AJCC TNM Staging System Research Articles

Prognostic significance of an innovative staging system based on the logarithmic odds of positive lymph nodes for resectable gastroesophageal cancer after neoadjuvant chemoradiation: a population-based study with external validation of data

BackgroundAfter receiving neoadjuvant chemoradiation, the number of examined lymph nodes in resectable gastroesophageal cancer (GEC) will decrease, this may not accurately determine the N staging. So our study evaluates the clinical significance of a new staging model based on the logarithmic odds of positive lymph nodes (LODDS) in patients with GEC after receiving neoadjuvant chemoradiation.MethodsA total of 1 130 patients with pathologically diagnosed GEC who received neoadjuvant chemoradiation from 2004 to 2019 included in the National Cancer Institute Surveillance, Epidemiology, and Results (SEER) database were selected for analysis. Lymph nodes were staged according to the AJCC TNM staging system (eighth edition) and LODDS. Patient prognosis across the two systems were evaluated by the Kaplan–Meier method, differences in node staging were evaluated by the Akaike information criterion and Bayesian information criterion. In addition, 914 patients from our center were externally validated.ResultsCompared to the traditional TNM staging system, the new TLODDSM staging system was comprised of stage I, stage II, stage IIIA, stage IIIB, and stage IVA, and decision curve analysis showed that the new staging system had higher benefits for different decision thresholds than the old staging system. The Akaike information criterion and Bayesian information criterion of the new staging system was lower than those of the old staging system, indicating the sensitivity of the TLODDSM staging system for predicting the prognosis of patients was higher. In addition, stage-IIIB or -IVA patients in the new staging system benefited from adjuvant chemotherapy. The externally validated data from our center supported this conclusion.ConclusionsCompared to the TNM staging system, the TLODDSM staging system has significant advantages in predicting prognosis of patients with GEC who have completed neoadjuvant chemoradiation, guiding the adjuvant chemotherapy for patients.

Clinical and pathological characteristics of and predictive model for colorectal neuroendocrine tumors

BackgroundThe incidence of colorectal neuroendocrine tumors (NETs) is increasing, causing a social burden. At present, there is no specific prognostic model for colorectal NETs. Thus, an accurate model is needed to predict the prognosis of patients with colorectal NETs. AimWe aimed to create a new nomogram to predict the prognosis of patients with colorectal NETs. Furthermore, we compared nomogram we established and the 8th edition of the AJCC TNM staging system in terms of prediction ability and accuracy. MethodsA total of 3353 patients with colorectal NETs were selected from the Surveillance, Epidemiology, and End Results (SEER) database. Kaplan-Meier analyses were used to assess overall survival (OS) and cancer-specific survival (CSS). Additionally, LASSO regression was used to select variables for constructing the nomogram. Furthermore, the C-index and time-dependent receiver operating characteristic (tdROC) curve were used to evaluate the nomogram. Decision curve analysis (DCA) was performed to compare the clinical utility of the nomogram with that of the TNM system. An external validation cohort (N = 61) was established to evaluate the nomogram's prediction accuracy. ResultsA total of 9 factors (age, sex, marital status, tumor size, T stage, M stage, N stage, grade, and surgery) were selected based on the results of LASSO analysis. The C-indexes of the nomogram in the training and validation sets were 0.807 and 0.775, respectively, which indicated that the nomogram had better prediction accuracy than TNM staging (C-index = 0.700 in the training set and 0.652 in the validation set). The C-index of the nomogram in the external validation cohort was 0.954, indicating that the nomogram had satisfactory prediction accuracy. The results of DCA revealed that the survival nomogram possessed greater utility in clinical practice. ConclusionWe determined the OS and CSS of patients with colorectal NETs and developed a robust and clinically useful survival nomogram.

Prognostic value analysis and survival model construction of different treatment methods for advanced intestinal type gastric adenocarcinoma

BackgroundIntestinal-type gastric adenocarcinoma, representing 95 % of gastric malignancies, originates from the malignant transformation of gastric gland cells. Despite its prevalence, existing methods for prognosis evaluation of this cancer subtype are inadequate. This study aims to enhance patient-specific prognosis evaluation by analyzing the clinicopathological characteristics and prognostic risk factors of intestinal-type gastric adenocarcinoma patients using data from the Surveillance, Epidemiology, and End Results (SEER) Program of the National Cancer Institute (NCI). MethodsWe extracted clinical data for patients diagnosed with intestinal-type gastric adenocarcinoma between 2010 and 2015 from the SEER database, selecting 257 cases based on predefined inclusion and exclusion criteria. Independent risk factors for overall survival (OS) and cancer-specific survival (CSS) were identified using a Cox regression model. A nomogram model for predicting OS or CSS was developed from the Cox risk regression analysis and validated through the consistency index (C-index), ROC curve, and calibration curve. ResultsAge, primary tumor resection, chemotherapy, lymph node metastasis, and tumor size were identified as independent prognostic factors for OS and CSS (P < 0.05). The nomogram model, constructed from these indicators, demonstrated superior predictive consistency for OS and CSS compared to the AJCC-TNM staging system. ROC curve analysis confirmed the model's higher accuracy, and calibration curve analysis indicated good agreement between the nomogram's predictions and actual observed outcomes. ConclusionThe nomogram model derived from SEER database analyses accurately predicts OS and CSS for patients with intestinal-type gastric adenocarcinoma. This model promises to facilitate more tailored treatments in clinical practice.

Establishment and validation of a prognostic nomogram for patients with colorectal neuroendocrine carcinoma

BackgroundColorectal neuroendocrine carcinoma is a relatively rare tumor, for which a prognosis prediction model is lacking. Based on the data from Surveillance, Epidemiology, and End Results (SEER) database and Fujian Cancer Hospital, the study constructed and validated a prognostic nomogram to assess overall survival of patients with colorectal neuroendocrine carcinoma(CRNEC). MethodsWe extracted data of patients diagnosed with CRNEC from the SEER database. These patients were randomly divided into a training cohort(N = 1425) and an internal validation cohort(N = 612). Data of patients diagnosed with CRNEC in Fujian Cancer Hospital was collected as an external validation cohort(N = 54). A prognostic nomogram was established. The performance of the nomogram was assessed with ROC curve, C-index and calibration curve. Decision curve analysis(DCA) and ROC curve were used to compare the prediction efficacy of nomogram with the seventh edition of the TNM classification of the American Joint Commission of Cancer. ResultsNine variables were identified as independent predictors. Nomogram were established by the nine variables. AUC of the nomogram in predicting 1-, 3- and 5-year OS were 0.900, 0.912 and 0.915 in training cohort, 0.900, 0.925 and 0.919 in internal validation cohort, 0.900, 0.903 and 0.928 in external validation cohort. C-index were 0.845, 0.854 and 0.837. Calibration curves overlapped well with reference lines. Compared with the AJCC TNM staging system, the nomogram performed more effectively. Patients classified into low-risk and high-risk groups by the nomogram scores and performed well in stratification. ConclusionThe prognostic nomogram established and validated in our study can accurately and effectively predict the prognosis of patients with CRNEC.

Survival nomogram for patients with de novo metastatic breast cancer based on the SEER database and an external validation cohort

BackgroundOn average, 5–10% of patients are diagnosed with metastatic breast cancer (MBC) at the initial diagnosis. This study aimed to develop a nomogram to predict the overall survival (OS) in these patients. MethodsThe nomogram was based on a retrospective study of 9435 patients with de novo MBC from the Surveillance, Epidemiology, and End Results (SEER) database. The predictive accuracy and discriminative ability of the nomogram were determined using the concordance index (C-index), area under the time-dependent receiver operating characteristic curve (AUC), and calibration curve. Decision curve analysis (DCA) was employed to evaluate the benefits and advantages of our new predicting model over the 8th edition of the American Joint Committee on Cancer (AJCC) Tumor Node Metastasis (TNM) staging system. The results were validated in a retrospective study of 103 patients with de novo MBC from January 2013 to June 2022 at an institution in northwest China. ResultsMultivariate analysis of the primary cohort revealed that independent factors for survival were age at diagnosis, pathological type, histological grade, T stage, N stage, molecular subtype, bone metastasis, brain metastasis, liver metastasis, lung metastasis, surgery, chemotherapy, and radiotherapy. The nomogram achieved a C-index of 0.688 (95% confidence interval [CI], 0.682–0.694) in the training cohort and 0.875 (95% CI, 0.816–0.934) in the validation cohort. The AUC of the nomograms indicated good specificity and sensitivity in the training and validation cohorts, respectively. Calibration curves showed favorable consistency between the predicted and actual survival probabilities. Additionally, the DCA curve produced higher net gains than by the AJCC-TNM staging system. Finally, risk stratification can accurately identify groups of patients with de novo MBC at different risk levels. ConclusionsThe nomogram showed favorable predictive and discriminative abilities for OS in patients with de novo MBC. Other populations from different countries or prospective studies are needed to further validate the nomogram.

Comparation between novel online models and the AJCC 8th TNM staging system in predicting cancer-specific and overall survival of small cell lung cancer.

Most of previous studies on predictive models for patients with small cell lung cancer (SCLC) were single institutional studies or showed relatively low Harrell concordance index (C-index) values. To build an optimal nomogram, we collected clinicopathological characteristics of SCLC patients from Surveillance, Epidemiology, and End Results (SEER) database. 24,055 samples with SCLC from 2010 to 2016 in the SEER database were analyzed. The samples were grouped into derivation cohort (n=20,075) and external validation cohort (n=3,980) based on America's different geographic regions. Cox regression analyses were used to construct nomograms predicting cancer-specific survival (CSS) and overall survival (OS) using derivation cohort. The nomograms were internally validated by bootstrapping technique and externally validated by calibration plots. C-index was computed to compare the accuracy and discrimination power of our nomograms with the 8th of version AJCC TNM staging system and nomograms built in previous studies. Decision curve analysis (DCA) was applied to explore whether the nomograms had better clinical efficiency than the 8th version of AJCC TNM staging system. Age, sex, race, marital status, primary site, differentiation, T classification, N classification, M classification, surgical type, lymph node ratio, radiotherapy, and chemotherapy were chosen as predictors of CSS and OS for SCLC by stepwise multivariable regression and were put into the nomograms. Internal and external validations confirmed the nomograms were accurate in prediction. C-indexes of the nomograms were relatively satisfactory in derivation cohort (CSS: 0.761, OS: 0.761) and external validation cohort (CSS: 0.764, OS: 0.764). The accuracy of the nomograms was superior to that of nomograms built in previous studies. DCA showed the nomograms conferred better clinical efficiency than 8th version of TNM staging system. We developed practical nomograms for CSS (https://guowei2020.shinyapps.io/DynNom-CSS-SCLC/) and OS (https://drboidedwater.shinyapps.io/DynNom-OS-SCLC/) prediction of SCLC patients which may facilitate clinicians in individualized therapeutics.

A new lymph node ratio-based staging system for rectosigmoid cancer: a retrospective study with external validation.

This study evaluated the clinical value of a new American Joint Committee on Cancer (AJCC) tumor node metastasis (TNM) staging prediction model based on lymph node ratio (LNR) in rectosigmoid cancer (RSC). The analysis included 1444 patients with nonmetastatic RSC diagnosed pathologically between 2010 and 2016 who were collected from the National Cancer Institute Surveillance, Epidemiology, and Results database. The AJCC N-stage was redefined according to the LNR cutoff point, and the ability of the new staging system to predict prognosis was compared with that of the AJCC TNM staging system. Data from 739 patients from our hospital were used for external validation. According to the number of examined lymph nodes and LNR, the N stage was divided into five groups (LNR0-5). The 5-year OS of patients divided according to the new T lymph node ratio M (TLNRM) staging into stage I (T1LNR1, T1LNR2), IIA (T1LNR3, T2LNR1, T2LNR2, T2LNR3, T1LNR4, T3LNR1), IIB (T2LNR4), IIC (T3LNR2, T4a LNR1, T1LNR5), IIIA (T3LNR3, T2LNR5, T4b LNR1, T4a LNR2, T3LNR4), IIIB (T3LNR5, T4a LNR3, T4a LNR4, T4b LNR2), and IIIC (T4b LNR3, T4a LNR5, T4b LNR4, T4b LNR5) was significantly different ( P <0.05). Decision curve analysis showed that the net income of the new TLNRM staging system for different decision thresholds was higher than the prediction line of the traditional eighth TNM staging system. The smaller Akaike information criterion and Bayesian information suggested that the new staging system had a higher sensitivity for predicting prognosis than the traditional staging system. TLNRM II and III patients benefited from adjuvant chemotherapy, while adjuvant chemotherapy did not improve the prognosis of TNM II patients. These findings were confirmed by the external validation data. The new TLNRM staging system was superior to the eighth edition AJCC staging system for staging and predicting the prognosis of patients with RSC and may become an effective tool in clinical practice.

Construction and validation of a nomogram for patients with multiple hepatocellular carcinoma: A SEER-based study

BackgroundAmerican Joint Committee on Cancer (AJCC)-TNM system doesn't accurately predict prognosis. Our study was designed to identify prognostic factors in patients with multiple hepatocellular carcinoma (MHCC), establish and validate a nomogram model to predict the risk and overall survival (OS) of MHCC patients. MethodsWe selected eligible HCC patients from the Surveillance, Epidemiology, and End Results (SEER) database, used univariate and multivariate COX regression to determine prognostic factors in MHCC patients, and used these factors to build a nomogram. The accuracy of the prediction was evaluated using the C-index, receiver operating characteristic (ROC) and calibration curve. Decision curve analysis (DCA), net reclassification index (NRI) and integrated discrimination improvement (IDI) were used to compare the nomogram with AJCC-TNM staging system. Finally, the prognosis of different risks was analyzed using Kaplan-Meier (K-M) method. Results4950 eligible patients with MHCC were enrolled in our study and randomly assigned to the training cohort and test cohort in a 7:3 ratio. After COX regression analysis, age, sex, histological grade, AJCC-TNM stage, tumor size, alpha-fetoprotein (AFP), surgery, radiotherapy and chemotherapy in total 9 factors could be used to independently determine OS of patients. the above factors were used to construct a nomogram, and the consistency C-index was 0.775. C-index, DCA, NRI and IDI showed that our nomogram was superior to the AJCC-TNM staging system. K-M plots for OS were performed using the log-rank test, the P-value of which was <0.001. ConclusionsThe practical nomogram can provide more accurate prognostic prediction for multiple hepatocellular carcinoma patients.

Risk stratification of pancreatic ductal adenocarcinoma patients undergoing curative-intent surgery after neoadjuvant therapy.

e16312 Background: To identify preoperative factors associated with survival in pancreatic ductal adenocarcinoma (PDAC) patients who underwent curative-intent surgery after neoadjuvant therapy (NAT) and develop risk stratification criteria. Methods: Consecutive PDAC patients who underwent post-NAT curative-intent surgeries between 2007 and 2020 at a high-volume center were retrospectively analyzed. Demographic, laboratory, surgical, and histopathologic variables were collected. Baseline, preoperative, and interval changes of CT findings proposed by the Society of Abdominal Radiology and the American Pancreatic Association were analyzed. Cox proportional hazard analyses were used to identify preoperative variables associated with survival since the time of diagnosis. We developed risk stratification criteria using the variables independently associated with survival, i.e., "post-NAT response criteria." We compared the discrimination performance of "post-NAT response criteria" with that of post-NAT pathological (yp) AJCC TNM staging system. Results: A total 145 PDAC patients who completed chemo- (n=47) or chemoradiation therapy (n=98) were included. Stable or increased tumor size on CT (hazard ratio, 2.58 [1.58, 4.21]; P &lt; 0.001) and elevated preop. CA 19-9 level (1.98 [1.11, 3.55]; P = 0.021) were independent poor prognostic factors. The survival of the patient groups stratified by "post-NAT response criteria" which combined changes in tumor size and CA 19-9 showed significant difference (P &lt; 0.001). Such stratification was comparable to ypTNM staging in discrimination performance (difference of C-index, 0.068 [-0.012, 0.142]). Conclusions: Decrease in tumor size and CA 19-9 normalization or staying normal until the time of surgery were independent favorable factors of survival. The combination of the two factors discriminated survival comparably to ypTNM staging.[Table: see text]

Cancer-Specific Survival after Limb Salvage versus Amputation in Children and Adolescents with Osteosarcoma: A Population-Based Analysis with Propensity Score Matching.

The study aims to identify whether osteosarcoma patients of children and young adults will benefit from a survival profit from the choice of the operation method. The National Cancer Institute SEER database from 2000 to 2018 was selected for a retrospective analysis of 1630 children and young adults with a primary diagnosis of osteosarcoma, 1222 who underwent limb-preserving surgery, and 408 who underwent amputation. Confounders were controlled for by propensity score matching (PSM), cancer-specific survival (CSS) was analyzed using the Kaplan-Meier method, and univariate and multivariate Cox regression was used to analyze the factors influencing the prognosis of children and young osteosarcoma patients after surgery. A nomogram plot predicted 1-, 3-, and 5- survival rate in osteosarcoma. The model's accuracy was validated by the area under the ROC and calibration curves. After PSM, multifactor Cox regression analysis found AJCC Stage III-IV (CSS : HR = 5.26, 95% CI 1.95-14.18, p=0. 001; HR = 5.54, 95% CI 2.56-12.01, p < 0. 001. Limb salvage surgery (CSS : HR = 0.58, 95% CI 0.44-0.77, p < 0. 001) has independent impact factors for CSS prognosis. The survival curve before and after PSM showed that patients with osteosarcoma of children and young adults who underwent limb salvage surgery had a survival benefit compared with those who underwent amputation surgery. Gender, chemotherapy, histology, primary tumor site, stage, and surgical modality were modeled in a total of six variables in the nomogram. The model exhibited good predictive performance. The AUC were 0.823, 0.74, and 0.757 for training set at 1, 3, and 5 years, respectively. The AUC of validation set 0.666, 0.722, and 0.699 at 1, 3, and 5 years, respectively. The model also predicted CSS with good fidelity for both datasets. This model was significantly superior to the 8th edition of the AJCC TNM staging system, with a better net benefit in predicting CSS in children and young adults with osteosarcoma. Limb salvage surgery is an option for children and young adults with osteosarcoma and cancer-specific survival rates can be improved by receiving limb salvage surgery.

Preoperative CA19-9 and GGT ratio as a prognostic indicator in ampullary carcinoma

Background and aimsIn recent years, more and more inflammatory indicators have been studied to predict the long-term survival of patients with ampullary carcinoma (AC) after radical resection, but these prognostic indicators are still controversial. Therefore, based on previous inflammation scores, this study established a novel, easily accessible, more feasible and more predictive prognostic marker [Carbohydrate antigen199 to gamma-glutamyltransferase ratio (CA19-9/GGT)] to better assess the prognostic significance in AC patients undergoing radical resection.MethodsOverall survival (OS) and recurrence-free survival (RFS) were analyzed by Cox regression model. Correlation between CA19-9/GGT and clinicopathological variables were analyzed by Chi-squared test, Fisher ' s exact test, independent sample t test and Mann–Whitney U test. The performance of prognostic indexes is compared by the consistency index (C-index). The prediction accuracy of nomogram is further confirmed by calibration curve and decision curve analysis (DCA).ResultsCA19-9/GGT was an independent risk factor affecting OS [P = 0.001, hazard ratio (HR) 2.459, 95% confidence intervals (CI) 1.450–4.167] and RFS (P = 0.002, HR 2.333, 95% CI 1.371–3.971) in multivariate analysis. The optimal cut-off value of CA19-9/GGT was 0.14. In CA19-9/GGT correlation analysis, high risk group (> 0.14) was significantly associated with poor prognosis. The predictive performance of CA19-9/GGT (OS: C-index = 0.753, RFS: C-index = 0.745) was confirmed to be superior to other prognostic indicators according to the C-index. Compared with the simple AJCC staging system, the Nomogram prediction model (OS: C-index = 0.787, RFS: C-index = 0.795) established by the combination of CA19-9/GGT and AJCC 8th TNM staging system has higher prediction accuracy.ConclusionsCA19-9/GGT was an independent prognostic indicator after radical resection of AC. Incorporating CA19-9/GGT into the AJCC TNM staging system optimized the prediction accuracy of the TNM staging system, and further verified the predictive value of CA19-9/GGT.

A nomogram and risk classification system for predicting cancer-specific survival in tall cell variant of papillary thyroid cancer: a SEER-based study.

Tall cell variant (TCV) of papillary thyroid cancer (PTC) is the most common aggressive subtype of PTC. The factors that affect survival of patients with TCV remain unclear. We aimed to develop a model to predict the cancer-specific survival (CSS). A total of 1615 patients diagnosed with TCV between 2004 and 2016 were identified from the Surveillance, Epidemiology, and End Results (SEER) database and randomized into training and validation cohorts (7:3). A predictive nomogram for predicting CSS was constructed by Cox proportional hazards regression and validated by concordance index (C-index), calibration curve, and decision curve analyses (DCA). A risk classification system was built based on the total nomogram scores of each case. A nomogram was constructed including five independent prognostic factors (age, tumor size, T stage, M stage, and extent of surgery) associated with CSS in TCV patients. Various validations proved that the nomogram model had good consistency and discrimination for TCV prognosis. The risk classification system could perfectly classify TCV patients into three risk groups with significantly different CSS. Compared with traditional AJCC TNM staging system, the nomogram could better predict CSS in TCV patients. A nomogram and corresponding risk classification system were developed for predicting CSS in TCV patients. The model has excellent performance and can be used to help clinicians make accurate prognostic assessment and individualized treatment.

448. THE PRIMARY TUMOR BURDEN SCORE: A NOVEL CLINICOPATHOLOGICAL STAGING TOOL FOR PATIENTS WITH ESOPHAGEAL CARCINOMA RECEIVING NEOADJUVANT CHEMORADIOTHERAPY PLUS SURGERY

Abstract The AJCC TNM staging system (8th edition) contains a ypTNM stage classification for patients with EC receiving neoadjuvant therapy prior to surgery. However, the ypTNM staging system may be unable to represent tumor changes occurring during the treatment. This study was to develop a prognostic model for esophageal carcinoma (EC) describing both the pretreatment pathological state of the tumor and its change after neoadjuvant chemoradiotherapy (nCRT) plus surgery. 187 patients with esophageal carcinoma treated with nCRT plus surgery were identified from two medical centers. The Primary Tumor Burden Score (PTBS) was calculated by multiplying the percentage of residual primary cancer cells and the pretreatment pathological T stage (prepT-stage). Univariate and multivariate Cox regression was used to determine the relationships between clinicopathological parameters and prognosis. The discriminatory power of the model was evaluated using the area under the curve (AUC) and goodness-of-fit Akaike information criterion (ΔAIC). The revised TNM stage was compared with the ypTNM stage using the AIC and the C-index. The PTBS model (AUC:0.727) outperformed to the residual tumor ratio (AUC:0.588) and the pretreatment T stage (AUC:0.6) in OS prediction (P &amp;lt; 0.05). High PTBS scores were associated with significantly worse outcomes: five-year OS of stage 1, 2, and 3 were 73.1%, 42.0%, and 11.4%, respectively, P &amp;lt; 0.001. The PTBS model was more accurate in predicting patient prognosis than cT-stage or ypT-stage (ΔAIC: PTBS vs cT-stage and pT-stage, 30.38 vs −1.87 and 7.61). Multivariate analysis indicated that PTBS independently predicted OS and DFS, The PTBS-based revised TNM stage showed superior prognostic strength to the 8th AJCC ypTNM stage. The PTBS model showed superior prognostic discriminatory ability than cT-stage or ypT-satge and may thus be useful for describing the influence of the primary tumor burden on survival in patients who had received neoadjuvant CRT before surgery. A revised TNM stage based on PTBS showed superior prognostic capability to the 8th AJCC ypTNM stage.

A Recurrence Predictive Model for Node-negative Esophageal Squamous Cell Carcinoma After Upfront Esophagectomy

The prognosis for pathologically node-negative (pN0) esophageal squamous cell carcinoma (ESCC) with surgery alone remains poor. We aimed to develop a model for a more precise prediction of recurrence, which will allow personalized management for pN0 ESCC after upfront complete resection. Clinical and pathological records of patients with completely resected pT1-3N0M0 ESCC were retrospectively analyzed between January 2014 and December 2019. A nomogram for the prediction of recurrence was established based on the Cox regression analysis and evaluated by C-index, AUC, and calibration curves. The model was further validated using bootstrap resampling and k-fold cross-validation and compared with the 8th edition of the AJCC TNM staging system using Td-ROC, NRI, IDI, and DCA. Two-hundred-and seventy cases were included in this study. The median follow-up was 45 months. Distant and/or loco-regional recurrences were noted in 89 (33.0%) patients. The predictive model revealed pT-category, differentiation, perineural invasion, examined lymph nodes (ELN), and prognostic nutritional index (PNI) as independent risk factors for recurrence, with a c-index of 0.725 in the bootstrapping cohort. Td-ROC, NRI, and IDI showed a better predictive ability than the AJCC 8th TNM staging system. Based on this model, patients in the low-risk group had a significantly lower recurrence incidence than those in the high-risk group (p < .001). The predictive model developed in this study may facilitate the precise prediction of recurrences for pN0 ESCC after upfront surgery. Stratifying management of those patients might bring significantly better survival benefits.

The prognostic value of machine learning techniques versus cox regression model for head and neck cancer.

Accurate prognostic prediction for head and neck cancer (HNC) is important for the improvement of clinical management. We aimed to compare the prognostic value of various machine learning techniques (MLTs) and statistical Cox regression model for different types of HNC. Clinical data of HNC patients were extracted from the Surveillance, Epidemiology, and End Results (SEER) database from 1974 to 2016. The prediction performance of five ML models, including random forest (RF), gradient boosting decision tree (GBDT), support vector machine (SVM), neural network (NN) and deep learning (DL), were compared with the statistical Cox regression model by estimating the concordance index (C-index), integrated Brier score (IBS), time-dependent receiver operating characteristic (ROC) curve and the area under the curve (AUC). Our results showed that the RF model outperformed all other models in prognostic prediction for all tumor sites of HNC, particularly for major salivary gland cancer (MSGC, C-index: 88.730±0.8700, IBS: 7.680±0.4800), oral cavity cancer (OCC, C-index: 84.250±0.6700, IBS: 11.480±0.3300) and oropharyngeal cancer (OPC, C-index: 82.510±0.5400, IBS: 10.120±0.1400). Meanwhile, we analyzed the importance of each clinical variable in the RF model, in which age and tumor size presented the strongest positive prognostic effects. Additionally, similar results can be observed in the internal (6th edition of the AJCC TNM staging system cohort) and external validations (the TCGA HNC cohort). The RF model is a promising prognostic prediction tool for HNC patients, regardless of the anatomic subsites.

Additional Postoperative Radiotherapy Prolonged the Survival of Patients with I-IIA Small Cell Lung Cancer: Analysis of the SEER Database.

Purpose Complete resection and adjuvant chemotherapy are recommended as the standard strategy for patients with stage I-IIA small cell lung cancer (SCLC). However, the role of additional postoperative radiotherapy (PORT) in treatment remains controversial. Methods Patients with stage I-IIA SCLC undergoing surgery and adjuvant chemotherapy were extracted from the Surveillance, Epidemiology, and End Results database. Stage I-IIA, defined as T1-2N0M0, was recalculated according to the 8th AJCC TNM staging system. Propensity score matching (PSM) was conducted to identify the therapeutic impact of PORT. Univariate Cox hazards regression and least absolute shrinkage and selection operator regression were utilized for primary screening of prognostic variables for I-IIA SCLC disease. A nomogram to predict overall survival (OS) was constructed based on the multivariate Cox proportional hazards model, evaluated with area under the curve, calibration curve, and decision curve analysis, and validated with bootstrap resampling. Results Our results demonstrated that compared with no PORT, PORT significantly prolonged the median OS (8.58 vs. 5.17 years, HR = 0.61 [0.39–0.96], P = 0.032) and median cancer-specific survival (11.33 vs. 8.08, HR = 0.47 [0.27–0.82], P = 0.0086) after PSM. The 5-year OS rate was 61.56% vs. 46.60%. Five variables including age at diagnosis, gender, T stage, surgical type, and PORT were elucidated to impact on prognosis and included in a nomogram to predict 3-/5-/10-year OS probability. The area under the curve values were 0.72, 0.71, and 0.81, respectively. The nomogram also exhibited satisfactory accuracy and clinical usefulness. Conclusion PORT was verified to improve the OS of patients with T1-2N0M0 SCLC after surgery and chemotherapy. A prognostic nomogram was developed and validated for OS prediction for these patients.

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

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). 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. 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). 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.

Breast Cancer Patients With Positive Apical or Infraclavicular/Ipsilateral Supraclavicular Lymph Nodes Should Be Excluded in the Application of the Lymph Node Ratio System.

Aim: Increasing studies have demonstrated lymph node ratio (LNR) to be an accurate prognostic indicator in breast cancer and an alternative to pN staging; however, the AJCC-TNM staging system classified apical or infraclavicular/ipsilateral supraclavicular lymph node-positive (APN(+)) patients with a worse prognosis as the pN3 stage. Until now, different reports on LNR in breast cancer have ignored this possibility. Consequently, it is necessary to discuss the role of APN(+) patients in the LNR system to obtain a precise LNR that predicts the prognosis accurately. Materials and Methods: We collected data on 10,120 breast cancer patients, including 3,936 lymph node-positive patients (3,283 APN(−) and 653 APN(+) patients), who visited our hospital from 2007 to 2012. Then we applied X-tile analysis to calculate cut-off values and conduct survival analysis and multivariate analysis to evaluate patients’ prognosis. Results: We confirmed that some APN(+) patients were mis-subgrouped according to previously reported LNR, indicating that APN(+) patients should be excluded in the application of LNR to predict prognosis. Then we applied X-tile analysis to calculate two cut-off values (0.15 and 0.34) for LNR-APN(−) patients and conducted survival analysis and found that LNR-APN(−) staging was superior to pN staging in predicting the prognosis of APN(−) breast cancer patients. Conclusion: From this study, we conclude that excluding APN(+) patients is the most necessary condition for effective implementation of the LNR system. LNR-APN(−) staging could be a more comprehensive approach in predicting prognosis and guiding clinicians to provide accurate and appropriate treatment.

A novel hematological classifier predicting chemotherapy benefit and recurrence hazard for locally advanced gastric cancer A multicenter IPTW analysis

BackgroundEffective classifiers for the prediction of individual adjuvant chemotherapy (AC) benefits are scarce. PurposeThis study aimed to construct a useful classifier to predict the AC benefit and recurrence hazard based on preoperative hematological indices through a multicenter database. Methods and resultsMultivariate analysis revealing GCRF (comprehensive deep learning classifier) as an independent prognostic factor associated with overall survival (OS) and disease-free survival (DFS). Locally advanced gastric cancer (LAGC) patients are categorized into the high-risk group (HRG) and low-risk group (LRG). In HRG, OS and DFS of the AC group are significantly higher than those of the non-AC group (all p˂0.05), whereas in LRG, OS and DFS of the AC group are comparable to those of the non-AC group (all p > 0.05). Furthermore, combined GCRF with 8th AJCC TNM staging system, only 650 (51.1%) patients can benefit most from AC among 1273 patients with pStage II-III. From the perspective of recurrence pattern, the recurrence rate of HRG is significantly higher than that of LRG in any recurrence type, including local recurrence, peritoneal recurrence, and distant recurrence (all p˂0.05). Furthermore, the mean time to peritoneal recurrence and lung metastasis in HRG is earlier than that in the LRG (p = 0.028 and 0.011, respectively). ConclusionIn summary, our novel classifier based on deep learning preoperative hematological indices can predict not only the AC benefit of LAGC patients, but also the recurrence hazard after surgery. This classifier is expected to be an effective supplement to the 8th AJCC TNM staging system for the prediction of AC benefits and is helpful for clinical decision in AC individual administration. Further large-scale western studies are warranted.

Indication to post-operative radiotherapy for oral cavity squamous cell carcinoma: what's new in the depth of infiltration (DOI) era?

The last edition of the American Joint Committee on Cancer (AJCC eighth) has introduced the depth of infiltration (DOI) as a new prognostic parameter in oral cavity squamous cell carcinomas (OCSCCs). The aim of this study is to analyze the impact of stage migration on the indication to post-operative radiotherapy (PORT). OCSCCs treated at two institutions between 2014 and 2019 were retrieved. As per the AJCC eighth, only pT3 primarily OCSCCs were considered; availability of the pathologic specimen was a further inclusion criterion. Risk factors considered for PORT were: pT3-pT4, nodal involvement, positive/close surgical margins, perineural and lymph vascular invasion. 149 patients staged as pT3 AJCC eighth were included. A four-fold increase in the number of patients staged as pT3 from the seventh to the eighth AJCC was found. Stage migration to pT3 was equally due to the downstaging from former pT4 (38%) and upstaging of former pT1-pT2 (35%). Considering the former pT1-pT2 53 patients, 13 (25%) had no risk factors for PORT other than DOI. Among 25 cases with former pT1-pT2 and negative lymph nodes, no additional risk factors were found in 11 (44%). 90% of patients had at least one risk factor besides DOI and would have received PORT also according to the AJCC seventh; notably, of former pT1-pT2N0, half of them have been upstaged to pT3 in the current TNM classification. The role of PORT in this cohort of patients has not been clarified yet. Other-than-DOI risk factors leading to PORT indication are highly prevalent in OCSSC patients classified as pT3 per the latest AJCC TNM staging system and should therefore be considered for a comprehensive oncological assessment.