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Figure 1.
Flowchart of Categorization of Patients With Major Salivary Gland Disease
Flowchart of Categorization of Patients With Major Salivary Gland Disease

Flowchart showing the categorization of 4381 patients with major salivary gland disease treated at Memorial Sloan Kettering Cancer Center from 1985 through 2009. BCC indicates basal cell carcinoma; and SCC, squamous cell carcinoma.

Figure 2.
Five-Year Recurrence Rate for Patients With Carcinoma of the Major Salivary Glands
Five-Year Recurrence Rate for Patients With Carcinoma of the Major Salivary Glands

The vertical dashed lined indicates the 5-year mark.

Figure 3.
Sites of Recurrence
Sites of Recurrence

Sites of recurrence for patients with carcinoma of the major salivary glands (18 local, 12 regional, and 56 distant).

Figure 4.
Nomogram and Concordance Index
Nomogram and Concordance Index

A, Nomogram for the prediction of recurrence in carcinoma of the major salivary glands. B, Concordance index for predictive nomogram of recurrence in carcinoma of the major salivary glands. The dotted line represents the ideal line where the actual probability of recurrence matches the predicted probability. The solid line represents the observed where the actual probability is slightly different from the predicted.

Figure 5.
Utility of the Nomogram in 2 Hypothetical Patients
Utility of the Nomogram in 2 Hypothetical Patients

A, Recurrence risk in T1N0 low-grade mucoepidermoid cancer in a 30-year-old man. B, Recurrence risk in a T4N1 high-grade salivary duct cancer in a 60-year-old woman.

Table 1.  
Patient Characteristics
Patient Characteristics
Table 2.  
Treatment Characteristics
Treatment Characteristics
Table 3.  
Tumor Characteristics
Tumor Characteristics
Table 4.  
Factors Predictive of Recurrence-Free Survival
Factors Predictive of Recurrence-Free Survival
1.
Kattan  MW, Marasco  J.  What is a real nomogram? Semin Oncol. 2010;37(1):23-26.
PubMedArticle
2.
Kattan  MW, Stapleton  AM, Wheeler  TM, Scardino  PT.  Evaluation of a nomogram used to predict the pathologic stage of clinically localized prostate carcinoma. Cancer. 1997;79(3):528-537.
PubMedArticle
3.
Kattan  MW.  Nomograms: introduction. Semin Urol Oncol. 2002;20(2):79-81.
PubMed
4.
Specht  MC, Kattan  MW, Gonen  M, Fey  J, Van Zee  KJ.  Predicting nonsentinel node status after positive sentinel lymph biopsy for breast cancer: clinicians versus nomogram. Ann Surg Oncol. 2005;12(8):654-659.
PubMedArticle
5.
Lee  HS, Kim  SW, Kim  BH,  et al.  Predicting nonsentinel lymph node metastasis using lymphoscintigraphy in patients with breast cancer. J Nucl Med. 2012;53(11):1693-1700.
PubMedArticle
6.
Kattan  MW.  Nomograms are superior to staging and risk grouping systems for identifying high-risk patients: preoperative application in prostate cancer. Curr Opin Urol. 2003;13(2):111-116.
PubMedArticle
7.
Kattan  MW.  Nomograms are difficult to beat. Eur Urol. 2008;53(4):671-672.
PubMedArticle
8.
Marko  NF, Xu  Z, Gao  T, Kattan  MW, Weil  RJ.  Predicting survival in women with breast cancer and brain metastasis: a nomogram outperforms current survival prediction models. Cancer. 2012;118(15):3749-3757.
PubMedArticle
9.
Hernando  M, Martín-Fragueiro  L, Eisenberg  G,  et al.  Surgical management of salivary gland tumours [in Spanish]. Acta Otorrinolaringol Esp. 2009;60(5):340-345.
PubMedArticle
10.
Völker  HU, Mühlmeier  G, Maier  H, Kraft  K, Müller-Hermelink  HK, Zettl  A.  True malignant mixed tumour (carcinosarcoma) of submandibular gland—a rare neoplasm of monoclonal origin? Histopathology. 2007;50(6):795-798.
PubMedArticle
11.
Terhaard  CH, Lubsen  H, Van der Tweel  I,  et al; Dutch Head and Neck Oncology Cooperative Group.  Salivary gland carcinoma: independent prognostic factors for locoregional control, distant metastases, and overall survival: results of the Dutch head and neck oncology cooperative group. Head Neck. 2004;26(8):681-693.
PubMedArticle
12.
Savera  AT, Sloman  A, Huvos  AG, Klimstra  DS.  Myoepithelial carcinoma of the salivary glands: a clinicopathologic study of 25 patients. Am J Surg Pathol. 2000;24(6):761-774.
PubMedArticle
13.
Cosentino  TB, Brazão-Silva  MT, Souza  KC,  et al.  Myoepithelial carcinoma of the submandibular gland: report of a case with multiple cutaneous metastases. Oral Surg Oral Med Oral Pathol Oral Radiol Endod. 2008;106(2):e26-e29.
PubMedArticle
14.
Spitz  MR, Batsakis  JG.  Major salivary gland carcinoma: descriptive epidemiology and survival of 498 patients. Arch Otolaryngol. 1984;110(1):45-49.
PubMedArticle
15.
Cheung  MC, Franzmann  E, Sola  JE, Pincus  DJ, Koniaris  LG.  A comprehensive analysis of parotid and salivary gland cancer: worse outcomes for male gender. J Surg Res. 2011;171(1):151-158.
PubMedArticle
16.
Ghosh-Laskar  S, Murthy  V, Wadasadawala  T,  et al.  Mucoepidermoid carcinoma of the parotid gland: factors affecting outcome. Head Neck. 2011;33(4):497-503.
PubMedArticle
17.
Kobayashi  K, Nakao  K, Yoshida  M,  et al.  Recurrent cancer of the parotid gland: how well does salvage surgery work for locoregional failure? ORL J Otorhinolaryngol Relat Spec. 2009;71(5):239-243.
PubMedArticle
18.
Hocwald  E, Korkmaz  H, Yoo  GH,  et al.  Prognostic factors in major salivary gland cancer. Laryngoscope. 2001;111(8):1434-1439.
PubMedArticle
19.
Kattan  MW.  Comparison of Cox regression with other methods for determining prediction models and nomograms. J Urol. 2003;170(6, pt 2):S6-S10.
PubMedArticle
20.
Westphalen  AC, Koff  WJ, Coakley  FV,  et al.  Prostate cancer: prediction of biochemical failure after external-beam radiation therapy—Kattan nomogram and endorectal MR imaging estimation of tumor volume. Radiology. 2011;261(2):477-486.
PubMedArticle
21.
Siegel  C.  Re: Prostate cancer: prediction of biochemical failure after external-beam radiation therapy—Kattan nomogram and endorectal MR imaging estimation of tumor volume. J Urol. 2012;188(2):432-433.
PubMedArticle
22.
Eifler  JB, Feng  Z, Lin  BM,  et al.  An updated prostate cancer staging nomogram (Partin tables) based on cases from 2006 to 2011. BJU Int. 2013;111(1):22-29.
PubMedArticle
23.
Hansen  J, Auprich  M, Ahyai  SA,  et al.  Initial prostate biopsy: development and internal validation of a biopsy-specific nomogram based on the prostate cancer antigen 3 assay. Eur Urol. 2013;63(2):201-209.
PubMedArticle
24.
Bevilacqua  JL, Kattan  MW, Changhong  Y,  et al.  Nomograms for predicting the risk of arm lymphedema after axillary dissection in breast cancer. Ann Surg Oncol. 2012;19(8):2580-2589.
PubMedArticle
25.
Gross  ND, Patel  SG, Carvalho  AL,  et al.  Nomogram for deciding adjuvant treatment after surgery for oral cavity squamous cell carcinoma. Head Neck. 2008;30(10):1352-1360.
PubMedArticle
26.
Nixon  IJ, Ganly  I, Hann  LE,  et al.  Nomogram for predicting malignancy in thyroid nodules using clinical, biochemical, ultrasonographic, and cytologic features. Surgery. 2010;148(6):1120-1128.
PubMedArticle
27.
Donahue  TR, Kattan  MW, Nelson  SD, Tap  WD, Eilber  FR, Eilber  FC.  Evaluation of neoadjuvant therapy and histopathologic response in primary, high-grade retroperitoneal sarcomas using the sarcoma nomogram. Cancer. 2010;116(16):3883-3891.
PubMedArticle
28.
Takakura  Y, Okajima  M, Kanemitsu  Y,  et al.  External validation of two nomograms for predicting patient survival after hepatic resection for metastatic colorectal cancer. World J Surg. 2011;35(10):2275-2282.
PubMedArticle
29.
Barlin  JN, Yu  C, Hill  EK,  et al.  Nomogram for predicting 5-year disease-specific mortality after primary surgery for epithelial ovarian cancer. Gynecol Oncol. 2012;125(1):25-30.
PubMedArticle
30.
Kondalsamy-Chennakesavan  S, Yu  C, Kattan  MW,  et al.  Nomograms to predict isolated loco-regional or distant recurrence among women with uterine cancer. Gynecol Oncol. 2012;125(3):520-525.
PubMedArticle
Original Investigation
July 2013

A Predictive Nomogram for Recurrence of Carcinoma of the Major Salivary Glands

Author Affiliations
  • 1Head and Neck Service, Department of Surgery, Memorial Sloan-Kettering Cancer Center, New York, New York
  • 2Department of Quantitative Health Sciences, The Cleveland Clinic, Cleveland, Ohio
JAMA Otolaryngol Head Neck Surg. 2013;139(7):698-705. doi:10.1001/jamaoto.2013.3347
Abstract

Importance  This nomogram quantifies the risk of recurrence in patients with carcinoma of the major salivary glands. It may facilitate patient counseling on prognosis and may help guide management and posttreatment surveillance in these patients.

Objectives  To identify factors predictive of recurrence after primary surgical treatment of carcinoma of the major salivary glands and create a nomogram that could be used to predict the risk of recurrence in an individual patient.

Design  Retrospective case series.

Setting  Single institution tertiary care cancer center.

Patients  After institutional review board approval, 301 patients with previously untreated malignant salivary gland tumors treated at our institution between the years 1985 and 2009 were identified. Among the 301 patients, the median age was 62 (range, 9-89) years and 156 (52%) were male. Patient, tumor, and treatment characteristics were recorded from a retrospective analysis of patient medical charts.

Main Outcomes and Measures  Overall mortality was calculated using the Kaplan-Meier method. Disease-specific mortality and recurrence risk were estimated with cumulative incidence rate. Factors predictive of recurrence were identified using univariate analysis. A Cox proportional hazard model was used to select predictors for the predictive nomogram.

Results  With a median follow-up of 43 (range, 1-264) months, the 5-year overall mortality, disease-specific mortality, and recurrence rate were 30%, 28%, and 33%, respectively. There were 70 recurrences (18 local, 12 regional, and 56 distant). The 5 variables most predictive for recurrence were age, grade, vascular and perineural invasion, and nodal metastasis. These variables were selected to generate the nomogram, which had a high concordance index of 0.85.

Conclusions and Relevance  We introduce a clinically useful nomogram that quantifies the risk of recurrence in carcinomas of the major salivary gland. By quantifying risk for an individual patient, this would enable the clinician to give more accurate prognostic information to the patient resulting in better patient counseling.

The field of nomography was invented in 1884 by the French engineer Philbert Maurice d’Ocagne and was used to provide engineers a means of simplifying complicated formulas. From their initial application in the fields of astronomy, chemistry, and aeronautics, nomograms have evolved in biostatistics as a means for predicting disease outcomes. There has been a recent resurgence in the use of these predictive tools to facilitate the physician-patient consultation. The evolution of statistical models and computing technology has simplified these models for clinical use.1 Nomograms are now used as predictive models in oncology, notably in prostate and breast cancer, to forecast clinical outcomes.25

Malignant salivary gland carcinomas are a very heterogeneous group because of not only the mixed histologic types but also their biological behavior. Although the American Joint Committee on Cancer (AJCC) TNM classification can help predict prognosis, this is applied to an entire population and does not predict the prognosis in an individual patient. Nomograms have been shown to be superior in predicting risk in patients compared with more traditional methods.68 Therefore, the objectives of this study were to identify factors predictive of recurrence after primary surgical treatment of carcinoma of the major salivary glands and create a nomogram that could be used to predict the risk of recurrence in an individual. Such a nomogram should be used as an aid in clinical decision making. By quantifying risk for an individual patient, this would enable the clinician to give more accurate prognostic information to the patient, resulting in better patient counseling. In addition it allows the clinician to assess the intensity of clinical follow-up, particularly with regard to frequency and type of imaging to identify recurrence.

Methods

After institutional review board approval, a data line search was made of all patients who had treatment of major salivary gland disease at Memorial Sloan Kettering Cancer Center from 1985 through 2009. Of 4381 patients, 451 patients had surgery for salivary gland cancer. A flowchart detailing the categorization of all patients is shown in Figure 1. Patients who had prior open biopsy (incisional or excisional), recurrent tumors, prior surgery, and prior radiotherapy were excluded. This left 301 patients who had primary surgery. Patient, tumor, and treatment characteristics were recorded from a retrospective analysis of patient medical charts.

Patient characteristics are given in Table 1. Among the 301 patients, 156 (52%) were male and 145 (48%) were female, and the median age was 62 (range, 9-89) years. The site of primary tumor was parotid in 266 patients (88%), submandibular in 30 patients (10%), and sublingual gland in 5 patients (2%). Regarding T classification, 54 patients (18%) had T1 tumors; 129 (43%), T2 tumors; 64 (21%), T3 tumors; 32 (11%), T4 tumors; and 22 (7%), Tx. Thirty-seven patients (12%) had a clinically positive neck.

Treatment characteristics are given in Table 2. Of the 301 patients, 123 (41%) were treated by superficial parotidectomy; 134 (44%) required total or extended parotidectomy; 97 (32%) had facial nerve sacrifice (either partial or total); 95 (32%) had an elective neck dissection; 36 (12%) had a therapeutic neck dissection; and 159 (53%) received postoperative radiotherapy. Indications for postoperative radiotherapy were patients with pathological T3/4 tumors, positive neck disease, perineural and vascular invasion, positive margins, and high-grade tumors.

Tumor characteristics are given in Table 3. The most common pathologic condition was mucoepidermoid carcinoma, which occurred in almost one-third of patients. Of the 131 patients who had a neck dissection, 64 (49%) had pathologically positive results. Ninety-nine patients (33%) had perineural invasion; 67 (22%), vascular invasion; 158 (53%), close or positive margins; and 110 (36%), high-grade tumors.

Overall mortality was calculated using the Kaplan-Meier method. Disease-specific mortality and recurrence risk were estimated with cumulative incidence rate. Factors predictive of recurrence were identified using univariate analysis. A Cox proportional hazard model was used to select predictors in the final nomogram. The nomogram was internally validated by assessing discrimination and calibration. Discrimination was measured with the concordance index, similar to the area under the receiver operating characteristic curve: values range from 0.5 (no discrimination) to 1.0 (perfect discrimination). Calibration was assessed by plotting the predicted vs the actual probability for quintiles of the predicted probability of recurrence. To fairly evaluate the predictive performance of the nomogram among new patients in future, bootstrapping was used (a statistical technique in which patients were resampled with replacement for a large number times, where in each resample some patients might be selected more than once and others were not selected at all). The nomogram was afterwards rebuilt on these bootstrap resamples and assessed on patients who were not selected to avoid overfitting bias.

Results

With a median follow-up of 43 (range, 1-264) months, the 5-year overall mortality, disease-specific mortality, and recurrence rate were 30%, 28%, and 33%, respectively (Figure 2). There were 70 recurrences (18 local, 12 regional, and 56 distant; Figure 3). Factors predictive of recurrence by univariate analysis are given in Table 4. Clinical factors included sex, comorbidity, facial nerve paralysis, skin involvement, and clinical T and N classifications. For example, patients with facial nerve paralysis had a 5-year recurrence-free survival (RFS) of 20.4% compared with 73.5% for those with no paralysis. Pathological factors predictive of recurrence included pathological T classification, positive nodes, vascular invasion, perineural invasion, margin status, and grade. For example, the 5-year RFS was poorer for patients with positive nodal disease (23.5% vs 83.7%; P < .001). The RFS was significantly poorer for high-grade tumors compared with low- and intermediate-grade tumors (5-year RFS, 42.5% for high-grade vs 88.9% and 95.9% for low- and intermediate-grade tumors; P < .001).

Five variables with the highest predictive power for recurrence were chosen to create the nomogram. These were age, grade, vascular invasion, perineural invasion, and nodal metastasis. The nomogram is shown in Figure 4A. The nomogram had a high concordance index of 0.85 (Figure 4B).

Discussion

Malignant tumors of the major salivary gland comprise 3% to 6% of all head and neck cancers.9 The most common histologic types are mucoepidermoid carcinoma, adenocarcinoma, adenoid cystic carcinoma, and acinic cell carcinoma. Rarer tumors include malignant mixed tumor, squamous cell carcinoma, myoepithelial carcinoma, and salivary duct carcinoma.1013 No specific etiological factors have been identified for salivary gland carcinoma, although exposure to ionizing radiation has been reported as late causation of salivary malignancy.14

In our series, local recurrence occurred in 18 patients (6%), neck recurrence in 12 patients (4%), and distant recurrence in 56 patients (19%). There were 58 disease specific deaths (19%). High-grade tumors accounted for the majority of the recurrences in our series of patients. On univariate analysis there were multiple factors predictive of recurrence. These were sex, medical comorbidity, facial nerve paralysis, skin involvement, clinical T classification, clinical N classification, clinical stage, pathological T classification, pathological N classification, perineural invasion, vascular invasion, margins, histological grade, and postoperative radiation. In other studies, clinicopathological factors such as advanced age (>65 years), male sex, advanced disease stage, close or positive margins, high tumor grade, perineural invasion, nodal disease, adjuvant chemoradiation therapy, and increased poverty have also been identified as independent predictors of recurrence and overall survival.1518 Improved outcomes are observed for female sex and tumors located in the parotid gland.15 The fact that there are so many factors predictive for recurrence is a reflection of the wide variety of histologic types that can occur in salivary gland cancers, many of which have a varied clinical behavior and outcome. Because of this, it is very difficult to estimate the risk of recurrence in an individual patient using this data. We therefore postulated that a predictive nomogram, designed using multiple predictors of recurrence, may be a very useful tool that could be used to counsel patients of recurrence risk. Combining factors predictive of recurrence together to create a nomogram allows us to create a numerical percentage of recurrence that applies to a specific individual patient. This is the benefit of a nomogram.

Nomograms have become widely accepted in many surgical specialties. They rely on the statistical methodology of Cox proportional hazards regression, and this has been tested against artificial neural networks and other machine learning methods and has proven to be comparable or superior in its predictive accuracy.19

Nomograms are well established in the prediction of outcomes in patients with prostate cancer.6,2023 In breast oncology, they have been used to predict nonsentinel node status after a positive sentinel lymph biopsy result for breast cancer and have assessed the risk of arm lymphedema after axillary dissection in breast cancer.4,5,24 Within head and neck oncology, there has been the development of a nomogram for deciding adjuvant radiation treatment after surgery for oral cavity squamous cell carcinoma25 and predicting malignancy in thyroid nodules.26 Other applications in oncology include evaluation of neoadjuvant therapy and response in high-grade retroperitoneal sarcomas27 and predicting patient survival after hepatic resection for metastatic colorectal carcinoma28 and various gynecological outcomes.29,30

The advantage nomograms offer is that they provide patients a tailored outcome and give them a risk assessment based on individual factors as opposed to a relative risk that may be applicable to a particular group or condition. With an increase in patient awareness about disease outcomes, there is an ever-growing desire to know an individualized risk of prognosis. Therefore, although the AJCC TNM classification can help predict prognosis, it only applies to an entire population and does not predict an individual’s risk. In other specialties, including urology and breast oncology, it has been shown that nomograms are superior to staging systems in predicting risk.68

Our nomogram uses best predictive power to model recurrence and has 5 variables: age, grade, perineural invasion, vascular invasion, and nodal metastasis. Each of the 5 variables gives a relative risk that can be quantified on a point scale. Once the total number of points has been tallied for an individual patient, we can use this score to assess how it corresponds with the relative risk of recurrence up to 5 years after surgery, thus giving the patient a relative risk of recurrence after salivary gland surgery. To illustrate the utility of the nomogram, Figure 5 shows 2 hypothetical patients. A 30-year-old man with T1N0 low-grade mucoepidermoid cancer (Figure 5A) has a recurrence-free probability of 95%. In contrast, a 60-year old woman with T4N1 high-grade salivary duct cancer has a recurrence-free probability of only 9% (Figure 5B). Thus, our nomogram can offer clinicians an adjunct to clinical surveillance in the postoperative setting to counsel patients on the risk of recurrence and ultimately on prognosis. By identifying patients at higher risk of recurrence, we can personalize treatment by providing more aggressive treatments to those at high risk and reserve less aggressive treatment to those with low risk of recurrence. The nomogram can also be used to tailor the frequency and extent of imaging, such as computed tomography, magnetic resonance imaging, and positron emission tomography, in the follow-up of patients to identify recurrence early in those patients at high risk.

Our nomogram has some limitations, however. First, the nomogram was generated based on retrospective data and is therefore susceptible to the deficiencies of retrospective data collection. Second, patients were selected on the basis of fitting our inclusion criteria, and this may represent a potential sampling bias. Before our nomogram can be used as a clinical aid to patient counseling and decision making, it is essential that the nomogram be validated externally on data sets from other institutions. We are currently approaching other national and international centers with a large experience in salivary gland malignancies. Validation on a combined data set from several institutions will undoubtedly add to the robustness of the nomogram and to its applicability to clinical decision making. This will be reported in a future communication.

In conclusion, we have created a clinically useful nomogram that quantifies the risk of recurrence in carcinoma of the major salivary glands. Its predictive utility may facilitate patient counseling on prognosis and may help guide management and posttreatment surveillance in these patients.

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Article Information

Corresponding Author: Ian Ganly, MD, PhD, Head and Neck Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, 1275 York Ave, New York, NY 10065 (ganlyi@mskcc.org).

Submitted for Publication: January 22, 2013; final revision received March 12, 2013; accepted April 11, 2013.

Published Online: June 20, 2013. doi:10.1001/jamaoto.2013.3347.

Author Contributions: Dr Ganly had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

Study concept and design: Ali, Palmer, Shah, Patel, Ganly.

Acquisition of data: Ali, Palmer, DiLorenzo, Patel, Ganly.

Analysis and interpretation of data: Ali, Palmer, Yu, DiLorenzo, Kattan, Patel, Ganly.

Drafting of the manuscript: Ali, Yu, Kattan, Ganly.

Critical revision of the manuscript for important intellectual content: Palmer, Yu, DiLorenzo, Shah, Kattan, Patel, Ganly.

Statistical analysis: Ali, Palmer, Yu, DiLorenzo, Kattan, Ganly.

Administrative, technical, and material support: Palmer, DiLorenzo.

Study supervision: Palmer, Shah, Kattan, Patel, Ganly.

Conflict of Interest Disclosures: None reported.

Previous Presentation: This study was given as an oral presentation at the American Head & Neck Society annual meeting; July 23, 2012; Toronto, Ontario, Canada.

References
1.
Kattan  MW, Marasco  J.  What is a real nomogram? Semin Oncol. 2010;37(1):23-26.
PubMedArticle
2.
Kattan  MW, Stapleton  AM, Wheeler  TM, Scardino  PT.  Evaluation of a nomogram used to predict the pathologic stage of clinically localized prostate carcinoma. Cancer. 1997;79(3):528-537.
PubMedArticle
3.
Kattan  MW.  Nomograms: introduction. Semin Urol Oncol. 2002;20(2):79-81.
PubMed
4.
Specht  MC, Kattan  MW, Gonen  M, Fey  J, Van Zee  KJ.  Predicting nonsentinel node status after positive sentinel lymph biopsy for breast cancer: clinicians versus nomogram. Ann Surg Oncol. 2005;12(8):654-659.
PubMedArticle
5.
Lee  HS, Kim  SW, Kim  BH,  et al.  Predicting nonsentinel lymph node metastasis using lymphoscintigraphy in patients with breast cancer. J Nucl Med. 2012;53(11):1693-1700.
PubMedArticle
6.
Kattan  MW.  Nomograms are superior to staging and risk grouping systems for identifying high-risk patients: preoperative application in prostate cancer. Curr Opin Urol. 2003;13(2):111-116.
PubMedArticle
7.
Kattan  MW.  Nomograms are difficult to beat. Eur Urol. 2008;53(4):671-672.
PubMedArticle
8.
Marko  NF, Xu  Z, Gao  T, Kattan  MW, Weil  RJ.  Predicting survival in women with breast cancer and brain metastasis: a nomogram outperforms current survival prediction models. Cancer. 2012;118(15):3749-3757.
PubMedArticle
9.
Hernando  M, Martín-Fragueiro  L, Eisenberg  G,  et al.  Surgical management of salivary gland tumours [in Spanish]. Acta Otorrinolaringol Esp. 2009;60(5):340-345.
PubMedArticle
10.
Völker  HU, Mühlmeier  G, Maier  H, Kraft  K, Müller-Hermelink  HK, Zettl  A.  True malignant mixed tumour (carcinosarcoma) of submandibular gland—a rare neoplasm of monoclonal origin? Histopathology. 2007;50(6):795-798.
PubMedArticle
11.
Terhaard  CH, Lubsen  H, Van der Tweel  I,  et al; Dutch Head and Neck Oncology Cooperative Group.  Salivary gland carcinoma: independent prognostic factors for locoregional control, distant metastases, and overall survival: results of the Dutch head and neck oncology cooperative group. Head Neck. 2004;26(8):681-693.
PubMedArticle
12.
Savera  AT, Sloman  A, Huvos  AG, Klimstra  DS.  Myoepithelial carcinoma of the salivary glands: a clinicopathologic study of 25 patients. Am J Surg Pathol. 2000;24(6):761-774.
PubMedArticle
13.
Cosentino  TB, Brazão-Silva  MT, Souza  KC,  et al.  Myoepithelial carcinoma of the submandibular gland: report of a case with multiple cutaneous metastases. Oral Surg Oral Med Oral Pathol Oral Radiol Endod. 2008;106(2):e26-e29.
PubMedArticle
14.
Spitz  MR, Batsakis  JG.  Major salivary gland carcinoma: descriptive epidemiology and survival of 498 patients. Arch Otolaryngol. 1984;110(1):45-49.
PubMedArticle
15.
Cheung  MC, Franzmann  E, Sola  JE, Pincus  DJ, Koniaris  LG.  A comprehensive analysis of parotid and salivary gland cancer: worse outcomes for male gender. J Surg Res. 2011;171(1):151-158.
PubMedArticle
16.
Ghosh-Laskar  S, Murthy  V, Wadasadawala  T,  et al.  Mucoepidermoid carcinoma of the parotid gland: factors affecting outcome. Head Neck. 2011;33(4):497-503.
PubMedArticle
17.
Kobayashi  K, Nakao  K, Yoshida  M,  et al.  Recurrent cancer of the parotid gland: how well does salvage surgery work for locoregional failure? ORL J Otorhinolaryngol Relat Spec. 2009;71(5):239-243.
PubMedArticle
18.
Hocwald  E, Korkmaz  H, Yoo  GH,  et al.  Prognostic factors in major salivary gland cancer. Laryngoscope. 2001;111(8):1434-1439.
PubMedArticle
19.
Kattan  MW.  Comparison of Cox regression with other methods for determining prediction models and nomograms. J Urol. 2003;170(6, pt 2):S6-S10.
PubMedArticle
20.
Westphalen  AC, Koff  WJ, Coakley  FV,  et al.  Prostate cancer: prediction of biochemical failure after external-beam radiation therapy—Kattan nomogram and endorectal MR imaging estimation of tumor volume. Radiology. 2011;261(2):477-486.
PubMedArticle
21.
Siegel  C.  Re: Prostate cancer: prediction of biochemical failure after external-beam radiation therapy—Kattan nomogram and endorectal MR imaging estimation of tumor volume. J Urol. 2012;188(2):432-433.
PubMedArticle
22.
Eifler  JB, Feng  Z, Lin  BM,  et al.  An updated prostate cancer staging nomogram (Partin tables) based on cases from 2006 to 2011. BJU Int. 2013;111(1):22-29.
PubMedArticle
23.
Hansen  J, Auprich  M, Ahyai  SA,  et al.  Initial prostate biopsy: development and internal validation of a biopsy-specific nomogram based on the prostate cancer antigen 3 assay. Eur Urol. 2013;63(2):201-209.
PubMedArticle
24.
Bevilacqua  JL, Kattan  MW, Changhong  Y,  et al.  Nomograms for predicting the risk of arm lymphedema after axillary dissection in breast cancer. Ann Surg Oncol. 2012;19(8):2580-2589.
PubMedArticle
25.
Gross  ND, Patel  SG, Carvalho  AL,  et al.  Nomogram for deciding adjuvant treatment after surgery for oral cavity squamous cell carcinoma. Head Neck. 2008;30(10):1352-1360.
PubMedArticle
26.
Nixon  IJ, Ganly  I, Hann  LE,  et al.  Nomogram for predicting malignancy in thyroid nodules using clinical, biochemical, ultrasonographic, and cytologic features. Surgery. 2010;148(6):1120-1128.
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