Evidence map›Paper›PMID 34784540›Full record

ArticleMedical image analysis2022

Selective identification and localization of indolent and aggressive prostate cancers via CorrSigNIA: an MRI-pathology correlation and deep learning framework.

Indrani Bhattacharya, Arun Seetharaman, Christian Kunder, Wei Shao, Leo C Chen, Simon J C Soerensen, Jeffrey B Wang, Nikola C Teslovich, Richard E Fan, Pejman Ghanouni and 3 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Medical image analysis, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers.

0numbers the graph read from it
0cells of the map it votes in
33citing 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

33 citing papers in PubMed.

  1. Article
  2. GUIDE-US: grade-informed unpaired distillation of encoder knowledge from histopathology to micro-ultrasound.International journal of computer assisted radiology and surgery · 2026
    Article
  3. Review
  4. Article
  5. Article
  6. Article
  7. Review
  8. Article
  9. Article
  10. Article
  11. Article
  12. Article
  13. Article
  14. Article
  15. Article
  16. Review
  17. Article
  18. Review
  19. Article
  20. Article
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

13 authors.

Indrani BhattacharyaDepartment of Radiology, Stanford University, 300 Pasteur Drive, Stanford, CA 94305, USA; Department of Urology, Stanford University, 300 Pasteur Drive, Stanford, CA 94305, USA. Electronic address: ibhatt@stanford.edu.
Arun SeetharamanDepartment of Electrical Engineering, Stanford University, 350 Jane Stanford Way, Stanford, CA 94305, USA.
Christian KunderDepartment of Pathology, Stanford University, 300 Pasteur Drive, Stanford, CA 94305, USA.
Wei ShaoDepartment of Radiology, Stanford University, 300 Pasteur Drive, Stanford, CA 94305, USA.
Leo C ChenDepartment of Urology, Stanford University, 300 Pasteur Drive, Stanford, CA 94305, USA.
Simon J C SoerensenDepartment of Urology, Stanford University, 300 Pasteur Drive, Stanford, CA 94305, USA; Department of Urology, Aarhus University Hospital, Aarhus, Denmark.
Jeffrey B WangDepartment of Radiology, Stanford University, 300 Pasteur Drive, Stanford, CA 94305, USA.
Nikola C TeslovichDepartment of Urology, Stanford University, 300 Pasteur Drive, Stanford, CA 94305, USA.
Richard E FanDepartment of Urology, Stanford University, 300 Pasteur Drive, Stanford, CA 94305, USA.
Pejman GhanouniDepartment of Radiology, Stanford University, 300 Pasteur Drive, Stanford, CA 94305, USA; Department of Urology, Stanford University, 300 Pasteur Drive, Stanford, CA 94305, USA.
James D BrooksDepartment of Urology, Stanford University, 300 Pasteur Drive, Stanford, CA 94305, USA.
Geoffrey A SonnDepartment of Urology, Stanford University, 300 Pasteur Drive, Stanford, CA 94305, USA; Department of Radiology, Stanford University, 300 Pasteur Drive, Stanford, CA 94305, USA.
Mirabela RusuDepartment of Radiology, Stanford University, 300 Pasteur Drive, Stanford, CA 94305, USA. Electronic address: mirabela.rusu@stanford.edu.

Funding

Stanford Molecular and Cellular Characterization LaboratoryU01CA196387 · NCI · STANFORD UNIVERSITY · PI BROOKS, JAMES D. · 2015 to 2020
$5.2M
NCI NIH HHS U01 CA196387
6 · The paper itself

Abstract

Automated methods for detecting prostate cancer and distinguishing indolent from aggressive disease on Magnetic Resonance Imaging (MRI) could assist in early diagnosis and treatment planning. Existing automated methods of prostate cancer detection mostly rely on ground truth labels with limited accuracy, ignore disease pathology characteristics observed on resected tissue, and cannot selectively identify aggressive (Gleason Pattern≥4) and indolent (Gleason Pattern=3) cancers when they co-exist in mixed lesions. In this paper, we present a radiology-pathology fusion approach, CorrSigNIA, for the selective identification and localization of indolent and aggressive prostate cancer on MRI. CorrSigNIA uses registered MRI and whole-mount histopathology images from radical prostatectomy patients to derive accurate ground truth labels and learn correlated features between radiology and pathology images. These correlated features are then used in a convolutional neural network architecture to detect and localize normal tissue, indolent cancer, and aggressive cancer on prostate MRI. CorrSigNIA was trained and validated on a dataset of 98 men, including 74 men that underwent radical prostatectomy and 24 men with normal prostate MRI. CorrSigNIA was tested on three independent test sets including 55 men that underwent radical prostatectomy, 275 men that underwent targeted biopsies, and 15 men with normal prostate MRI. CorrSigNIA achieved an accuracy of 80% in distinguishing between men with and without cancer, a lesion-level ROC-AUC of 0.81±0.31 in detecting cancers in both radical prostatectomy and biopsy cohort patients, and lesion-levels ROC-AUCs of 0.82±0.31 and 0.86±0.26 in detecting clinically significant cancers in radical prostatectomy and biopsy cohort patients respectively. CorrSigNIA consistently outperformed other methods across different evaluation metrics and cohorts. In clinical settings, CorrSigNIA may be used in prostate cancer detection as well as in selective identification of indolent and aggressive components of prostate cancer, thereby improving prostate cancer care by helping guide targeted biopsies, reducing unnecessary biopsies, and selecting and planning treatment.

Indexed as

Deep LearningProstatic NeoplasmsHumansMagnetic Resonance ImagingMaleProstateProstatectomyComputer-aided diagnosisCorrelated feature learningProstate cancerRadiology-pathology fusion

Identifiers

PMID34784540
PMCPMC8678366

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.