Accuracy of MRI Classification Algorithms in a Tertiary Memory Center Clinical Routine Cohort
AP-HP（巴黎公立医院集团）；Hôpital de la Pitié-Salpêtrière（皮蒂-萨尔皮特里医院）；Sorbonne Universités（索邦大学）；UPMC Univ Paris 06（巴黎第六大学（皮埃尔和玛丽·居里大学））；Inserm（法国国家健康与医学研究院）；CNRS（法国国家科学研究中心）；ICM（脑与脊髓研究所）；Inria（法国国家信息与自动化研究所）；Laboratoire Bordelais de Recherche en Informatique（波尔多计算机科学实验室）；Brainreader；AXA Research Fund（安盛研究基金）；Pierre et Marie Curie University（皮埃尔和玛丽·居里大学）
BACKGROUND:Automated volumetry software (AVS) has recently become widely available to neuroradiologists. MRI volumetry with AVS may support the diagnosis of dementias by identifying regional atrophy. Moreover, automatic classifiers using machine learning techniques have recently emerged as promising approaches to assist diagnosis. However, the performance of both AVS and automatic classifiers has been evaluated mostly in the artificial setting of research datasets.OBJECTIVE:Our aim was to evaluate the performance of two AVS and an automatic classifier in the clinical routine condition of a memory clinic.METHODS:We studied 239 patients with cognitive troubles from a single memory center cohort. Using clinical routine T1-weighted MRI, we evaluated the classification performance of: 1) univariate volumetry using two AVS (volBrain and Neuroreader$^{TM}$); 2) Support Vector Machine (SVM) automatic classifier, using either the AVS volumes (SVM-AVS), or whole gray matter (SVM-WGM); 3) reading by two neuroradiologists. The performance measure was the balanced diagnostic accuracy. The reference standard was consensus diagnosis by three neurologists using clinical, biological (cerebrospinal fluid) and imaging data and following international criteria.RESULTS:Univariate AVS volumetry provided only moderate accuracies (46% to 71% with hippocampal volume). The accuracy improved when using SVM-AVS classifier (52% to 85%), becoming close to that of SVM-WGM (52 to 90%). Visual classification by neuroradiologists ranged between SVM-AVS and SVM-WGM.CONCLUSION:In the routine practice of a memory clinic, the use of volumetric measures provided by AVS yields only moderate accuracy. Automatic classifiers can improve accuracy and could be a useful tool to assist diagnosis.
Erasmus MC（伊拉斯谟医学中心）；Maastricht University Medical Center（马斯特里赫特大学医学中心）；University Medical Center Groningen（格罗宁根大学医学中心）；University Medical Center Utrecht（乌得勒支大学医学中心）；Radboud University Medical Center（拉德堡德大学医学中心）；Leiden University Medical Center（莱顿大学医学中心）；Leiden University（莱顿大学）
This work validates the generalizability of MRI-based classification of Alzheimer's disease (AD) patients and controls (CN) to an external data set and to the task of prediction of conversion to AD in individuals with mild cognitive impairment (MCI). We used a conventional support vector machine (SVM) and a deep convolutional neural network (CNN) approach based on structural MRI scans that underwent either minimal pre-processing or more extensive pre-processing into modulated gray matter (GM) maps. Classifiers were optimized and evaluated using cross-validation in the ADNI (334 AD, 520 CN). Tr…
FTU-Seek: Foundation Model-Guided Hard-Negative Learning for Sparse Functional Tissue Unit Segmentation
Clinical Oncology School, Fujian Medical University（福建医科大学临床肿瘤学院）；Innovation Center for Cancer Research, Clinical Oncology School, Fujian Medical University（福建医科大学临床肿瘤学院癌症研究创新中心）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；University of Chinese Academy of Sciences（中国科学院大学）；College of Engineering, Boston University（波士顿大学工程学院）；Northeastern University（东北大学）；Fujian Cancer Hospital（福建省肿瘤医院）
Whole-body CT attenuation and volume charts from routine clinical scans via evidence-grounded LLM report filtering
Institute of Radiology, School of Medicine and Health, Technical University of Munich（医学与健康学院放射学研究所，慕尼黑技术大学）；Institute of Neuroradiology, School of Medicine and Health, Technical University of Munich（医学与健康学院神经放射学研究所，慕尼黑技术大学）；Munich Data Science Institute (MDSI), Technical University of Munich（慕尼黑数据科学研究所（MDSI），慕尼黑技术大学）；Munich Center for Machine Learning (MCML), Munich, Germany（慕尼黑机器学习中心（MCML），德国慕尼黑）
Large-Scale Pre-training Enables Multimodal AI Differentiation of Radiation Necrosis from Brain Metastasis Progression on Routine MRI
University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg（埃尔朗根大学医院，埃尔朗根-纽伦堡弗里德里希-亚历山大大学）；Comprehensive Cancer Center Erlangen-EMN (CCC ER-EMN)（埃尔朗根-欧洲大都会区纽伦堡综合癌症中心）；Bavarian Cancer Research Center (BZKF)（巴伐利亚癌症研究中心）；Universität Würzburg（维尔茨堡大学）；Peking University（北京大学）；Universitätsklinikum Würzburg（维尔茨堡大学医院）
Case Western Reserve University（凯斯西储大学）；Picture Health（Picture Health公司）；Stony Brook University（石溪大学）；University Hospitals Cleveland Medical Center（克利夫兰大学医院医学中心）；Yale School of Medicine（耶鲁医学院）；The Cleveland Clinic Foundation (CCF)（克利夫兰诊所基金会（CCF））；New York University (NYU) Langone Medical Center（纽约大学（NYU）朗格尼医学中心）；Louis Stokes Cleveland Veterans Medical Center（路易斯·斯托克斯克利夫兰退伍军人医学中心）
Purpose: Tumor-associated vasculature differs from healthy blood vessels by its chaotic architecture and twistedness, which promotes treatment resistance. Measurable differences in these attributes may help stratify patients by likely benefit of systemic therapy (e.g. chemotherapy). In this work, we present a new category of radiomic biomarkers called quantitative tumor-associated vasculature (QuanTAV) features, and demonstrate their ability to predict response and survival across multiple cancers, imaging modalities, and treatment regimens. Experimental Design: We segmented tumor vessels and …
DepViT-CAD: Deployable Vision Transformer-Based Cancer Diagnosis in Histopathology
University of Padova, Italy（意大利帕多瓦大学）；Unit of Surgical Pathology and Cytopathology, Ospedale dell’Angelo, Italy（意大利奥斯佩达莱-安吉洛外科病理学与细胞病理学单位）；Pathological Anatomy Unit, Padova University-Hospital, Italy（意大利帕多瓦大学医院病理学解剖单位）；Department of Medicine, University of Padova, Italy（意大利帕多瓦大学医学系）；Department of Integrated Diagnostics, Azienda Ospedale-Università Padova, Italy（意大利帕多瓦医院-大学整合诊断系）
Cyclic 2.5D Perceptual Loss for Cross-Modal 3D Medical Image Synthesis: T1w MRI to Tau PET
Department of Artificial Intelligence, Hanyang University, Seoul, South Korea（人工智能系，翰阳大学，首尔，韩国）；Department of Electronic Engineering, Hanyang University, Seoul, South Korea（电子工程系，翰阳大学，首尔，韩国）；Division of Computer Engineering, Hankuk University of Foreign Studies, Yongin, South Korea（计算机工程系，韩国外语大学， Yongin，韩国）；Division of AI Data Convergence, Hankuk University of Foreign Studies, Yongin, South Korea（AI数据融合系，韩国外语大学， Yongin，韩国）；Division of Language & AI, Hankuk University of Foreign Studies, Seoul, South Korea（语言与AI系，韩国外语大学，首尔，韩国）
Automated Landmark Detection for assessing hip conditions: A Cross-Modality Validation of MRI versus X-ray
MaLGa Center, DIBRIS University of Genoa（MaLGa中心，热那亚大学DIBRIS学院）；Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences University of Oxford（牛津大学风湿病学与肌肉骨骼科学系）；Department of Computer Science University of Oxford（牛津大学计算机科学系）
University Hospital Erlangen（埃尔朗根大学医院）；Comprehensive Cancer Center Erlangen-EMN (CCC ER-EMN)（埃尔朗根-EMN综合癌症中心）；Pattern Recognition Lab（模式识别实验室）
Stereotactic radiotherapy (SRT) is one of the most important treatment for patients with brain metastases (BM). Conventionally, following SRT patients are monitored by serial imaging and receive salvage treatments in case of significant tumor growth. We hypothesized that using radiomics and machine learning (ML), metastases at high risk for subsequent progression could be identified during follow-up prior to the onset of significant tumor growth, enabling personalized follow-up intervals and early selection for salvage treatment. All experiments are performed on a dataset from clinical routine…
Identifying acute illness phenotypes via deep temporal interpolation and clustering network on physiologic signatures
University of Florida（佛罗里达大学）；Intelligent Critical Care Center（智能重症监护中心）；College of Pharmacy（药学院）；Center for Natural Products, Drug Discovery and Development（天然产物、药物发现与开发中心）；J. Crayton Pruitt Family Department of Biomedical Engineering（J.克雷顿·普鲁伊特家族生物医学工程系）
