A predictive model of survival in early-stage medullary breast carcinoma: a population-based study.

Analysis of prognostic factors in patients with locally advanced cervical squamous cell carcinoma and development of a nomogram prediction model

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.

Development and validation of a SEER-derived prognostic nomogram for non-metastatic Asian breast cancer patients

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.

Survival Analysis of Male Breast Cancer Patients Based on SEER Data

Male breast cancer is uncommon, but its clinical management remains challenging because evidence is still far less abundant than that for female breast cancer. This study used data from the SEER Program to evaluate prognostic factors in male breast cancer and to compare the performance of Cox proportional hazards and accelerated failure time (AFT) models. After data cleaning and variable construction, 1,743 male patients diagnosed between 2000 and 2019 were included in the final analytic cohort. The dataset contained demographic information, tumor stage, receptor status, treatment variables, survival time, and outcome status. According to the above results, the 1-year, 3-year and 5-year survival rates are 93.7%, 82.7% and 71.5%, respectively. The test for proportional hazards showed that some variables did not meet the requirements of the Cox model, so an AFT model was selected. We can find that survival analysis of SEER data can provide statistical support for the prognosis prediction of patients.

OVERALL SURVIVAL ACCORDING TO MOLECULAR SUBTYPES AND CLINICAL STAGING IN INVASIVE BREAST CARCINOMA PATIENTS: KAPLAN-MEIER ANALYSIS IN A HOSPITAL COHORT

Mapping significant clinical factors to relapse-free survival prognosis in breast cancer patients

This study explores the role of clinical factors in influencing relapse-free survival (RFS) in breast cancer patients through comprehensive survival analysis (SA). The data were derived from a large cohort of breast cancer patients, encompassing clinical variables such as age at diagnosis, tumor size, number of positive lymph nodes, tumor grade, histological subtype, and receipt of chemotherapy and radiotherapy. Analytical methods included Kaplan-Meier plots, log-rank tests, Cox proportional hazards modeling, and feature ranking based on univariate and multivariate log-rank and Cox tests. The results show that patients who received chemotherapy demonstrated a shorter median RFS compared to those who did not (102.7 vs. 145.0 months, p = 0.03), likely due to high-risk patients being selected for chemotherapy. Meanwhile, radiotherapy was associated with improved prognosis (hazard ratio (HR) < 1 in Cox model, p < 10−5), in line with meta-analyses showing reduced breast cancer mortality post-radiotherapy. The contribution maps the key clinical determinants of RFS in breast cancer and highlights the practicality of performing SA.

Histological subtype and tumour differentiation as predictors of survival among patients with non-small cell lung cancer in specialised hospitals in Ethiopia

Introduction: non-small cell lung cancer accounts for most lung cancer-related deaths worldwide. Although tumour-node-metastasis staging remains the cornerstone of prognostication, histological subtype and tumour differentiation may provide additional prognostic value, particularly in low- and middle-income countries where advanced molecular testing is limited. Evidence on these factors in Ethiopia is scarce. Methods: an institution-based retrospective cohort study was conducted among 202 adult patients who received follow-up care at three specialised hospitals in Ethiopia between January 2020 and January 2025. Data were extracted from medical records and supplemented with telephone follow-up to ascertain survival outcomes. Overall survival was estimated using the Kaplan-Meier method, and Cox proportional hazards regression models were applied to identify independent predictors of mortality, with statistical significance set at p < 0.05. Results: the median overall survival was 10 months. Patients with squamous cell carcinoma had significantly better survival than those with non-squamous cell carcinoma (log-rank p = 0.020). Survival also differed significantly by histological differentiation (log-rank p = 0.001). In multivariate analysis, advanced primary tumour stage, contralateral nodal involvement, distant metastasis, non-squamous histology, moderately or poorly differentiated /undifferentiated tumours, and poor performance status were independently associated with worse survival. Similarly, Patients aged 40-59 years and ≥ 60 years had significantly lower hazards of death compared with patients aged under 40 years. Conclusion: histological subtype and tumour differentiation are independent predictors of survival among NSCLC patients in Ethiopia, beyond conventional TNM staging. These findings provide context-specific evidence from an Ethiopian cohort, where advanced molecular profiling is limited, and highlight the prognostic value of routinely available histopathological features in guiding clinical decision-making in resource-constrained settings.