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Deep Convolutional Neural Networks Detect Tumor Genotype from Pathological Tissue Images in Gastrointestinal Stromal
Cher-Wei Liang1,2,3, Pei-Wei Fang1, Hsuan-Ying Huang4
1Department of Pathology, Fu Jen Catholic University Hospital, Fu Jen Catholic University, New Taipei City 243, Taiwan.
Abstract:
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.
Insights
Deep convolutional neural networks (DCNNs) can predict drug-sensitive mutations in gastrointestinal stromal tumors (GIST) from pathology images. This AI approach offers a faster, cheaper alternative to traditional gene testing for GIST treatment.
Area of Science:
- Computational pathology
- Artificial intelligence in oncology
- Gastrointestinal stromal tumors (GIST) research
Background:
- Gastrointestinal stromal tumors (GIST) are common mesenchymal neoplasms.
- Effective GIST treatment relies on identifying specific KIT/PDGFRA gene mutations.
- Current genetic testing can be time-consuming and costly.
Purpose of the Study:
- To develop and validate deep convolutional neural network (DCNN) models for predicting drug-sensitive GIST mutation subtypes.
- To assess the feasibility of using histopathological images for rapid GIST mutation screening.
- To evaluate the impact of image features (color, subcellular components) on DCNN model performance.
Main Methods:
- Collected 5153 pathological images from 365 GIST cases across three institutions.
- Employed transfer learning with four DCNN architectures to identify drug-sensitive mutations.
- Analyzed the influence of grayscale conversion and isolation of nuclei or cytoplasm on prediction accuracy.
Main Results:
- DCNN models achieved prediction accuracies ranging from 75% to 87%.
- Cross-institutional consistency was observed, though with some variation.
- Reduced accuracy when using grayscale images (80%) or images focusing solely on nuclei (81%) or cytoplasm (79%), indicating a buffering effect.
Conclusions:
- The developed DCNN model accurately predicts drug-sensitive GIST mutations from pathological images.
- Image color and subcellular details play a role in DCNN interpretation.
- This AI-driven approach presents a promising avenue for a more cost-effective and rapid screening method for GIST gene testing.
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