Precise enhancement quantification in post-operative MRI as an indicator of residual tumor impact is associated with survival in patients with glioblastoma

Neuro-oncologic Emergencies

OBJECTIVE

Neuro-oncologic emergencies have become more frequent as cancer remains one of the leading causes of death in the United States, second only to heart disease. This article highlights key aspects of epidemiology, diagnosis, and management of acute neurologic complications in primary central nervous system malignancies and systemic cancer, following three thematic classifications: (1) complications that are anatomically or intrinsically tumor-related, (2) complications that are tumor-mediated, and (3) complications that are treatment-related.

LATEST DEVELOPMENTS

The main driver of mortality in patients with brain metastasis is systemic disease progression; however, intracranial hypertension, treatment-resistant seizures, and overall decline due to increased intracranial burden of disease are the main factors underlying neurologic-related deaths. Advances in the understanding of tumor-specific characteristics can better inform risk stratification of neurologic complications. Following standardized grading and management algorithms for neurotoxic syndromes related to newer immunologic therapies is paramount to achieving favorable outcomes.

ESSENTIAL POINTS

Neuro-oncologic emergencies span the boundaries of subspecialties in neurology and require a broad understanding of neuroimmunology, neuronal hyperexcitability, CSF flow dynamics, intracranial compliance, and neuroanatomy.

Radiomics for residual tumour detection and prognosis in newly diagnosed glioblastoma based on postoperative [11C] methionine PET and T1c-w MRI

Personalized treatment strategies based on non-invasive biomarkers have potential to improve patient management in patients with newly diagnosed glioblastoma (GBM). The residual tumour burden after surgery in GBM patients is a prognostic imaging biomarker. However, in clinical patient management, its assessment is a manual and time-consuming process that is at risk of inter-rater variability. Furthermore, the prediction of patient outcome prior to radiotherapy may identify patient subgroups that could benefit from escalated radiotherapy doses. Therefore, in this study, we investigate the capabilities of traditional radiomics and 3D convolutional neural networks for automatic detection of the residual tumour status and to prognosticate time-to-recurrence (TTR) and overall survival (OS) in GBM using postoperative [11C] methionine positron emission tomography (MET-PET) and gadolinium-enhanced T1-w magnetic resonance imaging (MRI). On the independent test data, the 3D-DenseNet model based on MET-PET achieved the best performance for residual tumour detection, while the logistic regression model with conventional radiomics features performed best for T1c-w MRI (AUC: MET-PET 0.95, T1c-w MRI 0.78). For the prognosis of TTR and OS, the 3D-DenseNet model based on MET-PET integrated with age and MGMT status achieved the best performance (Concordance-Index: TTR 0.68, OS 0.65). In conclusion, we showed that both deep-learning and conventional radiomics have potential value for supporting image-based assessment and prognosis in GBM. After prospective validation, these models may be considered for treatment personalization.

Artificial Intelligence Analysis Using MRI and PET Imaging in Gliomas: A Narrative Review

The lack of early detection and a high rate of recurrence/progression after surgery are defined as the most common causes of a very poor prognosis of Gliomas. The developments of quantification systems with special regards to artificial intelligence (AI) on medical images (CT, MRI, PET) are under evaluation in the clinical and research context in view of several applications providing different information related to the reconstruction of imaging, the segmentation of tissues acquired, the selection of features, and the proper data analyses. Different approaches of AI have been proposed as the machine and deep learning, which utilize artificial neural networks inspired by neuronal architectures. In addition, new systems have been developed using AI techniques to offer suggestions or make decisions in medical diagnosis, emulating the judgment of radiologist experts. The potential clinical role of AI focuses on the prediction of disease progression in more aggressive forms in gliomas, differential diagnosis (pseudoprogression vs. proper progression), and the follow-up of aggressive gliomas. This narrative Review will focus on the available applications of AI in brain tumor diagnosis, mainly related to malignant gliomas, with particular attention to the postoperative application of MRI and PET imaging, considering the current state of technical approach and the evaluation after treatment (including surgery, radiotherapy/chemotherapy, and prognostic stratification).

Postoperative Magnetic Resonance Imaging (MRI) Scans for the Surgical Resection of Cranial Glial Tumors According to National Institute of Health and Care Excellence (NICE) Guidelines: A Single-Center Experience

Background Glial tumours are the most common central nervous system (CNS) neoplastic lesions. They occur in 7 per 100,000 individuals in the United Kingdom (UK) and are categorized into astrocytomas, oligodendrogliomas, and glioblastomas in the adult population. The World Health Organization (WHO) has created a classification system in order to better categorise these lesions, placing them in a range from grade I to grade IV. The higher the grade, the poorer the prognosis. The National Institute of Health and Care Excellence (NICE) in the United Kingdom recommends that all surgical resections of glial brain tumours are followed by a postoperative magnetic resonance imaging (MRI) scan within a 72-hour to establish a baseline for further management. Objective We present a retrospective analysis that assessed the compliance rate with NICE guidelines among patients who underwent surgical resection of glial lesions at the Department of Neurosurgery, Queens Hospital Romford, between January 2022 and September 2023. Materials and methods A retrospective analysis was conducted on 136 glial tumour resections that were performed during the period between January 2022 and September 2023. The total time between the end of the operation and the MRI scan was calculated in hours for each procedure. This was analyzed into two groups with respect to compliance with the NICE guidelines, which are within 72 hours and after 72 hours. The non-compliant group was then further investigated regarding the reason for the delay. The cost related to delays was also determined by discussion with the hospital's finance department. Results All of the procedures were followed by a post-operative MRI scan but only 88% were within the timeframe recommended by NICE guidelines. The amount of delay was calculated in hours and the reasons for these delays were identified. We created two categories for delay: requesting delays and radiology department-related delays with an almost equivalent number of delays resulting from each category. This delay has resulted in approximately £19,845 of extra costs for inpatient stays. Conclusion A retrospective analysis at Queens Hospital, Romford, found good compliance with NICE guidelines for post-operative MRI scans in glial lesion resections from January 2022 to September 2023. Eighty-eight per cent of patients received scans within 72 hours, crucial for baseline assessment. A 12% non-compliance rate revealed areas for improvement, causing £19,845 in extra costs due to longer inpatient stays. Expediting scans to 36 hours could save around £30,876 annually and reduce complications like infections and thromboembolism. Proposed strategies include dedicated MRI slots and policy adjustments for MRI requests.

Imaging of heterogeneity in 3D spheroids of U87MG glioblastoma cells and its implications for photodynamic therapy

BACKGROUND

In recent years, pharmacology and toxicology have emphasised the intention to move from in vivo models to simplified 3D objects represented by spheroidal models of cancer. Mitochondria are one of the subcellular organelles responsible for cell metabolism and are often a lucrative target for cancer treatment including photodynamic therapy (PDT).

METHODS

Hanging droplet-grown glioblastoma cells were forced to form spheroids with heterogeneous environments that were characterised by fluorescence microscopy and flow cytometry using fluorescent probes sensitive to oxidative stress and apoptosis. PDT was induced with hypericin at 590 nm.

RESULTS

It was found that the metabolic activity of the cells in the periphery and core of the spheroid was different. Higher oxidative stress and induction of caspase-3 were observed in the peripheral layers after PDT. These parts were more destabilised and showed higher expression of LC3B, an autophagic marker. However, the response of the whole system to the treatment was controlled by the cells in the core of the spheroids, which were hardly affected by the treatment. It has been shown that the depth of penetration of hypericin into this system is an important limiting step for PDT and the induction of autophagy and apoptosis.

CONCLUSIONS

In this work, we have described the fluorescence imaging of vital mitochondria, caspase-3 production and immunostaining of autophagic LC3B in cells from glioblastoma spheroids before and after PDT. Overall, we can conclude that this model represents an in vitro and in vivo applicable alternative for the study of PDT in solid microtumours.

How to evaluate extent of resection in diffuse gliomas: from standards to new methods

PURPOSE OF REVIEW

Maximal safe tumor resection represents the current standard of care for patients with newly diagnosed diffuse gliomas. Recent efforts have highlighted the prognostic value of extent of resection measured as residual tumor volume in patients with isocitrate dehydrogenase (IDH)-wildtype and -mutant gliomas. Accurate assessment of such information therefore appears essential in the context of clinical trials as well as patient management.

RECENT FINDINGS

Current recommendations for evaluation of extent of resection rest upon standardized postoperative MRI including contrast-enhanced T1-weighted sequences, T2-weighted/fluid-attenuated-inversion-recovery sequences, and diffusion-weighted imaging to differentiate postoperative tumor volumes from ischemia and nonspecific imaging findings. In this context, correct timing of postoperative imaging within the postoperative period is of utmost importance. Advanced MRI techniques including perfusion-weighted MRI and MR-spectroscopy may add further insight when evaluating residual tumor remnants. Positron emission tomography (PET) using amino acid tracers proves beneficial in identifying metabolically active tumor beyond anatomical findings on conventional MRI.

SUMMARY

Future efforts will have to refine recommendations on postoperative assessment of residual tumor burden in respect to differences between IDH-wildtype and -mutant gliomas, and incorporate the emerging role of advanced imaging modalities like amino acid PET.

Segmentation of glioblastomas in early post-operative multi-modal MRI with deep neural networks

Extent of resection after surgery is one of the main prognostic factors for patients diagnosed with glioblastoma. To achieve this, accurate segmentation and classification of residual tumor from post-operative MR images is essential. The current standard method for estimating it is subject to high inter- and intra-rater variability, and an automated method for segmentation of residual tumor in early post-operative MRI could lead to a more accurate estimation of extent of resection. In this study, two state-of-the-art neural network architectures for pre-operative segmentation were trained for the task. The models were extensively validated on a multicenter dataset with nearly 1000 patients, from 12 hospitals in Europe and the United States. The best performance achieved was a 61% Dice score, and the best classification performance was about 80% balanced accuracy, with a demonstrated ability to generalize across hospitals. In addition, the segmentation performance of the best models was on par with human expert raters. The predicted segmentations can be used to accurately classify the patients into those with residual tumor, and those with gross total resection.

Pretreatment MR-based radiomics in patients with glioblastoma: A systematic review and meta-analysis of prognostic endpoints

PURPOSE

Recent studies have shown promise of MR-based radiomics in predicting the survival of patients with untreated glioblastoma. This study aimed to comprehensively collate evidence to assess the prognostic value of radiomics in glioblastoma.

METHODS

PubMed-MEDLINE, Embase, and Web of Science were searched to find original articles investigating the prognostic value of MR-based radiomics in glioblastoma published up to July 14, 2023. Concordance indexes (C-indexes) and Cox proportional hazards ratios (HRs) of overall survival (OS) and progression-free survival (PFS) were pooled via random-effects modeling. For studies aimed at classifying long-term and short-term PFS, a hierarchical regression model was used to calculate pooled sensitivity and specificity. Between-study heterogeneity was assessed using the Higgin inconsistency index (I2). Subgroup regression analysis was performed to find potential factors contributing to heterogeneity. Publication bias was assessed via funnel plots and the Egger test.

RESULTS

Among 1371 abstracts, 18 and 17 studies were included for qualitative and quantitative data synthesis, respectively. Respective pooled C-indexes and HRs for OS were 0.65 (95 % confidence interval [CI], 0.58-0.72) and 2.88 (95 % CI, 2.28-3.64), whereas those for PFS were 0.61 (95 % CI, 0.55-0.66) and 2.78 (95 % CI, 1.91-4.03). Among 4 studies that predicted short-term PFS, the pooled sensitivity and specificity were 0.77 (95 % CI, 0.58-0.89) and 0.60 (95 % CI, 0.45-0.73), respectively. There was a substantial between-study heterogeneity among studies with the survival endpoint of OS C-index (n = 9, I2 = 83.8 %). Publication bias was not observed overall.

CONCLUSION

Pretreatment MR-based radiomics provided modest prognostic value in both OS and PFS in patients with glioblastoma.

Predicting the WHO Grading of Pediatric Brain Tumors Based on Their MRI Appearance: A Retrospective Study

The treatment of central nervous system (CNS) tumors constitutes a significant part of a pediatric neurosurgeon's workload. The classification of such neoplasms spans many entities. These include low- and high-grade lesions, with both occurring in the population of patients under 18 years of age. Magnetic resonance imaging serves as the imaging method of choice for neoplastic lesions of the brain. Through its different modalities, such as T1, T2, T1 C+, apparent diffusion coefficient (ADC), diffusion-weighted imaging (DWI), susceptibility-weighted imaging (SWI), fluid-attenuated inversion recovery (FLAIR), etc., it allows the medical team to plan the therapeutic process accordingly while also possibly suggesting the specific tumor subtype prior to obtaining a definitive histological diagnosis. We conducted a retrospective study spanning 32 children treated surgically for brain tumors between July 2021 and January 2023 who had a precise histological diagnosis determined by using the 2021 WHO Classification of Tumors of the Central Nervous System. We divided them into two groups (high-grade and low-grade tumors, i.e., WHO grades 1 and 2, and grades 3 and 4, respectively) and analyzed their demographic data and preoperative MRI results. This was done using the following criteria: sub or supratentorial location of the tumor; lesion is circumscribed or infiltrating; solid, cystic, or mixed solid and cystic character of the tumor; number of compartments in cystic lesions; signal intensity (hypo-, iso-, hyperintensity sequences: T1, T2, T1 C+); presence of restricted diffusion; the largest diameter of the solid component and/or the largest diameter of the largest cyst in the transverse section. Then, we examined the results to find any correlation between the lesions' morphologies and their final assigned degree of malignancy. We found that the only radiological criteria correlating with the final WHO grade of the tumor were an infiltrative pattern of growth (25% of low-grade lesions, 75% of high-grade; p = 0.006) and the presence of a cystic component in the tumor (in 68.75% of low-grade tumors and 43.75% of high-grade tumors; p = 0.041). The only other feature close to attaining statistical significance was diffusion restriction (33.3% of low-grade tumors, 66.7% high-grade; p = 0.055). Older children tended to present with tumors of lower degrees of malignancy, and there was a predominance of female patients (21 female, 11 male).

Comparison of EPI DWI and STEAM DWI in Early Postoperative MRI Controls After Resection of Tumors of the Central Nervous System

PURPOSE

Diffusion-weighted imaging (DWI) is important for differentiating residual tumor and subacute infarctions in early postoperative magnetic resonance imaging (MRI) of central nervous system (CNS) tumors. In cases of pneumocephalus and especially in the presence of intraventricular trapped air, conventional echo-planar imaging (EPI) DWI is distorted by susceptibility artifacts. The performance and robustness of a newly developed DWI sequence using the stimulated echo acquisition mode (STEAM) was evaluated in patients after neurosurgical operations with early postoperative MRI.

METHODS

We compared EPI and STEAM DWI of 43 patients who received 3‑Tesla MRI within 72 h after a neurosurgical operation between 1 October 2019 and 30 September 2021. We analyzed susceptibility artifacts originating from air and blood and whether these artifacts compromised the detection of ischemic changes after surgery. The DWI sequences were (i) visually rated and (ii) volumetrically analyzed.

RESULTS

In 28 of 43 patients, we found severe and diagnostically relevant artifacts in EPI DWI, but none in STEAM DWI. In these cases, in which artifacts were caused by intracranial air, they led to a worse detection of ischemic lesions and thus to a possible failed diagnosis or lack of judgment using EPI DWI. Additionally, volumetric analysis demonstrated a 14% smaller infarct volume detected with EPI DWI. No significant differences in visual rating and volumetric analysis were detected among the patients without severe artifacts.

CONCLUSION

The newly developed version of STEAM DWI with highly undersampled radial encodings is superior to EPI DWI in patients with postoperative pneumocephalus.

Advanced Magnetic Resonance Imaging in the Evaluation of Treated Glioblastoma: A Pictorial Essay

MRI plays a key role in the evaluation of post-treatment changes, both in the immediate post-operative period and during follow-up. There are many different treatment's lines and many different neuroradiological findings according to the treatment chosen and the clinical timepoint at which MRI is performed. Structural MRI is often insufficient to correctly interpret and define treatment-related changes. For that, advanced MRI modalities, including perfusion and permeability imaging, diffusion tensor imaging, and magnetic resonance spectroscopy, are increasingly utilized in clinical practice to characterize treatment effects more comprehensively. This article aims to provide an overview of the role of advanced MRI modalities in the evaluation of treated glioblastomas. For a didactic purpose, we choose to divide the treatment history in three main timepoints: post-surgery, during Stupp (first-line treatment) and at recurrence (second-line treatment). For each, a brief introduction, a temporal subdivision (when useful) or a specific drug-related paragraph were provided. Finally, the current trends and application of radiomics and artificial intelligence (AI) in the evaluation of treated GB have been outlined.

Timing of Early Postoperative MRI following Primary Glioblastoma Surgery-A Retrospective Study of Contrast Enhancements in 311 Patients

An early postoperative MRI is recommended following Glioblastoma surgery. This retrospective, observational study aimed to investigate the timing of an early postoperative MRI among 311 patients. The patterns of the contrast enhancement (thin linear, thick linear, nodular, and diffuse) and time from surgery to the early postoperative MRI were recorded. The primary endpoint was the frequencies of the different contrast enhancements within and beyond the 48-h from surgery. The time dependence of the resection status and the clinical parameters were analysed as well. The frequency of the thin linear contrast enhancements significantly increased from 99/183 (50.8%) within 48-h post-surgery to 56/81 (69.1%) beyond 48-h post-surgery. Similarly, MRI scans with no contrast enhancements significantly declined from 41/183 (22.4%) within 48-h post-surgery to 7/81 (8.6%) beyond 48-h post-surgery. No significant differences were found for the other types of contrast enhancements and the results were robust in relation to the choice of categorisation of the postoperative periods. Both the resection status and the clinical parameters were not statistically different in patients with an MRI performed before and after 48 h. The findings suggest that surgically induced contrast enhancements are less frequent when an early postoperative MRI is performed earlier than 48-h, supporting the recommendation of a 48-h window for an early postoperative MRI.

T1-Weighted Contrast Enhancement, Apparent Diffusion Coefficient, and Cerebral-Blood-Volume Changes after Glioblastoma Resection: MRI within 48 Hours vs. beyond 48 Hours

BACKGROUND

The aim of the study is to identify the advantages, if any, of post-operative MRIs performed at 48 h compared to MRIs performed after 48 h in glioblastoma surgery.

MATERIALS AND METHODS

To assess the presence of a residual tumor, the T1-weighted Contrast Enhancement (CE), Apparent Diffusion Coefficient (ADC), and Cerebral Blood Volume (rCBV) in the proximity of the surgical cavity were considered. The rCBV ratio was calculated by comparing the rCBV with the contralateral normal white matter. After the blind image examinations by the two radiologists, the patients were divided into two groups according to time window after surgery: ≤48 h (group 1) and >48 h (group 2).

RESULTS

A total of 145 patients were enrolled; at the 6-month follow-up MRI, disease recurrence was 89.9% (125/139), with a mean patient survival of 8.5 months (SD 7.8). The mean ADC and rCBV ratio values presented statistical differences between the two groups (p < 0.05). Of these 40 patients in whom an ADC value was not obtained, the rCBV values could not be calculated in 52.5% (21/40) due to artifacts (p < 0.05).

CONCLUSION

The study showed differences in CE, rCBV, and ADC values between the groups of patients undergoing MRIs before and after 48 h. An MRI performed within 48 h may increase the ability of detecting GBM by the perfusion technique with the calculation of the rCBV ratio.

DeepEOR: automated perioperative volumetric assessment of variable grade gliomas using deep learning

PURPOSE

Volumetric assessments, such as extent of resection (EOR) or residual tumor volume, are essential criterions in glioma resection surgery. Our goal is to develop and validate segmentation machine learning models for pre- and postoperative magnetic resonance imaging scans, allowing us to assess the percentagewise tumor reduction after intracranial surgery for gliomas.

METHODS

For the development of the preoperative segmentation model (U-Net), MRI scans of 1053 patients from the Multimodal Brain Tumor Segmentation Challenge (BraTS) 2021 as well as from patients who underwent surgery at the University Hospital in Zurich were used. Subsequently, the model was evaluated on a holdout set containing 285 images from the same sources. The postoperative model was developed using 72 scans and validated on 45 scans obtained from the BraTS 2015 and Zurich dataset. Performance is evaluated using Dice Similarity score, Jaccard coefficient and Hausdorff 95%.

RESULTS

We were able to achieve an overall mean Dice Similarity Score of 0.59 and 0.29 on the pre- and postoperative holdout sets, respectively. Our algorithm managed to determine correct EOR in 44.1%.

CONCLUSION

Although our models are not suitable for clinical use at this point, the possible applications are vast, going from automated lesion detection to disease progression evaluation. Precise determination of EOR is a challenging task, but we managed to show that deep learning can provide fast and objective estimates.

Advances in the In Vivo Quantitative and Qualitative Imaging Characterization of Gliomas

Gliomas are the most common and aggressive intra-axial primary tumours of the central nervous system (CNS), arising from glial cells [...]

The Impact of Postoperative Tumor Burden on Patients With Brain Metastases

Background

Brain metastases were considered to be well-defined lesions, but recent research points to infiltrating behavior. Impact of postoperative residual tumor burden (RTB) and extent of resection are still not defined enough.

Patients and Methods

Adult patients with surgery of brain metastases between April 2007 and January 2020 were analyzed. Early postoperative MRI (<72 h) was used to segment RTB. Survival analysis was performed and cutoff values for RTB were revealed. Separate (subgroup) analyses regarding postoperative radiotherapy, age, and histopathological entities were performed.

Results

A total of 704 patients were included. Complete cytoreduction was achieved in 487/704 (69.2%) patients, median preoperative tumor burden was 12.4 cm³ (IQR 5.2–25.8 cm³), median RTB was 0.14 cm³ (IQR 0.0–2.05 cm³), and median postoperative tumor volume of the targeted BM was 0.0 cm³ (IQR 0.0–0.1 cm³). Median overall survival was 6 months (IQR 2–18). In multivariate analysis, preoperative KPSS (HR 0.981982, 95% CI, 0.9761–0.9873, p < 0.001), age (HR 1.012363; 95% CI, 1.0043–1.0205, p = 0.0026), and preoperative (HR 1.004906; 95% CI, 1.0003–1.0095, p = 0.00362) and postoperative tumor burden (HR 1.017983; 95% CI; 1.0058–1.0303, p = 0.0036) were significant. Maximally selected log rank statistics showed a significant cutoff for RTB of 1.78 cm³ (p = 0.0022) for all and 0.28 cm³ (p = 0.0047) for targeted metastasis and cutoff for the age of 67 years (p < 0.001). (Stereotactic) Radiotherapy had a significant impact on survival (p < 0.001).

Conclusions

RTB is a strong predictor for survival. Maximal cytoreduction, as confirmed by postoperative MRI, should be achieved whenever possible, regardless of type of postoperative radiotherapy.

MRI-based radiomics distinguish different pathological types of hepatocellular carcinoma

OBJECT

To distinguish combined hepatocellular cholangiocarcinoma (cHCC-CC), hepatocellular carcinoma (HCC) and cholangiocarcinoma (CC) before operation using MRI radiomics.

METHOD

This study retrospectively analyzed 196 liver cancers: 33 cHCC-CC, 88 HCC and 75 CC. They had confirmed by pathological analysis in the Affiliated Hospital of Hebei University. MRI lesions were manually segmented by a radiologist.1316 features were extracted from MRI lesions by Pyradiomics. Useful features were retained through two-level feature selection to establish a classification model. Receiver operating characteristic (ROC), area under curve (AUC) and F1-score were used to evaluate the performance of the model.

RESULTS

Compared with low-order image features, the performance of the model based on high-order features was improved by about 10%. The model showed better performance in identifying HCC tumors during the delay phase (AUC = 0.91, sensitivity = 0.88, specificity = 0.89, accuracy = 0.89, F1-Score = 0.88).

CONCLUSION

The classification ability of cHCC-CC, HCC and CC can be further improved by extracting MRI high-order features and using a two-level feature selection method.

Surgically Induced Contrast Enhancements on Intraoperative and Early Postoperative MRI Following High-Grade Glioma Surgery: A Systematic Review

For the radiological assessment of resection of high-grade gliomas, a 72-h diagnostic window is recommended to limit surgically induced contrast enhancements. However, such enhancements may occur earlier than 72 h post-surgery. This systematic review aimed to assess the evidence on the timing of the postsurgical MRI. PubMed, Embase, Web of Science and Cochrane were searched following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Only original research articles describing surgically induced contrast enhancements on MRI after resection for high-grade gliomas were included and analysed. The frequency of different contrast enhancement patterns on intraoperative MRI (iMRI) and early postoperative MRI (epMRI) was recorded. The search resulted in 1443 studies after removing duplicates, and a total of 12 studies were chosen for final review. Surgically induced contrast enhancements were reported at all time points after surgery, including on iMRI, but their type and frequency vary. Thin linear contrast enhancements were commonly found to be surgically induced and were less frequently recorded on postoperative days 1 and 2. This suggests that the optimal time to scan may be at or before this time. However, the evidence is limited, and higher-quality studies using larger and consecutively sampled populations are needed.