The added value of quantitative contrast-enhanced CT parameters in distinguishing malignant from benign solid pulmonary nodules by Peng Yun, Zhencheng Zhan, Zijing Wu, Yutong Rao, Ke Sun, Lianggeng Gong

“…Three multivariate logistic regression models (qualitative, quantitative, and combined models) were developed and evaluated through five-fold cross-validation, DeLong tests (Bonferroni-corrected α = 0.0167), decision/calibration curves, and Bootstrap-based threshold sensitivity analysis (1000 iterations; 0.1–0.9 thresholds). …”
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Computed tomography-based radiomics model for predicting station 4 lymph node metastasis in non-small cell lung cancer by Yanru Kang, Mei Li, Xizi Xing, Kaixuan Qian, Hongxia Liu, Yafei Qi, Yanguo Liu, Yi Cui, Hua Zhang

“…Models performance were evaluated using receiver operating characteristic (ROC) analysis, calibration curves, decision curve analysis (DCA), and DeLong’s test. …”
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Correlation between carotid artery ultrasound parameters and lacunar infarction by Peipei Yang, Ying Hui, Shuohua Chen, Xinyu Zhao, Wei Huang, Xiaoshuai Li, Shouling Wu, Yuntao Wu, Ling Yang, Jing Chen, Zhenchang Wang, Xian-Quan Shi

“…Internal diameter of CCA and ICA, plaque in CCA and ICA, resistance index (RI) of CCA and ICA, pulsatility indexes (PI) of CCA and ICA, systolic/diastolic(S/D) of CCA and ICA, and carotid-femoral pulse wave velocity (cf-PWV) were positively associated with LI; peak systolic velocities (PSV) of CCA, end-diastolic velocities (EDV) of CCA and ICA, average velocity (AVG) of CCA and ICA were negatively associated with LI. Multivariate regression analysis showed that CCA-internal diameter (95%CI, 1.04–1.93; p = 0.025), CCA-plaque (bilateral) (95%CI, 1.06–4.37; p = 0.034), and cf-PWV (95%CI, 1.01–1.15; p = 0.017) were independent factors of presence of LI. …”
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A short-term predictive model for disease progression in acute-on-chronic liver failure: integrating spectral CT extracellular liver volume and clinical characteristics by Yuan Xu, Fukai Li, Bo Liu, Tiezhu Ren, Jiachen Sun, Yufeng Li, Hong Liu, Jianli Liu, Junlin Zhou

“…ECVIC−liver was measured on the equilibrium period (EP) images of spectral CT, and L3-SMI was measured on unenhanced CT images, with sarcopenia assessed. …”
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A CT-based intratumoral and peritumoral radiomics nomogram for postoperative recurrence risk stratification in localized clear cell renal cell carcinoma by Xiaoxia Li, Yi Guo, Shunfa Huang, Funan Wang, Chenchen Dai, Jianjun Zhou, Dengqiang Lin

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Deep learning radiomics of left atrial appendage features for predicting atrial fibrillation recurrence by Yanping Yin, Sixiang Jia, Jing Zheng, Wei Wang, Ziwen Wang, Jiangbo Lin, Wenting Lin, Chao Feng, Shudong Xia, Weili Ge

“…The nnUNet segmentation model achieved a Dice coefficient of 0.89. Multivariate analysis showed that LAA volume was associated with a 5.8% increase in hazard risk per unit increase (aHR 1.058, 95% CI 1.021–1.095; p = 0.002). …”
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Associative memory is more strongly predicted by age-related differences in the prefrontal cortex than medial temporal lobes by Tiago Guardia, Negar Mazloum-Farzaghi, Rosanna K. Olsen, Kamen A. Tsvetanov, Karen L. Campbell

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Atypical ductal hyperplasia diagnosed by US-guided core needle biopsy: clinical, pathological and US features associated with upgrading to malignancy by Jun Kang Li, Yong Jie Xu, Rui Lan Niu, Nai Qin Fu, Zhi Ying Jin, Shi Yu Li, Yu Chen Liu, Zhi Li Wang

“…For ADH with intraductal papilloma, age and imaging-pathological discordance were the independent risk factors for malignancy upgrading. …”
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Characterising grey-white matter relationships in recent-onset psychosis and its association with cognitive function by Yoshito Saito, Christos Pantelis, Vanessa Cropley, Liliana Laskaris, Cassandra M.J. Wannan, Warda T. Syeda

“…Understanding characteristic GM-WM patterns may also clarify the basis of cognitive impairments, which are potentially linked to network dysfunction in psychosis. Using multivariate analysis, we examined whole-brain GM-WM relationships and their association with cognitive abilities in ROP.We used T1 and diffusion-weighted images from 71 non-affective ROP individuals (age 22.09 ± 3.08) and 71 matched controls (age 22.05 ± 3.21). …”
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CT-based radiomics model for noninvasive prediction of progression-free survival in high-grade serous ovarian carcinoma: a multicenter study incorporating preoperative and postoper... by Xinping Yu, Zidong Zhang, Yuwei Zou, Chang Wang, Jinwen Jiao, Chengjian Wang, Haiyang Yu, Shuai Zhang

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Nomogram-based prediction of the prognosis in patients with free floating venous thrombus after closed traumatic fracture by Yao Wei, Changxu Guo, Xiaoyu Chen, Yu Yuan

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Growth dynamics of splenic artery aneurysms: morphology, comorbidities, and vascular anatomical factors by Ahmet Tanyeri, Aygün Katmerlikaya, Rıdvan Akbulut, Mehmet Burak Çildağ

“…Data were analysed using the Mann-Whitney U test, Kruskal-Wallis test, and chi-square test. A multivariable regression analysis was performed to assess the impact of comorbidities on aneurysm growth, while receiver operating characteristic (ROC) analysis was conducted to determine the predictive threshold value. …”
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Incidence and predictors of discrepancies in radiology resident interpretations of coronary CT in the emergency department by Na Young Kim, Ji Hoon Kim, Young Joo Suh

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Association between non-alcoholic fatty liver disease and progression of abdominal aortic aneurysm: a multicenter study by Ximing Wang, Jingxiang Sun, Na Chang, Menghan Liu, Shuai Zhang

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Preoperative pectoralis muscle index predicts distant metastasis-free survival in non-small cell lung cancer patients: a retrospective study by Zhihui Shi, Lin Wu, Dengke Jiang, Ruiling Yang, Rui Liao, Lizhu Liu, Ruimin You, Yanli Li, Xingxiang Dong, Dafu Zhang, Jing Wang, Xuewen Zhang, Xiaobo Chen, Zhenhui Li

“…In the multivariable analysis, low PMI is still associated with shorter RFS (HR = 1.34, 95% CI: (1.10, 1.62), P = 0.004), DMFS (HR = 1.35, 95% CI: (1.11, 1.65), P = 0.003), lung MFS (HR = 1.47, 95% CI: (1.19, 1.81), P < 0.001) and bone MFS (HR = 1.38, 95% CI: (1.11, 1.73), P = 0.004). …”
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2.5D deep learning radiomics and clinical data for predicting occult lymph node metastasis in lung adenocarcinoma by Xiaoxin Huang, Xiaoxiao Huang, Kui Wang, Haosheng Bai, Xiuxian Lu, Guanqiao Jin

“…Multivariable analysis was performed to identify independent clinical risk factors for constructing clinical signatures. …”
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Hybrid ICA-Seed-Based Methods for fMRI Functional Connectivity Assessment: A Feasibility Study by Robert E. Kelly, Zhishun Wang, George S. Alexopoulos, Faith M. Gunning, Christopher F. Murphy, Sarah Shizuko Morimoto, Dora Kanellopoulos, Zhiru Jia, Kelvin O. Lim, Matthew J. Hoptman

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Predict status of axillary lymph node after neoadjuvant chemotherapy with dual-energy CT in breast cancer by Zhen Wang, Zhao-Qing Fan, Li-Ze Wang, Kun Cao, Rong Long, Yao Luo, Xiao-Ting Li, Liang You, Qing-Yang Li, Ying-Shi Sun

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Predicting memory decline from left temporal lobe epilepsy surgery using preoperative fMRI: a multicenter study by William L. Gross, Sara J. Swanson, Alexander I. Helfand, Sara B. Pillay, Colin J. Humphries, Manoj Raghavan, Wade M. Mueller, Chad E. Carlson, Lisa L. Conant, Robyn M. Busch, Mark Lowe, Madalina E. Tivarus, Daniel L. Drane, Monica Jacobs, Victoria L. Morgan, Jane B. Allendorfer, Jerzy P. Szaflarski, Leonardo Bonilha, Susan Bookheimer, Thomas Grabowski, Jennifer Vannest, Jeffrey R. Binder

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The value of post radiotherapy prostate specific antigen dynamics for prostate cancer risk stratification models by Jane Shortall, Eliana Vasquez Osorio, Andrew Green, Kimberley Reeves, David Wong, Tanuj Puri, Peter Hoskin, Ananya Choudhury, Marcel van Herk, Alan McWilliam

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