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Noninvasive Artificial Intelligence System for Early Predicting Residual Cancer Burden during Neoadjuvant Chemotherapy in Breast Cancer.
Li, Wei; Huang, Yu-Hong; Zhu, Teng; Zhang, Yi-Min; Zheng, Xing-Xing; Zhang, Ting-Feng; Lin, Ying-Yi; Wu, Zhi-Yong; Liu, Zai-Yi; Lin, Ying; Ye, Guo-Lin; Wang, Kun.
Afiliación
  • Li W; Department of Breast Cancer, The First People's Hospital of Foshan, Foshan 528000, China.
  • Huang YH; The Second School of Clinical Medicine, Southern Medical University, Guangzhou 510515, China.
  • Zhu T; Department of Breast Cancer, Cancer Center, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, 510080, Guangdong, China.
  • Zhang YM; Department of Breast Cancer, Cancer Center, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, 510080, Guangdong, China.
  • Zheng XX; Clinical research center & Breast disease diagnosis and treatment center, Shantou Central Hospital, Shantou 515000, China.
  • Zhang TF; Department of Breast Cancer, Cancer Center, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, 510080, Guangdong, China.
  • Lin YY; Department of Breast Cancer, Cancer Center, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, 510080, Guangdong, China.
  • Wu ZY; Department of Breast Cancer, Cancer Center, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, 510080, Guangdong, China.
  • Liu ZY; Clinical research center & Breast disease diagnosis and treatment center, Shantou Central Hospital, Shantou 515000, China.
  • Lin Y; Department of Radiology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, 510080, Guangdong, China.
  • Ye GL; Breast Disease Center, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou 510080, China.
  • Wang K; Department of Breast Cancer, The First People's Hospital of Foshan, Foshan 528000, China.
Ann Surg ; 2024 Apr 01.
Article en En | MEDLINE | ID: mdl-38557792
ABSTRACT

OBJECTIVE:

To develop an artificial intelligence (AI) system for the early prediction of residual cancer burden (RCB) scores during neoadjuvant chemotherapy (NAC) in breast cancer. SUMMARY BACKGROUND DATA RCB III indicates drug resistance in breast cancer, and early detection methods are lacking.

METHODS:

This study enrolled 1048 patients with breast cancer from four institutions, who were all receiving NAC. Magnetic resonance images were collected at the pre- and mid-NAC stages, and radiomics and deep learning features were extracted. A multitask AI system was developed to classify patients into three groups (RCB 0-I, II, and III ) in the primary cohort (PC, n=335). Feature selection was conducted using the Mann-Whitney U- test, Spearman analysis, least absolute shrinkage and selection operator regression, and the Boruta algorithm. Single-modality models were developed followed by model integration. The AI system was validated in three external validation cohorts. (EVCs, n=713).

RESULTS:

Among the patients, 442 (42.18%) were RCB 0-I, 462 (44.08%) were RCB II and 144 (13.74%) were RCB III. Model-I achieved an area under the curve (AUC) of 0.975 in the PC and 0.923 in the EVCs for differentiating RCB III from RCB 0-II. Model-II distinguished RCB 0-I from RCB II-III, with an AUC of 0.976 in the PC and 0.910 in the EVCs. Subgroup analysis confirmed that the AI system was consistent across different clinical T stages and molecular subtypes.

CONCLUSIONS:

The multitask AI system offers a noninvasive tool for the early prediction of RCB scores in breast cancer, supporting clinical decision-making during NAC.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Ann Surg Año: 2024 Tipo del documento: Article País de afiliación: China 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: Ann Surg Año: 2024 Tipo del documento: Article País de afiliación: China Pais de publicación: Estados Unidos