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Radiomics Analysis of Intratumoral and Various Peritumoral Regions From Automated Breast Volume Scanning for Accurate Ki-67 Prediction in Breast Cancer Using Machine Learning.
Hu, Bin; Xu, Yanjun; Gong, Huiling; Tang, Lang; Li, Hongchang.
Afiliación
  • Hu B; Department of Ultrasound, Minhang Hospital, Fudan University, Shanghai, China (B.H., H.G., L.T.). Electronic address: hubin_10945@fudan.edu.cn.
  • Xu Y; Department of Ultrasonography, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China (Y.X.); Shanghai Institute of Ultrasound in Medicine, Shanghai, China (Y.X.).
  • Gong H; Department of Ultrasound, Minhang Hospital, Fudan University, Shanghai, China (B.H., H.G., L.T.).
  • Tang L; Department of Ultrasound, Minhang Hospital, Fudan University, Shanghai, China (B.H., H.G., L.T.).
  • Li H; Department of General Surgery, Institute of Fudan-Minhang Academic Health System, Minhang Hospital, Fudan University, Shanghai, China (H.L.).
Acad Radiol ; 2024 Sep 09.
Article en En | MEDLINE | ID: mdl-39256084
ABSTRACT
RATIONALE AND

OBJECTIVES:

Current radiomics research primarily focuses on intratumoral regions and fixed peritumoral areas, lacking optimization for accurate Ki-67 prediction. This study aimed to develop machine learning (ML) models to analyze radiomic features from Automated Breast Volume Scanning (ABVS) images of different peritumoral region sizes to identify the optimal size for accurate preoperative Ki-67 prediction. MATERIALS AND

METHODS:

A total of 668 breast cancer patients were enrolled and divided into training (486) and testing (182) cohorts. In the training cohort, ML models were developed for intratumoral and peritumoral regions (2, 4, 6, 8, and 10 mm). Relevant Ki-67 features for each ROI were identified, and different models were compared to determine the optimal one. These models were validated using a testing cohort to find the most accurate peritumoral region for Ki-67 prediction. SHAP (Shapley Additive Explanations) analysis was performed to identify key radiomic features from the optimal model.

RESULTS:

The Extreme Gradient Boosting (XGBoost) model for the intratumoral region combined with the 6 mm peritumoral region achieved the highest predictive accuracy, with an AUC of 0.957 in the training cohort and 0.920 in the testing cohort. Calibration curves confirmed reliability, and decision curve analysis demonstrated the highest net benefit. SHAP indicated that 6 mm peritumoral radiomic features are more significant than intratumoral features.

CONCLUSION:

The XGBoost model using ABVS-derived radiomic features from both the intratumoral and 6 mm peritumoral regions provides the most accurate preoperative Ki-67 prediction.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Acad Radiol Asunto de la revista: RADIOLOGIA Año: 2024 Tipo del documento: Article Pais de publicación: Estados Unidos
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Acad Radiol Asunto de la revista: RADIOLOGIA Año: 2024 Tipo del documento: Article Pais de publicación: Estados Unidos