# Survival Prediction

Transferring drug sensitivity from cancer cell lines to patient tumours is hard because aligning the two domains can erase tumour-lineage structure. MODAE+ jointly optimizes domain alignment, deconfounding, drug-response prediction, survival prediction and lineage classification in one shared latent space, with adaptive multitask weighting to balance them. On matched treated TCGA subsets, its predicted sensitivity reached a 41.5% Cox-significant rate for overall survival, versus 4.9% for CODE-AE-ADV.

Transferring drug sensitivity from cancer cell lines to patient tumours is hard because aligning the two domains can erase tumour-lineage structure. MODAE+ jointly optimizes domain alignment, deconfounding, drug-response prediction, survival prediction and lineage classification in one shared latent space, with adaptive multitask weighting to balance them. On matched treated TCGA subsets, its predicted sensitivity reached a 41.5% Cox-significant rate for overall survival, versus 4.9% for CODE-AE-ADV.

Molecular data in lung adenocarcinoma are high-dimensional relative to the number of observed events, so concatenating them into a survival model invites overfitting and unstable feature selection. CAISL writes predicted risk as a clinical Cox risk plus a sparse molecular correction, and penalizes how much that correction varies across resampled training cohorts. On 470 TCGA-LUAD patients with 119 observed deaths, mean C-index across five independent cross-validation runs was 0.7246 versus 0.6970 for clinical Cox, a paired gain of 0.0276 (95% CI 0.005–0.050); this is internal validation and still needs an independent cohort.

Molecular data in lung adenocarcinoma are high-dimensional relative to the number of observed events, so concatenating them into a survival model invites overfitting and unstable feature selection. CAISL writes predicted risk as a clinical Cox risk plus a sparse molecular correction, and penalizes how much that correction varies across resampled training cohorts. On 470 TCGA-LUAD patients with 119 observed deaths, mean C-index across five independent cross-validation runs was 0.7246 versus 0.6970 for clinical Cox, a paired gain of 0.0276 (95% CI 0.005–0.050); this is internal validation and still needs an independent cohort.

CAPTOR, a vision-language foundation model pretrained on 25,512 chest-abdomen-pelvis CT-report pairs from cancer-focused exams, outperformed existing CT foundation models in its target setting. Disease classification reached an AUC of 0.767 versus 0.695 for the next-best model on the full chest-abdomen-pelvis subset, and 0.784 versus 0.734 on the oncologic subset. It encodes organ-centered crops of whole exams but makes study-level, not lesion-level, judgments.

CAPTOR, a vision-language foundation model pretrained on 25,512 chest-abdomen-pelvis CT-report pairs from cancer-focused exams, outperformed existing CT foundation models in its target setting. Disease classification reached an AUC of 0.767 versus 0.695 for the next-best model on the full chest-abdomen-pelvis subset, and 0.784 versus 0.734 on the oncologic subset. It encodes organ-centered crops of whole exams but makes study-level, not lesion-level, judgments.

In outcome-dependent sampling, covariates are collected for only part of the cohort, so standard survival models trained on the sampled data target a distribution that differs from the cohort. A gradient boosting machine carries the sampling weights into training and tuning, selecting iterations by a weighted integrated Brier score, and the weighted empirical risk is shown to converge to the corresponding integrated Brier risk. In simulations it outperformed the Cox model in high-dimensional and nonlinear settings, and on uranium miner cancer data the test C-index was 0.803 versus 0.801 for Cox.

In outcome-dependent sampling, covariates are collected for only part of the cohort, so standard survival models trained on the sampled data target a distribution that differs from the cohort. A gradient boosting machine carries the sampling weights into training and tuning, selecting iterations by a weighted integrated Brier score, and the weighted empirical risk is shown to converge to the corresponding integrated Brier risk. In simulations it outperformed the Cox model in high-dimensional and nonlinear settings, and on uranium miner cancer data the test C-index was 0.803 versus 0.801 for Cox.

The authors present SpaCEy (Spatial Clinical Explainability), an explainable graph neural network that builds each tissue sample into a spatial graph via Delaunay triangulation, with nodes as single-cell protein-marker abundances and no predefined cell-type or anatomical-region inputs, learns predictive embeddings with a GNN, and uses a GNNExplainer-style explainer to output edge masks that are aggregated over k-hop neighbourhoods into node importance, thereby localising contiguous outcome-associated spatial regions and key proteins; it predicted progression in a 416-patient lung adenocarcinoma cohort (accuracy 0.68, F1 0.68, AUC 0.62, versus Ali et al. 0.61/0.59/0.57 and SPACE-GM 0.55/0.52/0.

The authors present SpaCEy (Spatial Clinical Explainability), an explainable graph neural network that builds each tissue sample into a spatial graph via Delaunay triangulation, with nodes as single-cell protein-marker abundances and no predefined cell-type or anatomical-region inputs, learns predictive embeddings with a GNN, and uses a GNNExplainer-style explainer to output edge masks that are aggregated over k-hop neighbourhoods into node importance, thereby localising contiguous outcome-associated spatial regions and key proteins; it predicted progression in a 416-patient lung adenocarcinoma cohort (accuracy 0.68, F1 0.68, AUC 0.62, versus Ali et al. 0.61/0.59/0.57 and SPACE-GM 0.55/0.52/0.