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Radiomics in colorectal cancer patients.
Inchingolo, Riccardo; Maino, Cesare; Cannella, Roberto; Vernuccio, Federica; Cortese, Francesco; Dezio, Michele; Pisani, Antonio Rosario; Giandola, Teresa; Gatti, Marco; Giannini, Valentina; Ippolito, Davide; Faletti, Riccardo.
Afiliación
  • Inchingolo R; Unit of Interventional Radiology, F. Miulli Hospital, Acquaviva delle Fonti 70021, Italy. riccardoin@hotmail.it.
  • Maino C; Department of Radiology, Fondazione IRCCS San Gerardo dei Tintori, Monza 20900, Italy.
  • Cannella R; Department of Biomedicine, Neuroscience and Advanced Diagnostics (BiND), University of Palermo, Palermo 90127, Italy.
  • Vernuccio F; Institute of Radiology, University Hospital of Padova, Padova 35128, Italy.
  • Cortese F; Unit of Interventional Radiology, F. Miulli Hospital, Acquaviva delle Fonti 70021, Italy.
  • Dezio M; Unit of Interventional Radiology, F. Miulli Hospital, Acquaviva delle Fonti 70021, Italy.
  • Pisani AR; Interdisciplinary Department of Medicine, Section of Nuclear Medicine, University of Bari "Aldo Moro", Bari 70121, Italy.
  • Giandola T; Department of Radiology, Fondazione IRCCS San Gerardo dei Tintori, Monza 20900, Italy.
  • Gatti M; Department of Surgical Sciences, University of Turin, Turin 10126, Italy.
  • Giannini V; Department of Surgical Sciences, University of Turin, Turin 10126, Italy.
  • Ippolito D; Department of Radiology, Fondazione IRCCS San Gerardo dei Tintori, Monza 20900, Italy.
  • Faletti R; Department of Surgical Sciences, University of Turin, Turin 10126, Italy.
World J Gastroenterol ; 29(19): 2888-2904, 2023 May 21.
Article en En | MEDLINE | ID: mdl-37274803
ABSTRACT
The main therapeutic options for colorectal cancer are surgical resection and adjuvant chemotherapy in non-metastatic disease. However, the evaluation of the overall adjuvant chemotherapy benefit in patients with a high risk of recurrence is challenging. Radiological images can represent a source of data that can be analyzed by using automated computer-based techniques, working on numerical information coded within Digital Imaging and Communications in Medicine files This image numerical analysis has been named "radiomics". Radiomics allows the extraction of quantitative features from radiological images, mainly invisible to the naked eye, that can be further analyzed by artificial intelligence algorithms. Radiomics is expanding in oncology to either understand tumor biology or for the development of imaging biomarkers for diagnosis, staging, and prognosis, prediction of treatment response and diseases monitoring and surveillance. Several efforts have been made to develop radiomics signatures for colorectal cancer patient using computed tomography (CT) images with different

aims:

The preoperative prediction of lymph node metastasis, detecting BRAF and RAS gene mutations. Moreover, the use of delta-radiomics allows the analysis of variations of the radiomics parameters extracted from CT scans performed at different timepoints. Most published studies concerning radiomics and magnetic resonance imaging (MRI) mainly focused on the response of advanced tumors that underwent neoadjuvant therapy. Nodes status is the main determinant of adjuvant chemotherapy. Therefore, several radiomics model based on MRI, especially on T2-weighted images and ADC maps, for the preoperative prediction of nodes metastasis in rectal cancer has been developed. Current studies mostly focused on the applications of radiomics in positron emission tomography/CT for the prediction of survival after curative surgical resection and assessment of response following neoadjuvant chemoradiotherapy. Since colorectal liver metastases develop in about 25% of patients with colorectal carcinoma, the main diagnostic tasks of radiomics should be the detection of synchronous and metachronous lesions. Radiomics could be an additional tool in clinical setting, especially in identifying patients with high-risk disease. Nevertheless, radiomics has numerous shortcomings that make daily use extremely difficult. Further studies are needed to assess performance of radiomics in stratifying patients with high-risk disease.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Neoplasias del Recto / Inteligencia Artificial Tipo de estudio: Observational_studies / Prognostic_studies / Risk_factors_studies Límite: Humans Idioma: En Revista: World J Gastroenterol Asunto de la revista: GASTROENTEROLOGIA Año: 2023 Tipo del documento: Article País de afiliación: Italia

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Neoplasias del Recto / Inteligencia Artificial Tipo de estudio: Observational_studies / Prognostic_studies / Risk_factors_studies Límite: Humans Idioma: En Revista: World J Gastroenterol Asunto de la revista: GASTROENTEROLOGIA Año: 2023 Tipo del documento: Article País de afiliación: Italia
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