start-ver=1.4 cd-journal=joma no-vol=17 cd-vols= no-issue=19 article-no= start-page=3144 end-page= dt-received= dt-revised= dt-accepted= dt-pub-year=2025 dt-pub=20250927 dt-online= en-article= kn-article= en-subject= kn-subject= en-title= kn-title=Utility of Same-Modality, Cross-Domain Transfer Learning for Malignant Bone Tumor Detection on Radiographs: A Multi-Faceted Performance Comparison with a Scratch-Trained Model en-subtitle= kn-subtitle= en-abstract= kn-abstract=Background/Objectives: Developing high-performance artificial intelligence (AI) models for rare diseases like malignant bone tumors is limited by scarce annotated data. This study evaluates same-modality cross-domain transfer learning by comparing an AI model pretrained on chest radiographs with a model trained from scratch for detecting malignant bone tumors on knee radiographs. Methods: Two YOLOv5-based detectors differed only in initialization (transfer vs. scratch). Both were trained/validated on institutional data and tested on an independent external set of 743 radiographs (268 malignant, 475 normal). The primary outcome was AUC; prespecified operating points were high-sensitivity (?0.90), high-specificity (?0.90), and Youden-optimal. Secondary analyses included PR/F1, calibration (Brier, slope), and decision curve analysis (DCA). Results: AUC was similar (YOLO-TL 0.954 [95% CI 0.937?0.970] vs. YOLO-SC 0.961 [0.948?0.973]; DeLong p = 0.53). At the high-sensitivity point (both sensitivity = 0.903), YOLO-TL achieved higher specificity (0.903 vs. 0.867; McNemar p = 0.037) and PPV (0.840 vs. 0.793; bootstrap p = 0.030), reducing ~17 false positives among 475 negatives. At the high-specificity point (~0.902?0.903 for both), YOLO-TL showed higher sensitivity (0.798 vs. 0.764; p = 0.0077). At the Youden-optimal point, sensitivity favored YOLO-TL (0.914 vs. 0.892; p = 0.041) with a non-significant specificity difference. Conclusions: Transfer learning may not improve overall AUC but can enhance practical performance at clinically crucial thresholds. By maintaining high detection rates while reducing false positives, the transfer learning model offers superior clinical utility. Same-modality cross-domain transfer learning is an efficient strategy for developing robust AI systems for rare diseases, supporting tools more readily acceptable in real-world screening workflows. en-copyright= kn-copyright= en-aut-name=HaseiJoe en-aut-sei=Hasei en-aut-mei=Joe kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=1 ORCID= en-aut-name=NakaharaRyuichi en-aut-sei=Nakahara en-aut-mei=Ryuichi kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=2 ORCID= en-aut-name=OtsukaYujiro en-aut-sei=Otsuka en-aut-mei=Yujiro kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=3 ORCID= en-aut-name=TakeuchiKoichi en-aut-sei=Takeuchi en-aut-mei=Koichi kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=4 ORCID= en-aut-name=NakamuraYusuke en-aut-sei=Nakamura en-aut-mei=Yusuke kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=5 ORCID= en-aut-name=IkutaKunihiro en-aut-sei=Ikuta en-aut-mei=Kunihiro kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=6 ORCID= en-aut-name=OsakiShuhei en-aut-sei=Osaki en-aut-mei=Shuhei kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=7 ORCID= en-aut-name=TamiyaHironari en-aut-sei=Tamiya en-aut-mei=Hironari kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=8 ORCID= en-aut-name=MiwaShinji en-aut-sei=Miwa en-aut-mei=Shinji kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=9 ORCID= en-aut-name=OhshikaShusa en-aut-sei=Ohshika en-aut-mei=Shusa kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=10 ORCID= en-aut-name=NishimuraShunji en-aut-sei=Nishimura en-aut-mei=Shunji kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=11 ORCID= en-aut-name=KaharaNaoaki en-aut-sei=Kahara en-aut-mei=Naoaki kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=12 ORCID= en-aut-name=YoshidaAki en-aut-sei=Yoshida en-aut-mei=Aki kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=13 ORCID= en-aut-name=KondoHiroya en-aut-sei=Kondo en-aut-mei=Hiroya kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=14 ORCID= en-aut-name=FujiwaraTomohiro en-aut-sei=Fujiwara en-aut-mei=Tomohiro kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=15 ORCID= en-aut-name=KunisadaToshiyuki en-aut-sei=Kunisada en-aut-mei=Toshiyuki kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=16 ORCID= en-aut-name=OzakiToshifumi en-aut-sei=Ozaki en-aut-mei=Toshifumi kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=17 ORCID= affil-num=1 en-affil=Department of Medical Informatics and Clinical Support Technology Development, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University kn-affil= affil-num=2 en-affil=Science of Functional Recovery and Reconstruction, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University kn-affil= affil-num=3 en-affil=Department of Radiology, Juntendo University School of Medicine kn-affil= affil-num=4 en-affil=Graduate School of Environmental, Life Natural Science and Technology, Okayama University kn-affil= affil-num=5 en-affil=Plusman LCC kn-affil= affil-num=6 en-affil=Department of Orthopedic Surgery, Graduate School of Medicine, Nagoya University kn-affil= affil-num=7 en-affil=Department of Musculoskeletal Oncology and Rehabilitation, National Cancer Center Hospital kn-affil= affil-num=8 en-affil=Department of Musculoskeletal Oncology Service, Osaka International Cancer Institute, kn-affil= affil-num=9 en-affil=Department of Orthopedic Surgery, Graduate School of Medical Sciences, Kanazawa University kn-affil= affil-num=10 en-affil=Department of Orthopaedic Surgery, Hirosaki University Graduate School of Medicine kn-affil= affil-num=11 en-affil=Department of Orthopaedic Surgery, Kindai University Hospital kn-affil= affil-num=12 en-affil=Department of Orthopedic Surgery, Mizushima Central Hospital kn-affil= affil-num=13 en-affil=Science of Functional Recovery and Reconstruction, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University kn-affil= affil-num=14 en-affil=Science of Functional Recovery and Reconstruction, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University kn-affil= affil-num=15 en-affil=Science of Functional Recovery and Reconstruction, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University kn-affil= affil-num=16 en-affil=Science of Functional Recovery and Reconstruction, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University kn-affil= affil-num=17 en-affil=Science of Functional Recovery and Reconstruction, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University kn-affil= en-keyword=malignant bone tumors kn-keyword=malignant bone tumors en-keyword=artificial intelligence kn-keyword=artificial intelligence en-keyword=transfer learning kn-keyword=transfer learning en-keyword=YOLO kn-keyword=YOLO en-keyword=radiographs kn-keyword=radiographs en-keyword=cross-domain learning kn-keyword=cross-domain learning en-keyword=diagnostic imaging kn-keyword=diagnostic imaging END