Patient-Level Deep Fusion, Radiomics, and Ensemble Learning for CT-Based Differentiation of Multiple Primary Lung Cancers

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@article{1723737,
      author = {Arnab Dutta},
      title = {Patient-Level Deep Fusion, Radiomics, and Ensemble Learning for CT-Based Differentiation of Multiple Primary Lung Cancers},
      journal = {Iconic Research And Engineering Journals},
      year = {2026},
      volume = {10},
      number = {4},
      pages = {838-855},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1723737.pdf},
      abstract = {Background: Multiple primary lung cancers (MPLCs) are increasingly detected owing to advances in chest computed tomography (CT), yet distinguishing them from intrapulmonary metastases (IPMs) and multiple benign pulmonary lesions (MBPLs) remains difficult and strongly affects treatment planning.
Methods: We propose a patient-level framework that combines (i) a deep fusion network integrating 2D multiple-instance learning (2D-MIL) over axial slices, a 3D volumetric branch, and handcrafted radiomic features, with (ii) radiomics-based Random Forest and XGBoost classifiers, and (iii) a soft-voting ensemble of these models. Performance was assessed with accuracy, precision, recall, F1-score, specificity, and the area under the receiver operating characteristic curve (ROC-AUC).
Results: On the held-out test set, the radiomics-based XGBoost model achieved the highest ROC-AUC (0.9782), while Random Forest and XGBoost both reached an accuracy of 0.8571 and a precision of 0.9091. The deep fusion model obtained an accuracy of 0.7143 and an ROC-AUC of 0.9154, and the ensemble obtained an accuracy of 0.7619 and an ROC-AUC of 0.9376.
Conclusions: Radiomics-driven tree-based models were the most discriminative in this cohort, whereas the deep fusion model and ensemble were competitive in ROC-AUC but weaker in threshold-dependent metrics. Validation on larger, multicenter cohorts is required.},
      keywords = {ensemble learning; multiple primary lung cancer; intrapulmonary metastasis; CT; radiomics; deep learning; multiple-instance learning},
      month = {October},
  }