Development of a Malignancy Potential Binary Prediction Model Based on Deep Learning for the Mitotic Count of Local Primary Gastrointestinal Stromal Tumors.

Journal: Korean journal of radiology

Published Date: Oct 21, 2020

Abstract

OBJECTIVE: The mitotic count of gastrointestinal stromal tumors (GIST) is closely associated with the risk of planting and metastasis. The purpose of this study was to develop a predictive model for the mitotic index of local primary GIST, based on deep learning algorithm.

Authors

Jiejin Yang

Department of Radiology, Peking University First Hospital, Peking University, Beijing, China.
Zeyang Chen

Department of General Surgery, Peking University First Hospital, Peking University, Beijing, China.
Weipeng Liu

College of Materials and Energy, South China Agricultural University, Guangzhou, 510642, China.
Xiangpeng Wang

Beijing Smart Tree Medical Technology Co. Ltd., Beijing, China.
Shuai Ma
Feifei Jin

Department of Biostatistics, Peking University First Hospital, Beijing, China.
XiaoYing Wang

Keywords

Aged Area Under Curve Cohort Studies Contrast Media Deep Learning Female Gastrointestinal Stromal Tumors Humans Image Processing, Computer-Assisted Male Middle Aged Multidetector Computed Tomography Predictive Value of Tests Retrospective Studies ROC Curve Sensitivity and Specificity

External Resources

View on PubMed Access via DOI PubMed (33169545)

Development of a Malignancy Potential Binary Prediction Model Based on Deep Learning for the Mitotic Count of Local Primary Gastrointestinal Stromal Tumors.

Abstract

Authors

Keywords

External Resources

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