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      Computed tomography-based radiomics for prediction of neoadjuvant chemotherapy outcomes in locally advanced gastric cancer: A pilot study

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          Abstract

          Objective

          The standard treatment for patients with locally advanced gastric cancer has relied on perioperative radio-chemotherapy or chemotherapy and surgery. The aim of this study was to investigate the wealth of radiomics for pre-treatment computed tomography (CT) in the prediction of the pathological response of locally advanced gastric cancer with preoperative chemotherapy.

          Methods

          Thirty consecutive patients with CT-staged II/III gastric cancer receiving neoadjuvant chemotherapy were enrolled in this study between December 2014 and March 2017. All patients underwent upper abdominal CT during the unenhanced, late arterial phase (AP) and portal venous phase (PP) before the administration of neoadjuvant chemotherapy. In total, 19,985 radiomics features were extracted in the AP and PP for each patient. Four methods were adopted during feature selection and eight methods were used in the process of building the classifier model. Thirty-two combinations of feature selection and classification methods were examined. Receiver operating characteristic (ROC) curves were used to evaluate the capability of each combination of feature selection and classification method to predict a non-good response (non-GR) based on tumor regression grade (TRG).

          Results

          The mean area under the curve (AUC) ranged from 0.194 to 0.621 in the AP, and from 0.455 to 0.722 in the PP, according to different combinations of feature selection and the classification methods. There was only one cross-combination machine-learning method indicating a relatively higher AUC (>0.600) in the AP, while 12 cross-combination machine-learning methods presented relatively higher AUCs (all >0.600) in the PP. The feature selection method adopted by a filter based on linear discriminant analysis + classifier of random forest achieved a significantly prognostic performance in the PP (AUC, 0.722±0.108; accuracy, 0.793; sensitivity, 0.636; specificity, 0.889; Z=2.039; P=0.041).

          Conclusions

          It is possible to predict non-GR after neoadjuvant chemotherapy in locally advanced gastric cancers based on the radiomics of CT.

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          Most cited references27

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          Development and Validation of a Radiomics Nomogram for Preoperative Prediction of Lymph Node Metastasis in Colorectal Cancer.

          To develop and validate a radiomics nomogram for preoperative prediction of lymph node (LN) metastasis in patients with colorectal cancer (CRC).
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            Perioperative chemotherapy compared with surgery alone for resectable gastroesophageal adenocarcinoma: an FNCLCC and FFCD multicenter phase III trial.

            After curative resection, the prognosis of gastroesophageal adenocarcinoma is poor. This phase III trial was designed to evaluate the benefit in overall survival (OS) of perioperative fluorouracil plus cisplatin in resectable gastroesophageal adenocarcinoma. Overall, 224 patients with resectable adenocarcinoma of the lower esophagus, gastroesophageal junction (GEJ), or stomach were randomly assigned to either perioperative chemotherapy and surgery (CS group; n = 113) or surgery alone (S group; n = 111). Chemotherapy consisted of two or three preoperative cycles of intravenous cisplatin (100 mg/m(2)) on day 1, and a continuous intravenous infusion of fluorouracil (800 mg/m(2)/d) for 5 consecutive days (days 1 to 5) every 28 days and three or four postoperative cycles of the same regimen. The primary end point was OS. Compared with the S group, the CS group had a better OS (5-year rate 38% v 24%; hazard ratio [HR] for death: 0.69; 95% CI, 0.50 to 0.95; P = .02); and a better disease-free survival (5-year rate: 34% v 19%; HR, 0.65; 95% CI, 0.48 to 0.89; P = .003). In the multivariable analysis, the favorable prognostic factors for survival were perioperative chemotherapy (P = .01) and stomach tumor localization (P < .01). Perioperative chemotherapy significantly improved the curative resection rate (84% v 73%; P = .04). Grade 3 to 4 toxicity occurred in 38% of CS patients (mainly neutropenia) but postoperative morbidity was similar in the two groups. In patients with resectable adenocarcinoma of the lower esophagus, GEJ, or stomach, perioperative chemotherapy using fluorouracil plus cisplatin significantly increased the curative resection rate, disease-free survival, and OS.
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              Machine Learning methods for Quantitative Radiomic Biomarkers

              Radiomics extracts and mines large number of medical imaging features quantifying tumor phenotypic characteristics. Highly accurate and reliable machine-learning approaches can drive the success of radiomic applications in clinical care. In this radiomic study, fourteen feature selection methods and twelve classification methods were examined in terms of their performance and stability for predicting overall survival. A total of 440 radiomic features were extracted from pre-treatment computed tomography (CT) images of 464 lung cancer patients. To ensure the unbiased evaluation of different machine-learning methods, publicly available implementations along with reported parameter configurations were used. Furthermore, we used two independent radiomic cohorts for training (n = 310 patients) and validation (n = 154 patients). We identified that Wilcoxon test based feature selection method WLCX (stability = 0.84 ± 0.05, AUC = 0.65 ± 0.02) and a classification method random forest RF (RSD = 3.52%, AUC = 0.66 ± 0.03) had highest prognostic performance with high stability against data perturbation. Our variability analysis indicated that the choice of classification method is the most dominant source of performance variation (34.21% of total variance). Identification of optimal machine-learning methods for radiomic applications is a crucial step towards stable and clinically relevant radiomic biomarkers, providing a non-invasive way of quantifying and monitoring tumor-phenotypic characteristics in clinical practice.
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                Author and article information

                Contributors
                Journal
                Chin J Cancer Res
                Chin. J. Cancer Res
                CJCR
                Chinese Journal of Cancer Research
                AME Publishing Company
                1000-9604
                1993-0631
                August 2018
                : 30
                : 4
                : 406-414
                Affiliations
                [1 ] Department of Radiology, the Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Kunming 650118, China
                [2 ] Department of Abdominal Surgery, the Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Kunming 650118, China
                [3 ] Department of Pathology, the Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Kunming 650118, China
                [4 ] Department of Radiology, Guangdong General Hospital, Guangdong Academy of Medical Sciences, Guangzhou 510080, China
                [5 ] School of Medicine, South China University of Technology, Guangzhou 510641, China
                Author notes
                Yingying Ding, MD. Department of Radiology, the Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Kunming 650118, China. Email: d_yying@ 123456hotmail.com
                Zaiyi Liu, MD, PhD. Department of Radiology, Guangdong General Hospital, Guangdong Academy of Medical Sciences, Guangzhou 510080, China. Email: zyliu@ 123456163.com

                *These authors contributed equally to this article.

                Article
                cjcr-30-4-406
                10.21147/j.issn.1000-9604.2018.04.03
                6129565
                30210220
                cf288344-0d75-455c-88af-8df1069e258f
                Copyright © 2018 Chinese Journal of Cancer Research. All rights reserved.

                This work is licensed under a Creative Commons Attribution-Non Commercial-Share Alike 4.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by-nc-sa/4.0/

                History
                : 14 November 2017
                : 9 March 2018
                Categories
                Original Article

                gastric cancer,neoadjuvant chemotherapy,radiomics,tomography; spiral computed

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