The Role of Imaging Biomarkers Derived From Advanced Imaging and Radiomics in the Management of Brain Tumors

Shaikh, Faiq; Dupont-Roettger, Diana; Dehmeshki, Jamshid; Awan, Omer A.; Kubassova, Olga; Bisdas, Sotirios

doi:10.3389/fonc.2020.559946

Cited by 11 publications

(7 citation statements)

References 56 publications

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“…A non-invasive imaging biomarker is a characteristic feature of an imaging study that tells us about a disease process [13]. These new biomarkers need to be linked to ground truths, such as imaging-based "gold standards," clinical outcomes, or pathologic evidence.…”

Section: Quantitive Imaging Biomarkersmentioning

confidence: 99%

Artificial intelligence for radiomics; diagnostic biomarkers for neuro-oncology

Vahedifard¹,

Hassani²,

Afrasiabi³

et al. 2022

World J. Adv. Res. Rev.

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Recent advances in medical image analysis have been made to improve our understanding of how disease develops, behaves, and responds to treatment. Magnetic resonance imaging (MRI) and positron emission tomography (PET) advanced imaging strategies provide structural and functional phenotypic biomarkers that correlate with key disease processes. Through radiomics and radiogenomics, ML-medical imaging has opened up new perspectives in high-grade glioma diagnosis. As a result, non-invasive and in vivo biomarkers for patient survival, tumor recurrence, and genomics are identified. Tumor genomic imaging signatures can help identify patients who benefit from targeted therapies. Molecular characterization of gliomas and prediction of their evolution would allow treatment optimization. Radiomics-based biomarkers allow for a more in-depth analysis of pathophysiologic processes and insights into diagnosing better, classifying, stratifying, and prognosticating brain tumors and assessing their response to therapy. Radiomics is a new data-driven approach that can help answer clinical questions like diagnosis, prognosis, and treatment response. With encouraging outcomes in brain tumor patients, radiomics and deep learning are still not widely used in clinical practice, requiring more extensive and practical clinical studies.

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Section: Quantitive Imaging Biomarkersmentioning

confidence: 99%

Artificial intelligence for radiomics; diagnostic biomarkers for neuro-oncology

Vahedifard¹,

Hassani²,

Afrasiabi³

et al. 2022

World J. Adv. Res. Rev.

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“…Significant advances have been made in the realm of medical image analysis in the past few decades (Shaikh et al, 2020). Imaging biomarkers derived from advanced imaging techniques such as magnetic resonance imaging (MRI) data are used to characterize normal development (Oishi et al, 2013), disease (McEvoy and Brewer, 2010), and the effects of disease-modifying therapies (Paolillo et al, 1999).…”

Section: Introductionmentioning

confidence: 99%

A systematic review of (semi-)automatic quality control of T1-weighted MRI scans

Hendriks,

Mutsaerts,

Joules

et al. 2023

Preprint

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Artifacts in magnetic resonance imaging (MRI) scans degrade image quality and thus negatively affect the outcome measures of clinical and research scanning. Considering the time-consuming and subjective nature of visual quality control (QC), multiple (semi-)automatic QC algorithms have been developed. This systematic review presents an overview of the available (semi-)automatic QC algorithms and software packages designed for raw, structural T1-weighted (T1w) MRI datasets. The objective of this review was to identify the differences among these algorithms in terms of their features of interest, performance, and benchmarks. We queried PubMed, EMBASE (Ovid), and Web of Science databases on the fifth of January 2023, and cross-checked reference lists of retrieved papers. Bias assessment was performed using PROBAST (Prediction model Risk Of Bias ASsessment Tool). A total of 18 distinct algorithms were identified, demonstrating significant variations in methods, features, datasets, and benchmarks. The algorithms were categorized into rule-based, classical machine learning-based, and deep learning-based approaches. Numerous unique features were defined, which can be roughly divided into features capturing entropy, contrast, and normative measures. Due to dataset-specific optimization, it is challenging to draw broad conclusions about comparative performance. Additionally, large variations exist in the used datasets and benchmarks, further hindering direct algorithm comparison. The findings emphasize the need for standardization and comparative studies for advancing QC in MR imaging. Efforts should focus on identifying a dataset-independent measure as well as algorithm-independent methods for assessing the relative performance of different approaches.

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“…Significant advances have been made in the realm of medical image analysis in the past few decades [ 1 ]. Imaging biomarkers derived from advanced imaging techniques such as magnetic resonance imaging (MRI) data are used to characterize normal development [ 2 ], disease [ 3 ], and the effects of disease-modifying therapies [ 4 ].…”

Section: Introductionmentioning

confidence: 99%

A systematic review of (semi-)automatic quality control of T1-weighted MRI scans

Hendriks,

Mutsaerts,

Joules

et al. 2023

Neuroradiology

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Purpose Artifacts in magnetic resonance imaging (MRI) scans degrade image quality and thus negatively affect the outcome measures of clinical and research scanning. Considering the time-consuming and subjective nature of visual quality control (QC), multiple (semi-)automatic QC algorithms have been developed. This systematic review presents an overview of the available (semi-)automatic QC algorithms and software packages designed for raw, structural T1-weighted (T1w) MRI datasets. The objective of this review was to identify the differences among these algorithms in terms of their features of interest, performance, and benchmarks. Methods We queried PubMed, EMBASE (Ovid), and Web of Science databases on the fifth of January 2023, and cross-checked reference lists of retrieved papers. Bias assessment was performed using PROBAST (Prediction model Risk Of Bias ASsessment Tool). Results A total of 18 distinct algorithms were identified, demonstrating significant variations in methods, features, datasets, and benchmarks. The algorithms were categorized into rule-based, classical machine learning-based, and deep learning-based approaches. Numerous unique features were defined, which can be roughly divided into features capturing entropy, contrast, and normative measures. Conclusion Due to dataset-specific optimization, it is challenging to draw broad conclusions about comparative performance. Additionally, large variations exist in the used datasets and benchmarks, further hindering direct algorithm comparison. The findings emphasize the need for standardization and comparative studies for advancing QC in MR imaging. Efforts should focus on identifying a dataset-independent measure as well as algorithm-independent methods for assessing the relative performance of different approaches.

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The Role of Imaging Biomarkers Derived From Advanced Imaging and Radiomics in the Management of Brain Tumors

Cited by 11 publications

References 56 publications

Artificial intelligence for radiomics; diagnostic biomarkers for neuro-oncology

Artificial intelligence for radiomics; diagnostic biomarkers for neuro-oncology

A systematic review of (semi-)automatic quality control of T1-weighted MRI scans

A systematic review of (semi-)automatic quality control of T1-weighted MRI scans

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