Cancer survival prediction could potentially support individualized treatment planning of cancer patients. Healthcare centers accumulate several types of data that could be leveraged by artificial intelligence to create multimodal prognostic models. This study investigates the potential benefits of integrating radiological CT imaging of the thorax, laboratory blood tests, and clinical data to predict the 1-year survival in a pancancer cohort of non-small cell lung cancer, melanoma, and bladder cancer patients treated with immunotherapy. The study included 447 patients with longitudinal CT scans and 475 patients with longitudinal lab data. A 3D variant of the ResNet convolutional neural network was trained with imaging, and two random forest models were trained with lab and clinical data to perform the classification task. Monte Carlo cross-validation was conducted over 30 folds. A model-agnostic decision-based fusion strategy was adopted for integration. The highest result was obtained by integrating lab data and CT imaging (average AUC= 0.79, p-value< 0.05), while lab data and imaging individually scored average AUCs of 0.77 and 0.7, respectively (p-value< 0.05). Current results showed a modest improvement in the performance using specific complementary modalities over using single modalities. Clinical data did not provide any added value.

Survival Prediction of Cancer Immunotherapy Patients Using Integrative Diagnostics

[Networking hour](https://app.wonder.me/?spaceId=fe093845-759f-448f-9f55-01d3adccd820)
[COIs](https://drive.google.com/drive/folders/1nbfwAa9lf9Kz-wAZ7iLKhfpBFjpN_gkn?usp=sharing)