Evidence map›Paper›PMID 36580220›Full record

ArticleEJNMMI research2022

Deep learning and radiomics framework for PSMA-RADS classification of prostate cancer on PSMA PET.

Kevin H Leung, Steven P Rowe, Jeffrey P Leal, Saeed Ashrafinia, Mohammad S Sadaghiani, Hyun Woo Chung, Pejman Dalaie, Rima Tulbah, Yafu Yin, Ryan VanDenBerg and 5 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in EJNMMI research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.

0numbers the graph read from it
0cells of the map it votes in
23citing 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

23 citing papers in PubMed.

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  15. Deep Semisupervised Transfer Learning for Fully Automated Whole-Body Tumor Quantification and Prognosis of Cancer on PET/CT.Journal of nuclear medicine : official publication, Society of Nuclear Medicine · 2024
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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

15 authors.

Kevin H LeungDepartment of Biomedical Engineering, Johns Hopkins University School of Medicine, 601 N Caroline St. JHOC 4263, Baltimore, MD, 21287, USA. kleung8@jhmi.edu.ORCID http://orcid.org/0000-0002-8969-8737
Steven P RoweThe Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA.ORCID https://orcid.org/0000-0003-2897-4694
Jeffrey P LealThe Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
Saeed AshrafiniaThe Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
Mohammad S SadaghianiThe Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
Hyun Woo ChungDepartment of Nuclear Medicine, Konkuk University Medical Center, Konkuk University School of Medicine, Seoul, Korea.
Pejman DalaieThe Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
Rima TulbahThe Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
Yafu YinDepartment of Nuclear Medicine, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Ryan VanDenBergThe Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
Rudolf A WernerDepartment of Nuclear Medicine, University Hospital Würzburg, Würzburg, Germany.
Kenneth J PientaThe James Buchanan Brady Urological Institute and Department of Urology, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
Michael A GorinThe Milton and Carroll Petrie Department of Urology, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
Yong DuThe Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA.ORCID http://orcid.org/0000-0002-0237-6154
Martin G PomperDepartment of Biomedical Engineering, Johns Hopkins University School of Medicine, 601 N Caroline St. JHOC 4263, Baltimore, MD, 21287, USA.ORCID http://orcid.org/0000-0001-6753-3010

Funding

Training/Dissemination-Resource for Molecular Imaging Agents in Precision MedicineP41EB024495 · NIBIB · UT SOUTHWESTERN MEDICAL CENTER · PI MARTIN G POMPER · 2017 to 2026
$11.8M
PSMA-based Cancer Imaging AgentsR01CA134675 · NCI · UT SOUTHWESTERN MEDICAL CENTER · PI POMPER, MARTIN G · 2009 to 2025
$6.2M
Multi-Modality Quantitiative Imaging for Evaluation of Response to Cancer TherapyU01CA140204 · NCI · JOHNS HOPKINS UNIVERSITY · PI DU, YONG, SOLNES, LILJA · 2011 to 2022
$6.1M
Training for Clinician Scientists in Imaging ResearchT32EB006351 · NIBIB · JOHNS HOPKINS UNIVERSITY · PI POMPER, MARTIN G · 2006 to 2022
$4.0M
Optimization and Validation of Improved Quantitative I-123 Brain SPECT ImagingR01NS094227 · NINDS · JOHNS HOPKINS UNIVERSITY · PI DU, YONG · 2016 to 2019
$1.1M
Center for Strategic Scientific Initiatives, National Cancer Institute R01CA134675Center for Strategic Scientific Initiatives, National Cancer Institute R01CA184228Center for Strategic Scientific Initiatives, National Cancer Institute U01CA140204NCI NIH HHS R01 CA134675NCI NIH HHS U01 CA140204NIBIB NIH HHS P41 EB024495NIBIB NIH HHS P41EB024495NIBIB NIH HHS T32EB006351NINDS NIH HHS R01 NS094227NINDS NIH HHS R01NS094227
6 · The paper itself

Abstract

backgroundAccurate classification of sites of interest on prostate-specific membrane antigen (PSMA) positron emission tomography (PET) images is an important diagnostic requirement for the differentiation of prostate cancer (PCa) from foci of physiologic uptake. We developed a deep learning and radiomics framework to perform lesion-level and patient-level classification on PSMA PET images of patients with PCa.

methodsThis was an IRB-approved, HIPAA-compliant, retrospective study. Lesions on [

resultsPSMA PET scans from 267 men with PCa had 3794 lesions assigned to PSMA-RADS categories. The framework yielded AUROC values of 0.87 and 0.90 for lesion-level and patient-level PSMA-RADS classification, respectively, on the test set. The framework yielded AUROC values of 0.92 and 0.85 for lesion-level and patient-level PCa classification, respectively, on the test set. A t-SNE analysis revealed learned relationships between the PSMA-RADS categories and disease findings. Mean confidence scores reflected the expected accuracy and were significantly higher for correct predictions than for incorrect predictions (P < 0.05). Measured probability scores reflected the likelihood of PCa consistent with the PSMA-RADS framework.

conclusionThe framework provided lesion-level and patient-level PSMA-RADS and PCa classification on PSMA PET images. The framework was interpretable and provided confidence and probability scores that may assist physicians in making more informed clinical decisions.

Indexed as

ClassificationDeep learningProstate cancerPSMA PETPSMA-RADSt-SNE

Identifiers

PMID36580220
PMCPMC9800682

What OpenQuestion holds

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Registered trials

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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.