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This is a Systematic Review study.

October 8, 2025Open Access

Machine learning-based multimodal prognostic models integrating pathology images and high-throughput omic data for overall survival prediction in cancer: a systematic review

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Systematic review shows multimodal models improve overall survival prediction in cancer, highlighting the need for better validation.

Multimodal machine learning approaches outperformed unimodal models in predicting overall survival in cancer patients.
Forty-eight studies across 19 cancer types revealed c-indices between 0.550 and 0.857, indicating mixed predictive performance.
Data synthesis followed SWiM and PRISMA 2020 guidelines, assessing bias with PROBAST+AI for improved reliability.
Further methodological rigor and clinical evaluations are essential for translating these findings to clinical practice.

Jennings et al. (2025) studied this question.

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October 8, 2025Open Access

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Authors

Systematic review shows multimodal models improve overall survival prediction in cancer, highlighting the need for better validation.

Multimodal machine learning approaches outperformed unimodal models in predicting overall survival in cancer patients.
Forty-eight studies across 19 cancer types revealed c-indices between 0.550 and 0.857, indicating mixed predictive performance.
Data synthesis followed SWiM and PRISMA 2020 guidelines, assessing bias with PROBAST+AI for improved reliability.
Further methodological rigor and clinical evaluations are essential for translating these findings to clinical practice.

Jennings et al. (2025) studied this question.

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