Evidence map›Paper›PMID 33719168›Full record

ArticleJournal of magnetic resonance imaging : JMRI2021

A Deep Learning Approach to Diagnostic Classification of Prostate Cancer Using Pathology-Radiology Fusion.

Pegah Khosravi, Maria Lysandrou, Mahmoud Eljalby, Qianzi Li, Ehsan Kazemi, Pantelis Zisimopoulos, Alexandros Sigaras, Matthew Brendel, Josue Barnes, Camir Ricketts and 8 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of magnetic resonance imaging : JMRI, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 52 papers.

0numbers the graph read from it
0cells of the map it votes in
52citing 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

52 citing papers in PubMed.

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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

18 authors.

Pegah KhosraviComputational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, New York, USA.ORCID 0000-0003-3497-2820
Maria LysandrouNeuroscience Institute, The University of Chicago, Chicago, Illinois, USA.
Mahmoud EljalbyDepartment of Urology, Weill Cornell Medicine of Cornell University, New York, New York, USA.
Qianzi LiDepartment of Physiology and Biophysics, Institute for Computational Biomedicine, Weill Cornell Medicine of Cornell University, New York, New York, USA.
Ehsan KazemiYale University, Department of Electrical Engineering.
Pantelis ZisimopoulosDepartment of Physiology and Biophysics, Institute for Computational Biomedicine, Weill Cornell Medicine of Cornell University, New York, New York, USA.
Alexandros SigarasDepartment of Physiology and Biophysics, Institute for Computational Biomedicine, Weill Cornell Medicine of Cornell University, New York, New York, USA.
Matthew BrendelDepartment of Physiology and Biophysics, Institute for Computational Biomedicine, Weill Cornell Medicine of Cornell University, New York, New York, USA.
Josue BarnesDepartment of Physiology and Biophysics, Institute for Computational Biomedicine, Weill Cornell Medicine of Cornell University, New York, New York, USA.
Camir RickettsDepartment of Physiology and Biophysics, Institute for Computational Biomedicine, Weill Cornell Medicine of Cornell University, New York, New York, USA.
Dmitry MeleshkoDepartment of Physiology and Biophysics, Institute for Computational Biomedicine, Weill Cornell Medicine of Cornell University, New York, New York, USA.
Andy YatDepartment of Radiology, New York-Presbyterian Hospital, New York, New York, USA.
Timothy D McClureDepartment of Urology, Weill Cornell Medicine of Cornell University, New York, New York, USA.
Brian D RobinsonDepartment of Pathology, New York Presbyterian Hospital-Weill Cornell Medical College, New York, New York, USA.
Andrea SbonerDepartment of Physiology and Biophysics, Institute for Computational Biomedicine, Weill Cornell Medicine of Cornell University, New York, New York, USA.
Olivier ElementoDepartment of Physiology and Biophysics, Institute for Computational Biomedicine, Weill Cornell Medicine of Cornell University, New York, New York, USA.
Bilal ChughtaiDepartment of Urology, Weill Cornell Medicine of Cornell University, New York, New York, USA.
Iman HajirasoulihaDepartment of Physiology and Biophysics, Institute for Computational Biomedicine, Weill Cornell Medicine of Cornell University, New York, New York, USA.

Funding

X-RAY CRYSTALLOGRAPHYP30CA008748 · NCI · SLOAN-KETTERING INSTITUTE FOR CANCER RES · PI Hongbiao Carl Lekaye · 1985 to 2026
$347.4M
NCI NIH HHS P30 CA008748Weill Cornell Medicine
6 · The paper itself

Abstract

backgroundA definitive diagnosis of prostate cancer requires a biopsy to obtain tissue for pathologic analysis, but this is an invasive procedure and is associated with complications. PURPOSE: To develop an artificial intelligence (AI)-based model (named AI-biopsy) for the early diagnosis of prostate cancer using magnetic resonance (MR) images labeled with histopathology information. STUDY TYPE: Retrospective. POPULATION: Magnetic resonance imaging (MRI) data sets from 400 patients with suspected prostate cancer and with histological data (228 acquired in-house and 172 from external publicly available databases). FIELD STRENGTH/SEQUENCE: 1.5 to 3.0 Tesla, T2-weighted image pulse sequences. ASSESSMENT: MR images reviewed and selected by two radiologists (with 6 and 17 years of experience). The patient images were labeled with prostate biopsy including Gleason Score (6 to 10) or Grade Group (1 to 5) and reviewed by one pathologist (with 15 years of experience). Deep learning models were developed to distinguish 1) benign from cancerous tumor and 2) high-risk tumor from low-risk tumor. STATISTICAL TESTS: To evaluate our models, we calculated negative predictive value, positive predictive value, specificity, sensitivity, and accuracy. We also calculated areas under the receiver operating characteristic (ROC) curves (AUCs) and Cohen's kappa.

resultsOur computational method (https://github.com/ih-lab/AI-biopsy) achieved AUCs of 0.89 (95% confidence interval [CI]: [0.86-0.92]) and 0.78 (95% CI: [0.74-0.82]) to classify cancer vs. benign and high- vs. low-risk of prostate disease, respectively. DATA

conclusionAI-biopsy provided a data-driven and reproducible way to assess cancer risk from MR images and a personalized strategy to potentially reduce the number of unnecessary biopsies. AI-biopsy highlighted the regions of MR images that contained the predictive features the algorithm used for diagnosis using the class activation map method. It is a fully automatic method with a drag-and-drop web interface (https://ai-biopsy.eipm-research.org) that allows radiologists to review AI-assessed MR images in real time. LEVEL OF EVIDENCE: 1 TECHNICAL EFFICACY STAGE: 2.

Indexed as

Deep LearningProstatic NeoplasmsRadiologyArtificial IntelligenceHumansMagnetic Resonance ImagingMaleRetrospective Studiesartificial intelligencebiopsydeep neural networksMRI imagesPI-RADSprostate cancer

Identifiers

PMID33719168
PMCPMC8360022

What OpenQuestion holds

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LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

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.