A novel nomogram and risk stratification system predicting the cancer-specific survival of patients with gastric neuroendocrine carcinoma: a study based on SEER database and external validation.

BACKGROUND: Gastric neuroendocrine carcinoma (GNEC) is a rare histology of gastric cancer. The retrospective study was designed to construct and validate a nomogram for predicting the cancer-specific survival (CSS) of postoperative GNEC patients. METHODS: Data for 28 patients from the Hangzhou TCM Hospital were identified as the external validation cohort. A total of 1493 patients were included in the SEER database and randomly assigned to the training group (1045 patients) and internal validation group (448 patients). The nomogram was constructed using the findings of univariate and multivariate Cox regression studies. The model was evaluated by consistency index (C-index), calibration plots, and clinical net benefit. Finally, the effect between the nomogram and AJCC staging system was compared by net reclassification index (NRI) and integrated discrimination improvement (IDI). RESULTS: Age, gender, grade, T stage, N stage, metastasis, primary site, tumor size, RNE, and chemotherapy were incorporated in the nomogram. The C-indexes were 0.792 and 0.782 in the training and internal verification sets. The 1-, 3-, and 5-year CSS predicted by the nomogram and actual measurements had good agreement in calibration plots. The 1-, 3-, and 5-year NRI were 0.21, 0.29, and 0.37, respectively. The 1-, 3-, and 5-year IDI values were 0.10, 0.12, and 0.13 (P < 0.001), respectively. In 1-, 3-, and 5-year CSS prediction using DCA curves, the nomogram outperformed the AJCC staging system. The nomogram performed well in both the internal and external validation cohorts. CONCLUSION: We developed and validated a nomogram to predict 1-, 3-, and 5-year CSS for GNEC patients after surgical resection. This well-performing model could help doctors enhance the treatment plan.

Real-world implications of nonbiological factors with staging, clinical management, and prognostic prediction in pancreatic ductal adenocarcinoma.

BACKGROUND: The American Joint Committee on Cancer (AJCC) tumor-node-metastasis (TNM) staging system focuses on traditional biological factors (BFs). The present study incorporates nonbiological factors (NBFs) into the AJCC-TNM staging system in terms of the advanced clinical management and prognostic-prediction accuracy of pancreatic ductal adenocarcinoma (PDAC). METHODS: Eight thousand three hundred and thirty eligible patients with PDAC were obtained from Surveillance, Epidemiology, and End Results database between January 1, 2011, and December 31, 2015. Multivariate Cox proportional hazards regression analysis and Kaplan-Meier curves were used to testify the feasibility of cancer-specific survival (CSS) prediction based on TNM-NBF stages. RESULTS: The large population-based study demonstrated that NBFs (insurance status, marital status, county-level median household income, and unemployment) were significant prognostic indicators (p < 0.005), and multivariate Cox regression analysis demonstrated that the NBF1 stage carried a 29.4% increased risk of cancer-specific mortality than NBF0 stage (p < 0.001). The concordance index of TNM-NBF stage was 0.755 (95% confidence interval: 0.740-0.769). CONCLUSIONS: The novel NBF stage was independently associated with CSS of PDAC. In addition, combining TNM with the NBF stage could provide better clinical management and prognostic-prediction accuracy.

Surgical Methods and Social Factors Are Associated With Long-Term Survival in Follicular Thyroid Carcinoma: Construction and Validation of a Prognostic Model Based on Machine Learning Algorithms.

Background: This study aimed to establish and verify an effective machine learning (ML) model to predict the prognosis of follicular thyroid cancer (FTC), and compare it with the eighth edition of the American Joint Committee on Cancer (AJCC) model. Methods: Kaplan-Meier method and Cox regression model were used to analyze the risk factors of cancer-specific survival (CSS). Propensity-score matching (PSM) was used to adjust the confounding factors of different surgeries. Nine different ML algorithms,including eXtreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), Random Forests (RF), Logistic Regression (LR), Adaptive Boosting (AdaBoost), Gaussian Naive Bayes (GaussianNB), K-Nearest Neighbor (KNN), Support Vector Machine (SVM) and Multi-Layer Perceptron (MLP),were used to build prognostic models of FTC.10-fold cross-validation and SHapley Additive exPlanations were used to train and visualize the optimal ML model.The AJCC model was built by multivariate Cox regression and visualized through nomogram. The performance of the XGBoost model and AJCC model was mainly assessed using the area under the receiver operating characteristic (AUROC). Results: Multivariate Cox regression showed that age, surgical methods, marital status, T classification, N classification and M classification were independent risk factors of CSS. Among different surgeries, the prognosis of one-sided thyroid lobectomy plus isthmectomy (LO plus IO) was the best, followed by total thyroidectomy (hazard ratios: One-sided thyroid LO plus IO, 0.086[95% confidence interval (CI),0.025-0.290], P<0.001; total thyroidectomy (TT), 0.490[95%CI,0.295-0.814], P=0.006). PSM analysis proved that one-sided thyroid LO plus IO, TT, and partial thyroidectomy had no significant differences in long-term prognosis. Our study also revealed that married patients had better prognosis than single, widowed and separated patients (hazard ratios: single, 1.686[95%CI,1.146-2.479], P=0.008; widowed, 1.671[95%CI,1.163-2.402], P=0.006; separated, 4.306[95%CI,2.039-9.093], P<0.001). Among different ML algorithms, the XGBoost model had the best performance, followed by Gaussian NB, RF, LR, MLP, LightGBM, AdaBoost, KNN and SVM. In predicting FTC prognosis, the predictive performance of the XGBoost model was relatively better than the AJCC model (AUROC: 0.886 vs. 0.814). Conclusion: For high-risk groups, effective surgical methods and well marital status can improve the prognosis of FTC. Compared with the traditional AJCC model, the XGBoost model has relatively better prediction accuracy and clinical usage.

A Practical Nomogram and Risk Stratification System Predicting the Cancer-Specific Survival for Patients With Advanced Hepatocellular Carcinoma.

Background: Hepatocellular carcinoma (HCC) has the highest cancer-related mortality rate. This study aims to create a nomogram to predict the cancer-specific survival (CSS) in patients with advanced hepatocellular carcinoma. Methods: Patients diagnosed with advanced HCC (AJCC stage III and IV) during 1975 to 2018 were obtained from the Surveillance, Epidemiology, and End Results (SEER) database. Qualified patents were randomized into training cohort and validation cohort at a ratio of 7:3. The results of univariate and multivariate Cox regression analyses were used to construct the nomogram. Consistency index (C-index), area under the time-dependent receiver operating characteristic (ROC) curve [time-dependent area under the curve (AUC)], and calibration plots were used to identify and calibrate the nomogram. The net reclassification index (NRI), integrated discrimination improvement (IDI), and C-index, and decision curve analysis DCA were adopted to compare the nomogram's clinical utility with the AJCC criteria. Results: The 3,103 patients with advanced hepatocellular carcinoma were selected (the training cohort: 2,175 patients and the validation cohort: 928 patients). The C-index in both training cohort and validation cohort were greater than 0.7. The AUC for ROC in the training cohort was 0.781, 0.771, and 0.791 at 1, 2, and 3 years CSS, respectively. Calibration plots showed good consistency between actual observations and the 1-, 2-, and 3-year CSS predicted by the nomogram. The 1-, 2-, and 3-year NRI were 0.77, 0.46, and 0.48, respectively. The 1-, 2-, and 3-year IDI values were 0.16, 0.15, and 0.12 (P < 0.001), respectively. DCA curves in both the training and validation cohorts demonstrated that the nomogram showed better predicted 1-, 2-, and 3-year CSS probabilities than AJCC criteria. Conclusions: This study established a practical nomogram for predicting CSS in patients with advanced HCC and a risk stratification system that provided an applicable tool for clinical management.

The Studies of Prognostic Factors and the Genetic Polymorphism of Methylenetetrahydrofolate Reductase C667T in Thymic Epithelial Tumors.

Objective: To describe the clinical features of a cohort of patients with thymic epithelial tumors (TETs) and to analyze their prognostic factors. In particular, we investigated the correlation between the genetic polymorphism of methylenetetrahydrofolate reductase (MTHFR) C667T and the incidence of TETs. Methods: Pathological records were reviewed from the database of the Second Affiliated Hospital of Jiaxing University, from January 2010 to December 2020, and 84 patients with TETs were recruited for this study. Univariate and multivariate analyses were performed to determine the prognostic factors. The genetic polymorphism of MTHFR C667T was examined in the patients with TETs and in a group of healthy individuals. The correlation between MTHFR transcriptional levels and methylation was analyzed using The Cancer Genome Atlas (TCGA) thymoma dataset from the cBioPortal platform. Results: Kaplan-Meier univariate survival analysis showed that sex, age, the maximum tumor diameter, surgery, chemotherapy, radiotherapy, WHO histological classification, Masaoka-Koga stage, and 8th UICC/AJCC TNM staging, were statistically significantly correlated with the prognosis of patients with TETs. The Masaoka-Koga stage and 8th UICC/AJCC TNM staging were strongly correlated with each other in this study (r=0.925, P<0.001). Cox multivariate survival analysis showed that the maximum tumor diameter, Masaoka-Koga stage, and 8th UICC/AJCC TNM staging were independent prognostic factors affecting the overall survival (OS) of patients with TETs (P<0.05). The MTHFR C667T genotype (χ 2 = 7.987, P=0.018) and allele distribution (χ 2 = 5.750, P=0.016) were significantly different between the patients and healthy controls. CT heterozygous and TT homozygous genotypes at this MTHFR polymorphism significantly increased the risk of TETs (odds ratio [OR] =4.721, P=0.008). Kaplan-Meier univariate survival analysis showed that there was no correlation between different genotypes and the prognosis of TETs (CC versus CT + TT, χ2 = 0.003, P=0.959). Finally, a negative correlation between the transcriptional and methylation levels of MTHFR was observed in the TCGA thymoma dataset (r=-0.24, P=0.010). Conclusions: The Masaoka-Koga stage, 8th UICC/AJCC TNM staging, and maximum tumor diameter were independent prognostic factors for TETs. Reduced methylation levels of MTHFR and particular polymorphic variants may contribute to the susceptibility to developing TETs.

Critical appraisal of TNM versus HKU staging system for postoperative prognostic evaluation of hepatocellular carcinoma.

BACKGROUND: The 8th edition of the American Joint Committee on Cancer tumor-node-metastasis staging system (AJCC 8th) has been launched with modifications in T staging. The University of Hong Kong liver cancer staging system (HKUSS) has been proven to better categorize hepatocellular carcinoma (HCC) into different T stages. This study aimed to compare the two systems' predictive ability for HCC recurrence after primary surgical resection. METHODS: Patients who had primary, curative resection for HCC between 1989 and 2017 were reviewed. The Kaplan-Meier plot was used to estimate disease-free survival (DFS), and the log-rank test was used for survival comparison between subgroups. The two systems' prediction of recurrence was evaluated by the Cox regression model. RESULTS: Totally 1,815 patients were included. With AJCC 8th, the 5-year DFS was 58.9% for T1a, 52.3% for T1b, 30% for T2, 16.9% for T3, and 14.4% for T4. No survival difference was demonstrated between T1a and T1b (P=0.668) or between T3 and T4 (P=0.562). With HKUSS, the 5-year DFS was 57.7% for T1, 43.4% for T2, 28.9% for T3, and 15.7% for T4. The T staging in HKUSS showed significant survival differences (T1 vs. T2, T2 vs. T3, and T3 vs. T4; P<0.001). Using receiver operating characteristic curves to show the recurrence status in the two systems, HKUSS had the largest area under curve (AUC) (HKUSS: AUC =0.655, SE 0.014, P<0.001, 95% CI, 0.628-0.681; AJCC 8th: AUC =0.652, SE 0.013, P<0.001, 95% CI, 0.625-0.677). CONCLUSIONS: HKUSS showed better categorization of HCC. In the context of primary surgical resection, HKUSS may be more appropriate for stratification of patients with HCC with various T stages, and thus the choice of staging system when primary surgical resection is considered for patients of HCC.

Real-world clinical significance of nonbiological factors with staging, prognosis and clinical management in rectal cancer.

BACKGROUND: The clinical guidance of the American Joint Committee on Cancer (AJCC) tumor, node, metastasis (TNM) staging system is established only in biological factors and does not include nonbiological factors (NBFs). We assessed the clinical value of incorporating NBFs into the TNM staging system in point of the clinical management and prognostic prediction accuracy of rectal cancer. METHODS: We used the Surveillance, Epidemiology and End Results (SEER) database and identified 12,515 patients with rectal cancer who were diagnosed between 1 January 2011 and 31 December 2015. Multivariate Cox proportional hazards regression analysis and Kaplan-Meier curves were used to determine the probabilities of cancer-specific survival (CSS) according to different TNM-NBF stages. RESULTS: Multivariate Cox regression analysis showed that county percentage with a bachelor's degree, insurance status, unemployment status, and marital status were all significant prognostic NBFs (p < 0.05). The concordance index of TNM-NBF stages was 0.815 (95% confidence interval (CI) 0.8072-0.8228). Multivariate Cox analyses showed that, compared with NBF0-stage, NBF1-stage was contacted with a 54.5% increased risk of cancer-specific mortality in rectal cancer, which increased to 68.3% in non-metastatic rectal cancer (all p < 0.001). NBF0-stage showed a CSS benefit as compared with NBF1-stage (p < 0.001). CONCLUSIONS: We found that NBF-stage was an independent prognostic factor for survival in rectal cancer. The influence of NBFs on survival in rectal cancer warrants greater clinical attention. Furthermore, the consolidation of NBF-stage into the TNM staging system is crucial to better prognostic prediction accuracy and individualized risk-adaptive therapies.

Influence of adjuvant chemotherapy on survival for patients with stage IB and IIA non-small cell lung cancer.

BACKGROUND: The role of adjuvant chemotherapy (ACT) for patients with stage IB-IIA non-small cell lung cancer (NSCLC) according to the eighth edition of the AJCC TNM staging system remains controversial. METHODS: Data were collected from patients with NSCLC stage IB-IIA according to the eighth edition of the AJCC TNM staging system who underwent surgical resection from 2008 to 2015. The relationship between ACT and overall survival (OS) or disease-free survival (DFS) was analyzed using the Kaplan-Meier method and Cox proportional hazards model. RESULTS: The study included 648 patients with completely resected NSCLC stage IB-IIA; 312 underwent ACT after surgical resection and 336 were placed under observation. After propensity score matching, 247 pairs of patients were matched and the five-year OS was 88.08% and 83.12% (P = 0.13) in ACT and non-ACT settings, respectively. Subgroup analyses demonstrated that ACT treatment was correlated with an improved five-year OS in patients with visceral pleural invasion (VPI) in the 3 < tumor ≤ 4 cm subgroup (93.98% and 68.93%, P < 0.01). CONCLUSIONS: ACT was not significantly associated with improved five-year OS in stage IB-IIA NSCLC patients. However, further subgroup analysis showed that patients with VPI in the 3 < tumor ≤ 4 cm (T2aN0M0, stage IB) subgroup might benefit more from ACT. Further studies are required to validate the findings and better systemic strategies need to be developed in these patients. KEY POINTS: SIGNIFICANT FINDINGS OF THE STUDY: For patients with stage IB-IIA NSCLC according to the eighth edition of the AJCC TNM staging system, the effect of ACT remains unclear. ACT was not significantly associated with improved five-year OS in stage IB-IIA NSCLC patients. However, it was correlated with better DFS before or after PSM. Patients with VPI in the 3 < tumor ≤ 4 cm subgroup may benefit from ACT. WHAT THIS STUDY ADDS: ACT was not significantly associated with improved five-year OS in stage IB-IIA NSCLC patients. However, it was correlated with better DFS before or after PSM. Patients with VPI in the 3 < tumor ≤ 4 cm subgroup may benefit from ACT.

Comparisons of remnant primary, residual, and recurrent gastric cancer and applicability of the 8th AJCC TNM classification for remnant gastric cancer staging.

BACKGROUND: Because there is no well-established postoperative staging system for patients with remnant gastric cancer (RGC), we compared the overall survival of patients categorized with the 8th AJCC TNM staging system. METHOD: A total of 391 patients underwent surgery for RGC at our institution between 1996 and 2019. Among them, 201 patients received their first surgery at our institution and 190 received primary surgery elsewhere. We retrospectively reviewed their medical records and classified each according to Kaminishi's classification and the 8th AJCC TNM staging system for comparison and analysis. RESULTS: All 201 patients who underwent their first operation at our institution for malignancy were classified as primary (n = 41, 20.4%), residual (n = 103, 51.2%), and recurrent (n = 57, 28.4%) RGC. The 5-year overall survival (OS) rates for the primary, residual, and recurrent RGC groups were 78.1%, 73.8% and 56.0%, respectively (p = 0.004). In a multivariate analysis, RGC classification was an independent prognostic factor along with the TNM staging system (p = 0.001). However, there was no significant difference in OS between the three groups of the same TNM stage. In addition, the OS of each stage related to primary cancer was not significantly different from the OS of RGC patients classified in TNM staging. CONCLUSION: The RGC classification system we used may reflect the comprehensive aspects of previous disease states and predict the prognosis of patients with gastric cancer. In addition, the 8th AJCC TNM classification is a practical and applicable staging system for RGC.

Nomogram for Predicting the Overall Survival of Patients With Breast Cancer With Pathologic Nodal Status N3.

BACKGROUND: Patients with breast cancer with pathologic N3 (pN3) lymph node status have been proven to have a poor prognosis. This study aimed to establish a nomogram to predict overall survival (OS) in patients with pN3 breast cancer. MATERIALS AND METHODS: The eligible patients from the Surveillance, Epidemiology, and End Results (SEER) database were randomly divided into training and validation cohorts. χ2 tests and survival curves were performed to define the consistency between these 2 cohorts. Univariate and multivariate logistic regressions were carried out to identify the independent clinicopathologic factors of patients with pN3 breast cancer. A nomogram was developed and validated internally and externally by a calibration curve and compared with the seventh edition American Joint Committee on Cancer TNM staging classification in discrimination ability. RESULTS: Race, age at diagnosis, marital status, grade, T stage, N stage, breast cancer subtype, surgery, radiotherapy, and chemotherapy were independent predictive factors of OS in pN3 breast cancer. We developed a nomogram to predict 1-, 3-, and 5-year OS and further validated it in both cohorts, demonstrating better prediction capacity in OS than that of the seventh edition American Joint Committee on Cancer TNM staging classification (area under the curve in the receiver operating characteristic curve, 0.745 and 0.611 in the training cohort and 0.768 and 0.624 in the validation cohort, respectively). CONCLUSION: We have developed and validated the first nomogram for predicting the survival of pN3 breast cancer. This nomogram accurately and reliably predicted the OS of patients with pN3 breast cancer. However, more prognostic factors need to be further explored to improve the nomogram.

Proposed revision of N categories to the 8th edition of the AJCC-TNM staging system for non-surgical esophageal squamous cell cancer.

The 8th edition of the American Joint Committee on Cancer Tumor-Node-Metastasis (AJCC-TNM) staging system for esophageal cancer (EC) retained the definition of N categories based on the number of metastatic lymph nodes (LN). However, it is difficult to accurately determine the number of metastatic LN without surgery. This study aimed to propose a revision to the N categories of the 8th edition AJCC-TNM staging system that makes staging easier to perform and better represents the prognosis of non-surgical esophageal squamous cell cancer (ESCC). We retrospectively reviewed the data of 336 patients with ESCC. The revised N categories were based on the anatomic regions of LN metastasis (cervix, thorax and abdomen). Survival was analyzed using the Kaplan-Meier method and compared using the log-rank test. Multivariate analyses were performed using the Cox proportional hazard model. Survival differences were adequately discriminated when the revised N categories were used. Subgroup analyses by T stage showed significant difference in overall survival between the revised N categories. Multivariate analyses demonstrated that T stage, revised N category, age, sex and treatment modality were independent risk factors, with the revised N category being the most significant variable. The revised N categories determined in this study can be used to fill gaps in the staging system for patients with non-surgical ESCC, which can help clinicians to make better treatment decisions and more effectively predict patient prognoses. Future large-scale studies are required to validate these results.

Poorly differentiated thyroid cancer in the context of the revised 2015 American Thyroid Association Guidelines and the Updated American Joint Committee on Cancer/Tumor-Node-Metastasis Staging System (eighth edition).

OBJECTIVE: Poorly differentiated thyroid cancer (PDTC) is a rare, but aggressive thyroid cancer (TC) and a main cause of death from non-anaplastic follicular cell-derived TC. Assessing the risk of PDTC-related death and the risk of recurrence is important for clinicians. The recent American Thyroid Association (ATA) 2015 guidelines and the updated 8th edition of the American Joint Committee on Cancer/Tumor-Node-Metastasis (AJCC/TNM) staging system should support clinicians in the management approach to PDTC patients. PATIENTS: Forty-six consecutive PDTC patients treated in a single oncologic centre, 2000-2017. MEASUREMENTS: Retrospective analysis of TNM stage, initial risk, response-to-therapy categories, follow-up and final disease status incorporating the ATA 2015 criteria and the 8th AJCC/TNM staging system. Disease-specific survival (DSS) using the Kaplan-Meier method. RESULTS: Of the 46 PDTC 21 (45.6%) were ATA high risk (HR), 22 (47.8%), 17 (37%) and seven (15.2%) were TNM stages I, II, and III-IV, respectively. During a median follow-up of 55.5 months, two (4.3%) patients were recurrent, 18 (39.1%) died of PDTC. The 5-/10-year DSS were 65/57%, respectively. According to the AJCC/TNM, the 5-/10-year DSS of I, II, and III-IV stage were 83/83%; 77/55%, and 0/0%, respectively. According to the 2015 ATA initial risk, the 5-/10-year DSS were 91/72% for ATA intermediate risk and 38/38% for ATA HR patients. CONCLUSIONS: In PDTC patients, the updated AJCC/TNM staging system accurately predicts a high risk of death in stage III-IV, whereas it seems to be inadequate for predicting a very low or low risk of death expected for differentiated TC in stage I-II. The ATA initial HR may be also used to predict a high risk of PDTC-related death.

A comparison of the 7th and 8th editions of the AJCC staging system in terms of predicting recurrence and survival in patients with papillary thyroid carcinoma.

OBJECTIVES: The recently published 8th edition of the American Joint Committee on Cancer (AJCC) tumour-node-metastasis (TNM) staging system was significantly updated following the thyroid cancer-related guidelines to provide better predictability of survival but not focus on recurrence. Therefore, we compared the predictive values of the 7th and 8th editions of the AJCC staging systems for recurrence-free survival (RFS) and overall survival (OS) after thyroidectomy for papillary thyroid carcinoma (PTC). METHODS: This retrospective study included 2930 patients who underwent thyroidectomy and neck dissection for previously untreated PTC between 2006 and 2014. TNM stage was defined according to 7th and 8th editions. Univariate and multivariate Cox proportional hazard regression analyses were used to identify associations between variables and RFS or OS. Multivariate models for the AJCC TNM stages were adjusted for clinical and pathological variables. RESULTS: A significant number of patients classified as T3 with overall TNM stages II-IV in the AJCC 7th edition were down-staged in the 8th edition. Unadjusted T classification and overall TNM staging in both the 7th and 8th editions were significantly associated with RFS and OS rates (P < 0.001). After adjustment for clinicopathological factors, the overall TNM stage according to the AJCC 8th edition, but not the 7th edition, remained significantly associated with RFS and OS (P < 0.05), with better predictability of recurrence and survival, in patients with PTC. CONCLUSIONS: The 8th edition AJCC staging system down-staged a large proportion of PTC patients, resulting in better predictability of recurrence and survival compared to the previous staging system. CONDENSED ABSTRACT: This study compared the abilities of the 7th and 8th edition AJCC staging systems to predict recurrence and overall survival in 2930 patients with papillary thyroid carcinoma. The updated guidelines down-staged a large proportion of patients, resulting in better prediction of recurrence and survival than the previous staging system.

P-TNM staging system for colon cancer: combination of P-stage and AJCC TNM staging system for improving prognostic prediction and clinical management.

AIM: This study focused on improving the American Joint Committee on Cancer TNM staging system and demonstrated an improvement in prognostic accuracy and clinical management of colon cancer using the P-TNM staging system. PATIENTS AND METHODS: Eligible patients (N=56,800) were identified from the Surveillance, Epidemiology, and End Results database between January 1, 2010, and December 31, 2014. The P-stage (P0 or P1) was assigned to each patient based on age at diagnosis, tumor grade, and tumor size. The outcome of interest was cancer-specific survival (CSS). The Cox proportional hazards regression analyses were used to identify independent prognostic factors and analyze the CSS probabilities of patients with colon cancer having different P-TNM stages, respectively. RESULTS: A total of 29,627 patients were assigned to P0-stage and 27,173 patients were assigned to P1-stage. The P1-stage was associated with a 98.1% increased risk of cancer-specific mortality (hazard ratio =1.981, 95% confidence interval =1.891-2.076, P<0.001), which was higher in patients with nonmetastatic colon cancer. The P1-stage patients had improvement in CSS compared with those in P0-stage in respective stages (P<0.001). Moreover, CSS decreased in stage I-P1 compared with stage IIA-P0 or IIIA-P0 (P<0.001), stage IIIA-P1 compared with stage IIA-P0 (P<0.001), stage IIB-P1 compared with stage IIIB-P0 or IIC-P0 (P<0.001), stage IIIB-P1 compared with stage IIC-P0 (P<0.001), and stage IIC-P1 compared with stage IIIC-P0 (P<0.001). CONCLUSION: P-stage was an independent prognostic factor for colon cancer. This study strongly supported the incorporation of P-stage into the American Joint Committee on Cancer TNM staging system for a better approach to prognostication and, thus, more individualized risk-adaptive therapies in colon cancer.

Real-World Implications of Nonbiological Factors with Staging, Prognosis and Clinical Management in Colon Cancer.

Background: The present study analyzed the nonbiological factors (NBFs) together with the American Joint Committee on Cancer (AJCC) Tumor-Node-Metastasis (TNM) staging system to generate a refined, risk-adapted stage for the clinical treatment of colon cancer. Methods: Eligible patients (N = 28,818) with colon cancer between 1 January 2010 and 31 December 2014, were identified from the Surveillance, Epidemiology, and End Results (SEER) database. Kaplan-Meier curves and Cox proportional hazards regression, analyzed the probabilities of cancer-specific survival (CSS) in patients with colon cancer, with different NBF-TNM stages. Results: Insurance status, marital status, and median household income were significant prognostic NBFs in the current study (p < 0.05). The concordance index of NBF-TNM stage was 0.857 (95% confidence interval (CI) = 0.8472⁻0.8668). Multivariate Cox analyses, indicated that NBF1-stage was independently associated with a 50.4% increased risk of cancer-specific mortality in colon cancer (p < 0.001), which increased to 77.1% in non-metastatic colon cancer. NBF0-stage improved in CSS as compared to the NBF1-stage in the respective stages (p < 0.05). Conclusions: The new proposed NBF-stage was an independent prognostic factor in colon cancer. Effect of NBFs on the survival of colon cancer necessitates further clinical attention. Moreover, the incorporation of NBF-stage into the AJCC TNM staging system is essential for prognostic prediction, and clinical guidance of adjuvant chemotherapy in stage II and III colon cancer.