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Updated: Jun 13, 2025

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

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Predicting BRCA mutation and stratifying targeted therapy response using multimodal learning: a multicenter study.

Yi Li^1,2, Xiaomin Xiong^1,2, Xiaohua Liu³

¹School of Medicine, Chongqing University, Chongqing, China.

Annals of Medicine

|September 11, 2024

Summary

This summary is machine-generated.

This study developed a multimodal model (MIAM-C) to predict BRCA1/2 gene status and patient prognosis for PARPi cancer treatment. The MIAM-C model accurately identified BRCA1/2 mutations and improved risk stratification for better treatment decisions.

Keywords:

BRCA cancer deep learning interpretability multimodal targeted therapy

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Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

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Area of Science:

Oncology
Genetics
Computational Pathology

Background:

BRCA1/2 gene status is critical for cancer treatment decisions, but genetic testing is often inaccessible.
Not all patients benefit from poly (ADP-ribose) polymerase inhibitors (PARPi), necessitating improved risk stratification.
Developing predictive models for BRCA1/2 status and PARPi response is crucial for personalized cancer therapy.

Purpose of the Study:

To develop and validate a multimodal model for predicting BRCA1/2 gene status using histopathological images.
To assess the model's ability to predict prognosis and response to PARPi treatment in various cancer types.
To identify morphological features associated with BRCA1/2 mutations for model interpretability.

Main Methods:

A multi-instance attention model (MIAM) was developed to detect BRCA1/2 status from H&E images.
The MIAM-C model integrated tissue, cell, and clinical features for enhanced prediction.
Model performance was evaluated using AUC and Kaplan-Meier analysis across three independent cohorts (ovarian, breast, prostate, pancreatic cancers).

Main Results:

The MIAM-C model demonstrated superior performance in identifying BRCA1/2 genotype compared to the MIAM model.
High-attention regions, including high-grade tumors and lymphocytic infiltration, correlated with BRCA1/2 mutations.
MIAM-C accurately predicted PARPi therapy response and served as an independent prognostic factor for BRCA1/2-mutant ovarian cancer.

Conclusions:

The MIAM-C model accurately detects BRCA1/2 gene status from pathological images.
This multimodal approach effectively stratifies prognosis for patients with BRCA1/2 mutations.
The findings support the potential of AI-driven pathological analysis for personalized cancer treatment.