A Survey of Radiomics in Precision Diagnosis and Treatment of Adult Gliomas

Abstract

Glioma is the most common primary malignant tumor of the adult central nervous system (CNS), which mostly shows invasive growth. In most cases, surgery is often difficult to completely remove, and the recurrence rate and mortality of patients are high. With the continuous development of molecular genetics and the great progress of molecular biology technology, more and more molecular biomarkers have been proved to have important guiding significance in the individualized diagnosis, treatment, and prognosis evaluation of glioma. With the updates of the World Health Organization (WHO) classification of tumors of the CNS in 2021, the diagnosis and treatment of glioma has entered the era of precision medicine in the true sense. Due to its ability to non-invasively achieve accurate identification of glioma from other intracranial tumors, and to predict the grade, genotyping, treatment response, and prognosis of glioma, which provides a scientific basis for the clinical application of individualized diagnosis and treatment model of glioma, radiomics has become a research hotspot in the field of precision medicine. This paper reviewed the research related to radiomics of adult gliomas published in recent years and summarized the research proceedings of radiomics in differential diagnosis, preoperative grading and genotyping, treatment and efficacy evaluation, and survival prediction of adult gliomas.

Noninvasive prediction of CCL2 expression level in high‐grade glioma patients

“…Importantly, MR and CT images, protein sequences, and genetic patterns are valuable for extracting numerous features on the basis of a feature extraction strategy. 44,45 These features should be subsequently imported into the machine learning algorithms, while different contributions of different features to predictive models are noteworthy. Therefore, the results may be remarkably affected by selecting appropriate features.…”

“…Regarding machine learning, the development of a robust radiomics‐based algorithm is feasible using data exclusiveness. Importantly, MR and CT images, protein sequences, and genetic patterns are valuable for extracting numerous features on the basis of a feature extraction strategy 44,45 . These features should be subsequently imported into the machine learning algorithms, while different contributions of different features to predictive models are noteworthy.…”

Recapitulating the Key Advances in the Diagnosis and Prognosis of High-Grade Gliomas: Second Half of 2021 Update

“…As aforementioned, radiomics and radiogenomics may help with better defining the effectiveness of the different treatments (radio- and chemotherapeutic and immunological ones), since these computational methods have increased the comparability of the data, and analysis has become more independent from the subjectivity of the clinician [ 74 ]. Further, the characterization of tumor infiltration based on preoperative multiparametric MRI (MP-MRI) might predict the loci of the most probable future recurrence, permitting clinicians to anticipate their focused treatment [ 86 , 87 ]. The hope is that developments in the genetic classification of HGGs and the use of predictive models based on radiomics may provide important advances in tumor characterization and in the personalization of therapies.…”

The Utility of Prostate-Specific Membrane Antigen-11 PET in Detection and Management of Central Nervous System Neoplasms

“…Bottom row, PSMA-avid lesions within the left periventricular white matter right insula and temporal lobe corresponding to areas of enhancement on corresponding MR (Table 1). 1–38 …”