European journal of radiology · 2025
AI radiomics predicts spatial glioma recurrence on preoperative MRI: a systematic review.
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Bijlsma Sebastiaan, Maal Thomas, Rubbert Christian, Mannil Manoj, Meijer Anton, van der Kolk Anja, de Jong Guido, Henssen Dylan
Аннотация
BACKGROUND: Adult-type diffuse gliomas are highly infiltrative primary brain tumors in which, after a combination of surgery and chemoradiation therapy, tumor recurrence is inevitable. Artificial Intelligence (AI) models have been found capable to predict local and distant tumor recurrence at baseline, with the potential to guide surgical margins and enable focal dose escalation in radiotherapy. This study systematically reviews the literature on the performance of AI models in predicting local or distant tumor recurrence in glioma patients using preoperative MRI data.
METHODS: A systematic literature search was conducted across PubMed, EMBASE, and the Cochrane Library. Studies evaluating AI-based models for spatial recurrence prediction in gliomas using preoperative MRI were included. Study quality and methodological rigor were assessed using the PROBAST + AI tool.
FINDINGS: Eight studies, comprising 1004 high grade glioma patients, were included. A variety of machine learning and deep learning model architectures (e.g., Random Forest classifiers, Support Vector Machines and custom Convolutional Neural Networks) were employed. Input data were a heterogeneous combination of conventional MRI (e.g., T1CE, FLAIR) and more advanced imaging modalities (e.g., diffusion-weighted imaging). Considerable variability was reported with regard to sensitivity and specificity rates (ranging between 40 %-97 % and 29 %-98 %, respectively) for predicting tumor recurrence. The odds ratios for predicting regions of tumor recurrence, however, were generally high (ranging between 8.13-19.48). External validation was performed in 4 studies with one study using a multicenter cohort of 6 different institutions, demonstrating high generalizability in predictive performance. Risk of bias analysis was performed using the recently published PROBAST + AI tool and revealed generally low to unclear concern for risk of bias and low concern for applicability.
INTERPRETATION: AI models have been shown capable of predicting local and distant tumor recurrence in glioma patients from baseline MRI data. While the high odds ratios reported from the multicenter study are encouraging, the evidence comes mainly from small, single-center, retrospective cohorts, so larger prospective multicenter studies are needed before clinical adoption.
METHODS: A systematic literature search was conducted across PubMed, EMBASE, and the Cochrane Library. Studies evaluating AI-based models for spatial recurrence prediction in gliomas using preoperative MRI were included. Study quality and methodological rigor were assessed using the PROBAST + AI tool.
FINDINGS: Eight studies, comprising 1004 high grade glioma patients, were included. A variety of machine learning and deep learning model architectures (e.g., Random Forest classifiers, Support Vector Machines and custom Convolutional Neural Networks) were employed. Input data were a heterogeneous combination of conventional MRI (e.g., T1CE, FLAIR) and more advanced imaging modalities (e.g., diffusion-weighted imaging). Considerable variability was reported with regard to sensitivity and specificity rates (ranging between 40 %-97 % and 29 %-98 %, respectively) for predicting tumor recurrence. The odds ratios for predicting regions of tumor recurrence, however, were generally high (ranging between 8.13-19.48). External validation was performed in 4 studies with one study using a multicenter cohort of 6 different institutions, demonstrating high generalizability in predictive performance. Risk of bias analysis was performed using the recently published PROBAST + AI tool and revealed generally low to unclear concern for risk of bias and low concern for applicability.
INTERPRETATION: AI models have been shown capable of predicting local and distant tumor recurrence in glioma patients from baseline MRI data. While the high odds ratios reported from the multicenter study are encouraging, the evidence comes mainly from small, single-center, retrospective cohorts, so larger prospective multicenter studies are needed before clinical adoption.