AUTHOR=Guha Amrita , Goda Jayant S. , Dasgupta Archya , Mahajan Abhishek , Halder Soutik , Gawde Jeetendra , Talole Sanjay 

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

JOURNAL=Frontiers in Oncology

VOLUME=Volume 12 - 2022

YEAR=2022

URL=https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2022.884173

DOI=10.3389/fonc.2022.884173

ISSN=2234-943X

ABSTRACT=BACKGROUND
Glioblastoma (GBM) and primary central nervous system lymphoma (PCNSL) are common in the elderly yet difficult to differentiate on MRI. Their management and prognosis are quite different. The 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. 
METHODS 
The authors performed a systematic review of the literature using “PubMed” in accordance to PRISMA guidelines to select and evaluate studies that included themes of ML, DL, AI, GBM, PCNSL. All studies reporting on ML algorithms or DL 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 were synthesized and analyzed by forest plots. Outcomes assessed were test characteristics such as accuracy, sensitivity, specificity, and balanced accuracy.
RESULTS 
Ten articles meeting the eligibility criteria were identified addressing the 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 was in 4 studies. The lowest reported sensitivity was 80%, while the highest reported sensitivity was 99% in studies in which ML and DL were 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%.

CONCLUSIONS 
An extensive search of the database revealed a limited number of studies that have applied ML or DL to differentiate PCNSL from GBM. Of the 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 reduction in morbidities and being cost-effective.