Radiomics in immuno-oncology

Bodalal, Zuhir; Wamelink, Ivar

doi:10.1016/j.iotech.2021.100028

Cited by 2 publications

(2 citation statements)

References 131 publications

(149 reference statements)

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“…Prominent among the use cases for radiomics has been the domain of radiogenomics, where morphological phenotypes are linked to the underlying tumour genotype [ 29 , 30 ]. As more information is gathered from tumours, the scope of radiogenomics has expanded beyond strict somatic mutation analysis and now encompasses broader biological parameters, particularly from the tumour microenvironment [ 30 , 31 ].…”

Section: Introductionmentioning

confidence: 99%

Radiomic signatures from T2W and DWI MRI are predictive of tumour hypoxia in colorectal liver metastases

et al. 2023

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Background Tumour hypoxia is a negative predictive and prognostic biomarker in colorectal cancer typically assessed by invasive sampling methods, which suffer from many shortcomings. This retrospective proof-of-principle study explores the potential of MRI-derived imaging markers in predicting tumour hypoxia non-invasively in patients with colorectal liver metastases (CLM). Methods A single-centre cohort of 146 CLMs from 112 patients were segmented on preoperative T2-weighted (T2W) images and diffusion-weighted imaging (DWI). HIF-1 alpha immunohistochemical staining index (high/low) was used as a reference standard. Radiomic features were extracted, and machine learning approaches were implemented to predict the degree of histopathological tumour hypoxia. Results Radiomic signatures from DWI b200 (AUC = 0.79, 95% CI 0.61–0.93, p = 0.002) and ADC (AUC = 0.72, 95% CI 0.50–0.90, p = 0.019) were significantly predictive of tumour hypoxia. Morphological T2W TE75 (AUC = 0.64, 95% CI 0.42–0.82, p = 0.092) and functional DWI b0 (AUC = 0.66, 95% CI 0.46–0.84, p = 0.069) and b800 (AUC = 0.64, 95% CI 0.44–0.82, p = 0.071) images also provided predictive information. T2W TE300 (AUC = 0.57, 95% CI 0.33–0.78, p = 0.312) and b = 10 (AUC = 0.53, 95% CI 0.33–0.74, p = 0.415) images were not predictive of tumour hypoxia. Conclusions T2W and DWI sequences encode information predictive of tumour hypoxia. Prospective multicentre studies could help develop and validate robust non-invasive hypoxia-detection algorithms. Critical relevance statement Hypoxia is a negative prognostic biomarker in colorectal cancer. Hypoxia is usually assessed by invasive sampling methods. This proof-of-principle retrospective study explores the role of AI-based MRI-derived imaging biomarkers in non-invasively predicting tumour hypoxia in patients with colorectal liver metastases (CLM). Graphical Abstract

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Section: Introductionmentioning

confidence: 99%

Radiomic signatures from T2W and DWI MRI are predictive of tumour hypoxia in colorectal liver metastases

et al. 2023

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“…31,41 Recently, there has been much interest in exploring the role of radiomics in assessing treatment-related changes, such as atypical responses to novel therapies, and in predicting the risk of toxicity and adverse effects. 42,43 In particular, the ability of radiomics to capture the tumor's spatial and temporal heterogeneity, allowing analysis of the entire tumor, can be leveraged to assess a patient's individual risk for and predisposition to developing adverse effects. 41,44 Radiomics has also been demonstrated to accurately capture tumor biology and disease pathophysiology, 41,45 in addition to its ability to analyze multiple lesions.…”

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Novel Imaging Biomarkers to Assess Oncologic Treatment–Related Changes

Viswanathan

Gupta

Madabhushi

2022

American Society of Clinical Oncology Educational Book

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Cancer therapeutics cause various treatment-related changes that may impact patient follow-up and disease monitoring. Although atypical responses such as pseudoprogression may be misinterpreted as treatment nonresponse, other changes, such as hyperprogressive disease seen with immunotherapy, must be recognized early for timely management. Radiation necrosis in the brain is a known response to radiotherapy and must be distinguished from local tumor recurrence. Radiotherapy can also cause adverse effects such as pneumonitis and local tissue toxicity. Systemic therapies, like chemotherapy and targeted therapies, are known to cause long-term cardiovascular effects. Thus, there is a need for robust biomarkers to identify, distinguish, and predict cancer treatment–related changes. Radiomics, which refers to the high-throughput extraction of subvisual features from radiologic images, has been widely explored for disease classification, risk stratification, and treatment-response prediction. Lately, there has been much interest in investigating the role of radiomics to assess oncologic treatment–related changes. We review the utility and various applications of radiomics in identifying and distinguishing atypical responses to treatments, as well as in predicting adverse effects. Although artificial intelligence tools show promise, several challenges—including multi-institutional clinical validation, deployment in health care settings, and artificial-intelligence bias—must be addressed for seamless clinical translation of these tools.

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Radiomics in immuno-oncology

Cited by 2 publications

References 131 publications

Radiomic signatures from T2W and DWI MRI are predictive of tumour hypoxia in colorectal liver metastases

Radiomic signatures from T2W and DWI MRI are predictive of tumour hypoxia in colorectal liver metastases

Novel Imaging Biomarkers to Assess Oncologic Treatment–Related Changes

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