Dataset Information

Beyond Glioma: The Utility of Radiomic Analysis for Non-Glial Intracranial Tumors.

ABSTRACT: The field of radiomics is rapidly expanding and gaining a valuable role in neuro-oncology. The possibilities related to the use of radiomic analysis, such as distinguishing types of malignancies, predicting tumor grade, determining the presence of particular molecular markers, consistency, therapy response, and prognosis, can considerably influence decision-making in medicine in the near future. Even though the main focus of radiomic analyses has been on glial CNS tumors, studies on other intracranial tumors have shown encouraging results. Therefore, as the main focus of this review, we performed an analysis of publications on PubMed and Web of Science databases, focusing on radiomics in CNS metastases, lymphoma, meningioma, medulloblastoma, and pituitary tumors.

SUBMITTER: Kalasauskas D

PROVIDER: S-EPMC8834271 | biostudies-literature | 2022 Feb

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Beyond Glioma: The Utility of Radiomic Analysis for Non-Glial Intracranial Tumors.

Kalasauskas Darius D Kosterhon Michael M Keric Naureen N Korczynski Oliver O Kronfeld Andrea A Ringel Florian F Othman Ahmed A Brockmann Marc A MA

Cancers 20220207 3

The field of radiomics is rapidly expanding and gaining a valuable role in neuro-oncology. The possibilities related to the use of radiomic analysis, such as distinguishing types of malignancies, predicting tumor grade, determining the presence of particular molecular markers, consistency, therapy response, and prognosis, can considerably influence decision-making in medicine in the near future. Even though the main focus of radiomic analyses has been on glial CNS tumors, studies on other intrac ...[more]

PMID: 35159103

Similar Datasets

Project description:Quantitative analysis of computed tomography (CT) radiomic features is an indirect measure of tumor heterogeneity, which has been associated with prognosis in human lung carcinoma. Canine lung tumors share similar features to human lung tumors and serve as a model in which to investigate the utility of radiomic features in differentiating tumor type and prognostication. The purpose of this study was to correlate first-order radiomic features from canine pulmonary tumors to histopathologic characteristics and outcome. Disease-free survival, overall survival time and tumor-specific survival were calculated as days from the date of CT scan. Sixty-seven tumors from 65 dogs were evaluated. Fifty-six tumors were classified as primary pulmonary adenocarcinomas and 11 were non-adenocarcinomas. All dogs were treated with surgical resection; 14 dogs received adjuvant chemotherapy. Second opinion histopathology in 63 tumors confirmed the histologic diagnosis in all dogs and further characterized 53 adenocarcinomas. The median overall survival time was longer (p = 0.004) for adenocarcinomas (339d) compared to non-adenocarcinomas (55d). There was wide variation in first-order radiomic statistics across tumors. Mean Hounsfield units (HU) ratio (p = 0.042) and median mean HU ratio (p = 0.042) were higher in adenocarcinomas than in non-adenocarcinomas. For dogs with adenocarcinoma, completeness of excision was associated with overall survival (p<0.001) while higher mitotic index (p = 0.007) and histologic score (p = 0.037) were associated with shorter disease-free survival. CT-derived tumor variables prognostic for outcome included volume, maximum axial diameter, and four radiomic features: integral total, integral total mean ratio, total HU, and max mean HU ratio. Tumor volume was also significantly associated with tumor invasion (p = 0.044). Further study of radiomic features in canine lung tumors is warranted as a method to non-invasively interrogate CT images for potential predictive and prognostic utility.

Project description:Quantitative radiomic and iodine imaging features have been explored for diagnosis and characterization of tumors. In this work, we invistigate combined whole-lesion radiomic and iodine analysis for the differentiation of pulmonary tumors on contrast-enhanced dual-energy CT (DECT) chest images. 100 biopsy-proven solid lung lesions on contrast-enhanced DECT chest exams within 3 months of histopathologic sampling were identified. Lesions were volumetrically segmented using open-source software. Lesion segmentations and iodine density volumes were loaded into a radiomics prototype for quantitative analysis. Univariate analysis was performed to determine differences in volumetric iodine concentration (mean, median, maximum, minimum, 10th percentile, 90th percentile) and first and higher order radiomic features (n = 1212) between pulmonary tumors. Analyses were performed using a 2-sample t test, and filtered for false discoveries using Benjamini-Hochberg method. 100 individuals (mean age 65 ± 13 years; 59 women) with 64 primary and 36 metastatic lung lesions were included. Only one iodine concentration parameter, absolute minimum iodine, significantly differed between primary and metastatic pulmonary tumors (FDR-adjusted p = 0.015, AUC 0.69). 310 (FDR-adjusted p = 0.0008 to p = 0.0491) radiomic features differed between primary and metastatic lung tumors. Of these, 21 features achieved AUC ≥ 0.75. In subset analyses of lesions imaged by non-CTPA protocol (n = 72), 191 features significantly differed between primary and metastatic tumors, 19 of which achieved AUC ≥ 0.75. In subset analysis of tumors without history of prior treatment (n = 59), 40 features significantly differed between primary and metastatic tumors, 11 of which achieved AUC ≥ 0.75. Volumetric radiomic analysis provides differentiating capability beyond iodine quantification. While a high number of radiomic features differentiated primary versus metastatic pulmonary tumors, fewer features demonstrated good individual discriminatory utility.

Dataset Information

Beyond Glioma: The Utility of Radiomic Analysis for Non-Glial Intracranial Tumors.

Publications

Beyond Glioma: The Utility of Radiomic Analysis for Non-Glial Intracranial Tumors.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets