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Deep Learning Radiomics Model of Contrast-Enhanced CT for Differentiating the Primary Source of Liver Metastases.
Jia, Wenjing; Li, Fuyan; Cui, Yi; Wang, Yong; Dai, Zhengjun; Yan, Qingqing; Liu, Xinhui; Li, Yuting; Chang, Huan; Zeng, Qingshi.
Afiliação
  • Jia W; Department of Radiology, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Jinan, China; Shandong First Medical University, Jinan, China. Electronic address: jiawj12@163.com.
  • Li F; Department of Radiology, Shandong Provincial Hospital affiliated to Shandong First Medical University, Jinan, China. Electronic address: fuyanli1234@sina.com.
  • Cui Y; Department of Radiology, Qilu Hospital of Shandong University, Jinan, China. Electronic address: 22hua@sina.com.
  • Wang Y; Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China. Electronic address: doctorwy@163.com.
  • Dai Z; Scientific Research Department, Huiying Medical Technology Co., Ltd, Beijing, China. Electronic address: daizhengjun@huiyihuiying.com.
  • Yan Q; Department of Radiology, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Jinan, China. Electronic address: yanqing10207@163.com.
  • Liu X; Department of Radiology, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Jinan, China. Electronic address: lxh0725hxl@163.com.
  • Li Y; Department of Radiology, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Jinan, China. Electronic address: 523072559@qq.com.
  • Chang H; Department of Radiology, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Jinan, China. Electronic address: 17863991278@163.com.
  • Zeng Q; Department of Radiology, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Jinan, China. Electronic address: zengqingshi@sina.com.
Acad Radiol ; 2024 May 02.
Article em En | MEDLINE | ID: mdl-38702214
ABSTRACT
RATIONALE AND

OBJECTIVES:

To develop and validate a deep learning radiomics (DLR) model based on contrast-enhanced computed tomography (CT) to identify the primary source of liver metastases. MATERIALS AND

METHODS:

In total, 657 liver metastatic lesions, including breast cancer (BC), lung cancer (LC), colorectal cancer (CRC), gastric cancer (GC), and pancreatic cancer (PC), from 428 patients were collected at three clinical centers from January 2018 to October 2023 series. The lesions were randomly assigned to the training and validation sets in a 73 ratio. An additional 112 lesions from 61 patients at another clinical center served as an external test set. A DLR model based on contrast-enhanced CT of the liver was developed to distinguish the five pathological types of liver metastases. Stepwise classification was performed to improve the classification efficiency of the model. Lesions were first classified as digestive tract cancer (DTC) and non-digestive tract cancer (non-DTC). DTCs were divided into CRC, GC, and PC and non-DTCs were divided into LC and BC. To verify the feasibility of the DLR model, we trained classical machine learning (ML) models as comparison models. Model performance was evaluated using accuracy (ACC) and area under the receiver operating characteristic curve (AUC).

RESULTS:

The classification model constructed by the DLR algorithm showed excellent performance in the classification task compared to ML models. Among the five categories task, highest ACC and average AUC were achieved at 0.563 and 0.796 in the validation set, respectively. In the DTC and non-DTC and the LC and BC classification tasks, AUC was achieved at 0.907 and 0.809 and ACC was achieved at 0.843 and 0.772, respectively. In the CRC, GC, and PC classification task, ACC and average AUC were the highest, at 0.714 and 0.811, respectively.

CONCLUSION:

The DLR model is an effective method for identifying the primary source of liver metastases.
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Texto completo: 1 Base de dados: MEDLINE Idioma: En Ano de publicação: 2024 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Idioma: En Ano de publicação: 2024 Tipo de documento: Article