Large language models in radiology reporting: Bridging semantics, education, and safety [0.03%]
大型语言模型在放射学报告中的应用：语义、教育和安全性的桥梁
Eren Çamur,Turay Cesur,Yasin Celal Güneş
Eren Çamur
Development and validation of a radiomics model based on the ASPECTS framework using CT imaging for predicting malignant cerebral edema [0.03%]
基于ASPECTS框架的CT影像放射组学模型预测恶性脑水肿的研究与验证
LiJun Huang,XiaoQuan Xu,Bing Tian et al.
LiJun Huang et al.
Purpose: Accurate delineation of the infarct region on acute-phase Computed Tomography (CT) remains challenging, and radiomics applications in stroke are limited. We aimed to develop and validate a multimodal prediction m...
Efficient T staging in nasopharyngeal carcinoma via deep Learning-Based Multi-Modal classification [0.03%]
基于深度学习的多模态分类在鼻咽癌T分期中的高效应用研究
Dili Song,Xu Han,Yong Li et al.
Dili Song et al.
Background: Accurate T staging of nasopharyngeal carcinoma (NPC) is crucial for precision therapeutic strategies. The T staging process is associated with significant challenges, including time consumption and variability...
Quantification of tumor heterogeneity based on fractal dimension for predicting the response to neoadjuvant chemotherapy in triple-negative breast cancer [0.03%]
基于分形维数量化三阴性乳腺癌新辅助化疗反应的肿瘤异质性预测模型
Jiamin Guo,Ying Liu,Wei Ren et al.
Jiamin Guo et al.
Background: Triple-negative breast cancer (TNBC) exhibits high heterogeneity, leading to variable responses to neoadjuvant chemotherapy (NAC) among patients. Noninvasive quantification of intratumoral heterogeneity (ITH) ...
Metal artifact reduction from surgical clips for intracranial aneurysms in photon-counting detector CT angiography [0.03%]
光子计数检测CT血管造影中颅内动脉瘤手术夹金属伪影减少
Masahiro Nakashima,Tatsuya Kawai,Kazuhisa Matsumoto et al.
Masahiro Nakashima et al.
Objectives: To identify conditions that reduce metal artifacts from surgical clips for intracranial aneurysms while preserving parent vessel visualization in CT angiography (CTA) by using a reconstruction method combining...
A novel multimodal framework combining habitat radiomics, deep learning, and conventional radiomics for predicting MGMT gene promoter methylation in Glioma: Superior performance of integrated models [0.03%]
一种结合放射组学、深度学习和传统影像组学的多模态框架预测胶质瘤MGMT启动子甲基化的研究：集成模型具有更好的性能
Feng-Ying Zhu,Wen-Jing Chen,Hao-Yan Chen et al.
Feng-Ying Zhu et al.
Purpose: The present study aimed to develop a noninvasive predictive framework that integrates clinical data, conventional radiomics, habitat imaging, and deep learning for the preoperative stratification of MGMT gene pro...
Dynamic AI-assisted ipsilateral tissue matching for digital breast tomosynthesis [0.03%]
动态人工智能辅助的乳房数字断层合成术同侧组织匹配技术
Stephen Morrell,Michael Hutel,Oeslle Lucena et al.
Stephen Morrell et al.
Purpose: To evaluate whether AI-assisted ipsilateral tissue matching in digital breast tomosynthesis (DBT) reduces localization errors beyond typical tumor boundaries, particularly for non-expert radiologists. The technol...
Short occipital circulation time derived from quantitative digital subtraction angiography is associated with headache risk in patients with unruptured brain arteriovenous malformations [0.03%]
定量数字减影血管造影衍生的短枕部灌注时间与未破裂脑动静脉畸形患者的头痛风险相关
Yong-Sin Hu,Jr-Wei Wu,Huai-Che Yang et al.
Yong-Sin Hu et al.
Purpose: To explored key angiographic markers associated with headache risk in patients with unruptured brain arteriovenous malformations (BAVMs). Methods...
Spatial heterogeneity and distribution of CT-Based pulmonary vascular volumes in chronic thromboembolic pulmonary hypertension [0.03%]
基于CT的慢性血栓性肺动脉高压患者的肺血管容积分布及异质性分析
Andrew J Synn,Pietro Nardelli,Rahul Renapurkar et al.
Andrew J Synn et al.
Rationale/objectives: Image-based vascular biomarkers may help expedite evaluation of chronic thromboembolic pulmonary hypertension (CTEPH), which remains difficult to diagnose despite available effective therapies. We so...
End-to-end deep learning model with multi-channel and attention mechanisms for multi-class diagnosis in CT-T staging of advanced gastric cancer [0.03%]
多通道和注意机制的端到端深度学习模型在CT-T分期的晚期胃癌多分类诊断中的应用
Bowen Liu,Pengcheng Jiang,Zehui Wang et al.
Bowen Liu et al.
Background: Homogeneous AI assessment is required for CT-T staging of gastric cancer. Purpose: To construct an End-to-End CT-based Deep...