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Deep Convolutional Neural Networks Detect Tumor Genotype from Pathological Tissue Images in Gastrointestinal Stromal Tumors
| Content Provider | MDPI |
|---|---|
| Author | Liang, Cher-Wei Fang, Pei-Wei Huang, Hsuan-Ying Lo, Chung-Ming |
| Copyright Year | 2021 |
| Description | Gastrointestinal stromal tumors (GIST) are common mesenchymal tumors, and their effective treatment depends upon the mutational subtype of the KIT/PDGFRA genes. We established deep convolutional neural network (DCNN) models to rapidly predict drug-sensitive mutation subtypes from images of pathological tissue. A total of 5153 pathological images of 365 different GISTs from three different laboratories were collected and divided into training and validation sets. A transfer learning mechanism based on DCNN was used with four different network architectures, to identify cases with drug-sensitive mutations. The accuracy ranged from 87% to 75%. Cross-institutional inconsistency, however, was observed. Using gray-scale images resulted in a 7% drop in accuracy (accuracy 80%, sensitivity 87%, specificity 73%). Using images containing only nuclei (accuracy 81%, sensitivity 87%, specificity 73%) or cytoplasm (accuracy 79%, sensitivity 88%, specificity 67%) produced 6% and 8% drops in accuracy rate, respectively, suggesting buffering effects across subcellular components in DCNN interpretation. The proposed DCNN model successfully inferred cases with drug-sensitive mutations with high accuracy. The contribution of image color and subcellular components was also revealed. These results will help to generate a cheaper and quicker screening method for tumor gene testing. |
| Starting Page | 5787 |
| e-ISSN | 20726694 |
| DOI | 10.3390/cancers13225787 |
| Journal | Cancers |
| Issue Number | 22 |
| Volume Number | 13 |
| Language | English |
| Publisher | MDPI |
| Publisher Date | 2021-11-18 |
| Access Restriction | Open |
| Subject Keyword | Cancers Oncology Gastrointestinal Stromal Tumor Kit Pdgfra Deep Convolutional Neural Network Machine Learning |
| Content Type | Text |
| Resource Type | Article |
