Validation of the Memorial Sloan-Kettering Cancer Center Nomogram to Predict Disease-Specific Survival after R0 Resection in a Chinese Gastric Cancer Population

Background

Prediction of disease-specific survival (DSS) for individual patient with gastric cancer after R0 resection remains a clinical concern. Since the clinicopathologic characteristics of gastric cancer vary widely between China and western countries, this study is to evaluate a nomogram from Memorial Sloan-Kettering Cancer Center (MSKCC) for predicting the probability of DSS in patients with gastric cancer from a Chinese cohort.

Methods

From 1998 to 2007, clinical data of 979 patients with gastric cancer who underwent R0 resection were retrospectively collected from Peking University Cancer Hospital & Institute and used for external validation. The performance of the MSKCC nomogram in our population was assessed using concordance index (C-index) and calibration plot.

Results

The C-index for the MSKCC predictive nomogram was 0.74 in the Chinese cohort, compared with 0.69 for American Joint Committee on Cancer (AJCC) staging system (P<0.0001). This suggests that the discriminating value of MSKCC nomogram is superior to AJCC staging system for prognostic prediction in the Chinese population. Calibration plots showed that the actual survival of Chinese patients corresponded closely to the MSKCC nonogram-predicted survival probabilities. Moreover, MSKCC nomogram predictions demonstrated the heterogeneity of survival in stage IIA/IIB/IIIA/IIIB disease of the Chinese patients.

Conclusion

In this study, we externally validated MSKCC nomogram for predicting the probability of 5- and 9-year DSS after R0 resection for gastric cancer in a Chinese population. The MSKCC nomogram performed well with good discrimination and calibration. The MSKCC nomogram improved individualized predictions of survival, and may assist Chinese clinicians and patients in individual follow-up scheduling, and decision making with regard to various treatment options.     

Value and prognostic significance of mitotic rate in a retrospective series of pT1 cutaneous malignant melanoma patients

Introduction: Patients affected by thin melanomas (≤1 mm) generally have a good prognosis; however, some have a recurrence and eventually die of the disease. The seventh edition of the American Joint Committee on Cancer (AJCC) melanoma staging system, introduced mitotic rate (MR) as one of the primary criteria for staging thin melanoma. Materials and methods In this study, we sought to determine the prognostic value of mitotic rate in a retrospective cohort of localized primary cutaneous melanoma patients. Results: In total, 286 cases of pT1 primary malignant melanoma occurring in the period 2003–2008 were evaluated. Mitotic counts were re-assessed on standard sections of cases without mitosis and with at least 1 mitosis at diagnosis; 5-year follow-up and recurrence-free survival were available for all patients. Of the 56 radically treated pT1b melanoma patients, 4 (7.1%) had recurrent disease. These data support the efficacy of the incorporation of mitogenicity into AJCC staging for localized cutaneous melanoma and indicate the difficulties in the accuracy and reproducibility of the mitotic count system.     

Five-Year Prognosis Model of Esophageal Cancer Based on Genetic Algorithm Improved Deep Neural Network

ObjectivesEsophageal cancer is a high occult malignant tumor. Even with good diagnosis and treatment, the 5-year survival rate of esophageal cancer patients is still less than 30%. Considering the influence of clinical characteristics on postoperative esophageal cancer patients, the construction of a neural network model will help improve the poor prognosis of patients in the five years.Material and methodsIn this study, genetic algorithm optimized deep neural network is exploited to the clinical dataset of esophageal cancer. The independent prognostic factors are screened by Relief algorithm and Cox proportional risk regression. FTD prognostic staging system is established to assess the risk level of esophageal cancer patients.ResultsFTD staging system and independent prognostic factors are integrated into the genetic algorithm optimized deep neural network. The Area Under Curve (AUC) of FTD staging system is 0.802. FTD staging system is verified by the Kaplan-Meier survival curve, and the median survival time is divided for different risk grades. The FTD staging system is superior to the TNM stages in the prognosis effect. The AUC of deep neural network optimized by genetic algorithm is 0.91.ConclusionThe deep neural network optimized by genetic algorithm has good performance in predicting the 5-year survival status of esophageal cancer patients. The FTD staging system has a significant prognostic effect. The FTD staging system and genetic algorithm optimized deep neural network can be successfully availed in clinical diagnosis and treatment.     

C-Terminal Tensin-Like Protein Is a Novel Prognostic Marker for Primary Melanoma Patients

Background

C-terminal tensin-like protein (Cten) is a focal adhesion protein originally identified as a tumor suppressor in prostate cancer. It has since been found to be overexpressed and function as an oncogene in numerous other cancers, but the expression status of Cten in melanoma is still unknown.

Methods

Using tissue microarrays containing 562 melanocytic lesions, we evaluated Cten protein expression by immunohistochemistry. The association between Cten expression and patient survival was examined using Kaplan-Meier survival analysis, and univariate and multivariate Cox regression analyses were used to estimate the crude and adjusted hazard ratios.

Results

Strong Cten expression was detected in 7%, 24%, 41%, and 46% of normal nevi, dysplastic nevi, primary melanoma, and metastatic melanoma samples, respectively, and Cten expression was found to be significantly higher in dysplastic nevi compared to normal nevi (P = 0.046), and in primary melanoma compared to dysplastic nevi (P = 0.003), but no difference was observed between metastatic and primary melanoma. Cten staining also correlated with AJCC stages (P = 0.015) and primary tumor thickness (P = 0.002), with Cten expression being induced in the transition from thin (<1mm) to thick (≥1mm) melanomas. Strong Cten expression was significantly associated with a worse 5-year overall (P = 0.008) and disease-specific survival (P = 0.004) for primary melanoma patients, and multivariate Cox regression analysis revealed that Cten expression was an independent prognostic marker for these patients (P = 0.038 for overall survival; P = 0.021 for disease-specific survival).

Conclusion

Our findings indicate that induction of Cten protein expression is a relatively early event in melanoma progression, and that Cten has the potential to serve as a prognostic marker for primary melanoma patients.     

Is Above Age 45 Appropriate for Upstaging Well-Differentiated Papillary Thyroid Cancer?

ObjectiveAge greater than 45 years old is a prognostic marker in well-differentiated papillary thyroid cancer (PTC) using the American Joint Cancer Committee/Union Internationale Contre le Cancer Tumor Nodes Metastasis (AJCC/UICC TNM) staging system. Our clinical observation has been that patients aged 45 to 64 years have similar outcomes when compared to patients younger than 45 years, and we questioned the origin and accuracy of this prognostic variable.MethodsUsing SEERstat software, we analyzed the Surveillance, Epidemiology, and End Result (SEER) database for PTC using the following International Classification of Diseases for Oncology (ICD-O) codes: 8050, 8260, 8340, 8341, 8342, 8243, and 8344. Data were stratified in 5-year categories by age at diagnosis from 20 to 84 years old, with patients 85 years old and above categorized together. Survival is reported as cause specific.ResultsA total of 53,581 patients were identified. The 5-year survival rate decreased with each increasing age category with no inflection point at age 45 in the survival curve. While the prognosis was less favorable in each advancing age group, survival remained above 90% for all age groups under 65 years.ConclusionA review of the literature reveals a lack of data supporting the use of age 45 as a prognostic variable. Our SEER database review revealed a continuum of disease-specific mortality for each incremental 5-year time period above age 45. We conclude that the current use of age 45 as a single prognostic age marker does not accurately reflect the progressive mortality risk that is apparent with each 5-year increment in age. (Endocr Pract. 2013; 19:995-997)     

NLR、PLR、LMR与乳腺癌新辅助化疗疗效及预后的关系研究

摘要 目的：探讨化疗前外周血中性粒细胞与淋巴细胞比值（NLR）、血小板与淋巴细胞比值（PLR）、淋巴细胞与单核细胞比值（LMR）与乳腺癌患者新辅助化疗疗效及预后的关系。方法：选择2016年10月至2018年1月在安徽医科大学附属安庆第一人民医院进行新辅助化疗的乳腺癌患者105例为研究对象,根据新辅助化疗疗效分为病理完全缓解（pCR）组（26例）和非pCR组（79例）。比较pCR组和非pCR组化疗前外周血NLR、PLR、LMR;采用受试者工作特征（ROC）曲线分析化疗前外周血NLR、PLR、LMR对乳腺癌患者新辅助化疗病理疗效预测价值。所有患者术后随访5年,根据ROC曲线确定的NLR、PLR、LMR最佳截断值分为高NLR、PLR、LMR组和低NLR、PLR、LMR组,采用K-M生存曲线分析不同NLR、PLR、LMR组5年无病生存期（DFS）;单因素和多因素COX回归分析预后不良的影响因素。结果：pCR组化疗前NLR、PLR均低于非pCR组（P＜0.05）,LMR高于非pCR组（P＜0.05）。化疗前NLR、PLR、LMR三项联合预测新辅助化疗病理疗效的曲线下面积（AUC）均大于各指标单独预测。K-M生存曲线分析显示,化疗前高NLR、PLR组5年DFS分别低于低NLR、PLR组（P＜0.05）,高LMR组5年DFS高于低LMR组（P＜0.05）;多因素COX回归分析显示,NLR、PLR升高是乳腺癌预后的危险因素,LMR升高是保护因素（P<0.05）。结论：pCR组化疗前NLR、PLR更低,LMR更高,高NLR、PLR和低LMR患者5年DFS更低。NLR、PLR、LMR对新辅助化疗病理疗效具有一定的预测价值,三项联合能为乳腺癌的新辅助化疗评估提供重要参考依据。     

Evaluation of the WHO 2010 Grading and AJCC/UICC Staging Systems in Prognostic Behavior of Intestinal Neuroendocrine Tumors

Background

The increasing incidence and heterogeneous behavior of intestinal neuroendocrine tumors (iNETs) pose a clinicopathological challenge. Our goal was to decribe the prognostic value of the new WHO 2010 grading and the AJCC/UICC TNM staging systems for iNETs. Moreover, outcomes of patients treated with somatostatin analogs were assessed.

Methods

We collected epidemiological and clinicopathological data from 93 patients with histologically proven iNETs including progression and survival outcomes. The WHO 2010 grading and the AJCC/UICC TNM staging systems were applied for all cases. RECIST criteria were used to define progression. Kaplan-Meier analyses for progression free survival (PFS) and overall survival (OS) were performed.

Results

Mean follow-up was 58.6 months (4–213 months). WHO 2010 grading yielded PFS and disease-specific OS of 125.0 and 165.8 months for grade 1 (G1), 100.0 and 144.2 months for G2 and 15.0 and 15.8 months for G3 tumors (p = 0.004 and p = 0.001). Using AJCC staging, patients with stage I and II tumors had no progression and no deaths. Stage III and IV patients demonstrated PFS of 138.4 and 84.7 months (p = 0.003) and disease-specific OS of 210.0 and 112.8 months (p = 0.017). AJCC staging also provided informative PFS (91.2 vs. 50.0 months, p = 0.004) and OS (112.3 vs. 80.0 months, p = 0.005) measures with somatostatin analog use in stage IV patients.

Conclusion

Our findings underscore the complementarity of WHO 2010 and AJCC classifications in providing better estimates of iNETS disease outcomes and extend the evidence for somatostatin analog benefit in patients with metastatic disease.     

Tumor cell intrinsic and extrinsic features predict prognosis in estrogen receptor positive breast cancer

Although estrogen-receptor-positive (ER+) breast cancer is generally associated with favorable prognosis, clinical outcome varies substantially among patients. Genomic assays have been developed and applied to predict patient prognosis for personalized treatment. We hypothesize that the recurrence risk of ER+ breast cancer patients is determined by both genomic mutations intrinsic to tumor cells and extrinsic immunological features in the tumor microenvironment. Based on the Cancer Genome Atlas (TCGA) breast cancer data, we identified the 72 most common genomic aberrations (including gene mutations and indels) in ER+ breast cancer and defined sample-specific scores that systematically characterized the deregulated pathways intrinsic to tumor cells. To further consider tumor cell extrinsic features, we calculated immune infiltration scores for six major immune cell types. Many individual intrinsic features are predictive of patient prognosis in ER+ breast cancer, and some of them achieved comparable accuracy with the Oncotype DX assay. In addition, statistical learning models that integrated these features predicts the recurrence risk of patients with significantly better performance than the Oncotype DX assay (our optimized random forest model AUC = 0.841, Oncotype DX model AUC = 0.792, p = 0.04). As a proof-of-concept, our study indicates the great potential of genomic and immunological features in prognostic prediction for improving breast cancer precision medicine. The framework introduced in this work can be readily applied to other cancers.     

The new TNM classification of malignant melanoma

The identification of increasingly powerful prognostic factors has led to sequential modifications of the cutaneous melanoma staging system. The American Joint Committee on Cancer (AJCC) recently proposed major revision of tumor node metastasis (TNM) categories and stage groupings for melanoma. The authors summarize the main characteristics of this new TNM classification of malignant melanoma. The importance of the novel technique - sentinel node biopsy - in the management of malignant melanoma is discussed.     

Estudio de cohorte retrospectivo de pacientes diagnosticados de cáncer de tiroides del área suroeste de Madrid. Factores pronósticos en el cáncer diferenciado de tiroides

ObjectivesTo analyze the clinical and histopathological features of patients with thyroid cancer in the southwest Madrid area and to identify poor prognostic factors in the subgroup with differentiated thyroid carcinoma (DTC) of the follicular epitelium.Patients and methodsA retrospective cohort study of patients diagnosed with thyroid cancer at our hospital from 1998 to 2009. Significant clinical, surgical, and histopathological variables were included in Cox proportional hazard and logistic regression models to identify baseline factors predicting for death, recurrence, and persistent disease in DTC.ResultsA total of 150 patients with a median age of 49 years and a median follow-up of 5.4 years were enrolled. Histological subtypes were: papillary carcinoma (86%), follicular carcinoma (6.6%), medullary carcinoma (4%), poorly differentiated carcinoma (2.7%), and anaplastic carcinoma (0.7%). At the end of the study, 68% of patients were cured, 3.3% had died (disease-specific mortality, 1.3%), 1.3% were lost to follow-up, 6.7% had persistent biochemical disease, and 2.7% persistent clinical disease, while 18% of patients were pending assessment. The best prognostic model for DTC recurrence was TNM staging (stage II-IV vs. I: HR 5.9, 95% CI 1.3-26.6), while the best model for persistent disease or death was ETA clinical staging (high risk vs. low or very low risk: OR 9.2, 95% CI 2.6-33.2).ConclusionsIn our study, disease-specific mortality and persistent clinical disease were low. Classification of DTC patients based on ETA staging after initial treatment was a good predictor of persistent disease or death.     

Application of step-wise discriminant analysis and Bayesian classification procedure in determining prognosis of acute myocardial infarction.

A retrospective study was carried out to assess the feasibility of computer-assisted prognostication by discriminant analysis and the Bayesian classification procedure based on clinical information collected on patients with acute myocardial infarction. The overall accuracy was 94.2% in predicting hospital death but the prediction of late death after discharge was less accurate. It was found that not all of the 44 variables used for analysis were necessary to reach the same level of predictive accuracy--16 to 20 variables would result in almost the identical prediction. The Bayesian classification procedure was applied to estimate probabilities of individual patients belonging to the different prognostic categories.     

Development and Validation of Nomograms for Predicting Overall and Breast Cancer–Specific Survival in Young Women with Breast Cancer: A Population-Based Study

INTRODUCTION: The objective of current study was to develop and validate comprehensive nomograms for predicting the survival of young women with breast cancer. METHODS: Women aged <40 years diagnosed with invasive breast cancer between 1990 and 2010 were selected from the Surveillance, Epidemiology, and End Results database and randomly divided into training (n = 12,465) and validation (n = 12,424) cohorts. A competing-risks model was used to estimate the probability of breast cancer–specific survival (BCSS). We identified and integrated significant prognostic factors for overall survival (OS) and BCSS to construct nomograms. The performance of the nomograms was assessed with respect to calibration, discrimination, and risk group stratification. RESULTS: The entire cohort comprised 24,889 patients. The 5- and 10-year probabilities of breast cancer–specific mortality were 11.6% and 20.5%, respectively. Eight independent prognostic factors for both OS and BCSS were identified and integrated for the construction of the nomograms. The calibration curves showed optimal agreement between the predicted and observed probabilities. The C-indexes of the nomograms in the training cohort were higher than those of the TNM staging system for predicting OS (0.724 vs 0.694; P < .001) and BCSS (0.733 vs 0.702; P < .001). Additionally, significant differences in survival were observed in patients stratified into different risk groups within respective TNM categories. CONCLUSIONS: We developed and validated novel nomograms that can accurately predict OS and BCSS in young women with breast cancer. These nomograms may help clinicians in making decisions on an individualized basis.     

Can the Tumor Deposits Be Counted as Metastatic Lymph Nodes in the UICC TNM Staging System for Colorectal Cancer?

Objective

The 7th edition of AJCC staging manual implicitly states that only T1 and T2 lesions that lack regional lymph node metastasis but have tumor deposit(s) will be classified in addition as N1c, though it is not consistent in that pN1c is also an option for pT3/T4a tumors in the staging table. Nevertheless, in this TNM classification, how to classify tumor deposits (TDs) in colorectal cancer patients with lymph node metastasis (LNM) and TDs simultaneously is still not clear. The aim of this study is to investigate the possibility of counting TDs as metastatic lymph nodes in TNM classification and to indentify its prognostic value for colorectal cancer patients.

Methods and Results

In this retrospective study, 513 cases of colorectal cancer with LNM were reviewed. We proposed a novel pN (npN) category in which TDs were counted as metastatic lymph nodes in the TNM classification. Cancer-specific survival according to the npN or pN category was analyzed using Kaplan-Meier survival curves. Univariate and multivariate analyses were performed to indentify significant prognostic factors. Harrell''s C statistic was used to test the predictive capacity of the prognostic models. The results revealed that the TD was a significant prognostic factor in colorectal cancer. Univariate and multivariate analyses uniformly indicated that the npN category was significantly correlated with prognosis. The results of Harrell''s C statistical analysis demonstrated that the npN category exhibited a superior predictive capacity compared to the pN category of the 7th edition TNM classification. Moreover, we also found no significant prognostic differences in patients with or without TD in the same npN categories.

Conclusions

The counting of TDs as metastatic lymph nodes in the TNM classification system is potentially superior to the classification in the 7th edition of the TNM staging system to assess prognosis and survival for colorectal cancer patients.     

Activity of the National Oncology R&D Consortium in 2004

We have prepared the map of regional distribution of cervical cancer in Hungary. Serial HPV genotyping of sexual partners provided evidence for the sexually transmitted infections. Molecular epidemiology studies revealed activating c-kit mutation in bilateral testicular cancers. A cost-effective molecular staging method was introduced to the management of breast cancer patients. Genomic profiling identified the gene signature of Herceptin and taxane sensitivity of breast cancer. In colon cancer patients we have determined the mutational spectrum of hMLH1 and hMSH2 genes in Hungary. The prognostic power of SHMT and MTHFR polymorphism was determined in colorectal cancer patients. In head and neck cancer the gene signature of cisplatin sensitivity and the EGFR polymorphism was determined. We have introduced a cost-effective in vitro assay to determine the drug resistance of pediatric leukemias. The prognostic power of N-myc genotyping was determined in neuroblastoma patients. A phase I trial for gene therapy of brain cancer was started by using a GM-CSF adenoviral vector system. Using global genomic approaches the gene signature of malignant melanoma and its metastatic disease was determined. We have found that Ca-channel blockers and EGFR tyrosine kinase inhibitors are effective in preclinical human melanoma models in breaking the apoptosis resistance of this tumor.     

Combining gene signatures improves prediction of breast cancer survival

Background

Several gene sets for prediction of breast cancer survival have been derived from whole-genome mRNA expression profiles. Here, we develop a statistical framework to explore whether combination of the information from such sets may improve prediction of recurrence and breast cancer specific death in early-stage breast cancers. Microarray data from two clinically similar cohorts of breast cancer patients are used as training (n = 123) and test set (n = 81), respectively. Gene sets from eleven previously published gene signatures are included in the study.

Principal Findings

To investigate the relationship between breast cancer survival and gene expression on a particular gene set, a Cox proportional hazards model is applied using partial likelihood regression with an L2 penalty to avoid overfitting and using cross-validation to determine the penalty weight. The fitted models are applied to an independent test set to obtain a predicted risk for each individual and each gene set. Hierarchical clustering of the test individuals on the basis of the vector of predicted risks results in two clusters with distinct clinical characteristics in terms of the distribution of molecular subtypes, ER, PR status, TP53 mutation status and histological grade category, and associated with significantly different survival probabilities (recurrence: p = 0.005; breast cancer death: p = 0.014). Finally, principal components analysis of the gene signatures is used to derive combined predictors used to fit a new Cox model. This model classifies test individuals into two risk groups with distinct survival characteristics (recurrence: p = 0.003; breast cancer death: p = 0.001). The latter classifier outperforms all the individual gene signatures, as well as Cox models based on traditional clinical parameters and the Adjuvant! Online for survival prediction.

Conclusion

Combining the predictive strength of multiple gene signatures improves prediction of breast cancer survival. The presented methodology is broadly applicable to breast cancer risk assessment using any new identified gene set.     

RB-pathway disruption in breast cancer

In breast cancer, inactivation of the RB tumor suppressor gene is believed to occur via multiple mechanisms to facilitate tumorigenesis. However, the prognostic and predictive value of RB status in disease-specific clinical outcomes has remained uncertain. We investigated RB pathway deregulation in the context of both ER-positive and ER-negative disease using combined microarray datasets encompassing over 900 breast cancer patient samples. Disease-specific characteristics of RB pathway deregulation were investigated in this dataset by evaluating correlation among pathway genes as well as differential expression across patient tumor populations defined by ER status. Survival analysis among these breast cancer samples demonstrates that the RB-loss signature is associated with poor disease outcome within several independent cohorts. Within the ER-negative subpopulation, the RB-loss signature is associated with improved response to chemotherapy and longer relapse-free survival. Additionally, while individual genes in the RB target signature closely reproduce its prognostic value, they also serve to predict and monitor response to therapeutic compounds, such as the cytostatic agent PD-0332991. These results indicate that the RB-loss signature expression is associated with poor outcome in breast cancer, but predicts improved response to chemotherapy based on data in ER-negative populations. While the RB-loss signature, as a whole, demonstrates prognostic and predictive utility, a small subset of markers could be sufficient to stratify patients based on RB function and inform the selection of appropriate therapeutic regimens.     

RB-pathway disruption in breast cancer: Differential association with disease subtypes,disease-specific prognosis and therapeutic response

In breast cancer, inactivation of the RB tumor suppressor gene is believed to occur via multiple mechanisms to facilitate tumorigenesis. However, the prognostic and predictive value of RB status in disease-specific clinical outcomes has remained uncertain. We investigated RB pathway deregulation in the context of both ER-positive and ER-negative disease using combined microarray datasets encompassing over 900 breast cancer patient samples. Disease-specific characteristics of RB pathway deregulation were investigated in this dataset by evaluating correlation among pathway genes as well as differential expression across patient tumor populations defined by ER status. Survival analysis among these breast cancer samples demonstrates that the RB-loss signature is associated with poor disease outcome within several independent cohorts. Within the ER-negative subpopulation, the RB-loss signature is associated with improved response to chemotherapy and longer relapse-free survival. Additionally, while individual genes in the RB target signature closely reproduce its prognostic value, they also serve to predict and monitor response to therapeutic compounds, such as the cytostatic agent PD-0332991. These results indicate that the RB-loss signature expression is associated with poor outcome in breast cancer, but predicts improved response to chemotherapy based on data in ER-negative populations. While the RB-loss signature, as a whole, demonstrates prognostic and predictive utility, a small subset of markers could be sufficient to stratify patients based on RB function and inform the selection of appropriate therapeutic regimens.Key words: RB, breast cancer, microarray, proliferation, cytostatics     

Tumor Size is an Independent Predictor of Mortality Risk in Differentiated Thyroid Cancer Patients with T4 Disease

Objective: The eighth edition of the American Joint Committee on Cancer (AJCC) guideline on the tumor-node-metastasis staging system has been applied in clinical practice for thyroid cancer since 2018. However, using these criteria, a few studies have shown no significant difference between stage III and IV diseases amongst the differentiated thyroid cancer (DTC) patients. Thus, we aimed to study the underlying reason behind this observation.Methods: Patients were selected from the Surveillance, Epidemiology, and End Results database between 2004 and 2015. The Cox proportional hazards regression model was used for the univariate and multivariate analyses to plot the Kaplan-Meier survival curves for overall survival (OS) and disease-specific survival (DSS).Results: A total of 1,431 patients had a median tumor size of 3.0 cm (range: 0.1 to 50 cm). When stratified by tumor size (≤2 cm, 2 to 4 cm, and >4 cm), lower survival rates were observed in patients with stage III (T4a) cancer and large tumor size than in those with stage IVA (T4b) cancer and small tumor size. Univariate and multivariate analyses showed that tumor size (≤4 cm versus >4 cm) is an independent prognostic factor for OS (P<.001) and DSS (P<.001) in DTC patients with T4a and T4b diseases.Conclusion: Tumor size is an independent prognostic factor for OS and DSS in DTC patients with T4 disease; tumor size-related modification of the T4 category can improve the AJCC staging system for DTC patient with stage III–IV diseases.Abbreviations: AJCC = American Joint Committee on Cancer; CI = confidence interval; DSS = disease-specific survival; DTC = differentiated thyroid cancer; FTC = follicular thyroid cancer; HR = hazard ratio; OS = overall survival; PTC = papillary thyroid cancer; SEER = Surveillance, Epidemiology, and End Results; TNM = tumor-node-metastasis     

Prognostic Factors of Melanoma Patients with Satellite or In-Transit Metastasis at the Time of Stage III Diagnosis

Background

Prognosis of patients with loco-regional skin metastases has not been analyzed in detail and the presence or absence of concurrent lymph node metastasis represents the only established prognostic factor thus far. Most studies were limited to patients already presenting with skin lesions at the time of initial diagnosis. We aimed to analyze the impact of a broad penal of prognostic factors in patients with skin metastases at the time of first metastatic spread, including patients with synchronous lesions already present at the time of initial diagnosis, stage I/II patients with loco-regional recurrence and patients initially presenting with skin metastasis but unknown primary melanoma.

Patients and Methods

We investigated disease-specific survival of 380 patients treated at our department between 1996 and 2010 using Kaplan Meier survival probabilities and Cox-proportional hazard analysis.

Results

Five-year survival probability was 60.1% for patients with skin metastases only and 36.3% for those with synchronous nodal metastases. The number of involved nodes and a tumor thickness of at least 3 mm had independent negative impact on prognosis. A strong relationship was identified between the risk of death and the number of involved nodes. Neither ulceration nor the timing of the first occurrence of metastases as either in stage I/II patients, at the time of excision of the primary melanoma or initially in patients with unknown primary tumor, had additional effects on survival.

Conclusion

Lymph node involvement was confirmed as the most important prognostic factor for melanoma patients with loco-regional skin metastasis including those with unknown primary tumor and stage I/II patients with skin recurrence. Consideration of the tumor thickness and of the number of involved lymph nodes instead of the exclusive differentiation into presence vs. absence of nodal disease may allow a more accurate prediction of prognosis for patients with satellite or in-transit metastases.     

Successful management of differentiated thyroid cancer in a community-based endocrine practice

ObjectiveTo describe the range of differentiated thyroid cancer (DTC) cases, disease complexity, and treatment outcomes seen in our 3-physician community-based general endocrine practice during an 8-year period in order to make comparisons with published cohorts from university settings.MethodsMedical records of patients with DTC treated between 2002 and 2009 at Mountain Diabetes and Endocrine Center (Asheville, North Carolina) were reviewed. Pathologic features, staging, and disease status at last contact were determined. Multivariate analyses of adverse prognostic risk factors at diagnosis, recombinant human thyroid-stimulating hormone use, and radioiodine use were compared with the ultimate outcome of patients.ResultsWe treated a total of 167 patients with DTC during the study period (mean age at diagnosis, 44.4 years; mean duration of follow-up, 6.2 years). In our study cohort, 88.6% had papillary thyroid cancer, 74% had stage I disease, and 32.4% of those with papillary thyroid cancer had microscopic tumors (≤ 1 cm). Remission occurred in 67.1%, 17.1% had persistent disease, and 11.8% were indeterminate for remission; non-thyroid cancer death occurred in 2.6% and disease-specific death in 1.3%. The mean number of adverse prognostic risk factors per patient was 2.0 in those with remission and 4.7 in those with persistent disease.ConclusionCommunity-based endocrinologists evaluate the full spectrum of thyroid cancer disease complexity and can achieve excellent outcomes. In our current study group, disease persistence and diseasespecific death occurred in 17.1% and 1.3%, respectively. Individualization of care based on prognostic variables guided our diagnostic and therapeutic decisions. (Endocr Pract. 2012;18:170-178)