Purpose: To analyse the changes in brain white matter before and after radiotherapy (RT) by applying multisequence MR radiomics features and to establish a relationship between the changes in radiomics features and radiation dose.
Methods: Eighty-eight patients with brain tumours who had undergone RT were selected in this study, and MR images (T1, T1+C, T2FLAIR, T2, DWI, and ASL) before and after RT were obtained. The brain white matter was delineated as an ROI under dose gradients of 0-5 Gy, 5-10 Gy, 10-15 Gy, 15-20 Gy, 20-30 Gy, 30-40 Gy, and 40-50 Gy. The radiomics features of each ROI were extracted, and the changes in radiomics features before and after RT for different sequences under different dose gradients were compared.
Results: At each dose gradient, statistically significant features of different MR sequences were mainly concentrated in three dose gradients, 5-10 Gy, 20-30 Gy, and 30-40 Gy. The T1+C sequence held the most features (66) under the 20-30 Gy dose gradient. There were 20 general features at dose gradients of 20-30 Gy, 30-40 Gy, and 40-50 Gy, and the changes in features first decreased and then increased following dose escalation. With dose gradients of 5-10 Gy and 10-15 Gy, only T1 and T2FLAIR had general features, and the rates of change were - 24.57% and - 29.32% for T1 and - 3.08% and - 10.87% for T2FLAIR, respectively. The changes showed an upward trend with increasing doses. For different MR sequences that were analysed under the same dose gradient, all sequences with 5-10 Gy, 20-30 Gy and 30-40 Gy had general features, except the T2FLAIR sequence, which was concentrated in the FirstOrder category feature, and the changes in features of T1 and T1+C were more significant than those of the other sequences.
Conclusions: MR radiomics features revealed microscopic changes in brain white matter before and after RT, although there was no constant dose-effect relationship for each feature. The changes in radiomics features in different sequences could reveal the radiation response of brain white matter to different doses.
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http://dx.doi.org/10.1186/s12880-022-00816-3 | DOI Listing |
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.
Front Oncol
January 2025
Departments of Ultrasound, Jiading District Central Hospital Affiliated Shanghai University of Medicine &Health Sciences, Shanghai, China.
Background: Skip lymph node metastasis (SLNM) in papillary thyroid cancer (PTC) involves cancer cells bypassing central nodes to directly metastasize to lateral nodes, often undetected by standard preoperative ultrasonography. Although multiple models exist to identify SLNM, they are inadequate for clinically node-negative (cN0) patients, resulting in underestimated metastatic risks and compromised treatment effectiveness. Our study aims to develop and validate a machine learning (ML) model that combines elastography radiomics with clinicopathological data to predict pre-surgical SLNM risk in cN0 PTC patients with increased risk of lymph node metastasis (LNM), improving their treatment strategies.
View Article and Find Full Text PDFFront Oncol
January 2025
Department of Urology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China.
Purpose: To create a system to enable the identification of histological variants of bladder cancer in a simple, efficient, and noninvasive manner.
Material And Methods: In this multicenter diagnostic study, we retrospectively collected basic information and CT images about the patients concerned from three hospitals. An interactive deep learning-based bladder cancer image segmentation framework was constructed using the Swin UNETR algorithm for further features extraction.
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.
J Med Imaging (Bellingham)
January 2025
Case Western Reserve University, Department of Biomedical Engineering, Cleveland, Ohio, United States.
Purpose: We investigated the feasibility and advantages of using non-contrast CT calcium score (CTCS) images to assess pericoronary adipose tissue (PCAT) and its association with major adverse cardiovascular events (MACE). PCAT features from coronary computed tomography angiography (CCTA) have been shown to be associated with cardiovascular risk but are potentially confounded by iodine. If PCAT in CTCS images can be similarly analyzed, it would avoid this issue and enable its inclusion in formal risk assessment from readily available, low-cost CTCS images.
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