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      The development and validation of a CT-based radiomics signature for the preoperative discrimination of stage I-II and stage III-IV colorectal cancer

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          Abstract

          Objectives

          To investigative the predictive ability of radiomics signature for preoperative staging (I-II vs.III-IV) of primary colorectal cancer (CRC).

          Methods

          This study consisted of 494 consecutive patients (training dataset: n=286; validation cohort, n=208) with stage I–IV CRC. A radiomics signature was generated using LASSO logistic regression model. Association between radiomics signature and CRC staging was explored. The classification performance of the radiomics signature was explored with respect to the receiver operating characteristics(ROC) curve.

          Results

          The 16-feature-based radiomics signature was an independent predictor for staging of CRC, which could successfully categorize CRC into stage I-II and III-IV ( p <0.0001) in training and validation dataset. The median of radiomics signature of stage III-IV was higher than stage I-II in the training and validation dataset. As for the classification performance of the radiomics signature in CRC staging, the AUC was 0.792(95%CI:0.741-0.853) with sensitivity of 0.629 and specificity of 0.874. The signature in the validation dataset obtained an AUC of 0.708(95%CI:0.698-0.718) with sensitivity of 0.611 and specificity of 0.680.

          Conclusions

          A radiomics signature was developed and validated to be a significant predictor for discrimination of stage I-II from III-IV CRC, which may serve as a complementary tool for the preoperative tumor staging in CRC.

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          Most cited references20

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          The meaning and use of the area under a receiver operating characteristic (ROC) curve.

          A representation and interpretation of the area under a receiver operating characteristic (ROC) curve obtained by the "rating" method, or by mathematical predictions based on patient characteristics, is presented. It is shown that in such a setting the area represents the probability that a randomly chosen diseased subject is (correctly) rated or ranked with greater suspicion than a randomly chosen non-diseased subject. Moreover, this probability of a correct ranking is the same quantity that is estimated by the already well-studied nonparametric Wilcoxon statistic. These two relationships are exploited to (a) provide rapid closed-form expressions for the approximate magnitude of the sampling variability, i.e., standard error that one uses to accompany the area under a smoothed ROC curve, (b) guide in determining the size of the sample required to provide a sufficiently reliable estimate of this area, and (c) determine how large sample sizes should be to ensure that one can statistically detect differences in the accuracy of diagnostic techniques.
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            Multivariable analysis: a primer for readers of medical research.

            Lior Katz (2003)
            Many clinical readers, especially those uncomfortable with mathematics, treat published multivariable models as a black box, accepting the author's explanation of the results. However, multivariable analysis can be understood without undue concern for the underlying mathematics. This paper reviews the basics of multivariable analysis, including what multivariable models are, why they are used, what types exist, what assumptions underlie them, how they should be interpreted, and how they can be evaluated. A deeper understanding of multivariable models enables readers to decide for themselves how much weight to give to the results of published analyses.
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              Preoperative evaluation of colorectal cancer using CT colonography, MRI, and PET/CT.

              Imaging studies are a major component in the evaluation of patients for the screening, staging and surveillance of colorectal cancer. This review presents commonly encountered findings in the diagnosis and staging of patients with colorectal cancer using computed tomography (CT) colonography, magnetic resonance imaging (MRI), and positron emission tomography (PET)/CT colonography. CT colonography provides important information for the preoperative assessment of T staging. Wall deformities are associated with muscular or subserosal invasion. Lymph node metastases from colorectal cancer often present with calcifications. CT is superior to detect calcified metastases. Three-dimensional CT to image the vascular anatomy facilitates laparoscopic surgery. T staging of rectal cancer by MRI is an established modality because MRI can diagnose rectal wall laminar structure. N staging in patients with colorectal cancer is still challenging using any imaging modality. MRI is more accurate than CT for the evaluation of liver metastases. PET/CT colonography is valuable in the evaluation of extra-colonic and hepatic disease. PET/CT colonography is useful for obstructing colorectal cancers that cannot be traversed colonoscopically. PET/CT colonography is able to localize synchronous colon cancers proximal to the obstruction precisely. However, there is no definite evidence to support the routine clinical use of PET/CT colonography.
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                Author and article information

                Journal
                Oncotarget
                Oncotarget
                Oncotarget
                ImpactJ
                Oncotarget
                Impact Journals LLC
                1949-2553
                24 May 2016
                22 April 2016
                : 7
                : 21
                : 31401-31412
                Affiliations
                1 Department of Radiology, Guangdong General Hospital, Guangdong Academy of Medical Sciences, Guangzhou, 510080, China
                2 Graduate College, Southern Medical University, Guangzhou, 510515, China
                3 School of Medicine, South China University of Technology, Guangzhou, Guangdong, 510006, China
                4 Department of Radiology, The Affiliated Guangzhou First People’ Hospital, Guangzhou Medical University, Guangzhou, 510180, China
                5 Key Laboratory of Molecular Imaging, Chinese Academy of Sciences, Beijing, 100190, China
                Author notes
                Correspondence to: Zaiyi Liu, zyliu@ 123456163.com
                Article
                8919
                10.18632/oncotarget.8919
                5058766
                27120787
                bdb3d32d-0bdd-476b-ba9a-69635fd1c4aa
                Copyright: © 2016 Liang et al.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 25 December 2015
                : 2 April 2016
                Categories
                Research Paper

                Oncology & Radiotherapy
                colorectal cancer,computed tomography,radiomics signature,predictor,stage
                Oncology & Radiotherapy
                colorectal cancer, computed tomography, radiomics signature, predictor, stage

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