Predictor Of Cancer-specific Survival Research Articles

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.

Construction and validation of a prognostic nomogram for predicting cancer-specific survival in patients with intermediate and advanced colon cancer after receiving surgery and chemotherapy

BackgroundExisting predictive models often focus solely on overall survival (OS), neglecting the bias that other causes of death might introduce into survival rate predictions. To date, there is no strict predictive model established for cancer-specific survival (CSS) in patients with intermediate and advanced colon cancer after receiving surgery and chemotherapy.MethodsWe extracted the data from the Surveillance, Epidemiology, and End Results (SEER) database on patients with stage-III and -IV colon cancer treated with surgery and chemotherapy between 2010 and 2015. The cancer-specific survival (CSS) was assessed using a competitive risk model, and the associated risk factors were identified via univariate and multivariate analyses. A nomogram predicting 1-, 3-, and 5-year CSS was constructed. The c-index, area under the curve (AUC), and calibration curve were adopted to assess the predictive performance of the model. Additionally, the model was externally validated.ResultsA total of 18 risk factors were identified by univariate and multivariate analyses for constructing the nomogram. The AUC values of the nomogram for the 1-, 3-, and 5-year CSS prediction were 0.831, 0.842, and 0.848 in the training set; 0.842, 0.853, and 0.849 in the internal validation set; and 0.815, 0.823, and 0.839 in the external validation set. The C-index were 0.826 (se: 0.001), 0.836 (se: 0.002) and 0.763 (se: 0.013), respectively. Moreover, the calibration curve showed great calibration.ConclusionThe model we have constructed is of great accuracy and reliability, and can help physicians develop treatment and follow-up strategies that are beneficial to the survival of the patients.

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

BackgroundGastric 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.MethodsData 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).ResultsAge, 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.ConclusionWe 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.

Development and validation of a nomogram to predict long-term cancer-specific survival for patients with osteosarcoma

The present work aimed to establish a new model to accurately estimate overall survival (OS) as well as cancer-specific survival (CSS) of osteosarcoma. Osteosarcoma cases were collected from the Surveillance, Epidemiology, and End Results (SEER) database between 2004 and 2017 and randomized as training or validation sets. Then, the OS- and CSS-related variables were discovered through multivariate Cox regression analysis to develop new nomograms to predict the 1-, 3- and 5-year OS and CSS. Besides, consistency index (C-index), decision curve analysis (DCA), along with calibration curve were adopted for assessing the predicting ability of our constructed nomograms after calibrating for 1-, 3- and 5-year OS and CSS. Altogether, 1727 osteosarcoma cases were enrolled in the present study and randomly divided as training (n = 1149, 70%) or validation (n = 576, 30%) set. As shown by univariate as well as multivariate Cox regression analyses, age, grade, T stage, M stage, surgery, chemotherapy, and histological type were identified to be the adverse factors to independently predict OS and CSS among the osteosarcoma cases. Besides, based on results of multivariate Cox regression analysis, we constructed the OS and CSS prediction nomograms. The C-index in training set was 0.806 (95% CI 0.769–0.836) for OS nomogram and 0.807 (95% CI 0.769–0.836) for CSS nomogram. In the meantime, C-index value in validation set was 0.818 (95% CI 0.789–0.847) for OS nomogram, while 0.804 (95% CI 0.773–0.835) for CSS nomogram. Besides, those calibration curves regarding the 3- and 5-year CSS of our constructed nomogram were highly consistent between the predicted values and the measurements in the training set as well as the external validation set. Our constructed nomogram outperformed the TNM stage in prediction. Our constructed nomogram is facile, creditable, and feasible; it efficiently predicts OS and CSS for osteosarcoma cases and can assist clinicians in assessing the prognosis for individuals and making decisions.

Comparison of the cox regression to machine learning in predicting the survival of anaplastic thyroid carcinoma

SummaryBackgroundTo compare the ability of the Cox regression and machine learning algorithms to predict the survival of patients with Anaplastic thyroid carcinoma (ATC).MethodsPatients diagnosed with ATC were extracted from the Surveillance, Epidemiology, and End Results database. The outcomes were overall survival (OS) and cancer-specific survival (CSS), divided into: (1) binary data: survival or not at 6 months and 1 year; (2): time-to-event data. The Cox regression method and machine learnings were used to construct models. Model performance was evaluated using the concordance index (C-index), brier score and calibration curves. The SHapley Additive exPlanations (SHAP) method was deployed to interpret the results of machine learning models.ResultsFor binary outcomes, the Logistic algorithm performed best in the prediction of 6-month OS, 12-month OS, 6-month CSS, and 12-month CSS (C-index = 0.790, 0.811, 0.775, 0.768). For time-event outcomes, traditional Cox regression exhibited good performances (OS: C-index = 0.713; CSS: C-index = 0.712). The DeepSurv algorithm performed the best in the training set (OS: C-index = 0.945; CSS: C-index = 0.834) but performs poorly in the verification set (OS: C-index = 0.658; CSS: C-index = 0.676). The brier score and calibration curve showed favorable consistency between the predicted and actual survival. The SHAP values was deployed to explain the best machine learning prediction model.ConclusionsCox regression and machine learning models combined with the SHAP method can predict the prognosis of ATC patients in clinical practice. However, due to the small sample size and lack of external validation, our findings should be interpreted with caution.

Prognosis prediction in esophageal signet-ring-cell carcinoma: a competing risk analysis

ObjectiveThis study aims to construct and validate a competing risk nomogram model to predict 1-year, 3-year, and 5-year cancer-specific survival (CSS) for patients with esophageal signet-ring-cell carcinoma.MethodsPatients diagnosed with esophageal signet-ring-cell carcinoma (ESRCC) between 2010 and 2015 were abstracted from the Surveillance, Epidemiology, and End Results (SEER) database. We performed the competing risk model to select significant variables to build a competing risk nomogram, which was used to estimate 1-year, 3-year, and 5-year CSS probability. The C-index, receiver operating characteristic (ROC) curve, calibration plot, Brier score, and decision curve analysis were performed in the internal validation.ResultsA total of 564 patients with esophageal signet-ring-cell carcinoma fulfilled the eligibility criteria. The competing risk nomogram identified 4 prognostic variables, involving the gender, lung metastases, liver metastases, and receiving surgery. The C indexes of nomogram were 0.61, 0.75, and 0.70, respectively for 5-year, 3-year, and 1-year CSS prediction. The calibration plots displayed high consistency. The Brier scores and decision curve analysis respectively favored good prediction ability and clinical utility of the nomogram.ConclusionsA competing risk nomogram for esophageal signet-ring-cell carcinoma was successfully constructed and internally validated. This model is expected to predict 1-year, 3-year, and 5-year CSS, and help oncologists and pathologists in clinical decision making and health care management for esophageal signet-ring-cell carcinoma patients.

Prognostic Value of T-Cell Density in the Tumor Center and Outer Margins in Gastric Cancer

Tumor-infiltrating lymphocytes are associated with the survival of gastric cancer patients. T-cell densities in the tumor and its periphery were previously identified as prognostic T-cell markers for resectable gastric cancer. Immunohistochemistry for 5 T-cell markers, CD3, CD45RO, CD8, FOXP3, and granzyme B was performed on serial sections of N = 251 surgical resection specimens of patients treated with surgery only in the D1/D2 trial. Positive T cells were digitally quantified into tiles of 0.25 mm2 across 3 regions: the tumor center (TC), the inner invasive margin, and the outer invasive margin (OIM). A classification and regression tree model was employed to identify the optimal combination of median T-cell densities per region with cancer-specific survival (CSS) as the outcome. All statistical tests were 2-sided. CD8OIM was identified as the most dominant prognostic factor, followed by FOXP3TC, resulting in a decision tree containing 3 prognostically distinct subgroups with high (Hi) or low (Lo) density of the markers: CD8OIMHi, CD8OIMLo/FOXP3TCHi, and CD8OIMLo/FOXP3TCLo. In a multivariable Cox regression analysis, which included pathological T and N stages, Lauren histologic types, EBV status, microsatellite instability, and type of surgery, the immune subgroups were independent predictors for CSS. CSS was lower for CD8OIMLo/FOXP3TCHi (HR: 5.02; 95% CI: 2.03-12.42) and for CD8OIMLo/FOXP3TCLo (HR: 7.99; 95% CI: 3.22-19.86), compared with CD8OIMHi (P < .0001). The location and density of both CD8+ and FOXP3+ T cells in resectable gastric cancer are independently associated with survival. The combination of CD8OIM and FOXP3TC T-cell densities is a promising stratification factor that should be validated in independent studies.

Unradical Surgery for Locally-Advanced Thymoma: Is it time to evolve Perspectives?

ObjectivesNearly-one-third of thymomas are locally-advanced at diagnosis. The traditional dogma that surgery is justified in case a complete resection can be achieved has remained unmovable until today. This study aimed to investigate feasibility and oncologic efficacy of incomplete resection for locally-advanced thymomas in a contest of multimodality therapy. Materials and MethodsA retrospective analysis was conducted using data of prospectively maintained thymomas database in a single high-volume centre. Data on 285 consecutive patients undergoing surgery for stage III and IVa thymomas between 1995 and 2019 were reviewed. Patients who underwent incomplete resection with curative-intent (removal of at least 90% of tumour burden) were included. Long-term outcomes and predictors of cancer-specific survival (CSS) and progression-free survival (PFS) were analyzed. Secondary endpoint was to assess adjuvant therapy efficacy. ResultsThe study included 79 patients, 60 with microscopic residual tumour (76%, R1) and 19 with macroscopic residual disease (24%, R2). Masaoka-Koga stage was: III in 41 patients (52%) and IVa in 38 (48%). Histology was B2-thymomas (n = 31, 39.2%) followed by B3 (n = 27, 34.2%). Five- and 10-years CSS was 88% and 80%. Seventy patients (90%) underwent adjuvant treatment; they showed CSS comparable to radical resected patients (5-years: 89.1% vs 98.9%, respectively; 10-years: 81.8% vs 92.7%, respectively, p = 0.43). The site of residual disease, Masaoka-Koga stage and WHO histology did not affect prognosis. Stepwise multivariable analysis confirmed adjuvant therapy as a favourable CSS prognostic factor (HR, 0.51; 95% CI, 0.33–0.79, p = 0.003). Stratifying by subgroups, R2-patients who received postoperative chemo(radio)therapy (pCRT) showed a significantly better prognosis than R2-patients treated by consolidation radiotherapy alone (10-years CSS: 60%, p < 0.001). ConclusionIn locally-advanced thymomas, whenever a radical surgery cannot be achieved, incomplete resection has proved to be effective in a contest of multimodality strategy, independently of WHO histology, Masaoka-Koga stage and site of residual disease.

Preoperative fibrinogen/CRP score predicts survival in upper urothelial tract carcinoma patients undergoing radical curative surgery

PurposeUpper tract urothelial carcinoma (UTUC) represents an often aggressive malignancy associated with poor prognosis. Therefore, finding reliable prognostic biomarkers in patients undergoing curative surgery for improved risk stratification is crucial. We evaluated the prognostic value of the Fibrinogen/C-reactive protein (FC)-score in a cohort of surgically treated UTUC patients.Methods170 patients with radiologically and histologically verified UTUC who underwent radical curative surgery between 1990 and 2020, were included. The FC-score was calculated for each patient, with patients receiving 1 point each if Fibrinogen and/or CRP levels were elevated above the 25th or 75th percentile, respectively. Patients were divided into three subgroups according to their FC-score of 0, 1 or 2 point(s). Kaplan–Meier analysis, uni- and multivariable Cox proportional hazard models were implemented. We determined cancer-specific survival (CSS) as primary endpoint, whereas overall survival (OS) and recurrence-free survival (RFS) were considered secondary endpoints.ResultsHigh FC-score (2 points) was significantly associated with adverse histological features such as vascular invasion (OR = 4.08, 95%CI 1.18–14.15, p = .0027) and tumour necrosis (OR = 6.67, 95%CI 1.35–32.96, p = 0.020). Both, uni- and multivariable Cox proportional hazard models showed the FC-score as a significant predictor for CSS (univariable analysis: FC-score = 1: HR = 1.90, 95%CI 0.92–3.93, p = 0.085 | FC-score = 2: HR = 2.86, 95%CI 1.22–6.72, p = 0.016). Furthermore, in univariable analysis, patients with higher FC-score had significantly shorter OS (FC-score = 1: HR = 1.32, 95%CI 0.70–2.49, p = 0.387 | FC-score = 2: HR = 2.19, 95%CI 1.02–4.67, p = 0.043). However, this did not prevail in multivariable analysis.ConclusionThe FC-score represents a novel potential biomarker in patients with UTUC undergoing radical curative surgery.

Critical Appraisal of Leibovich 2018 and GRANT Models for Prediction of Cancer-Specific Survival in Non-Metastatic Chromophobe Renal Cell Carcinoma.

Within the Surveillance, Epidemiology, and End Results database (2000-2019), we identified 5522 unilateral surgically treated non-metastatic chromophobe kidney cancer (chRCC) patients. This population was randomly divided into development vs. external validation cohorts. In the development cohort, the original Leibovich 2018 and GRANT categories were applied to predict 5- and 10-year cancer-specific survival (CSS). Subsequently, a novel multivariable nomogram was developed. Accuracy, calibration and decision curve analyses (DCA) tested the Cox regression-based nomogram as well as the Leibovich 2018 and GRANT risk categories in the external validation cohort. The accuracy of the Leibovich 2018 and GRANT models was 0.65 and 0.64 at ten years, respectively. The novel prognostic nomogram had an accuracy of 0.78 at ten years. All models exhibited good calibration. In DCA, Leibovich 2018 outperformed the novel nomogram within selected ranges of threshold probabilities at ten years. Conversely, the novel nomogram outperformed Leibovich 2018 for other values of threshold probabilities. In summary, Leibovich 2018 and GRANT risk categories exhibited borderline low accuracy in predicting CSS in North American non-metastatic chRCC patients. Conversely, the novel nomogram exhibited higher accuracy. However, in DCA, all examined models exhibited limitations within specific threshold probability intervals. In consequence, all three examined models provide individual predictions that might be suboptimal and be affected by limitations determined by the natural history of chRCC, where few deaths occur within ten years from surgery. Further investigations regarding established and novel predictors of CSS and relying on large sample sizes with longer follow-up are needed to better stratify CSS in chRCC.

Surgical resection as a predictor of cancer-specific survival by stage at diagnosis and cancer type, United States, 2006–2015

BackgroundWhen solid tumors are amenable to definitive resection, clinical outcomes are generally superior to when those tumors are inoperable. However, the population-level cancer survival benefit of eligibility for surgery by cancer stage has not yet been quantified. MethodsUsing Surveillance, Epidemiology and End Results data allowing us to identify patients who were deemed eligible for and received surgical resection, we examined the stage-specific association of surgical resection with 12-year cancer-specific survival. The 12-year endpoint was selected to maximize follow-up time and thereby minimize the influence of lead time bias. ResultsAcross a variety of solid tumor types, earlier stage at diagnosis allowed for surgical intervention at a much higher rate than later-stage diagnosis. At every stage, surgical intervention was associated with a substantially higher rate of 12-year cancer-specific survival, with absolute differences of up to 51% for stage I, 51% for stage II, and 44% for stage III cancer, and stage-specific mortality relative risks of 3.6, 2.4, and 1.7, respectively. ConclusionsDiagnosis of solid cancers in early stages often enables surgical resection, which reduces the risk of death from cancer. Receipt of surgical resection is an informative endpoint that is strongly associated with long-term cancer-specific survival at every stage.

The Prognostic Impact of Angiolymphatic Invasion in Bladder Urothelial Carcinoma Patients Undergoing Radical Cystectomy

Purpose: The aim of this study was to investigate the association between angiolymphatic invasion (ALI) and bladder cancer in patients who underwent radical cystectomy (RC).Materials and Methods: Multicenter retrospective data from 495 bladder cancer patients who underwent RC between 2007 and 2019 were enrolled in this study. Patients were stratified into 2 groups according to the presence of ALI. The effect of ALI was analyzed by the Kaplan-Meier method and Cox regression hazard models for patients’ cancer-specific survival (CSS), overall survival (OS), and recurrence-free survival (RFS).Results: The median age of the 495 patients in the study was 65 years, with median and mean follow-up durations of 23.3 months and 37.1 months, respectively. ALI was present in 182 patients (36.8%). ALI was significantly associated with worse RFS as well as CSS and OS (p&lt;0.001, p=0.012, and p=0.01, respectively). Adjusting for significant variables, a multivariate analysis showed that tumor stage (over T2) and ALI were independent predictors for CSS, whereas lymph node (LN) metastasis was not. Meanwhile, the adjusted multivariate analysis showed that tumor stage over T2, ALI, LN metastasis, and positive surgical margin were independent predictors for RFS. Otherwise, tumor grade (over grade 2) was not a significant predictor.Conclusions: The presence of ALI was an independent predictor influencing both CSS and RFS.

Establishment and validation of systematic prognostic nomograms in patients over 60 years of age with osteosarcoma: A multicenter external verification study.

The aim of this study was to develop and validate systematic nomograms to predict cancer specific survival (CSS) and overall survival (OS) in osteosarcoma patients aged over 60 years. We used data from the Surveillance, Epidemiology, and End Results (SEER) database and identified 982 patients with osteosarcoma over 60 years of age diagnosed between 2004 and 2015. Overall, 306 patients met the requirements for the training group. Next, we enrolled 56 patients who met the study requirements from multiple medical centers as the external validation group to validate and analyze our model. We collected all available variables and finally selected eight that were statistically associated with CSS and OS through Cox regression analysis. Integrating the identified variables, we constructed 3- and 5-year OS and CSS nomograms, respectively, which were further evaluated by calculating the C-index. A calibration curve was used to evaluate the accuracy of the model. Receiver operating characteristic (ROC) curves measured the predictive capacity of the nomograms. The Kaplan-Meier analysis was used for all patient-based variables to explore the influence of various factors on patient survival. Finally, a decision curve analysis (DCA) curve was used to analyze whether our model would be suitable for application in clinical practice. Cox regression analysis of clinical variables identified age, sex, marital status, tumor grade, tumor laterality, tumor size, M-stage, and surgical treatment as prognostic factors. Nomograms showed good predictive capacity for OS and CSS. We calculated that the C-index of the OS nomogram of the training population was 0.827 (95% CI 0.778-0.876), while that of the CSS nomogram was 0.722 (95% CI 0.665-0.779). The C-index of the OS nomogram evaluated on the external validation population was 0.716 (95% CI 0.575-0.857), while that of the CSS nomogram was 0.642 (95% CI 0.50-0.788). Furthermore, the calibration curve of our prediction models indicated the nomograms could accurately predict patient outcome. The constructed nomogram is a useful tool for accurately predicting OS and CSS at 3 and 5 years for patients over 60 years of age with osteosarcoma and can assist clinicians in making appropriate decisions in practice.

Radiological lymph-node size improves the prognostic value of systemic inflammation index in rectal cancer with pathologically negative nodes.

The relationship between the radiological lymph node (rLN) size and survival outcome in node-negative rectal cancer is still uncertain. In this study, we aimed to explore the role of enlarged rLN in predicting the survival of node-negative rectal cancers. We retrospectively reviewed the records of 722 node-negative rectal cancer who underwent curative resection. Factors associated with DFS (disease-free survival) and CSS (cancer-specific survival) were assessed with univariate and multivariate analysis. Survival analysis was performed according to presence with or without enlarged rLN. Combining rLN with NLR as a new index-inflammation immune score (IIS) for predicting survival. Comparing different models to assess the predictive powers. A total of 119 patients had tumor recurrence and 73 patients died due to cancer. Patients with enlarged rLN (≥5 mm) was significantly associated with better DFS (HR:0.517, 95%CI:0.339-0.787, p=0.002) and CSS (HR:0.43, 95%CI:0.242-0.763, p=0.004). The risk factors of recurrence were rLN, neutrophil-lymphocyte ratio (NLR), CEA level, and distance from the anal verge. The risk of recurrence increased by 1.88- and 2.83-fold for the high score in IIS compared with the low and intermediate score group (All p < 0.001). Similarly, the high score in IIS also increased the risk of cancer-specific death. In the model comparison, the AIC and LR were improved by including the rLN into the NLR model for DFS and CSS prediction (All p < 0.05). Node-negative rectal cancer patients with enlarged rLN had a better survival outcome. IIS might be a more comprehensive and complete inflammation immune index for survival prediction.

Prognostic value of the sarcomatoid component in bladder cancer: A propensity score matching study.

Sarcomatoid carcinoma of the bladder is rare, and little is known about the prognostic impact of the proportion of sarcomatoid components of the bladder. The present study aimed to assess the prognostic value of the proportion of sarcomatoid components with regard to death and recurrence rates in patients with bladder cancer (BC), and to validate the worse survival results of sarcomatoid carcinomas of the bladder using propensity score matching. Patients with sarcomatoid carcinoma of the bladder who were treated at the Affiliated Hospital of Qingdao University between August 2010 and May 2021 were included in the study. A 1:2 propensity score matching system based on age, sex and pathological T stage was used for sarcomatoid and non-sarcomatoid carcinoma matching. Finally, 114 patients with BC were included. Patients with sarcomatoid carcinoma had worse 5-year cancer-specific survival (CSS) (69.1 vs. 86.9%; log-rank P=0.008) and recurrence-free survival (RFS) (64.1 vs. 83.6%; log-rank P=0.001) rates compared with patients with non-sarcomatoid carcinoma, as had the subgroup with muscle invasion. Multivariate analysis revealed sarcomatoid carcinoma as an independent prognostic factor. Patients with a low proportion of sarcomatoid components (1-50%) had a better prognosis than patients with a high proportion (>50%), and no significant difference was found compared with the non-sarcomatoid group. Overall, a proportion of sarcomatoid components >50% was a predictor of CSS and RFS. Sarcomatoid components markedly increased the risk of death and recurrence in muscle-invasive BC, but not in non-muscle-invasive BC. A higher proportion of sarcomatoid components was significantly associated with poorer survival.

Development and validation of a nomogram to predict cancer-specific survival with unresected cholangiocarcinoma undergoing external radiotherapy.

To analyze the prognostic factors of patients with cholangiocarcinoma (CCA) who were unresected and received radiotherapy to establish a nomogram model for the prediction of patient cancer-specific survival (CSS). Suitable patient cases were selected from the Surveillance, Epidemiology, and End Results (SEER) database, survival rates were calculated using the Kaplan-Meier method, prognostic factors were analyzed by Lasso, Cox regression, and nomogram was developed based on independent prognostic factors to predict 6 and 12 months CSS. The consistency index (C-index), calibration curve, and decision curve analysis (DCA) were tested for the predictive efficacy of the model, respectively. The primary site, tumor size, T-stage, M-stage, and chemotherapy (P < 0.05) were identified as independent risk factors after Cox and Lasso regression analysis. Patients in training cohort had a 6 months CSS rates was 68.6 ± 2.6%, a 12-month CSS rates was 49.0 ± 2.8%. The median CSS time of 12.00 months (95% CI: 10.17-13.83 months). The C-index was 0.664 ± 0.039 for the training cohort and 0.645 ± 0.042 for the validation cohort. The nomogram predicted CSS and demonstrated satisfactory and consistent predictive performance in 6 (73.4 vs. 64.9%) and 12 months (72.2 vs. 64.9%), respectively. The external validation calibration plot is shown AUC for 6- and 12-month compared with AJCC stage was (71.2 vs. 63.0%) and (65.9 vs. 59.8%). Meanwhile, the calibration plot of the nomogram for the probability of CSS at 6 and 12 months indicates that the actual and nomogram predict that the CSS remains largely consistent. DCA showed that using a nomogram to predict CSS results in better clinical decisions compared to the AJCC staging system. A nomogram model based on clinical prognostic characteristics can be used to provide CSS prediction reference for patients with CCA who have not undergone surgery but have received radiotherapy.

Deep learning of bone metastasis in small cell lung cancer: A large sample-based study.

Bone is a common metastatic site for small cell lung cancer (SCLC). Bone metastasis (BM) in patients have are known to show poor prognostic outcomes. We explored the epidemiological characteristics of BM in SCLC patients and create a new deep learning model to predict outcomes for cancer-specific survival (CSS) and overall survival (OS). Data for SCLC patients diagnosed with or without BM from 2010 to 2016 were retrieved from the Surveillance, Epidemiology, and End Results (SEER) database. Univariate and multivariate Cox proportional hazards regression models were used to evaluate the effects of prognostic variables on OS and CSS. Through integration of these variables, nomograms were created for the prediction of CSS and OS rates at 3-month,6- month,and 12-month. Harrell's coordination index, calibration curves,and time- dependent ROC curves were used to assess the nomograms' accuracy. Decision tree analysis was used to evaluate the clinical application value of the established nomogram. In this study, 4201 patients were enrolled. Male sex, tumor size 25 but <10, brain and liver metastases, as well as chemotherapy were associated with a high risk for BM. Tumor size, Age, N stage, gender, liver metastasis, radiotherapy as well as chemotherapy were shown to be prognostic variables for OS, and the prognostic variables for CSS were added to the tumor number in addition. Based on these results, nomograms for CSS and OS were established separately. Internal as well as external validation showed that the C-index, calibration cuurve and DCA had good constructive correction effect and clinical application value. Decision tree analysis further confirmed the prognostic factors of OS and CSS. The nomogram and decision tree models developed in this study effectively guided treatment decisions for SCLC patients with BM. The creation of prediction models for BM SCLC patients may be facilitated by deep learning models.

Endometrial Cancer Individualized Scoring System (ECISS): A machine learning-based prediction model of endometrial cancer prognosis.

To establish a prognostic model for endometrial cancer (EC) that individualizes a risk and management plan per patient and disease characteristics. A multicenter retrospective study conducted in nine European gynecologic cancer centers. Women with confirmed EC between January 2008 to December 2015 were included. Demographics, disease characteristics, management, and follow-up information were collected. Cancer-specific survival (CSS) and disease-free survival (DFS) at 3 and 5 years comprise the primary outcomes of the study. Machine learning algorithms were applied to patient and disease characteristics. Model I: pretreatment model. Calculated probability was added to management variables (model II: treatment model), and the second calculated probability was added to perioperative and postoperative variables (model III). Of 1150 women, 1144 were eligible for 3-year survival analysis and 860 for 5-year survival analysis. Model I, II, and III accuracies of prediction of 5-year CSS were 84.88%/85.47% (in train and test sets), 85.47%/84.88%, and 87.35%/86.05%, respectively. Model I predicted 3-year CSS at an accuracy of 91.34%/87.02%. Accuracies of models I, II, and III in predicting 5-year DFS were 74.63%/76.72%, 77.03%/76.72%, and 80.61%/77.78%, respectively. The Endometrial Cancer Individualized Scoring System (ECISS) is a novel machine learning tool assessing patient-specific survival probability with high accuracy.

Inversed albumin-to-globulin ratio and underlying liver disease severity as a prognostic factor for survival in hepatocellular carcinoma patients undergoing transarterial chemoembolization.

Previous studies have shown that an inversed albumin-to-globulin ratio (IAGR) is a predictor of the prognosis of many cancers. However, the prognostic value of an IAGR for patients with hepatocellular carcinoma (HCC) who undergo transarterial chemoembolization (TACE) is still unclear. This study aims to evaluate the predictive value of an IAGR for the prognosis of those patients. This study retrospectively analyzed 396 patients with HCC who received TACE. Using a cut-off value of 1.0 for the albumin-to-globulin ratio, patients were divided into a normal albumin-to-globulin ratio (NAGR) (≥1) and an IAGR (<1) group. Univariate and multivariate analyses and time-dependent receiver operating characteristic analyses were performed to identify risk factors of overall survival (OS) and cancer-specific survival (CSS). Survival nomograms were constructed based on the multivariable analysis results and further evaluated using the consistency index (C-index) and calibration curve. A total of 396 patients were included in the final analysis and were divided into the NAGR group (n = 298, 75.3%) and the IAGR (n = 98, 24.7%) group. The median OS and CSS were significantly worse in the IAGR group than in the NAGR group (OS: 8 vs. 26 months, CSS: 10 vs. 41 months, both P < 0.001). Multivariate analyses demonstrated that an IAGR was an independent risk factor for predicting worse OS [hazard ratio (HR), 2.024; 95% confidence interval (CI): 1.460-2.806] and CSS (HR: 2.439; 95% CI: 1.651-3.601). The nomogram-based model-related C-indexes for OS and CSS prediction were 0.715 (95% CI: 0.697-0.733) and 0.750 (95% CI: 0.729-0.771), and the calibration of the nomogram showed good consistency. The IAGR along with underlying liver disease severity were the useful prognostic predictors of OS and CSS among patients with HCC undergoing TACE and might be useful to identify high-risk patients.

External validation of an 18F-FDG-PET radiomic model predicting survival after radiotherapy for oropharyngeal cancer.

The purpose of the study is to externally validate published 18F-FDG-PET radiomic models for outcome prediction in patients with oropharyngeal cancer treated with chemoradiotherapy. Outcome data and pre-radiotherapy PET images of 100 oropharyngeal cancer patients (stage IV:78) treated with concomitant chemotherapy to 66-69 Gy/30 fr were available. Tumors were segmented using a previously validated semi-automatic method; 450 radiomic features (RF) were extracted according to IBSI (Image Biomarker Standardization Initiative) guidelines. Only one model for cancer-specific survival (CSS) prediction was suitable to be independently tested, according to our criteria. This model, in addition to HPV status, SUVmean and SUVmax, included two independent meta-factors (Fi), resulting from combining selected RF clusters. In a subgroup of 66 patients with complete HPV information, the global risk score R was computed considering the original coefficients and was tested by Cox regression as predictive of CSS. Independently, only the radiomic risk score RF derived from Fi was tested on the same subgroup to learn about the radiomics contribution to the model. The metabolic tumor volume (MTV) was also tested as a single predictor and its prediction performances were compared to the global and radiomic models. Finally, the validation of MTV and the radiomic score RF were also tested on the entire dataset. Regarding the analysis of the subgroup with HPV information, with a median follow-up of 41.6 months, seven patients died due to cancer. R was confirmed to be associated to CSS (p value = 0.05) with a C-index equal 0.75 (95% CI=0.62-0.85). The best cut-off value (equal to 0.15) showed high ability in patient stratification (p=0.01, HR=7.4, 95% CI=1.6-11.4). The 5-year CSS for R were 97% (95% CI: 93-100%) vs 74% (56-92%) for low- and high-risk groups, respectively. RF and MTV alone were also significantly associated to CSS for the subgroup with an almost identical C-index. According to best cut-off value (RF>0.12 and MTV>15.5cc), the 5-year CSS were 96% (95% CI: 89-100%) vs 65% (36-94%) and 97% (95% CI: 88-100%) vs 77% (58-93%) for RF and MTV, respectively. Results regarding RF and MTV were confirmed in the overall group. A previously published PET radiomic model for CSS prediction was independently validated. Performances of the model were similar to the ones of using only the MTV, without improvement of prediction accuracy.