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Our study enrolled 386 breast nodules from 279 patients with DLBCL and IDC, which were pathologically confirmed and underwent 18F-fluorodeoxyglucose (18F-FDG) positron emission tomography\/computed tomography (PET\/CT) examination. Patients from two centers were separated into internal and external cohorts. Notably, we introduced 2.5D deep learning and machine learning to extract features, develop models, and discover biomarkers. Performances were assessed using the area under curve (AUC) and confusion matrix. Additionally, the Shapley additive explanation (SHAP) and local interpretable model-agnostic explanations (LIME) techniques were employed to interpret the model. On the internal cohort, the optimal model PT_TDC_SVM achieved an accuracy of 0.980 (95% confidence interval (CI): 0.957\u20130.991) and an AUC of 0.992 (95% CI: 0.946\u20130.998), surpassing the other models. On the external cohort, the accuracy was 0.975 (95% CI: 0.913\u20130.993) and the AUC was 0.996 (95% CI: 0.972\u20130.999). The optimal imaging biomarker PET_LBP-2D_gldm_DependenceEntropy demonstrated an average accuracy of 0.923\/0.937 on internal\/external testing. Our study presented an innovative automated model for DLBCL and IDC, identifying reliable digital imaging biomarkers with significant potential.<\/jats:p>","DOI":"10.3390\/bioengineering12080873","type":"journal-article","created":{"date-parts":[[2025,8,13]],"date-time":"2025-08-13T07:45:31Z","timestamp":1755071131000},"page":"873","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["2.5D Deep Learning and Machine Learning for Discriminative DLBCL and IDC with Radiomics on PET\/CT"],"prefix":"10.3390","volume":"12","author":[{"given":"Fei","family":"Liu","sequence":"first","affiliation":[{"name":"Department of Nuclear Medicine, Fudan University Shanghai Cancer Center, Shanghai 200032, China"},{"name":"Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, China"},{"name":"Center for Biomedical Imaging, Fudan University, Shanghai 200032, China"},{"name":"Shanghai Engineering Research 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China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jianping","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Nuclear Medicine, Fudan University Shanghai Cancer Center, Shanghai 200032, China"},{"name":"Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, China"},{"name":"Center for Biomedical Imaging, Fudan University, Shanghai 200032, China"},{"name":"Shanghai Engineering Research Center of Molecular Imaging Probes, Shanghai 200032, China"},{"name":"Key Laboratory of Nuclear Physics and Ion-beam Application (MOE), Fudan University, Shanghai 200433, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jianling","family":"Zou","sequence":"additional","affiliation":[{"name":"Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, China"},{"name":"Department of Gastrointestinal Medical Oncology, Fudan University Shanghai Cancer Center, Shanghai 200032, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bingxin","family":"Gu","sequence":"additional","affiliation":[{"name":"Department of Nuclear Medicine, Fudan University Shanghai Cancer Center, Shanghai 200032, China"},{"name":"Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, China"},{"name":"Center for Biomedical Imaging, Fudan University, Shanghai 200032, China"},{"name":"Shanghai Engineering Research Center of Molecular Imaging Probes, Shanghai 200032, China"},{"name":"Key Laboratory of Nuclear Physics and Ion-beam Application (MOE), Fudan University, Shanghai 200433, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hongxing","family":"Yang","sequence":"additional","affiliation":[{"name":"Department of Nuclear Medicine, Fudan University Shanghai Cancer Center, Shanghai 200032, China"},{"name":"Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, China"},{"name":"Center for 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