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

Role of apparent diffusion coefficient (ADC) and MRI-derived parameters in identifying p53-abnormal subtypes of endometrial cancer.

European journal of radiology open·2026
Same author

Multi-Institutional Annotated Multiparametric MRI Dataset of Pediatric High-Grade Gliomas.

Radiology. Artificial intelligence·2026
Same author

Extracorporeal shock wave therapy (ESWT) for treatment of osteonecrosis of the femoral head: a systematic review and meta-analysis.

European journal of orthopaedic surgery & traumatology : orthopedie traumatologie·2026
Same author

The 2024 Brain Tumor Segmentation Challenge Meningioma Radiotherapy (BraTS-MEN-RT) dataset.

Scientific data·2026
Same author

The added value of apparent diffusion coefficient assessments in O-RADS MRI evaluation for characterizing ovarian masses with solid components.

Abdominal radiology (New York)·2025
Same author

Enhancing Prostate Cancer Classification: A Comprehensive Review of Multiparametric MRI and Deep Learning Integration.

Journal of magnetic resonance imaging : JMRI·2025

Related Experiment Video

Updated: Feb 23, 2026

Author Spotlight: Integrating Ultrasound Imaging with Biochemical Markers for Thyroid Disease Diagnosis
05:41

Author Spotlight: Integrating Ultrasound Imaging with Biochemical Markers for Thyroid Disease Diagnosis

Published on: February 9, 2024

1.1K

ADC-derived spatial features can accurately classify adnexal lesions.

Anahita Fathi Kazerooni1,2, Mahnaz Nabil3, Hamidreza Haghighat Khah4

  • 1Quantitative MR Imaging and Spectroscopy Group, Research Center for Molecular and Cellular Imaging, Tehran University of Medical Sciences, Iran.

Journal of Magnetic Resonance Imaging : JMRI
|September 14, 2017
PubMed
Summary

This study introduces a novel spatial quantification method using apparent diffusion coefficient (ADC) maps to accurately differentiate benign from malignant adnexal masses. The approach achieved over 92% accuracy, offering a potential computer-aided diagnostic tool.

Keywords:
adnexal lesionsapparent diffusion coefficientdiffusion-weighted MRImagnetic resonance imagingspatial quantificationtextural analysis

More Related Videos

Tracking the Mammary Architectural Features and Detecting Breast Cancer with Magnetic Resonance Diffusion Tensor Imaging
15:48

Tracking the Mammary Architectural Features and Detecting Breast Cancer with Magnetic Resonance Diffusion Tensor Imaging

Published on: December 15, 2014

23.3K
A Cognitive Fusion-guided Prostate Biopsy Using Multiparametric Magnetic Resonance Imaging and Transrectal Ultrasound
06:08

A Cognitive Fusion-guided Prostate Biopsy Using Multiparametric Magnetic Resonance Imaging and Transrectal Ultrasound

Published on: March 21, 2025

1.7K

Related Experiment Videos

Last Updated: Feb 23, 2026

Author Spotlight: Integrating Ultrasound Imaging with Biochemical Markers for Thyroid Disease Diagnosis
05:41

Author Spotlight: Integrating Ultrasound Imaging with Biochemical Markers for Thyroid Disease Diagnosis

Published on: February 9, 2024

1.1K
Tracking the Mammary Architectural Features and Detecting Breast Cancer with Magnetic Resonance Diffusion Tensor Imaging
15:48

Tracking the Mammary Architectural Features and Detecting Breast Cancer with Magnetic Resonance Diffusion Tensor Imaging

Published on: December 15, 2014

23.3K
A Cognitive Fusion-guided Prostate Biopsy Using Multiparametric Magnetic Resonance Imaging and Transrectal Ultrasound
06:08

A Cognitive Fusion-guided Prostate Biopsy Using Multiparametric Magnetic Resonance Imaging and Transrectal Ultrasound

Published on: March 21, 2025

1.7K

Area of Science:

  • Radiology
  • Medical Imaging
  • Oncology

Background:

  • The diagnostic role of quantitative apparent diffusion coefficient (ADC) maps in characterizing adnexal masses remains unclear.
  • Accurate differentiation of benign and malignant adnexal masses is crucial for appropriate patient management.

Purpose of the Study:

  • To develop an objective diagnostic method using spatial features from ADC maps for predicting the benignity or malignancy of adnexal masses.
  • To evaluate the efficacy of this method in classifying adnexal lesions.

Main Methods:

  • Prospective study involving 70 women with indeterminate adnexal masses.
  • Analysis of conventional and diffusion-weighted MRI (3T scanner) using spatial models (first-order histogram, GLCM, RLM, Gabor filters) on ADC maps.
  • Feature selection and cross-validated classification to identify models discriminating benign from malignant lesions.

Main Results:

  • A feature subspace derived from run-length matrix (RLM) features achieved approximately 92% accuracy in differentiating benign from malignant adnexal masses.
  • The model demonstrated 87% accuracy for benign, borderline, and malignant lesions, and 100% for borderline versus malignant lesions.
  • This spatial quantification method outperformed qualitative assessment by radiologists (80% accuracy).

Conclusions:

  • The proposed spatial quantification methodology, analyzing cellular distributions within ADC maps, offers a promising computer-aided strategy for objective adnexal mass characterization.
  • This approach may enhance diagnostic accuracy and aid in clinical decision-making for adnexal lesions.