Prediction of response after chemoradiation for esophageal cancer using a combination of dosimetry and CT radiomics.
Purpose: To investigate the treatment response prediction feasibility and accuracy of an integrated model combining computed tomography (CT) radiomic features and dosimetric parameters for patients with esophageal cancer (EC) who underwent concurrent chemoradiation (CRT) using machine learning.Metho...
| Publicado en: | European Radiology Vol. 29; no. 11; pp. 6080 - 6089 |
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| Autores principales: | , , , , , , , , , , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
Nov2019
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=139163662&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 139163662 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 09387994 NPH jtl: European Radiology issn: 09387994 maglogo: N pubinfo: dt: Nov2019 vid: 29 iid: 11 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 139163662 139163662 144029577 NLM31028447 10.1007/s00330-019-06193-w NLM31028447 139163662 ppf: 6080 ppct: 9 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Prediction of response after chemoradiation for esophageal cancer using a combination of dosimetry and CT radiomics. aug: au: Jin, Xiance Zheng, Xiaomin Chen, Didi Jin, Juebin Zhu, Guojie Deng, Xia Han, Ce Gong, Changfei Zhou, Yongqiang Liu, Cong Xie, Congying affil: Department of Radiation and Medical Oncology, The 1st Affiliated Hospital of Wenzhou Medical University, No.2 Fuxue Lane, 325000, Wenzhou, People's Republic of China sug: subj: Esophageal Neoplasms Therapy Tomography, X-Ray Computed Methods Carcinoma, Squamous Cell Therapy Radiometry Methods Female Aged, 80 and Over Aged Esophageal Neoplasms Diagnosis Middle Age ROC Curve Male Carcinoma, Squamous Cell Diagnosis Funding Source Boosting Machine Learning Algorithms Aged, 80 & over Aged: 65+ years Middle Aged: 45-64 years Female Male ab: Purpose: To investigate the treatment response prediction feasibility and accuracy of an integrated model combining computed tomography (CT) radiomic features and dosimetric parameters for patients with esophageal cancer (EC) who underwent concurrent chemoradiation (CRT) using machine learning.Methods: The radiomic features and dosimetric parameters of 94 EC patients were extracted and modeled using Support Vector Classification (SVM) and Extreme Gradient Boosting algorithm (XGBoost). The 94-sample dataset was randomly divided into a 70-sample training subset and a 24-sample independent test set while keeping the class proportions intact via stratification. A receiver operating characteristic (ROC) curve was used to assess the performance of models using radiomic features alone and using combined radiomic features and dosimetric parameters.Results: A total of 42 radiomic features and 18 dosimetric parameters plus the patients' characteristic parameters were extracted for these 94 cases (58 responders and 36 non-responders). XGBoost plus principal component analysis (PCA) achieved an accuracy and area under the curve of 0.708 and 0.541, respectively, for models with radiomic features combined with dosimetric parameters, and 0.689 and 0.479, respectively, for radiomic features alone. Image features of GlobalMean X.333.1, Coarseness, Skewness, and GlobalStd contributed most to the model. The dosimetric parameters of gross tumor volume (GTV) homogeneity index (HI), Cord Dmax, Prescription dose, Heart-Dmean, and Heart-V50 also had a strong contribution to the model.Conclusions: The model with radiomic features combined with dosimetric parameters is promising and outperforms that with radiomic features alone in predicting the treatment response of patients with EC who underwent CRT.Key Points: • The model with radiomic features combined with dosimetric parameters is promising in predicting the treatment response of patients with EC who underwent CRT. • The model with radiomic features combined with dosimetric parameters (prediction accuracy of 0.708 and AUC of 0.689) outperforms that with radiomic features alone (best prediction accuracy of 0.625 and AUC of 0.412). • The image features of GlobalMean X.333.1, Coarseness, Skewness, and GlobalStd contributed most to the treatment response prediction model. The dosimetric parameters of GTV HI, Cord Dmax, Prescription dose, Heart-Dmean, and Heart-V50 also had a strong contribution to the model. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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