Jul 2026· The American surgeon· pp.
31348261472872
· 0 citations· 21 references
Medicine
TL;DR
This nomogram provides a population-level estimate of DSS in advanced FTC and may support preliminary risk stratification using readily available registry variables, but its clinical interpretation should consider the absence of key biologic and treatment-response factors not captured in SEER, including vascular invasion, molecular alterations, and systemic therapy data.
Abstract
BackgroundAdvanced follicular thyroid carcinoma (FTC) represents a clinically heterogeneous subgroup of differentiated thyroid cancer with variable long-term outcomes. We aimed to develop and internally validate a population-based nomogram for predicting 3-, 5-, and 10-year disease-specific survival (DSS) in patients with stage III-IV FTC using SEER registry data.Materials and MethodsThis retrospective cohort study utilized data from the SEER 17 registry (2010-2022). A total of 1027 patients with stage III-IV FTC were included and randomly assigned to training (n = 718) and validation (n = 309) cohorts. Clinical and demographic variables were analyzed. Univariable and multivariable Cox proportional hazards regression analyses were performed to identify independent prognostic factors. A nomogram was constructed based on the final multivariable model. Predictive performance was assessed using ROC and calibration analyses.ResultsThe mean age of the cohort was 63.5 years (±11.7), and 59.3% of patients were female. Multivariable analysis identified age, stage IV disease, N1 nodal status, unmarried status, and surgical treatment category as independent predictors of DSS. The nomogram demonstrated strong discriminative performance, with AUC values of 0.953, 0.966, and 0.903 for predicting 3-, 5-, and 10-year DSS, respectively. Calibration analyses showed acceptable agreement.ConclusionThis nomogram provides a population-level estimate of DSS in advanced FTC. While the model may support preliminary risk stratification using readily available registry variables, its clinical interpretation should consider the absence of key biologic and treatment-response factors not captured in SEER, including vascular invasion, molecular alterations, and systemic therapy data.
Background At present, the risk factors contributing to the recurrence of papillary thyroid carcinoma (PTC) in residual thyroid tissue post-surgery are not yet fully understood. This study sought to identify multivariable predictors (MVPs) associated with such recurrence and to develop a dynamic, individualized nomogram. The objective is to establish a stratification framework to inform precision “risk-intervention” decision-making for patients at elevated risk of recurrence. Methods A cohort of 2,512 patients diagnosed with thyroid cancer and treated at the First Affiliated Hospital of Bengbu Medical University between January 2014 and December 2024 was analyzed. The cohort was stratified into a training set (n = 1,760) and a validation set (n = 752) using stratified random sampling with a 7:3 ratio. To address the class imbalance between recurrent and non-recurrent cases in the residual thyroid, the Random Over-Sampling Examples (ROSE) method was employed on the training set. Variable selection was conducted using univariate and multivariate logistic regression, LASSO regression, and standard stepwise regression based on the Akaike Information Criterion (AIC). A nomogram was subsequently developed by integrating the coefficients of the selected variables from the logistic regression model. The performance of the nomogram was assessed in both cohorts in terms of discrimination, calibration, and clinical utility. Results In a study of 2,512 thyroid cancer patients who received primary treatment, 146 experienced recurrence in the remaining thyroid tissue. Using univariate and multivariate logistic regression and LASSO regression for feature selection, four independent predictors were identified for the final predictive model: maximum tumor diameter, number of metastatic lymph nodes, lymph node positivity rate, and age. The nomogram showed strong discriminatory ability, with an AUC of 0.7518 in the training set and 0.8347 in the validation set. It also demonstrated excellent calibration and clinical utility across all groups. The best cut-off value for the nomogram score to identify patients with residual thyroid recurrence was 0.245, with scores above this linked to a significantly worse postoperative outcome. Conclusions We have developed and validated an innovative clinical prediction model specifically designed to assess the risk of recurrence in residual thyroid tissue. This model serves as a valuable tool for clinicians, facilitating personalized decision-making and representing a significant advancement in the postoperative management of papillary thyroid carcinoma.
Ruohan Shi, Lisen Zhu, Jia Liu et al.· Frontiers in Oncology· 0 citations
Patients with obstructive colorectal cancer (OCRC) have poor prognosis, and conventional prognostic tools such as TNM staging alone are insufficient for accurate assessment; thus, this study aimed to develop and validate nomograms for predicting overall survival (OS) and disease-free survival (DFS) in OCRC patients. A total of 1,650 eligible colorectal cancer patients were enrolled, including 1,520 from Center A (randomly split 6:4 into training and internal validation cohorts) and 130 from Center B as the external validation cohort; clinicopathological and lipid profile data were collected retrospectively, independent prognostic factors were identified via univariate and multivariate Cox regression, and OS/DFS nomograms were constructed and evaluated using ROC curves, C-index, calibration curves, and DCA. The OS nomogram included 8 factors with 1-, 3-, 5-year AUCs of 0.761–0.793 (training), 0.759–0.788 (internal validation), and 0.723–0.856 (external validation), while the DFS nomogram included 6 factors with corresponding AUCs of 0.777–0.822, 0.750–0.841, and 0.775–0.820, and all evaluations confirmed excellent model performance. In conclusion, two nomograms incorporating lipid and clinicopathological factors were developed and validated, which effectively predict OS and DFS in OCRC patients with good discrimination, calibration, and clinical utility, providing new tools for individualized risk prediction.
Hanwenchen Wang, Falong Zou, Denglong Cheng et al.· Frontiers in Medicine· 0 citations
Background Extrahepatic cholangiocarcinoma (ECC) is a highly aggressive malignancy. This study aimed to develop and validate a comprehensive prognostic nomogram incorporating treatment data to enable individualized risk stratification and refine patient selection for palliative interventions. Methods Data were extracted from the Surveillance, Epidemiology, and End Results (SEER) program (for the period from 2018 to 2022). There were 2,254 patients identified and randomly divided into model development (n=1,577) and validation (n=677) cohorts. Prognostic factors were selected through multivariate Cox regression using backward elimination based on the Akaike Information Criterion (AIC). A nomogram was developed to estimate 6-month, 1-year, and 3-year survival probabilities. Model performance was evaluated using the consistency index (C-index), calibration curves, and decision curve analysis (DCA). Finally, risk stratification cutoffs were determined to facilitate individualized prognostic assessment in palliative settings. Results The final nomogram incorporated age, N stage, M stage, surgery, and chemotherapy status. The model demonstrated good discrimination, with C-indices of 0.770 [95% confidence interval (CI): 0.750–0.790] in the training cohort and 0.789 (95% CI: 0.768–0.802) in the validation cohort. Calibration curves indicated good agreement between predicted and observed probabilities. DCA revealed good net benefit across clinically relevant threshold probabilities. Risk stratification identified high-risk (<3 months, score ≥70), intermediate-risk (3–12 months, score 59–69), and low-risk (>1 year, score ≤58) groups, potentially guiding the selection between plastic and metal stents in palliative settings. Conclusions This prognostic model provides accurate individualized survival prediction for ECC and facilitates objective risk stratification.
Guan-Jun Zhang, Ya-Qi Zhai, Ke Meng et al.· Journal of Gastrointestinal...· 0 citations
Background Breast cancer (BC) is the most prevalent cancer among American women. Asian Americans are confronting a rapidly increasing incidence, facing unique challenges within the healthcare system that contribute to disparities in cancer outcomes. This study aimed to develop and validate a prognostic nomogram for predicting long-term survival in Asian BC patients utilizing the Surveillance, Epidemiology, and End Results (SEER) database. Methods Asian female BC patients diagnosed between 2000 and 2021 were identified from the SEER database. Following the application of inclusion and exclusion criteria, 38,269 patients were included and randomly divided into training (70%) and validation (30%) cohorts. Univariate and multivariate Cox regression analyses were conducted to identify independent prognostic factors, which were incorporated into a nomogram. The model’s discriminative ability, calibration, and clinical utility were evaluated. Results Independent predictors of overall survival included age, tumor grade, marital status, T stage, N stage, subtype, radiotherapy, and chemotherapy. The nomogram demonstrated strong discriminative performance, with concordance indices of 0.794 and 0.811 in the training and validation cohorts, respectively, demonstrating better discriminative performance than the traditional tumor-node-metastasis (TNM) staging system. Calibration plots and decision curve analysis confirmed excellent calibration and clinical applicability. Conclusions This study presents a prognostic nomogram developed and validated exclusively in a large cohort of non-metastatic Asian American BC patients from the SEER database, incorporating modern molecular subtypes to improve prognostic stratification for this understudied population. By integrating readily available clinicopathological factors, the nomogram provides personalized survival forecasts, serving as a valuable tool for individualized management in this unique population. Further external validation and refinements are warranted to enhance its clinical utility.
Background Gallbladder cancer (GBC) remains an aggressive malignancy with a generally poor long-term outlook. In this study, we sought to develop and internally validate a prognostic nomogram for predicting overall survival (OS), aiming to improve individualized risk assessment for these patients. Methods We identified 3,892 patients diagnosed with GBC between 2000 and 2021 from the Surveillance, Epidemiology, and End Results (SEER) database. Eligible cases were randomly divided into a training cohort (70%, n=2,724) and an internal validation cohort (30%, n=1,168). Candidate predictors covered demographic characteristics, tumor‑related factors and treatment information. Univariate and multivariate Cox proportional hazards regression were performed sequentially on the training set to identify independent prognostic factors, which were then used to construct a nomogram predicting 3-, 5- and 8-year OS. Model performance was evaluated comprehensively using the concordance index (C‑index), time‑dependent receiver operating characteristic (ROC) curves, area under the curve (AUC), calibration plots, net reclassification improvement (NRI), integrated discrimination improvement (IDI), and decision curve analysis (DCA). All statistical tests were two‑sided, with significance set at P<0.05. Results Independent prognostic factors included age, tumor grade, T stage, N stage, M stage, surgery, and radiotherapy. The nomogram yielded a C‑index of 0.708 [95% confidence interval (CI): 0.670–0.745] in the training set and 0.623 (95% CI: 0.583–0.622) in the internal validation set. Time‑dependent AUCs for 3-, 5- and 8‑year OS were 0.752, 0.757, and 0.763 in the training set, and 0.758, 0.767, and 0.771 in the validation set, respectively. Compared with the American Joint Committee on Cancer (AJCC) staging system, our nomogram showed significantly improved risk reclassification at 3, 5 and 8 years (NRI =0.460, 0.467 and 0.494; all P<0.001) as well as better IDI (IDI =0.058, 0.044 and 0.041; all P<0.001) in the internal validation cohort. Calibration curves indicated excellent agreement between predicted and observed survival probabilities. DCA confirmed that the nomogram offered a higher net clinical benefit across a wide range of threshold probabilities, suggesting its potential usefulness in clinical decision‑making. Conclusions Compared to the AJCC system, this novel nomogram provides better risk stratification for 3-, 5- and 8‑year survival in GBC, with moderate discrimination, good calibration, and clinical utility. It may serve as a practical tool for individualized prognosis and treatment planning.
Hao Song, Zuo-Biao Li, Qin-Wen Tai et al.· Journal of Gastrointestinal...· 0 citations
To develop and validate a predictive model integrating patient demographics and clinical data for estimating 3-, 4-, and 5-year overall survival in patients with locally advanced cervical squamous cell carcinoma (LACSC).
Clinical data from 670 LACSC patients at Shanxi Cancer Hospital were collected and then randomly assigned to a training cohort and an internal validation cohort at a 6:4 ratio by stratified sampling. Independent prognostic factors were identified using LASSO regression and multivariate Cox regression analysis, with which a predictive model was constructed. Model performance was assessed using the concordance index (C-index), receiver operating characteristic (ROC) curves, and calibration curves. Clinical utility was evaluated via decision curve analysis (DCA).
A robust prognostic model was developed and visualized as a nomogram comprising six variables: NEUT, MONO, CA125, SII, lymph node metastasis status, and treatment modality. Patients were stratified into high- and low-risk groups based on the median risk score in the training cohort. The high-risk group exhibited significantly poorer overall survival (OS) in both cohorts (
P
< 0.05).
A clinical predictive model was established to estimate 3-, 4-, and 5-year survival rates for LACSC patients.
Ting-Ting Zou, Xu-Peng Wang, Hai-Xia Wu et al.· Frontiers in Oncology· 0 citations
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