Paper Detail
Sofie Allgöwer, Mikael Johansson, Andreas Hallqvist, Jonas Andersson, Åse Johnsson, Ida Häggström, Jennifer Alvén
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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@article{allgwer2026ct,
title = {CT-CLIP Representations for Multimodal Lung Cancer Survival Prediction},
author = {Sofie Allgöwer and Mikael Johansson and Andreas Hallqvist and Jonas Andersson and Åse Johnsson and Ida Häggström and Jennifer Alvén},
year = {2026},
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 clini},
url = {https://arxiv.org/abs/2607.08503},
keywords = {cs.CV},
eprint = {2607.08503},
archiveprefix = {arXiv},
}
{}