Very Early Recurrence After Liver Resection for Intrahepatic Cholangiocarcinoma: Considering Alternative Treatment Approaches | Gastroenterology | JAMA Surgery | JAMA Network
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Figure 1.  Kaplan-Meier Curve Demonstrating the Differences in Overall Survival Between Patients With and Without Very Early Recurrence (VER)
Kaplan-Meier Curve Demonstrating the Differences in Overall Survival Between Patients With and Without Very Early Recurrence (VER)
Figure 2.  Kaplan-Meier Curves Demonstrating the Differences in Recurrence-Free Survival Among Low-, Intermediate-, and High-Risk Patients for Very Early Recurrence Based on the Preoperative and Postoperative Models
Kaplan-Meier Curves Demonstrating the Differences in Recurrence-Free Survival Among Low-, Intermediate-, and High-Risk Patients for Very Early Recurrence Based on the Preoperative and Postoperative Models
Figure 3.  Calibration Plots for the Preoperative and Postoperative Models Associated With the Prediction of Very Early Recurrence (VER)
Calibration Plots for the Preoperative and Postoperative Models Associated With the Prediction of Very Early Recurrence (VER)

The dots represent the deciles of patients’ observed frequency of VER plotted against the estimated/predicted probability of VER. The smooth lines are cubic splines representing the relationship between the frequency and the predicted probability of VER.

Table 1.  Comparison of Baseline Characteristics and Operative Variables Between Patients With and Without Very Early Recurrence Within 6 Months After Curative-Intent Liver Resection for Intrahepatic Cholangiocarcinoma
Comparison of Baseline Characteristics and Operative Variables Between Patients With and Without Very Early Recurrence Within 6 Months After Curative-Intent Liver Resection for Intrahepatic Cholangiocarcinoma
Table 2.  Bivariate and Multivariable Logistic Regression Analyses of Factors Associated With Very Early Recurrence in Patients Who Underwent Curative-Intent Liver Resection of Intrahepatic Cholangiocarcinomaa
Bivariate and Multivariable Logistic Regression Analyses of Factors Associated With Very Early Recurrence in Patients Who Underwent Curative-Intent Liver Resection of Intrahepatic Cholangiocarcinomaa
Supplement.

eTable 1. Bivariate and multivariable logistic regression analyses of factors associated with Very Early Recurrence after excluding patients who underwent re-resection of the recurrent tumor (n=870)

eTable 2. Bivariate and multivariable logistic regression analyses of factors associated with Very Early Recurrence among patients who underwent curative-intent liver resection of intrahepatic cholangiocarcinoma after 2000.

eTable 3. Baseline Characteristics and Operative Variables Among Patients Undergoing Curative Liver Resection for Intrahepatic Cholangiocarcinoma in the external validation dataset

eTable 4. Multivariable logistic regression analyses of factors associated with Very Early Recurrence to weight a pre- and post-operative scoring points

eTable 5. Treatment of Recurrence

eFigure 1. Illustration of the online calculator with preoperative (a), and postoperative characteristics (b).

eFigure 2. Kaplan Meier curves demonstrating differences in recurrence-free survival among low, intermediate and high-risk patients for VER in the external validation cohort.

eFigure 3. Kaplan Meier curves demonstrating differences in recurrence-free survival among patients with a preoperative score 0-3, 4-5 and 6-9 (a), as well as a postoperative score 0-4, 5-6 and 7-10 (b) in the test cohort.

eFigure 4. Kaplan Meier curves demonstrating differences in recurrence-free survival among patients with a postoperative score 0-4, 5-6 and 7-10 in the external validation cohort.

eFigure 5. Kaplan-Meier curves demonstrating differences in overall survival among patients treated with different treatment modalities for VER.

1.
Chang  KY, Chang  JY, Yen  Y.  Increasing incidence of intrahepatic cholangiocarcinoma and its relationship to chronic viral hepatitis.   J Natl Compr Canc Netw. 2009;7(4):423-427. doi:10.6004/jnccn.2009.0030 PubMedGoogle ScholarCrossref
2.
Amini  N, Ejaz  A, Spolverato  G, Kim  Y, Herman  JM, Pawlik  TM.  Temporal trends in liver-directed therapy of patients with intrahepatic cholangiocarcinoma in the United States: a population-based analysis.   J Surg Oncol. 2014;110(2):163-170. doi:10.1002/jso.23605 PubMedGoogle ScholarCrossref
3.
Mavros  MN, Economopoulos  KP, Alexiou  VG, Pawlik  TM.  Treatment and prognosis for patients with intrahepatic cholangiocarcinoma: systematic review and meta-analysis.   JAMA Surg. 2014;149(6):565-574. doi:10.1001/jamasurg.2013.5137 PubMedGoogle ScholarCrossref
4.
Kim  DH, Choi  DW, Choi  SH, Heo  JS, Kow  AW.  Is there a role for systematic hepatic pedicle lymphadenectomy in intrahepatic cholangiocarcinoma? a review of 17 years of experience in a tertiary institution.   Surgery. 2015;157(4):666-675. doi:10.1016/j.surg.2014.11.006 PubMedGoogle ScholarCrossref
5.
Zhang  XF, Beal  EW, Bagante  F,  et al.  Early versus late recurrence of intrahepatic cholangiocarcinoma after resection with curative intent.   Br J Surg. 2018;105(7):848-856. doi:10.1002/bjs.10676 PubMedGoogle ScholarCrossref
6.
Doussot  A, Gonen  M, Wiggers  JK,  et al.  Recurrence patterns and disease-free survival after resection of intrahepatic cholangiocarcinoma: preoperative and postoperative prognostic models.   J Am Coll Surg. 2016;223(3):493-505.e2. doi:10.1016/j.jamcollsurg.2016.05.019 PubMedGoogle ScholarCrossref
7.
Wang  C, Pang  S, Si-Ma  H,  et al.  Specific risk factors contributing to early and late recurrences of intrahepatic cholangiocarcinoma after curative resection.   World J Surg Oncol. 2019;17(1):2. doi:10.1186/s12957-018-1540-1 PubMedGoogle ScholarCrossref
8.
Tsilimigras  DI, Sahara  K, Moris  D,  et al.  Effect of surgical margin width on patterns of recurrence among patients undergoing R0 hepatectomy for T1 hepatocellular carcinoma: an international multi-institutional analysis.   J Gastrointest Surg. 2019. doi:10.1007/s11605-019-04275-0 PubMedGoogle Scholar
9.
Xu  XF, Xing  H, Han  J,  et al.  Risk factors, patterns, and outcomes of late recurrence after liver resection for hepatocellular carcinoma: a multicenter study from China.   JAMA Surg. 2019;154(3):209-217. doi:10.1001/jamasurg.2018.4334 PubMedGoogle ScholarCrossref
10.
Tsilimigras  DI, Hyer  JM, Moris  D,  et al; International Intrahepatic Cholangiocarcinoma Study Group.  Prognostic utility of albumin-bilirubin grade for short- and long-term outcomes following hepatic resection for intrahepatic cholangiocarcinoma: a multi-institutional analysis of 706 patients.   J Surg Oncol. 2019;120(2):206-213. doi:10.1002/jso.25486 PubMedGoogle Scholar
11.
Tsilimigras  DI, Mehta  R, Moris  D,  et al.  A machine-based approach to preoperatively identify patients with the most and least benefit associated with resection for intrahepatic cholangiocarcinoma: an international multi-institutional analysis of 1,146 patients.   Ann Surg Oncol. 2020;27(4):1110-1119. doi:10.1245/s10434-019-08067-3PubMedGoogle ScholarCrossref
12.
Tsilimigras  DI, Mehta  R, Aldrighetti  L,  et al; International Intrahepatic Cholangiocarcinoma Study Group.  Development and validation of a laboratory risk score (LabScore) to predict outcomes after resection for intrahepatic cholangiocarcinoma.   J Am Coll Surg. 2020;230(4):381-391.e2. doi:10.1016/j.jamcollsurg.2019.12.025 PubMedGoogle ScholarCrossref
13.
Simon  R, Sasaki  K, Margonis  GA,  et al.  Risk factors for very early recurrence of hepatocellular carcinoma: a retrospective review.   HPB (Oxford). 2018;20:2. doi:10.1016/j.hpb.2018.02.327 Google ScholarCrossref
14.
Hirokawa  F, Hayashi  M, Asakuma  M, Shimizu  T, Inoue  Y, Uchiyama  K.  Risk factors and patterns of early recurrence after curative hepatectomy for hepatocellular carcinoma.   Surg Oncol. 2016;25(1):24-29. doi:10.1016/j.suronc.2015.12.002 PubMedGoogle ScholarCrossref
15.
Jung  SW, Kim  DS, Yu  YD, Han  JH, Suh  SO.  Risk factors for cancer recurrence or death within 6 months after liver resection in patients with colorectal cancer liver metastasis.   Ann Surg Treat Res. 2016;90(5):257-264. doi:10.4174/astr.2016.90.5.257 PubMedGoogle ScholarCrossref
16.
Takahashi  S, Konishi  M, Nakagohri  T, Gotohda  N, Saito  N, Kinoshita  T.  Short time to recurrence after hepatic resection correlates with poor prognosis in colorectal hepatic metastasis.   Jpn J Clin Oncol. 2006;36(6):368-375. doi:10.1093/jjco/hyl027 PubMedGoogle ScholarCrossref
17.
Strasberg  SM.  Nomenclature of hepatic anatomy and resections: a review of the Brisbane 2000 system.   J Hepatobiliary Pancreat Surg. 2005;12(5):351-355. doi:10.1007/s00534-005-0999-7 PubMedGoogle ScholarCrossref
18.
Amin  MB, Greene  FL, Edge  SB,  et al.  The Eighth Edition AJCC Cancer Staging Manual: continuing to build a bridge from a population-based to a more “personalized” approach to cancer staging.   CA Cancer J Clin. 2017;67(2):93-99. doi:10.3322/caac.21388PubMedGoogle ScholarCrossref
19.
Hu  LS, Zhang  XF, Weiss  M,  et al.  Recurrence patterns and timing courses following curative-intent resection for intrahepatic cholangiocarcinoma.   Ann Surg Oncol. 2019;26(8):2549-2557. doi:10.1245/s10434-019-07353-4 PubMedGoogle ScholarCrossref
20.
Lee  KJ, Carlin  JB.  Multiple imputation for missing data: fully conditional specification versus multivariate normal imputation.   Am J Epidemiol. 2010;171(5):624-632. doi:10.1093/aje/kwp425 PubMedGoogle ScholarCrossref
21.
Camp  RL, Dolled-Filhart  M, Rimm  DL.  X-tile: a new bio-informatics tool for biomarker assessment and outcome-based cut-point optimization.   Clin Cancer Res. 2004;10(21):7252-7259. doi:10.1158/1078-0432.CCR-04-0713 PubMedGoogle ScholarCrossref
22.
Facciabene  A, Peng  X, Hagemann  IS,  et al.  Tumour hypoxia promotes tolerance and angiogenesis via CCL28 and T(reg) cells.   Nature. 2011;475(7355):226-230. doi:10.1038/nature10169 PubMedGoogle ScholarCrossref
23.
Chan  AWH, Zhong  J, Berhane  S,  et al.  Development of pre and post-operative models to predict early recurrence of hepatocellular carcinoma after surgical resection.   J Hepatol. 2018;69(6):1284-1293. doi:10.1016/j.jhep.2018.08.027 PubMedGoogle ScholarCrossref
24.
Margonis  GA, Sasaki  K, Gholami  S,  et al.  Genetic And Morphological Evaluation (GAME) score for patients with colorectal liver metastases.   Br J Surg. 2018;105(9):1210-1220. doi:10.1002/bjs.10838 PubMedGoogle ScholarCrossref
25.
Valle  J, Wasan  H, Palmer  DH,  et al; ABC-02 Trial Investigators.  Cisplatin plus gemcitabine versus gemcitabine for biliary tract cancer.   N Engl J Med. 2010;362(14):1273-1281. doi:10.1056/NEJMoa0908721 PubMedGoogle ScholarCrossref
26.
Bagante  F, Spolverato  G, Merath  K,  et al.  Intrahepatic cholangiocarcinoma tumor burden: a classification and regression tree model to define prognostic groups after resection.   Surgery. 2019;166(6):983-990. doi:10.1016/j.surg.2019.06.005 PubMedGoogle ScholarCrossref
27.
Sahara  K, Tsilimigras  DI, Mehta  R,  et al.  A novel online prognostic tool to predict long-term survival after liver resection for intrahepatic cholangiocarcinoma: the “metro-ticket” paradigm.   J Surg Oncol. 2019;120(2):223-230. doi:10.1002/jso.25480 PubMedGoogle Scholar
28.
Yeh  CN, Jan  YY, Chen  MF.  Influence of age on surgical treatment of peripheral cholangiocarcinoma.   Am J Surg. 2004;187(4):559-563. doi:10.1016/j.amjsurg.2003.12.051 PubMedGoogle ScholarCrossref
29.
Beheshti  A, Benzekry  S, McDonald  JT,  et al.  Host age is a systemic regulator of gene expression impacting cancer progression.   Cancer Res. 2015;75(6):1134-1143. doi:10.1158/0008-5472.CAN-14-1053 PubMedGoogle ScholarCrossref
30.
Kleeff  J, Reiser  C, Hinz  U,  et al.  Surgery for recurrent pancreatic ductal adenocarcinoma.   Ann Surg. 2007;245(4):566-572. doi:10.1097/01.sla.0000245845.06772.7d PubMedGoogle ScholarCrossref
31.
Youngwirth  LM, Nussbaum  DP, Thomas  S,  et al.  Nationwide trends and outcomes associated with neoadjuvant therapy in pancreatic cancer: an analysis of 18 243 patients.   J Surg Oncol. 2017;116(2):127-132. doi:10.1002/jso.24630 PubMedGoogle ScholarCrossref
32.
Heinrich  S, Schäfer  M, Weber  A,  et al.  Neoadjuvant chemotherapy generates a significant tumor response in resectable pancreatic cancer without increasing morbidity: results of a prospective phase II trial.   Ann Surg. 2008;248(6):1014-1022. doi:10.1097/SLA.0b013e318190a6da PubMedGoogle ScholarCrossref
33.
Leonard  GD, Brenner  B, Kemeny  NE.  Neoadjuvant chemotherapy before liver resection for patients with unresectable liver metastases from colorectal carcinoma.   J Clin Oncol. 2005;23(9):2038-2048. doi:10.1200/JCO.2005.00.349 PubMedGoogle ScholarCrossref
34.
Greer  SE, Pipas  JM, Sutton  JE,  et al.  Effect of neoadjuvant therapy on local recurrence after resection of pancreatic adenocarcinoma.   J Am Coll Surg. 2008;206(3):451-457. doi:10.1016/j.jamcollsurg.2007.10.002 PubMedGoogle ScholarCrossref
35.
Darwish Murad  S, Kim  WR, Harnois  DM,  et al.  Efficacy of neoadjuvant chemoradiation, followed by liver transplantation, for perihilar cholangiocarcinoma at 12 US centers.   Gastroenterology. 2012;143(1):88-98.e3. doi:10.1053/j.gastro.2012.04.008 PubMedGoogle ScholarCrossref
36.
Buettner  S, Koerkamp  BG, Ejaz  A,  et al.  The effect of preoperative chemotherapy treatment in surgically treated intrahepatic cholangiocarcinoma patients—a multi-institutional analysis.   J Surg Oncol. 2017;115(3):312-318. doi:10.1002/jso.24524 PubMedGoogle ScholarCrossref
37.
Borad  MJ, Champion  MD, Egan  JB,  et al.  Integrated genomic characterization reveals novel, therapeutically relevant drug targets in FGFR and EGFR pathways in sporadic intrahepatic cholangiocarcinoma.   PLoS Genet. 2014;10(2):e1004135. doi:10.1371/journal.pgen.1004135 PubMedGoogle Scholar
38.
Farshidfar  F, Zheng  S, Gingras  M-C,  et al; Cancer Genome Atlas Network.  Integrative genomic analysis of cholangiocarcinoma identifies distinct IDH-mutant molecular profiles.   Cell Rep. 2017;18(11):2780-2794. doi:10.1016/j.celrep.2017.02.033 PubMedGoogle ScholarCrossref
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    Original Investigation
    July 8, 2020

    Very Early Recurrence After Liver Resection for Intrahepatic Cholangiocarcinoma: Considering Alternative Treatment Approaches

    Author Affiliations
    • 1James Comprehensive Cancer Center, Department of Surgery, Division of Surgical Oncology, The Ohio State University Wexner Medical Center, Columbus
    • 2Department of Gastroenterological Surgery, Yokohama City University School of Medicine, Yokohama, Japan
    • 3Department of Surgery, Eastern Hepatobiliary Surgery Hospital, Shanghai, China
    • 4Department of Surgery, University of Verona, Verona, Italy
    • 5Department of Surgery, Ospedale San Raffaele, Milano, Italy
    • 6Department of Surgery, Johns Hopkins Hospital, Baltimore, Maryland
    • 7Department of Surgery, University of Virginia, Charlottesville
    • 8Department of Surgery, Fundeni Clinical Institute, Bucharest, Romania
    • 9Department of Surgery, Stanford University, Stanford, California
    • 10Department of Surgery, Emory University, Atlanta, Georgia
    • 11Department of Surgery, Curry Cabral Hospital, Lisbon, Portugal
    • 12Department of Surgery, University of Ottawa, Ottawa, Canada
    • 13Department of Surgery, Royal Prince Alfred Hospital, University of Sydney, Sydney, Australia
    • 14Department of Hepatobiliopancreatic Surgery and Liver Transplantation, AP-HP, Beaujon Hospital, Clichy, France
    • 15Department of Surgery, Erasmus University Medical Centre, Rotterdam, the Netherlands
    • 16Digestive Disease and Surgery Institute, Department of General Surgery, Cleveland Clinic, Cleveland, Ohio
    • 17Institute of Advanced Surgical Technology and Engineering, Department of Hepatobiliary Surgery, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
    • 18Deputy Editor, JAMA Surgery
    JAMA Surg. 2020;155(9):823-831. doi:10.1001/jamasurg.2020.1973
    Key Points

    Question  Which patients will develop very early recurrence (VER) (ie, recurrence within 6 months) after resection for intrahepatic cholangiocarcinoma and are the best candidates for neoadjuvant chemotherapy?

    Findings  In this multi-institutional cohort study, 196 patients (22.3%) developed VER following resection with a detrimental association with overall survival (5-year overall survival, 8.9%). Two predictive models were developed to identify high-risk patients for VER in the pre- and postoperative setting with a good predictive accuracy in the training as well as the internal and external validation data sets.

    Meaning  These data emphasize that VER is common after intrahepatic cholangiocarcinoma resection and highlight the need for an alternative treatment approach (ie, neoadjuvant chemotherapy) for high-risk patients.

    Abstract

    Importance  Although surgery offers the best chance of a potential cure for patients with localized, resectable intrahepatic cholangiocarcinoma (ICC), prognosis of patients remains dismal largely because of a high incidence of recurrence.

    Objective  To predict very early recurrence (VER) (ie, recurrence within 6 months after surgery) following resection for ICC in the pre- and postoperative setting.

    Design, Setting, and Participants  Patients who underwent curative-intent resection for ICC between May 1990 and July 2016 were identified from an international multi-institutional database. The study was conducted at The Ohio State University in collaboration with all other participating institutions. The data were analyzed in December 2019.

    Main Outcomes and Measures  Two logistic regression models were constructed to predict VER based on pre- and postoperative variables. The final models were used to develop an online calculator to predict VER and the tool was internally and externally validated.

    Results  Among 880 patients (median age, 59 years [interquartile range, 51-68 years]; 388 women [44.1%]; 428 [50.2%] white; 377 [44.3%] Asian; 27 [3.2%] black]), 196 (22.3%) developed VER. The 5-year overall survival among patients with and without VER was 8.9% vs 49.8%, respectively (P < .001). A preoperative model was able to stratify patients relative to the risk for VER: low risk (6-month recurrence-free survival [RFS], 87.7%), intermediate risk (6-month RFS, 72.3%), and high risk (6-month RFS, 49.5%) (log-rank P < .001). The postoperative model similarly identified discrete cohorts of patients based on probability for VER: low risk (6-month RFS, 90.0%), intermediate risk (6-month RFS, 73.1%), and high risk (6-month RFS, 48.5%) (log-rank, P < .001). The calibration and predictive accuracy of the pre- and postoperative models were good in the training (C index: preoperative, 0.710; postoperative, 0.722) as well as the internal (C index: preoperative, 0.715; postoperative, 0.728; bootstrapping resamples, n = 5000) and external (C index: postoperative, 0.672) validation data sets.

    Conclusion and Relevance  An easy-to-use online calculator was developed to help clinicians predict the chance of VER after curative-intent resection for ICC. The tool performed well on internal and external validation. This tool may help clinicians in the preoperative selection of patients for neoadjuvant therapy as well as during the postoperative period to inform surveillance strategies.

    Introduction

    Intrahepatic cholangiocarcinoma (ICC) ranks as the second most common primary liver malignancy, with a growing incidence in Western and Eastern countries over the past 3 decades.1,2 Although surgery offers the best chance of a potential cure for patients with localized, resectable ICC, the prognosis of these patients is still discouraging, with a median overall survival (OS) ranging from 12 to 31 months.3,4 In fact, 50% to 70% of patients with ICC will experience a recurrence following resection.5,6

    Previous studies defined recurrence following resection for ICC as early vs late using a cutoff of 2 years.5,6 Patients with early (<24 months) vs late recurrence (>24 months) had distinct recurrence patterns, predictors, and outcomes.5,6 Patients with late recurrence generally had a better prognosis compared with patients who developed early recurrence following ICC resection.5,7 In addition, certain tumor characteristics, including tumor size and tumor multifocality, were predominantly associated with early but not late recurrence.5,7 Although such a categorization (ie, early [<24 months] vs late [>24 months]) aligns with previous studies on hepatocellular carcinoma (HCC),8,9 it may not be appropriate for patients with ICC given that most recurrences occur within the first 2 years after resection of ICC.5,6 In fact, a previous study from our own group noted that approximately one-quarter of patients with ICC had very early recurrence (VER) (ie, recurrence within 6 months after initial resection).5 Patients with VER were even more common than individuals who experienced a late recurrence (>2 years).5 As such, identifying patients who are at risk for VER is important to construct individualized surveillance strategies following resection for ICC or even recommend an alternative treatment strategy for these patients, including neoadjuvant therapy or other nonsurgical treatment modalities. To our knowledge, no predictive tool exists to predict VER among patients undergoing curative-intent liver resection for ICC. As such, the objective of this study was to characterize patients who develop VER following curative-intent resection for ICC. In addition, we sought to develop preoperative and postoperative models to predict VER based on factors known before and after surgery using a large, multi-institutional database. To facilitate the clinical applicability of the models, an easy-to-use online calculator was developed to predict the risk of VER among individuals with resectable ICC in the pre- and postoperative setting.

    Methods
    Patient Cohort and Data Collection

    Patients who underwent liver resection for histologically proven ICC between May 1990 and July 2016 were identified in an international multi-institutional database that incorporated data from 15 major hepatobiliary institutions involved in the International Intrahepatic Cholangiocarcinoma Collaboration.10-12 The VER of ICC was defined as the incidence of recurrence within 6 months after resection based on previous studies.13-16 Only patients who received curative-intent hepatectomy were included in the analysis. Patients were excluded for (1) macroscopically positive surgical margins, (2) lack of follow-up data, and (3) death or loss to follow-up without any evidence of recurrence within 6 months following resection. The institutional review boards of all the participating facilities approved the study. Patient consent was waived as retrospective deidentified data were analyzed.

    Clinicopathologic variables of patients with ICC extracted included age, sex, race, body mass index (calculated as weight in kilograms divided by height in meters squared), cirrhosis, American Society of Anesthesiologists class, preoperative serum levels of carcinoembryonic antigen and carbohydrate antigen (CA) 19-9, preoperative lymph node (LN) assessment, tumor size, tumor number, location, macro- or microvascular invasion, perineural invasion, American Joint Committee on Cancer (AJCC) tumor stage, AJCC N stage, tumor grade, morphological type (ie, mass-forming, intraductal growth, and periductal infiltrating), extent of resection, resection margin status, intraoperative blood loss, operative time, use of perioperative chemotherapy or radiotherapy, and postoperative complications.5 Major hepatectomy was defined as resection of 3 or more Couinaud segments.17 Macrovascular invasion was defined as invasion of the portal vein, hepatic artery, or hepatic veins, whereas microvascular invasion was defined as intraparenchymal vascular involvement identified on histology testing results.18 Tumor stage was defined following the AJCC seventh edition staging manual.

    After liver resection, patients were monitored for recurrence with serum tumor markers and imaging studies, including ultrasonography, computed tomography, and/or magnetic resonance imaging. In general, patients were followed up once every 3 to 4 months for the first 3 years, once every 6 months from years 4 to 5, and then annually.19 Recurrence was defined as suspicious or positive findings on surveillance imaging or histologically confirmed disease. The treatment of tumor recurrence was decided following consensus among the multidisciplinary team in each institution.

    Statistical Analysis

    Categorical and continuous variables were presented as frequency (%) and median (interquartile range [IQR]), respectively. The association of several clinicopathological factors with the incidence of VER following ICC resection was assessed by means of logistic regression analysis. Variables significant on bivariate analysis were subsequently included in the multivariable logistic regression model and a stepwise selection method was used (forward selection method using the lowest bayesian information criterion). Two risk scores to predict VER of ICC before and after resection were developed based on the final step of the multivariable logistic regression model. Specifically, the β coefficients of the risk factors of VER identified in the final step of the respective multivariable logistic regression models were used to construct a weighted composite preoperative and postoperative score. Estimated probabilities of developing VER were calculated according to the following formula: P = 1/{1+exp[−(Preoperative or Postoperative Score)]}, in which P is the probability of developing VER. For the multivariable logistic regression analysis, multivariate normal imputations were performed for missing data.20 By using the X-tile program,21,22 the optimal cutoffs of pre- and postoperative risk scores were determined to stratify patients at low, intermediate, or high risk for VER.23 In addition, a model using discrete categorical variables was developed. In this model, the hazard ratio (HR) of factors that were significant in the multivariable model was assigned discrete points to create a simple scoring system, as previously reported.24 Differences in recurrence-free survival (RFS) or OS between different subgroups of patients were assessed using the Kaplan-Meier method and the log-rank test.

    To assess the performance of the prognostic model, the C index was calculated for the entire data set (training data set) as well as with the bootstrapping resample method (n = 5000) (internal validation). Calibration of the models was performed by plotting the predicted probabilities against the observed outcomes of the cohort. The accuracy of the prognostic model to predict VER was also externally validated using data from the Cleveland Clinic (Cleveland, Ohio) and the First Affiliated Hospital of Xi'an Jiaotong University (Xi'an, China). Because of data collection limitations, only the postoperative VER model was externally validated. The level of statistical significance was set at α = .05. To account for the possible association of a period effect, additional sensitivity analyses were also performed after excluding patients who underwent liver resection before 2000. All statistical analyses were performed using SPSS, version 25 (IBM), along with JMP statistical package, version 14 (SAS Institute).

    Results
    Patient Characteristics With or Without VER

    A total of 880 patients met the inclusion criteria and were included in the final analytic cohort (Table 1). The median patient age was 59 years (IQR, 51-68 years), 491 patients (55.9%) were men, and 562 (70.4%) had an American Society of Anesthesiologists class of 2 or lower. Most patients underwent a major hepatectomy (491 [56.9%]) for a T1 or T2 tumor (708 [84.0%]); a subset of patients had LN metastases (165 [18.8%]). Approximately one-third of patients received adjuvant chemotherapy or radiotherapy (279 [32.7%]) (Table 1). Overall, 196 patients (22.3%) had VER, whereas 684 (77.7%) did not (non-VER group); 374 patients (42.5%) had a recurrence more than 6 months after resection and 310 patients (35.3%) did not experience a recurrence during the follow-up period. Differences in the characteristics of patients with and without VER are summarized in Table 1.

    Survival and Risk Factors of Patients With VER

    After a median follow-up time of 24.1 months (IQR, 13.2-43.6 months), the median and 5-year OS among patients with and without VER was 13.8 months (IQR, 11.6-15.3 months) and 8.9% vs 59.7 months (IQR, 48.2-73.8 months) and 49.8%, respectively (P < .001) (Figure 1). On multivariable analysis of preoperative factors, race of color (odds ratio [OR],  1.79; 95% CI, 1.23-2.60), liver cirrhosis (OR,  2.06; 95% CI, 1.25-3.40), larger tumor size (OR, 1.12; 95% CI, 1.06-1.17), higher number of tumors (OR,  1.36; 95% CI, 1.15-1.60), and suspicious/metastatic LNs on preoperative imaging (OR,  1.90; 95% CI, 1.28-2.84) remained associated with a higher likelihood of VER, whereas higher age was associated with lower odds of VER (OR, 0.97; 95% CI, 0.96-0.99) (Table 2). A separate multivariable analysis that included all pre- and postoperative factors demonstrated that race of color (OR,  2.04; 95% CI, 1.38-3.00), larger tumor size (OR, 1.11; 95% CI, 1.06-1.17), higher number of tumors (OR, 1.36; 95% CI, 1.15-1.60), microvascular invasion (OR,  1.55; 95% CI, 1.06-2.26), N1 or Nx disease (OR, 1.94; 95% CI, 1.29-2.94), and R1 resection (OR, 2.14; 95% CI, 1.27-3.60) were each associated with greater odds of VER, whereas older age was again associated with lower odds of VER (OR, 0.97; 95% CI, 0.95-0.98) (Table 2). Neither hospital location (Eastern vs Western: OR, 1.33; 95% CI, 0.77-2.30) nor year of surgery (OR, 0.96; 95% CI, 0.92-1.02) were associated with VER. A sensitivity analysis after excluding patients who underwent reresection of a recurrent tumor (total number of patients analyzed, 870 [98.9%]) revealed the same variables were associated with VER, with only slightly changed ORs compared with the aforementioned models (eTable 1 in the Supplement).

    Development of Preoperative and Postoperative Risk Scores to Predict VER

    Pre- and postoperative risk scores were developed based on the factors identified in the respective multivariable models (Table 2). Subsequently, patients were categorized into 3 different risk categories for VER based on the preoperative risk score: low risk (455 [51.7%]; 6-month RFS, 87.7%), intermediate risk (332 [37.7%]; 6-month RFS, 72.3%), and high risk (93 [10.6%]; 6-month RFS, 49.5%) (P < .001) (Table 2 and Figure 2A). Similarly, patients were categorized into 3 different risk groups for VER based on the postoperative risk score: low risk (440 [50.0%]; 6-month RFS, 90.0%), intermediate risk (308 [35.0%]; 6-month RFS, 73.1%), and high risk (132 [15.0%]; 6-month RFS, 48.5%) (P < .001) (Table 2 and Figure 2B). To facilitate clinical applicability of the preoperative and postoperative models, a convenient online calculator able to calculate the probability of VER and the risk group of VER assigned on the basis of the pre- and postoperative scores was developed (eFigure 1 in the Supplement), which is available at: https://k-sahara.shinyapps.io/Veryearly-recurrence/.

    Predictive Performance of the Models to Predict VER

    The discriminative accuracy of the preoperative model was very good in the training data set (C index: 0.710; 95% CI, 0.666-0.750) and the validation data set with bootstrapping resamples (C index: 0.715; 95% CI, 0.700-0.730). Similarly, the predictive accuracy of the postoperative model was very good in the training data set (C index: 0.722; 95% CI, 0.677-0.759) as well as the validation data set with bootstrapping resamples (C index: 0.728; 95% CI, 0.715-0.742). The calibration plots demonstrated overall good agreement between the estimated probability of VER and the observed frequency of VER in the pre- and postoperative models (Figure 3). A sensitivity analysis was conducted that included only patients who underwent surgery after 2000 (eTable 2 in the Supplement). The differences in the predicted probability of VER were minor (0.6% in the pre- and post-operative models).

    The postoperative VER model performed well in the external validation cohort (C index: 0.672; 95% CI, 0.595-0.742). Specifically, patients deemed high risk had a worse RFS compared with patients who were either intermediate or low risk for VER (6-month RFS: low risk, 80.4% vs intermediate risk, 75.3% vs high risk, 44.4%; P < .01) (eFigure 2 and eTable 3 in the Supplement).

    Development and Validation of a Simple Scoring System

    A simple discrete scoring system was also developed to facilitate prognostic classification of patients without the need of the online calculator (eTable 4 in the Supplement). Specifically, patients with a preoperative score of 0 to 3, 4 to 5, and 6 to 9 had incrementally worse 6-month RFS (90.1% vs 75.6% vs 55.2%; P < .001; eFigure 3 in the Supplement). Similarly, based on the postoperative scoring system, patients with a score of 0 to 4, 5 to 6, and 7 to 10 had an incrementally worse 6-month RFS (91.1% vs 82.4% vs 57.7%; P < .001; eFigure 3 in the Supplement); this discrete postoperative scoring system was also able to stratify patient prognosis in the external validation cohort (6-month RFS: score of 0-4, 84.3% vs score of 5-6, 72.0% vs score of 7-10, 56.2%; P < .001) (eFigure 4 in the Supplement). The predictive accuracy of the pre- and postoperative models based on the scoring system was also very good in the training (C index: preoperative, 0.716; postoperative, 0.726) as well as the internal (C index: preoperative, 0.716; postoperative, 0.725; bootstrapping resamples, n = 5000) and external (C index: postoperative, 0.692) validation data sets.

    Treatment and Outcomes of Patients With VER

    Among 196 patients who had VER, most had intrahepatic recurrence only (117 [60.3%]); a subset had extrahepatic recurrence (29 [15.0%]) or intra- and extrahepatic recurrence (48 [24.7%]). Among patients with VER, only 10 patients (5.1%) underwent reresection compared with 45 individuals (12.0%) among those who experienced a later recurrence (P < .001), and most received the best supportive care (100 [51.0%]) (eTable 5 in the Supplement). The median OS following VER was 9.3 months (95% CI, 8.0–10.5 months). Three-year OS after recurrence was better among patients who underwent reresection vs individuals who received other types of treatment (54.0% vs 13.0%; P = .001) (eFigure 5 in the Supplement).

    Discussion

    Several previous studies have used the term VER to characterize recurrence within 6 months following resection for HCC and colorectal liver metastases.14-16 Although there is no consensus about the exact timing of early recurrence among patients with ICC, using a cutoff of 2 years for ICC may be problematic because many patients with ICC have recurrence much earlier within the very first months following resection.5 This study demonstrated that approximately one-fourth of patients (22.3%) developed recurrence within 6 months after resection for ICC. Patients with a VER had a median OS as low as 13.8 months, which is similar to patients with advanced cholangiocarcinoma who received systemic chemotherapy in a phase 3 randomized clinical trial (median OS, 11.7 months).25 In addition, 2 models, one preoperative and one postoperative, were developed to calculate the risk of VER among patients with resectable ICC. Using the preoperative model, patients were categorized into low- (455 [51.7%]), intermediate- (332 [37.7%]), and high-risk groups (93 [10.6%]) with an incrementally worse 6-month RFS (87.7% vs 72.3% vs 49.5%; P < .001). Similarly, a postoperative model identified 3 groups of patients with an incrementally worse RFS (6-month RFS: 90.0% vs 73.1% vs 48.5%; P < .001). Using an online calculator developed in this study, physicians can calculate the individualized possibility of patients to develop VER in the pre- and postoperative setting. An additional simple discrete scoring system to predict VER was developed and validated that can be used by physicians without requiring the use of an online calculator. To our knowledge, this is the first study to define the incidence and risk of VER as well as provide a prediction tool to assess the likelihood of VER among patients undergoing surgery for ICC.

    This study developed 2 models to predict VER following resection for ICC. Based on variables, such as age, race, liver cirrhosis, tumor size and number, and radiologic LN status, the preoperative model was able to identify 3 groups of patients with different risk for VER (ie, low-, intermediate-, and high-risk groups) who had an incrementally worse 6-month RFS (87.7% vs 72.3% vs 49.5%; P < .001) (Figure 2A). Incorporating pathologic data, including microvascular invasion, nodal status, and resection margins, the postoperative model was able to stratify patients according to risk for VER (90.0% vs 73.1% vs 48.5%; P < .001). Tumor size and number (eg, tumor burden),26 microvascular and nodal invasion,6 liver cirrhosis, and resection margins have been associated with risk of recurrence among patients with ICC.10,27 In contrast, data on age as a predictor of outcomes among patients with cancer have been more equivocal.3,28 In this study, younger age remained associated with a higher chance for VER in the pre- and postoperative models. Although the explanation is likely multifactorial, the increased proliferative and angiogenic activity of tumor cells in younger individuals may be significantly associated with recurrence rates.29 The presence of cirrhosis was only included in the preoperative predictive model; the former did not remain associated with VER after accounting for other variables available during the postoperative period (eg, vascular invasion). In addition, suspicious or metastatic LNs on preoperative imaging were replaced in the postoperative model with actual pathologic nodal status. Taken together, the data highlight how patients with multiple nodules, large tumor size, and suspicious or metastatic LNs on preoperative imaging have a markedly higher likelihood of experiencing VER and, in turn, a poor survival.11 Thus, identifying patients who are likely to experience a VER is particularly important because these patients should be considered for clinical trials, neoadjuvant therapy, or other nonsurgical treatment modalities. In addition, characterizing patients at risk for VER in the postoperative setting may be useful in determining the intensity of the surveillance strategy, as well as identifying patients who might benefit more from adjuvant chemotherapy following resection of ICC.

    The finding that 1 in 4 patients experienced VER with an overall survival of roughly 1 year after surgery was particularly notable. These data were comparable with outcomes among many patients with pancreatic adenocarcinoma who often have recurrence early and have a very poor survival rate.30 Because of these poor outcomes, there has been a marked increase over the last decade in the routine use of neoadjuvant chemotherapy among patients with pancreatic cancer.31 Neoadjuvant therapy is used for many reasons, including early treatment of micrometastatic disease, in addition to treatment of the index lesion.32 In this manner, early systemic chemotherapy provides a therapeutic and selection role to help determine which patients may benefit most from an attempt at curative-intent surgery.33 In fact, neoadjuvant therapy has been demonstrated to be effective in increasing disease-free survival among patients with pancreatic and perihilar cholangiocarcinoma.34,35 Despite this, the use of neoadjuvant therapy among patients with ICC remains extremely low.36 However, more recently, epidermal growth factor receptor signaling pathway, as well as isocitrate dehydrogenase mutations, have been identified as specific therapeutic targets for systemic therapy.37,38 Therefore, use of the VER tool may be important to inform a potential paradigm shift in treating patients with ICC. Specifically, by using the tool proposed in this study (https://k-sahara.shinyapps.io/Veryearly-recurrence/), surgeons can estimate the individualized risk of a specific patient to experience VER. In addition, by using the simple scoring system developed in this study, physicians can also estimate the risk of VER without requiring the use of an online calculator (eTable 4 and eFigures 3 and 4 in the Supplement). In turn, patients at high risk for VER should be considered candidates for clinical trials, neoadjuvant systemic chemotherapy, or alternative liver-directed treatment options rather than upfront surgery.

    Limitations

    Several limitations should be considered when interpreting the results of this study. While the multi-institutional nature of the database was a strength, there may have been some heterogeneity in patient selection and surgical techniques among the different participating centers. The duration of the cohort may have also contributed to a period effect and associated heterogeneity; however, a sensitivity analysis that excluded patients who underwent an operation before 2000 (n = 33) demonstrated similar results. In addition, data on CA19-9 levels 1 month after surgery were not available in the data set and we were thus unable to assess whether this information could predict VER following liver resection of ICC. However, not all patients with ICC express CA19-9 and the more comprehensive postoperative model developed in the context of this study did predict VER well in the test and external validation cohorts. Information on α-fetoprotein levels was also not available in the database because most centers routinely measure CA19-9 and not α-fetoprotein for ICC patients; all patients underwent resection for pure ICC and none had mixed HCC-ICC. Furthermore, there may have been slight variations in radiologic or pathologic assessment of tumor size and number at different centers depending on the method of assessment; however, these are unlikely to be clinically significant.23

    Conclusions

    Approximately one-fourth of patients undergoing curative-intent hepatectomy for ICC developed VER, which was associated with a very discouraging prognosis. An easy-to-use online calculator to predict the risk of VER was developed based on clinicopathological variables available before and after resection for ICC. The VER calculator demonstrated a very good accuracy on internal and external validation. The online calculator may help clinicians to use neoadjuvant therapy more often among high-risk patients with ICC as well as inform the intensity of surveillance following resection.

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    Article Information

    Accepted for Publication: March 14, 2020.

    Corresponding Author: Timothy M. Pawlik, MD, MPH, PhD, Department of Surgery, The Ohio State University, Wexner Medical Center, 395 W 12th Ave, Ste 670, Columbus, OH 43210 (tim.pawlik@osumc.edu).

    Published Online: July 8, 2020. doi:10.1001/jamasurg.2020.1973

    Author Contributions: Drs Tsilimigras and Pawlik had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis. Drs Tsilimigras, Sahara, and Wu contributed equally to this work.

    Concept and design: Tsilimigras, Sahara, Wu, Moris, Guglielmi, Shen, Groot Koerkamp, Aucejo, Zhang, Pawlik.

    Acquisition, analysis, or interpretation of data: Tsilimigras, Sahara, Wu, Moris, Bagante, Aldrighetti, Weiss, Bauer, Alexandrescu, Poultsides, Maithel, Marques, Martel, Pulitano, Soubrane, Moro, Sasaki, Zhang, Matsuyama, Endo, Pawlik.

    Drafting of the manuscript: Tsilimigras, Sahara, Wu, Moris, Weiss, Alexandrescu, Zhang, Matsuyama, Pawlik.

    Critical revision of the manuscript for important intellectual content: Tsilimigras, Sahara, Bagante, Guglielmi, Aldrighetti, Weiss, Bauer, Poultsides, Maithel, Marques, Martel, Pulitano, Shen, Soubrane, Groot Koerkamp, Moro, Sasaki, Aucejo, Zhang, Endo, Pawlik.

    Statistical analysis: Tsilimigras, Sahara, Wu, Bagante, Weiss, Shen, Moro, Zhang, Pawlik.

    Administrative, technical, or material support: Tsilimigras, Wu, Moris, Bauer, Poultsides, Pulitano, Sasaki, Zhang, Matsuyama, Pawlik.

    Supervision: Tsilimigras, Moris, Guglielmi, Aldrighetti, Marques, Groot Koerkamp, Sasaki, Aucejo, Pawlik.

    Conflict of Interest Disclosures: Dr Alexandrescu reported personal fees from Merck and nonfinancial support from Merck outside the submitted work. No other disclosures were reported.

    Disclaimer: Dr Pawlik is a deputy editor of JAMA Surgery, but he was not involved in any of the decisions regarding review of the manuscript or its acceptance.

    References
    1.
    Chang  KY, Chang  JY, Yen  Y.  Increasing incidence of intrahepatic cholangiocarcinoma and its relationship to chronic viral hepatitis.   J Natl Compr Canc Netw. 2009;7(4):423-427. doi:10.6004/jnccn.2009.0030 PubMedGoogle ScholarCrossref
    2.
    Amini  N, Ejaz  A, Spolverato  G, Kim  Y, Herman  JM, Pawlik  TM.  Temporal trends in liver-directed therapy of patients with intrahepatic cholangiocarcinoma in the United States: a population-based analysis.   J Surg Oncol. 2014;110(2):163-170. doi:10.1002/jso.23605 PubMedGoogle ScholarCrossref
    3.
    Mavros  MN, Economopoulos  KP, Alexiou  VG, Pawlik  TM.  Treatment and prognosis for patients with intrahepatic cholangiocarcinoma: systematic review and meta-analysis.   JAMA Surg. 2014;149(6):565-574. doi:10.1001/jamasurg.2013.5137 PubMedGoogle ScholarCrossref
    4.
    Kim  DH, Choi  DW, Choi  SH, Heo  JS, Kow  AW.  Is there a role for systematic hepatic pedicle lymphadenectomy in intrahepatic cholangiocarcinoma? a review of 17 years of experience in a tertiary institution.   Surgery. 2015;157(4):666-675. doi:10.1016/j.surg.2014.11.006 PubMedGoogle ScholarCrossref
    5.
    Zhang  XF, Beal  EW, Bagante  F,  et al.  Early versus late recurrence of intrahepatic cholangiocarcinoma after resection with curative intent.   Br J Surg. 2018;105(7):848-856. doi:10.1002/bjs.10676 PubMedGoogle ScholarCrossref
    6.
    Doussot  A, Gonen  M, Wiggers  JK,  et al.  Recurrence patterns and disease-free survival after resection of intrahepatic cholangiocarcinoma: preoperative and postoperative prognostic models.   J Am Coll Surg. 2016;223(3):493-505.e2. doi:10.1016/j.jamcollsurg.2016.05.019 PubMedGoogle ScholarCrossref
    7.
    Wang  C, Pang  S, Si-Ma  H,  et al.  Specific risk factors contributing to early and late recurrences of intrahepatic cholangiocarcinoma after curative resection.   World J Surg Oncol. 2019;17(1):2. doi:10.1186/s12957-018-1540-1 PubMedGoogle ScholarCrossref
    8.
    Tsilimigras  DI, Sahara  K, Moris  D,  et al.  Effect of surgical margin width on patterns of recurrence among patients undergoing R0 hepatectomy for T1 hepatocellular carcinoma: an international multi-institutional analysis.   J Gastrointest Surg. 2019. doi:10.1007/s11605-019-04275-0 PubMedGoogle Scholar
    9.
    Xu  XF, Xing  H, Han  J,  et al.  Risk factors, patterns, and outcomes of late recurrence after liver resection for hepatocellular carcinoma: a multicenter study from China.   JAMA Surg. 2019;154(3):209-217. doi:10.1001/jamasurg.2018.4334 PubMedGoogle ScholarCrossref
    10.
    Tsilimigras  DI, Hyer  JM, Moris  D,  et al; International Intrahepatic Cholangiocarcinoma Study Group.  Prognostic utility of albumin-bilirubin grade for short- and long-term outcomes following hepatic resection for intrahepatic cholangiocarcinoma: a multi-institutional analysis of 706 patients.   J Surg Oncol. 2019;120(2):206-213. doi:10.1002/jso.25486 PubMedGoogle Scholar
    11.
    Tsilimigras  DI, Mehta  R, Moris  D,  et al.  A machine-based approach to preoperatively identify patients with the most and least benefit associated with resection for intrahepatic cholangiocarcinoma: an international multi-institutional analysis of 1,146 patients.   Ann Surg Oncol. 2020;27(4):1110-1119. doi:10.1245/s10434-019-08067-3PubMedGoogle ScholarCrossref
    12.
    Tsilimigras  DI, Mehta  R, Aldrighetti  L,  et al; International Intrahepatic Cholangiocarcinoma Study Group.  Development and validation of a laboratory risk score (LabScore) to predict outcomes after resection for intrahepatic cholangiocarcinoma.   J Am Coll Surg. 2020;230(4):381-391.e2. doi:10.1016/j.jamcollsurg.2019.12.025 PubMedGoogle ScholarCrossref
    13.
    Simon  R, Sasaki  K, Margonis  GA,  et al.  Risk factors for very early recurrence of hepatocellular carcinoma: a retrospective review.   HPB (Oxford). 2018;20:2. doi:10.1016/j.hpb.2018.02.327 Google ScholarCrossref
    14.
    Hirokawa  F, Hayashi  M, Asakuma  M, Shimizu  T, Inoue  Y, Uchiyama  K.  Risk factors and patterns of early recurrence after curative hepatectomy for hepatocellular carcinoma.   Surg Oncol. 2016;25(1):24-29. doi:10.1016/j.suronc.2015.12.002 PubMedGoogle ScholarCrossref
    15.
    Jung  SW, Kim  DS, Yu  YD, Han  JH, Suh  SO.  Risk factors for cancer recurrence or death within 6 months after liver resection in patients with colorectal cancer liver metastasis.   Ann Surg Treat Res. 2016;90(5):257-264. doi:10.4174/astr.2016.90.5.257 PubMedGoogle ScholarCrossref
    16.
    Takahashi  S, Konishi  M, Nakagohri  T, Gotohda  N, Saito  N, Kinoshita  T.  Short time to recurrence after hepatic resection correlates with poor prognosis in colorectal hepatic metastasis.   Jpn J Clin Oncol. 2006;36(6):368-375. doi:10.1093/jjco/hyl027 PubMedGoogle ScholarCrossref
    17.
    Strasberg  SM.  Nomenclature of hepatic anatomy and resections: a review of the Brisbane 2000 system.   J Hepatobiliary Pancreat Surg. 2005;12(5):351-355. doi:10.1007/s00534-005-0999-7 PubMedGoogle ScholarCrossref
    18.
    Amin  MB, Greene  FL, Edge  SB,  et al.  The Eighth Edition AJCC Cancer Staging Manual: continuing to build a bridge from a population-based to a more “personalized” approach to cancer staging.   CA Cancer J Clin. 2017;67(2):93-99. doi:10.3322/caac.21388PubMedGoogle ScholarCrossref
    19.
    Hu  LS, Zhang  XF, Weiss  M,  et al.  Recurrence patterns and timing courses following curative-intent resection for intrahepatic cholangiocarcinoma.   Ann Surg Oncol. 2019;26(8):2549-2557. doi:10.1245/s10434-019-07353-4 PubMedGoogle ScholarCrossref
    20.
    Lee  KJ, Carlin  JB.  Multiple imputation for missing data: fully conditional specification versus multivariate normal imputation.   Am J Epidemiol. 2010;171(5):624-632. doi:10.1093/aje/kwp425 PubMedGoogle ScholarCrossref
    21.
    Camp  RL, Dolled-Filhart  M, Rimm  DL.  X-tile: a new bio-informatics tool for biomarker assessment and outcome-based cut-point optimization.   Clin Cancer Res. 2004;10(21):7252-7259. doi:10.1158/1078-0432.CCR-04-0713 PubMedGoogle ScholarCrossref
    22.
    Facciabene  A, Peng  X, Hagemann  IS,  et al.  Tumour hypoxia promotes tolerance and angiogenesis via CCL28 and T(reg) cells.   Nature. 2011;475(7355):226-230. doi:10.1038/nature10169 PubMedGoogle ScholarCrossref
    23.
    Chan  AWH, Zhong  J, Berhane  S,  et al.  Development of pre and post-operative models to predict early recurrence of hepatocellular carcinoma after surgical resection.   J Hepatol. 2018;69(6):1284-1293. doi:10.1016/j.jhep.2018.08.027 PubMedGoogle ScholarCrossref
    24.
    Margonis  GA, Sasaki  K, Gholami  S,  et al.  Genetic And Morphological Evaluation (GAME) score for patients with colorectal liver metastases.   Br J Surg. 2018;105(9):1210-1220. doi:10.1002/bjs.10838 PubMedGoogle ScholarCrossref
    25.
    Valle  J, Wasan  H, Palmer  DH,  et al; ABC-02 Trial Investigators.  Cisplatin plus gemcitabine versus gemcitabine for biliary tract cancer.   N Engl J Med. 2010;362(14):1273-1281. doi:10.1056/NEJMoa0908721 PubMedGoogle ScholarCrossref
    26.
    Bagante  F, Spolverato  G, Merath  K,  et al.  Intrahepatic cholangiocarcinoma tumor burden: a classification and regression tree model to define prognostic groups after resection.   Surgery. 2019;166(6):983-990. doi:10.1016/j.surg.2019.06.005 PubMedGoogle ScholarCrossref
    27.
    Sahara  K, Tsilimigras  DI, Mehta  R,  et al.  A novel online prognostic tool to predict long-term survival after liver resection for intrahepatic cholangiocarcinoma: the “metro-ticket” paradigm.   J Surg Oncol. 2019;120(2):223-230. doi:10.1002/jso.25480 PubMedGoogle Scholar
    28.
    Yeh  CN, Jan  YY, Chen  MF.  Influence of age on surgical treatment of peripheral cholangiocarcinoma.   Am J Surg. 2004;187(4):559-563. doi:10.1016/j.amjsurg.2003.12.051 PubMedGoogle ScholarCrossref
    29.
    Beheshti  A, Benzekry  S, McDonald  JT,  et al.  Host age is a systemic regulator of gene expression impacting cancer progression.   Cancer Res. 2015;75(6):1134-1143. doi:10.1158/0008-5472.CAN-14-1053 PubMedGoogle ScholarCrossref
    30.
    Kleeff  J, Reiser  C, Hinz  U,  et al.  Surgery for recurrent pancreatic ductal adenocarcinoma.   Ann Surg. 2007;245(4):566-572. doi:10.1097/01.sla.0000245845.06772.7d PubMedGoogle ScholarCrossref
    31.
    Youngwirth  LM, Nussbaum  DP, Thomas  S,  et al.  Nationwide trends and outcomes associated with neoadjuvant therapy in pancreatic cancer: an analysis of 18 243 patients.   J Surg Oncol. 2017;116(2):127-132. doi:10.1002/jso.24630 PubMedGoogle ScholarCrossref
    32.
    Heinrich  S, Schäfer  M, Weber  A,  et al.  Neoadjuvant chemotherapy generates a significant tumor response in resectable pancreatic cancer without increasing morbidity: results of a prospective phase II trial.   Ann Surg. 2008;248(6):1014-1022. doi:10.1097/SLA.0b013e318190a6da PubMedGoogle ScholarCrossref
    33.
    Leonard  GD, Brenner  B, Kemeny  NE.  Neoadjuvant chemotherapy before liver resection for patients with unresectable liver metastases from colorectal carcinoma.   J Clin Oncol. 2005;23(9):2038-2048. doi:10.1200/JCO.2005.00.349 PubMedGoogle ScholarCrossref
    34.
    Greer  SE, Pipas  JM, Sutton  JE,  et al.  Effect of neoadjuvant therapy on local recurrence after resection of pancreatic adenocarcinoma.   J Am Coll Surg. 2008;206(3):451-457. doi:10.1016/j.jamcollsurg.2007.10.002 PubMedGoogle ScholarCrossref
    35.
    Darwish Murad  S, Kim  WR, Harnois  DM,  et al.  Efficacy of neoadjuvant chemoradiation, followed by liver transplantation, for perihilar cholangiocarcinoma at 12 US centers.   Gastroenterology. 2012;143(1):88-98.e3. doi:10.1053/j.gastro.2012.04.008 PubMedGoogle ScholarCrossref
    36.
    Buettner  S, Koerkamp  BG, Ejaz  A,  et al.  The effect of preoperative chemotherapy treatment in surgically treated intrahepatic cholangiocarcinoma patients—a multi-institutional analysis.   J Surg Oncol. 2017;115(3):312-318. doi:10.1002/jso.24524 PubMedGoogle ScholarCrossref
    37.
    Borad  MJ, Champion  MD, Egan  JB,  et al.  Integrated genomic characterization reveals novel, therapeutically relevant drug targets in FGFR and EGFR pathways in sporadic intrahepatic cholangiocarcinoma.   PLoS Genet. 2014;10(2):e1004135. doi:10.1371/journal.pgen.1004135 PubMedGoogle Scholar
    38.
    Farshidfar  F, Zheng  S, Gingras  M-C,  et al; Cancer Genome Atlas Network.  Integrative genomic analysis of cholangiocarcinoma identifies distinct IDH-mutant molecular profiles.   Cell Rep. 2017;18(11):2780-2794. doi:10.1016/j.celrep.2017.02.033 PubMedGoogle ScholarCrossref
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