SynthesisNeuro-oncology2025
Automated longitudinal treatment response assessment of brain tumors: A systematic review.
Synthesis in Neuro-oncology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
3 citing papers in PubMed.
- Temporal Dynamics and Heterogeneity in Brain Metastases: A Single-Center Retrospective Analysis of Vulnerabilities in Current MRI Surveillance Practices.Medicina (Kaunas, Lithuania) · 2026Observational
- Review
- Validation of open-source deep learning segmentation tools for automated glioma volumetry: a narrative review of Dice scores, workflow efficiency, and clinical RANO 2.0 implementation.Frontiers in neurologyReview
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
backgroundLongitudinal assessment of tumor burden using imaging helps to determine whether there has been a response to treatment both in trial and real-world settings. From a patient and clinical trial perspective alike, the time to develop disease progression, or progression-free survival, is an important endpoint. However, manual longitudinal response assessment is time-consuming and subject to interobserver variability. Automated response assessment techniques based on machine learning (ML) promise to enhance accuracy and reduce reliance on manual measurement. This paper evaluates the quality and performance accuracy of recently published studies.
methodsFollowing PRISMA guidelines and the CLAIM checklist, we searched PUBMED, EMBASE, and Web of Science for articles (January 2010-November 2024). Our PROSPERO-registered study (CRD42024496126) focused on adult brain tumor automated treatment response assessment studies using ML methodologies. We determined the extent of development and validation of the tools and employed QUADAS-2 for study appraisal.
resultsTwenty (including 17 retrospective and 3 prospective) studies were included. Data extracted included information on the dataset, automated response assessment including pertinent steps within the pipeline (index tests), and reference standards. Only limited conclusions are appropriate given the high bias risk and applicability concerns (particularly regarding reference standards and patient selection), and the low-level evidence. There was insufficient homogenous data for meta-analysis.
conclusionsThe study highlights the potential of ML to improve brain tumor longitudinal treatment response assessment. Interpretation is limited due to study bias and limited evidence of generalizability. Prospective studies with external datasets validating the latest neuro-oncology criteria are now required.
Indexed as
Identifiers
What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.