Literature DB >> 32756021

Prediction of the Ki-67 marker index in hepatocellular carcinoma based on CT radiomics features.

Hongzhen Wu1,2, Xiaorui Han1, Zihua Wang3, Lei Mo1, Weifeng Liu1, Yuan Guo1, Xinhua Wei1, Xinqing Jiang1,2.   

Abstract

The noninvasive detection of tumor proliferation is of great value and the Ki-67 is a biomarker of tumor proliferation. We hypothesized that radiomics characteristics may be related to tumor proliferation. To evaluate whether computed tomography (CT) radiomics feature analyses could aid in assessing the Ki-67 marker index in hepatocellular carcinoma (HCC), we retrospectively analyzed preoperative CT findings of 74 patients with HCC. The texture feature calculations were computed from MaZda 4.6 software, and the sequential forward selection algorithm was used as the selection method. The correlation between radiomics features and the Ki-67 marker index, as well as the difference between low Ki-67 (<10%) and high Ki-67 (≥10%) groups were evaluated. A simple logistic regression model was used to evaluate the associations between texture features and high Ki-67, and receiver operating characteristic analysis was performed on important parameters to assess the ability of radiomics characteristics to distinguish the high Ki-67 group from the low Ki-67 group. Contrast, correlation, and inverse difference moment (IDM) were significantly different (P < 0.001) between the low and high Ki-67 groups. Contrast (odds ratio [OR] = 0.957; 95% confidence interval [CI]: 0.926-0.990, P = 0.01) and correlation (OR = 2.5☆105; 95% CI: 7.560-8.9☆109; P = 0.019) were considered independent risk factors for combined model building with logistic regression. Angular second moment (r = -0.285, P = 0.014), contrast (r = -0.449, P < 0.001), correlation (r = 0.552, P < 0.001), IDM (r = 0.458, P < 0.001), and entropy (r = 0.285, P = 0.014) strongly correlated with the Ki-67 scores. Contrast, correlation, and the combined predictor were predictive of Ki-67 status (P < 0.001), with areas under the curve ranging from 0.777 to 0.836. The radiomics characteristics of CT have potential as biomarkers for predicting Ki-67 status in patients with HCC. These findings suggest that the radiomics features of CT might be used as a noninvasive measure of cellular proliferation in HCC.

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Year:  2020        PMID: 32756021     DOI: 10.1088/1361-6560/abac9c

Source DB:  PubMed          Journal:  Phys Med Biol        ISSN: 0031-9155            Impact factor:   3.609


  5 in total

1.  Prediction of the Ki-67 expression level and prognosis of gastrointestinal stromal tumors based on CT radiomics nomogram.

Authors:  Qiuxia Feng; Bo Tang; Yudong Zhang; Xisheng Liu
Journal:  Int J Comput Assist Radiol Surg       Date:  2022-02-23       Impact factor: 2.924

Review 2.  AI in spotting high-risk characteristics of medical imaging and molecular pathology.

Authors:  Chong Zhang; Jionghui Gu; Yangyang Zhu; Zheling Meng; Tong Tong; Dongyang Li; Zhenyu Liu; Yang Du; Kun Wang; Jie Tian
Journal:  Precis Clin Med       Date:  2021-12-04

3.  Nomogram Based on CT Radiomics Features Combined With Clinical Factors to Predict Ki-67 Expression in Hepatocellular Carcinoma.

Authors:  Cuiyun Wu; Junfa Chen; Yuqian Fan; Ming Zhao; Xiaodong He; Yuguo Wei; Weidong Ge; Yang Liu
Journal:  Front Oncol       Date:  2022-07-06       Impact factor: 5.738

4.  CT Texture Analysis of Pulmonary Neuroendocrine Tumors-Associations with Tumor Grading and Proliferation.

Authors:  Hans-Jonas Meyer; Jakob Leonhardi; Anne Kathrin Höhn; Johanna Pappisch; Hubert Wirtz; Timm Denecke; Armin Frille
Journal:  J Clin Med       Date:  2021-11-26       Impact factor: 4.241

5.  Radiomic signatures based on multiparametric MR images for predicting Ki-67 index expression in medulloblastoma.

Authors:  Lili Zhou; Hong Peng; Qiang Ji; Bo Li; Lexin Pan; Feng Chen; Zishan Jiao; Yali Wang; Mengqian Huang; Gaifen Liu; Yaou Liu; Wenbin Li
Journal:  Ann Transl Med       Date:  2021-11
  5 in total

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