QIMCA-Net: A Leakage-Controlled Multimodal Self-Attention Framework for 90-Day Functional Outcome Prediction After Ischaemic Stroke

Reliable multimodal prognosis after acute ischemic stroke requires methods that integrate heterogeneous information under rigorous leakage control and are evaluated against strong simpler alternatives. We developed QIMCA-Net, a multimodal residual self-attention framework integrating six-channel three-dimensional computed tomography, 17 baseline clinical variables with 17 missingness indicators, and 77 quantitative whole-brain imaging biomarkers. Poor 90-day functional outcome was defined as a modified Rankin Scale score of 3–6. The locked ISLES’24 cohort comprised 119 patients, including 41 with poor outcomes. Patient-level five-fold outer cross-validation with an inner training–validation hold-out split confined preprocessing and model fitting to inner-training data, while checkpoint and threshold selection and, where applicable, temperature scaling used only inner-validation data. The frozen primary QIMCA-Net achieved an out-of-fold AUROC of 0.7301 (95% CI, 0.6304–0.8280), an AUPRC of 0.5869, and a Brier score of 0.2038. Strong structured-data benchmarks remained competitive, and no model consistently dominated discrimination, positive-class retrieval, and probabilistic accuracy. Across five alternative patient-level five-fold assignments, QIMCA-Net showed a mean AUROC difference of +0.0421 relative to parameter-matched direct concatenation; however, the Nadeau–Bengio corrected interval included zero (95% CI, −0.0373 to 0.1215; p = .215). CT permutation produced negligible performance change, center-related signal was substantial, and uncertainty measures only weakly discriminated prediction errors. Lesion-auxiliary learning, Bayesian optimization, and uncertainty-aware extensions did not replace the frozen primary model. These findings support the feasibility of leakage-controlled multimodal self-attention while showing uncertain incremental architectural benefit and the need for independent external validation before clinical use.