Xiaomeng Lei
University of Southern California
46 Papers
15 Citations
Xiaomeng Lei is an academic researcher from University of Southern California. The author has contributed to research in topics: Medicine & Internal medicine. The author has an hindex of 4, co-authored 22 publications. Previous affiliations of Xiaomeng Lei include Claremont Graduate University.
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Papers
Machine learning based predictors for COVID-19 disease severity.
Dhruv V. Patel,Vikram Kher,Bhushan Desai,Xiaomeng Lei,Steven Cen,Neha Nanda,Ali Gholamrezanezhad,Vinay Duddalwar,Bino Varghese,Assad A. Oberai +9 more
TL;DR: The relative importance of blood panel profile data was determined and it was noted that the AUC dropped by 0.12 units when this data was not included, thus indicating that it provided valuable data in predicting disease severity.
CT-based radiomics stratification of tumor grade and TNM stage of clear cell renal cell carcinoma.
Natalie L. Demirjian,Bino Varghese,Steven Cen,Darryl Hwang,Manju Aron,Imran Siddiqui,Brandon K. K. Fields,Xiaomeng Lei,Felix Y. Yap,Marielena Rivas,Sharath S. Reddy,Haris Zahoor,Derek Liu,Mihir M. Desai,Suhn K. Rhie,Inderbir S. Gill,Vinay Duddalwar +16 more
TL;DR: In this article, CT-based radiomics signatures were used to discriminate low grade (grades 1-2) clear cell renal cell carcinomas (ccRCC) from high-grade (grades 3-4) and low TNM stage (stages I-II) ccRCC from high stage (stage III-IV).
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Shape and texture-based radiomics signature on CT effectively discriminates benign from malignant renal masses.
Felix Y. Yap,Bino Varghese,Steven Cen,Darryl Hwang,Xiaomeng Lei,Bhushan Desai,Christopher Lau,Lindsay Yang,Austin Fullenkamp,Simin Hajian,Marielena Rivas,Megha Gupta,Brian D. Quinn,Manju Aron,Mihir M. Desai,Monish Aron,Assad A. Oberai,Inderbir S. Gill,Vinay Duddalwar +18 more
TL;DR: Current radiomics research is heavily weighted towards texture analysis, but quantitative shape metrics should not be ignored in their potential to distinguish benign from malignant renal tumors, and future radiomics platforms powered by machine learning should harness both shape and texture metrics.
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Whole-tumor 3D volumetric MRI-based radiomics approach for distinguishing between benign and malignant soft tissue tumors
Brandon K.K. Fields,Natalie L. Demirjian,Darryl Hwang,Bino Varghese,Steven Cen,Xiaomeng Lei,Bhushan Desai,Vinay Duddalwar,George R. Matcuk +8 more
TL;DR: In this paper, the authors used a combination of MRI-based radiomics metrics and machine learning to differentiate malignant from benign soft tissue neoplasms using a set of 3D ROIs drawn on 1 sequence of interest and co-registered to other sequences obtained during the same study.
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Identification of robust and reproducible CT-texture metrics using a customized 3D-printed texture phantom.
Bino Varghese,Darryl Hwang,Steven Cen,Xiaomeng Lei,Joshua R. Levy,Bhushan Desai,D Goodenough,Vinay Duddalwar +7 more
TL;DR: In this paper, the authors evaluated the robustness and reproducibility of computed tomography-based texture analysis (CTTA) metrics extracted from CT images of a customized texture phantom built for assessing the association of texture metrics to 3D printed progressively increasing textural heterogeneity.
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