Radiomics and radiogenomics in gliomas: a contemporary update

• Published in: British journal of cancer Vol. 125; no. 5; pp. 641 - 657
• Main Authors: Singh, Gagandeep, Manjila, Sunil, Sakla, Nicole, True, Alan, Wardeh, Amr H., Beig, Niha, Vaysberg, Anatoliy, Matthews, John, Prasanna, Prateek, Spektor, Vadim
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 31.08.2021; Nature Publishing Group
• Subjects: 692/4028/67; 692/700/1421; Biomedical and Life Sciences; Biomedicine; Brain cancer; Brain Neoplasms - diagnostic imaging; Brain Neoplasms - genetics; Brain Neoplasms - pathology; Brain tumors; Cancer Research; Chemotherapy; Drug Resistance; Epidemiology; Genomics - methods; Glioma; Glioma - diagnostic imaging; Glioma - genetics; Glioma - pathology; Humans; Image processing; Immunotherapy; Information processing; Learning algorithms; Machine Learning; Magnetic Resonance Imaging; Molecular Medicine; Neoplasm Grading; Oncology; Population studies; Prognosis; Radiation; Radiation therapy; Radiographic Image Interpretation, Computer-Assisted - methods; Radiomics; Review; Review Article
• ISSN: 0007-0920; 1532-1827
• DOI: 10.1038/s41416-021-01387-w

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.