Vincenza Granata1, Roberta Fusco2, Maria Luisa Barretta1, Carmine Picone1, Antonio Avallone3, Andrea Belli4, Renato Patrone4, Marilina Ferrante5, Diletta Cozzi6, Roberta Grassi5, Roberto Grassi5,7, Francesco Izzo4, Antonella Petrillo1. 1. Radiology Division, "ISTITUTO NAZIONALE TUMORI - IRCCS - FONDAZIONE G. PASCALE, Napoli, Italy", Via Mariano Semmola, Naples, Italy. 2. Radiology Division, "ISTITUTO NAZIONALE TUMORI - IRCCS - FONDAZIONE G. PASCALE, Napoli, Italy", Via Mariano Semmola, Naples, Italy. r.fusco@istitutotumori.na.it. 3. Abdominal Oncology Division, "ISTITUTO NAZIONALE TUMORI - IRCCS - FONDAZIONE G. PASCALE, NAPOLI, ITALIA", Via Mariano Semmola, Naples, Italy. 4. Hepatobiliary Surgical Oncology Division, "ISTITUTO NAZIONALE TUMORI - IRCCS - FONDAZIONE G. PASCALE, NAPOLI, ITALIA", Via Mariano Semmola, Naples, Italy. 5. Division of Radiology, "Università degli Studi della Campania Luigi Vanvitelli", Naples, Italy. 6. Division of Radiology, "Azienda Ospedaliera Universitaria Careggi", Florence, Italy. 7. Italian Society of Medical and Interventional Radiology SIRM, SIRM Foundation, Via della Signora 2, 20122, Milan, Italy.
Abstract
BACKGROUND: Radiomics is an emerging field and has a keen interest, especially in the oncology field. The process of a radiomics study consists of lesion segmentation, feature extraction, consistency analysis of features, feature selection, and model building. Manual segmentation is one of the most critical parts of radiomics. It can be time-consuming and suffers from variability in tumor delineation, which leads to the reproducibility problem of calculating parameters and assessing spatial tumor heterogeneity, particularly in large or multiple tumors. Radiomic features provides data on tumor phenotype as well as cancer microenvironment. Radiomics derived parameters, when associated with other pertinent data and correlated with outcomes data, can produce accurate robust evidence based clinical decision support systems. The principal challenge is the optimal collection and integration of diverse multimodal data sources in a quantitative manner that delivers unambiguous clinical predictions that accurately and robustly enable outcome prediction as a function of the impending decisions. METHODS: The search covered the years from January 2010 to January 2021. The inclusion criterion was: clinical study evaluating radiomics of liver colorectal metastases. Exclusion criteria were studies with no sufficient reported data, case report, review or editorial letter. RESULTS: We recognized 38 studies that assessed radiomics in mCRC from January 2010 to January 2021. Twenty were on different tpics, 5 corresponded to most criteria; 3 are review, or letter to editors; so 10 articles were included. CONCLUSIONS: In colorectal liver metastases radiomics should be a valid tool for the characterization of lesions, in the stratification of patients based on the risk of relapse after surgical treatment and in the prediction of response to chemotherapy treatment.
BACKGROUND: Radiomics is an emerging field and has a keen interest, especially in the oncology field. The process of a radiomics study consists of lesion segmentation, feature extraction, consistency analysis of features, feature selection, and model building. Manual segmentation is one of the most critical parts of radiomics. It can be time-consuming and suffers from variability in tumor delineation, which leads to the reproducibility problem of calculating parameters and assessing spatial tumor heterogeneity, particularly in large or multiple tumors. Radiomic features provides data on tumor phenotype as well as cancer microenvironment. Radiomics derived parameters, when associated with other pertinent data and correlated with outcomes data, can produce accurate robust evidence based clinical decision support systems. The principal challenge is the optimal collection and integration of diverse multimodal data sources in a quantitative manner that delivers unambiguous clinical predictions that accurately and robustly enable outcome prediction as a function of the impending decisions. METHODS: The search covered the years from January 2010 to January 2021. The inclusion criterion was: clinical study evaluating radiomics of liver colorectal metastases. Exclusion criteria were studies with no sufficient reported data, case report, review or editorial letter. RESULTS: We recognized 38 studies that assessed radiomics in mCRC from January 2010 to January 2021. Twenty were on different tpics, 5 corresponded to most criteria; 3 are review, or letter to editors; so 10 articles were included. CONCLUSIONS: In colorectal liver metastases radiomics should be a valid tool for the characterization of lesions, in the stratification of patients based on the risk of relapse after surgical treatment and in the prediction of response to chemotherapy treatment.
Authors: Peter J Campbell; Shinichi Yachida; Laura J Mudie; Philip J Stephens; Erin D Pleasance; Lucy A Stebbings; Laura A Morsberger; Calli Latimer; Stuart McLaren; Meng-Lay Lin; David J McBride; Ignacio Varela; Serena A Nik-Zainal; Catherine Leroy; Mingming Jia; Andrew Menzies; Adam P Butler; Jon W Teague; Constance A Griffin; John Burton; Harold Swerdlow; Michael A Quail; Michael R Stratton; Christine Iacobuzio-Donahue; P Andrew Futreal Journal: Nature Date: 2010-10-28 Impact factor: 49.962
Authors: E J Limkin; R Sun; L Dercle; E I Zacharaki; C Robert; S Reuzé; A Schernberg; N Paragios; E Deutsch; C Ferté Journal: Ann Oncol Date: 2017-06-01 Impact factor: 32.976
Authors: Okker D Bijlstra; Maud M E Boreel; Sietse van Mossel; Mark C Burgmans; Ellen H W Kapiteijn; Daniela E Oprea-Lager; Daphne D D Rietbergen; Floris H P van Velden; Alexander L Vahrmeijer; Rutger-Jan Swijnenburg; J Sven D Mieog; Lioe-Fee de Geus-Oei Journal: Diagnostics (Basel) Date: 2022-03-15