PMID- 31252296 OWN - NLM STAT- MEDLINE DCOM- 20200521 LR - 20200521 IS - 1879-0887 (Electronic) IS - 0167-8140 (Linking) VI - 138 DP - 2019 Sep TI - Radiomics analysis of magnetic resonance imaging improves diagnostic performance of lymph node metastasis in patients with cervical cancer. PG - 141-148 LID - S0167-8140(19)30398-6 [pii] LID - 10.1016/j.radonc.2019.04.035 [doi] AB - BACKGROUND AND PURPOSE: Robust parameters are needed to predict lymph node metastasis (LNM) in locally advanced cervical cancer patients in order to select optimal treatment regimen. The aim of this study is to utilize radiomics analysis of magnetic resonance imaging (MRI) to improve diagnostic performance of LNM in cervical cancer patients. MATERIALS AND METHODS: A total of 189 cervical cancer patients were divided into a training cohort (n = 126) and a validation cohort (n = 63). For each patient, we extracted radiomic features from intratumoral and peritumoral tissues on sagittal T2WI and axial apparent diffusion coefficient (ADC) maps. Afterward, the radiomic features associated with LNM status were selected by univariate ROC testing and logistic regression with the least absolute shrinkage and selection operator (LASSO) penalty in the training cohort. Based on the selected features, a support vector machine (SVM) model was established to predict LNM status. To further improve the diagnostic performance, a decision tree which combines the radiomics model with clinical factors was built. RESULTS: Radiomics model of the intratumoral and peritumoral tissues on T2WI (T2(tumor+peri)) showed best sensitivity and clinical LN (c-LN) status showed best specificity to predict LNM. The decision tree that combines radiomics model of T2(tumor+peri) and c-LN status achieved best diagnostic performance, with AUC and sensitivity of 0.895 and 94.3%, 0.847 and 100% in the training and validation cohort respectively. CONCLUSIONS: The decision tree, which incorporates radiomics model of T2(tumor+peri) and c-LN status can be potentially applied in the preoperative prediction of LNM in locally advanced cervical cancer patients. CI - Copyright (c) 2019 Elsevier B.V. All rights reserved. FAU - Wu, Qingxia AU - Wu Q AD - Department of Medical Imaging, Henan Provincial People's Hospital, Zhengzhou, China; Zhengzhou University People's Hospital, Zhengzhou, China; Henan University People's Hospital, Zhengzhou, China. FAU - Wang, Shuo AU - Wang S AD - Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, School of Medicine, Beihang University, Beijing, China; Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China; University of Chinese Academy of Sciences, Beijing, China. FAU - Chen, Xi AU - Chen X AD - School of Information and Electronics, Beijing Institute of Technology, Beijing, China.; Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China. FAU - Wang, Yan AU - Wang Y AD - Department of Medical Imaging, Henan Provincial People's Hospital, Zhengzhou, China; Zhengzhou University People's Hospital, Zhengzhou, China; Henan University People's Hospital, Zhengzhou, China. FAU - Dong, Li AU - Dong L AD - Department of Gynaecology, Henan Provincial People's Hospital, Zhengzhou, China; Zhengzhou University People's Hospital, Zhengzhou, China; Henan University People's Hospital, Zhengzhou, China. FAU - Liu, Zhenyu AU - Liu Z AD - Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China; University of Chinese Academy of Sciences, Beijing, China. Electronic address: zhenyu.liu@ia.ac.cn. FAU - Tian, Jie AU - Tian J AD - Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China; Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, School of Medicine, Beihang University, Beijing, China; Engineering Research Center of Molecular and Neuro Imaging of Ministry of Education, School of Life Science and Technology, Xidian University, Xi'an, Shanxi, China.; University of Chinese Academy of Sciences, Beijing, China. Electronic address: jie.tian@ia.ac.cn. FAU - Wang, Meiyun AU - Wang M AD - Department of Medical Imaging, Henan Provincial People's Hospital, Zhengzhou, China; Zhengzhou University People's Hospital, Zhengzhou, China; Henan University People's Hospital, Zhengzhou, China. Electronic address: mywang@ha.edu.cn. LA - eng PT - Journal Article PT - Research Support, Non-U.S. Gov't DEP - 20190625 PL - Ireland TA - Radiother Oncol JT - Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology JID - 8407192 SB - IM MH - Adult MH - Aged MH - Cohort Studies MH - Female MH - Humans MH - Logistic Models MH - Lymph Nodes/*diagnostic imaging/pathology MH - Lymphatic Metastasis/diagnostic imaging MH - Magnetic Resonance Imaging/methods MH - Male MH - Middle Aged MH - Neoplasm Staging MH - Retrospective Studies MH - Uterine Cervical Neoplasms/*diagnostic imaging/pathology OTO - NOTNLM OT - Cervical cancer OT - Lymph nodes OT - Magnetic resonance imaging OT - Radiomics EDAT- 2019/06/30 06:00 MHDA- 2020/05/22 06:00 CRDT- 2019/06/29 06:00 PHST- 2019/01/10 00:00 [received] PHST- 2019/03/21 00:00 [revised] PHST- 2019/04/29 00:00 [accepted] PHST- 2019/06/30 06:00 [pubmed] PHST- 2020/05/22 06:00 [medline] PHST- 2019/06/29 06:00 [entrez] AID - S0167-8140(19)30398-6 [pii] AID - 10.1016/j.radonc.2019.04.035 [doi] PST - ppublish SO - Radiother Oncol. 2019 Sep;138:141-148. doi: 10.1016/j.radonc.2019.04.035. Epub 2019 Jun 25.