Association of the Collagen Signature in the Tumor Microenvironment With Lymph Node Metastasis in Early Gastric Cancer | Cancer Biomarkers | JAMA Surgery | JAMA Network
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Figure 1.  Schematic Illustration of Collagen Signature Construction
Schematic Illustration of Collagen Signature Construction

A, A representative region of interest with a field of view of 200 × 200 μm was selected in the hematoxylin-eosin (H-E) stain (original magnification ×200). The corresponding multiphoton imaging, including 2-photon excitation fluorescence (TPEF) and second harmonic generation (SHG), was obtained, and the SHG imaging was chosen for collagen feature extraction. B, A computation framework was used to establish the collagen signature. The SHG imaging of multiphoton imaging was chosen for collagen feature extraction, including morphologic features and texture features. Next, the potential predictors were selected using LASSO (least-absolute shrinkage and selection operator) logistic regression. The collagen signature can be calculated by these potential predictors.

Figure 2.  Nomogram for Estimating Lymph Node Metastasis (LNM) in Early Gastric Cancer
Nomogram for Estimating Lymph Node Metastasis (LNM) in Early Gastric Cancer

The nomogram indicates the risk of LNM in early gastric cancer. For clinical use, tumor differentiation is determined by drawing a line straight up to the point axis to establish the score associated with the differentiation. Next, this process is repeated for the other 2 covariates (depth of tumor invasion and collagen signature). The scores of each covariate are added, and the total score is located on the total score points axis. Last, a line is drawn straight down to the risk of LNM axis to obtain the probability.

Figure 3.  Comparison Between the Traditional Model and the New Collagen Signature–Based Prediction Model
Comparison Between the Traditional Model and the New Collagen Signature–Based Prediction Model

The orange line represents the new model (area under the receiver operating characteristic curve [AUROC], 0.950; 95% CI, 0.923-0.977), including the depth of tumor invasion, tumor differentiation, and the collagen signature. The blue line represents the traditional model (AUROC, 0.798; 95% CI, 0.749-0.847), including the depth of tumor invasion, tumor differentiation, and lymphovascular infiltration.

Table 1.  Characteristics of Patients in the Primary and Validation Cohorts
Characteristics of Patients in the Primary and Validation Cohorts
Table 2.  Univariate and Multivariate Logistic Regression of Lymph Node Metastasis in the Primary Cohort
Univariate and Multivariate Logistic Regression of Lymph Node Metastasis in the Primary Cohort
1.
Van Cutsem  E, Sagaert  X, Topal  B, Haustermans  K, Prenen  H.  Gastric cancer.  Lancet. 2016;388(10060):2654-2664. doi:10.1016/S0140-6736(16)30354-3PubMedGoogle ScholarCrossref
2.
Isomoto  H, Shikuwa  S, Yamaguchi  N,  et al.  Endoscopic submucosal dissection for early gastric cancer: a large-scale feasibility study.  Gut. 2009;58(3):331-336. doi:10.1136/gut.2008.165381PubMedGoogle ScholarCrossref
3.
Japanese Gastric Cancer Association.  Japanese gastric cancer treatment guidelines 2014 (ver. 4).  Gastric Cancer. 2017;20(1):1-19. doi:10.1007/s10120-016-0622-4PubMedGoogle ScholarCrossref
4.
Choi  JH, Kim  ES, Lee  YJ,  et al.  Comparison of quality of life and worry of cancer recurrence between endoscopic and surgical treatment for early gastric cancer.  Gastrointest Endosc. 2015;82(2):299-307. doi:10.1016/j.gie.2015.01.019PubMedGoogle ScholarCrossref
5.
Hirasawa  T, Gotoda  T, Miyata  S,  et al.  Incidence of lymph node metastasis and the feasibility of endoscopic resection for undifferentiated-type early gastric cancer.  Gastric Cancer. 2009;12(3):148-152. doi:10.1007/s10120-009-0515-xPubMedGoogle ScholarCrossref
6.
An  JY, Baik  YH, Choi  MG, Noh  JH, Sohn  TS, Kim  S.  Predictive factors for lymph node metastasis in early gastric cancer with submucosal invasion: analysis of a single institutional experience.  Ann Surg. 2007;246(5):749-753. doi:10.1097/SLA.0b013e31811f3fb7PubMedGoogle ScholarCrossref
7.
Oh  SY, Lee  KG, Suh  YS,  et al.  Lymph node metastasis in mucosal gastric cancer: reappraisal of expanded indication of endoscopic submucosal dissection.  Ann Surg. 2017;265(1):137-142. doi:10.1097/SLA.0000000000001649PubMedGoogle ScholarCrossref
8.
Spolverato  G, Ejaz  A, Kim  Y,  et al.  Use of endoscopic ultrasound in the preoperative staging of gastric cancer: a multi-institutional study of the US gastric cancer collaborative.  J Am Coll Surg. 2015;220(1):48-56. doi:10.1016/j.jamcollsurg.2014.06.023PubMedGoogle ScholarCrossref
9.
Fujikawa  H, Yoshikawa  T, Hasegawa  S,  et al.  Diagnostic value of computed tomography for staging of clinical T1 gastric cancer.  Ann Surg Oncol. 2014;21(9):3002-3007. doi:10.1245/s10434-014-3667-9PubMedGoogle ScholarCrossref
10.
Park  JW, Ahn  S, Lee  H,  et al.  Predictive factors for lymph node metastasis in early gastric cancer with lymphatic invasion after endoscopic resection.  Surg Endosc. 2017;31(11):4419-4424. doi:10.1007/s00464-017-5490-4PubMedGoogle ScholarCrossref
11.
Pyo  JH, Shin  CM, Lee  H,  et al; JHP and CMS contributed equally as the first authors of this study. A risk-prediction model based on lymph-node metastasis for incorporation into a treatment algorithm for signet ring cell-type intramucosal gastric cancer.  Ann Surg. 2016;264(6):1038-1043. doi:10.1097/SLA.0000000000001602PubMedGoogle ScholarCrossref
12.
Guo  CG, Zhao  DB, Liu  Q,  et al.  A nomogram to predict lymph node metastasis in patients with early gastric cancer.  Oncotarget. 2017;8(7):12203-12210. doi:10.18632/oncotarget.14660PubMedGoogle ScholarCrossref
13.
Jung  DH, Huh  CW, Kim  JH,  et al.  Risk-stratification model based on lymph node metastasis after noncurative endoscopic resection for early gastric cancer.  Ann Surg Oncol. 2017;24(6):1643-1649. doi:10.1245/s10434-017-5791-9PubMedGoogle ScholarCrossref
14.
Buchheit  CL, Weigel  KJ, Schafer  ZT.  Cancer cell survival during detachment from the ECM: multiple barriers to tumour progression.  Nat Rev Cancer. 2014;14(9):632-641. doi:10.1038/nrc3789PubMedGoogle ScholarCrossref
15.
Han  W, Chen  S, Yuan  W,  et al.  Oriented collagen fibers direct tumor cell intravasation.  Proc Natl Acad Sci U S A. 2016;113(40):11208-11213. doi:10.1073/pnas.1610347113PubMedGoogle ScholarCrossref
16.
Conklin  MW, Gangnon  RE, Sprague  BL,  et al.  Collagen alignment as a predictor of recurrence after ductal carcinoma in situ.  Cancer Epidemiol Biomarkers Prev. 2018;27(2):138-145. doi:10.1158/1055-9965.EPI-17-0720PubMedGoogle ScholarCrossref
17.
Pointer  KB, Clark  PA, Schroeder  AB, Salamat  MS, Eliceiri  KW, Kuo  JS.  Association of collagen architecture with glioblastoma patient survival.  J Neurosurg. 2017;126(6):1812-1821.PubMedGoogle ScholarCrossref
18.
Penet  MF, Kakkad  S, Pathak  AP,  et al.  Structure and function of a prostate cancer dissemination-permissive extracellular matrix.  Clin Cancer Res. 2017;23(9):2245-2254. doi:10.1158/1078-0432.CCR-16-1516PubMedGoogle ScholarCrossref
19.
Zipfel  WR, Williams  RM, Christie  R, Nikitin  AY, Hyman  BT, Webb  WW.  Live tissue intrinsic emission microscopy using multiphoton-excited native fluorescence and second harmonic generation.  Proc Natl Acad Sci U S A. 2003;100(12):7075-7080. doi:10.1073/pnas.0832308100PubMedGoogle ScholarCrossref
20.
Zipfel  WR, Williams  RM, Webb  WW.  Nonlinear magic: multiphoton microscopy in the biosciences.  Nat Biotechnol. 2003;21(11):1369-1377. doi:10.1038/nbt899PubMedGoogle ScholarCrossref
21.
Campagnola  P.  Second harmonic generation imaging microscopy: applications to diseases diagnostics.  Anal Chem. 2011;83(9):3224-3231. doi:10.1021/ac1032325PubMedGoogle ScholarCrossref
22.
Cicchi  R, Kapsokalyvas  D, De Giorgi  V,  et al.  Scoring of collagen organization in healthy and diseased human dermis by multiphoton microscopy.  J Biophotonics. 2010;3(1-2):34-43. doi:10.1002/jbio.200910062PubMedGoogle ScholarCrossref
23.
Wen  BL, Brewer  MA, Nadiarnykh  O,  et al.  Texture analysis applied to second harmonic generation image data for ovarian cancer classification.  J Biomed Opt. 2014;19(9):096007. doi:10.1117/1.JBO.19.9.096007PubMedGoogle ScholarCrossref
24.
Kakkad  SM, Solaiyappan  M, Argani  P,  et al.  Collagen I fiber density increases in lymph node positive breast cancers: pilot study.  J Biomed Opt. 2012;17(11):116017. doi:10.1117/1.JBO.17.11.116017PubMedGoogle ScholarCrossref
25.
Huang  YQ, Liang  CH, He  L,  et al.  Development and validation of a radiomics nomogram for preoperative prediction of lymph node metastasis in colorectal cancer.  J Clin Oncol. 2016;34(18):2157-2164. doi:10.1200/JCO.2015.65.9128PubMedGoogle ScholarCrossref
26.
Agesen  TH, Sveen  A, Merok  MA,  et al.  ColoGuideEx: a robust gene classifier specific for stage II colorectal cancer prognosis.  Gut. 2012;61(11):1560-1567. doi:10.1136/gutjnl-2011-301179PubMedGoogle ScholarCrossref
27.
Zhuo  S, Chen  J, Luo  T, Zou  D.  Multimode nonlinear optical imaging of the dermis in ex vivo human skin based on the combination of multichannel mode and Lambda mode.  Opt Express. 2006;14(17):7810-7820. doi:10.1364/OE.14.007810PubMedGoogle ScholarCrossref
28.
Xu  S, Wang  Y, Tai  DCS,  et al.  qFibrosis: a fully-quantitative innovative method incorporating histological features to facilitate accurate fibrosis scoring in animal model and chronic hepatitis B patients.  J Hepatol. 2014;61(2):260-269. doi:10.1016/j.jhep.2014.02.015PubMedGoogle ScholarCrossref
29.
Xu  S, Kang  CH, Gou  X,  et al.  Quantification of liver fibrosis via second harmonic imaging of the Glisson’s capsule from liver surface.  J Biophotonics. 2016;9(4):351-363. doi:10.1002/jbio.201500001PubMedGoogle ScholarCrossref
30.
Sauerbrei  W, Royston  P, Binder  H.  Selection of important variables and determination of functional form for continuous predictors in multivariable model building.  Stat Med. 2007;26(30):5512-5528. doi:10.1002/sim.3148PubMedGoogle ScholarCrossref
31.
Iasonos  A, Schrag  D, Raj  GV, Panageas  KS.  How to build and interpret a nomogram for cancer prognosis.  J Clin Oncol. 2008;26(8):1364-1370. doi:10.1200/JCO.2007.12.9791PubMedGoogle ScholarCrossref
32.
Kerr  KF, Brown  MD, Zhu  K, Janes  H.  Assessing the clinical impact of risk prediction models with decision curves: guidance for correct interpretation and appropriate use.  J Clin Oncol. 2016;34(21):2534-2540. doi:10.1200/JCO.2015.65.5654PubMedGoogle ScholarCrossref
33.
Dormann  CF, Elith  J, Bacher  S,  et al.  Collinearity: a review of methods to deal with it and a simulation study evaluating their performance.  Ecography. 2013;36(1):27-46. doi:10.1111/j.1600-0587.2012.07348.xGoogle ScholarCrossref
34.
Chen  J, Zhuo  S, Chen  G,  et al.  Establishing diagnostic features for identifying the mucosa and submucosa of normal and cancerous gastric tissues by multiphoton microscopy.  Gastrointest Endosc. 2011;73(4):802-807. doi:10.1016/j.gie.2010.12.016PubMedGoogle ScholarCrossref
35.
Mocellin  S, Pasquali  S.  Diagnostic accuracy of endoscopic ultrasonography (EUS) for the preoperative locoregional staging of primary gastric cancer.  Cochrane Database Syst Rev. 2015;(2):CD009944.PubMedGoogle Scholar
36.
Ajani  JA, D’Amico  TA, Almhanna  K,  et al.  Gastric Cancer, Version 3.2016, NCCN Clinical Practice Guidelines in Oncology.  J Natl Compr Canc Netw. 2016;14(10):1286-1312. doi:10.6004/jnccn.2016.0137PubMedGoogle ScholarCrossref
37.
Provenzano  PP, Eliceiri  KW, Campbell  JM, Inman  DR, White  JG, Keely  PJ.  Collagen reorganization at the tumor-stromal interface facilitates local invasion.  BMC Med. 2006;4(1):38. doi:10.1186/1741-7015-4-38PubMedGoogle ScholarCrossref
38.
Wyckoff  JB, Wang  Y, Lin  EY,  et al.  Direct visualization of macrophage-assisted tumor cell intravasation in mammary tumors.  Cancer Res. 2007;67(6):2649-2656. doi:10.1158/0008-5472.CAN-06-1823PubMedGoogle ScholarCrossref
39.
Levental  KR, Yu  H, Kass  L,  et al.  Matrix crosslinking forces tumor progression by enhancing integrin signaling.  Cell. 2009;139(5):891-906. doi:10.1016/j.cell.2009.10.027PubMedGoogle ScholarCrossref
40.
Brown  CM, Rivera  DR, Pavlova  I,  et al.  In vivo imaging of unstained tissues using a compact and flexible multiphoton microendoscope.  J Biomed Opt. 2012;17(4):040505. doi:10.1117/1.JBO.17.4.040505PubMedGoogle ScholarCrossref
41.
Huland  DM, Brown  CM, Howard  SS,  et al.  In vivo imaging of unstained tissues using long gradient index lens multiphoton endoscopic systems.  Biomed Opt Express. 2012;3(5):1077-1085. doi:10.1364/BOE.3.001077PubMedGoogle ScholarCrossref
42.
Rivera  DR, Brown  CM, Ouzounov  DG,  et al.  Compact and flexible raster scanning multiphoton endoscope capable of imaging unstained tissue.  Proc Natl Acad Sci U S A. 2011;108(43):17598-17603. doi:10.1073/pnas.1114746108PubMedGoogle ScholarCrossref
43.
Collins  GS, Reitsma  JB, Altman  DG, Moons  KG.  Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis (TRIPOD): the TRIPOD statement.  Ann Intern Med. 2015;162(1):55-63. doi:10.7326/M14-0697PubMedGoogle ScholarCrossref
44.
Hsieh  FY.  Sample size tables for logistic regression.  Stat Med. 1989;8(7):795-802. doi:10.1002/sim.4780080704PubMedGoogle ScholarCrossref
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    Original Investigation
    January 30, 2019

    Association of the Collagen Signature in the Tumor Microenvironment With Lymph Node Metastasis in Early Gastric Cancer

    Author Affiliations
    • 1Department of General Surgery, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong, People's Republic of China
    • 2Key Laboratory of OptoElectronic Science and Technology for Medicine of Ministry of Education, Fujian Provincial Key Laboratory of Photonics Technology, Fujian Normal University, Fuzhou, Fujian, People's Republic of China
    • 3Department of Pathology, Fujian Provincial Cancer Hospital, Teaching Hospital of Fujian Medical University, Fuzhou, Fujian, People's Republic of China
    • 4Department of Gastroenterology, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong, People's Republic of China
    • 5Department of Gastrointestinal Surgery, Fujian Provincial Hospital, Teaching Hospital of Fujian Medical University, Fuzhou, Fujian, People's Republic of China
    • 6Department of Radiology, Sun Yat-sen University Cancer Center, Guangzhou, Guangdong, People's Republic of China
    • 7Department of Endoscopy Center, Fujian Provincial Hospital, Teaching Hospital of Fujian Medical University, Fuzhou, Fujian, People's Republic of China
    JAMA Surg. 2019;154(3):e185249. doi:10.1001/jamasurg.2018.5249
    Key Points

    Question  How can lymph node metastasis in early gastric cancer be accurately assessed?

    Finding  In this study of 375 patients with early gastric cancer, collagen signature was statistically significantly associated with lymph node metastasis. A newly developed lymph node metastasis prediction model based on the collagen signature showed good discrimination in the primary cohort and was externally validated.

    Meaning  The new prediction model appears to be useful in decision making associated with tailored surgical interventions in patients with early gastric cancer.

    Abstract

    Importance  Lymph node status is the primary determinant in treatment decision making in early gastric cancer (EGC). Current evaluation methods are not adequate for estimating lymph node metastasis (LNM) in EGC.

    Objective  To develop and validate a prediction model based on a fully quantitative collagen signature in the tumor microenvironment to estimate the individual risk of LNM in EGC.

    Design, Setting, and Participants  This retrospective study was conducted from August 1, 2016, to May 10, 2018, at 2 medical centers in China (Nanfang Hospital and Fujian Provincial Hospital). Participants included a primary cohort (n = 232) of consecutive patients with histologically confirmed gastric cancer who underwent radical gastrectomy and received a T1 gastric cancer diagnosis from January 1, 2008, to December 31, 2012. Patients with neoadjuvant radiotherapy, chemotherapy, or chemoradiotherapy were excluded. An additional consecutive cohort (n = 143) who received the same diagnosis from January 1, 2011, to December 31, 2013, was enrolled to provide validation. Baseline clinicopathologic data of each patient were collected. Collagen features were extracted in specimens using multiphoton imaging, and the collagen signature was constructed. An LNM prediction model based on the collagen signature was developed and was internally and externally validated.

    Main Outcomes and Measures  The area under the receiver operating characteristic curve (AUROC) of the prediction model and decision curve were analyzed for estimating LNM.

    Results  In total, 375 patients were included. The primary cohort comprised 232 consecutive patients, in whom the LNM rate was 16.4% (n = 38; 25 men [65.8%] with a mean [SD] age of 57.82 [10.17] years). The validation cohort consisted of 143 consecutive patients, in whom the LNM rate was 20.9% (n = 30; 20 men [66.7%] with a mean [SD] age of 54.10 [13.19] years). The collagen signature was statistically significantly associated with LNM (odds ratio, 5.470; 95% CI, 3.315-9.026; P < .001). Multivariate analysis revealed that the depth of tumor invasion, tumor differentiation, and the collagen signature were independent predictors of LNM. These 3 predictors were incorporated into the new prediction model, and a nomogram was established. The model showed good discrimination in the primary cohort (AUROC, 0.955; 95% CI, 0.919-0.991) and validation cohort (AUROC, 0.938; 95% CI, 0.897-0.981). An optimal cutoff value was selected in the primary cohort, which had a sensitivity of 86.8%, a specificity of 93.3%, an accuracy of 92.2%, a positive predictive value of 71.7%, and a negative predictive value of 97.3%. The validation cohort had a sensitivity of 90.0%, a specificity of 90.3%, an accuracy of 90.2%, a positive predictive value of 71.1%, and a negative predictive value of 97.1%. Among the 375 patients, a sensitivity of 87.3%, a specificity of 92.1%, an accuracy of 91.2%, a positive predictive value of 72.1%, and a negative predictive value of 96.9% were found.

    Conclusions and Relevance  This study’s findings suggest that the collagen signature in the tumor microenvironment is an independent indicator of LNM in EGC, and the prediction model based on this collagen signature may be useful in treatment decision making for patients with EGC.

    Introduction

    Early gastric cancer (EGC) is defined as cancer limited to the mucosa or submucosa, regardless of nodal status.1 Currently, endoscopic submucosal dissection (ESD) has become more popular than surgical procedures in treating EGC because it is minimally invasive, preserves function, and results in better quality of life.2-4 The principal indication for ESD is a tumor with a low risk of lymph node metastasis (LNM) that can undergo en bloc resection.3 The incidence of LNM is less than 3% when cancer is limited to the mucosa and increases to approximately 20% after cancer invades the submucosa.5 Thus, the accurate assessment of nodal status in EGC is integral to providing tailored surgical procedure.6,7 So far, the diagnostic accuracy of endoscopic ultrasonography and computed tomography for the nodal status of EGC is limited.8,9 Early gastric cancer with undifferentiated histologic result, submucosal invasion, and lymphovascular infiltration is deemed a high risk for LNM, and radical surgical procedure is considered,3,5,10 but a unanimous consensus has not been reached. To estimate the likelihood of LNM for EGC, several studies have developed different prediction models.11-13 However, these models focused on the clinical-pathologic characteristics, and the association of the tumor microenvironment with LNM was not investigated.

    The extracellular matrix constitutes the scaffold of the tumor microenvironment, which regulates cancer behavior.14 As the main component of the extracellular matrix, collagen accounts for its major functions. The arrangement and orientation of collagen were proven to be indicators of tumor metastasis in breast cancer,15,16 glioblastoma17 and prostate cancer.18 Nevertheless, the role of collagen in the process of LNM in EGC is still unclear.

    Multiphoton imaging could provide detailed information about tissue architecture and cell morphology in specimens through a combination of 2-photon excitation fluorescence from cells and second harmonic generation from collagen.19 Because of the underlying physical origin, multiphoton imaging has emerged as a powerful modality for collagen imaging in diverse tissues.20,21 Moreover, multiphoton imaging could be converted into high-dimensional and quantitative components of collagen via automatic extraction of multiple features. Collagen features analysis, including morphologic and textural features extracted from multiphoton imaging, has been applied and demonstrated to be a powerful quantitative indicator for diagnosis in several diseases.22-24

    Integrating multiple biomarkers into a single signature, rather than performing individual biomarker analysis, is a promising approach that would improve clinical management.25,26 Currently, an appropriate method of integrating multiple collagen features into a single signature has not yet been developed. Hence, we propose the collagen signature, deduced by multiple morphologic and textural features of collagen using multiphoton imaging. The aim of this study was to develop and validate a prediction model based on the collagen signature that can distinguish genuine high-risk EGC with LNM. To our knowledge, this is the first study to investigate the role of collagen in EGC and to develop a prediction model for LNM based on the fully quantitative collagen signature.

    Methods

    The institutional review board at each participating center in China (Nanfang Hospital, Guangzhou, Guangdong, People's Republic of China and Fujian Provincial Hospital, Fuzhou, Fujian, People's Republic of China) approved this study. Patient informed consent was waived by the institutional review board because of the retrospective design of the study and patients' information was protected. The study was conducted from August 1, 2016, to May 10, 2018.

    Patients and Specimens

    The primary cohort (n = 232) was retrospectively assembled using the medical database of Nanfang Hospital. Consecutive patients who received a diagnosis from January 1, 2008, to December 31, 2012 (eFigure 1 in the Supplement) comprised the cohort. The inclusion criteria were patients with histologically confirmed gastric cancer who underwent radical gastrectomy and received a T1 gastric cancer diagnosis after surgical intervention. We excluded patients with neoadjuvant radiotherapy, chemotherapy, or chemoradiotherapy. An additional consecutive cohort (n = 143) who received the same diagnosis at the Fujian Provincial Hospital another hospital from January 1, 2011, to December 31, 2013, and who met the same criteria as the primary cohort was enrolled to provide validation. The formalin-fixed paraffin-embedded specimens of all patients were used.

    Baseline clinicopathologic data of each patient, including sex, age at surgical intervention, macroscopic classification, tumor location, tumor size, tumor differentiation, lymphovascular infiltration, and depth of invasion, were collected. The tumor differentiation was divided into differentiated and undifferentiated types according to the 2014 Japanese gastric cancer treatment guidelines (version 4).3

    Selection of Regions of Interest, Multiphoton Image Acquisition, and Collagen Feature Extraction

    All specimens were processed for hematoxylin-eosin staining (original magnification ×200). Two of our independent pathologists (W. L. and J. L.), who were blinded to the nodal status, evaluated the region of the invasive margin of the EGC using a microscope at ×200 magnification. The interrater reliability was evaluated (κ = 0.437; 95% CI, 0.295-0.569) with approximately 87.3% (95% CI, 84.3%-90.1%) agreement. When the 2 pathologists differed in opinion, they consulted with the director (G.C.) of the Department of Pathology, Fujian Provincial Cancer Hospital, to make a decision. Five regions of interest with a field of view of 200 × 200 μm per specimen, which were equidistantly spread throughout the invasive margin, were selected to provide a realistic representation of each EGC sample.

    Image acquisition for multiphoton imaging was performed with a 200× original magnification objective on another unstained serial section and then compared with hematoxylin-eosin staining for histologic assessment. The multiphoton imaging system used in this study has been described previously (eMethods in the Supplement).27

    The extraction of collagen features was performed using MATLAB 2015b (MathWorks) as previously reported.28,29 A total of 146 features, including 12 morphologic features and 134 textural features, were extracted (eMethods and eTable 1 in the Supplement).

    Feature Selection and Collagen Signature Construction

    The LASSO (least-absolute shrinkage and selection operator) logistic regression, which has been broadly applied for high-dimensional data, was used to select the most predictive features in the primary cohort.30 The collagen signature construction was calculated through a combination of selected features (eMethods in the Supplement).

    Prediction Model Development and Evaluation

    Both the 8 clinicopathologic variables and the collagen signature were included in the univariate analysis to explore the association with LNM in the primary cohort, and variables with P < .05 were selected for the multivariate analysis. Backward stepwise regression was applied to select the independent predictors. The multicollinearity of the multivariate model was assessed using the tolerance and variance inflation factor. In addition, the effect modification was evaluated. A nomogram was constructed according to independent predictors. For quantification of the discrimination of the nomogram, the area under the receiver operating characteristic curve (AUROC) was measured. The calibration of the nomogram was evaluated by the calibration curve to assess the goodness of fit, accompanied by the Hosmer-Lemeshow test.

    Prediction Model Internal and External Validation

    The bootstrap method was applied for internal validation, in which the random samples drawn with a replacement from the original data set were the same size as the primary cohort.31 One thousand bootstrap repetitions were performed.

    The prediction model was applied in the validation cohort. Ultimately, the AUROC was calculated, and the calibration curve was plotted.

    Clinical Application

    To evaluate the clinical application of the nomogram, decision curve analysis was used to assess the net benefits of the prediction model at different threshold probabilities (eMethods in the Supplement).32 The maximum Youden index was selected as the cutoff value to evaluate the sensitivity, specificity, accuracy, positive predictive value, and negative predictive value of the prediction model.

    Statistical Analysis

    An independent-samples, unpaired, 2-tailed t test or Mann-Whitney H test, where appropriate, was used to assess the differences in continuous variables, and a χ2 test or Fisher exact probability test was used to compare the differences between categorical variables. A multivariate logistic regression was performed to estimate the odds ratio (OR) with a 95% CI and to identify the independent predictors for LNM. Statistical analysis was conducted with R software, version 3.4.2 (R Foundation for Statistical Computing). Differences with a 2-sided P < .05 were considered statistically significant.

    Results
    Participants

    The primary cohort included 232 consecutive patients, in whom the LNM rate was 16.4% (n = 38; 25 men [65.8%] with a mean [SD] age of 57.82 [10.17] years). The validation cohort included 143 consecutive patients, in whom the LNM rate was 20.9% (n = 30; 20 men [66.7%] with a mean [SD] age of 54.10 [13.19] years). Patient characteristics in the primary and validation cohorts are given in Table 1. No statistically significant difference in LNM prevalence was observed between the 2 cohorts (OR, 1.355; 95% CI, 0.796-2.307; P = .26). The clinicopathologic characteristics were similar between the primary and validation cohorts (eTable 2 in the Supplement).

    Collagen Signature Construction

    The construction framework of the collagen signature is presented in Figure 1. All collagen features were reduced to the 6 best potential predictors, using LASSO logistic regression (eFigures 2A and 2B in the Supplement; the 6 features are presented in eAppendix 1 in the Supplement). A statistically significant difference in the collagen signature (median [interquartile range (IQR)]) was found between patients with LNM (0.284 [–0.836 to 0.872]) and patients without LNM (–2.856 [–3.630 to –2.088]) in the primary cohort (median difference, 2.059; 95% CI, 1.413 to 2.757; P < .001). This finding was consistent with the patients with LNM (–0.522 [–0.887 to –0.125]) and patients without LNM (–2.277 [–2.851 to –1.794]) in the validation cohort (median difference, 1.793; 95% CI, 1.176 to 2.361; P < .001) (Table 1). The collagen signature indicated a favorable prediction of LNM with an AUROC of 0.944 (95% CI, 0.905-0.982) in the primary cohort and 0.933 (95% CI, 0.889-0.977) in the validation cohort (eFigures 2C and 2D in the Supplement).

    Prediction Model Development and Evaluation

    A univariate analysis was performed for each variable in the primary cohort. A tumor size larger than 2 cm (OR, 3.249; 95% CI, 1.578-6.693; P = .001), undifferentiated tumor (OR, 2.956; 95% CI, 1.452-6.017; P = .002), lymphovascular infiltration (OR, 7.341; 95% CI, 2.743-19.646; P < .001), submucosal invasion (OR, 9.231; 95% CI, 3.155-27.010; P < .001), and collagen signature (OR, 5.470; 95% CI, 3.315-9.026; P < .001) were statistically significantly associated with LNM in EGC (Table 2). Furthermore, a multivariate analysis identified that tumor differentiation (OR, 4.585; 95% CI, 1.310-16.041; P = .02), the depth of tumor invasion (OR, 6.773; 95% CI, 1.636-28.039; P = .008), and the collagen signature (OR, 5.335; 95% CI, 3.042-9.358; P < .001) were independent predictors of LNM (Table 2). The variance inflation factor of each predictor was less than 10, and the corresponding tolerance was more than 0.1; therefore, no multicollinearity among these predictors was noted (eTable 3 in the Supplement).33 No effect modification was found in the prediction model (eTables 4 and 5 in the Supplement). The association between the collagen signature and the risk of LNM with different combinations of tumor differentiation states (differentiated or undifferentiated) and depths of tumor invasion (mucosa or submucosa) is presented in eFigure 3 in the Supplement. A nomogram was produced by incorporating these 3 independent predictors (Figure 2).

    The newly developed prediction model showed good discrimination with an AUROC of 0.955 (95% CI, 0.919-0.991), and the calibration curve showed good agreement between the nomogram-estimated probability of LNM and the actual LNM rate in the primary cohort (eFigure 4A and B in the Supplement). The Hosmer-Lemeshow test demonstrated a P = .47, indicating no departure from a good fit.

    Internal and External Prediction Model Validation

    For internal validation, we used the bootstrap method with 1000 bootstrap repetitions. The results remained largely unchanged between iterations, with a mean concordance index of 0.911.

    Good discrimination with an AUROC of 0.938 (95% CI, 0.897-0.981) was externally validated, and the favorable calibration was also confirmed in the validation cohort (eFigure 4C and D in the Supplement). A Hosmer-Lemeshow test demonstrated a nonsignificant P = .15.

    Clinical Application

    In the decisive curve, the x-axis is a measure of patient or physician preference, and the threshold probability indicates that the expected advantage of treatment is equal to the expected advantage of avoiding treatment.33 The decision curve revealed that if the threshold probability of a patient or physician was greater than 5%, more advantages would be added by using the nomogram to estimate LNM in EGC than the advantage achieved in either the treat-all-patient scheme or the treat-none scheme (eFigure 5 in the Supplement).

    In addition, in the primary cohort, the maximum Youden index of 0.301 was selected as the cutoff value, and the cohort had a sensitivity of 86.8%, a specificity of 93.3%, an accuracy of 92.2%, a positive predictive value of 71.7%, and a negative predictive value of 97.3%. The validation cohort had a sensitivity of 90.0%, a specificity of 90.3%, an accuracy of 90.2%, a positive predictive value of 71.1%, and a negative predictive value of 97.1%. Among the 375 patients, a sensitivity of 87.3%, a specificity of 92.1%, an accuracy of 91.2%, a positive predictive value of 72.1%, and a negative predictive value of 96.9% were found (eTable 6 in the Supplement).

    Comparison With the Traditional Prediction Model

    To elucidate the superiority of the model we built over the clinicopathologic characteristic-based model (ie, the traditional model), we eliminated the collagen signature and developed the traditional model on the basis of tumor differentiation (OR, 2.576; 95% CI, 1.167-5.685; P = .02), lymphovascular infiltration (OR, 3.333; 95% CI, 1.145-9.703; P = .03), and the depth of tumor invasion (OR, 9.923; 95% CI, 3.305-29.793; P < .001) (eTable 7 in the Supplement) after univariate and multivariate analyses. No multicollinearity in the traditional model was found (eTable 8 in the Supplement). Sex, age at surgical intervention, macroscopic classification, and tumor location were chosen as variables, but these variables were not statistically significant after univariate analysis. The performance of the traditional model was similar to the performance previously reported, with an AUROC of 0.812 (95% CI, 0.752-0.872) in the primary cohort and 0.768 (95% CI, 0.688-0.849) in the validation cohort (eFigure 6 in the Supplement).11,12 Compared with the traditional model, the new model based on the collagen signature showed a more robust ability to estimate the risk of LNM in EGC in all 375 patients (AUROC comparison, 0.950 [95% CI, 0.923-0.977] vs 0.798 [95% CI, 0.749-0.847]; P < .001) (Figure 3).

    Discussion

    Accurate assessment of the nodal status in EGC is important in the decision making for lymph node dissection. In this study, we developed and validated a nomogram for individual estimation of LNM in EGC, including the depth of tumor invasion, tumor differentiation, and the collagen signature.

    Two key factors determine the construction of the collagen signature. The first is the use of a suitable imaging approach to selectively visualize the collagen. In this study, multiphoton imaging was used because of its underlying physical origin.20,21 Previous research indicated that multiphoton imaging can distinguish between the mucosa and submucosa of cancerous gastric tissues, and collagen can be quantified by second harmonic generation in a stain-free section.34 Thus, multiphoton imaging is an ideal method for collagen imaging. The second factor is the quantitative analysis of collagen from multiphoton imaging. For this purpose, we have established a stable framework for achieving precise quantification.28,29

    After considering these 2 factors, we constructed the collagen signature. The collagen signature was substantially different in EGC with and without LNM. To develop a clinically practicable prediction tool, we used other clinicopathologic characteristics. We also built a nomogram with good discrimination and calibration. Our findings suggest that LNM is more likely to appear in patients with an undifferentiated histologic result, submucosal invasion, and a high collagen signature.

    Compared with the traditional model based on tumor differentiation, the depth of tumor invasion, and lymphovascular infiltration, the prediction model was more powerful in estimating the risk of LNM in EGC. Although a tumor size larger than 2 cm was statistically significantly associated with LNM, it was excluded after backward stepwise multivariate analysis in both the prediction model and the traditional model. The reason for this exclusion was that the depth of tumor invasion was much more important than tumor size in the clinic.

    Currently, endoscopic ultrasonography and computed tomography are the 2 most common examination methods for N staging of gastric cancer. Endoscopic ultrasonography for N staging had a sensitivity of 83% and a specificity of 67%.35 Meanwhile, computed tomography for detecting LNM had a sensitivity of 78% and a specificity of 62% in gastric cancer.36 In the prediction model, the sensitivity was 87.3% and the specificity was 92.1%, with the cutoff value of the maximum Youden index. Therefore, the prediction model was adequate for base clinical decisions.

    In this study, tumor differentiation and the depth of tumor invasion were categorical variables, and the collagen signature was a continuous variable. The risk of LNM was always contributed to by these 3 predictors. For example, for a patient without LNM with a collagen signature of –2.856, the risk of LNM was less than 1% for differentiated tumors that invaded only the mucosa. When the tumor was undifferentiated, the risk of LNM was approximately 2%, and if the tumor also invaded the submucosa, the risk of LNM increased to 8%. Similarly, for a patient with LNM with a collagen signature of 0.284, the risk of LNM was approximately 30% for differentiated tumors that invaded the mucosa. In the case of undifferentiated tumors, the risk of LNM increased to approximately 70%. Once the tumors invaded the submucosa, the risk of LNM increased to approximately 90%. As tumor differentiation and the depth of tumor invasion are routinely assessed in endoscopic resection specimens, and the collagen signature could be quantified using multiphoton imaging, the individual risk of LNM could be conveniently estimated by the nomogram after ESD. For a low risk of LNM, the nomogram indicates that ESD is adequate. Inversely, for a high risk of LNM, additional lymph node dissection might be needed.

    Collagen was identified as a component of cancer metastasis. Local collagen orientations have been shown to play an important role in promoting cell breakage into the basement membrane before entering the circulation systems.15 Kakkad et al24 reported that multiphoton imaging revealed that a substantially increased density of collagen was associated with LNM in breast cancer. In our study, the collagen signature was positively correlated with collagen straightness and cross-link density. This result indicated that the collagen arrangement was far straighter in the invasive margin of EGC with LNM. Straighter collagen in the tumor microenvironment could facilitate invasion.37,38 Meanwhile, increased collagen cross-link density could stiffen the extracellular matrix, enhance growth factor signaling activity, and induce the invasion of an oncogene-initiated epithelium.39 Our data showed the association between the collagen signature and LNM for EGC. Future studies should focus on the underlying molecular mechanisms.

    The collagen features in this study were extracted from multiphoton imaging. Because the components of the multiphoton imaging system were fixed, pathologists could conduct multiphoton imaging using a microscope. Finishing multiphoton imaging took approximately 5 to 10 minutes. Multiphoton imaging was good at showing collagen and did not change the tissue architecture and cell morphology. Therefore, pathologists could understand and analyze multiphoton imaging after training. Meanwhile, the ESD of EGC has no special requirements, and specimens can be processed regularly, which would not affect the multiphoton imaging. Multiphoton imaging is a promising method for realizing real-time in vivo optical biopsy, and several groups have reported the possible clinical applications in different organs.40-42 We foresee that clinicians could obtain collagen signature in the near future using multiphoton imaging. With the assistance of the prediction model, EGC with a genuine high risk of LNM would be distinguished, and more tailored surgical interventions could be performed.

    Limitations

    This study has some limitations. First, because it was a retrospective study, it might result in a potential selection bias. Thus, a multicenter prospective clinical trial is required to confirm the prediction model we developed. We are comfortable with the application of this technique in a clinical trial. Second, the clinicopathologic characteristics between the primary and validation cohorts were similar, which made our validation less robust, and the distribution of clinicopathologic characteristics might be different in other countries. Therefore, cohorts from Western countries are needed to further validate our findings. Third, the sample-size calculation for logistic regression analysis is still debated. We used 2 methods to calculate the sample size: one requires at least 10 events per variable,43 and the other is based on the variance inflation factor and does not explicitly require knowledge of the number of variables in the regression model.44 The sample size might not be adequate for the former method but was enough for the latter method (eAppendix 2 in the Supplement). Thus, we hope that this limitation will be solved in our upcoming clinical trial. Fourth, the weak interrater reliability between the 2 pathologists is also a limitation. One pathologist was a senior attending pathologist, and the other was a junior attending pathologist. The weak interrater reliability was the result of the difference in their experiences. In our next trial, we will require 2 senior attending pathologists.

    Conclusions

    The collagen signature in the tumor microenvironment is an independent risk factor for LNM in EGC. The prediction model we developed and validated is useful for decision making in tailored surgical intervention.

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    Article Information

    Accepted for Publication: October 28, 2018.

    Corresponding Authors: Jun Yan, MD, PhD, Department of General Surgery, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong 510515, People's Republic of China (yanjunfudan@163.com); Shuangmu Zhuo, PhD, Key Laboratory of OptoElectronic Science and Technology for Medicine of Ministry of Education, Fujian Provincial Key Laboratory of Photonics Technology, Fujian Normal University, Fuzhou, Fujian 350007, People's Republic of China (shuangmuzhuo@gmail.com).

    Published Online: January 30, 2019. doi:10.1001/jamasurg.2018.5249

    Author Contributions: Drs D. Chen, G. Chen, Jiang, and Fu contributed equally to this article. Drs Yan and Zhuo jointly directed this work and contributed equally as corresponding authors. Dr Yan had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

    Concept and design: D. Chen, Chi, G. Li, Zhuo, Yan.

    Acquisition, analysis, or interpretation of data: D. Chen, G. Chen, Jiang, Fu, W. Liu, Sui, Xu, Z. Liu, Zheng, Lin, K. Li, W. Chen, Zuo, Lu, J. Chen, Yan.

    Drafting of the manuscript: D. Chen, Jiang, Fu, W. Liu, Sui, Xu, Z. Liu, Zheng, Lin, K. Li, W. Chen, Zuo, Lu, J. Chen, Zhuo, Yan.

    Critical revision of the manuscript for important intellectual content: D. Chen, G. Chen, Jiang, Xu, Chi, G. Li, Zhuo, Yan.

    Statistical analysis: D. Chen, Fu, Sui, Chi, Zhuo, Yan.

    Obtained funding: D. Chen, J. Chen, Zhuo, Yan.

    Administrative, technical, or material support: G. Chen, Xu, Zuo, J. Chen, G. Li, Zhuo, Yan.

    Supervision: D. Chen, G. Li, Zhuo.

    Conflict of Interest Disclosures: None reported.

    Funding/Support: This work was supported by grants 81773117, 81771881, 81700576, and 81672446 from the National Natural Science Foundation of China; grant 2015CB352006 from the National Key Basic Research Program of China; grant [2011]170 from the National Clinical Key Specialty Construction Program; grants 2017YFC0108300 and 2017YFC0108302 from the State’s Key Project of Research and Development Plan; grant 201402015 from the Research Fund of Public Welfare in the National Health and Family Planning Commission of China; grant 2014B020215002 from the Special Fund for Guangdong Province Public Research and Capacity Building; grant 2015A030308006 from the Natural Science Foundation of Guangdong Province; grant 2018J07004 from the Natural Science Foundation of Fujian Province; grant 2017L3009 from the Special Funds of the Central Government Guiding Local Science and Technology Development; grant 2014A020215014 from the Guangdong Provincial Science and Technology Key Project; grant 2012-CXB-7 from the Innovation Research of Fujian Health Bureau; grant IRT_15R10 from the Program for Changjiang Scholars and Innovative Research Team in University; grant 201704020062 from the Guangzhou Industry University Research Cooperative Innovation Major Project; grant 320.2710.1851 from the Special Fund from Clinical Research of Wu Jieping Medical Foundation; grant LC2016PY010 from the Clinical Research Project of Southern Medical University; grant 2014067 from the High-Level Research Matching Foundation of Nanfang Hospital; grant 201404280056 from the Scientific Research Foundation for High-Level Talents in Nanfang Hospital of Southern Medical University; grants pdjhb0100 and pdjh2017a0093 from the Special Funds for the Cultivation of Guangdong College Students’ Scientific and Technological Innovation; grants 201612121008, 201612121080, 201712121052, 201712121132, 201712121149, 201812121265, and 201812121039S from the Training Program for Undergraduate Innovation and Entrepreneurship; and grant B1000494 from the Scientific Enlightenment Plan of Southern Medical University.

    Role of the Funder/Sponsor: The funding sources had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

    References
    1.
    Van Cutsem  E, Sagaert  X, Topal  B, Haustermans  K, Prenen  H.  Gastric cancer.  Lancet. 2016;388(10060):2654-2664. doi:10.1016/S0140-6736(16)30354-3PubMedGoogle ScholarCrossref
    2.
    Isomoto  H, Shikuwa  S, Yamaguchi  N,  et al.  Endoscopic submucosal dissection for early gastric cancer: a large-scale feasibility study.  Gut. 2009;58(3):331-336. doi:10.1136/gut.2008.165381PubMedGoogle ScholarCrossref
    3.
    Japanese Gastric Cancer Association.  Japanese gastric cancer treatment guidelines 2014 (ver. 4).  Gastric Cancer. 2017;20(1):1-19. doi:10.1007/s10120-016-0622-4PubMedGoogle ScholarCrossref
    4.
    Choi  JH, Kim  ES, Lee  YJ,  et al.  Comparison of quality of life and worry of cancer recurrence between endoscopic and surgical treatment for early gastric cancer.  Gastrointest Endosc. 2015;82(2):299-307. doi:10.1016/j.gie.2015.01.019PubMedGoogle ScholarCrossref
    5.
    Hirasawa  T, Gotoda  T, Miyata  S,  et al.  Incidence of lymph node metastasis and the feasibility of endoscopic resection for undifferentiated-type early gastric cancer.  Gastric Cancer. 2009;12(3):148-152. doi:10.1007/s10120-009-0515-xPubMedGoogle ScholarCrossref
    6.
    An  JY, Baik  YH, Choi  MG, Noh  JH, Sohn  TS, Kim  S.  Predictive factors for lymph node metastasis in early gastric cancer with submucosal invasion: analysis of a single institutional experience.  Ann Surg. 2007;246(5):749-753. doi:10.1097/SLA.0b013e31811f3fb7PubMedGoogle ScholarCrossref
    7.
    Oh  SY, Lee  KG, Suh  YS,  et al.  Lymph node metastasis in mucosal gastric cancer: reappraisal of expanded indication of endoscopic submucosal dissection.  Ann Surg. 2017;265(1):137-142. doi:10.1097/SLA.0000000000001649PubMedGoogle ScholarCrossref
    8.
    Spolverato  G, Ejaz  A, Kim  Y,  et al.  Use of endoscopic ultrasound in the preoperative staging of gastric cancer: a multi-institutional study of the US gastric cancer collaborative.  J Am Coll Surg. 2015;220(1):48-56. doi:10.1016/j.jamcollsurg.2014.06.023PubMedGoogle ScholarCrossref
    9.
    Fujikawa  H, Yoshikawa  T, Hasegawa  S,  et al.  Diagnostic value of computed tomography for staging of clinical T1 gastric cancer.  Ann Surg Oncol. 2014;21(9):3002-3007. doi:10.1245/s10434-014-3667-9PubMedGoogle ScholarCrossref
    10.
    Park  JW, Ahn  S, Lee  H,  et al.  Predictive factors for lymph node metastasis in early gastric cancer with lymphatic invasion after endoscopic resection.  Surg Endosc. 2017;31(11):4419-4424. doi:10.1007/s00464-017-5490-4PubMedGoogle ScholarCrossref
    11.
    Pyo  JH, Shin  CM, Lee  H,  et al; JHP and CMS contributed equally as the first authors of this study. A risk-prediction model based on lymph-node metastasis for incorporation into a treatment algorithm for signet ring cell-type intramucosal gastric cancer.  Ann Surg. 2016;264(6):1038-1043. doi:10.1097/SLA.0000000000001602PubMedGoogle ScholarCrossref
    12.
    Guo  CG, Zhao  DB, Liu  Q,  et al.  A nomogram to predict lymph node metastasis in patients with early gastric cancer.  Oncotarget. 2017;8(7):12203-12210. doi:10.18632/oncotarget.14660PubMedGoogle ScholarCrossref
    13.
    Jung  DH, Huh  CW, Kim  JH,  et al.  Risk-stratification model based on lymph node metastasis after noncurative endoscopic resection for early gastric cancer.  Ann Surg Oncol. 2017;24(6):1643-1649. doi:10.1245/s10434-017-5791-9PubMedGoogle ScholarCrossref
    14.
    Buchheit  CL, Weigel  KJ, Schafer  ZT.  Cancer cell survival during detachment from the ECM: multiple barriers to tumour progression.  Nat Rev Cancer. 2014;14(9):632-641. doi:10.1038/nrc3789PubMedGoogle ScholarCrossref
    15.
    Han  W, Chen  S, Yuan  W,  et al.  Oriented collagen fibers direct tumor cell intravasation.  Proc Natl Acad Sci U S A. 2016;113(40):11208-11213. doi:10.1073/pnas.1610347113PubMedGoogle ScholarCrossref
    16.
    Conklin  MW, Gangnon  RE, Sprague  BL,  et al.  Collagen alignment as a predictor of recurrence after ductal carcinoma in situ.  Cancer Epidemiol Biomarkers Prev. 2018;27(2):138-145. doi:10.1158/1055-9965.EPI-17-0720PubMedGoogle ScholarCrossref
    17.
    Pointer  KB, Clark  PA, Schroeder  AB, Salamat  MS, Eliceiri  KW, Kuo  JS.  Association of collagen architecture with glioblastoma patient survival.  J Neurosurg. 2017;126(6):1812-1821.PubMedGoogle ScholarCrossref
    18.
    Penet  MF, Kakkad  S, Pathak  AP,  et al.  Structure and function of a prostate cancer dissemination-permissive extracellular matrix.  Clin Cancer Res. 2017;23(9):2245-2254. doi:10.1158/1078-0432.CCR-16-1516PubMedGoogle ScholarCrossref
    19.
    Zipfel  WR, Williams  RM, Christie  R, Nikitin  AY, Hyman  BT, Webb  WW.  Live tissue intrinsic emission microscopy using multiphoton-excited native fluorescence and second harmonic generation.  Proc Natl Acad Sci U S A. 2003;100(12):7075-7080. doi:10.1073/pnas.0832308100PubMedGoogle ScholarCrossref
    20.
    Zipfel  WR, Williams  RM, Webb  WW.  Nonlinear magic: multiphoton microscopy in the biosciences.  Nat Biotechnol. 2003;21(11):1369-1377. doi:10.1038/nbt899PubMedGoogle ScholarCrossref
    21.
    Campagnola  P.  Second harmonic generation imaging microscopy: applications to diseases diagnostics.  Anal Chem. 2011;83(9):3224-3231. doi:10.1021/ac1032325PubMedGoogle ScholarCrossref
    22.
    Cicchi  R, Kapsokalyvas  D, De Giorgi  V,  et al.  Scoring of collagen organization in healthy and diseased human dermis by multiphoton microscopy.  J Biophotonics. 2010;3(1-2):34-43. doi:10.1002/jbio.200910062PubMedGoogle ScholarCrossref
    23.
    Wen  BL, Brewer  MA, Nadiarnykh  O,  et al.  Texture analysis applied to second harmonic generation image data for ovarian cancer classification.  J Biomed Opt. 2014;19(9):096007. doi:10.1117/1.JBO.19.9.096007PubMedGoogle ScholarCrossref
    24.
    Kakkad  SM, Solaiyappan  M, Argani  P,  et al.  Collagen I fiber density increases in lymph node positive breast cancers: pilot study.  J Biomed Opt. 2012;17(11):116017. doi:10.1117/1.JBO.17.11.116017PubMedGoogle ScholarCrossref
    25.
    Huang  YQ, Liang  CH, He  L,  et al.  Development and validation of a radiomics nomogram for preoperative prediction of lymph node metastasis in colorectal cancer.  J Clin Oncol. 2016;34(18):2157-2164. doi:10.1200/JCO.2015.65.9128PubMedGoogle ScholarCrossref
    26.
    Agesen  TH, Sveen  A, Merok  MA,  et al.  ColoGuideEx: a robust gene classifier specific for stage II colorectal cancer prognosis.  Gut. 2012;61(11):1560-1567. doi:10.1136/gutjnl-2011-301179PubMedGoogle ScholarCrossref
    27.
    Zhuo  S, Chen  J, Luo  T, Zou  D.  Multimode nonlinear optical imaging of the dermis in ex vivo human skin based on the combination of multichannel mode and Lambda mode.  Opt Express. 2006;14(17):7810-7820. doi:10.1364/OE.14.007810PubMedGoogle ScholarCrossref
    28.
    Xu  S, Wang  Y, Tai  DCS,  et al.  qFibrosis: a fully-quantitative innovative method incorporating histological features to facilitate accurate fibrosis scoring in animal model and chronic hepatitis B patients.  J Hepatol. 2014;61(2):260-269. doi:10.1016/j.jhep.2014.02.015PubMedGoogle ScholarCrossref
    29.
    Xu  S, Kang  CH, Gou  X,  et al.  Quantification of liver fibrosis via second harmonic imaging of the Glisson’s capsule from liver surface.  J Biophotonics. 2016;9(4):351-363. doi:10.1002/jbio.201500001PubMedGoogle ScholarCrossref
    30.
    Sauerbrei  W, Royston  P, Binder  H.  Selection of important variables and determination of functional form for continuous predictors in multivariable model building.  Stat Med. 2007;26(30):5512-5528. doi:10.1002/sim.3148PubMedGoogle ScholarCrossref
    31.
    Iasonos  A, Schrag  D, Raj  GV, Panageas  KS.  How to build and interpret a nomogram for cancer prognosis.  J Clin Oncol. 2008;26(8):1364-1370. doi:10.1200/JCO.2007.12.9791PubMedGoogle ScholarCrossref
    32.
    Kerr  KF, Brown  MD, Zhu  K, Janes  H.  Assessing the clinical impact of risk prediction models with decision curves: guidance for correct interpretation and appropriate use.  J Clin Oncol. 2016;34(21):2534-2540. doi:10.1200/JCO.2015.65.5654PubMedGoogle ScholarCrossref
    33.
    Dormann  CF, Elith  J, Bacher  S,  et al.  Collinearity: a review of methods to deal with it and a simulation study evaluating their performance.  Ecography. 2013;36(1):27-46. doi:10.1111/j.1600-0587.2012.07348.xGoogle ScholarCrossref
    34.
    Chen  J, Zhuo  S, Chen  G,  et al.  Establishing diagnostic features for identifying the mucosa and submucosa of normal and cancerous gastric tissues by multiphoton microscopy.  Gastrointest Endosc. 2011;73(4):802-807. doi:10.1016/j.gie.2010.12.016PubMedGoogle ScholarCrossref
    35.
    Mocellin  S, Pasquali  S.  Diagnostic accuracy of endoscopic ultrasonography (EUS) for the preoperative locoregional staging of primary gastric cancer.  Cochrane Database Syst Rev. 2015;(2):CD009944.PubMedGoogle Scholar
    36.
    Ajani  JA, D’Amico  TA, Almhanna  K,  et al.  Gastric Cancer, Version 3.2016, NCCN Clinical Practice Guidelines in Oncology.  J Natl Compr Canc Netw. 2016;14(10):1286-1312. doi:10.6004/jnccn.2016.0137PubMedGoogle ScholarCrossref
    37.
    Provenzano  PP, Eliceiri  KW, Campbell  JM, Inman  DR, White  JG, Keely  PJ.  Collagen reorganization at the tumor-stromal interface facilitates local invasion.  BMC Med. 2006;4(1):38. doi:10.1186/1741-7015-4-38PubMedGoogle ScholarCrossref
    38.
    Wyckoff  JB, Wang  Y, Lin  EY,  et al.  Direct visualization of macrophage-assisted tumor cell intravasation in mammary tumors.  Cancer Res. 2007;67(6):2649-2656. doi:10.1158/0008-5472.CAN-06-1823PubMedGoogle ScholarCrossref
    39.
    Levental  KR, Yu  H, Kass  L,  et al.  Matrix crosslinking forces tumor progression by enhancing integrin signaling.  Cell. 2009;139(5):891-906. doi:10.1016/j.cell.2009.10.027PubMedGoogle ScholarCrossref
    40.
    Brown  CM, Rivera  DR, Pavlova  I,  et al.  In vivo imaging of unstained tissues using a compact and flexible multiphoton microendoscope.  J Biomed Opt. 2012;17(4):040505. doi:10.1117/1.JBO.17.4.040505PubMedGoogle ScholarCrossref
    41.
    Huland  DM, Brown  CM, Howard  SS,  et al.  In vivo imaging of unstained tissues using long gradient index lens multiphoton endoscopic systems.  Biomed Opt Express. 2012;3(5):1077-1085. doi:10.1364/BOE.3.001077PubMedGoogle ScholarCrossref
    42.
    Rivera  DR, Brown  CM, Ouzounov  DG,  et al.  Compact and flexible raster scanning multiphoton endoscope capable of imaging unstained tissue.  Proc Natl Acad Sci U S A. 2011;108(43):17598-17603. doi:10.1073/pnas.1114746108PubMedGoogle ScholarCrossref
    43.
    Collins  GS, Reitsma  JB, Altman  DG, Moons  KG.  Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis (TRIPOD): the TRIPOD statement.  Ann Intern Med. 2015;162(1):55-63. doi:10.7326/M14-0697PubMedGoogle ScholarCrossref
    44.
    Hsieh  FY.  Sample size tables for logistic regression.  Stat Med. 1989;8(7):795-802. doi:10.1002/sim.4780080704PubMedGoogle ScholarCrossref
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