Tensor Decomposition of Largest Convolutional Eigenvalues Reveals Pathologic Predictive Power of RhoB in Rectal Cancer Biopsy

被引:4
|
作者
Pham, Tuan D. [1 ,4 ]
Ravi, Vinayakumar [1 ]
Fan, Chuanwen [2 ]
Luo, Bin [2 ,3 ]
Sun, Xiao-Feng [2 ]
机构
[1] Prince Mohammad Bin Fahd Univ, Ctr Artificial Intelligence, Khobar, Saudi Arabia
[2] Linkoping Univ, Dept Clin & Expt Med, Linkoping, Sweden
[3] Sichuan Prov Peoples Hosp, Dept Gastrointestinal Surg, Chengdu, Peoples R China
[4] Prince Mohammad Bin Fahd Univ, Ctr Artificial Intelligence, POB 1664, Khobar 31952, Saudi Arabia
来源
AMERICAN JOURNAL OF PATHOLOGY | 2023年 / 193卷 / 05期
关键词
ARTIFICIAL-INTELLIGENCE; PRECISION MEDICINE; COLORECTAL-CARCINOMA; AUTOMATED-ANALYSIS; APOPTOSIS; CLASSIFICATION; ALGORITHMS; BIOMARKERS; PROTEINS; SUPPORT;
D O I
10.1016/j.ajpath.2023.01.007
中图分类号
R36 [病理学];
学科分类号
100104 ;
摘要
RhoB protein belongs to the Rho GTPase family, which plays an important role in governing cell signaling and tissue morphology. Its expression is known to have implications in pathologic processes of diseases. In particular, the role of RhoB in rectal cancer is not well understood. Investigation in the regulation and communication of this protein, detected by immunohistochemical staining on the mi-croscope, can help gain insightful information leading to optimal disease treatment options. Herein, deep learning-based image analysis and the decomposition of multiway arrays were used to study the predictive factor of RhoB in two cohorts of patients with rectal cancer having survival rates of <5 and >5 years. The results show distinctions between the tensor decomposition factors of the two cohorts. (Am J Pathol 2023, 193: 579-590; https://doi.org/10.1016/j.ajpath.2023.01.007)
引用
收藏
页码:579 / 590
页数:12
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