Pre- and Post-Treatment Imaging of Primary Central Nervous System Tumors in the Molecular and Genetic Era

A Radiologist's Guide to IDH-Wildtype Glioblastoma for Efficient Communication With Clinicians: Part I-Essential Information on Preoperative and Immediate Postoperative Imaging

The paradigm of isocitrate dehydrogenase (IDH)-wildtype glioblastoma is rapidly evolving, reflecting clinical, pathological, and imaging advancements. Thus, it remains challenging for radiologists, even those who are dedicated to neuro-oncology imaging, to keep pace with this rapidly progressing field and provide useful and updated information to clinicians. Based on current knowledge, radiologists can play a significant role in managing patients with IDH-wildtype glioblastoma by providing accurate preoperative diagnosis as well as preoperative and postoperative treatment planning including accurate delineation of the residual tumor. Through active communication with clinicians, extending far beyond the confines of the radiology reading room, radiologists can impact clinical decision making. This Part 1 review provides an overview about the neuropathological diagnosis of glioblastoma to understand the past, present, and upcoming revisions of the World Health Organization classification. The imaging findings that are noteworthy for radiologists while communicating with clinicians on preoperative and immediate postoperative imaging of IDH-wildtype glioblastomas will be summarized.

Performance of amide proton transfer imaging to differentiate true progression from therapy-related changes in gliomas and metastases

OBJECTIVES

Differentiating true progression or recurrence (TP/TR) from therapy-related changes (TRC) is complex in brain tumours. Amide proton transfer-weighted (APT) imaging is a chemical exchange saturation transfer (CEST) MRI technique that may improve diagnostic accuracy during radiological follow-up. This systematic review and meta-analysis elucidated the level of evidence and details of state-of-the-art imaging for APT-CEST in glioma and brain metastasis surveillance.

METHODS

PubMed, EMBASE, Web of Science, and Cochrane Library were systematically searched for original articles about glioma and metastasis patients who received APT-CEST imaging for suspected TP/TR within 2 years after (chemo)radiotherapy completion. Modified Quality Assessment of Diagnostic Accuracy Studies-2 criteria were applied. A meta-analysis was performed to pool results and to compare subgroups.

RESULTS

Fifteen studies were included for a narrative synthesis, twelve of which (500 patients) were deemed sufficiently homogeneous for a meta-analysis. Magnetisation transfer ratio asymmetry performed well in gliomas (sensitivity 0.88 [0.82-0.92], specificity 0.84 [0.72-0.91]) but not in metastases (sensitivity 0.64 [0.38-0.84], specificity 0.56 [0.33-0.77]). APT-CEST combined with conventional/advanced MRI rendered 0.92 [0.86-0.96] and 0.88 [0.72-0.95] in gliomas. Tumour type, TR prevalence, sex, and acquisition protocol were sources of significant inter-study heterogeneity in sensitivity (I2 = 62.25%; p < 0.01) and specificity (I2 = 66.31%; p < 0.001).

CONCLUSION

A growing body of literature suggests that APT-CEST is a promising technique for improving the discrimination of TP/TR from TRC in gliomas, with limited data on metastases.

CLINICAL RELEVANCE STATEMENT

This meta-analysis identified a utility for APT-CEST imaging regarding the non-invasive discrimination of brain tumour progression from therapy-related changes, providing a critical evaluation of sequence parameters and cut-off values, which can be used to improve response assessment and patient outcome.

KEY POINTS

Therapy-related changes mimicking progression complicate brain tumour treatment. Amide proton imaging improves the non-invasive discrimination of glioma progression from therapy-related changes. Magnetisation transfer ratio asymmetry measurement seems not to have added value in brain metastases.

The 2021 WHO Classification for Gliomas and Implications on Imaging Diagnosis: Part 2-Summary of Imaging Findings on Pediatric-Type Diffuse High-Grade Gliomas, Pediatric-Type Diffuse Low-Grade Gliomas, and Circumscribed Astrocytic Gliomas

The fifth edition of the World Health Organization (WHO) classification of central nervous system tumors published in 2021 advances the role of molecular diagnostics in the classification of gliomas by emphasizing integrated diagnoses based on histopathology and molecular information and grouping tumors based on genetic alterations. This Part 2 review focuses on the molecular diagnostics and imaging findings of pediatric-type diffuse high-grade gliomas, pediatric-type diffuse low-grade gliomas, and circumscribed astrocytic gliomas. Each tumor type in pediatric-type diffuse high-grade glioma mostly harbors a distinct molecular marker. On the other hand, in pediatric-type diffuse low-grade gliomas and circumscribed astrocytic gliomas, molecular diagnostics may be extremely complicated at a glance in the 2021 WHO classification. It is crucial for radiologists to understand the molecular diagnostics and imaging findings and leverage the knowledge in clinical practice. EVIDENCE LEVEL: 3 TECHNICAL EFFICACY: Stage 3.

The 2021 WHO Classification for Gliomas and Implications on Imaging Diagnosis: Part 1-Key Points of the Fifth Edition and Summary of Imaging Findings on Adult-Type Diffuse Gliomas

The fifth edition of the World Health Organization (WHO) classification of central nervous system tumors published in 2021 advances the role of molecular diagnostics in the classification of gliomas by emphasizing integrated diagnoses based on histopathology and molecular information and grouping tumors based on genetic alterations. Importantly, molecular biomarkers that provide important prognostic information are now a parameter for establishing tumor grades in gliomas. Understanding the 2021 WHO classification is crucial for radiologists for daily imaging interpretation as well as communication with clinicians. Although imaging features are not included in the 2021 WHO classification, imaging can serve as a powerful tool to impact the clinical practice not only prior to tissue confirmation but beyond. This review represents the first of a three-installment review series on the 2021 WHO classification for gliomas, glioneuronal tumors, and neuronal tumors and implications on imaging diagnosis. This Part 1 Review focuses on the major changes to the classification of gliomas and imaging findings on adult-type diffuse gliomas. EVIDENCE LEVEL: 3. TECHNICAL EFFICACY: Stage 3.

A Multimodal Knowledge-Based Deep Learning Approach for MGMT Promoter Methylation Identification

Glioblastoma Multiforme (GBM) is considered one of the most aggressive malignant tumors, characterized by a tremendously low survival rate. Despite alkylating chemotherapy being typically adopted to fight this tumor, it is known that O(6)-methylguanine-DNA methyltransferase (MGMT) enzyme repair abilities can antagonize the cytotoxic effects of alkylating agents, strongly limiting tumor cell destruction. However, it has been observed that MGMT promoter regions may be subject to methylation, a biological process preventing MGMT enzymes from removing the alkyl agents. As a consequence, the presence of the methylation process in GBM patients can be considered a predictive biomarker of response to therapy and a prognosis factor. Unfortunately, identifying signs of methylation is a non-trivial matter, often requiring expensive, time-consuming, and invasive procedures. In this work, we propose to face MGMT promoter methylation identification analyzing Magnetic Resonance Imaging (MRI) data using a Deep Learning (DL) based approach. In particular, we propose a Convolutional Neural Network (CNN) operating on suspicious regions on the FLAIR series, pre-selected through an unsupervised Knowledge-Based filter leveraging both FLAIR and T1-weighted series. The experiments, run on two different publicly available datasets, show that the proposed approach can obtain results comparable to (and in some cases better than) the considered competitor approach while consisting of less than 0.29% of its parameters. Finally, we perform an eXplainable AI (XAI) analysis to take a little step further toward the clinical usability of a DL-based approach for MGMT promoter detection in brain MRI.

Adding radiomics to the 2021 WHO updates may improve prognostic prediction for current IDH-wildtype histological lower-grade gliomas with known EGFR amplification and TERT promoter mutation status

OBJECTIVES

To assess whether radiomic features could improve the accuracy of survival predictions of IDH-wildtype (IDHwt) histological lower-grade gliomas (LGGs) over clinicopathological features.

METHODS

Preoperative MRI data of 61 patients with IDHwt histological LGGs were included as the institutional training set. The test set consisted of 32 patients from The Cancer Genome Atlas. Radiomic features (n = 186) were extracted using conventional MRIs. The radiomics risk score (RRS) for overall survival (OS) was derived from the elastic net. Multivariable Cox regression analyses with clinicopathological features (including epidermal growth factor receptor [EGFR] amplification and telomerase reverse transcriptase promoter [TERTp] mutation status) and the RRS were performed. The integrated area under the receiver operating curves (iAUCs) from the models with and without the RRS were compared. The net reclassification index (NRI) for 1-year OS was also calculated. The prognostic value of the RRS was evaluated using the external validation set.

RESULTS

The RRS independently predicted OS (hazard ratio = 48.08; p = 0.001). Compared with the clinicopathological model alone, adding the RRS had a better OS prediction performance (iAUCs 0.775 vs. 0.910), which was internally validated (iAUCs 0.726 vs. 0.884, 1-year OS NRI = 0.497), and a similar trend was found on external validation (iAUCs 0.683 vs. 0.705, 1-year OS NRI = 0.733). The prognostic significance of the RRS was confirmed in the external validation set (p = 0.001).

CONCLUSIONS

Integrating radiomics with clinicopathological features (including EGFR amplification and TERTp mutation status) can improve survival prediction in patients with IDHwt LGGs.

KEY POINTS

• Radiomics risk score has the potential to improve survival prediction when added to clinicopathological features (iAUCs increased from 0.775 to 0.910). • NRIs for 1-year OS showed that the radiomics risk score had incremental value over the clinicopathological model. • The prognostic significance of the radiomics risk score was confirmed in the external validation set (p = 0.001).