Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Cancer Survival Analysis

Cancer Survival Analysis

Cancer survival analysis focuses on quantifying and interpreting the time from a key starting point, such as diagnosis or the initiation of treatment, to a specific endpoint, such as remission or death. This analysis provides critical insights into treatment effectiveness and factors that influence patient outcomes, helping to shape clinical decisions and guide prognostic evaluations. A cornerstone of oncology research, survival analysis tackles the challenges of skewed, non-normally...

You might also read

Related Articles

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

Sort by

Same author

Chromophobe renal cell carcinoma with nodal involvement: A rare case report highlighting unusual molecular features.

Urology case reports·2026

Same author

GLP-1 receptor agonists and weight-loss strategies for individuals with obesity and hip or knee osteoarthritis: a scoping review.

British journal of sports medicine·2026

Same author

Clinical and immunological effects of adding cyclophosphamide to pomalidomide-dexamethasone in relapsed-refractory multiple myeloma: The randomized MUKseven trial.

British journal of haematology·2026

Same author

Ensemble learning for predicting the damped response of granular materials and interpretation through feature importance analysis.

The Journal of the Acoustical Society of America·2026

Same author

Deep learning layer-specific collagen quantification correlates with activity and is associated with outcomes in Crohn's disease.

Journal of Crohn's & colitis·2026

Same author

Building a Mississippi Military-Civilian Surgical Partnership for Readiness and Community Service.

Military medicine·2026

Search research articles

Related Experiment Video

Updated: Mar 6, 2026

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

A methodology for automated CPA extraction using liver biopsy image analysis and machine learning techniques.

Markos G Tsipouras¹, Nikolaos Giannakeas¹, Alexandros T Tzallas¹

¹Division of Gastroenterology, Faculty of Medicine, School of Health Sciences, University of Ioannina, GR45110 Ioannina, Greece; Department of Computer Engineering, School of Applied Technology, Technological Educational Institute of Epirus, Kostakioi, GR47100, Arta, Greece.

Computer Methods and Programs in Biomedicine

|March 4, 2017

Summary

This study presents an automated method for quantifying liver fibrosis using collagen proportional area (CPA) in biopsy images. The machine learning approach improves accuracy and efficiency, overcoming limitations of current manual scoring methods for hepatitis C virus (HCV) patients.

Keywords:

Classification Clustering Collagen proportional area Liver biopsy image analysis Machine learning techniques

More Related Videos

Author Spotlight: Investigating Immune Cell Dynamics in the Tumor Microenvironment — Challenges and Innovations in Cancer Prognosis

Author Spotlight: Investigating Immune Cell Dynamics in the Tumor Microenvironment — Challenges and Innovations in Cancer Prognosis

Published on: April 12, 2024

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

Related Experiment Videos

Last Updated: Mar 6, 2026

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

Author Spotlight: Investigating Immune Cell Dynamics in the Tumor Microenvironment — Challenges and Innovations in Cancer Prognosis

Author Spotlight: Investigating Immune Cell Dynamics in the Tumor Microenvironment — Challenges and Innovations in Cancer Prognosis

Published on: April 12, 2024

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

Area of Science:

Digital pathology
Medical image analysis
Hepatology

Background:

Collagen proportional area (CPA) quantifies liver fibrosis, a key indicator in hepatitis C virus (HCV) infection.
Current assessment relies on semiquantitative scores (e.g., Ishak, Metavir), which are less accurate than CPA.
Widespread clinical adoption of CPA is hindered by the lack of standardized, automated image analysis methods.

Purpose of the Study:

To introduce a fully automated, three-stage methodology for CPA extraction from liver biopsy images.
To develop a robust and efficient computational approach for fibrosis assessment.
To overcome the limitations of manual and semi-automated CPA calculation techniques.

Main Methods:

A three-stage methodology utilizing machine learning for automated CPA extraction.
Clustering algorithms for background-tissue separation and fibrosis detection.
Classification algorithms to identify liver tissue and exclude non-liver regions for accurate CPA computation.

Main Results:

The methodology was evaluated on 79 liver biopsy images.
Achieved a mean absolute CPA error of 1.31%.
Demonstrated high agreement with a concordance correlation coefficient of 0.923.

Conclusions:

The proposed methodology automates CPA extraction, eliminating manual thresholding and region selection.
It significantly minimizes the time required for CPA calculation.
Offers a standardized and robust computational tool for liver fibrosis assessment in clinical practice.