Classification of Alzheimer's disease using unsupervised diffusion component analysis by Dominique Duncan, Thomas Strohmer

“…The new algorithm constructs coordinates as an extension of diffusion maps and generates efficient geometric representations of the complex structure of the MRI data. …”
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Radiomics prediction of MGMT promoter methylation in adult diffuse gliomas: a combination of structural MRI, DCE, and DTI by Yuying Liu, Yuying Liu, Zhengyang Zhu, Jianan Zhou, Han Wang, Huiquan Yang, Jinfeng Yin, Yitong Wang, Xin Li, Futao Chen, Qian Li, Zhuoru Jiang, Xi Wu, Danni Ge, Yi Zhang, Xin Zhang, Bing Zhang, Bing Zhang, Bing Zhang, Bing Zhang, Bing Zhang

“…PurposeTo assess the predictive value of radiomics features extracted from structural MRI, dynamic contrast enhanced (DCE), and diffusion tensor imaging (DTI) in detecting O6-methylguanine-DNA methyltransferase (MGMT) promoter methylation in patients with diffuse gliomas.MethodsRetrospective MRI data of 110 patients were enrolled in this study. …”
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Multilevel Deficiency of White Matter Connectivity Networks in Alzheimer’s Disease: A Diffusion MRI Study with DTI and HARDI Models by Tao Wang, Feng Shi, Yan Jin, Pew-Thian Yap, Chong-Yaw Wee, Jianye Zhang, Cece Yang, Xia Li, Shifu Xiao, Dinggang Shen

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Ultra high density imaging arrays in diffuse optical tomography for human brain mapping improve image quality and decoding performance by Zachary E. Markow, Jason W. Trobaugh, Edward J. Richter, Kalyan Tripathy, Sean M. Rafferty, Alexandra M. Svoboda, Mariel L. Schroeder, Tracy M. Burns-Yocum, Karla M. Bergonzi, Mark A. Chevillet, Emily M. Mugler, Adam T. Eggebrecht, Joseph P. Culver

“…Functional near-infrared spectroscopy (fNIRS) and high-density diffuse optical tomography (HD-DOT) non-invasively measure blood oxygen fluctuations related to brain activity, like fMRI, at the brain surface, using more-lightweight equipment that circumvents ergonomic and logistical limitations of fMRI. …”
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Evaluation of vascular cognitive impairment and identification of imaging markers using machine learning: a multimodal MRI study by Haoying He, Dongwei Lu, Sisi Peng, Jiu Jiang, Fan Fan, Dong Sun, Tianqi Sun, Zhipeng Xu, Ping Zhang, Xiaoxiang Peng, Ming Lei, Junjian Zhang

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Evaluation of Clinical Symptoms of Unilateral S1 Nerve Injury Caused by Disc Herniation the via High Resolution MRI and DTI by Zhang C, Hao W, Guo X, Zhang Y, Fu H, Zhang J

“…The sensitivity, specificity and area under the curve of the damaged nerve fiber bundle were detected by multi-factor logistic regression models constructed with FA+rFA and FA+rFA+rADC of the affected nerve root, respectively 95.20%, 72.00%, 0.939, and 97.60%, 80.00%, 0.944.Conclusion: High-resolution MRI and DTI can quantitatively evaluate the degree of nerve fiber bundle injury and clinical symptoms caused by lumbar disc herniation.Keywords: lumbar disc herniation, high-resolution MRI, DTI, fractional anisotropy, apparent diffusion coefficient…”
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Spherical-deconvolution informed filtering of tractograms changes laterality of structural connectome by Yifei He, Yoonmi Hong, Ye Wu

“…Diffusion MRI-driven tractography, a non-invasive technique that reveals how the brain is connected, is widely used in brain lateralization studies. …”
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Combining multi-parametric MRI radiomics features with tumor abnormal protein to construct a machine learning-based predictive model for prostate cancer by Chi Zhang, Zewen Wang, Peicheng Shang, Yibin Zhou, Jin Zhu, Lijun Xu, Zeyu Chen, Mengqi Yu, Yachen Zang

“…Notably, combining mpMRI radiomic features with TAP and clinical characteristics, or integrating dADC (b = 100/2000 s/mm²) sequence with TAP and clinical characteristics to construct random forest models, improved the AUCs to 0.91 and 0.92, respectively. …”
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Structural brain network organization in children with prenatal alcohol exposure by Xiaoyun Liang, Claire E. Kelly, Chun-Hung Yeh, Thijs Dhollander, Stephen Hearps, Peter J. Anderson, Deanne K. Thompson

“…However, investigations on how PAE affects brain networks are limited. We aim to compare diffusion magnetic resonance imaging (MRI) tractography-based structural networks between children with low-to-moderate PAE in trimester 1 only (T1) or throughout all trimesters (T1-T3) with those without alcohol exposure prenatally. …”
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7 Tesla multimodal MRI dataset of ex-vivo human brain by Qinfeng Zhu, Sihui Li, Zuozhen Cao, Yao Shen, Haoan Xu, Guojun Xu, Haotian Li, Zhaopeng Cui, Keqing Zhu, Zhiyong Zhao, Jing Zhang, Dan Wu

“…In our study, we aimed to address these limitations by constructing a comprehensive multimodal MRI database acquired from six ex-vivo Chinese human brains. …”
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POSSIBILITIES OF DIFFUSION-WEIGHTED MAGNETIC RESONANCE IMAGING IN THE DIAGNOSIS OF SPINAL CORD TUMORS by V. A. Byval’tsev, I. A. Stepanov, A. I. Kichigin

“…It reflects the possibilities of using diffusion-weighted MRI (DW-MRI), by constructing the maps of measured diffusion coefficients (ICD) and calculating the mean value of ICD in this pathology. …”
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Multiparametric MRI-based radiomics for preoperative prediction of parametrial invasion in early-stage cervical cancer by Chongshuang Yang, Man Li, Xin Yi, Lin Wang, Guangxian Kuang, Chunfang Zhang, Benyong Yao, Zhihong Qin, Tianliang Shi, Qiang Jiang

“…Radiomics features were extracted separately from T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI), apparent diffusion coefficient (ADC), and contrast-enhanced T1-weighted imaging (T1C). …”
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Research on the application of distinguishing between benign and malignant breast nodules using MRI and US radiomics by Yifan Liu, Dan Zhou, Jing Liu, Jinding Wei, Xiao Hu, Xiaoli Yu

“…All patients’ dynamic contrast-enhanced MRI (DCE-MRI), diffusion-weighted imaging (DWI), T1-weighted imaging (T1WI), T2-weighted imaging (T2WI), and ultrasound (US) images were uploaded to the 3D Slicer software. …”
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Predicting the efficacy of chemoradiotherapy in advanced nasopharyngeal carcinoma patients: an MRI radiomics and machine learning approach by Liucheng Chen, Zhiyuan Wang, Ji Zhang, Ying Meng, Xuelian Wang, Cancan Zhao, Longshan Shen

“…DCA results showed that patients could get good benefits.ConclusionsThe machine learning model based on multimodal MRI radiomic features may serve as a promising tool for predicting the efficacy of chemoradiotherapy in patients with advanced NPC.…”
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DIFFUSION-WEIGHTED MAGNETIC RESONANCE IMAGING IN THE DIAGNOSIS OF INTERVERTEBRAL DISC DEGENERATION IN THE LUMBOSACRAL SPINE by V. A. Byval’tsev, I. A. Stepanov, A. A. Kalinin, E. G. Belykh

“…Objective: to quantify the degree of degeneration of intervertebral discs (IVDs), by constructing functional diffusion-weighted imaging (DWI) maps, and to determine a correlation between the measured diffusion coefficient (DC) values and IVD changes on T1- and T2-weighted images. …”
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Intratumoral and peritumoral multiparametric MRI-based radiomics nomogram for preoperative risk stratification in patients with endometrial cancer by Bin Yan, Tingting Zhao, Ying Deng, Jianrong Lu, Guoqing Wang

“…This study aimed to establish and validate a multiparametric magnetic resonance imaging (MRI) radiomics nomogram that incorporates the peritumoral region for preoperative risk stratification in EC patients.MethodsThree-hundred seventy-four women with histologically confirmed EC were divided into training (1.5-T MRI, n=163), test (1.5-T MRI, n=70), and independent validation (3.0-T MRI, n=141) cohorts. …”
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Predicting recurrence risk in endometrial cancer: a multisequence MRI intratumoral and peritumoral radiomics nomogram approach by Jie Li, Jie Li, Dianpei Ma, Dianpei Ma, Xiuting Chen, Xiuting Chen, Junting Wei, Junting Wei, Jiali Xu, Yingming Zhao, Zhizhen Gao

“…ObjectiveTo assess the predictive value of a nomogram model incorporating clinical factors and multisequence MRI intratumoral and peritumoral radiomics features for estimating recurrence risk in endometrial cancer (EC) patients.Materials and methodsThis retrospective study included 184 patients with EC. …”
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Identification of the pathological subtypes of lung cancer brain metastases with multiparametric MRI radiomics: A feasibility study by Lian-Yu Sui, Shuai Quan, Li-Hong Xing, Yu Zhang, Huan Meng, Jia-Liang Ren, Jia-Ning Wang, Xiao-Ping Yin

“…Logistic regression analysis was used to construct classification models based on the radiomics features extracted from contrast-enhanced T1-weighted imaging (T1CE), T2-fluid-attenuated inversion recovery (T2-FLAIR), and diffusion-weighted imaging (DWI) images. …”
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Multi-parametric MRI-based radiomics nomogram for predicting lymphovascular space invasion in early-stage cervical adenocarcinoma by Ke-Ying Wang, Mei-Ling Xiao, Mei-Ling Xiao, Yu-Han Fang, Yu-Han Fang, Jie-Jun Cheng, Zi-Jing Lin, Ying Li, Jin-Wei Qiang

“…PurposeTo develop a magnetic resonance imaging (MRI)-based radiomics nomogram to predict lymphovascular space invasion (LVSI) status in patients with early-stage cervical adenocarcinoma (CAC).MethodsClinicopathological and MRI data from 310 patients with histopathologically confirmed early-stage CAC were retrospectively analyzed. …”
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Assessment of prostate cancer aggressiveness through the combined analysis of prostate MRI and 2.5D deep learning models by Yalei Wang, Yuqing Xin, Baoqi Zhang, Fuqiang Pan, Xu Li, Manman Zhang, Yushan Yuan, Lei Zhang, Peiqi Ma, Bo Guan, Yang Zhang

“…Before pathological biopsy, all patients underwent biparametric MRI, including T2-weighted imaging, diffusion-weighted imaging, and apparent diffusion coefficient scans. …”
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