Integrating radiomics into predictive models for low nuclear grade DCIS using machine learning

Integrating radiomics into predictive models for low nuclear grade DCIS using machine learning

Abstract Predicting low nuclear grade DCIS before surgery can improve treatment choices and patient care, thereby reducing unnecessary treatment. Due to the high heterogeneity of DCIS and the limitations of biopsies in fully characterizing tumors, current diagnostic methods relying on invasive biops...

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Bibliographic Details
Main Authors: Yimin Wu, Daojing Xu, Zongyu Zha, Li Gu, Jieqing Chen, Jiagui Fang, Ziyang Dou, Pingyang Zhang, Chaoxue Zhang, Junli Wang
Format: Article
Language:English
Published: Nature Portfolio 2025-03-01
Series:Scientific Reports
Subjects:
Ductal carcinoma in situ
Machine learning
Radiomic
Ultrasound
Mammography
Online Access:https://doi.org/10.1038/s41598-025-92080-y
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Summary:Abstract Predicting low nuclear grade DCIS before surgery can improve treatment choices and patient care, thereby reducing unnecessary treatment. Due to the high heterogeneity of DCIS and the limitations of biopsies in fully characterizing tumors, current diagnostic methods relying on invasive biopsies face challenges. Here, we developed an ensemble machine learning model to assist in the preoperative diagnosis of low nuclear grade DCIS. We integrated preoperative clinical data, ultrasound images, mammography images, and Radiomic scores from 241 DCIS cases. The ensemble model, based on Elastic Net, Generalized Linear Models with Boosting (glmboost), and Ranger, improved the ability to predict low nuclear grade DCIS preoperatively, achieving an AUC of 0.92 on the validation set, outperforming the model using clinical data alone. The comprehensive model also demonstrated notable enhancements in integrated discrimination improvement and net reclassification improvement (p < 0.001). Furthermore, the Radiomic ensemble model effectively stratified DCIS patients by risk based on disease-free survival. Our findings emphasize the importance of integrating Radiomic into DCIS prediction models, offering fresh perspectives for personalized treatment and clinical management of DCIS.
ISSN:2045-2322

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