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      CT texture features are associated with overall survival in pancreatic ductal adenocarcinoma a quantitative analysis

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          Abstract

          Background

          To assess whether CT-derived texture features predict survival in patients undergoing resection for pancreatic ductal adenocarcinoma (PDAC).

          Methods

          Thirty patients with pre-operative CT from 2007 to 2012 for PDAC were included. Tumor size and five texture features namely uniformity, entropy, dissimilarity, correlation, and inverse difference normalized were calculated. Mann–Whitney rank sum test was used to compare tumor with normal pancreas. Receiver operating characteristics (ROC) analysis, Cox regression and Kaplan-Meier tests were used to assess association of texture features with overall survival (OS).

          Results

          Uniformity ( p < 0.001), entropy ( p = 0.009), correlation ( p < 0.001), and mean intensity ( p < 0.001) were significantly different in tumor regions compared to normal pancreas. Tumor dissimilarity ( p = 0.045) and inverse difference normalized ( p = 0.046) were associated with OS whereas tumor intensity ( p = 0.366), tumor size ( p = 0.611) and other textural features including uniformity ( p = 0.334), entropy ( p = 0.330) and correlation ( p = 0.068) were not associated with OS.

          Conclusion

          CT-derived PDAC texture features of dissimilarity and inverse difference normalized are promising prognostic imaging biomarkers of OS for patients undergoing curative intent surgical resection.

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          Most cited references16

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          1423 pancreaticoduodenectomies for pancreatic cancer: A single-institution experience.

          Pancreaticoduodenectomy (PD) with the possible addition of neoadjuvant or adjuvant therapy is the standard of care in the United States for adenocarcinoma originating in the pancreatic head, neck, and uncinate process. We reviewed 1423 patients who underwent a PD for a malignancy originating in the pancreas at our institution between 1970 and 2006. We examined 1175 PDs for ductal adenocarcinomas in greater detail. Eighteen different histological types of pancreatic cancer were identified; the most common diagnoses included ductal adenocarcinoma, neuroendocrine carcinoma, and IPMN with invasive cancer. Patients with ductal adenocarcinoma were analyzed in detail. The median age was 66 years, with patients in the present decade significantly older (68 years), on average, than patients in the three prior decades (e.g., 60 years in 1970, P = 0.02). The median tumor diameter was 3 cm; 42% of the resections had positive margins and 78% had positive lymph nodes. The perioperative morbidity was 38%. The median postoperative stay declined over time, from 16 days in the 1980s to 8 days in the 2000s (P < 0.001). The perioperative mortality declined from 30% in the 1970s to 1% in the 2000s (P < 0.001). The median survival for all patients with ductal adenocarcinoma was 18 months (1-year survival = 65 %, 2-year survival = 37%, 5-year survival = 18%). In a Cox proportional hazards model, pathological factors having a significant impact on survival included tumor diameter, resection margin status, lymph node status, and histologic grade. This is the largest single-institution experience with PD for pancreatic cancer. Patients who have cancers with favorable pathological features have a statistically significant improved long-term survival.
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            18F-FDG PET uptake characterization through texture analysis: investigating the complementary nature of heterogeneity and functional tumor volume in a multi-cancer site patient cohort.

            Intratumoral uptake heterogeneity in (18)F-FDG PET has been associated with patient treatment outcomes in several cancer types. Textural feature analysis is a promising method for its quantification. An open issue associated with textural features for the quantification of intratumoral heterogeneity concerns its added contribution and dependence on the metabolically active tumor volume (MATV), which has already been shown to be a significant predictive and prognostic parameter. Our objective was to address this question using a larger cohort of patients covering different cancer types.
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              An analysis of co-occurrence texture statistics as a function of grey level quantization

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                Author and article information

                Contributors
                armin.eilaghi@utoronto.ca
                sameer.baig@sri.utoronto.ca
                lance.zhang@mail.utoronto.ca
                jzhang@sri.utoronto.ca
                paul.karanicolas@sunnybrook.ca
                steven.gallinger@uhn.ca
                farzad.khalvati@sri.utoronto.ca
                416-480-6100 , masoom.haider@sunnybrook.ca
                Journal
                BMC Med Imaging
                BMC Med Imaging
                BMC Medical Imaging
                BioMed Central (London )
                1471-2342
                19 June 2017
                19 June 2017
                2017
                : 17
                : 38
                Affiliations
                [1 ]ISNI 0000 0001 2157 2938, GRID grid.17063.33, Department of Medical Imaging and Sunnybrook Research Institute, , Sunnybrook Health Sciences Center, University of Toronto, ; 2075 Bayview Ave., Room Rm AG 46, Toronto, M4N 3 M5 ON Canada
                [2 ]ISNI 0000 0001 2157 2938, GRID grid.17063.33, Department of Surgery, Sunnybrook Health Sciences Center, , University of Toronto, ; Toronto, ON Canada
                [3 ]ISNI 0000 0004 0626 690X, GRID grid.419890.d, PanCuRx Translational Research Initiative, , Ontario Institute for Cancer Research, ; Toronto, ON Canada
                [4 ]ISNI 0000 0004 0473 9881, GRID grid.416166.2, Lunenfeld-Tanenbaum Research Institute, , Mount Sinai Hospital, ; Toronto, ON Canada
                [5 ]ISNI 0000 0004 0474 0428, GRID grid.231844.8, Hepatobiliary/pancreatic Surgical Oncology Program, , University Health Network, ; Toronto, ON Canada
                [6 ]ISNI 0000 0004 0637 3588, GRID grid.462040.4, Mechanical Engineering Department, , Australian College of Kuwait, ; Kuwait City, Kuwait
                Article
                209
                10.1186/s12880-017-0209-5
                5477257
                28629416
                d45521d1-1ddf-4667-a6c0-da517944abc6
                © The Author(s). 2017

                Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

                History
                : 5 January 2017
                : 31 May 2017
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/501100004203, Ontario Institute for Cancer Research;
                Categories
                Research Article
                Custom metadata
                © The Author(s) 2017

                Radiology & Imaging
                texture features,pancreatic ductal adenocarcinoma,overall survival prediction,dissimilarity,inverse difference normalized

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