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2.5D Deep Learning and Machine Learning for Discriminative DLBCL and IDC with Radiomics on PET/CT

dc.contributor.authorFei Liu
dc.contributor.authorWen Chen
dc.contributor.authorJianping Zhang
dc.contributor.authorJianling Zou
dc.contributor.authorBingxin Gu
dc.contributor.authorHongxing Yang
dc.contributor.authorSilong Hu
dc.contributor.authorXiaosheng Liu
dc.contributor.authorShaoli Song
dc.contributor.otherDepartment of Nuclear Medicine, Fudan University Shanghai Cancer Center, Shanghai 200032, China
dc.contributor.otherDepartment of Nuclear Medicine, Fudan University Shanghai Cancer Center, Shanghai 200032, China
dc.contributor.otherDepartment of Nuclear Medicine, Fudan University Shanghai Cancer Center, Shanghai 200032, China
dc.contributor.otherDepartment of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, China
dc.contributor.otherDepartment of Nuclear Medicine, Fudan University Shanghai Cancer Center, Shanghai 200032, China
dc.contributor.otherDepartment of Nuclear Medicine, Fudan University Shanghai Cancer Center, Shanghai 200032, China
dc.contributor.otherDepartment of Nuclear Medicine, Fudan University Shanghai Cancer Center, Shanghai 200032, China
dc.contributor.otherDepartment of Nuclear Medicine, Fudan University Shanghai Cancer Center, Shanghai 200032, China
dc.contributor.otherDepartment of Nuclear Medicine, Fudan University Shanghai Cancer Center, Shanghai 200032, China
dc.date.accessioned2025-08-27T14:11:54Z
dc.date.accessioned2025-10-08T08:38:45Z
dc.date.available2025-10-08T08:38:45Z
dc.date.issued01-08-2025
dc.identifier.urihttp://digilib.fisipol.ugm.ac.id/repo/handle/15717717/36485
dc.description.abstractWe aimed to establish non-invasive diagnostic models comparable to pathology testing and explore reliable digital imaging biomarkers to classify diffuse large B-cell lymphoma (DLBCL) and invasive ductal carcinoma (IDC). Our study enrolled 386 breast nodules from 279 patients with DLBCL and IDC, which were pathologically confirmed and underwent <sup>18</sup>F-fluorodeoxyglucose (<sup>18</sup>F-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): 0.957–0.991) and an AUC of 0.992 (95% CI: 0.946–0.998), surpassing the other models. On the external cohort, the accuracy was 0.975 (95% CI: 0.913–0.993) and the AUC was 0.996 (95% CI: 0.972–0.999). 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.
dc.language.isoEN
dc.publisherMDPI AG
dc.subject.lccTechnology
dc.title2.5D Deep Learning and Machine Learning for Discriminative DLBCL and IDC with Radiomics on PET/CT
dc.typeArticle
dc.description.keywords2.5D deep learning
dc.description.keywordsmachine learning
dc.description.keywordsPET/CT
dc.description.keywordsdiffuse large B-cell lymphoma
dc.description.keywordsinvasive ductal carcinoma
dc.description.doi10.3390/bioengineering12080873
dc.title.journalBioengineering
dc.identifier.e-issn2306-5354
dc.identifier.oaioai:doaj.org/journal:851ea4df2721469e84a30268fcc43f06
dc.journal.infoVolume 12, Issue 8


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