What type of study is this?

This is a Meta-Analysis study (also classified as: Systematic Review).

What question did this study set out to answer?

This research aims to evaluate the diagnostic accuracy of deep learning and machine learning in differentiating primary CNS lymphomas from glioblastomas using MRI.

November 1, 2025Neuro-OncologyOpen Access

IMG-47. How does deep learning/machine learning perform in comparison to radiologists in distinguishing glioblastomas (or grade IV astrocytomas) from primary CNS lymphomas?: a meta-analysis and systematic review

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Authors

AGAmrita Guha JGJayant Goda

Discussion

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Overview

Meta-analysis reveals that machine learning outperforms radiologists in diagnostic accuracy for CNS lymphomas and glioblastomas, suggesting improved patient outcomes.

Key Points

This research aims to evaluate the diagnostic accuracy of deep learning and machine learning in differentiating primary CNS lymphomas from glioblastomas using MRI.
Conducted a meta-analysis according to PRISMA guidelines.
Data extracted by two experienced researchers with extensive background.
Quality and risk-bias assessed using the QUADAS-2 tool.
Constructed contingency tables to calculate sensitivity, specificity, accuracy, SROC curve, and AUC.
Analyzed 11 studies, with 8 meeting inclusion criteria, involving 1159 patients.
Pooled sensitivity for machine learning was 0.89, compared to 0.82 for radiologists.
Pooled specificity for machine learning was 0.88, compared to 0.90 for radiologists.
Pooled accuracy for machine learning was 0.88, compared to 0.86 for radiologists.
Pooled AUC for machine learning was 0.94, versus 0.90 for radiologists.

Cite This Study

Guha et al. (2025) studied this question.

synapsesocial.com/papers/69254366c0ce034ddc358550 https://doi.org/10.1093/neuonc/noaf201.1126

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November 1, 2025Neuro-OncologyOpen Access

IMG-47. How does deep learning/machine learning perform in comparison to radiologists in distinguishing glioblastomas (or grade IV astrocytomas) from primary CNS lymphomas?: a meta-analysis and systematic review

View Full Paper

Ask AI

Bookmark

Authors

AGAmrita Guha JGJayant Goda

Discussion

Member takes

Overview

Meta-analysis reveals that machine learning outperforms radiologists in diagnostic accuracy for CNS lymphomas and glioblastomas, suggesting improved patient outcomes.

Key Points

This research aims to evaluate the diagnostic accuracy of deep learning and machine learning in differentiating primary CNS lymphomas from glioblastomas using MRI.
Conducted a meta-analysis according to PRISMA guidelines.
Data extracted by two experienced researchers with extensive background.
Quality and risk-bias assessed using the QUADAS-2 tool.
Constructed contingency tables to calculate sensitivity, specificity, accuracy, SROC curve, and AUC.
Analyzed 11 studies, with 8 meeting inclusion criteria, involving 1159 patients.
Pooled sensitivity for machine learning was 0.89, compared to 0.82 for radiologists.
Pooled specificity for machine learning was 0.88, compared to 0.90 for radiologists.
Pooled accuracy for machine learning was 0.88, compared to 0.86 for radiologists.
Pooled AUC for machine learning was 0.94, versus 0.90 for radiologists.

Cite This Study

Guha et al. (2025) studied this question.

synapsesocial.com/papers/69254366c0ce034ddc358550 https://doi.org/10.1093/neuonc/noaf201.1126

Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

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