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Cited by in F6Publishing
For: Feng Q, Ding Z. MRI Radiomics Classification and Prediction in Alzheimer's Disease and Mild Cognitive Impairment: A Review. Curr Alzheimer Res 2020;17:297-309. [PMID: 32124697 DOI: 10.2174/1567205017666200303105016] [Cited by in Crossref: 6] [Cited by in F6Publishing: 5] [Article Influence: 6.0] [Reference Citation Analysis]
Number Citing Articles
1 Sheng C, Yang K, He B, Li T, Wang X, Du W, Hu X, Jiang J, Jiang X, Jessen F, Han Y. Cross-Cultural Longitudinal Study on Cognitive Decline (CLoCODE) for Subjective Cognitive Decline in China and Germany: A Protocol for Study Design. JAD 2022. [DOI: 10.3233/jad-215452] [Reference Citation Analysis]
2 Lin W, Gao Q, Du M, Chen W, Tong T. Multiclass diagnosis of stages of Alzheimer's disease using linear discriminant analysis scoring for multimodal data. Comput Biol Med 2021;134:104478. [PMID: 34000523 DOI: 10.1016/j.compbiomed.2021.104478] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
3 Coupé P, Manjón JV, Mansencal B, Tourdias T, Catheline G, Planche V. Hippocampal-amygdalo-ventricular atrophy score: Alzheimer disease detection using normative and pathological lifespan models. Hum Brain Mapp 2022. [PMID: 35388950 DOI: 10.1002/hbm.25850] [Reference Citation Analysis]
4 George-jones NA, Wang K, Wang J, Hunter JB. Prediction of Vestibular Schwannoma Enlargement After Radiosurgery Using Tumor Shape and MRI Texture Features. Otology & Neurotology 2021;42:e348-54. [DOI: 10.1097/mao.0000000000002938] [Cited by in Crossref: 2] [Article Influence: 1.0] [Reference Citation Analysis]
5 Shu ZY, Mao DW, Xu YY, Shao Y, Pang PP, Gong XY. Prediction of the progression from mild cognitive impairment to Alzheimer's disease using a radiomics-integrated model. Ther Adv Neurol Disord 2021;14:17562864211029551. [PMID: 34349837 DOI: 10.1177/17562864211029551] [Reference Citation Analysis]
6 Zheng J, Yu H, Batur J, Shi Z, Tuerxun A, Abulajiang A, Lu S, Kong J, Huang L, Wu S, Wu Z, Qiu Y, Lin T, Zou X. A multicenter study to develop a non-invasive radiomic model to identify urinary infection stone in vivo using machine-learning. Kidney Int 2021:S0085-2538(21)00587-1. [PMID: 34129883 DOI: 10.1016/j.kint.2021.05.031] [Reference Citation Analysis]
7 Davidovic LM, Cumic J, Dugalic S, Vicentic S, Sevarac Z, Petroianu G, Corridon P, Pantic I. Gray-Level Co-occurrence Matrix Analysis for the Detection of Discrete, Ethanol-Induced, Structural Changes in Cell Nuclei: An Artificial Intelligence Approach. Microsc Microanal 2021;:1-7. [PMID: 34937605 DOI: 10.1017/S1431927621013878] [Reference Citation Analysis]
8 Yang S, Wang Y, Shi Y, Yang G, Yan Q, Shen J, Wang Q, Zhang H, Yang S, Shan F, Zhang Z. Radiomics nomogram analysis of T2-fBLADE-TSE in pulmonary nodules evaluation. Magn Reson Imaging 2022;85:80-6. [PMID: 34666158 DOI: 10.1016/j.mri.2021.10.010] [Reference Citation Analysis]