European journal of medical research · 2025
A population-based analysis of a risk stratification system for predicting radiotherapy benefits in invasive breast carcinoma of no special type with medullary pattern.
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Zhang Jian, Wang Lizhao, Chen Heyan, Yan Yu, Ge Guanqun, Wang Ke, He Jianjun
Аннотация
BACKGROUND: Invasive breast carcinoma of no special type (IBC-NST) with medullary pattern is a rare histological subtype, accounting for 3-5% of breast malignancies. Its distinct clinicopathological features and uncertain benefit from radiotherapy (RT) pose unique therapeutic challenges. Current guidelines extrapolate treatment protocols from invasive ductal carcinoma, yet prognostic heterogeneity and the absence of validated biomarkers underscore the need for precision stratification tools to guide RT decisions.
METHODS: Using data from the SEER database (2010-2018), we conducted univariate and multivariate Cox regression analyses to develop a prognostic stratification model and stratified the whole cohort into different risk groups to determine the optimal candidates to benefit from radiotherapy. The accuracy of the nomogram was evaluated by discrimination and calibration evaluation.
RESULTS: Among 667 eligible patients, 535 were allocated to the training set and 132 to the validation set (8:2 ratio). Five independent prognostic factors were identified: age, T stage, N stage, molecular subtype, and chemotherapy status. These were incorporated into a nomogram predicting 3- and 5-year overall survival (OS). Using an optimal cutoff, patients were stratified into low- and high-risk groups. Radiotherapy significantly improved OS in low-risk patients compared to those not receiving RT (P = 0.017), but not in high-risk patients (P = 0.47). The model demonstrated strong predictive performance, with 3- and 5-year AUC values of 0.777 and 0.775 in the training set, and 0.747 and 0.712 in the validation set. Calibration curves indicated close agreement between predicted and observed outcomes.
CONCLUSION: We developed and validated a prognostic nomogram for IBC-NST with medullary pattern that accurately stratifies patients by risk. Our findings indicate that radiotherapy is associated with a survival benefit primarily in low-risk patients, offering a practical tool to optimize RT personalization and avoid overtreatment in high-risk individuals.
METHODS: Using data from the SEER database (2010-2018), we conducted univariate and multivariate Cox regression analyses to develop a prognostic stratification model and stratified the whole cohort into different risk groups to determine the optimal candidates to benefit from radiotherapy. The accuracy of the nomogram was evaluated by discrimination and calibration evaluation.
RESULTS: Among 667 eligible patients, 535 were allocated to the training set and 132 to the validation set (8:2 ratio). Five independent prognostic factors were identified: age, T stage, N stage, molecular subtype, and chemotherapy status. These were incorporated into a nomogram predicting 3- and 5-year overall survival (OS). Using an optimal cutoff, patients were stratified into low- and high-risk groups. Radiotherapy significantly improved OS in low-risk patients compared to those not receiving RT (P = 0.017), but not in high-risk patients (P = 0.47). The model demonstrated strong predictive performance, with 3- and 5-year AUC values of 0.777 and 0.775 in the training set, and 0.747 and 0.712 in the validation set. Calibration curves indicated close agreement between predicted and observed outcomes.
CONCLUSION: We developed and validated a prognostic nomogram for IBC-NST with medullary pattern that accurately stratifies patients by risk. Our findings indicate that radiotherapy is associated with a survival benefit primarily in low-risk patients, offering a practical tool to optimize RT personalization and avoid overtreatment in high-risk individuals.