Automated longitudinal treatment response assessment of brain tumors: A systematic review.

ABSTRACT:

Background

Longitudinal 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.

Methods

Following PRISMA guidelines and the CLAIM checklist, we searched PUBMED, EMBASE, and Web of Science for articles (Ja

SUBMITTER: Shi T 

PROVIDER: S-EPMC12448867 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature