학술논문
Deep Learning-Based Detection and Classification of Bone Lesions on Staging Computed Tomography in Prostate Cancer: A Development Study.
Document Type
Academic Journal
Author
Belue MJ; Molecular Imaging Branch, National Cancer Institute, National Institutes of Health, 10 Center Dr., MSC 1182, Building 10, Room B3B85, Bethesda, Maryland, USA (M.J.B., S.A.H., N.S.L., E.C.Y., T.E.P., B.S., L.L., E.M., P.L.C., B.T.).; Harmon SA; Molecular Imaging Branch, National Cancer Institute, National Institutes of Health, 10 Center Dr., MSC 1182, Building 10, Room B3B85, Bethesda, Maryland, USA (M.J.B., S.A.H., N.S.L., E.C.Y., T.E.P., B.S., L.L., E.M., P.L.C., B.T.).; Yang D; NVIDIA Corporation, Santa Clara, California, USA (D.Y., Z.X., J.T., D.X.).; An JY; Department of Radiology, University of California, San Diego, California, USA (J.Y.A.).; Gaur S; Department of Radiology, Massachusetts General Hospital, Boston, Massachusetts, USA (S.G.).; Law YM; Department of Radiology, Singapore General Hospital, Singapore (Y.M.L.).; Turkbey E; Department of Radiology, Clinical Center, National Institutes of Health, Bethesda, Maryland, USA (E.T., B.J.W.).; Xu Z; NVIDIA Corporation, Santa Clara, California, USA (D.Y., Z.X., J.T., D.X.).; Tetreault J; NVIDIA Corporation, Santa Clara, California, USA (D.Y., Z.X., J.T., D.X.).; Lay NS; Molecular Imaging Branch, National Cancer Institute, National Institutes of Health, 10 Center Dr., MSC 1182, Building 10, Room B3B85, Bethesda, Maryland, USA (M.J.B., S.A.H., N.S.L., E.C.Y., T.E.P., B.S., L.L., E.M., P.L.C., B.T.).; Yilmaz EC; Molecular Imaging Branch, National Cancer Institute, National Institutes of Health, 10 Center Dr., MSC 1182, Building 10, Room B3B85, Bethesda, Maryland, USA (M.J.B., S.A.H., N.S.L., E.C.Y., T.E.P., B.S., L.L., E.M., P.L.C., B.T.).; Phelps TE; Molecular Imaging Branch, National Cancer Institute, National Institutes of Health, 10 Center Dr., MSC 1182, Building 10, Room B3B85, Bethesda, Maryland, USA (M.J.B., S.A.H., N.S.L., E.C.Y., T.E.P., B.S., L.L., E.M., P.L.C., B.T.).; Simon B; Molecular Imaging Branch, National Cancer Institute, National Institutes of Health, 10 Center Dr., MSC 1182, Building 10, Room B3B85, Bethesda, Maryland, USA (M.J.B., S.A.H., N.S.L., E.C.Y., T.E.P., B.S., L.L., E.M., P.L.C., B.T.).; Lindenberg L; Molecular Imaging Branch, National Cancer Institute, National Institutes of Health, 10 Center Dr., MSC 1182, Building 10, Room B3B85, Bethesda, Maryland, USA (M.J.B., S.A.H., N.S.L., E.C.Y., T.E.P., B.S., L.L., E.M., P.L.C., B.T.).; Mena E; Molecular Imaging Branch, National Cancer Institute, National Institutes of Health, 10 Center Dr., MSC 1182, Building 10, Room B3B85, Bethesda, Maryland, USA (M.J.B., S.A.H., N.S.L., E.C.Y., T.E.P., B.S., L.L., E.M., P.L.C., B.T.).; Pinto PA; Urologic Oncology Branch, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA (P.A.P.).; Bagci U; Radiology and Biomedical Engineering Department, Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA (U.B.).; Wood BJ; Department of Radiology, Clinical Center, National Institutes of Health, Bethesda, Maryland, USA (E.T., B.J.W.); Center for Interventional Oncology, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA (B.J.W.).; Citrin DE; Radiation Oncology Branch, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA (D.E.C.).; Dahut WL; Genitourinary Malignancies Branch, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA (W.L.D., R.A.M.).; Madan RA; Genitourinary Malignancies Branch, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA (W.L.D., R.A.M.).; Gulley JL; Center for Immuno-Oncology, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA (J.L.G.).; Xu D; NVIDIA Corporation, Santa Clara, California, USA (D.Y., Z.X., J.T., D.X.).; Choyke PL; Molecular Imaging Branch, National Cancer Institute, National Institutes of Health, 10 Center Dr., MSC 1182, Building 10, Room B3B85, Bethesda, Maryland, USA (M.J.B., S.A.H., N.S.L., E.C.Y., T.E.P., B.S., L.L., E.M., P.L.C., B.T.).; Turkbey B; Molecular Imaging Branch, National Cancer Institute, National Institutes of Health, 10 Center Dr., MSC 1182, Building 10, Room B3B85, Bethesda, Maryland, USA (M.J.B., S.A.H., N.S.L., E.C.Y., T.E.P., B.S., L.L., E.M., P.L.C., B.T.). Electronic address: turkbeyi@mail.nih.gov.
Source
Publisher: Association Of University Radiologists Country of Publication: United States NLM ID: 9440159 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1878-4046 (Electronic) Linking ISSN: 10766332 NLM ISO Abbreviation: Acad Radiol Subsets: MEDLINE
Subject
Language
English
Abstract
Rationale and Objectives: Efficiently detecting and characterizing metastatic bone lesions on staging CT is crucial for prostate cancer (PCa) care. However, it demands significant expert time and additional imaging such as PET/CT. We aimed to develop an ensemble of two automated deep learning AI models for 1) bone lesion detection and segmentation and 2) benign vs. metastatic lesion classification on staging CTs and to compare its performance with radiologists.
Materials and Methods: This retrospective study developed two AI models using 297 staging CT scans (81 metastatic) with 4601 benign and 1911 metastatic lesions in PCa patients. Metastases were validated by follow-up scans, bone biopsy, or PET/CT. Segmentation AI (3DAISeg) was developed using the lesion contours delineated by a radiologist. 3DAISeg performance was evaluated with the Dice similarity coefficient, and classification AI (3DAIClass) performance on AI and radiologist contours was assessed with F1-score and accuracy. Training/validation/testing data partitions of 70:15:15 were used. A multi-reader study was performed with two junior and two senior radiologists within a subset of the testing dataset (n = 36).
Results: In 45 unseen staging CT scans (12 metastatic PCa) with 669 benign and 364 metastatic lesions, 3DAISeg detected 73.1% of metastatic (266/364) and 72.4% of benign lesions (484/669). Each scan averaged 12 extra segmentations (range: 1-31). All metastatic scans had at least one detected metastatic lesion, achieving a 100% patient-level detection. The mean Dice score for 3DAISeg was 0.53 (median: 0.59, range: 0-0.87). The F1 for 3DAIClass was 94.8% (radiologist contours) and 92.4% (3DAISeg contours), with a median false positive of 0 (range: 0-3). Using radiologist contours, 3DAIClass had PPV and NPV rates comparable to junior and senior radiologists: PPV (semi-automated approach AI 40.0% vs. Juniors 32.0% vs. Seniors 50.0%) and NPV (AI 96.2% vs. Juniors 95.7% vs. Seniors 91.9%). When using 3DAISeg, 3DAIClass mimicked junior radiologists in PPV (pure-AI 20.0% vs. Juniors 32.0% vs. Seniors 50.0%) but surpassed seniors in NPV (pure-AI 93.8% vs. Juniors 95.7% vs. Seniors 91.9%).
Conclusion: Our lesion detection and classification AI model performs on par with junior and senior radiologists in discerning benign and metastatic lesions on staging CTs obtained for PCa.
Competing Interests: Declaration of Competing Interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Bradford J. Wood: Principal investigator on cooperative research and development agreement (CRADA) between National Institutes of Health (NIH) and Philips and CRADAs with industry partners unrelated to this work; travel support related to CRADAs; royalties from NIH related to Philips licensing agreement; patents planned, issued, or pending. Peter L. Choyke: Receives payment from royalties paid to the U.S. government for patents on MRI US fusion biopsy licensed to Philips Medical. Peter A. Pinto: Institutional CRADA with Philips; royalties from NIH related to Philips licensing agreement; NIH-related patents planned, issued, or pending (U.S. patent nos. 8 447 384 and 10 215 830). Baris Turkbey: CRADAs with NVIDIA and Philips; royalties from NIH; patents planned, issued, or pending in the field of artificial intelligence. Dong Yang, Ziyue Xu, Jesse Tetreault, Daguang Xu: employee of NVIDIA Corporation. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
(Published by Elsevier Inc.)
Materials and Methods: This retrospective study developed two AI models using 297 staging CT scans (81 metastatic) with 4601 benign and 1911 metastatic lesions in PCa patients. Metastases were validated by follow-up scans, bone biopsy, or PET/CT. Segmentation AI (3DAISeg) was developed using the lesion contours delineated by a radiologist. 3DAISeg performance was evaluated with the Dice similarity coefficient, and classification AI (3DAIClass) performance on AI and radiologist contours was assessed with F1-score and accuracy. Training/validation/testing data partitions of 70:15:15 were used. A multi-reader study was performed with two junior and two senior radiologists within a subset of the testing dataset (n = 36).
Results: In 45 unseen staging CT scans (12 metastatic PCa) with 669 benign and 364 metastatic lesions, 3DAISeg detected 73.1% of metastatic (266/364) and 72.4% of benign lesions (484/669). Each scan averaged 12 extra segmentations (range: 1-31). All metastatic scans had at least one detected metastatic lesion, achieving a 100% patient-level detection. The mean Dice score for 3DAISeg was 0.53 (median: 0.59, range: 0-0.87). The F1 for 3DAIClass was 94.8% (radiologist contours) and 92.4% (3DAISeg contours), with a median false positive of 0 (range: 0-3). Using radiologist contours, 3DAIClass had PPV and NPV rates comparable to junior and senior radiologists: PPV (semi-automated approach AI 40.0% vs. Juniors 32.0% vs. Seniors 50.0%) and NPV (AI 96.2% vs. Juniors 95.7% vs. Seniors 91.9%). When using 3DAISeg, 3DAIClass mimicked junior radiologists in PPV (pure-AI 20.0% vs. Juniors 32.0% vs. Seniors 50.0%) but surpassed seniors in NPV (pure-AI 93.8% vs. Juniors 95.7% vs. Seniors 91.9%).
Conclusion: Our lesion detection and classification AI model performs on par with junior and senior radiologists in discerning benign and metastatic lesions on staging CTs obtained for PCa.
Competing Interests: Declaration of Competing Interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Bradford J. Wood: Principal investigator on cooperative research and development agreement (CRADA) between National Institutes of Health (NIH) and Philips and CRADAs with industry partners unrelated to this work; travel support related to CRADAs; royalties from NIH related to Philips licensing agreement; patents planned, issued, or pending. Peter L. Choyke: Receives payment from royalties paid to the U.S. government for patents on MRI US fusion biopsy licensed to Philips Medical. Peter A. Pinto: Institutional CRADA with Philips; royalties from NIH related to Philips licensing agreement; NIH-related patents planned, issued, or pending (U.S. patent nos. 8 447 384 and 10 215 830). Baris Turkbey: CRADAs with NVIDIA and Philips; royalties from NIH; patents planned, issued, or pending in the field of artificial intelligence. Dong Yang, Ziyue Xu, Jesse Tetreault, Daguang Xu: employee of NVIDIA Corporation. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
(Published by Elsevier Inc.)