  The aim of the systematic review was to assess recently published studies on
diagnostic test accuracy of glioblastoma treatment response monitoring
biomarkers in adults, developed through machine learning (ML). Articles were
searched for using MEDLINE, EMBASE, and the Cochrane Register. Included study
participants were adult patients with high grade glioma who had undergone
standard treatment (maximal resection, radiotherapy with concomitant and
adjuvant temozolomide) and subsequently underwent follow-up imaging to
determine treatment response status. Risk of bias and applicability was
assessed with QUADAS 2 methodology. Contingency tables were created for
hold-out test sets and recall, specificity, precision, F1-score, balanced
accuracy calculated. Fifteen studies were included with 1038 patients in
training sets and 233 in test sets. To determine whether there was progression
or a mimic, the reference standard combination of follow-up imaging and
histopathology at re-operation was applied in 67% of studies. The small numbers
of patient included in studies, the high risk of bias and concerns of
applicability in the study designs (particularly in relation to the reference
standard and patient selection due to confounding), and the low level of
evidence, suggest that limited conclusions can be drawn from the data. There is
likely good diagnostic performance of machine learning models that use MRI
features to distinguish between progression and mimics. The diagnostic
performance of ML using implicit features did not appear to be superior to ML
using explicit features. There are a range of ML-based solutions poised to
become treatment response monitoring biomarkers for glioblastoma. To achieve
this, the development and validation of ML models require large, well-annotated
datasets where the potential for confounding in the study design has been
carefully considered.
