Evidence map›Paper›PMID 38100831›Full record

ArticleThe Journal of urology2024

Stimulated Raman Histology Interpretation by Artificial Intelligence Provides Near-Real-Time Pathologic Feedback for Unprocessed Prostate Biopsies.

M P Mannas, F M Deng, A Ion-Margineanu, D Jones, D Hoskoppal, J Melamed, S Pastore, C Freudiger, D A Orringer, S S Taneja

Open access · greenAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in The Journal of urology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed
3.2field-weighted citation impact, top 8% of its field
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

11 citing papers in PubMed, 9 citations in OpenAlex.

  1. Trial
  2. Prostate biopsy - current discussions and future perspectives.Prostate cancer and prostatic diseases · 2026
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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

10 authors at 3 institutions in 3 countries.

M P MannasDepartment of Urologic Sciences, University of British Columbia, Vancouver, British Columbia, Canada.ORCID 0000-0002-7706-3830
F M DengDepartment of Pathology, NYU Langone Health, New York, New York.ORCID 0000-0001-9837-4227
A Ion-MargineanuInvenio Imaging, Santa Clara, California.
D JonesDepartment of Pathology, NYU Langone Health, New York, New York.
D HoskoppalDepartment of Pathology, NYU Langone Health, New York, New York.
J MelamedDepartment of Pathology, NYU Langone Health, New York, New York.
S PastoreInvenio Imaging, Santa Clara, California.
C FreudigerInvenio Imaging, Santa Clara, California.
D A OrringerDepartment of Neurosurgery, NYU Langone Health, New York, New York.
S S TanejaDepartment of Urology, NYU Langone Health, New York, New York.
NYU Langone Health · USInvenio Imaging (United States) · USUniversity of British Columbia · CA

Funding

Project-005UL1TR001445 · NCATS · NEW YORK UNIVERSITY SCHOOL OF MEDICINE · PI BREDELLA, MIRIAM ANTOINETTE, HOCHMAN, JUDITH S · 2015 to 2025
$103.5M
Clinical Translation of Stimulated Raman HistologyR01CA226527 · NCI · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI ORRINGER, DANIEL · 2018 to 2025
$4.4M
NCATS NIH HHS UL1 TR001445NCI NIH HHS R01 CA226527
6 · The paper itself

Abstract

purposeStimulated Raman histology is an innovative technology that generates real-time, high-resolution microscopic images of unprocessed tissue, significantly reducing prostate biopsy interpretation time. This study aims to evaluate the ability for an artificial intelligence convolutional neural network to interpretate prostate biopsy histologic images created with stimulated Raman histology. MATERIALS AND

methodsUnprocessed, unlabeled prostate biopsies were prospectively imaged using a stimulated Raman histology microscope. Following stimulated Raman histology creation, the cores underwent standard pathological processing and interpretation by at least 2 genitourinary pathologists to establish a ground truth assessment. A network, trained on 303 prostate biopsies from 100 participants, was used to measure the accuracy, sensitivity, and specificity of detecting prostate cancer on stimulated Raman histology relative to conventional pathology. The performance of the artificial intelligence was evaluated on an independent 113-biopsy test set.

resultsProstate biopsy images obtained through stimulated Raman histology can be generated within a time frame of 2 to 2.75 minutes. The artificial intelligence system achieved a rapid classification of prostate biopsies with cancer, with a potential identification time of approximately 1 minute. The artificial intelligence demonstrated an impressive accuracy of 96.5% in detecting prostate cancer. Moreover, the artificial intelligence exhibited a sensitivity of 96.3% and a specificity of 96.6%.

conclusionsStimulated Raman histology generates microscopic images capable of accurately identifying prostate cancer in real time, without the need for sectioning or tissue processing. These images can be interpreted by artificial intelligence, providing physicians with near-real-time pathological feedback during the diagnosis or treatment of prostate cancer.

Indexed as

Artificial IntelligenceProstatic NeoplasmsBiopsyFeedbackHumansMaleProstateartificial intelligenceprostate cancerRaman spectroscopy

Identifiers

PMID38100831
PMCPMC12279078
OpenAlexW4389774881

What OpenQuestion holds

Textmetadata
LicenceTDM
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.