Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

How does AI perform compared to human expert panels in medical Delphi studies? A pilot study through the lens of pathology.

Journal of pathology informatics·2026
Same author

Finding Holes: Pathologist-Level Performance Using AI for Cribriform Morphology Detection in Prostate Cancer.

European urology open science·2026
Same author

Redefining the Landscape of Genitourinary Cancer Precursors: The International Society of Urological Pathology Consensus Recommendations.

European urology·2026
Same author

AI-discovered cellular morphometric biomarkers in needle biopsy of prostate cancer predict neoadjuvant androgen deprivation therapy response and enable therapeutic targeting of mTOR in androgen deprivation therapy-resistant tumors.

Cancer letters·2026
Same author

Ciliary p75 neurotrophin receptor (p75NTR) facilitates the enrichment of exogenous amyloid beta (Aβ 1-42) peptide and promotes oxidative stress in human hippocampal astrocytes.

BMC molecular and cell biology·2026
Same author

Exosomal ZFPL1 Identifies Neuroendocrine and Stem Cell-Like Prostate Cancer Subtypes?

The Prostate·2026
Same journal

Changes in Three-Dimensional Intrahepatic Biliary Structures in Patients With Hepatobiliary Diseases Visualized Using Tissue-Clearing Methods.

Laboratory investigation; a journal of technical methods and pathology·2026
Same journal

Genome-wide SNP-based Profiling of Loss of Heterozygosity Reveals Distinct Molecular Subgroup-specific Patterns in Gastrointestinal Stromal Tumors (GIST).

Laboratory investigation; a journal of technical methods and pathology·2026
Same journal

AI-Assisted HER2 Scoring in Breast Cancer: Diagnostic Agreement and Understanding Discordance.

Laboratory investigation; a journal of technical methods and pathology·2026
Same journal

Corrigendum to "POU2F3 in Small Cell Lung Cancer (SCLC): Diagnostic Utility in Neuroendocrine-Low/Negative SCLC and Discrimination From Other Thoracic Malignancies and Other Small Blue Round Cell Tumors" [Laboratory Investigation 2026;106(6):106124].

Laboratory investigation; a journal of technical methods and pathology·2026
Same journal

Assessing the Effects of a 3D Pathology Tissue-Processing Workflow on Downstream Molecular Analyses.

Laboratory investigation; a journal of technical methods and pathology·2026
Same journal

Transcription Factor Ets-1 is a Central Regulator of Redox Balance and Liver Regeneration Through EGF and TGF-β1 Signaling.

Laboratory investigation; a journal of technical methods and pathology·2026
See all related articles

Related Experiment Video

Updated: Jul 12, 2025

Author Spotlight: Advancing Prostate Cancer Research Through Improved Tissue Sampling and Biobanking
07:34

Author Spotlight: Advancing Prostate Cancer Research Through Improved Tissue Sampling and Biobanking

Published on: November 17, 2023

705

Quantitative Histomorphometric Features of Prostate Cancer Predict Patients Who Biochemically Recur Following

Savannah R Duenweg1, Michael Brehler2, Allison K Lowman2

  • 1Departments of Biophysics, Medical College of Wisconsin, Milwaukee, Wisconsin.

Laboratory Investigation; a Journal of Technical Methods and Pathology
|October 28, 2023
PubMed
Summary
This summary is machine-generated.

Quantitative pathomic features from digital prostate cancer histology show potential for predicting recurrence after surgery. These novel metrics offer insights comparable to traditional assessments, aiding clinical decision-making.

Keywords:
annotationsdigital pathologyimage processingpathomic featuresprostate cancerwhole slide images

More Related Videos

Microarray-based Identification of Individual HERV Loci Expression: Application to Biomarker Discovery in Prostate Cancer
13:19

Microarray-based Identification of Individual HERV Loci Expression: Application to Biomarker Discovery in Prostate Cancer

Published on: November 2, 2013

16.6K
Use of Magnetic Resonance Imaging and Biopsy Data to Guide Sampling Procedures for Prostate Cancer Biobanking
05:49

Use of Magnetic Resonance Imaging and Biopsy Data to Guide Sampling Procedures for Prostate Cancer Biobanking

Published on: October 10, 2019

6.6K

Related Experiment Videos

Last Updated: Jul 12, 2025

Author Spotlight: Advancing Prostate Cancer Research Through Improved Tissue Sampling and Biobanking
07:34

Author Spotlight: Advancing Prostate Cancer Research Through Improved Tissue Sampling and Biobanking

Published on: November 17, 2023

705
Microarray-based Identification of Individual HERV Loci Expression: Application to Biomarker Discovery in Prostate Cancer
13:19

Microarray-based Identification of Individual HERV Loci Expression: Application to Biomarker Discovery in Prostate Cancer

Published on: November 2, 2013

16.6K
Use of Magnetic Resonance Imaging and Biopsy Data to Guide Sampling Procedures for Prostate Cancer Biobanking
05:49

Use of Magnetic Resonance Imaging and Biopsy Data to Guide Sampling Procedures for Prostate Cancer Biobanking

Published on: October 10, 2019

6.6K

Area of Science:

  • Digital pathology
  • Oncology
  • Medical image analysis

Background:

  • Prostate cancer is a leading male cancer diagnosis.
  • Radical prostatectomy offers a cure for organ-confined disease, but recurrence remains a challenge.
  • Predicting biochemical recurrence after surgery is crucial for patient prognosis.

Purpose of the Study:

  • To investigate differences in quantitative pathomic features of prostate cancer in patients experiencing biochemical recurrence post-surgery.
  • To assess the predictive value of these pathomic features compared to traditional clinical information.

Main Methods:

  • Analysis of whole-mount prostate histology from 78 patients.
  • Digitization of hematoxylin and eosin stained slides into whole slide images (WSI).
  • Automated identification of prostate glands and calculation of histomorphometric features using image processing algorithms.

Main Results:

  • Logistic regression models using quantitative pathomic features achieved >80% accuracy and AUCs up to 0.82.
  • These results were comparable to established clinical factors like Gleason Grade Groups and CAPRA score.
  • Pathomic features provided predictive information across different tile groups (all, cancer only, noncancer only) and WSI.

Conclusions:

  • Quantitative pathomic features derived from digital histology offer valuable information beyond traditional pathologist assessments.
  • These novel features show potential for improving the prediction of prostate cancer recurrence.
  • Further research is needed to integrate these findings into clinical treatment guidance.