Deep Learning Model for Differentiating Between Neoplastic Pathologic Fracture and Nonpathologic Fracture Using Hip Radiographs.

Journal: The Journal of bone and joint surgery. American volume
Published Date: Nov 26, 2025

Abstract

BACKGROUND: Although radiographs are the first-line imaging modality, differentiating between neoplastic pathologic fractures and nonpathologic fractures on radiographs can sometimes be challenging. In this study, we aimed to develop and evaluate a deep learning model capable of distinguishing neoplastic pathologic fractures from nonpathologic fractures on hip radiographs to enhance diagnostic accuracy. METHODS: This retrospective, multicenter study analyzed anteroposterior hip radiographs from patients who visited the emergency department at 4 different institutions. The deep learning model was trained on, and tested using, data from 338 patients at a single institution and externally validated on data from 488 patients across 3 additional institutions. RESULTS: The model achieved an overall accuracy of 0.880, with a sensitivity of 0.882 and a specificity of 0.879, on the internal test set. It was then externally validated using the data of 488 patients (67 with neoplastic pathologic fracture and 421 with nonpathologic fracture) from institutions separate from where the model was developed. The model achieved an overall accuracy of 0.848, sensitivity of 0.910, and specificity of 0.786. Its performance was comparable with that of general orthopaedic surgeons. CONCLUSIONS: The developed deep learning model is a reliable and valid tool for distinguishing neoplastic pathologic fractures from nonpathologic fractures on hip radiographs. It has the potential to assist orthopaedic surgeons in resource-limited settings, where optimizing the interpretation of radiographs is critical for patient care. The model is publicly available at https://pathfxdx.org . LEVEL OF EVIDENCE: Diagnostic Level III . See Instructions for Authors for a complete description of levels of evidence.

Authors

  • Shinn Kim
    Department of Orthopaedic Surgery, Seoul National University Hospital, Seoul, Republic of Korea.
  • Kyoungseob Shin
    Department of Electrical and Computer Engineering, Seoul National University, Seoul, South Korea.
  • Han-Soo Kim
    Department of Orthopaedic Surgery, Seoul National University Hospital, Seoul, Republic of Korea.
  • Yongsung Kim
    Department of Technology Education, Chungnam National University, Daejeon 34134, Korea.
  • June Hyuk Kim
    Orthopaedic Oncology Clinic, National Cancer Center, Goyang, Republic of Korea.
  • Min Wook Joo
    Department of Orthopedic Surgery, St. Vincent's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea.
  • Wanlim Kim
    Department of Orthopaedic Surgery, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea.
  • Jay Hoon Park
    Department of Orthopaedic Surgery, Seoul National University Hospital, Seoul, Republic of Korea.
  • Yoon Joo Cho
    Department of Orthopedic Surgery, St. Vincent's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea.
  • Minsu Kim
    School of Chemical and Biological Engineering, Institute of Chemical Processes, Seoul National University, Seoul 08826, Republic of Korea.
  • Sunghoon Kwon
    Bio-MAX Institute, Seoul National University, Seoul, South Korea. [email protected].
  • Ilkyu Han
    Department of Orthopaedic Surgery, Seoul National University Hospital, Seoul, Republic of Korea.

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