Background: Achieving pathological complete response (pCR) to neoadjuvant chemotherapy (NAC) improves survival outcomes for breast cancer patients. Currently, conventional histopathological biomarkers predicting such responses are inconsistent. Studies investigating radiomic texture analysis from breast magnetic resonance imaging (MRI) to predict pCR have varied radiomic protocols introducing heterogeneity between results. Thus, the efficacy of radiomic profiles compared to conventional strategies to predict pCR are inconclusive.
Purpose: Comparing the predictive accuracy of different breast MRI radiomic protocols to identify the optimal strategy in predicting pCR to NAC.
Material And Methods: A systematic review and network meta-analysis was performed according to PRISMA guidelines. Four databases were searched up to October 4th, 2021. Nine predictive strategies were compared, including conventional biomarker parameters, MRI radiomic analysis conducted before, during, or after NAC, combination strategies and nomographic methodology.
Results: 14 studies included radiomic data from 2,722 breast cancers, of which 994 were used in validation cohorts. All MRI derived radiomic features improved predictive accuracy when compared to biomarkers, except for pre-NAC MRI radiomics (odds ratio [OR]: 0.00; 95 % CI: -0.07-0.08). During-NAC and post-NAC MRI improved predictive accuracy compared to Pre-NAC MRI (OR: 0.14, 95 % CI: 0.02-0.26) and (OR: 0.26, 95 % CI: 0.07-0.45) respectively. Combining multiple MRIs did not improve predictive performance compared to Mid- or Post-NAC MRIs individually.
Conclusion: Radiomic analysis of breast MRIs improve identification of patients likely to achieve a pCR to NAC. Post-NAC MRI are the most accurate imaging method to extrapolate radiomic data to predict pCR.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1016/j.ejrad.2022.110561 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
A clinical study exploring the prediction of microvascular invasion in hepatocellular carcinoma through the use of combined enhanced CT and MRI radiomics.
PLoS One
January 2025
Department of Geriatric Medicine, the Affiliated Hospital of Guilin Medical University, Guilin, Guangxi, China.
Objective: To develop a predictive model for microvascular invasion (MVI) in hepatocellular carcinoma (HCC) through radiomics analysis, integrating data from both enhanced computed tomography (CT) and magnetic resonance imaging (MRI).
Methods: A retrospective analysis was conducted on 93 HCC patients who underwent partial hepatectomy. The gold standard for MVI was based on the histopathological diagnosis of the tissue.
Value of MRI-based radiomics models for predicting visual outcome in patients with MOG-optic neuritis: A preliminary study.
Eur J Ophthalmol
January 2025
Department of Radiology, 3rd Medical Center of Chinese PLA General Hospital, Beijing, China.
Purpose: To investigate the predictive value of MRI-based radiomics models for the recovery of visual acuity after 12 months in patients with acute phase MOG-optic neuritis(MOG-ON).
Materials And Methods: Clinical and MRI imaging data were collected consecutively from January 2021 to April 2022 from patients with acute stage MOG-ON, and the visual acuity of patients were followed up after 12 months. After stratified random sampling, patients were divided into training and test sets, and prediction models based on CE-T1WI, FS-T2WI, and combined CE-T1WI and FS-T2WI were developed.
Radiogenomics and machine learning predict oncogenic signaling pathways in glioblastoma.
J Transl Med
January 2025
Department of Computer Science, Islamic University of Science and Technology (IUST), Kashmir, 192122, India.
Background: Glioblastoma (GBM) is a highly aggressive brain tumor associated with a poor patient prognosis. The survival rate remains low despite standard therapies, highlighting the urgent need for novel treatment strategies. Advanced imaging techniques, particularly magnetic resonance imaging (MRI), are crucial in assessing GBM.
View Article and Find Full Text PDFDistinguishing benign and malignant myxoid soft tissue tumors: Performance of radiomics vs. radiologists.
PLoS One
January 2025
Department of Biostatistics, Vanderbilt University Medical Center, Nashville, Tennessee, United States of America.
Introduction: Benign and malignant myxoid soft tissue tumors have shared clinical, imaging, and histologic features that can make diagnosis challenging. The purpose of this study is comparison of the diagnostic performance of a radiomic based machine learning (ML) model to musculoskeletal radiologists.
Methods: Manual segmentation of 90 myxoid soft tissue tumors (45 myxomas and 45 myxofibrosarcomas) was performed on axial T1, and T2FS or STIR magnetic resonance imaging sequences.
Hippocampal Functional Radiomic Features for Identification of the Cognitively Impaired Patients from Low-Back-Related Pain: A Prospective Machine Learning Study.
J Pain Res
January 2025
Jiangxi Provincial Key Laboratory for Precision Pathology and Intelligent Diagnosis, Department of Radiology, the First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, 330006, People's Republic of China.
Purpose: To investigate whether functional radiomic features in bilateral hippocampi can identify the cognitively impaired patients from low-back-related leg pain (LBLP).
Patients And Methods: For this retrospective study, a total of 95 clinically definite LBLP patients (40 cognitively impaired patients and 45 cognitively preserved patients) were included, and all patients underwent functional MRI and clinical assessments. After calculating the amplitude of low-frequency fluctuations (ALFF), regional homogeneity (ReHo), voxel-mirrored homotopic connectivity (VMHC) and degree centrality (DC) imaging, the radiomic features (n = 819) of bilateral hippocampi were extracted from these images, respectively.
Want AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!