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Conference Paper: Deep learning CT-based radiomics for prediction of treatment response to neoadjuvant chemoradiation in oesophageal squamous cell carcinoma

TitleDeep learning CT-based radiomics for prediction of treatment response to neoadjuvant chemoradiation in oesophageal squamous cell carcinoma
Authors
Issue Date2020
PublisherSpringerOpen. The Journal's web site is located at http://www.springer.com/medicine/radiology/journal/13244
Citation
European Congress of Radiology (ECR), Vienna, Austria, 15-19 July 2020. Book of Abstracts In Insights into Imaging, 2020, v. 11 n. Suppl. 1, p. 306 How to Cite?
AbstractPurpose: Neoadjuvant chemoradiotherapy (NCRT) plus surgery improves long-term survival of patients with locally advanced esophageal squamous cell carcinoma (ESCC). Treatment response prediction remains a great challenge. We aimed to evaluate the value of deep learning radiomics models based on computed tomography (CT) for predicting pathologic complete response (pCR) in ESCC patients receiving NCRT. Methods and materials: We identified 161 patients with ESCC (mean age: 58. mate: 83.5%. pCR: 46.0%). A total of 2048 deep learning radiomics features were analysed by the convolutional neural network (Xception) from CT images. After feature selection, a radiomics signature was butt with an extreme gradient boosting (XGBoost) algonthm. Two models were built. Model A. for post-NCRT evaluation, incorporates both pre-NCRT and post-NCRT CT images into the analysts, while Model B. for pretreatment assessment, was built based on pre- NCRT CT images only. Results: Model A comprised 9 selected features and showed good discrimination performance in test set for treatment response to NCRT. with an accuracy of 0.78. area under the receiver operating characteristic curve (AUC) of 0.89. sensitivity of 0.70, and specificity of 0.96. Calibration curves demonstrated good agreement between the prediction probability and the observed pCR (Hosmer-Lemeshow test. P-value = 0.66). Decision curve analysts confirmed the clinical benefits. Model B was also found to have predictrve potential, with an accuracy of 0.67. AUC of 0.73. sensitrvity of 0.87. and specificity of 0.62. Conclusion: Deep learning radiomics analysis based on CT demonstrated promising predictive value for NCRT treatment response in locally advanced ESCC. Both pretreatment and post-NCRT models could be potentially used for treatment strategy decision-making. Limitations: Future studies in larger prospective cohorts are needed to further confinn dmical practicability.
DescriptionGI Tract - RPS 701: Upper GI tract: what is new? - no. RPS 701-3
Congress was originally planned for 11-15 March 2020 could not be held, due to Convid-19. The ECR 2020 Online Congress Programme taking place between July 15-19, 2020
Persistent Identifierhttp://hdl.handle.net/10722/284955
ISSN
2019 Impact Factor: 3.579
2015 SCImago Journal Rankings: 1.004

 

DC FieldValueLanguage
dc.contributor.authorXie, C-
dc.contributor.authorHu, Y-
dc.contributor.authorHan, L-
dc.contributor.authorFu, J-
dc.contributor.authorVardhanabhuti, V-
dc.contributor.authorChiu, WHK-
dc.date.accessioned2020-08-07T09:04:49Z-
dc.date.available2020-08-07T09:04:49Z-
dc.date.issued2020-
dc.identifier.citationEuropean Congress of Radiology (ECR), Vienna, Austria, 15-19 July 2020. Book of Abstracts In Insights into Imaging, 2020, v. 11 n. Suppl. 1, p. 306-
dc.identifier.issn1869-4101-
dc.identifier.urihttp://hdl.handle.net/10722/284955-
dc.descriptionGI Tract - RPS 701: Upper GI tract: what is new? - no. RPS 701-3-
dc.descriptionCongress was originally planned for 11-15 March 2020 could not be held, due to Convid-19. The ECR 2020 Online Congress Programme taking place between July 15-19, 2020-
dc.description.abstractPurpose: Neoadjuvant chemoradiotherapy (NCRT) plus surgery improves long-term survival of patients with locally advanced esophageal squamous cell carcinoma (ESCC). Treatment response prediction remains a great challenge. We aimed to evaluate the value of deep learning radiomics models based on computed tomography (CT) for predicting pathologic complete response (pCR) in ESCC patients receiving NCRT. Methods and materials: We identified 161 patients with ESCC (mean age: 58. mate: 83.5%. pCR: 46.0%). A total of 2048 deep learning radiomics features were analysed by the convolutional neural network (Xception) from CT images. After feature selection, a radiomics signature was butt with an extreme gradient boosting (XGBoost) algonthm. Two models were built. Model A. for post-NCRT evaluation, incorporates both pre-NCRT and post-NCRT CT images into the analysts, while Model B. for pretreatment assessment, was built based on pre- NCRT CT images only. Results: Model A comprised 9 selected features and showed good discrimination performance in test set for treatment response to NCRT. with an accuracy of 0.78. area under the receiver operating characteristic curve (AUC) of 0.89. sensitivity of 0.70, and specificity of 0.96. Calibration curves demonstrated good agreement between the prediction probability and the observed pCR (Hosmer-Lemeshow test. P-value = 0.66). Decision curve analysts confirmed the clinical benefits. Model B was also found to have predictrve potential, with an accuracy of 0.67. AUC of 0.73. sensitrvity of 0.87. and specificity of 0.62. Conclusion: Deep learning radiomics analysis based on CT demonstrated promising predictive value for NCRT treatment response in locally advanced ESCC. Both pretreatment and post-NCRT models could be potentially used for treatment strategy decision-making. Limitations: Future studies in larger prospective cohorts are needed to further confinn dmical practicability.-
dc.languageeng-
dc.publisherSpringerOpen. The Journal's web site is located at http://www.springer.com/medicine/radiology/journal/13244-
dc.relation.ispartofInsights into Imaging-
dc.relation.ispartofEuropean Congress of Radiology (ECR)-
dc.titleDeep learning CT-based radiomics for prediction of treatment response to neoadjuvant chemoradiation in oesophageal squamous cell carcinoma-
dc.typeConference_Paper-
dc.identifier.emailVardhanabhuti, V: varv@hku.hk-
dc.identifier.emailChiu, WHK: kwhchiu@hku.hk-
dc.identifier.authorityVardhanabhuti, V=rp01900-
dc.identifier.authorityChiu, WHK=rp02074-
dc.identifier.hkuros312613-
dc.identifier.volume11-
dc.identifier.issueSuppl. 1-
dc.identifier.spage306-
dc.identifier.epage306-
dc.publisher.placeGermany-
dc.identifier.partofdoi10.1186/s13244-020-00851-0-

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