Live(r) Die: Predicting Survival in Colorectal Liver Metastasis

• URL: http://arxiv.org/abs/2509.08935v1
• Date: Wed, 10 Sep 2025 19:02:59 GMT
• Title: Live(r) Die: Predicting Survival in Colorectal Liver Metastasis
• Authors: Muhammad Alberb, Helen Cheung, Anne Martel,
• Abstract summary: Colorectal cancer frequently metastasizes to the liver, significantly reducing long-term survival.<n>Current prognostic models, often based on limited clinical or molecular features, lack sufficient predictive power.<n>We present a fully automated framework for surgical outcome prediction from pre- and post-contrast MRI.
• Score: 0.01268579273097071
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Colorectal cancer frequently metastasizes to the liver, significantly reducing long-term survival. While surgical resection is the only potentially curative treatment for colorectal liver metastasis (CRLM), patient outcomes vary widely depending on tumor characteristics along with clinical and genomic factors. Current prognostic models, often based on limited clinical or molecular features, lack sufficient predictive power, especially in multifocal CRLM cases. We present a fully automated framework for surgical outcome prediction from pre- and post-contrast MRI acquired before surgery. Our framework consists of a segmentation pipeline and a radiomics pipeline. The segmentation pipeline learns to segment the liver, tumors, and spleen from partially annotated data by leveraging promptable foundation models to complete missing labels. Also, we propose SAMONAI, a novel zero-shot 3D prompt propagation algorithm that leverages the Segment Anything Model to segment 3D regions of interest from a single point prompt, significantly improving our segmentation pipeline's accuracy and efficiency. The predicted pre- and post-contrast segmentations are then fed into our radiomics pipeline, which extracts features from each tumor and predicts survival using SurvAMINN, a novel autoencoder-based multiple instance neural network for survival analysis. SurvAMINN jointly learns dimensionality reduction and hazard prediction from right-censored survival data, focusing on the most aggressive tumors. Extensive evaluation on an institutional dataset comprising 227 patients demonstrates that our framework surpasses existing clinical and genomic biomarkers, delivering a C-index improvement exceeding 10%. Our results demonstrate the potential of integrating automated segmentation algorithms and radiomics-based survival analysis to deliver accurate, annotation-efficient, and interpretable outcome prediction in CRLM.

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