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arXiv · 2607.08503

CT-CLIP Representations for Multimodal Lung Cancer Survival Prediction

Sofie Allgöwer·Mikael Johansson·Andreas Hallqvist·Jonas Andersson·Åse Johnsson·Ida Häggström·Jennifer Alvén
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Abstract

Accurate prognosis prediction is important for treatment planning in lung cancer, but deep learning-driven survival modelling is often limited by the scarcity of curated imaging cohorts with reliable outcome data. This study evaluates whether representations from a domain-specific foundation model can be used for multimodal survival prediction in data-constrained clinical settings. We assess the foundation model CT-CLIP as a feature extractor for pretreatment computed tomography images and clinical variables from 242 diagnosed lung cancer patients. The evaluation includes adaptation strategies based on frozen encoders, full fine-tuning, and low-rank adaptation, together with modality ablations and comparisons with clinical and multimodal baselines. The results show that a frozen CT-CLIP model combined with a trainable lightweight survival head outperforms the clinical baseline and achieves comparable or improved performance relative to other multimodal approaches, and separates patients into clinically meaningful high- and low-risk groups.

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Sofie Allgöwer, Mikael Johansson, Andreas Hallqvist, Jonas Andersson, Åse Johnsson, Ida Häggström, Jennifer Alvén. 2026-07-09. CT-CLIP Representations for Multimodal Lung Cancer Survival Prediction. https://arxiv.org/abs/2607.08503

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