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Magnetic Resonance Imaging

Magnetic Resonance Imaging

Magnetic resonance imaging (MRI) is a noninvasive medical imaging technique based on a phenomenon of nuclear physics discovered in the 1930s, in which matter exposed to magnetic fields and radio waves was found to emit radio signals. In 1970, a physician and researcher named Raymond Damadian noticed that malignant (cancerous) tissue gave off different signals than normal body tissue. He applied for a patent for the first MRI scanning device in clinical use by the early 1980s. The early MRI...

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Updated: May 23, 2025

Murine Model for Non-invasive Imaging to Detect and Monitor Ovarian Cancer Recurrence

Murine Model for Non-invasive Imaging to Detect and Monitor Ovarian Cancer Recurrence

Published on: November 2, 2014

Diagnostic value of magnetic resonance imaging for malignant ovarian tumors mis-subclassified by the ultrasound-based

Meijiao Jiang¹, Congcong Yuan², Siwei Lu¹

¹Department of Radiology, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.

Frontiers in Oncology

|March 12, 2025

Summary

This study reveals MRI features like DWI signal and ADC values can improve diagnosis of ovarian tumors misclassified by the ADNEX model. These imaging findings aid in differentiating primary and metastatic ovarian cancers.

Keywords:

ADNEX model diagnosis magnetic resonance imaging malignant ovarian tumors ultrasound

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In Vivo, Percutaneous, Needle Based, Optical Coherence Tomography of Renal Masses

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Published on: March 30, 2015

Area of Science:

Oncology
Radiology
Medical Imaging

Background:

Accurate preoperative differentiation of ovarian tumors is critical for patient management.
The ADNEX (Assessment of Different Neoplasms on Early Ovarian Neoplasia) model, based on ultrasound, is a key tool for this differentiation.
However, some malignant ovarian tumors are mis-subclassified by the ADNEX model, necessitating further diagnostic refinement.

Purpose of the Study:

To analyze Magnetic Resonance Imaging (MRI) features of malignant ovarian tumors.
To evaluate the diagnostic value of MRI in cases mis-subclassified by the ADNEX model.
To correlate MRI findings with diverse histopathologic types of ovarian malignancies.

Main Methods:

Retrospective analysis of 164 patients with pathologically confirmed ovarian malignancies from January 2018 to September 2022.
Focus on 51 patients mis-subclassified by the ADNEX model.
Comparison of clinical and MRI characteristics with histopathological diagnoses.

Main Results:

MRI correctly diagnosed 90.20% (46/51) of malignant ovarian tumors.
Significant differences in mean ADC values were observed between clear cell carcinoma (CCC) and other tumor types (P=0.000).
MRI features such as lobulation, solid-cystic components, necrosis (common in HGSOC, colorectal, and gastric metastases), and hemorrhage (common in CCC) were noted.

Conclusions:

Diffusion-weighted imaging (DWI) signal, apparent diffusion coefficient (ADC) values, enhancement patterns, and internal mass components on MRI offer valuable supplementary information.
These MRI features can aid in correctly classifying malignant ovarian tumors that are mis-subclassified by the ADNEX model.
Enhanced preoperative characterization of ovarian tumors using MRI improves diagnostic accuracy.