Damiano Caruso1, Marta Zerunian1, Maria Ciolina1, Domenico de Santis1, Marco Rengo1, Mumtaz H Soomro2, Gaetano Giunta2, Silvia Conforto2, Maurizio Schmid2, Emanuele Neri3, Andrea Laghi4. 1. Department of Radiological Sciences, Oncology and Pathology, "Sapienza" - University of Rome, I.C.O.T. Hospital, Via Franco Faggiana 1668, 04100, Latina, Italy. 2. Department of Engineering, University of Roma Tre, Via Vito Volterra 62, 00146, Rome, Italy. 3. Department of Radiological Sciences, AOUP, Via Savi 10, 56126, Pisa, Italy. 4. Department of Radiological Sciences, Oncology and Pathology, "Sapienza" - University of Rome, I.C.O.T. Hospital, Via Franco Faggiana 1668, 04100, Latina, Italy. andrea.laghi@uniroma1.it.
Abstract
PURPOSE: Haralick features Texture analysis is a recent oncologic imaging biomarker used to assess quantitatively the heterogeneity within a tumor. The aim of this study is to evaluate which Haralick's features are the most feasible in predicting tumor response to neoadjuvant chemoradiotherapy (CRT) in colorectal cancer. MATERIALS AND METHODS: After MRI and histological assessment, eight patients were enrolled and divided into two groups based on response to neoadjuvant CRT in complete responders (CR) and non-responders (NR). Oblique Axial T2-weighted MRI sequences before CRT were analyzed by two radiologists in consensus drawing a ROI around the tumor. 14 over 192 Haralick's features were extrapolated from normalized gray-level co-occurrence matrix in four different directions. A dedicated statistical analysis was performed to evaluate distribution of the extracted Haralick's features computing mean and standard deviation. RESULTS: Pretreatment MRI examination showed significant value (p < 0.05) of 5 over 14 computed Haralick texture. In particular, the significant features are the following: concerning energy, contrast, correlation, entropy and inverse difference moment. CONCLUSIONS: Five Haralick's features showed significant relevance in the prediction of response to therapy in colorectal cancer and might be used as additional imaging biomarker in the oncologic management of colorectal patients.
PURPOSE: Haralick features Texture analysis is a recent oncologic imaging biomarker used to assess quantitatively the heterogeneity within a tumor. The aim of this study is to evaluate which Haralick's features are the most feasible in predicting tumor response to neoadjuvant chemoradiotherapy (CRT) in colorectal cancer. MATERIALS AND METHODS: After MRI and histological assessment, eight patients were enrolled and divided into two groups based on response to neoadjuvant CRT in complete responders (CR) and non-responders (NR). Oblique Axial T2-weighted MRI sequences before CRT were analyzed by two radiologists in consensus drawing a ROI around the tumor. 14 over 192 Haralick's features were extrapolated from normalized gray-level co-occurrence matrix in four different directions. A dedicated statistical analysis was performed to evaluate distribution of the extracted Haralick's features computing mean and standard deviation. RESULTS: Pretreatment MRI examination showed significant value (p < 0.05) of 5 over 14 computed Haralick texture. In particular, the significant features are the following: concerning energy, contrast, correlation, entropy and inverse difference moment. CONCLUSIONS: Five Haralick's features showed significant relevance in the prediction of response to therapy in colorectal cancer and might be used as additional imaging biomarker in the oncologic management of colorectalpatients.
Authors: Amanda Drumstas Nussi; Sérgio Lucio Pereira de Castro Lopes; Catharina Simioni De Rosa; João Pedro Perez Gomes; Celso Massahiro Ogawa; Paulo Henrique Braz-Silva; Andre Luiz Ferreira Costa Journal: Oral Radiol Date: 2022-05-18 Impact factor: 1.852
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
Authors: Damiano Caruso; Marta Zerunian; Domenico De Santis; Tommaso Biondi; Pasquale Paolantonio; Marco Rengo; Davide Bellini; Riccardo Ferrari; Maria Ciolina; Elena Lucertini; Michela Polici; Elsa Iannicelli; Vincenzo Tombolini; Andrea Laghi Journal: Biomed Res Int Date: 2020-10-10 Impact factor: 3.411