Literature DB >> 23985274

Primary esophageal cancer: heterogeneity as potential prognostic biomarker in patients treated with definitive chemotherapy and radiation therapy.

Connie Yip1, David Landau, Robert Kozarski, Balaji Ganeshan, Robert Thomas, Andriana Michaelidou, Vicky Goh.   

Abstract

PURPOSE: To determine the association between tumor heterogeneity, morphologic tumor response, and overall survival in primary esophageal cancer treated with chemotherapy and radiation therapy (CRT).
MATERIALS AND METHODS: After an institutional review board waiver was obtained, contrast material-enhanced computed tomographic (CT) studies in 36 patients with stage T2 or greater esophageal tumors who underwent contrast-enhanced CT before and after CRT between 2005 and 2008 were analyzed in terms of whole-tumor texture, with quantification of entropy, uniformity, mean gray-level intensity, kurtosis, standard deviation of the histogram, and skewness for fine to coarse textures (filters 1.0-2.5, respectively). The association between texture parameters and survival time was assessed by using Kaplan-Meier analysis and a Cox proportional hazards model. Survival models involving texture parameters and combinations of texture and morphologic response assessment were compared with morphologic assessment alone by means of receiver operating characteristic (ROC) analysis.
RESULTS: Posttreatment medium entropy of less than 7.356 (median overall survival, 33.2 vs 11.7 months; P = .0002), coarse entropy of less than 7.116 (median overall survival, 33.2 vs 11.7 months; P = .0002), and medium uniformity of 0.007 or greater (median overall survival, 33.2 vs 11.7 months; P = .0002) were associated with improved survival time. These remained significant prognostic factors after adjustment for stage and age: entropy (filter 2.0: hazard ratio [HR] = 5.038, P = .0004; filter 2.5: HR = 5.038, P = .0004) and uniformity (HR = 0.199, P = .0004). Survival models that included a combination of pretreatment entropy and uniformity with maximal wall thickness assessment, respectively, performed better than morphologic assessment alone (area under the ROC curve, 0.767 vs 0.487 [P = .00005] and 0.802 vs 0.487 [P = .0003]).
CONCLUSION: Posttreatment texture parameters are associated with survival time, and the combination of pretreatment texture parameters and maximal wall thickness performed better in survival models than morphologic tumor response alone. © RSNA, 2013.

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Year:  2013        PMID: 23985274     DOI: 10.1148/radiol.13122869

Source DB:  PubMed          Journal:  Radiology        ISSN: 0033-8419            Impact factor:   11.105


  83 in total

1.  Pre-treatment magnetic resonance-based texture features as potential imaging biomarkers for predicting event free survival in anal cancer treated by chemoradiotherapy.

Authors:  Arnaud Hocquelet; Thibaut Auriac; Cynthia Perier; Clarisse Dromain; Marie Meyer; Jean-Baptiste Pinaquy; Alban Denys; Hervé Trillaud; Baudouin Denis De Senneville; Véronique Vendrely
Journal:  Eur Radiol       Date:  2018-02-05       Impact factor: 5.315

2.  Predicting the grade of hepatocellular carcinoma based on non-contrast-enhanced MRI radiomics signature.

Authors:  Minghui Wu; Hongna Tan; Fei Gao; Jinjin Hai; Peigang Ning; Jian Chen; Shaocheng Zhu; Meiyun Wang; Shewei Dou; Dapeng Shi
Journal:  Eur Radiol       Date:  2018-11-07       Impact factor: 5.315

3.  Texture features of colorectal liver metastases on pretreatment contrast-enhanced CT may predict response and prognosis in patients treated with bevacizumab-containing chemotherapy: a pilot study including comparison with standard chemotherapy.

Authors:  Marco Ravanelli; Giorgio Maria Agazzi; Elena Tononcelli; Elisa Roca; Paolo Cabassa; Gianluca Baiocchi; Alfredo Berruti; Roberto Maroldi; Davide Farina
Journal:  Radiol Med       Date:  2019-06-06       Impact factor: 3.469

4.  Fractal analysis of contrast-enhanced CT images to predict survival of patients with hepatocellular carcinoma treated with sunitinib.

Authors:  Koichi Hayano; Hiroyuki Yoshida; Andrew X Zhu; Dushyant V Sahani
Journal:  Dig Dis Sci       Date:  2014-02-22       Impact factor: 3.199

Review 5.  Texture analysis of medical images for radiotherapy applications.

Authors:  Elisa Scalco; Giovanna Rizzo
Journal:  Br J Radiol       Date:  2016-11-25       Impact factor: 3.039

6.  CT texture analysis of pancreatic cancer.

Authors:  Kumar Sandrasegaran; Yuning Lin; Michael Asare-Sawiri; Tai Taiyini; Mark Tann
Journal:  Eur Radiol       Date:  2018-08-16       Impact factor: 5.315

Review 7.  The role of texture analysis in imaging as an outcome predictor and potential tool in radiotherapy treatment planning.

Authors:  S Alobaidli; S McQuaid; C South; V Prakash; P Evans; A Nisbet
Journal:  Br J Radiol       Date:  2014-07-23       Impact factor: 3.039

8.  CT texture analysis in the differentiation of major renal cell carcinoma subtypes and correlation with Fuhrman grade.

Authors:  Yu Deng; Erik Soule; Aster Samuel; Sakhi Shah; Enming Cui; Michael Asare-Sawiri; Chandru Sundaram; Chandana Lall; Kumaresan Sandrasegaran
Journal:  Eur Radiol       Date:  2019-05-24       Impact factor: 5.315

9.  Metastatic melanoma: pretreatment contrast-enhanced CT texture parameters as predictive biomarkers of survival in patients treated with pembrolizumab.

Authors:  Carole Durot; Sébastien Mulé; Philippe Soyer; Aude Marchal; Florent Grange; Christine Hoeffel
Journal:  Eur Radiol       Date:  2019-01-15       Impact factor: 5.315

10.  Diagnostic accuracy of MRI texture analysis for grading gliomas.

Authors:  Austin Ditmer; Bin Zhang; Taimur Shujaat; Andrew Pavlina; Nicholas Luibrand; Mary Gaskill-Shipley; Achala Vagal
Journal:  J Neurooncol       Date:  2018-08-25       Impact factor: 4.130

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