Predictor Of Cancer-specific Survival Research Articles

Clinical significance of the modified Naples prognostic score in patients with stage II-III colon cancer undergoing curative resection: a retrospective study from the real world.

The Naples prognostic score (NPS) determined by the nutritional and inflammatory condition of an individual is attracting growing attention for predicting postoperative outcomes in a variety of malignancies. The study aimed to assess the clinical significance of a modified NPS (M-NPS) and establish and validate nomograms incorporating M-NPS in curative stage II-III colon cancer patients. We retrospectively analyzed 328 stage II-III colon cancer patients receiving radical surgical resection at our hospital from January 2011 to December 2016. Kaplan-Meier (KM) survival analysis and Cox regression analysis were executed for overall survival (OS) and cancer-specific survival (CSS). Independent predictive indicators were applied to develop nomograms. The model's performance was evaluated using many different methods. Of a total of 328 cases, 153 cases were in group 0, 145 in group 1, and 30 in group 2. In terms of OS or CSS, there were obvious differences between groups 0 and 1, and between groups 0 and 2. Age, obstruction, N stage, gross tumor type, and M-NPS group were independent prognostic indicators for OS, while obstruction, gross tumor type, M-NPS group, and N stage were independent predictive parameters for CSS. Furthermore, the training and validation sets were randomly allocated among a cohort of 328 patients. OS and CSS prediction nomograms were developed. In the training and validation cohort, the C-index and ROC analysis showed good discrimination, calibration curves exhibited an excellent level of consistency between model-predicted survival and actual survival outcomes, and DCA curves demonstrated good clinical performance. M-NPS is a reliable survival predictor in patients with curative stage II-III colon cancer. Nomograms incorporating M-NPS for OS and CSS have good predictive performance and clinical utility.

Preoperative platelet count as an independent predictor of long-term outcomes among patients undergoing resection for intrahepatic cholangiocarcinoma.

An elevated platelet count may reflect neoplastic and inflammatory states, with cytokine-driven overproduction of platelets. The objective of this study was to evaluate the prognostic utility of high platelet count among patients undergoing curative-intent liver surgery for intrahepatic cholangiocarcinoma (ICC). An international, multi-institutional cohort was used to identify patients undergoing curative-intent liver resection for ICC (2000-2020). A high platelet count was defined as platelets >300 *109/L. The relationship between preoperative platelet count, cancer-specific survival (CSS), and overall survival (OS) was examined. Among 825 patients undergoing curative-intent resection for ICC, 139 had a high platelet count, which correlated with multifocal disease, lymph nodes metastasis, poor to undifferentiated grade, and microvascular invasion. Patients with high platelet counts had worse 5-year (35.8% vs. 46.7%, p = 0.009) CSS and OS (24.8% vs. 39.8%, p < 0.001), relative to patients with a low platelet count. After controlling for relevant clinicopathologic factors, high platelet count remained an adverse independent predictor of CSS (HR = 1.46, 95% CI 1.02-2.09) and OS (HR = 1.59, 95% CI 1.14-2.22). High platelet count was associated with worse tumor characteristics and poor long-term CSS and OS. Platelet count represents a readily-available laboratory value that may preoperatively improve risk-stratification of patients undergoing curative-intent liver resection for ICC.

A Nomogram for Predicting Cancer-Specific Survival in Young Patients With Advanced Lung Cancer Based on Competing Risk Model.

Young lung cancer is a rare subgroup accounting for 5% of lung cancer. The aim of this study was to compare the causes of death (COD) among lung cancer patients of different age groups and construct a nomogram to predict cancer-specific survival (CSS) in young patients with advanced stage. Lung cancer patients diagnosed between 2004 and 2015 were extracted from the Surveillance, Epidemiology, and End Results (SEER) database and stratified into the young (18-45 years) and old (> 45 years) groups to compare their COD. Young patients diagnosed with advanced stage (IVa and IVb) from 2010 to 2015 were reselected and divided into training and validation cohorts (7:3). Independent prognostic factors were identified through the Fine-Gray's test and further integrated to the competing risk model. The area under the receiver operating characteristic curve (AUC), consistency index (C-index), and calibration curve were applied for validation. The proportion of cancer-specific death (CSD) in young patients was higher than that in old patients with early-stage lung cancer (p < 0.001), while there was no difference in the advanced stage (p = 0.999). Through univariate and multivariate analysis, 10 variables were identified as independent prognostic factors for CSS. The AUC of the 1-, 3-, and 5-year prediction of CSS was 0.688, 0.706, and 0.791 in the training cohortand 0.747, 0.752, and 0.719 in the validation cohort. The calibration curves demonstrated great accuracy. The C-index of the competing risk model was 0.692 (95% CI: 0.636-0.747) in the young patient cohort. Young lung cancer is a distinct entity with a different spectrum of competing risk events. The construction of our nomogramcan provide new insights into the management of young patients with lung cancer.

Human papillomavirus prevalence, genotype distribution, and prognostic factors of vaginal cancer.

We aimed to investigate human papillomavirus (HPV) prevalence and genotype distribution and prognostic factors in vaginal cancer (VC). VC patients who received treatment between 1989 and 2020 were retrospectively reviewed. L1 general polymerase chain reaction (PCR) followed by HPV Blot (King Car, I-Lan, Taiwan) and E6 type-specific-PCR were performed for genotyping firstly. P16 and p53 immunohistochemistry staining was performed. Univariate and multivariate analyses identified predictors of clinical outcomes.79 VC patients were eligible for analysis. 73 patients (92.4%) were squamous cell carcinoma (SCC) and 6 (7.6%) as non-SCC. The median follow-up time was 134.3 months (range 0.9-273.4). Among nine initially HPV-negative cases, seven were identified as being positive through HPV16/18/45/52/58 whole-genome amplification followed by Sanger sequencing (WGASS). HPV DNA sequences were detected in 98.6% of SCC and 83.3% of non-SCC, respectively, with HPV16 (49.4%), HPV52 (15.2%) and HPV58 (8.9%) being predominant. Patients with paraaortic lymph node (LN) metastasis had a 5-year cancer-specific survival (CSS) rate of 0%. Multivariate analysis revealed that only p16 and stage were significantly correlated with prognosis. Variables with strong correlations (p16- and HPV-positivity, LN metastasis and stage), were included in models 2-5 alternatively. Stage III/IV (hazard ratio [HR] = 3.64-4.56) and LN metastasis (HR = 2.81-3.44) were significant negative predictors of CSS, whereas p16-positivity (HR = 0.29-0.32) and HPV-positivity (HR = 0.14) were related to better prognosis. In conclusion, 97.5% of VCs were HPV-positive with WGASS. Stage III/IV and LN metastasis were significant negative predictors, whereas p16- and HPV-positivity were significantly associated with better prognosis.

Clinical features and prediction of long-term survival after surgery for perihilar cholangiocarcinoma.

The treatment of perihilar Cholangiocarcinoma (pCCA) poses specific challenges not only due to its high perioperative complication rates but also due its dismal long-term prognosis with only a few long-term survivors (LTS) among the patients. Therefore, in this analysis characteristics and predictors of LTS in pCCA patients are investigated. In this single center analysis, patients undergoing curative-intent liver resection for pCCA between 2010 and 2022 were categorized into long-term and short-term survivors (STS) excluding perioperative mortality. Binary logistic regression was used to determine key differences between the groups and to develop a prognostic composite variable. This composite variable was subsequently tested in the whole cohort of surgically treated pCCA patients using Cox Regression analysis for cancer-specific survival (CSS). Within a cohort of 209 individuals, 27 patients were identified as LTS (median CSS = 125 months) and 55 patients as STS (median CSS = 16 months). Multivariable analysis identified preoperative portal vein infiltration (OR = 5.85, p = 0.018) and intraoperative packed red blood cell (PRBC) transfusions (OR = 10.29, p = 0.002) as key differences between the groups. A prognostic composite variable based on these two features was created and transferred into a Cox regression model of the whole cohort. Here, the composite variable (HR = 0.35, p<0.001), lymph node metastases (HR = 2.15, p = 0.001) and postoperative complications (HR = 3.06, p<0.001) were identified as independent predictors of CSS. Long-term survival after surgery for pCCA is possible and is strongly negatively associated with preoperative portal vein infiltration and intraoperative PRBC transfusion. As these variables are part of preoperative staging or can be modulated by intraoperative technique, the proposed prognostic composite variable can easily be transferred into clinical management to predict the oncological outcome of patients undergoing surgery for pCCA.

Impact of treatment on the prognosis of childhood in hepatoblastoma: A SEER based analysis

BackgroundThe prognosis of patients with hepatoblastoma has been unsatisfactory. This study analyzed the effects of different treatment methods on cancer-specific survival (CSS) in children with hepatoblastoma. MethodFrom 2000 to 2018, patients with hepatoblastoma were included in the Surveillance, Epidemiology, and End Results (SEER) database. CSS was estimated using the Kaplan–Meier method. Cox regression analysis assessed prognostic factors. The predictive models were validated using the concordance index (C-index), calibration curve and receiver operating characteristic (ROC) curve. ResultOf the 785 included patients, 730 (93.0 %) underwent chemotherapy, 516 (65.7 %) underwent liver tumour resection and 129 (16.4 %) underwent liver transplantation. Both chemotherapy and surgery could significantly improve the CSS rate (all p < 0.001). However, there was no difference in CSS rate between the two surgical methods (liver tumour resection and liver transplantation) (p = 0.613). Further subgroup analysis revealed that children who underwent liver tumour resection or liver transplantation based on chemotherapy (all p > 0.05) had a similar prognosis. Multivariate analysis revealed that age (p = 0.003), race (p = 0.001), operative method (p < 0.001), chemotherapy (p < 0.001), distant metastasis (p < 0.001) and tumour size (p < 0.001) were independent factors related to CSS. The C-index of the new nomogram was 0.759, and its consistency was good. The ROC curves verified that the nomogram had a better prediction ability for 1-, 3- and 5-year CSS rates. ConclusionIn children with hepatoblastoma, there was no statistically significant difference in CSS between chemotherapy combined with liver transplantation and liver tumour resection. The nomogram we constructed demonstrated satisfactory CSS prediction ability.

Prognostic Nomograms for Elderly Patients with Small Cell Lung Cancer Brain Metastasis: A Surveillance, Epidemiology, and End Results Population-Based Study with Temporal External Validation

This study aimed to pinpoint independent predictors influencing overall survival (OS) and cancer-specific survival (CSS) in elderly patients with small cell lung cancer (SCLC) brain metastasis (BM), and to create and validate nomograms for OS and CSS prediction. Data from elderly SCLC BM patients were extracted out of the Surveillance, Epidemiology, and End Results database, including 1200 patients identified from 2010 and 2015 who were randomly allocated into a training set and an internal validation set at a proportion of 7:3, and 666 patients diagnosed between 2018 and 2020 as a temporal external validation set. Independent predictors for OS and CSS were determined through univariate Cox analysis, least absolute shrinkage and selection operator analysis, and multivariate Cox analysis sequentially. Nomograms for OS and CSS were constructed, and validated by the internal and temporal external validation sets. Age, N stage, chemotherapy, and liver metastasis were determined as independent predictors of OS and CSS, while radiotherapy and surgery were not. Nomograms were constructed based on these independent predictors. The results of the receiver operator characteristic curves, the areas under the curve and calibration curve demonstrated that the nomograms exhibited commendable discriminative ability and calibration. Moreover, decision curve analysis, net reclassification improvement, and integrated discrimination improvement also suggested that the nomograms possessed superior clinical usefulness and predictive capability relative to the TNM system. Prognostic nomograms for elderly patients with SCLC BM have been developed, demonstrating good performance in terms of accuracy, reliability, and practicality.

Evaluating the prognostic value of tumor deposits in non-metastatic lymph node-positive colon adenocarcinoma using Cox regression and machine learning

BackgroundThe 8th AJCC TNM staging for non-metastatic lymph node-positive colon adenocarcinoma patients(NMLP-CA) stages solely by lymph node status, irrespective of the positivity of tumor deposits (TD). This study uses machine learning and Cox regression to predict the prognostic value of tumor deposits in NMLP-CA.MethodsPatient data from the SEER registry (2010–2019) was used to develop CSS nomograms based on prognostic factors identified via multivariate Cox regression. Model performance was evaluated by c-index, dynamic calibration, and Schmid score. Shapley additive explanations (SHAP) were used to explain the selected models.ResultsThe study included 16,548 NMLP-CA patients, randomized 7:3 into training (n = 11,584) and test (n = 4964) sets. Multivariate Cox analysis identified TD, age, marital status, primary site, grade, pT stage, and pN stage as prognostic for cancer-specific survival (CSS). In the test set, the gradient boosting machine (GBM) model achieved the best C-index (0.733) for CSS prediction, while the Cox model and GAMBoost model optimized dynamic calibration(6.473) and Schmid score (0.285), respectively. TD ranked among the top 3 most important features in the models, with increasing predictive significance over time.ConclusionsPositive tumor deposit status confers worse prognosis in NMLP-CA patients. Tumor deposits may confer higher TNM staging. Furthermore, TD could play a more significant role in the staging system.

A prognostic nomogram to predict the cancer-specific survival of patients with initially diagnosed metastatic gastric cancer: a validation study in a Chinese cohort.

Few studies have been designed to predict the survival of Chinese patients initially diagnosed with metastatic gastric cancer (mGC). Therefore, the objective of this study was to construct and validate a new nomogram model to predict cancer-specific survival (CSS) in Chinese patients. We collected 328 patients with mGC from Northern Jiangsu People's Hospital as the training cohort and 60 patients from Xinyuan County People's Hospital as the external validation cohort. Multivariate Cox regression was used to identify risk factors, and a nomogram was created to predict CSS. The predictive performance of the nomogram was evaluated using the consistency index (C-index), the calibration curve, and the decision curve analysis (DCA) in the training cohort and the validation cohort. Multivariate Cox regression identified differentiation grade (P < 0.001), T-stage (P < 0.05), N-stage (P < 0.001), surgery (P < 0.05), and chemotherapy (P < 0.001) as independent predictors of CSS. Nomogram of chemotherapy regimens and cycles was also designed by us for the prediction of mGC. Thus, these factors are integrated into the nomogram model: the C-index value was 0.72 (95% CI 0.70-0.85) for the nomogram model and 0.82 (95% CI 0.79-0.89) and 0.73 (95% CI 0.70-0.86) for the internal and external validation cohorts, respectively. Calibration curves and DCA also demonstrated adequate fit and ideal net benefit in prediction and clinical applications. We established a practical nomogram to predict CSS in Chinese patients initially diagnosed with mGC. Nomograms can be used to individualize survival predictions and guide clinicians in making therapeutic decisions.

Different prognosis in cutaneous early-onset and late-onset Merkel cell carcinoma: a population-based retrospective study.

Merkel cell carcinoma (MCC) is a rare and highly aggressive form of skin cancer. However, there is limited research on the clinicopathological features of early-onset MCC (EOMCC) and the differences between EOMCC and late-onset MCC (LOMCC). Our objective was to evaluate the clinicopathological features and cancer-specific survival (CSS) of EOMCC. Our cohort study analyzed data from the Surveillance, Epidemiology, and End Results (SEER) database from January 1, 2018, to December 31, 2020. Data from 1941 patients who were diagnosed with primary cutaneous MCC were included. We then divided the patients with MCC into two groups: those with EOMCC (526 patients) and those with LOMCC (1415 patients). CSS is used as the primary outcome. The EOMCC group exhibited trends toward advanced tumor progression, an expanded surgical scope, increased lymph node retrieval, intensified radiotherapy, greater utilization of systemic therapy, and a better prognosis. Multivariate analysis revealed that LOMCC (HR 3.305 [2.002-5.456], P < 0.001), advanced T stage (HR 1.430 [1.139-1.797], P = 0.002), advanced N stage (HR 1.522 [1.221-1.897], P < 0.001), M1 stage (HR 2.587 [1.480-4.521], P < 0.001), and radiation (HR 0.586 [0.410-0.837], P = 0.003) were significantly associated with CSS. Among these factors, EOMCC/LOMCC was most strongly associated with CSS, indicating that LOMCC is an independent risk factor for CSS. Interestingly, we found that regional EOMCC and localized or in situ LOMCC had almost completely overlapping survival curves (Plog-rank = 0.620). Additionally, we observed that the TNM staging + age model was a more accurate predictor of CSS among MCC patients than using TNM staging alone. We found that EOMCC has distinct clinicopathological features compared to LOMCC. EOMCC is associated with better CSS. The combination of TNM staging and age was more accurate for predicting patient outcomes than TNM staging alone.

Development and validation of a nomogram for predicting cancer-specific survival in small-bowel adenocarcinoma patients using the SEER database

BackgroundSmall bowel adenocarcinoma (SBA) is a rare gastrointestinal malignancy forwhich survival is hampered by late diagnosis, complex responses to treatment, and poor prognosis. Accurate prognostic tools are crucial for optimizing treatment strategies and improving patient outcomes. This study aimed to develop and validate a nomogram based on the Surveillance, Epidemiology, and End Results (SEER) database to predict cancer-specific survival (CSS) in patients with SBA and compare it to traditional American Joint Committee on Cancer (AJCC) staging.MethodsWe analyzed data from 2,064 patients diagnosed with SBA between 2010 and 2020 from the SEER database. Patients were randomly assigned to training and validation cohorts (7:3 ratio). Kaplan‒Meier survival analysis, Cox multivariate regression, and nomograms were constructed for analysis of 3-year and 5-year CSS. The performance of the nomograms was evaluated using Harrell’s concordance index (C-index), the area under the receiver operating characteristic (ROC) curve, calibration curves, decision curve analysis (DCA), net reclassification improvement (NRI), and integrated discrimination improvement (IDI).ResultsMultivariate Cox regression identified sex, age at diagnosis, marital status, tumor site, pathological grade, T stage, N stage, M stage, surgery, retrieval of regional lymph nodes (RORLN), and chemotherapy as independent covariates associated with CSS. In both the training and validation cohorts, the developed nomograms demonstrated superior performance to that of the AJCC staging system, with C-indices of 0.764 and 0.759, respectively. The area under the curve (AUC) values obtained by ROC analysis for 3-year and 5-year CSS prediction significantly surpassed those of the AJCC model. The nomograms were validated using calibration and decision curves, confirming their clinical utility and superior predictive accuracy. The NRI and IDI indicated the enhanced predictive capability of the nomogram model.ConclusionThe SEER-based nomogram offers a significantly superior ability to predict CSS in SBA patients, supporting its potential application in clinical decision-making and personalized approaches to managing SBA to improve survival outcomes.

Enhancing clinical decision-making: A novel nomogram for stratifying cancer-specific survival in middle-aged individuals with follicular thyroid carcinoma utilizing SEER data

BackgroundThyroid cancer (TC) is the most common malignant tumor in the endocrine system, is also one of the head and neck tumor. Follicular Thyroid Carcinoma (FTC) plays an important role in the pathological classification of thyroid cancer. This study aimed to develop an innovative predictive tool, a nomogram, for predicting cancer specific survival (CSS) in middle-aged FTC patients. MethodsWe collected patient data from the Surveillance, Epidemiology, and End Results (SEER) database. The data from patients between 2004 and 2015 were used as the training set, and the data from patients between 2016 and 2018 were used as the validation set. To identify independent risk factors affecting patient survival, univariate and multivariate Cox regression analyses were performed. Based on this, we developed a nomogram model aimed at predicting CSS in middle-aged patients with FTC. The consistency index (C-index), the area under the receiver operating characteristic (ROC) curve (AUC), and the calibration curve were used to evaluate the accuracy and confidence of the model. ResultsA total of 2470 patients were enrolled in this study, in which patients from 2004 to 2015 were randomly assigned to the training cohort (N = 1437) and validation cohort (N = 598), and patients from 2016 to 2018 were assigned to the external validation cohort (N = 435) in terms of time. Univariate and multivariate Cox regression analysis showed that marriage, histological grade and TNM stage were independent risk factors for survival. The C-index for the training cohort was 0.866 (95 % CI: 0.805–0.927), for the validation cohort it was 0.944 (95 % CI: 0.903–0.985), and for the external validation cohort, it reached 0.999 (95 % CI: 0.997–1.001). Calibration curves and AUC suggest that the model has good accuracy. ConclusionsWe developed an innovative nomogram to predict CSS in middle-aged patients with FTC. Our model after a rigorous internal validation and external validation process, based on the time proved that the high level of accuracy and reliability. This tool helps healthcare professionals and patients make informed clinical decisions.

Surgically treated pelvic liposarcoma and leiomyosarcoma: The effect of tumor size on cancer-specific survival

IntroductionIn soft tissue pelvic liposarcoma and leiomyosarcoma, it is unknown whether a specific tumor size cut-off may help to better predict prognosis, defined as cancer-specific survival (CSS). We tested whether different tumor size cut-offs, could improve CSS prediction. Materials and methodsSurgically treated non-metastatic soft tissue pelvic sarcoma patients were identified (Surveillance, Epidemiology, and End Results 2004–2019). Kaplan-Meier plots, univariable and multivariable Cox-regression models and receiver operating characteristic-derived area under the curve (AUC) estimates were used. ResultsOverall, 672 (65 %) liposarcoma (median tumor size 11 cm, interquartile range [IQR] 7–16) and 367 (35 %) leiomyosarcoma (median tumor size 8 cm, IQR 5–12) patients were identified. The p-value derived ideal tumor size cut-off was 17.1 cm, in liposarcoma and 7.0 cm, in leiomyosarcoma. In liposarcoma, according to p-value derived cut-off, five-year CSS rates were 92 vs 83 % (≤17.1 vs > 17.1 cm). This cut-off represented an independent predictor of CSS and improved prognostic ability from 83.8 to 86.8 % (Δ = 3 %). Similarly, among previously established cut-offs (5 vs 10 vs 15 cm), also 15 cm represented an independent predictor of CSS and improved prognostic ability from 83.8 to 87.0 % (Δ = 3.2 %). In leiomyosarcoma, according to p-value derived cut-off, five-year CSS rates were 86 vs 55 % (≤7.0 vs > 7.0 cm). This cut-off represented an independent predictor of CSS and improved prognostic ability from 68.6 to 76.5 % (Δ = 7.9 %). ConclusionsIn liposarcoma, the p-value derived tumor size cut-off was 17.1 cm vs 7.0 cm, in leiomyosarcoma. In both histologic subtypes, these cut-offs exhibited the optimal statistical characteristics (univariable, multivariable and AUC analyses). In liposarcoma, the 15 cm cut-off represented a valuable alternative.

Predictors of cancer-specific survival and overall survival among patients aged ≥60 years with lung adenocarcinoma using the SEER database.

We developed a simple, rapid predictive model to evaluate the prognosis of older patients with lung adenocarcinoma. Demographic characteristics and clinical information of patients with lung adenocarcinoma aged ≥60 years were retrospectively analyzed using Surveillance, Epidemiology, and End Results (SEER) data. We built nomograms of overall survival and cancer-specific survival using Cox single-factor and multi-factor regression. We used the C-index, calibration curve, receiver operating characteristic (ROC) curves, and decision curve analysis (DCA) to evaluate performance of the nomograms. We included 14,117 patients, divided into a training set and validation set. We used the chi-square test to compare baseline data between groups and found no significant differences. We used Cox regression analysis to screen out independent prognostic factors affecting survival time and used these factors to construct the nomogram. The ROC curve, calibration curve, C-index, and DCA curve were used to verify the model. The final results showed that our predictive model had good predictive ability, and showed better predictive ability compared with tumor-node-metastasis (TNM) staging. We also achieved good results using data of our center for external verification. The present nomogram could accurately predict prognosis in older patients with lung adenocarcinoma.

Identifying optimal surgical approach among T1N2-3M0 non-small cell lung cancer patients: a population-based analysis.

Whether stage T1N2-3M0 non-small cell lung cancer (NSCLC) patients could benefit from surgery and the optimal surgical procedure have remained controversial and unclear. This study aimed to investigate whether stage T1N2-3M0 NSCLC can benefit from different surgery types and develop a tool for survival prediction. The Surveillance, Epidemiology, and End Results (SEER) database was used to identify patients diagnosed with stage T1N2-3M0 NSCLC between 2000 and 2015. A 1:1 propensity score-matched (PSM) analysis was used to balance the distribution of clinical characteristics. Survival analyses were performed by using the Kaplan-Meier (KM) curves and Cox proportional hazards regression. All patients were randomly split at a ratio of 7:3 into training and validation cohorts. The nomogram was constructed by integrating all independent predictors for overall survival (OS) and cancer-specific survival (CSS). The model's performance was evaluated by discrimination, calibration ability, and risk stratification ability. A total of 4,671 patients were enrolled. After 1:1 PSM, the distribution proportions of clinical characteristics in 1,146 patients were balanced (all P>0.05). The non-surgical approach was associated with worse survival compared with sublobectomy and lobectomy in the unmatched and matched cohorts. The multivariate Cox analysis showed that sublobectomy and lobectomy were both related to better OS and CSS rates compared with no surgery (P<0.001). Moreover, the results of subgroup analyses based on age, N stage, and radiotherapy or chemotherapy strategy were consistent. A total of 801 patients were included in the training cohort and 345 cases constituted the validation cohort. The nomogram constructed for the 1-, 3-, and 5-year OS and CSS prediction showed good discrimination, performance, and calibration both in the training and validation sets. Significant distinctions in survival curves between different risk groups stratified by prognostic scores were also observed (all P<0.001). Stage T1N2-3M0 NSCLC patients could benefit from sublobectomy or lobectomy, and lobectomy provides better survival benefits. We developed and validated nomograms, which could offer clinicians instructions for strategy making.

Individualized prediction of conditional survival for colorectal signet-ring cell carcinoma patients.

Conditional survival (CS) considers the time already survived after surgery and may provide additional survival information. The authors sought to construct and validate novel conditional survival nomograms for the prediction of conditional overall survival (OS) and cancer-specific survival (CSS) of colorectal signet-ring cell carcinoma (SRCC) patients. Patients diagnosed with stage I-III SRCC between 2010 and 2019 were identified from the Surveillance, Epidemiology, and End Results database. The formula calculating CS was: CS(x|y) = S(x+y)/S(x), where S(x) represents the survival at x years. CS nomograms were then constructed to predict the 5-year conditional OS and CSS, followed by internal validation. A total of 944 colorectal SRCC patients were finally identified in this study. The 5-year OS and CSS improved gradually with additional survival time. Univariate and multivariate Cox regression analysis conducted in training set revealed that age, race, T stage, LNR, and perineural invasion were independent risk factors for both OS and CSS. Two nomograms with considerable predictive ability were successfully constructed [area under the curve (AUC) for OS: 0.788; AUC for CSS: 0.847] and validated (AUC for OS: 0.773; AUC for CSS: 0.799) for the prediction of 5-year OS and CSS, based on the duration of 1-4 years post-surgery survival. The probability of achieving 5-year OS and 5-year CSS in colorectal SRCC patients improved gradually with additional time. Conditional nomograms considering survival time will be more reliable and informative for risk stratification and postoperative follow-up.

Construction and validation of log odds of positive lymph nodes (LODDS)-based nomograms for predicting overall survival and cancer-specific survival in ovarian clear cell carcinoma patients.

Ovarian clear cell carcinoma (OCCC) is one of the special histologic subtypes of ovarian cancer. This study aimed to construct and validate log odds of positive lymph nodes (LODDS)-based nomograms for predicting the overall survival (OS) and cancer-specific survival (CSS) in patients with OCCC. Patients who underwent surgical treatment between 2010 and 2016 were extracted from the Surveillance Epidemiology and End Results (SEER) database and the data of OCCC patients from the First Affiliated Hospital of Dalian Medical University were used as the external validation group to test the validity of the prognostic model. The best-fitting models were selected by stepwise Cox regression analysis. Survival probability was calculated by the Kaplan-Meier method, and the differences in survival time between subgroups were compared using the log-rank test. Each nomogram's performance was assessed by the calibration plots, decision curve analysis (DCA), and receiver operating characteristics (ROC) curves. T stage, distant metastasis, marital status, and LODDS were identified as significant risk factors for OS. A model with four risk factors (age, T stage, stage, and LODDS value) was obtained for CSS. Nomograms were constructed by incorporating the prognostic factors to predict 1-, 3- and 5-year OS and CSS for OCCC patients, respectively. The area under the curve (AUC) range of our nomogram model for OS and CSS prediction ranged from 0.738-0.771 and 0.769-0.794, respectively, in the training cohort. The performance of this model was verified in the internal and external validation cohorts. Calibration plots illustrated nomograms have good prognostic reliability. Predictive nomograms were constructed and validated to evaluate the OS and CSS of OCCC patients. These nomograms may provide valuable prognostic information and guide postoperative personalized care in OCCC.

Serum α-hydroxybutyrate dehydrogenase as a biomarker for predicting survival outcomes in patients with UTUC after radical nephroureterectomy.

We aimed to determine the prognostic value of α-hydroxybutyrate dehydrogenase (α-HBDH) in upper tract urothelial carcinoma (UTUC) patients after radical nephroureterectomy (RNU). We retrospectively enrolled the data of 544 UTUC patients at West China Hospital from May 2003 to June 2019. Cancer-specific survival (CSS) was the endpoint of interest. The optimal cutoff value of α-HBDH was identified by X-Tile program. After propensity score matching (PSM), we utilized Kaplan‒Meier curves to estimate survival and Cox proportional hazard model for risk assessment. A nomogram was built based on the results of multivariate analysis, and calibration curve, time-dependent receiver operating characteristic (ROC) curves and decision curve analysis were also performed to evaluate the predictive accuracy. Overall, 394 and 150 patients were divided into the α-HBDH-low group and α-HBDH -high group at the cutoff value of 158 U/L, respectively. After PSM, the two groups were well matched for all confounding factors. High α-HBDH was associated with inferior CSS (P = 0.006), and preoperative α-HBDH was an independent predictor for CSS (HR: 1.36; 95% CI:1.08, 1.80), especially in localized UTUC patients (HR: 2.04; 95% CI:1.11, 3.74). Furthermore, the nomogram based on α-HBDH achieved great predictive ability for CSS with areas under the curves of 0.800 and 0.778 for 3-year and 5-year CSS, respectively. Serum α-HBDH was a novel and reliable biomarker for predicting survival outcomes in UTUC patients after RNU but should be further explored.

The development of a prediction model based on deep learning for prognosis prediction of gastrointestinal stromal tumor: a SEER-based study

Accurately predicting the prognosis of Gastrointestinal stromal tumor (GIST) patients is an important task. The goal of this study was to create and assess models for GIST patients' survival patients using the Surveillance, Epidemiology, and End Results Program (SEER) database based on the three different deep learning models. Four thousand five hundred thirty-eight patients were enrolled in this study and divided into training and test cohorts with a 7:3 ratio; the training cohort was used to develop three different models, including Cox regression, RSF, and DeepSurv model. Test cohort was used to evaluate model performance using c-index, Brier scores, calibration, and the area under the curve (AUC). The net benefits at risk score stratification of GIST patients based on the optimal model was compared with the traditional AJCC staging system using decision curve analysis (DCA). The clinical usefulness of risk score stratification compared to AJCC tumor staging was further assessed using the Net Reclassification Index (NRI) and Integrated Discrimination Improvement (IDI). The DeepSurv model predicted cancer-specific survival (CSS) in GIST patients showed a higher c-index (0.825), lower Brier scores (0.142), and greater AUC of receiver operating characteristic (ROC) analysis (1-year ROC:0.898; 3-year:0.853, and 5-year ROC: 0.856). The calibration plots demonstrated good agreement between the DeepSurv model's forecast and actual results. The NRI values ( training cohort: 0.425 for 1-year, 0.329 for 3-year and 0.264 for 5-year CSS prediction; test cohort:0.552 for 1-year,0.309 for 3-year and 0.255 for 5-year CSS prediction) and IDI (training cohort: 0.130 for 1-year,0.141 for 5-year and 0.155 for 10-year CSS prediction; test cohort: 0.154 for 1-year,0.159 for 3-year and 0.159 for 5-year CSS prediction) indicated that the risk score stratification performed significantly better than the AJCC staging alone (P < 0.001). DCA demonstrated the risk score stratification as more clinically beneficial and discriminatory than AJCC staging. Finally, an interactive native web-based prediction tool was constructed for the survival prediction of GIST patients. This study established a high-performance prediction model for projecting GIST patients based on deep learning, which has advantages in predicting each person's prognosis and risk stratification.

Prognostic value of tumor deposits and positive lymph node ratio in stage III colorectal cancer: a retrospective cohort study.

In colorectal cancer (CRC), tumor deposits (TD) have been used to guide the N staging only in node-negative patients. It remains unknown about the prognostic value of TD in combination with positive lymph node ratio (LNR) in stage III CRC. We analyzed data from 31,139 eligible patients diagnosed with stage III CRC, including 30,230 from the Surveillance, Epidemiology, and End Results (SEER) database as a training set and 909 from two Chinese hospitals as a validation set. The associations of TD and LNR with cancer-specific survival (CSS) and overall survival (OS) were evaluated using the Kaplan-Meier method and Cox regression models. Both TD-positive and high LNR (value≥0.4) were associated with worse CSS in the training (multivariable hazard ratio [HR], 1.50; 95% confidence interval [CI], 1.43-1.58 and HR, 1.74; 95% CI, 1.62-1.86, respectively) and validation sets (HR,1.90; 95%CI, 1.41-2.54 and HR,2.01; 95%CI, 1.29-3.15, respectively). Compared to patients with TD-negative and low LNR (value<0.4), those with TD-positive and high LNR had a 4.09-fold risk of CRC-specific death in the training set (HR, 4.09; 95% CI, 3.54-4.72) and 4.60-fold risk in the validation set (HR, 4.60; 95% CI, 2.88-7.35). Patients with TD-positive/H-LNR CRC on the right side had the worst prognosis (P<0.001). The combined variable of TD and LNR contributed the most to CSS prediction in the training (24.26%) and validation (32.31%) sets. A nomogram including TD and LNR showed satisfactory discriminative ability, and calibration curves indicated favorable consistency in both the training and validation sets. TD and LNR represent independent prognostic predictors for stage III CRC. A combination of TD and LNR could be used to identify those at high risk of CRC deaths.