Computer methods and programs in biomedicine · 2025
Neighboring tissues as diagnostic windows: Neighborhood effects in radiomic detection of pancreatic ductal adenocarcinoma.
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Rezaeijo Seyed Masoud, Eftekhar Alireza, Rouhi Saleh, Keshavarzi Behnaz, Mohammadi Zahra, Firouzabad Leila Alipour, Abidi Maryam, Ghafeli Salimeh
Аннотация
OBJECTIVE: This study introduces a pioneering, tumor-independent diagnostic approach for Pancreatic Ductal Adenocarcinoma (PDAC), utilizing radiomic and deep features from non-tumorous CT regions to detect subtle, systemic tissue alterations beyond conventional imaging limits.
MATERIALS AND METHODS: A retrospective cohort of 1263 patients was analyzed, including both PDAC and non-PDAC cases, with anatomical segmentation masks encompassing veins, arteries, pancreatic parenchyma, pancreatic duct, and common bile duct. Radiomic features (n = 107 per region) were extracted using the PyRadiomics library, while deep features were derived from a 3D convolutional autoencoder trained on cropped and normalized anatomical volumes. Three analytical approaches were implemented: (1) healthy tissue analysis using features exclusively from non-tumorous structures, (2) row-wise label combination treating each anatomical label as a separate instance, and (3) column-wise patient-level fusion aggregating multi-tissue features. Each dataset underwent multiple feature selection methods and was classified using ensemble and neural machine learning models. SHAP and t-SNE analyses were conducted for model interpretability and visualization.
RESULTS: Radiomic analysis of non-tumorous anatomical regions demonstrated high diagnostic performance for PDAC detection, particularly in the pancreatic duct and parenchyma. Among the three approaches, patient-level feature aggregation (Approach 3) achieved the best results, with an F1-score of 88.33 % and AUC of 97.98 %. In contrast, deep features exhibited limited discriminative power when used in isolation but improved moderately in fusion strategies. SHAP and t-SNE analyses confirmed that tissue-wide radiomic signatures serve as robust biomarkers, supporting the hypothesis that PDAC induces detectable changes beyond the tumor region. These findings validate a novel, tumor-independent diagnostic framework for early PDAC classification.
CONCLUSIONS: Non-tumorous anatomical structures encode valuable diagnostic information for PDAC. Systemic feature integration provides a robust, interpretable framework for early detection, particularly in radiologically occult or ambiguous cases.
MATERIALS AND METHODS: A retrospective cohort of 1263 patients was analyzed, including both PDAC and non-PDAC cases, with anatomical segmentation masks encompassing veins, arteries, pancreatic parenchyma, pancreatic duct, and common bile duct. Radiomic features (n = 107 per region) were extracted using the PyRadiomics library, while deep features were derived from a 3D convolutional autoencoder trained on cropped and normalized anatomical volumes. Three analytical approaches were implemented: (1) healthy tissue analysis using features exclusively from non-tumorous structures, (2) row-wise label combination treating each anatomical label as a separate instance, and (3) column-wise patient-level fusion aggregating multi-tissue features. Each dataset underwent multiple feature selection methods and was classified using ensemble and neural machine learning models. SHAP and t-SNE analyses were conducted for model interpretability and visualization.
RESULTS: Radiomic analysis of non-tumorous anatomical regions demonstrated high diagnostic performance for PDAC detection, particularly in the pancreatic duct and parenchyma. Among the three approaches, patient-level feature aggregation (Approach 3) achieved the best results, with an F1-score of 88.33 % and AUC of 97.98 %. In contrast, deep features exhibited limited discriminative power when used in isolation but improved moderately in fusion strategies. SHAP and t-SNE analyses confirmed that tissue-wide radiomic signatures serve as robust biomarkers, supporting the hypothesis that PDAC induces detectable changes beyond the tumor region. These findings validate a novel, tumor-independent diagnostic framework for early PDAC classification.
CONCLUSIONS: Non-tumorous anatomical structures encode valuable diagnostic information for PDAC. Systemic feature integration provides a robust, interpretable framework for early detection, particularly in radiologically occult or ambiguous cases.