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      Radiomic signature of 18F fluorodeoxyglucose PET/CT for prediction of gastric cancer survival and chemotherapeutic benefits

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          Abstract

          We aimed to evaluate whether radiomic feature-based fluorine 18 ( 18F) fluorodeoxyglucose (FDG) positron emission tomography (PET) imaging signatures allow prediction of gastric cancer (GC) survival and chemotherapy benefits.

          Methods: A total of 214 GC patients (training (n = 132) or validation (n = 82) cohort) were subjected to radiomic feature extraction (80 features). Radiomic features of patients in the training cohort were subjected to a LASSO cox analysis to predict disease-free survival (DFS) and overall survival (OS) and were validated in the validation cohort. A radiomics nomogram with the radiomic signature incorporated was constructed to demonstrate the incremental value of the radiomic signature to the TNM staging system for individualized survival estimation, which was then assessed with respect to calibration, discrimination, and clinical usefulness. The performance was assessed with concordance index (C-index) and integrated Brier scores.

          Results: Significant differences were found between the high- and low-radiomic score (Rad-score) patients in 5-year DFS and OS in training and validation cohorts. Multivariate analysis revealed that the Rad-score was an independent prognostic factor. Incorporating the Rad-score into the radiomics-based nomogram resulted in better performance (C -index: DFS, 0.800; OS, 0.786; in the training cohort) than TNM staging system and clinicopathologic nomogram. Further analysis revealed that patients with higher Rad-scores were prone to benefit from chemotherapy.

          Conclusion: The newly developed radiomic signature was a powerful predictor of OS and DFS. Moreover, the radiomic signature could predict which patients could benefit from chemotherapy.

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          Regularization Paths for Cox's Proportional Hazards Model via Coordinate Descent.

          Noah Simon, Jerome Friedman, Trevor J. Hastie … (2011)
          We introduce a pathwise algorithm for the Cox proportional hazards model, regularized by convex combinations of ℓ1 and ℓ2 penalties (elastic net). Our algorithm fits via cyclical coordinate descent, and employs warm starts to find a solution along a regularization path. We demonstrate the efficacy of our algorithm on real and simulated data sets, and find considerable speedup between our algorithm and competing methods.
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            Radiomics Signature: A Potential Biomarker for the Prediction of Disease-Free Survival in Early-Stage (I or II) Non-Small Cell Lung Cancer.

            Zelan Ma, Cuishan Liang, Jie Tian … (2016)
            Purpose To develop a radiomics signature to estimate disease-free survival (DFS) in patients with early-stage (stage I-II) non-small cell lung cancer (NSCLC) and assess its incremental value to the traditional staging system and clinical-pathologic risk factors for individual DFS estimation. Materials and Methods Ethical approval by the institutional review board was obtained for this retrospective analysis, and the need to obtain informed consent was waived. This study consisted of 282 consecutive patients with stage IA-IIB NSCLC. A radiomics signature was generated by using the least absolute shrinkage and selection operator, or LASSO, Cox regression model. Association between the radiomics signature and DFS was explored. Further validation of the radiomics signature as an independent biomarker was performed by using multivariate Cox regression. A radiomics nomogram with the radiomics signature incorporated was constructed to demonstrate the incremental value of the radiomics signature to the traditional staging system and other clinical-pathologic risk factors for individualized DFS estimation, which was then assessed with respect to calibration, discrimination, reclassification, and clinical usefulness. Results The radiomics signature was significantly associated with DFS, independent of clinical-pathologic risk factors. Incorporating the radiomics signature into the radiomics-based nomogram resulted in better performance (P < .0001) for the estimation of DFS (C-index: 0.72; 95% confidence interval [CI]: 0.71, 0.73) than with the clinical-pathologic nomogram (C-index: 0.691; 95% CI: 0.68, 0.70), as well as a better calibration and improved accuracy of the classification of survival outcomes (net reclassification improvement: 0.182; 95% CI: 0.02, 0.31; P = .02). Decision curve analysis demonstrated that in terms of clinical usefulness, the radiomics nomogram outperformed the traditional staging system and the clinical-pathologic nomogram. Conclusion The radiomics signature is an independent biomarker for the estimation of DFS in patients with early-stage NSCLC. Combination of the radiomics signature, traditional staging system, and other clinical-pathologic risk factors performed better for individualized DFS estimation in patients with early-stage NSCLC, which might enable a step forward precise medicine. (©) RSNA, 2016 Online supplemental material is available for this article.
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              Evaluating Random Forests for Survival Analysis using Prediction Error Curves.

              Ulla Mogensen, Hemant Ishwaran, Thomas A. Gerds (2012)
              Prediction error curves are increasingly used to assess and compare predictions in survival analysis. This article surveys the R package pec which provides a set of functions for efficient computation of prediction error curves. The software implements inverse probability of censoring weights to deal with right censored data and several variants of cross-validation to deal with the apparent error problem. In principle, all kinds of prediction models can be assessed, and the package readily supports most traditional regression modeling strategies, like Cox regression or additive hazard regression, as well as state of the art machine learning methods such as random forests, a nonparametric method which provides promising alternatives to traditional strategies in low and high-dimensional settings. We show how the functionality of pec can be extended to yet unsupported prediction models. As an example, we implement support for random forest prediction models based on the R-packages randomSurvivalForest and party. Using data of the Copenhagen Stroke Study we use pec to compare random forests to a Cox regression model derived from stepwise variable selection. Reproducible results on the user level are given for publicly available data from the German breast cancer study group.
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                Author and article information

                Journal
                Journal ID (nlm-ta): Theranostics
                Journal ID (iso-abbrev): Theranostics
                Journal ID (publisher-id): thno
                Title: Theranostics
                Publisher: Ivyspring International Publisher (Sydney )
                ISSN (Electronic): 1838-7640
                Publication date Collection: 2018
                Publication date (Electronic): 12 November 2018
                Volume: 8
                Issue: 21
                Pages: 5915-5928
                Affiliations
                [1 ]Department of General Surgery, Nanfang Hospital, Southern Medical University, 1838 North Guangzhou Avenue, Guangzhou, China;
                [2 ]Nanfang PET Center, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong, 510515, China;
                [3 ]School of Biomedical Engineering and Guangdong Provincal Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, Guangdong, 510515, China;
                [4 ]Guangdong Key Laboratory of Liver Disease Research, the 3rd Affiliated Hospital of Sun Yat-sen University, Guangzhou, 510630, China;
                [5 ]Department of Infectious Disease, the 3rd Affiliated Hospital of Sun Yat-sen University, Guangzhou, China.
                Author notes
                ✉ Corresponding authors: Guoxin Li, Department of General Surgery, Nanfang Hospital, Southern Medical University, 1838 North Guangzhou Avenue, Guangzhou, 510515, China; Tel and Fax: +86-20-6278 7170; E-mail: gzliguoxin@ 123456163.com or jiangymbest@ 123456163.com ; Quanshi Wang, Nanfang PET Center, Nanfang Hospital, Southern Medical University, 1838 Guangzhou Avenue North, Guangzhou, Guangdong Province 510515, China Tel/fax: +86-20-6164 2127; e-mail: wqslph@ 123456163.net ; Jiang Yu, Department of General Surgery, Nanfang Hospital, Southern Medical University, 1838 North Guangzhou Avenue, Guangzhou, 510515, China; Tel and Fax: +86-20-6278 7170; E-mail: balbc@ 123456163.com ; Tuanjie Li, Department of General Surgery, Nanfang Hospital, Southern Medical University, 1838 North Guangzhou Avenue, Guangzhou, 510515, China; Tel and Fax: +86-20-6278 7170; E-mail: ehbhltj@ 123456hotmail.com .

                *These authors contributed equally to this work.

                Competing Interests: The authors have declared that no competing interest exists.

                Article
                Publisher ID: thnov08p5915
                DOI: 10.7150/thno.28018
                PMC ID: 6299427
                PubMed ID: 30613271
                SO-VID: 5afc7058-95d2-4abc-956f-6a83de798b03
                Copyright © © Ivyspring International Publisher
                License:

                This is an open access article distributed under the terms of the Creative Commons Attribution (CC BY-NC) license ( https://creativecommons.org/licenses/by-nc/4.0/). See http://ivyspring.com/terms for full terms and conditions.

                History
                Date received : 20 June 2018
                Date accepted : 22 October 2018
                Categories
                Subject: Research Paper

                ScienceOpen disciplines: Molecular medicine
                Keywords: gastric cancer,prognosis,chemotherapy,predictive signature,pet/ct
                Data availability:
                ScienceOpen disciplines: Molecular medicine
                Keywords: gastric cancer, prognosis, chemotherapy, predictive signature, pet/ct

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