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Radiomics and Machine Learning in Brain Tumors and Their Habitat: A Systematic Review.

ABSTRACT: Radiomics is a rapidly evolving field that involves extracting and analysing quantitative features from medical images, such as computed tomography or magnetic resonance images. Radiomics has shown promise in brain tumor diagnosis and patient-prognosis prediction by providing more detailed and objective information about tumors' features than can be obtained from the visual inspection of the images alone. Radiomics data can be analyzed to determine their correlation with a tumor's genetic status and grade, as well as in the assessment of its recurrence vs. therapeutic response, among other features. In consideration of the multi-parametric and high-dimensional space of features extracted by radiomics, machine learning can further improve tumor diagnosis, treatment response, and patients' prognoses. There is a growing recognition that tumors and their microenvironments (habitats) mutually influence each other-tumor cells can alter the microenvironment to increase their growth and survival. At the same time, habitats can also influence the behavior of tumor cells. In this systematic review, we investigate the current limitations and future developments in radiomics and machine learning in analysing brain tumors and their habitats.

SUBMITTER: Tabassum M

PROVIDER: S-EPMC10417709 | biostudies-literature | 2023 Jul

REPOSITORIES: biostudies-literature

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Radiomics and Machine Learning in Brain Tumors and Their Habitat: A Systematic Review.

Tabassum Mehnaz M Suman Abdulla Al AA Suero Molina Eric E Pan Elizabeth E Di Ieva Antonio A Liu Sidong S

Cancers 20230728 15

Radiomics is a rapidly evolving field that involves extracting and analysing quantitative features from medical images, such as computed tomography or magnetic resonance images. Radiomics has shown promise in brain tumor diagnosis and patient-prognosis prediction by providing more detailed and objective information about tumors' features than can be obtained from the visual inspection of the images alone. Radiomics data can be analyzed to determine their correlation with a tumor's genetic status ...[more]

PMID: 37568660

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Project description:BackgroundBrain tumors present a complex challenge in clinical oncology, where precise diagnosis and classification are pivotal for effective treatment planning. Radiomics, a burgeoning field in neuro-oncology, involves extracting and analyzing numerous quantitative features from medical images. This approach captures subtle spatial and textural information imperceptible to the human eye. However, implementation in clinical practice is still distant, and concerns have been raised regarding the methodological quality of radiomic studies.MethodologyA systematic literature search was performed to identify original articles focused on the use of radiomics for brain tumors from 2015 based on the inclusion and exclusion criteria. The radiomic features train machine learning models for glioma classification, and data are split into training and testing subsets to validate the model accuracy, reliability, and generalizability. The present study systematically reviews the status of radiomic studies concerning brain tumors, also using the radiomics quality score (RQS) to assess the quality of the methodology used in each study.ResultsA systematic search of PubMed identified 300 articles, with 18 studies meeting the inclusion criteria for qualitative synthesis. These studies collectively demonstrate the potential of radiomics-based machine learning models in accurately distinguishing between glioma subtypes and grades. Various imaging modalities, including MRI, PET/CT, and advanced techniques like ASL and DTI, were utilized to extract radiomic features for analysis. Machine learning algorithms such as deep learning networks, support vector machines, random forests, and logistic regression were applied to develop predictive models.ConclusionsThe present study indicates high accuracies in glioma classification, outperforming traditional imaging methods and inexperienced radiologists in some cases. Further validation and standardization efforts are warranted to facilitate the clinical integration of radiomics into routine practice, ultimately enhancing glioma management and patient outcomes. Open science practices: Machine learning using MRI radiomic features provides a simple, noninvasive, and cost-effective method for glioma classification, enhancing transparency, reproducibility, and collaboration within the scientific community.

Project description:Purpose To identify precise three-dimensional radiomics features in CT images that enable computation of stable and biologically meaningful habitats with machine learning for cancer heterogeneity assessment. Materials and Methods This retrospective study included 2436 liver or lung lesions from 605 CT scans (November 2010-December 2021) in 331 patients with cancer (mean age, 64.5 years ± 10.1 [SD]; 185 male patients). Three-dimensional radiomics were computed from original and perturbed (simulated retest) images with different combinations of feature computation kernel radius and bin size. The lower 95% confidence limit (LCL) of the intraclass correlation coefficient (ICC) was used to measure repeatability and reproducibility. Precise features were identified by combining repeatability and reproducibility results (LCL of ICC ≥ 0.50). Habitats were obtained with Gaussian mixture models in original and perturbed data using precise radiomics features and compared with habitats obtained using all features. The Dice similarity coefficient (DSC) was used to assess habitat stability. Biologic correlates of CT habitats were explored in a case study, with a cohort of 13 patients with CT, multiparametric MRI, and tumor biopsies. Results Three-dimensional radiomics showed poor repeatability (LCL of ICC: median [IQR], 0.442 [0.312-0.516]) and poor reproducibility against kernel radius (LCL of ICC: median [IQR], 0.440 [0.33-0.526]) but excellent reproducibility against bin size (LCL of ICC: median [IQR], 0.929 [0.853-0.988]). Twenty-six radiomics features were precise, differing in lung and liver lesions. Habitats obtained with precise features (DSC: median [IQR], 0.601 [0.494-0.712] and 0.651 [0.52-0.784] for lung and liver lesions, respectively) were more stable than those obtained with all features (DSC: median [IQR], 0.532 [0.424-0.637] and 0.587 [0.465-0.703] for lung and liver lesions, respectively; P < .001). In the case study, CT habitats correlated quantitatively and qualitatively with heterogeneity observed in multiparametric MRI habitats and histology. Conclusion Precise three-dimensional radiomics features were identified on CT images that enabled tumor heterogeneity assessment through stable tumor habitat computation. Keywords: CT, Diffusion-weighted Imaging, Dynamic Contrast-enhanced MRI, MRI, Radiomics, Unsupervised Learning, Oncology, Liver, Lung Supplemental material is available for this article. © RSNA, 2024 See also the commentary by Sagreiya in this issue.

Dataset Information

Radiomics and Machine Learning in Brain Tumors and Their Habitat: A Systematic Review.

Publications

Radiomics and Machine Learning in Brain Tumors and Their Habitat: A Systematic Review.

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