Q. Xue, B. Xie, Y. Sun, Y. Niu, H. Yin, J. Duan, Z. Li, K. Wang, R. Yan
The First Affiliated Hospital of Xinxiang Medical University, Weihui, China

Impact: This study enhances preoperative diagnosis of lymphovascular invasion in rectal cancer using deep learning and multi-parameter MRI, leading to potential improved treatment strategies, reduced unnecessary surgeries, and better patient outcomes.

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J. Cao, Y. Liu, Y. Li, X. Luo
Zibo Central Hospital, Zibo, China

Impact: Deep learning based on multimodal MRI can be a helpful tool for predicts brain metastases originating from different pathological subtypes of lung cancer. Apparent diffusion coefficient maps and T1-weighted contrast enhancement sequence exhibiting optimal predictive performance.

H. Shang, Y. Fan, Y. Yang, B. Li, Y. Yi, W. Wang
The First People's Hospital of Foshan, Foshan, China

Impact: This clinical-radiomics model combining T2WI and DWI imaging offers a powerful tool for preoperative prediction of perineural invasion in rectal cancer, aiding personalized treatment planning and prognosis assessment for better clinical decision-making.

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X. Jun, X. Lizhi, N. Lang
Peking Third Hospital, Beijing, China

Impact: DWI-based habitat imaging shows clinical potential for noninvasively and preoperatively differentiating between spine MM and OM.

J. Yoon, J. S. Lee, Y. T. Oh
Severance hospital, Yonsei University College of Medicine, Seoul, Korea, Republic of

Impact: MRI-based radiomics models offer a non-invasive, cost-effective alternative to NGS, potentially reducing the need for NGS testing. These models could aid in drug selection and prognosis prediction, enhancing personalized treatment strategies.

J. Yoon, H. Han, Y. T. Oh
Severance hospital, Yonsei University College of Medicine, Seoul, Korea, Republic of

Impact: Our pilot study demonstrated promising results for a radiomics model capable of predicting genetic subtypes of prostate cancer. This model demonstrated its ability to predict AR inhibitor-resistant and docetaxel-resistant genetic subtypes with AUROCs of 0.84 and 0.77, respectively. 

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M. Tariq, J. Ruffle, S. Mohinta, S. Brandner, P. Nachev, H. Hyare
UCL, London, United Kingdom

Impact: Identification of features in the peri-lesional oedema and disconnectome associated with early tumour recurrence will enable precision treatment planning and personalised imaging surveillance in glioblastomas.

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G. Yu, A. Eresen, Z. Zhang, Q. Hou, F. Amirrad, S. Webster, S. Nauli, V. Yaghmai, Z. Zhang
University of California, Irvine, Irvine, United States

Impact: This study showcases the potential of ViT-based MRI analysis in distinguishing HCC treatment outcomes, enabling more precise combination therapy selection and advancing personalized care for improved patient outcomes.

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W. Zhao, Y. Wang, H. Kukun, R. Xu, P. Tuxunjiang, G. Han
The First Affiliated Hospital of Xinjiang Medical University, urumqi, China

Impact: Our study predicts biomarkers using glioma subregion radiomics and machine learning , enabling physicians to personalize treatment non-invasively. This approach will inspire other researchers to validate larger datasets and incorporate clinical genetic data.

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G. Valizadeh, F. Moodi, F. Khodadadi Shoushtari, M. Morafegh, M. Ghafoori, H. Saligheh Rad
Quantitative MR Imaging and Spectroscopy Group (QMISG), Tehran University of Medical Sciences, Tehran, Iran (Islamic Republic of)

Impact: This study’s integration of AI, multiparametric MRI, and clinical data refines prostate cancer diagnostics, equipping clinicians with a robust tool for precise lesion classification. This approach fosters personalized patient management, reduces overtreatment, and encourages further advancements in AI-driven oncology.

J. Bai
Xuanwu Hospital, Beijing, China

Impact: The radiomics models, which incorporate structural, proliferative, and metabolic information based on multiparameter 18F-FET PET/MRI, demonstrated effective predictive performance for the molecular biomarker status of glioma and had the potential to optimize diagnostic processes.

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F. La Rosa, E. Dereskewicz, J. Dos Santos Silva, J. Galasso, N. Garcia, R. Graney, S. Levy, H. Greenspan, D. Reich, J. Sumowski, M. Cuadra, E. Beck
Icahn School of Medicine at Mount Sinai, New York, United States

Impact: This study investigates 7T MRI for precise brain age prediction. Its application to individuals with multiple sclerosis demonstrates the potential for brain age as a valuable biomarker for assessing accelerated aging and the risk of disability progression in neurological conditions.

X. Wang, H. Zhang, S. Hu, P-Y Wu
Affiliated Hospital of Jiangnan University, Nanjing, China

Impact: The high predictive accuracy of the DL-Fusion model can enhance clinical decision-making, allowing for more precise assessment of CLNM and reducing the frequency of unnecessary CLND in PTC patients.

Z. Sadeghi Adl, S. Naghizadehkashani, L. Krisa, D. Middleton, M. Alizadeh, A. Flanders, S. Faro, F. Mohamed
Thomas Jefferson University, Philadelphia, United States

Impact: This research identifies structural MRI biomarkers for pediatric SCI severity, offering a precise tool for assessing injury severity. The approach offers clinicians a potential tool for refined injury assessment and sets a foundation for further advancements in pediatric SCI management.

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J. Lasek, A. Słowik, A. Krzyżak
AGH University of Kraków, Krakow, Poland

Impact: Using DTI metrics allows the development of machine learning models that are capable of distinguishing multiple sclerosis patients from healthy controls, enabling accurate early-stage diagnosis, and providing clinicians with a non-invasive diagnostic tool.

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P. Johnson, T. Dutt, A. S. Saimbhi, L. Ginocchio, K. T. Block, D. Sodickson, S. Chopra, A. Tong, H. Chandarana
Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, United States

Impact: This study demonstrates that a DL model can guide the selective use of mpMRI based on bpMRI, optimizing resources. This approach could streamline PCa screening, improve patient care, and inspire further research into adaptive and personalized MRI protocols.

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M. Winter, L. Kiefer, F. Schick, B. Yang
University Hospital Tuebingen, Tuebingen, Germany

Impact:

This study shows that MR-based skeletal muscle features, particularly fat fractions of the gluteus and psoas muscles, can improve diabetes classification model’s predictions. Using several MRI-derived predictors, diabetes diagnosis may become possible purely from MRI, i.e., without targeted diabetes-specific measurements.

h. Tao, Z. Zhang, S. Zhou
The First Clinical Medical College of Gansu University of Chinese Medicine, Lanzhou, China

Impact: The present meta-analysis demonstrated the substantial diagnostic value of AI in identifying BM, with CT exhibiting superior performance compared to MR.  However, further large-scale prospective studies are needed to validate the clinical utility of AI in managing BM.

X. Lu, Y. Yan, X. Chen, W. Liu, Y. Zha
Renmin Hospital of Wuhan University, Wuhan, China

Impact: The combination of AI-ACS and 4D Flow MRI provides a quantitative assessment of aortic remodeling in CKD patients, enhancing cardiovascular risk prediction and offering a novel approach for cardiovascular management in CKD.