ArticleFrontiers in artificial intelligence2026
LungCraft: a hybrid 3D-2D deep learning and radiomics framework with explainable AI for precision diagnosis of lung cancer.
Article in Frontiers in artificial intelligence, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Background and objective: Lung cancer is the top cause of cancer-related death, globally. The morphological complexity of tumours, intra-tumoural heterogeneity and limited interpretability of current imaging systems all contribute to the difficulty of early and reliable detection of tumours. This paper introduces LungCraft, a diagnostic framework composed of 3D medical modelling, quantitative radiomics and explainable artificial intelligence (XAI) for lung adenocarcinoma classification, using computed tomography (CT) scans as input. Methods: LungCraft uses a hybrid deep learning architecture (HybridNET) which has a combination of layers that use 3D convolutional layers for volumetric feature learning and 2D convolutional layers for contextual refinement. Sixty-one TCIA CT volumes were used for internal validation, while the LIDC-IDRI and NSCLC-Radiomics datasets were kept as held-out test sets for external validation. The spatial and feature level interpretability is given by Grad-CAM and KernelSHAP. Unlike previous methods which focussed on achieving one or more of these, LungCraft's main strength is that it brings these in a systematic way as part of an integrated pipeline that includes a two-stage feature-level fusion strategy, dual spatial and feature level explainability and interactive 3D tumour visualization. Results: On internal test set, LungCraft was able to classify with an accuracy of 91.3% and an AUC of 0.93, whereas on external cohorts it was able to classify with an accuracy of 88.7% and an AUC of 0.91 (LIDC-IDRI) and 89.6% and 0.92 (NSCLC-Radiomics). 95% confidence intervals are given for all measurements. The results are statistically significant ( Conclusion: LungCraft achieves accurate and interpretable diagnosis of lung adenocarcinoma, with demonstrated external generalisation and has a potential use as an AI assistant in clinical decision making which is subject to formal prospective validation.
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