Literature DB >> 33924854

The Heterogeneity of Skewness in T2W-Based Radiomics Predicts the Response to Neoadjuvant Chemoradiotherapy in Locally Advanced Rectal Cancer.

Francesca Coppola1,2, Margherita Mottola3, Silvia Lo Monaco1, Arrigo Cattabriga1, Maria Adriana Cocozza1, Jia Cheng Yuan1, Caterina De Benedittis1, Dajana Cuicchi4, Alessandra Guido5, Fabiola Lorena Rojas Llimpe6, Antonietta D'Errico7, Andrea Ardizzoni6, Gilberto Poggioli4, Lidia Strigari8, Alessio Giuseppe Morganti5, Franco Bazzoli9, Luigi Ricciardiello9, Rita Golfieri1, Alessandro Bevilacqua3,10.   

Abstract

Our study aimed to investigate whether radiomics on MRI sequences can differentiate responder (R) and non-responder (NR) patients based on the tumour regression grade (TRG) assigned after surgical resection in locally advanced rectal cancer (LARC) treated with neoadjuvant chemoradiotherapy (nCRT). Eighty-five patients undergoing primary staging with MRI were retrospectively evaluated, and 40 patients were finally selected. The ROIs were manually outlined in the tumour site on T2w sequences in the oblique-axial plane. Based on the TRG, patients were grouped as having either a complete or a partial response (TRG = (0,1), n = 15). NR patients had a minimal or poor nCRT response (TRG = (2,3), n = 25). Eighty-four local first-order radiomic features (RFs) were extracted from tumour ROIs. Only single RFs were investigated. Each feature was selected using univariate analysis guided by a one-tailed Wilcoxon rank-sum. ROC curve analysis was performed, using AUC computation and the Youden index (YI) for sensitivity and specificity. The RF measuring the heterogeneity of local skewness of T2w values from tumour ROIs differentiated Rs and NRs with a p-value ≈ 10-5; AUC = 0.90 (95%CI, 0.73-0.96); and YI = 0.68, corresponding to 80% sensitivity and 88% specificity. In conclusion, higher heterogeneity in skewness maps of the baseline tumour correlated with a greater benefit from nCRT.

Entities:  

Keywords:  MRI; radiomics; rectal cancer

Year:  2021        PMID: 33924854     DOI: 10.3390/diagnostics11050795

Source DB:  PubMed          Journal:  Diagnostics (Basel)        ISSN: 2075-4418


  34 in total

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Authors:  Zhihua Lu; Lei Wang; Kaijian Xia; Heng Jiang; Xiaoyan Weng; Jianlong Jiang; Mei Wu
Journal:  J Med Syst       Date:  2019-11-07       Impact factor: 4.460

2.  Artificial intelligence: radiologists' expectations and opinions gleaned from a nationwide online survey.

Authors:  Francesca Coppola; Lorenzo Faggioni; Daniele Regge; Andrea Giovagnoni; Rita Golfieri; Corrado Bibbolino; Vittorio Miele; Emanuele Neri; Roberto Grassi
Journal:  Radiol Med       Date:  2020-04-29       Impact factor: 3.469

3.  Fractal-based radiomic approach to predict complete pathological response after chemo-radiotherapy in rectal cancer.

Authors:  Davide Cusumano; Nicola Dinapoli; Luca Boldrini; Giuditta Chiloiro; Roberto Gatta; Carlotta Masciocchi; Jacopo Lenkowicz; Calogero Casà; Andrea Damiani; Luigi Azario; Johan Van Soest; Andre Dekker; Philippe Lambin; Marco De Spirito; Vincenzo Valentini
Journal:  Radiol Med       Date:  2017-12-11       Impact factor: 3.469

4.  Radiomics Analysis for Evaluation of Pathological Complete Response to Neoadjuvant Chemoradiotherapy in Locally Advanced Rectal Cancer.

Authors:  Zhenyu Liu; Xiao-Yan Zhang; Yan-Jie Shi; Lin Wang; Hai-Tao Zhu; Zhenchao Tang; Shuo Wang; Xiao-Ting Li; Jie Tian; Ying-Shi Sun
Journal:  Clin Cancer Res       Date:  2017-09-22       Impact factor: 12.531

5.  Texture analysis as imaging biomarker of tumoral response to neoadjuvant chemoradiotherapy in rectal cancer patients studied with 3-T magnetic resonance.

Authors:  Carlo N De Cecco; Balaji Ganeshan; Maria Ciolina; Marco Rengo; Felix G Meinel; Daniela Musio; Francesca De Felice; Nicola Raffetto; Vincenzo Tombolini; Andrea Laghi
Journal:  Invest Radiol       Date:  2015-04       Impact factor: 6.016

Review 6.  Endoscopic ultrasound for the characterization and staging of rectal cancer. Current state of the method. Technological advances and perspectives.

Authors:  Mariana M Gersak; Radu Badea; Florin Graur; Nadim Al Hajja; Luminita Furcea; Sorin M Dudea
Journal:  Med Ultrason       Date:  2015-06       Impact factor: 1.611

7.  Rectal Cancer, Version 2.2018, NCCN Clinical Practice Guidelines in Oncology.

Authors:  Al B Benson; Alan P Venook; Mahmoud M Al-Hawary; Lynette Cederquist; Yi-Jen Chen; Kristen K Ciombor; Stacey Cohen; Harry S Cooper; Dustin Deming; Paul F Engstrom; Jean L Grem; Axel Grothey; Howard S Hochster; Sarah Hoffe; Steven Hunt; Ahmed Kamel; Natalie Kirilcuk; Smitha Krishnamurthi; Wells A Messersmith; Jeffrey Meyerhardt; Mary F Mulcahy; James D Murphy; Steven Nurkin; Leonard Saltz; Sunil Sharma; David Shibata; John M Skibber; Constantinos T Sofocleous; Elena M Stoffel; Eden Stotsky-Himelfarb; Christopher G Willett; Evan Wuthrick; Kristina M Gregory; Lisa Gurski; Deborah A Freedman-Cass
Journal:  J Natl Compr Canc Netw       Date:  2018-07       Impact factor: 11.908

Review 8.  Epidemiology of colorectal cancer: incidence, mortality, survival, and risk factors.

Authors:  Prashanth Rawla; Tagore Sunkara; Adam Barsouk
Journal:  Prz Gastroenterol       Date:  2019-01-06

9.  Clinical utility of radiomics at baseline rectal MRI to predict complete response of rectal cancer after chemoradiation therapy.

Authors:  Iva Petkovska; Florent Tixier; Eduardo J Ortiz; Jennifer S Golia Pernicka; Viktoriya Paroder; David D Bates; Natally Horvat; James Fuqua; Juliana Schilsky; Marc J Gollub; Julio Garcia-Aguilar; Harini Veeraraghavan
Journal:  Abdom Radiol (NY)       Date:  2020-11

10.  Radiomics: Images Are More than Pictures, They Are Data.

Authors:  Robert J Gillies; Paul E Kinahan; Hedvig Hricak
Journal:  Radiology       Date:  2015-11-18       Impact factor: 11.105

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  4 in total

1.  Analysis of MRI and CT-based radiomics features for personalized treatment in locally advanced rectal cancer and external validation of published radiomics models.

Authors:  Iram Shahzadi; Alex Zwanenburg; Annika Lattermann; Annett Linge; Christian Baldus; Jan C Peeken; Stephanie E Combs; Markus Diefenhardt; Claus Rödel; Simon Kirste; Anca-Ligia Grosu; Michael Baumann; Mechthild Krause; Esther G C Troost; Steffen Löck
Journal:  Sci Rep       Date:  2022-06-17       Impact factor: 4.996

2.  Automated Prediction of the Response to Neoadjuvant Chemoradiotherapy in Patients Affected by Rectal Cancer.

Authors:  Giuseppe Filitto; Francesca Coppola; Nico Curti; Enrico Giampieri; Daniele Dall'Olio; Alessandra Merlotti; Arrigo Cattabriga; Maria Adriana Cocozza; Makoto Taninokuchi Tomassoni; Daniel Remondini; Luisa Pierotti; Lidia Strigari; Dajana Cuicchi; Alessandra Guido; Karim Rihawi; Antonietta D'Errico; Francesca Di Fabio; Gilberto Poggioli; Alessio Giuseppe Morganti; Luigi Ricciardiello; Rita Golfieri; Gastone Castellani
Journal:  Cancers (Basel)       Date:  2022-04-29       Impact factor: 6.575

3.  Simulation CT-based radiomics for prediction of response after neoadjuvant chemo-radiotherapy in patients with locally advanced rectal cancer.

Authors:  Pierluigi Bonomo; Jairo Socarras Fernandez; Daniela Thorwarth; Marta Casati; Lorenzo Livi; Daniel Zips; Cihan Gani
Journal:  Radiat Oncol       Date:  2022-04-28       Impact factor: 4.309

4.  Performance of Machine Learning and Texture Analysis for Predicting Response to Neoadjuvant Chemoradiotherapy in Locally Advanced Rectal Cancer with 3T MRI.

Authors:  Davide Bellini; Iacopo Carbone; Marco Rengo; Simone Vicini; Nicola Panvini; Damiano Caruso; Elsa Iannicelli; Vincenzo Tombolini; Andrea Laghi
Journal:  Tomography       Date:  2022-08-19
  4 in total

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