Evidence map›Paper›PMID 38262813›Full record

ArticleAcademic radiology2024

Deep Learning-Based Detection and Classification of Bone Lesions on Staging Computed Tomography in Prostate Cancer: A Development Study.

Mason J Belue, Stephanie A Harmon, Dong Yang, Julie Y An, Sonia Gaur, Yan Mee Law, Evrim Turkbey, Ziyue Xu, Jesse Tetreault, Nathan S Lay and 15 more

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Article in Academic radiology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

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4 · The record

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PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

25 authors.

Mason J BelueMolecular 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.).
Stephanie A HarmonMolecular 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.).
Dong YangNVIDIA Corporation, Santa Clara, California, USA (D.Y., Z.X., J.T., D.X.).
Julie Y AnDepartment of Radiology, University of California, San Diego, California, USA (J.Y.A.).
Sonia GaurDepartment of Radiology, Massachusetts General Hospital, Boston, Massachusetts, USA (S.G.).
Yan Mee LawDepartment of Radiology, Singapore General Hospital, Singapore (Y.M.L.).
Evrim TurkbeyDepartment of Radiology, Clinical Center, National Institutes of Health, Bethesda, Maryland, USA (E.T., B.J.W.).
Ziyue XuNVIDIA Corporation, Santa Clara, California, USA (D.Y., Z.X., J.T., D.X.).
Jesse TetreaultNVIDIA Corporation, Santa Clara, California, USA (D.Y., Z.X., J.T., D.X.).
Nathan S LayMolecular 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.).
Enis C YilmazMolecular 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.).
Tim E PhelpsMolecular 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.).
Benjamin SimonMolecular 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.).
Liza LindenbergMolecular 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.).
Esther MenaMolecular 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.).
Peter A PintoUrologic Oncology Branch, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA (P.A.P.).
Ulas BagciRadiology and Biomedical Engineering Department, Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA (U.B.).
Bradford J WoodDepartment 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.).
Deborah E CitrinRadiation Oncology Branch, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA (D.E.C.).
William L DahutGenitourinary Malignancies Branch, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA (W.L.D., R.A.M.).
Ravi A MadanGenitourinary Malignancies Branch, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA (W.L.D., R.A.M.).
James L GulleyCenter for Immuno-Oncology, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA (J.L.G.).
Daguang XuNVIDIA Corporation, Santa Clara, California, USA (D.Y., Z.X., J.T., D.X.).
Peter L ChoykeMolecular 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.).
Baris TurkbeyMolecular 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.

Funding

Prostate Cancer ImagingZIABC010655 · NCI · DIVISION OF BASIC SCIENCES - NCI · PI CHOYKE, PETER L · 2009 to 2025
$49.2M
Cancer Therapy Clinical Trials Using Novel Recombinant VaccinesZIABC010666 · NCI · DIVISION OF BASIC SCIENCES - NCI · PI GULLEY, JAMES L. · 2009 to 2025
$22.4M
Center for Interventional OncologyZIDBC011242 · NCI · DIVISION OF BASIC SCIENCES - NCI · PI WOOD, BRADFORD J · 2009 to 2025
$18.7M
Prostate SectionZIABC011081 · NCI · DIVISION OF BASIC SCIENCES - NCI · PI PINTO, PETER · 2009 to 2025
$15.2M
Targeting mechanisms of radiation resistanceZIABC011552 · NCI · DIVISION OF BASIC SCIENCES - NCI · PI CITRIN, DEBORAH · 2014 to 2025
$12.2M
Evaluation of Immune-based Combinations in Prostate CancerZIABC011897 · NCI · DIVISION OF BASIC SCIENCES - NCI · PI MADAN, RAVI · 2019 to 2025
$3.1M
Imaging, image guided biopsy and treatment of prostate cancerZIABC012062 · NCI · DIVISION OF BASIC SCIENCES - NCI · PI TÜRKBEY, BARIS · 2021 to 2025
$1.3M
Development of COVID-19 Imaging Tools with Artificial IntelligenceZIABC011936 · NCI · DIVISION OF BASIC SCIENCES - NCI · PI WOOD, BRADFORD J · 2020 to 2025
$1.2M
Interventional OncologyZIACL040015 · CLC · CLINICAL CENTER · PI WOOD, BRADFORD · 2009 to 2025
$0k
Artificial Intelligence with Chest Imaging in COVID-19 and Isolation and Ventilator Devices for COVID-19ZIACL090072 · CLC · CLINICAL CENTER · PI WOOD, BRADFORD · 2020 to 2025
$0k
Intramural NIH HHS ZIA BC012062
6 · The paper itself

Abstract

RATIONALE AND

objectivesEfficiently 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

methodsThis 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).

resultsIn 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%).

conclusionOur 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.

Indexed as

Bone NeoplasmsDeep LearningNeoplasm StagingProstatic NeoplasmsTomography, X-Ray ComputedAgedHumansMaleMiddle AgedRadiographic Image Interpretation, Computer-AssistedRetrospective StudiesArtificial intelligenceBone metastasisComputed tomographyDeep learningMONAIOligometastaticProstate cancer

Identifiers

PMID38262813
PMCPMC11214604

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.