Radiomics and radiogenomics in gliomas: a contemporary update.
Gagandeep SinghSunil ManjilaNicole SaklaAlan TrueAmr H WardehNiha BeigAnatoliy VaysbergJohn MatthewsPrateek PrasannaVadim SpektorPublished in: British journal of cancer (2021)
The natural history and treatment landscape of primary brain tumours are complicated by the varied tumour behaviour of primary or secondary gliomas (high-grade transformation of low-grade lesions), as well as the dilemmas with identification of radiation necrosis, tumour progression, and pseudoprogression on MRI. Radiomics and radiogenomics promise to offer precise diagnosis, predict prognosis, and assess tumour response to modern chemotherapy/immunotherapy and radiation therapy. This is achieved by a triumvirate of morphological, textural, and functional signatures, derived from a high-throughput extraction of quantitative voxel-level MR image metrics. However, the lack of standardisation of acquisition parameters and inconsistent methodology between working groups have made validations unreliable, hence multi-centre studies involving heterogenous study populations are warranted. We elucidate novel radiomic and radiogenomic workflow concepts and state-of-the-art descriptors in sub-visual MR image processing, with relevant literature on applications of such machine learning techniques in glioma management.
Keyphrases
- high grade
- low grade
- contrast enhanced
- machine learning
- radiation therapy
- high throughput
- magnetic resonance imaging
- deep learning
- magnetic resonance
- computed tomography
- big data
- diffusion weighted imaging
- lymph node metastasis
- locally advanced
- systematic review
- single cell
- artificial intelligence
- radiation induced
- squamous cell carcinoma
- multiple sclerosis
- resting state
- gene expression
- subarachnoid hemorrhage
- rectal cancer
- mass spectrometry
- case control
- bioinformatics analysis
- dna methylation