دورية أكاديمية
CT radiomic features for predicting resectability of oesophageal squamous cell carcinoma as given by feature analysis: a case control study
العنوان: | CT radiomic features for predicting resectability of oesophageal squamous cell carcinoma as given by feature analysis: a case control study |
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المؤلفون: | Jing Ou, Rui Li, Rui Zeng, Chang-qiang Wu, Yong Chen, Tian-wu Chen, Xiao-ming Zhang, Lan Wu, Yu Jiang, Jian-qiong Yang, Jin-ming Cao, Sun Tang, Meng-jie Tang, Jiani Hu |
المصدر: | Cancer Imaging, Vol 19, Iss 1, Pp 1-10 (2019) |
بيانات النشر: | BMC, 2019. |
سنة النشر: | 2019 |
المجموعة: | LCC:Medical physics. Medical radiology. Nuclear medicine LCC:Neoplasms. Tumors. Oncology. Including cancer and carcinogens |
مصطلحات موضوعية: | Esophagus, Squamous cell carcinoma, Computed tomography, Esophagectomy, Diagnosis, Medical physics. Medical radiology. Nuclear medicine, R895-920, Neoplasms. Tumors. Oncology. Including cancer and carcinogens, RC254-282 |
الوصف: | Abstract Background Computed tomography (CT) is commonly used in all stages of oesophageal squamous cell carcinoma (SCC) management. Compared to basic CT features, CT radiomic features can objectively obtain more information about intratumour heterogeneity. Although CT radiomics has been proved useful for predicting treatment response to chemoradiotherapy in oesophageal cancer, the best way to use CT radiomic biomarkers as predictive markers for determining resectability of oesophageal SCC remains to be developed. This study aimed to develop CT radiomic features related to resectability of oesophageal SCC with five predictive models and to determine the most predictive model. Methods Five hundred ninety-one patients with oesophageal SCC undergoing contrast-enhanced CT were enrolled in this study, and were composed by 270 resectable cases and 321 unresectable cases. Of the 270 resectable oesophageal SCCs, 91 cases were primary resectable tumours; and the remained 179 cases received neoadjuvant therapy after CT, shrank on therapy, and changed to resectable tumours. Four hundred thirteen oesophageal SCCs including 189 resectable cancers and 224 unresectable cancers were randomly allocated to the training cohort; and 178 oesophageal SCCs including 81 resectable tumours and 97 unresectable tumours were allocated to the validation group. Four hundred ninety-five radiomic features were extracted from CT data for identifying resectability of oesophageal SCC. Useful radiomic features were generated by dimension reduction using least absolute shrinkage and selection operator. The optimal radiomic features were chosen using multivariable logistic regression, random forest, support vector machine, X-Gradient boost and decision tree classifiers. Discriminating performance was assessed with area under receiver operating characteristic curve (AUC), accuracy and F-1score. Results Eight radiomic features were selected to create radiomic models related to resectability of oesophageal SCC (P-values |
نوع الوثيقة: | article |
وصف الملف: | electronic resource |
اللغة: | English |
تدمد: | 1470-7330 |
Relation: | http://link.springer.com/article/10.1186/s40644-019-0254-0; https://doaj.org/toc/1470-7330 |
DOI: | 10.1186/s40644-019-0254-0 |
URL الوصول: | https://doaj.org/article/eae9362064b345808aa82cfde8160cd2 |
رقم الأكسشن: | edsdoj.9362064b345808aa82cfde8160cd2 |
قاعدة البيانات: | Directory of Open Access Journals |
تدمد: | 14707330 |
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DOI: | 10.1186/s40644-019-0254-0 |