Literature DB >> 33778733

Radiomics Texture Features in Advanced Colorectal Cancer: Correlation with BRAF Mutation and 5-year Overall Survival.

Adrian A Negreros-Osuna1, Anushri Parakh1, Ryan B Corcoran1, Ali Pourvaziri1, Avinash Kambadakone1, David P Ryan1, Dushyant V Sahani1.   

Abstract

Purpose: To explore the potential of radiomics texture features as potential biomarkers to enable detection of the presence of BRAF mutation and prediction of 5-year overall survival (OS) in stage IV colorectal cancer (CRC). Materials and
Methods: In this retrospective study, a total of 145 patients (mean age, 61 years ± 14 [standard deviation {SD}]; 68 female patients and 77 male patients) with stage IV CRC who underwent molecular profiling and pretreatment contrast material-enhanced CT scans between 2004 and 2018 were included. Tumor radiomics texture features, including the mean, the SD, the mean value of positive pixels (MPP), skewness, kurtosis, and entropy, were extracted from regions of interest on CT images after applying three Laplacian-of-Gaussian filters known as spatial scaling factors (SSFs) (SSF = 2, fine; SSF = 4, medium; SSF = 6, coarse) by using specialized software; values of these parameters were also obtained without filtration (SSF = 0). The Wilcoxon rank sum test was used to assess differences between mutated versus wild-type BRAF tumors. Associations between radiomics texture features and 5-year OS were determined by using Kaplan-Meier estimators using the log-rank test and multivariate Cox proportional-hazards regression analysis.
Results: The SDs and MPPs of radiomic texture features were significantly lower in BRAF mutant tumors than in wild-type BRAF tumors at SSFs of 0, 4, and 6 (P = .006, P = .007, and P = .005, respectively). Patients with skewness less than or equal to -0.75 at an SSF of 0 and a mean of greater than or equal to 17.76 at an SSF of 2 showed better 5-year OS (hazard ratio [HR], 0.53 [95% confidence interval {CI}: 0.29, 0.94]; HR, 0.40 [95% CI: 0.22, 0.71]; log-rank P = .025 and P = .002, respectively). Tumor location (right colon vs left colon vs rectum) had no significant impact on the clinical outcome (log-rank P = .53).
Conclusion: Radiomics texture features can serve as potential biomarkers for determining BRAF mutation status and as predictors of 5-year OS in patients with advanced-stage CRC.Keywords: Abdomen/GI, CT, Comparative Studies, Large BowelSupplemental material is available for this article.© RSNA, 2020. 2020 by the Radiological Society of North America, Inc.

Entities:  

Year:  2020        PMID: 33778733      PMCID: PMC7983710          DOI: 10.1148/rycan.2020190084

Source DB:  PubMed          Journal:  Radiol Imaging Cancer        ISSN: 2638-616X


  33 in total

1.  In search of biologic correlates for liver texture on portal-phase CT.

Authors:  Balaji Ganeshan; Kenneth A Miles; Rupert C D Young; Chris R Chatwin
Journal:  Acad Radiol       Date:  2007-09       Impact factor: 3.173

2.  Opportunities and challenges to utilization of quantitative imaging: Report of the AAPM practical big data workshop.

Authors:  Thomas R Mackie; Edward F Jackson; Maryellen Giger
Journal:  Med Phys       Date:  2018-09-24       Impact factor: 4.071

3.  Assessment of tumor heterogeneity by CT texture analysis: can the largest cross-sectional area be used as an alternative to whole tumor analysis?

Authors:  Francesca Ng; Robert Kozarski; Balaji Ganeshan; Vicky Goh
Journal:  Eur J Radiol       Date:  2012-11-26       Impact factor: 3.528

4.  CT textural analysis of hepatic metastatic colorectal cancer: pre-treatment tumor heterogeneity correlates with pathology and clinical outcomes.

Authors:  Meghan G Lubner; Nicholas Stabo; Sam J Lubner; Alejandro Munoz del Rio; Chihwa Song; Richard B Halberg; Perry J Pickhardt
Journal:  Abdom Imaging       Date:  2015-10

5.  Molecular Biomarkers for the Evaluation of Colorectal Cancer: Guideline From the American Society for Clinical Pathology, College of American Pathologists, Association for Molecular Pathology, and American Society of Clinical Oncology.

Authors:  Antonia R Sepulveda; Stanley R Hamilton; Carmen J Allegra; Wayne Grody; Allison M Cushman-Vokoun; William K Funkhouser; Scott E Kopetz; Christopher Lieu; Noralane M Lindor; Bruce D Minsky; Federico A Monzon; Daniel J Sargent; Veena M Singh; Joseph Willis; Jennifer Clark; Carol Colasacco; R Bryan Rumble; Robyn Temple-Smolkin; Christina B Ventura; Jan A Nowak
Journal:  Arch Pathol Lab Med       Date:  2017-02-06       Impact factor: 5.534

Review 6.  CT Texture Analysis: Definitions, Applications, Biologic Correlates, and Challenges.

Authors:  Meghan G Lubner; Andrew D Smith; Kumar Sandrasegaran; Dushyant V Sahani; Perry J Pickhardt
Journal:  Radiographics       Date:  2017 Sep-Oct       Impact factor: 5.333

7.  Can CT-based radiomics signature predict KRAS/NRAS/BRAF mutations in colorectal cancer?

Authors:  Lei Yang; Di Dong; Mengjie Fang; Yongbei Zhu; Yali Zang; Zhenyu Liu; Hongmei Zhang; Jianming Ying; Xinming Zhao; Jie Tian
Journal:  Eur Radiol       Date:  2018-01-15       Impact factor: 5.315

8.  Quantifying tumour heterogeneity with CT.

Authors:  Balaji Ganeshan; Kenneth A Miles
Journal:  Cancer Imaging       Date:  2013-03-26       Impact factor: 3.909

9.  BRAF-Mutated Colorectal Cancer Exhibits Distinct Clinicopathological Features from Wild-Type BRAF-Expressing Cancer Independent of the Microsatellite Instability Status.

Authors:  Min Hye Jang; Sehun Kim; Dae Yong Hwang; Wook Youn Kim; So Dug Lim; Wan Seop Kim; Tea Sook Hwang; Hye Seung Han
Journal:  J Korean Med Sci       Date:  2017-01       Impact factor: 2.153

10.  CT texture analysis in patients with locally advanced rectal cancer treated with neoadjuvant chemoradiotherapy: A potential imaging biomarker for treatment response and prognosis.

Authors:  Choong Guen Chee; Young Hoon Kim; Kyoung Ho Lee; Yoon Jin Lee; Ji Hoon Park; Hye Seung Lee; Soyeon Ahn; Bohyoung Kim
Journal:  PLoS One       Date:  2017-08-10       Impact factor: 3.240

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  10 in total

1.  Coupling radiomics analysis of CT image with diversification of tumor ecosystem: A new insight to overall survival in stage I-III colorectal cancer.

Authors:  Yanqi Huang; Lan He; Zhenhui Li; Xin Chen; Chu Han; Ke Zhao; Yuan Zhang; Jinrong Qu; Yun Mao; Changhong Liang; Zaiyi Liu
Journal:  Chin J Cancer Res       Date:  2022-02-28       Impact factor: 5.087

Review 2.  Overview of serum and tissue markers in colorectal cancer: a primer for radiologists.

Authors:  Apurva Bonde; Daniel A Smith; Elias Kikano; Jennifer M Yoest; Sree H Tirumani; Nikhil H Ramaiya
Journal:  Abdom Radiol (NY)       Date:  2021-08-20

Review 3.  What Genetics Can Do for Oncological Imaging: A Systematic Review of the Genetic Validation Data Used in Radiomics Studies.

Authors:  Rebeca Mirón Mombiela; Anne Rix Arildskov; Frederik Jager Bruun; Lotte Harries Hasselbalch; Kristine Bærentz Holst; Sine Hvid Rasmussen; Consuelo Borrás
Journal:  Int J Mol Sci       Date:  2022-06-10       Impact factor: 6.208

Review 4.  Predicting cancer outcomes with radiomics and artificial intelligence in radiology.

Authors:  Kaustav Bera; Nathaniel Braman; Amit Gupta; Vamsidhar Velcheti; Anant Madabhushi
Journal:  Nat Rev Clin Oncol       Date:  2021-10-18       Impact factor: 65.011

Review 5.  The application of radiomics in predicting gene mutations in cancer.

Authors:  Yana Qi; Tingting Zhao; Mingyong Han
Journal:  Eur Radiol       Date:  2022-01-20       Impact factor: 5.315

Review 6.  Multi-Omic Approaches in Colorectal Cancer beyond Genomic Data.

Authors:  Emilia Sardo; Stefania Napolitano; Carminia Maria Della Corte; Davide Ciardiello; Antonio Raucci; Gianluca Arrichiello; Teresa Troiani; Fortunato Ciardiello; Erika Martinelli; Giulia Martini
Journal:  J Pers Med       Date:  2022-01-18

7.  Adjusted CT Image-Based Radiomic Features Combined with Immune Genomic Expression Achieve Accurate Prognostic Classification and Identification of Therapeutic Targets in Stage III Colorectal Cancer.

Authors:  Yi-Ching Huang; Yi-Shan Tsai; Chung-I Li; Ren-Hao Chan; Yu-Min Yeh; Po-Chuan Chen; Meng-Ru Shen; Peng-Chan Lin
Journal:  Cancers (Basel)       Date:  2022-04-08       Impact factor: 6.575

8.  Nomogram based on radiomics analysis of ultrasound images can improve preoperative BRAF mutation diagnosis for papillary thyroid microcarcinoma.

Authors:  Jiajia Tang; Shitao Jiang; Jiaojiao Ma; Xuehua Xi; Huilin Li; Liangkai Wang; Bo Zhang
Journal:  Front Endocrinol (Lausanne)       Date:  2022-08-19       Impact factor: 6.055

Review 9.  Brain Tumor Characterization Using Radiogenomics in Artificial Intelligence Framework.

Authors:  Biswajit Jena; Sanjay Saxena; Gopal Krishna Nayak; Antonella Balestrieri; Neha Gupta; Narinder N Khanna; John R Laird; Manudeep K Kalra; Mostafa M Fouda; Luca Saba; Jasjit S Suri
Journal:  Cancers (Basel)       Date:  2022-08-22       Impact factor: 6.575

10.  Elevated CEA and CA 19-9 Levels within the Normal Ranges Increase the Likelihood of CRC Recurrence in the Chinese Han Population.

Authors:  Lujia Wang; Guangkai Zhang; Jiafeng Shen; Yujiang Shen; Guojun Cai
Journal:  Appl Bionics Biomech       Date:  2022-09-22       Impact factor: 1.664

  10 in total

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