Predicting Peritoneal Metastasis of Gastric Cancer Patients Based on Machine Learning
Objective: The aim is to explore the prediction effect of 5 machine learning algorithms on peritoneal metastasis of gastric cancer. Methods: 1080 patients with postoperative gastric cancer were divided into a training group and test group according to the ratio of 7:3. The model of peritoneal metast...
Main Authors: | Chengmao Zhou PhD, Ying Wang MD, Mu-Huo Ji MD, Jianhua Tong MD, Jian-Jun Yang PhD, Hongping Xia PhD |
---|---|
Format: | Article |
Language: | English |
Published: |
SAGE Publishing
2020-10-01
|
Series: | Cancer Control |
Online Access: | https://doi.org/10.1177/1073274820968900 |
Similar Items
-
Perforated gastric metastasis of Merkel cell carcinoma: Case report and review of the literature
by: Darshan Trivedi, MD, PhD, et al.
Published: (2017-06-01) -
A machine learning-based predictor for the identification of the recurrence of patients with gastric cancer after operation
by: Chengmao Zhou, et al.
Published: (2021-01-01) -
Mature ovarian cystic teratoma with disseminated nodular lesions in the pleural and peritoneal cavities: A case report
by: Takamichi Minato, MD, et al.
Published: (2018-06-01) -
Metabolic Profiling of Human Gastric Cancer Cells Treated With Salazosulfapyridine
by: Kohei Takizawa MD, et al.
Published: (2020-06-01) -
Gastric CLTC-ALK fusion-positive inflammatory myofibroblastic tumor showing an endoscopic superficial depressed-type appearance
by: Mai Nakanishi, MD, et al.
Published: (2019-03-01)