METnet: A novel deep learning model predicting MET dysregulation in non-small-cell lung cancer on computed tomography images

被引：1

作者：

Sun, Yige ^{[1
,2
]}

Guo, Jirui ^{[3
]}

Liu, Yang ^{[1
]}

Wang, Nan ^{[4
]}

Xu, Yanwei ^{[5
]}

Wu, Fei ^{[6
]}

Xiao, Jianxin ^{[1
]}

Li, Yingpu ^{[7
]}

Wang, Xinxin ^{[1
]}

Hu, Yang ^{[3
]}

Zhou, Yang ^{[1
]}

机构：

[1] Harbin Med Univ, Canc Hosp, Dept Radiol, 150 Haping Rd, Harbin 150010, Heilongjiang, Peoples R China

[2] Harbin Med Univ, State Prov Key Lab Biomed Pharmaceut China, Genom Res Ctr Key Lab Gut Microbiota & Pharmacogen, Coll Pharm, 157 Baojian Rd, Harbin 150081, Peoples R China

[3] Harbin Inst Technol, Fac Comp, Ctr Bioinformat, Harbin 150001, Heilongjiang, Peoples R China

[4] Beidahuang Ind Grp Gen Hosp, Harbin 150088, Peoples R China

[5] Beidahuang Grp Neuropsychiat Hosp, Jiamusi 154000, Peoples R China

[6] Harbin Med Univ, Dept Geriatr, Affiliated Hosp 2, Harbin 150086, Heilongjiang, Peoples R China

[7] Harbin Med Univ, Dept Oncol Surg, Dept Thorac Surg, Canc Hosp, Harbin 150000, Heilongjiang, Peoples R China

来源：

COMPUTERS IN BIOLOGY AND MEDICINE | 2024年 / 171卷

关键词：

Non -small cell lung cancer; MET; Deep learning; Computed tomography; MedSAM; MUTATIONS; EGFR;

D O I：

10.1016/j.compbiomed.2024.108136

中图分类号：

Q [生物科学];

学科分类号：

07 ; 0710 ; 09 ;

摘要：

Background: Mesenchymal epithelial transformation (MET) is a key molecular target for diagnosis and treatment of non -small cell lung cancer (NSCLC). The corresponding molecularly targeted therapeutics have been approved by Food and Drug Administration (FDA), achieving promising results. However, current detection of MET dysregulation requires biopsy and gene sequencing, which is invasive, time-consuming and difficult to obtain tumor samples. Methods: To address the above problems, we developed a noninvasive and convenient deep learning (DL) model based on Computed tomography (CT) imaging data for prediction of MET dysregulation. We introduced the unsupervised algorithm RK-net for automated image processing and utilized the MedSAM large model to achieve automated tissue segmentation. Based on the processed CT images, we developed a DL model (METnet). The model based on the grouped convolutional block. We evaluated the performance of the model over the internal test dataset using the area under the receiver operating characteristic curve (AUROC) and accuracy. We conducted subgroup analysis on the basis of clinical data of the lung cancer patients and compared the performance of the model in different subgroups. Results: The model demonstrated a good discriminative ability over the internal test dataset. The accuracy of METnet was 0.746 with an AUC value of 0.793 (95% CI 0.714-0.871). The subgroup analysis revealed that the model exhibited similar performance across different subgroups. Conclusions: METnet realizes prediction of MET dysregulation in NSCLC, holding promise for guiding precise tumor diagnosis and treatment at the molecular level.

引用

页数：10