-
- Jiejin Yang, Zeyang Chen, Weipeng Liu, Xiangpeng Wang, Shuai Ma, Feifei Jin, and Xiaoying Wang.
- Department of Radiology, Peking University First Hospital, Peking University, Beijing, China.
- Korean J Radiol. 2021 Mar 1; 22 (3): 344-353.
ObjectiveThe 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.Materials And MethodsAbdominal contrast-enhanced CT images of 148 pathologically confirmed GIST cases were retrospectively collected for the development of a deep learning classification algorithm. The areas of GIST masses on the CT images were retrospectively labelled by an experienced radiologist. The postoperative pathological mitotic count was considered as the gold standard (high mitotic count, > 5/50 high-power fields [HPFs]; low mitotic count, ≤ 5/50 HPFs). A binary classification model was trained on the basis of the VGG16 convolutional neural network, using the CT images with the training set (n = 108), validation set (n = 20), and the test set (n = 20). The sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were calculated at both, the image level and the patient level. The receiver operating characteristic curves were generated on the basis of the model prediction results and the area under curves (AUCs) were calculated. The risk categories of the tumors were predicted according to the Armed Forces Institute of Pathology criteria.ResultsAt the image level, the classification prediction results of the mitotic counts in the test cohort were as follows: sensitivity 85.7% (95% confidence interval [CI]: 0.834-0.877), specificity 67.5% (95% CI: 0.636-0.712), PPV 82.1% (95% CI: 0.797-0.843), NPV 73.0% (95% CI: 0.691-0.766), and AUC 0.771 (95% CI: 0.750-0.791). At the patient level, the classification prediction results in the test cohort were as follows: sensitivity 90.0% (95% CI: 0.541-0.995), specificity 70.0% (95% CI: 0.354-0.919), PPV 75.0% (95% CI: 0.428-0.933), NPV 87.5% (95% CI: 0.467-0.993), and AUC 0.800 (95% CI: 0.563-0.943).ConclusionWe developed and preliminarily verified the GIST mitotic count binary prediction model, based on the VGG convolutional neural network. The model displayed a good predictive performance.Copyright © 2021 The Korean Society of Radiology.
Notes
Knowledge, pearl, summary or comment to share?You can also include formatting, links, images and footnotes in your notes
- Simple formatting can be added to notes, such as
*italics*
,_underline_
or**bold**
. - Superscript can be denoted by
<sup>text</sup>
and subscript<sub>text</sub>
. - Numbered or bulleted lists can be created using either numbered lines
1. 2. 3.
, hyphens-
or asterisks*
. - Links can be included with:
[my link to pubmed](http://pubmed.com)
- Images can be included with:
![alt text](https://bestmedicaljournal.com/study_graph.jpg "Image Title Text")
- For footnotes use
[^1](This is a footnote.)
inline. - Or use an inline reference
[^1]
to refer to a longer footnote elseweher in the document[^1]: This is a long footnote.
.