Toward uncertainty-aware clinical decision support for treatment response prediction in metastatic NSCLC: integrating FDG-PET, T-cell repertoire, and cytokines with conformal prediction
Abstract
Abstract Background Variable response to chemoimmunotherapy in metastatic non-small cell lung cancer (mNSCLC), together with the limited discriminative accuracy of PD-L1 tumor proportion score, highlights the need for reliable, uncertainty-aware early response prediction using multimodal biomarkers. We developed and prototyped a multimodal clinical decision support (CDS) framework integrating longitudinal FDG-PET, T-cell receptor (TCR), and cytokine biomarkers with conformal prediction to deliver uncertainty-quantified, patient-level response predictions. Methods Thirty-five patients with mNSCLC receiving first-line carboplatin–pemetrexed–pembrolizumab on the PET-BRIGHT trial (NCT04151940) underwent FDG-PET/CT and blood collection at baseline and week 3. Multimodal biomarkers included FDG-PET metrics (standardized uptake value/total lesion glycolysis), TCR diversity metrics, and inflammatory cytokines. Nested leave-one-out cross-validation with automated feature selection identified a single biomarker per modality. Class-balanced logistic regression was used for classification, with discrimination assessed by the area under the receiver operating characteristic curve (AUROC) and calibration by the Brier score. Conformal prediction (targeting 80% reliability) quantified patient-level uncertainty via prediction sets and singleton rate. Unimodal and multimodal early- and late-fusion models were evaluated using baseline-only and combined baseline plus mid-treatment biomarkers.…
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