Classifying primary central nervous system lymphoma from glioblastoma using deep learning and radiomics based machine learning approach - a systematic review and meta-analysis

Abstract

BackgroundGlioblastoma (GBM) and primary central nervous system lymphoma (PCNSL) are common in elderly yet difficult to differentiate on MRI. Their management and prognosis are quite different. Recent surge of interest in predictive analytics, using machine learning (ML) from radiomic features and deep learning (DL) for diagnosing, predicting response and prognosticating disease has evinced interest among radiologists and clinicians. The objective of this systematic review and meta-analysis was to evaluate the deep learning & ML algorithms in classifying PCNSL from GBM.MethodsThe authors performed a systematic review of the literature from MEDLINE, EMBASE and the Cochrane central trials register for the search strategy in accordance with PRISMA guidelines to select and evaluate studies that included themes of ML, DL, AI, GBM, PCNSL. All studies reporting on ML algorithms or DL that for differentiating PCNSL from GBM on MR imaging were included. These studies were further narrowed down to focus on works published between 2018 and 2021. Two researchers independently conducted the literature screening, database extraction and risk bias assessment. The extracted data was synthesised and analysed by forest plots. Outcomes assessed were test characteristics such as accuracy, sensitivity, specificity and balanced accuracy.ResultsTen articles meeting the eligibility criteria were identified addressing use of ML and DL in training and validation classifiers to distinguish PCNSL from GBM on MR imaging. The total sample size was 1311 in the included studies. ML approach was used in 6 studies while DL in 4 studies. The lowest reported sensitivity was 80%, while the highest reported sensitivity was 99% in studies in which ML and DL was directly compared with the gold standard histopathology. The lowest reported specificity was 87% while the highest reported specificity was 100%. The highest reported balanced accuracy was 100% and the lowest was 84%.ConclusionsExtensive search of the database revealed a limited number of studies that have applied ML or DL to differentiate PCNSL from GBM. Of the currently published studies, Both DL & ML algorithms have demonstrated encouraging results and certainly have the potential to aid neurooncologists in taking preoperative decisions in the future leading to not only reduction in morbidities but also be cost effective.

A multicenter proof-of-concept study on deep learning-based intraoperative discrimination of primary central nervous system lymphoma

“…Secondly, refinement of the online platform is necessary before deployment for clinical use. Thirdly, we did not use these modern tools, including radiomics 12 , genetic biomarkers 32 , and cell-free DNA 33 to select doubtful cases for analysis. Fourthly, this is only a proof-of-concept study that needs for future analyses on a suitable number of pathologists and on better-selected cases (small biopsies and difficult neuroimaging interpretation) to draw more reliable conclusions.…”

“…Neuroimaging provides valuable insights into the distinction between these tumors; however, it lacks the precision to differentiate them accurately 11 . Despite the promising results of radiomics-based machine learning approaches in discerning PCNSL and glioma, some models still exhibit room for improvement in performance, as highlighted in pooled analyses 12 . Therefore, given the intraoperative histopathological diagnosis as the gold standard for brain tumors, accurate differentiation of PCNSL remains crucial.…”

Primary central nervous system lymphoma in elderly: An illustrative case of the new role of surgery and integrative medical management

“…PCNSLs typically exhibit a diffuse distribution, with the most common sites being the hemispheres (38%), that is, the frontoparietal, followed by the temporal lobe, basal ganglia (16%), corpus callosum (14%), periventricular regions (12%), and rarely the cerebellum (9%). [ 4 ] The brainstem is less frequently affected, and about 1% of patients have spinal cord involvement. [ 4 ] Symptoms can manifest suddenly or gradually and range in severity depending on the intracranial pressure.…”

“…[ 4 ] The brainstem is less frequently affected, and about 1% of patients have spinal cord involvement. [ 4 ] Symptoms can manifest suddenly or gradually and range in severity depending on the intracranial pressure. Common manifestations include focal neurological deficits, headaches, and seizures.…”

Preoperative Discrimination of CDKN2A/B Homozygous Deletion Status in Isocitrate Dehydrogenase‐Mutant Astrocytoma: A Deep Learning‐Based Radiomics Model Using MRI

“…Recently, radiomics methods and deep learning-based radiomics (DLR) that leveraged potential image features by various network architectures or and linear/ nonlinear function stacking algorithm have made a substantially and extensively positive contributions to precision medicine for glioma, including grading, differential diagnosis, genotyping prediction, and disease progression and survival prediction. [8][9][10][11][12][13][14][15][16][17][18][19][20][21][22] A machine learning model based on a combination of deep learning features and radiomics features from contrast enhanced T1 weighted imaging (CE-T1WI) achieved positive outcomes in brain glioma grading (training: AUC = 0.847; test: AUC = 0.898). 19 The molecular subtypes of diffuse glioma were preoperatively assessed by radiomics and deep convolutional neural network (DCNN) models with AUCs ranging from 0.67 to 0.84 and 0.66 to 0.89, respectively.…”