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題名 Deep Convolutional Neural Networks Detect Tumor Genotype from Pathological Tissue Images in Gastrointestinal Stromal Tumors 作者 羅崇銘
Lo, Chung-Ming
Liang, Cher-Wei
Fang, Pei-Wei
Huang , Hsuan-Ying貢獻者 圖檔所 關鍵詞 KIT; PDGFRA; deep convolutional neural network; gastrointestinal stromal tumor; machine learning. 日期 2021-11 上傳時間 14-Apr-2022 15:26:22 (UTC+8) 摘要 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. 關聯 Cancers, pp.13, 5787 資料類型 article DOI https://doi.org/10.3390/cancers13225787 dc.contributor 圖檔所 dc.creator (作者) 羅崇銘 dc.creator (作者) Lo, Chung-Ming dc.creator (作者) Liang, Cher-Wei dc.creator (作者) Fang, Pei-Wei dc.creator (作者) Huang , Hsuan-Ying dc.date (日期) 2021-11 dc.date.accessioned 14-Apr-2022 15:26:22 (UTC+8) - dc.date.available 14-Apr-2022 15:26:22 (UTC+8) - dc.date.issued (上傳時間) 14-Apr-2022 15:26:22 (UTC+8) - dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/139948 - dc.description.abstract (摘要) 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. dc.format.extent 815865 bytes - dc.format.mimetype application/pdf - dc.relation (關聯) Cancers, pp.13, 5787 dc.subject (關鍵詞) KIT; PDGFRA; deep convolutional neural network; gastrointestinal stromal tumor; machine learning. dc.title (題名) Deep Convolutional Neural Networks Detect Tumor Genotype from Pathological Tissue Images in Gastrointestinal Stromal Tumors dc.type (資料類型) article dc.identifier.doi (DOI) 10.3390/cancers13225787 dc.doi.uri (DOI) https://doi.org/10.3390/cancers13225787
