Machine Learning and Glioblastoma: Treatment Response Monitoring Biomarkers in 2021

Abstract

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 published 09/2018-09/2020 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 (specifically, distinguishing progression/recurrence from progression/recurrence mimics -the target condition). Risk of bias and applicability was assessed with QADAS 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% (10/15) of studies. External hold-out test sets were used in 27% (4/15) to give ranges of diagnostic accuracy measures: recall = 0.70-1.00; specificity = 0.67-0.90; precision = 0.78-0.88; F1 score = 0.74-0.94; balanced accuracy = 0.74-0.83; AUC = 0.80-0.85. 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. Therefore, multidisciplinary efforts and multicentre collaborations are necessary.

Imaging Biomarkers of Glioblastoma Treatment Response: A Systematic Review and Meta-Analysis of Recent Machine Learning Studies

“…The aim of the study is to systematically review and perform a meta-analysis of diagnostic accuracy of ML-based treatment response monitoring biomarkers for glioblastoma patients using recently published peer-reviewed studies. The study builds on previous work to incorporate the rapidly growing body of knowledge in this field (11,16), providing promising avenues for further research.…”

High-Grade Glioma Treatment Response Monitoring Biomarkers: A Position Statement on the Evidence Supporting the Use of Advanced MRI Techniques in the Clinic, and the Latest Bench-to-Bedside Developments. Part 2: Spectroscopy, Chemical Exchange Saturation, Multiparametric Imaging, and Radiomics

“…Many incorporate machine learning as a central pillar of the process. A review of studies up to 2018 ( 91 ), a systematic review of studies from 2018 – 2020 ( 122 ) using PRISMA-DTA methodology and a meta-analysis from 2018–2021 ( 123 ) indicated that those taking advantage of enhanced computational processing power to build monitoring biomarker models (e.g., using deep learning methods such as convolutional neural networks) have yet to show an advantage in performance compared with machine learning techniques using explicit feature engineering and less computationally expensive classifiers (e.g., using “classical” machine learning methods support vector machine). It is also notable that studies applying machine learning to build monitoring biomarker models have yet to show an overall advantage over those using traditional statistical methods.…”

“…It is also notable that studies applying machine learning to build monitoring biomarker models have yet to show an overall advantage over those using traditional statistical methods. There is good diagnostic performance of machine learning models that use MRI features to distinguish between progressive disease and diagnostic accuracy measures comprise recall = 0.61 – 1.00, specificity = 0.47 – 0.90, balanced accuracy = 0.54 – 0.83, precision = 0.58 – 0.88, F1 score = 0.59 – 0.94, and AUC = 0.65 – 0.85 ( 122 , 123 ). The recent meta-analysis of ten studies showed a pooled true positive rate (sensitivity) = 0.769 (0.649 – 0.858), a false positive rate (1-specificity) = 0.352 (0.251 – 0.468) and a summary AUC-ROC = 0.765.…”