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AI model predicts breast cancer therapy response using transcriptome and histopathology

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a novel two-stage AI model designed to predict pathological complete response (pCR) to neoadjuvant therapy in breast cancer patients. The model first infers the transcriptome from histopathology images across a large dataset, then uses this inferred expression along with clinical variables to predict treatment response. This approach achieved a pooled AUROC of 0.79 and demonstrated superior performance compared to traditional histopathological biomarkers, while also proving robust even with minimal biopsy tissue. AI

IMPACT This AI model could improve precision oncology by enabling more accurate prediction of treatment response from biopsy data.

RANK_REASON The cluster contains an academic paper detailing a new AI model and its evaluation. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

AI model predicts breast cancer therapy response using transcriptome and histopathology

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The cluster contains an academic paper detailing a new AI model and its evaluation. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Jungkyu Park, Dhruva Biswas, Joseph Cappadona, Cerise Tang, Ken G. Zeng, Bartosz Machura, Chuwen Liu, Paolo Tarantino, Coral Omene, Francisco J. Esteva, Rohit Bhargava, Marcin Braun, Kamila Pa\'zdzierz, Jakub Czerwi\'nski, Hanna Roma\'nska-Knight, Albert… ·

    Transcriptome-informed multi-modal AI for predicting neoadjuvant therapy response from breast cancer biopsies

    arXiv:2610.03693v1 Announce Type: new Abstract: Scarcity of labeled data limits development of deep learning biomarkers in oncology. We develop a two-stage AI model predicting pathological complete response (pCR) to neoadjuvant therapy in breast cancer. The first stage learns the…

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