Radiomics in hepatic metastasis by colorectal cancer

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.

Radiomic features of breast parenchyma: assessing differences between FOR PROCESSING and FOR PRESENTATION digital mammography

“…In the last decade there has been growing consensus regarding the role of breast parenchyma as an independent risk factor for breast cancer [4][5][6]: consequently, a number of approaches to breast parenchyma assessment have been proposed, among which radiomic texture feature extraction is the most spread [7][8][9]. Radiomics is an emerging field and has a keen interest, especially in the oncology field [10][11][12]: it has been shown that radiomics could be predictive of TNM grade, histological grade, response to therapy and survival in various tumors [13][14][15]. Textural radiomic features of breast parenchyma have been shown to be useful for cancer classification, too [16].…”

Computed Tomography Structured Reporting in the Staging of Lymphoma: A Delphi Consensus Proposal

“…Radiomic features provide data on tumor phenotype as well as cancer microenvironment. Radiomics-derived parameters, when associated with other clinical pertinent data and correlated with outcomes data, can produce accurate robust evidence-based clinical-decision support systems (CDSS) [25][26][27][28][29][30][31][32]. The possibility to connect radiological and clinical data in the present SR template could create the basis for a large database, allowing not only epidemiological statistical analyses, but also the building of radiomics models [29].…”

Structured Reporting of Computed Tomography in the Staging of Neuroendocrine Neoplasms: A Delphi Consensus Proposal

“…The "Clinical Evaluation" section collected previous examinations findings, a genetic panel, results of histopathological examination on biopsy specimen, chromogranin A (CgA) level, neuronspecific enolase (NSE) level, 5-hydroxyindoleacetic acid (5-HIAA) 24-h urine level, serum gastrin level, serum insulin level, serum glucagon level, serum VIP level, blood count, serum creatinine, liver function and clinical symptoms. This data could serve as a basis for creating potentially large databases, allowing not only for epidemiological statistical analysis, but also for building a radiomics model through the combination of radiological features and clinical data (24)(25)(26)(27)(28)(29). To this end, genomic data could also be leveraged to build a radiogenomics model, which may be useful in the upper levels of personalised risk stratification and advanced precision medicine for early cancer diagnosis, cancer therapy selection, prognosis prediction, and assessment of treatment response and resistance to therapy (30)(31)(32)(33)(34).…”