Evidence map›Paper›PMID 40634223›Full record

ArticleAcademic radiology2025

A Deep Learning Model for Comprehensive Automated Bone Lesion Detection and Classification on Staging Computed Tomography Scans.

Benjamin D Simon, Stephanie A Harmon, Dong Yang, Mason J Belue, Ziyue Xu, Jesse Tetreault, Peter A Pinto, Bradford J Wood, Deborah E Citrin, Ravi A Madan and 4 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Academic radiology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

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

14 authors.

Benjamin D SimonMolecular Imaging Branch, NCI, NIH, Bethesda, Maryland (B.D.S., S.A.H., M.J.B., P.L.C., B.T.); Institute of Biomedical Engineering, Department Engineering Science, University of Oxford, England, UK (B.D.S.).
Stephanie A HarmonMolecular Imaging Branch, NCI, NIH, Bethesda, Maryland (B.D.S., S.A.H., M.J.B., P.L.C., B.T.).
Dong YangNVIDIA Corporation, Santa Clara, California (D.Y., Z.X., J.T., D.X.).
Mason J BelueMolecular Imaging Branch, NCI, NIH, Bethesda, Maryland (B.D.S., S.A.H., M.J.B., P.L.C., B.T.).
Ziyue XuNVIDIA Corporation, Santa Clara, California (D.Y., Z.X., J.T., D.X.).
Jesse TetreaultNVIDIA Corporation, Santa Clara, California (D.Y., Z.X., J.T., D.X.).
Peter A PintoUrology Oncology Branch, NCI, NIH, Bethesda, Maryland (P.A.P.).
Bradford J WoodCenter for Interventional Oncology, NCI, NIH, Bethesda, Maryland (B.J.W.); Department of Radiology, Clinical Center, NIH, Bethesda, Maryland (B.J.W.).
Deborah E CitrinRadiation Oncology Branch, NCI, NIH, Bethesda, Maryland (D.E.C.).
Ravi A MadanGenitourinary Malignancies Branch, NCI, NIH, Bethesda, Maryland (R.A.M.).
Daguang XuNVIDIA Corporation, Santa Clara, California (D.Y., Z.X., J.T., D.X.).
Peter L ChoykeMolecular Imaging Branch, NCI, NIH, Bethesda, Maryland (B.D.S., S.A.H., M.J.B., P.L.C., B.T.).
James L GulleyCenter for Immuno-Oncology, NCI, NIH, Bethesda, Maryland (J.L.G.).
Baris TurkbeyMolecular Imaging Branch, NCI, NIH, Bethesda, Maryland (B.D.S., S.A.H., M.J.B., P.L.C., B.T.). Electronic address: turkbeyi@mail.nih.gov.

Funding

CCR Artificial Intelligence ResourceZIHBC012032 · NCI · DIVISION OF BASIC SCIENCES - NCI · PI TÜRKBEY, BARIS · 2021 to 2025
$10.1M
Intramural NIH HHS ZIH BC012032
6 · The paper itself

Abstract

RATIONALE AND

objectivesA common site of metastases for a variety of cancers is the bone, which is challenging and time consuming to review and important for cancer staging. Here, we developed a deep learning approach for detection and classification of bone lesions on staging CTs. MATERIALS AND

methodsThis study developed an nnUNet model using 402 patients' CTs, including prostate cancer patients with benign or malignant osteoblastic (blastic) bone lesions, and patients with benign or malignant osteolytic (lytic) bone lesions from various primary cancers. An expert radiologist contoured ground truth lesions, and the model was evaluated for detection on a lesion level. For classification performance, accuracy, sensitivity, specificity, and other metrics were calculated. The held-out test set consisted of 69 patients (32 with bone metastases). The AUC of AI-predicted burden of disease was calculated on a patient level.

resultsIn the independent test set, 70% of ground truth lesions were detected (67% of malignant lesions and 72% of benign lesions). The model achieved accuracy of 85% in classifying lesions as malignant or benign (91% sensitivity and 81% specificity). Although AI identified false positives in several benign patients, the patient-level AUC was 0.82 using predicted disease burden proportion.

conclusionOur lesion detection and classification AI model performs accurately and has the potential to correct physician errors. Further studies should investigate if the model can impact physician review in terms of detection rate, classification accuracy, and review time.

Indexed as

Bone NeoplasmsDeep LearningRadiographic Image Interpretation, Computer-AssistedTomography, X-Ray ComputedAgedFemaleHumansMaleMiddle AgedNeoplasm StagingProstatic NeoplasmsSensitivity and SpecificityArtificial intelligenceBone metastasisComputed tomographyDeep learningOsteoblasticOsteolytic

Identifiers

PMID40634223
PMCPMC12252225

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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.