Cross-modal deep learning model for predicting pathologic complete response to neoadjuvant chemotherapy in breast cancer

Pathological complete response (pCR) serves as a critical measure of the success of neoadjuvant chemotherapy (NAC) in breast cancer, directly influencing subsequent therapeutic decisions. With the continuous advancement of artificial intelligence, methods for early and accurate prediction of pCR are being extensively explored. In this study, we propose a cross-modal multi-pathway automated prediction model that integrates temporal and spatial information. This model fuses digital pathology images from biopsy specimens and multi-temporal ultrasound (US) images to predict pCR status early in NAC. The model demonstrates exceptional predictive efficacy. Our findings lay the foundation for developing personalized treatment paradigms based on individual responses. This approach has the potential to become a critical auxiliary tool for the early prediction of NAC response in breast cancer patients.

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Publication Details

Journal
npj Precision Oncology
Published
2024-09-05
DOI
https://doi.org/10.1038/s41698-024-00678-8
Citations
16
Primary Topic
AI in cancer detection
Type
article
Field-Weighted Citation Impact
5.56

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article

Cross-modal deep learning model for predicting pathologic complete response to neoadjuvant chemotherapy in breast cancer

Hongda Cao, Quan Dai, Baihui Chen, Dongxu Yang et al.
16 citations
npj Precision Oncology
AI in cancer detection
5.56
article

Cross-modal deep learning model for predicting pathologic complete response to neoadjuvant chemotherapy in breast cancer

Hongda Cao, Quan Dai, Baihui Chen, Dongxu Yang, Youxue Zhang, Tianyu Chen, Jinfeng Zhang, Xue Gong, Yuan Sui, Dalin Li, Huanyu Zhang, Ling Qin, Jianming Guo
article en
16 citations

Abstract

Pathological complete response (pCR) serves as a critical measure of the success of neoadjuvant chemotherapy (NAC) in breast cancer, directly influencing subsequent therapeutic decisions. With the continuous advancement of artificial intelligence, methods for early and accurate prediction of pCR are being extensively explored. In this study, we propose a cross-modal multi-pathway automated prediction model that integrates temporal and spatial information. This model fuses digital pathology images from biopsy specimens and multi-temporal ultrasound (US) images to predict pCR status early in NAC. The model demonstrates exceptional predictive efficacy. Our findings lay the foundation for developing personalized treatment paradigms based on individual responses. This approach has the potential to become a critical auxiliary tool for the early prediction of NAC response in breast cancer patients.

npj Precision OncologyVol. 8(1)
Harbin Medical University (CN), Third Affiliated Hospital of Harbin Medical University (CN), Sichuan Cancer Hospital (CN), Beihang University (CN)
Servier, Wu Jieping Medical Foundation
Openalex Percentile: Top 4%
AI in cancer detection
5.56
DOIOpen Access PDFOpenAlex
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