ArticleJournal of applied clinical medical physics2026
An interpretable radiomics-deep learning nomogram from whole-body bone scintigraphy for MDP-avid bone metastasis prediction in NSCLC.
Article in Journal of applied clinical medical physics, 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
backgroundWhole-body bone scintigraphy (WBS) remains a widely used first-line screening tool for bone metastasis, but differentiating MDP-avid metastatic lesions from benign bone abnormalities in non-small cell lung cancer (NSCLC) remains challenging. PURPOSE: To develop and externally validate a radiomics and deep learning bone signature (RDB) derived from planar WBS images for prediction of MDP-avid bone metastasis and to explore its prognostic value in NSCLC.
methodsA retrospective analysis was conducted on NSCLC patients who underwent WBS imaging across two centers. Eight hundred twenty-nine patients from Center 1 were used for training and internal validation, whereas 574 patients from Center 2 were used for external validation. Radiomics features from 2 segmentation methods and deep learning features were extracted from planar WBS images. The RDB nomogram was generated from clinical, radiomics, and deep learning features using the best-performing machine learning algorithm. Model performance was assessed using the area under the receiver operating characteristic curve. Shapley additive explanations were used to interpret the model output, and Kaplan-Meier survival analysis evaluated prognostic stratification. The locked model was also translated into a research-use-only local desktop application.
resultsThere were 720 patients with bone metastasis (202 solitary and 518 multiple) and 683 without bone metastasis. The final RDB nomogram included 8 radiomics features, 11 deep learning features, and 2 clinical factors. The RDB nomogram achieved an area under the curve of 0.857 (95% CI, 0.811-0.904) in the internal validation cohort and 0.870 (95% CI, 0.840-0.900) in the external validation cohort for predicting MDP-avid bone metastasis. The Rad-score was the most influential predictor. Kaplan-Meier analysis showed significant survival differences for solitary-lesion patients in both centers. A deployable desktop implementation of the final model was completed to support reproducibility and external use.
conclusionsThe RDB nomogram is a promising noninvasive tool for predicting MDP-avid bone metastasis in NSCLC patients and may provide prognostic value, particularly for solitary lesions.
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