The utility of intraoperative ultrasonography for spinal cord surgery.

OBJECTIVES: Intraoperative ultrasonography (IOUS) offers the advantage of providing real-time imaging features, yet it is not generally used. This study aims to discuss the benefits of utilizing IOUS in spinal cord surgery and review related literature. MATERIALS AND METHODS: Patients who underwent spinal cord surgery utilizing IOUS at a single institution were retrospectively collected and analyzed to evaluate the benefits derived from the use of IOUS. RESULTS: A total of 43 consecutive patients were analyzed. Schwannoma was the most common tumor (35%), followed by cavernous angioma (23%) and ependymoma (16%). IOUS confirmed tumor extent and location before dura opening in 42 patients (97.7%). It was particularly helpful for myelotomy in deep-seated intramedullary lesions to minimize neural injury in 13 patients (31.0% of 42 patients). IOUS also detected residual or hidden lesions in 3 patients (7.0%) and verified the absence of hematoma post-tumor removal in 23 patients (53.5%). In 3 patients (7.0%), confirming no intradural lesions after removing extradural tumors avoided additional dural incisions. IOUS identified surrounding blood vessels and detected dural defects in one patient (2.3%) respectively. CONCLUSIONS: The IOUS can be a valuable tool for spinal cord surgery in identifying the exact location of the pathologic lesions, confirming the completeness of surgery, and minimizing the risk of neural and vascular injury in a real-time fashion.

Disappearance of "Elongated Pony Tail Sign" Following Chemoradiotherapy in a Case of Primary Cerebellopontine Angle Ependymoma With Spinal Drop Metastasis: 18 F-FDG PET/CT Scan Findings.

ABSTRACT: Ependymomas are rare glial tumors that commonly arise from the lining cells of ventricular system and constitute ~10% of intracranial pediatric malignancies. The incidence of ependymoma in adults is rare. Due to close approximation with the ventricular system, subtentorial ependymomas are more prone to show cerebrospinal fluid metastasis compared with supratentorial ependymomas. We present a case of subtentorial cerebellopontine angle ependymoma with diffuse spinal drop metastases showing "elongated pony tail appearance" in a 69-year-old man with complete metabolic response on 18 F-FDG PET/CT imaging following chemoradiotherapy.

Differentiating Low-Grade from High-Grade Intracranial Ependymomas: Comparison of Dynamic Contrast-Enhanced MRI and Diffusion-Weighted Imaging.

BACKGROUND AND PURPOSE: The aim of this study was to determine the diagnostic value of fractional plasma volume derived from dynamic contrast-enhanced perfusion MR imaging versus ADC, obtained from DWI in differentiating between grade 2 (low-grade) and grade 3 (high-grade) intracranial ependymomas. MATERIALS AND METHODS: A hospital database was created for the period from January 2013 through June 2022, including patients with histologically-proved ependymoma diagnosis with available dynamic contrast-enhanced MR imaging. Both dynamic contrast-enhanced perfusion and DWI were performed on each patient using 1.5T and 3T scanners. Fractional plasma volume maps and ADC maps were calculated. ROIs were defined by a senior neuroradiologist manually by including the enhancing tumor on every section and conforming a VOI to obtain the maximum value of fractional plasma volume (Vpmax) and the minimum value of ADC (ADCmin). A Mann-Whitney U test at a significance level of corrected P = .01 was used to evaluate the differences. Additionally, receiver operating characteristic curve analysis was applied to assess the sensitivity and specificity of Vpmax and ADCmin values. RESULTS: A total of 20 patients with ependymomas (10 grade 2 tumors and 10 grade 3 tumors) were included. Vpmax values for grade 3 ependymomas were significantly higher (P < .002) than those for grade 2. ADCmin values were overall lower in high-grade lesions. However, no statistically significant differences were found (P = .12114). CONCLUSIONS: As a dynamic contrast-enhanced perfusion MR imaging metric, fractional plasma volume can be used as an indicator to differentiate grade 2 and grade 3 ependymomas. Dynamic contrast-enhanced perfusion MR imaging plays an important role with high diagnostic value in differentiating low- and high-grade ependymoma.

Extraneural metastatic ependymoma: distant metastasis to the pleura, lungs, lymph nodes and bone.

Ependymomas are neuroepithelial tumours arising from ependymal cells surrounding the cerebral ventricles that rarely metastasise to extraneural structures. This spread has been reported to occur to the lungs, lymph nodes, liver and bone. We describe the case of a patient with recurrent CNS WHO grade 3 ependymoma with extraneural metastatic disease. He was treated with multiple surgical resections, radiation therapy and salvage chemotherapy for his extraneural metastasis to the lungs, bone, pleural space and lymph nodes.

Technical aspects of fourth ventricle ependymomas in adults: how I do it.

BACKGROUND: Ependymomas in the fourth ventricle in adults are rare entity. Surgical treatment of adult ependymomas is the only treatment modality since no other effective alternative is available. Radical resection often means cure but it is hindered by the nature and location of the lesion. METHODS: Technical aspects of the fourth ventricle ependymoma surgery in adults are discussed. Anatomy of the area is provided with the step-by-step surgical algorithm. CONCLUSION: Radical resection of low-grade ependymoma with a detailed understanding of the anatomy in this area is vital considering the high effectiveness of the treatment and its excellent prognosis.

Single-Center Experience of Resection of 120 Cases of Intradural Spinal Tumors.

BACKGROUND: Our study presents a single-center experience of resection of intradural spinal tumors either with or without using intraoperative computed tomography-based registration and microscope-based augmented reality (AR). Microscope-based AR was recently described for improved orientation in the operative field in spine surgery, using superimposed images of segmented structures of interest in a two-dimensional or three-dimensional mode. METHODS: All patients who underwent surgery for resection of intradural spinal tumors at our department were retrospectively included in the study. Clinical outcomes in terms of postoperative neurologic deficits and complications were evaluated, as well as neuroradiologic outcomes for tumor remnants and recurrence. RESULTS: 112 patients (57 female, 55 male; median age 55.8 ± 17.8 years) who underwent 120 surgeries for resection of intradural spinal tumors with the use of intraoperative neuromonitoring were included in the study, with a median follow-up of 39 ± 34.4 months. Nine patients died during the follow-up for reasons unrelated to surgery. The most common tumors were meningioma (n = 41), schwannoma (n = 37), myopapillary ependymomas (n = 12), ependymomas (n = 10), and others (20). Tumors were in the thoracic spine (n = 46), lumbar spine (n = 39), cervical spine (n = 32), lumbosacral spine (n = 1), thoracic and lumbar spine (n = 1), and 1 tumor in the cervical, thoracic, and lumbar spine. Four biopsies were performed, 10 partial resections, 13 subtotal resections, and 93 gross total resections. Laminectomy was the common approach. In 79 cases, patients experienced neurologic deficits before surgery, with ataxia and paraparesis as the most common ones. After surgery, 67 patients were unchanged, 49 improved and 4 worsened. Operative time, extent of resection, clinical outcome, and complication rate did not differ between the AR and non-AR groups. However, the use of AR improved orientation in the operative field by identification of important neurovascular structures. CONCLUSIONS: High rates of gross total resection with favorable neurologic outcomes in most patients as well as low recurrence rates with comparable complication rates were noted in our single-center experience. AR improved intraoperative orientation and increased surgeons' comfort by enabling early identification of important anatomic structures; however, clinical and radiologic outcomes did not differ, when AR was not used.

Recurrence Patterns and Surveillance Imaging in Pediatric Brain Tumor Survivors.

Surveillance magnetic resonance imaging (MRI) is routinely used to detect recurrence in pediatric central nervous system (CNS) tumors. The frequency of neuroimaging surveillance varies without a standardized approach. A single-institutional retrospective cohort study evaluated the frequency of recurrences. This study included 476 patients with the majority diagnosed with low-grade glioma (LGG) (n=138, 29%), high-grade glioma (HGG) (n=77, 16%), ependymoma (n=70, 15%), or medulloblastoma (n=61, 13%). LGG, HGG, and ependymoma patients more commonly had multiply recurrent disease ( P =0.08), with ependymoma patients demonstrating ≥2 relapses in 47% of cases. Recurrent disease was identified by imaging more often than clinical symptoms (65% vs. 32%; P =<0.01). Patients diagnosed with meningioma demonstrated the longest mean time to first relapse (74.7 mo) whereas those with atypical teratoid rhabdoid tumor and choroid plexus carcinoma tended to have the shortest time to relapse (8.9 and 9 mo, respectively). Overall, 22 patients sustained first relapse >10 years from initial diagnosis. With a higher tendency toward detection of tumor recurrence/progression on MRI surveillance in comparison to clinical progression, surveillance imaging is necessary in routine follow up of pediatric CNS tumor survivors. With some relapses >10 years from initial diagnosis, imaging beyond this time point may be useful in particular tumor types. While the study is limited in outcome analysis, earlier detection of recurrence would lead to earlier initiation of treatment and implementation of salvage treatment regimens which can impact survival and quality of life.

Multiparametric MRI-Based Interpretable Radiomics Machine Learning Model Differentiates Medulloblastoma and Ependymoma in Children: A Two-Center Study.

RATIONALE AND OBJECTIVES: Medulloblastoma (MB) and Ependymoma (EM) in children, share similarities in age group, tumor location, and clinical presentation. Distinguishing between them through clinical diagnosis is challenging. This study aims to explore the effectiveness of using radiomics and machine learning on multiparametric magnetic resonance imaging (MRI) to differentiate between MB and EM and validate its diagnostic ability with an external set. MATERIALS AND METHODS: Axial T2 weighted image (T2WI) and contrast-enhanced T1weighted image (CE-T1WI) MRI sequences of 135 patients from two centers were collected as train/test sets. Volume of interest (VOI) was manually delineated by an experienced neuroradiologist, supervised by a senior. Feature selection analysis and the least absolute shrinkage and selection operator (LASSO) algorithm identified valuable features, and Shapley additive explanations (SHAP) evaluated their significance. Five machine-learning classifiers-extreme gradient boosting (XGBoost), Bernoulli naive Bayes (Bernoulli NB), Logistic Regression (LR), support vector machine (SVM), linear support vector machine (Linear SVC) classifiers were built based on T2WI (T2 model), CE-T1WI (T1 model), and T1 + T2WI (T1 + T2 model). A human expert diagnosis was developed and corrected by senior radiologists. External validation was performed at Sun Yat-Sen University Cancer Center. RESULTS: 31 valuable features were extracted from T2WI and CE-T1WI. XGBoost demonstrated the highest performance with an area under the curve (AUC) of 0.92 on the test set and maintained an AUC of 0.80 during external validation. For the T1 model, XGBoost achieved the highest AUC of 0.85 on the test set and the highest accuracy of 0.71 on the external validation set. In the T2 model, XGBoost achieved the highest AUC of 0.86 on the test set and the highest accuracy of 0.82 on the external validation set. The human expert diagnosis had an AUC of 0.66 on the test set and 0.69 on the external validation set. The integrated T1 + T2 model achieved an AUC of 0.92 on the test set, 0.80 on the external validation set, achieved the best performance. Overall, XGBoost consistently outperformed in different classification models. CONCLUSION: The combination of radiomics and machine learning on multiparametric MRI effectively distinguishes between MB and EM in childhood, surpassing human expert diagnosis in training and testing sets.

Convolutional neural network-based magnetic resonance image differentiation of filum terminale ependymomas from schwannomas.

PURPOSE: Preoperative diagnosis of filum terminale ependymomas (FTEs) versus schwannomas is difficult but essential for surgical planning and prognostic assessment. With the advancement of deep-learning approaches based on convolutional neural networks (CNNs), the aim of this study was to determine whether CNN-based interpretation of magnetic resonance (MR) images of these two tumours could be achieved. METHODS: Contrast-enhanced MRI data from 50 patients with primary FTE and 50 schwannomas in the lumbosacral spinal canal were retrospectively collected and used as training and internal validation datasets. The diagnostic accuracy of MRI was determined by consistency with postoperative histopathological examination. T1-weighted (T1-WI), T2-weighted (T2-WI) and contrast-enhanced T1-weighted (CE-T1) MR images of the sagittal plane containing the tumour mass were selected for analysis. For each sequence, patient MRI data were randomly allocated to 5 groups that further underwent fivefold cross-validation to evaluate the diagnostic efficacy of the CNN models. An additional 34 pairs of cases were used as an external test dataset to validate the CNN classifiers. RESULTS: After comparing multiple backbone CNN models, we developed a diagnostic system using Inception-v3. In the external test dataset, the per-examination combined sensitivities were 0.78 (0.71-0.84, 95% CI) based on T1-weighted images, 0.79 (0.72-0.84, 95% CI) for T2-weighted images, 0.88 (0.83-0.92, 95% CI) for CE-T1 images, and 0.88 (0.83-0.92, 95% CI) for all weighted images. The combined specificities were 0.72 based on T1-WI (0.66-0.78, 95% CI), 0.84 (0.78-0.89, 95% CI) based on T2-WI, 0.74 (0.67-0.80, 95% CI) for CE-T1, and 0.81 (0.76-0.86, 95% CI) for all weighted images. After all three MRI modalities were merged, the receiver operating characteristic (ROC) curve was calculated, and the area under the curve (AUC) was 0.93, with an accuracy of 0.87. CONCLUSIONS: CNN based MRI analysis has the potential to accurately differentiate ependymomas from schwannomas in the lumbar segment.

Intracranial ependymoma with extremely rare extraneural metastasis.

Ependymomas account for 1-8% of overall brain tumors. They are most common at the age of 3-4 years. Their metastasis is very rare, and extraneural metastasis is even more unusual. In this report, the ependymoma localized in the posterior fossa with metastasis into femoral diaphysis in a 27-year-old male patient, who was treated in 2001, is presented. As we did not have any other cases of patients having a brain and spinal tumor with extraneural metastases even after 21 years, until 2022, this case was found worthy of being presented. When the literature was examined, it was observed that there is still no standard treatment after surgery for ependymomas and their metastasis. Due to their rarity, the general treatment of extraneural metastasis of ependymomas is also under discussion. It is recommended that clinicians consider admitting patients with rare or hard-to-treat tumors to ongoing clinical trials.

Brainstem toxicity after proton or photon therapy in children and young adults with localized intracranial ependymoma: A French retrospective study.

BACKGROUND AND PURPOSE: Ependymoma is the third most frequent childhood braintumor. Standard treatment is surgery followed by radiation therapy including proton therapy (PBT). Retrospective studies have reported higher rates of brainstem injury after PBT than after photon therapy (XRT). We report a national multicenter study of the incidence of brainstem injury after XRT versus PBT, and their correlations with dosimetric data. MATERIAL AND METHODS: We included all patients aged < 25 years who were treated with PBT or XRT for intracranial ependymoma at five French pediatric oncology reference centers between 2007 and 2020. We reviewed pre-irradiation MRI, follow-up MRIs over the 12 months post-treatment and clinical data. RESULTS: Of the 83 patients, 42 were treated with PBT, 37 with XRT, and 4 with both (median dose: 59.4 Gy, range: 53­60). No new or progressive symptomatic brainstem injury was found. Four patients presented asymptomatic radiographic changes (punctiform brainstem enhancement and FLAIR hypersignal), with median onset at 3.5 months (range: 3.0­9.4) after radiation therapy, and median offset at 7.6 months (range: 3.7­7.9). Two had been treated with PBT, one with XRT, and one with mixed XRT-PBT. Prescribed doses were 59.4, 55.8, 59.4 and 54 Gy. CONCLUSION: Asymptomatic radiographic changes occurred in 4.8% of patients with ependymoma in a large national series. There was no correlation with dose or technique. No symptomatic brainstem injury was identified.

LETd Optimization Verification With an SOI Microdosimeter.

PURPOSE: A first of its kind experimental verification of dose-averaged linear energy transfer (LETd) optimized treatment plans for proton therapy has been carried out using a silicon-on-insulator microdosimeter at the Massachusetts General Hospital (MGH), Boston, USA. METHODS AND MATERIALS: Three clinical treatment plans of a typical ependymoma structure set were designed using the standard clinical approach, the proposed protocol approach, and a one-field approach. The plans were then reoptimized to reduce the LETd-weighted dose in the brain stem. All six plans were delivered in a solid water phantom and the experimental yD‾ measured. RESULTS: After LETd optimization, a reduction in yD‾ was found within the brain stem by an average of 12%, 19%, and 4% for the clinical, protocol, and one-field plans, respectively, while maintaining adequate coverage of the tumor structure. The experimental LETd-weighted doses were in agreement with the treatment planning system calculations and Monte Carlo simulations and reinforced the improvement of the optimization. CONCLUSIONS: This work demonstrates the first experimental verification of the clinical implementation of LETd optimization for patient treatment with proton therapy.

Giant Myxopapillary Ependymoma with Multi-Site Neural Axis Metastases: A Rare Case with Suboptimal Outcome.

BACKGROUND Myxopapillary ependymoma is a rare type of slow-growing tumor that mainly occurs in the spinal cord, particularly in the region of the conus medullaris and the cauda equina. It originates from the ependymal glial cells found in the filum terminale. CASE REPORT We present a clinical case of a 44-year-old male patient who presented with symptoms of non-specific pain in the lower back persisting for the past 2 years. He did not report any specific neurological deficits or radicular symptoms. Unenhanced MRI of the lumbar spine showed a giant intradural, extramedullary, heterogenous, expansive tumor at the level L1-S4 with erosion of the sacral bone and invasion of presacral tissue. Based on its characteristic localization and growth pattern, suspicion arose for myxopapillary ependymoma. Biopsy confirmed the initial diagnosis. Partial resection of the tumor with laminectomy and laminoplasty was deemed necessary. Preoperative neural axis MRI showed contrast-enhancing lesions in the cerebellum and the cervical and thoracic spine; therefore, adjuvant radiation therapy was administered. Following the surgery, the patient experienced intermittent episodes of neurological deficits and required physiotherapy. Control MRI a year after the operation showed tumor growth and more metastases along the neural axis. CONCLUSIONS Complete surgical excision of the tumor is the preferred treatment approach, but there is a risk of recurrence even after total excision, so radiotherapy is recommended to minimize the risk of recurrence. Prior to surgery, it is essential to conduct MRI/PET/CT of the head and spine to assess the possibility of metastases.

Development of a novel prediction model for differential diagnosis between spinal myxopapillary ependymoma and schwannoma.

Spinal myxopapillary ependymoma (MPE) and schwannoma represent clinically distinct intradural extramedullary tumors, albeit with shared and overlapping magnetic resonance imaging (MRI) characteristics. We aimed to identify significant MRI features that can differentiate between MPE and schwannoma and develop a novel prediction model using these features. In this study, 77 patients with MPE (n = 24) or schwannoma (n = 53) who underwent preoperative MRI and surgical removal between January 2012 and December 2022 were included. MRI features, including intratumoral T2 dark signals, subarachnoid hemorrhage (SAH), leptomeningeal seeding, and enhancement patterns, were analyzed. Logistic regression analysis was conducted to distinguish between MPE and schwannomas based on MRI parameters, and a prediction model was developed using significant MRI parameters. The model was validated internally using a stratified tenfold cross-validation. The area under the curve (AUC) was calculated based on the receiver operating characteristic curve analysis. MPEs had a significantly larger mean size (p = 0.0035), higher frequency of intratumoral T2 dark signals (p = 0.0021), associated SAH (p = 0.0377), and leptomeningeal seeding (p = 0.0377). Focal and diffuse heterogeneous enhancement patterns were significantly more common in MPEs (p = 0.0049 and 0.0038, respectively). Multivariable analyses showed that intratumoral T2 dark signal (p = 0.0439) and focal (p = 0.0029) and diffuse enhancement patterns (p = 0.0398) were independent factors. The prediction model showed an AUC of 0.9204 (95% CI 0.8532-0.9876) and the average AUC for internal validation was 0.9210 (95% CI 0.9160-0.9270). MRI provides useful data for differentiating spinal MPEs from schwannomas. The prediction model developed based on the MRI features demonstrated excellent discriminatory performance.

Resection of an Intradural Intramedullary C7-T1 Tumor: Technical Nuances and Complication Management.

Primary spinal cord tumors are relatively rare, comprising approximately 4%-16% of all tumors originating from the central nervous system. These tumors are anatomically separable into 2 broad categories: intradural intramedullary and intradural extramedullary. Intramedullary tumors are composed predominantly of gliomas (infiltrative astrocytoma) and ependymomas.1-4 The primary treatment approach for these tumors is surgical resection, aiming to preserve neurologic function.5-9 In Video 1, the authors showcase a step-by-step approach for microsurgical resection of a primary spinal ependymoma, with emphasis on microsurgical technique and utility of adjunct equipment, such as intraoperative ultrasound and neuromonitoring.10,11 The patient consented to the procedure.

Imaging features to distinguish posterior fossa ependymoma subgroups.

OBJECTIVES: Posterior fossa ependymoma group A (EPN_PFA) and group B (EPN_PFB) can be distinguished by their DNA methylation and give rise to different prognoses. We compared the MRI characteristics of EPN_PFA and EPN_PFB at presentation. METHODS: Preoperative imaging of 68 patients with posterior fossa ependymoma from two centers was reviewed by three independent readers, blinded for histomolecular grouping. Location, tumor extension, tumor volume, hydrocephalus, calcifications, tissue component, enhancement or diffusion signal, and histopathological data (cellular density, calcifications, necrosis, mitoses, vascularization, and microvascular proliferation) were compared between the groups. Categorical data were compared between groups using Fisher's exact tests, and quantitative data using Mann-Whitney tests. We performed a Benjamini-Hochberg correction of the p values to account for multiple tests. RESULTS: Fifty-six patients were categorized as EPN_PFA and 12 as EPN_PFB, with median ages of 2 and 20 years, respectively (p = 0.0008). The median EPN_PFA tumoral volume was larger (57 vs 29 cm3, p = 0.003), with more pronounced hydrocephalus (p = 0.002). EPN_PFA showed an exclusive central position within the 4th ventricle in 61% of patients vs 92% for EPN_PFB (p = 0.01). Intratumor calcifications were found in 93% of EPN_PFA vs 40% of EPN_PFB (p = 0.001). Invasion of the posterior fossa foramina was mostly found for EPN_PFA, particularly the foramina of Luschka (p = 0.0008). EPN_PFA showed whole and homogeneous tumor enhancement in 5% vs 75% of EPN_PFB (p = 0.0008). All mainly cystic tumors were EPN_PFB (p = 0.002). The minimal and maximal relative ADC was slightly lower in EPN_PFA (p = 0.02 and p = 0.01, respectively). CONCLUSION: Morphological characteristics from imaging differ between posterior fossa ependymoma subtypes and may help to distinguish them preoperatively. CLINICAL RELEVANCE STATEMENT: This study provides a tool to differentiate between group A and group B ependymomas, which will ultimately allow the therapeutic strategy to be adapted in the early stages of patient management. KEY POINTS: • Posterior fossa ependymoma subtypes often have different imaging characteristics. • Posterior fossa ependymomas group A are commonly median or lateral tissular calcified masses, with incomplete enhancement, affecting young children and responsible for pronounced hydrocephalus and invasion of the posterior fossa foramina. • Posterior fossa ependymomas group B are commonly median non-calcified masses of adolescents and adults, predominantly cystic, and minimally invasive, with total and homogeneous enhancement.

A Fully Automated Deep-Learning Model for Predicting the Molecular Subtypes of Posterior Fossa Ependymomas Using T2-Weighted Images.

PURPOSE: We aimed to develop and validate a deep learning (DL) model to automatically segment posterior fossa ependymoma (PF-EPN) and predict its molecular subtypes [Group A (PFA) and Group B (PFB)] from preoperative MR images. EXPERIMENTAL DESIGN: We retrospectively identified 227 PF-EPNs (development and internal test sets) with available preoperative T2-weighted (T2w) MR images and molecular status to develop and test a 3D nnU-Net (referred to as T2-nnU-Net) for tumor segmentation and molecular subtype prediction. The network was externally tested using an external independent set [n = 40; subset-1 (n = 31) and subset-2 (n =9)] and prospectively enrolled cases [prospective validation set (n = 27)]. The Dice similarity coefficient was used to evaluate the segmentation performance. Receiver operating characteristic analysis for molecular subtype prediction was performed. RESULTS: For tumor segmentation, the T2-nnU-Net achieved a Dice score of 0.94 ± 0.02 in the internal test set. For molecular subtype prediction, the T2-nnU-Net achieved an AUC of 0.93 and accuracy of 0.89 in the internal test set, an AUC of 0.99 and accuracy of 0.93 in the external test set. In the prospective validation set, the model achieved an AUC of 0.93 and an accuracy of 0.89. The predictive performance of T2-nnU-Net was superior or comparable to that of demographic and multiple radiologic features (AUCs ranging from 0.87 to 0.95). CONCLUSIONS: A fully automated DL model was developed and validated to accurately segment PF-EPNs and predict molecular subtypes using only T2w MR images, which could help in clinical decision-making.