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Quantitative Magnetic Resonance Imaging Methods for the Assessment and Prediction of Treatment Response to Transarterial Chemoembolization in Hepatocellular Carcinoma.
Acad Radiol
March 2025
Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing 100070, China. Electronic address:
This article reviews the state-of-the-art applications of quantitative magnetic resonance imaging (qMRI) in predicting and evaluating response to transarterial chemoembolization (TACE) in patients with hepatocellular carcinoma (HCC). HCC is a highly heterogeneous tumor, and its response to TACE varies significantly among patients. Early identification of treatment response is critical for optimizing management.
View Article and Find Full Text PDFDual-Modality Virtual Biopsy System Integrating MRI and MG for Noninvasive Predicting HER2 Status in Breast Cancer.
Acad Radiol
March 2025
Department of Radiology, The Affiliated Huai'an Clinical College of Xuzhou Medical University, Huai'an, Jiangsu Province, China (Q.W., C.-C.H., H.-W.X., G.-J.B.). Electronic address:
Rationale And Objectives: Accurate determination of human epidermal growth factor receptor 2 (HER2) expression is critical for guiding targeted therapy in breast cancer. This study aimed to develop and validate a deep learning (DL)-based decision-making visual biomarker system (DM-VBS) for predicting HER2 status using radiomics and DL features derived from magnetic resonance imaging (MRI) and mammography (MG).
Materials And Methods: Radiomics features were extracted from MRI, and DL features were derived from MG.
Development and validation of radiomics and deep transfer learning models to assess cognitive impairment in patients with cerebral small vessel disease.
Neuroscience
March 2025
Department of Radiology, Affiliated Hospital of Youjiang Medical University for Nationalities, 533000 Baise, China; Life Science and Clinical Medicine Research Center, Affiliated Hospital of Youjiang Medical University for Nationalities, 533000 Baise, China. Electronic address:
Cognitive impairment in cerebral small vessel disease (CSVD) progresses subtly but carries significant clinical consequences, necessitating effective diagnostic tools. This study developed and validated predictive models for CSVD-related cognitive impairment using deep transfer learning (DTL) and radiomics features extracted from hippocampal 3D T1-weighted MRI. A total of 145 CSVD patients and 99 control subjects were enrolled in the study.
View Article and Find Full Text PDFA CT-based interpretable deep learning signature for predicting PD-L1 expression in bladder cancer: a two-center study.
Cancer Imaging
March 2025
Department of Radiology, The Affiliated Hospital of Qingdao University, 16 Jiangsu Road, Qingdao, Shandong, 266003, China.
Background: To construct and assess a deep learning (DL) signature that employs computed tomography imaging to predict the expression status of programmed cell death ligand 1 in patients with bladder cancer (BCa).
Methods: This retrospective study included 190 patients from two hospitals who underwent surgical removal of BCa (training set/external validation set, 127/63). We used convolutional neural network and radiomics machine learning technology to generate prediction models.
Incorporating radiomic MRI models for presurgical response assessment in patients with early breast cancer undergoing neoadjuvant systemic therapy: collaborative insights from breast oncologists and radiologists.
Crit Rev Oncol Hematol
March 2025
IRCCS Humanitas Research Hospital, Via Manzoni 56, 20089 Rozzano - Milan, Italy; Department of Biomedical Sciences, Humanitas University, Via Rita Levi Montalcini 4, 20090 Pieve Emanuele (Milan), Italy.
The assessment of neoadjuvant treatment's response is critical for selecting the most suitable therapeutic options for patients with breast cancer to reduce the need for invasive local therapies. Breast magnetic resonance imaging (MRI) is so far one of the most accurate approaches for assessing pathological complete response, although this is limited by the qualitative and subjective nature of radiologists' assessment, often making it insufficient for deciding whether to forgo additional locoregional therapy measures. To increase the accuracy and prediction of radiomic MRI with the aid of machine learning models and deep learning methods, as part of artificial intelligence, have been used to analyse the different subtypes of breast cancer and the specific changes observed before and after therapy.
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