Radiomics: Phases of osteoclastic metastasis status in breast cancer identified by morphologic markers

In the practical work of radiologists or oncologists, particularly in individualized treatment, a rapid and accurate diagnosis, timely assessments of drug effects, and direction of disease progression are essential. Radiomics and neural networks offer significant help in analyzing data from diagnostic imaging studies. This study examines quantitative biomarkers derived from magnetic resonance imaging, tentatively categorized as mathematical morphological markers, and explores their relationship with osteoclast tumor regression in breast cancer. This study aims to determine the consistency of imaging biomarkers in the stabilization, healing, and progression of breast cancer bone metastases.

Authors

Publication Details

Journal
Cancer Plus
Published
2025-03-21
DOI
https://doi.org/10.36922/cp.6649
Primary Topic
Radiomics and Machine Learning in Medical Imaging
Type
article
Field-Weighted Citation Impact
0.00
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article

Radiomics: Phases of osteoclastic metastasis status in breast cancer identified by morphologic markers

Н. И. Сергеев, Valentin Steinhauer
Cancer Plus
Radiomics and Machine Learning in Medical Imaging
article

Radiomics: Phases of osteoclastic metastasis status in breast cancer identified by morphologic markers

Н. И. Сергеев, Valentin Steinhauer
article en

Abstract

In the practical work of radiologists or oncologists, particularly in individualized treatment, a rapid and accurate diagnosis, timely assessments of drug effects, and direction of disease progression are essential. Radiomics and neural networks offer significant help in analyzing data from diagnostic imaging studies. This study examines quantitative biomarkers derived from magnetic resonance imaging, tentatively categorized as mathematical morphological markers, and explores their relationship with osteoclast tumor regression in breast cancer. This study aims to determine the consistency of imaging biomarkers in the stabilization, healing, and progression of breast cancer bone metastases.

Cancer PlusVol. 7(1)
Openalex Percentile: Top 92%
Radiomics and Machine Learning in Medical Imaging
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