A feasibility study on multimodal CT-MRI registration using segmentation aid and CoLlAGe feature extraction approach [0.03%]
基于分割辅助和CoLlAGe特征提取的多模态CT-MRI配准研究
Hang Phuong Nguyen,Se Young Jang,Sungmin Kim
Hang Phuong Nguyen
Purpose: This study proposes a framework to address the problem of multimodal MRI-to-CT image registration by incorporating feature-based registration approach and segmentation, focusing especially on liver-specific clini...
MKNet-family architectures for auto-segmentation of the residual pancreas after pancreatic resection: a deep learning comparative study [0.03%]
基于深度学习的胰腺切除术后残余胰腺自动分割的MKNet家族网络架构研究
Dennis Böhm,Paul C M Andel,Paul A Akkermans et al.
Dennis Böhm et al.
Purpose: Accurate interpretation of CT scans after pancreatic resection is crucial for detecting abnormalities, including postoperative complications and cancer recurrence. This study investigates the feasibility and clin...
Appendicolith detection in dual-energy CT of adult acute appendicitis: comparing portovenous phase and virtual noncontrast with true noncontrast images [0.03%]
双能量CT在成人急性阑尾炎中的附件石检测：门静脉期和虚拟非对比图像与真正的非对比图像的比较
Rathachai Kaewlai,Jitti Chatpuwaphat,Sasima Tongsai et al.
Rathachai Kaewlai et al.
Objectives: Appendicoliths are associated with failed nonoperative management in acute appendicitis and are used to exclude patients from this treatment. This study evaluated whether portovenous phase (PVP) and virtual no...
Imaging findings in fumarate hydratase-deficient renal cell carcinoma: a case series of 11 patients [0.03%]
富马酸水合酶缺乏型肾细胞癌的影像学表现：11例分析
Naoya Ebisu,Yoshiko Ueno,Takamichi Murakami et al.
Naoya Ebisu et al.
Purpose: Fumarate hydratase (FH)-deficient renal cell carcinoma (RCC) is a rare and aggressive RCC subtype defined in the 2022 WHO classification. This study aimed to describe its imaging and clinicopathological features....
Physics-aware imaging AI for quantitative MASLD biomarker mapping: a systematic review of deep learning and radiomics across ultrasound, CT, and MRI [0.03%]
基于物理的成像人工智能用于定量MASLD生物标志物映射：超声、CT和MRI深度学习和影像组学系统综述
Houshyar Maghsoudi,Ahmad Khonche,Reza Gereami et al.
Houshyar Maghsoudi et al.
Objective: This systematic review critically appraises the current landscape of physics-aware artificial intelligence (AI) in medical imaging for quantitative biomarker mapping in Metabolic dysfunction-associated steatoti...
Multiparametric MRI-based radiomics machine learning nomogram for predicting aggressive histology in endometrial cancer [0.03%]
基于多参数MRI的影像组学机器学习预测子宫内膜癌恶性组织学的列线图模型
Ruqi Fang,Xiaojuan Zheng,Keyi Wu et al.
Ruqi Fang et al.
Objective: To develop and validate a radiomics-based machine learning nomogram using multiparametric MRI for preoperative prediction of aggressive histology in endometrial cancer (EC) patients. ...
CT‑based radiomics of bowel wall at baseline predicts the efficacy of Ustekinumab at week 16 in patients with Crohn's disease [0.03%]
基线结肠壁的CT影像组学可预测克罗恩病患者第16周ustekinumab疗效
Minyi Guo,Yilin Guan,Siqi Hu et al.
Minyi Guo et al.
Objectives: Ustekinumab is a biological treatment for Crohn's disease, but some patients do not respond. This study aimed to assess the role of radiomic techniques in predicting the treatment response by quantifying trans...
Charlotte Charbel,Samuel J Withey,Eva Serrao et al.
Charlotte Charbel et al.
Hereditary renal cell carcinoma (RCC) accounts for approximately 5-8% of all renal cancers. This review provides a comprehensive overview of the seven hereditary RCC syndromes recognized by the National Comprehensive Cancer Network: Tuberou...
Hepatic fat fraction and lipid content of adrenal adenomas: insights from unenhanced CT [0.03%]
无 Enhancement CT 下肝脏脂肪分数与肾上腺腺瘤脂质含量的关系研究
Osman Konukoglu,Murat Kaya,Ergul Cindemir
Osman Konukoglu
Background: Adrenal incidentalomas (AI) are increasingly detected with the widespread use of abdominal computed tomography (CT). Although a relationship between AI, metabolic syndrome, and metabolic dysfunction-associated...
Deep learning for non-invasive detection of steatosis and fibrosis in MASLD: a multicenter study with a new fibroscan-labelled ultrasound dataset [0.03%]
深度学习在非酒精性脂肪肝病中的无创检测：一项新的超声和fibroscan联合数据集的多中心研究
Kartik Bose,Priya Mudgil,Pankaj Gupta et al.
Kartik Bose et al.
Purpose: This study aimed to develop and validate deep learning models for non-invasive assessment of hepatic steatosis and fibrosis using conventional B-mode ultrasound images, with Fibroscan-derived measurement as refer...