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Machine Learning Predicts Pathologic Complete Response to Neoadjuvant Chemotherapy for ER+HER2- Breast Cancer: Integrating Tumoral and Peritumoral MRI Radiomic Features

Authors
 Jiwoo Park  ;  Min Jung Kim  ;  Jong-Hyun Yoon  ;  Kyunghwa Han  ;  Eun-Kyung Kim  ;  Joo Hyuk Sohn  ;  Young Han Lee  ;  Yangmo Yoo 
Citation
 DIAGNOSTICS, Vol.13(19) : 3031, 2023-09 
Journal Title
DIAGNOSTICS
Issue Date
2023-09
Keywords
ER+HER2- locally advanced breast cancer ; machine learning ; neoadjuvant chemotherapy ; pathological complete response ; pretreatment MRI ; radiomics ; segmentation
Abstract
Background: This study aimed to predict pathologic complete response (pCR) in neoadjuvant chemotherapy for ER+HER2- locally advanced breast cancer (LABC), a subtype with limited treatment response.

Methods: We included 265 ER+HER2- LABC patients (2010-2020) with pre-treatment MRI, neoadjuvant chemotherapy, and confirmed pathology. Using data from January 2016, we divided them into training and validation cohorts. Volumes of interest (VOI) for the tumoral and peritumoral regions were segmented on preoperative MRI from three sequences: T1-weighted early and delayed contrast-enhanced sequences and T2-weighted fat-suppressed sequence (T2FS). We constructed seven machine learning models using tumoral, peritumoral, and combined texture features within and across the sequences, and evaluated their pCR prediction performance using AUC values.

Results: The best single sequence model was SVM using a 1 mm tumor-to-peritumor VOI in the early contrast-enhanced phase (AUC = 0.9447). Among the combinations, the top-performing model was K-Nearest Neighbor, using 1 mm tumor-to-peritumor VOI in the early contrast-enhanced phase and 3 mm peritumoral VOI in T2FS (AUC = 0.9631).

Conclusions: We suggest that a combined machine learning model that integrates tumoral and peritumoral radiomic features across different MRI sequences can provide a more accurate pretreatment pCR prediction for neoadjuvant chemotherapy in ER+HER2- LABC.
Files in This Item:
T202306714.pdf Download
DOI
10.3390/diagnostics13193031
Appears in Collections:
1. College of Medicine (의과대학) > Research Institute (부설연구소) > 1. Journal Papers
1. College of Medicine (의과대학) > Dept. of Internal Medicine (내과학교실) > 1. Journal Papers
1. College of Medicine (의과대학) > Dept. of Radiology (영상의학교실) > 1. Journal Papers
Yonsei Authors
Kim, Min Jung(김민정) ORCID logo https://orcid.org/0000-0003-4949-1237
Kim, Eun-Kyung(김은경) ORCID logo https://orcid.org/0000-0002-3368-5013
Park, Jiwoo(박지우)
Sohn, Joo Hyuk(손주혁) ORCID logo https://orcid.org/0000-0002-2303-2764
Lee, Young Han(이영한) ORCID logo https://orcid.org/0000-0002-5602-391X
Han, Kyung Hwa(한경화)
URI
https://ir.ymlib.yonsei.ac.kr/handle/22282913/197302
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