PRRT with Lu-177 DOTATATE in Treatment-Refractory Progressive Meningioma: Initial Experience from a Tertiary-Care Neuro-Oncology Center.

PURPOSE: Refractory and/or recurrent meningiomas have poor outcomes, and the treatment options are limited. Peptide receptor radionuclide therapy (PRRT) has been used in this setting with promising results. We have documented our experience of using intravenous (IV) and intra-arterial (IA) approaches of Lu-177 DOTATATE PRRT. METHODS: Eight patients with relapsed/refractory high-grade meningioma received PRRT with Lu-177 DOTATATE by IV and an IA route. At least 2 cycles were administered. Time to progression was calculated from the first PRRT session to progression. The response was assessed on MRI using RANO criteria, and visual analysis of uptake was done on Ga-68 DOTANOC PET/CT. Post-therapy dosimetry calculations for estimating the absorbed dose were performed. RESULTS: Median time to progression was 8.9 months. One patient showed disease progression, whereas seven patients showed stable disease at 4 weeks following 2 cycles of PRRT. Dosimetric analysis showed higher dose and retention time by IA approach. No significant peri-procedural or PRRT associated toxicity was seen. CONCLUSION: PRRT is a safe and effective therapeutic option for relapsed/refractory meningioma. The IA approach yields better dose delivery and should be routinely practised.

Unusual Extra-Axial and Extracranial Recurrence in an IDH Mutant Astrocytoma: A Case Report.

Isocitrate dehydrogenase mutant gliomas generally have a better prognosis than their wild-type counterpart. Recurrences are generally within the radiation field in the primary tumoral bed. Remote recurrence is uncommon and is usually intraparenchymal. Transformation to a higher grade has been observed with TP53 mutants. Presentation of glioma as an extra-axial lesion is extremely uncommon. No such cases of remote intracranial extra-axial recurrence have been reported in the literature. We describe the unique imaging findings in this case and attempt to formulate possible diagnoses. Intraoperative and pathological findings confirmed this unusual recurrence pattern.

Tumor location and neurocognitive function-Unravelling the association and identifying relevant anatomical substrates in intra-axial brain tumors.

Background: Neurocognitive function is a key outcome indicator of therapy in brain tumors. Understanding the underlying anatomical substrates involved in domain function and the pathophysiological basis of dysfunction can help ameliorate the effects of therapy and tailor directed rehabilitative strategies. Methods: Hundred adult diffuse gliomas were co-registered onto a common demographic-specific brain template to create tumor localization maps. Voxel-based lesion symptom (VLSM) technique was used to assign an association between individual voxels and neuropsychological dysfunction in various domains (attention and executive function (A & EF), language, memory, visuospatial/constructive abilities, and visuomotor speed). The probability maps thus generated were further co-registered to cortical and subcortical atlases. A permutation-based statistical testing method was used to evaluate the statistically and clinically significant anatomical parcels associated with domain dysfunction and to create heat maps. Results: Neurocognition was affected in a high proportion of subjects (93%), with A & EF and memory being the most affected domains. Left-sided networks were implicated in patients with A & EF, memory, and language deficits with the perisylvian white matter tracts being the most common across domains. Visuospatial dysfunction was associated with lesions involving the right perisylvian cortical regions, whereas deficits in visuomotor speed were associated with lesions involving primary visual and motor output pathways. Conclusions: Significant baseline neurocognitive deficits are prevalent in gliomas. These are multidomain and the perisylvian network especially on the left side seems to be very important, being implicated in dysfunction of many domains.

The Great Imposter: An Atypical Case of Pleomorphic Adenoma With Review of Literature.

Pleomorphic adenomas (PA) are the most common type of salivary gland tumors. These slow-growing benign tumors most commonly involve the parotid gland, but can sometimes occur at atypical sites such as the submandibular or minor salivary glands. We describe an atypical case of pleomorphic adenoma with multicentric involvement of the parotid, the submandibular gland, and the parapharyngeal space in a 35-year-old male which mimicked a slow-flow malformation on magnetic resonance imaging (MRI). Diagnosis was confirmed on fine needle aspiration cytology, and conservative approach was opted for the patient in view of perioperative risks. This case highlights the uncommon multicentricity and atypical presentation of PA, challenging the initial differential diagnosis based on MRI features. It also underscores the importance of considering atypical presentations and utilizing accurate diagnostic tools like cytology for managing complex salivary gland tumors.

Clinico-Radiological Outcomes in WNT-Subgroup Medulloblastoma.

Medulloblastoma (MB) comprises four broad molecular subgroups, namely wingless (WNT), sonic hedgehog (SHH), Group 3, and Group 4, respectively, with subgroup-specific developmental origins, unique genetic profiles, distinct clinico-demographic characteristics, and diverse clinical outcomes. This is a retrospective audit of clinical outcomes in molecularly confirmed WNT-MB patients treated with maximal safe resection followed by postoperative standard-of-care risk-stratified adjuvant radio(chemo)therapy at a tertiary-care comprehensive cancer centre. Of the 74 WNT-MB patients registered in a neuro-oncology unit between 2004 to 2020, 7 patients accrued on a prospective clinical trial of treatment deintensification were excluded, leaving 67 patients that constitute the present study cohort. The median age at presentation was 12 years, with a male preponderance (2:1). The survival analysis was restricted to 61 patients and excluded 6 patients (1 postoperative mortality plus 5 without adequate details of treatment or outcomes). At a median follow-up of 72 months, Kaplan-Meier estimates of 5-year progression-free survival and overall survival were 87.7% and 91.2%, respectively. Traditional high-risk features, large residual tumour (≥1.5 cm2), and leptomeningeal metastases (M+) did not significantly impact upon survival in this molecularly characterized WNT-MB cohort treated with risk-stratified contemporary multimodality therapy. The lack of a prognostic impact of conventional high-risk features suggests the need for refined risk stratification and potential deintensification of therapy.

CDKN2A/B deletion in IDH-mutant astrocytomas: An evaluation by Fluorescence in-situ hybridization.

INTRODUCTION: CDKN2A/B homozygous deletion is one of the defining features of grade 4 in IDH-mutant astrocytic tumours. AIM: To evaluate CDKN2A/B-deletion in IDH-mutant astrocytic tumours and its clinicopathological impact. MATERIALS AND METHODS: CDKN2A/B-deletion was evaluated by Fluorescence in-situ hybridisation (FISH) and interpreted by two recently accepted methods. RESULTS: Eighty-three out of 94 cases (histologically-grade 2: 3, grade 3: 46, grade 4: 34) were interpretable on FISH. Concordant CDKN2A/B-deletion was observed in 71% (27/38) of lower-grade tumours (n = 49) and 90% (27/30) of histological grade 4 tumours (n = 34). Both the interpretation methods showed good agreement (Kappa = 0.75). CDKN2A/B-deletion showed an inverse correlation for < 10% MIB-1 labeling index (p = 0.01) while that by method-2 showed a significant correlation for grade 4 (p = 0.02). No significant correlation was observed for any other clinicopathological parameters. Twenty-four patients showed progression/recurrence (including deaths), and no significant difference in frequency of CDKN2A/B deletion was observed among cases with disease progression across different histological grades. CONCLUSIONS: CDKN2A/B-deletion was observed across all the histological grades of IDH-mutant astrocytic tumours, expectedly more in the higher grade. FISH, as a method, can be used for the detection of CDKN2A/B homozygous deletion, when there is concordant interpretation.

Post-ictal changes presenting as late pseudoprogression on MRI and PET in a patient with diffuse glioma: Case report and brief literature review.

Following completion of adjuvant radiation and chemotherapy imaging surveillance forms a major role in the management of diffuse gliomas. The primary role of imaging is to detect recurrences earlier than clinical symptomatology. Magnetic resonance imaging (MRI) is considered the gold standard in follow-up protocols owing to better soft tissue delineation and multiparametric nature. True recurrence can often mimic treatment-related changes, it is of paramount importance to differentiate between the two entities as the clinical course is divergent. Addition of functional sequences like perfusion, spectroscopy and metabolic imaging can provide further details into the microenvironment. In equivocal cases, a follow-up short interval imaging might be obtained to settle the diagnostic dilemma. Here, we present a patient with diagnosis of recurrent oligodendroglioma treated with adjuvant chemoradiation, presenting with seizures five years post-completion of chemotherapy for recurrence. On MRI, subtle new onset gyral thickening of the left frontal region with mild increase in perfusion and patchy areas of raised choline. FET-PET (fluoro-ethyltyrosine) showed an increased tumour-to-white matter (T/Wm) ratio favouring tumour recurrence. Based on discussion in a multi-disciplinary joint clinic, short interval follow-up MRI was undertaken at two months showing decrease in gyral thickening and resolution of enhancing areas in left frontal lobe. Repeat imaging one year later demonstrated stable disease status without further new imaging findings. Given the changes resolving completely without any anti-tumoral intervention, we conclude this to be peri-ictal pseudoprogression, being the second such case described in India.

Patterns of Care in Craniopharyngioma: Clinical Outcomes After Surgery and Radiation Therapy in a Real-World Setting.

OBJECTIVE: Craniopharyngioma (CP) is a benign neuroepithelial tumor generally treated with maximal safe resection and radiation therapy (RT) in incompletely resected CP or in recurrent tumors to achieve long-term control. We analyzed the clinical outcomes of patients with CPs treated with a multimodality approach. PATIENTS AND METHODS: A retrospective clinical audit of histologically proven CPs registered between 2008 and 2019 at a specialized neuro-oncology center in India was performed. Time-to-event outcomes (overall survival [OS] and progression-free survival [PFS]) were analyzed. RESULTS: One hundred and twenty-two patients with CP were analyzed. The median age of the population was 14 years (interquartile range [IQR], 8-26) with a significant male preponderance. Gross total resection was achieved in only 25% of patients. At a median follow-up of 57.1 months (IQR, 27.8-87.8), 5-year estimates of PFS and OS were 52% (95% confidence interval, 46%-63.4%) and 85.8% (95% confidence interval, 78.6%-93%), respectively. Recurrence or progression was observed in 48 of 122 patients (39.3%) at a median time of 84.4 months (IQR, 24.7-174.8). On multivariate analysis, the absence of residual disease (P = 0.004), near-total resection (P = 0.035), and use of up-front adjuvant RT (P < 0.001) significantly improved the 5-year PFS, whereas the absence of extracavernous extension (P = 0.058) and any use of postoperative RT (P = 0.026) significantly improved the 5-year OS. CONCLUSIONS: This study represents one of the largest single-institutional series of CPs, showing improved PFS with up-front adjuvant RT in most cases of CP. Deferring adjuvant RT should be considered only in patients with no evidence of residual disease (as shown on dedicated sellar imaging) after primary surgery.

Deep learning based clinico-radiological model for paediatric brain tumor detection and subtype prediction.

Aim: Early diagnosis of paediatric brain tumors significantly improves the outcome. The aim is to study magnetic resonance imaging (MRI) features of paediatric brain tumors and to develop an automated segmentation (AS) tool which could segment and classify tumors using deep learning methods and compare with radiologist assessment. Methods: This study included 94 cases, of which 75 were diagnosed cases of ependymoma, medulloblastoma, brainstem glioma, and pilocytic astrocytoma and 19 were normal MRI brain cases. The data was randomized into training data, 64 cases; test data, 21 cases and validation data, 9 cases to devise a deep learning algorithm to segment the paediatric brain tumor. The sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy of the deep learning model were compared with radiologist's findings. Performance evaluation of AS was done based on Dice score and Hausdorff95 distance. Results: Analysis of MRI semantic features was done with necrosis and haemorrhage as predicting features for ependymoma, diffusion restriction and cystic changes were predictors for medulloblastoma. The accuracy of detecting abnormalities was 90%, with a specificity of 100%. Further segmentation of the tumor into enhancing and non-enhancing components was done. The segmentation results for whole tumor (WT), enhancing tumor (ET), and non-enhancing tumor (NET) have been analyzed by Dice score and Hausdorff95 distance. The accuracy of prediction of all MRI features was compared with experienced radiologist's findings. Substantial agreement observed between the classification by model and the radiologist's given classification [K-0.695 (K is Cohen's kappa score for interrater reliability)]. Conclusions: The deep learning model had very high accuracy and specificity for predicting the magnetic resonance (MR) characteristics and close to 80% accuracy in predicting tumor type. This model can serve as a potential tool to make a timely and accurate diagnosis for radiologists not trained in neuroradiology.

Computerized tomography-based first order tumor texture features in non-small cell lung carcinoma treated with concurrent chemoradiation: A simplistic and potential surrogate imaging marker for survival.

Objectives: This study examines the role of tumor texture on computed tomography (CT) images as a complement to clinical prognostic factors in predicting survival in patients of non-small cell lung carcinoma (NSCLC) treated with radical chemo-radiation (CRT). Methods: A total of 93 patients with confirmed NSCLC treated with CRT accrued in a study approved by the institutional ethics committee were analyzed for CT-based radiomic features. Pretreatment CT images were used to contour the primary tumor and texture features were computed by the image filtration method to differentially highlight fine to coarse textures. Texture parameters included mean intensity, entropy, kurtosis, standard deviation, and mean positive pixel and skewness. Optimal threshold cut-off values of the above tumor texture features were analyzed. These features were explored as imaging biomarkers to predict survival using Kaplan-Meier and Cox proportional hazard model. Results: Median follow-up of the entire cohort was 23.5 months [Interquartile range, IQR: 14-37] while for alive patients, median follow-up was 31 months (IQR: 23-49), 47 (50.6%) patients had died at the last follow-up. Univariate analysis revealed certain features like age, gender, response to therapy, and texture features like mean and kurtosis in CT images to be significant predictors of survival. In multivariate analysis, age (P = 0.006), gender (P = 0.004), treatment response (P< 0.0001), and two CT texture parameters: mean (P = 0.027) and kurtosis (P= 0.002) were independent prognostic factors of survival. Interpretation and Conclusion: CT-derived tumor heterogeneity (mean and kurtosis) complements clinical factors for predicting survival in NSCLC patients treated with CRT. Tumor radiomics warrants further validation as potential prognostic biomarkers for these patients.

Machine-Learning-Based Radiomics for Classifying Glioma Grade from Magnetic Resonance Images of the Brain.

Grading of gliomas is a piece of critical information related to prognosis and survival. Classifying glioma grade by semantic radiological features is subjective, requires multiple MRI sequences, is quite complex and clinically demanding, and can very often result in erroneous radiological diagnosis. We used a radiomics approach with machine learning classifiers to determine the grade of gliomas. Eighty-three patients with histopathologically proven gliomas underwent MRI of the brain. Whenever available, immunohistochemistry was additionally used to augment the histopathological diagnosis. Segmentation was performed manually on the T2W MR sequence using the TexRad texture analysis softwareTM, Version 3.10. Forty-two radiomics features, which included first-order features and shape features, were derived and compared between high-grade and low-grade gliomas. Features were selected by recursive feature elimination using a random forest algorithm method. The classification performance of the models was measured using accuracy, precision, recall, f1 score, and area under the curve (AUC) of the receiver operating characteristic curve. A 10-fold cross-validation was adopted to separate the training and the test data. The selected features were used to build five classifier models: support vector machine, random forest, gradient boost, naive Bayes, and AdaBoost classifiers. The random forest model performed the best, achieving an AUC of 0.81, an accuracy of 0.83, f1 score of 0.88, a recall of 0.93, and a precision of 0.85 for the test cohort. The results suggest that machine-learning-based radiomics features extracted from multiparametric MRI images can provide a non-invasive method for predicting glioma grades preoperatively. In the present study, we extracted the radiomics features from a single cross-sectional image of the T2W MRI sequence and utilized these features to build a fairly robust model to classify low-grade gliomas from high-grade gliomas (grade 4 gliomas).

Primary Intracranial Germ Cell Tumors: A Study with an Integrated Clinicopathological Approach.

Background and Objective: Primary intracranial germ cell tumors (ICGCTs) are rare and are histologically classified as germinomas and non-germinomatous with distinctive prognostic and therapeutic implications. ICGCTs, essentially due to the inherent difficulty of surgical access, pose different challenges and management connotations than their extracranial counterparts. This is a retrospective analysis of histologically verified ICGCTs, which was undertaken to evaluate various clinicopathological features and their implications on patient management. Materials and Methods: Eighty-eight histologically diagnosed cases (over 14 years) of ICGCT at our institute formed the study cohort and were classified into germinoma and non-germinomatous germ cell tumors (NGGCTs). Additionally, germinomas were further subdivided on the basis of 1) tumor marker (TM) levels, as germinoma with normal TM, mildly elevated TM, and markedly elevated TM and 2) radiology features, as germinomas with typical radiology and atypical radiological features. Results: ICGCT with age ≤6 years (P = 0.049), elevated TM (P = 0.047), and NGGCT histology (P < 0.001) showed significantly worse outcomes. Furthermore, germinomas with markedly elevated TM and certain atypical radiological features showed prognosis akin to NGGCT. Conclusions: Analysis of our largest single cancer center Indian patient cohort of ICGCT shows that inclusion of age ≤6 years, raised TM, and certain radiological features may assist clinicians in overcoming the limitations of surgical sampling, with better prognostication of histologically diagnosed germinomas.

Imaging-Based Patterns of Failure following Re-Irradiation for Recurrent/Progressive High-Grade Glioma.

BACKGROUND: Re-irradiation (ReRT) is an effective treatment modality in appropriately selected patients with recurrent/progressive high-grade glioma (HGG). The literature is limited regarding recurrence patterns following ReRT, which was investigated in the current study. METHODS: Patients with available radiation (RT) contours, dosimetry, and imaging-based evidence of recurrence were included in the retrospective study. All patients were treated with fractionated focal conformal RT. Recurrence was detected on imaging with magnetic resonance imaging (MRI) and/ or amino-acid positron emission tomography (PET), which was co-registered with the RT planning dataset. Failure patterns were classified as central, marginal, and distant if >80%, 20-80%, or <20% of the recurrence volumes were within 95% isodose lines, respectively. RESULTS: Thirty-seven patients were included in the current analysis. A total of 92% of patients had undergone surgery before ReRT, and 84% received chemotherapy. The median time to recurrence was 9 months. Central, marginal, and distant failures were seen in 27 (73%), 4 (11%), and 6 (16%) patients, respectively. None of the patient-, disease-, or treatment-related factors were significantly different across different recurrence patterns. CONCLUSION: Failures are seen predominantly within the high-dose region following ReRT in recurrent/ progressive HGG.

Pearls & Oy-sters: Primary Diffuse Leptomeningeal Melanocytosis: A Diagnostic Conundrum.

Primary diffuse leptomeningeal melanocytosis (PDLM) is an extremely rare CNS tumor with nonspecific clinicoradiologic features that overlap considerably with aseptic meningitis posing significant diagnostic and therapeutic challenges. We present one such case report of a patient treated empirically at first presentation as aseptic viral meningitis based on MRI and CSF analysis. Diagnosis of PDLM was established subsequently through meningeal biopsy that demonstrated a melanocytic tumor with fine granular melanin pigment without significant mitoses. Her systemic and ocular examination was unremarkable. Whole-body 18F-fluorodeoxyglucose PET/CT (FDG-PET/CT) did not identify any other primary site. Following ventriculoperitoneal shunt to relieve hydrocephalus, she was treated with definitive craniospinal irradiation plus whole-brain boost and remains stable on periodic clinicoradiologic surveillance. Optimal management of PDLM lacks consensus with role of radiotherapy, chemotherapy, targeted therapy and immunotherapy being controversial.

Institutional Patterns of Care of Diffuse Intrinsic Pontine Glioma.

Background and Aim: Despite recent advances, the outcomes of diffuse intrinsic pontine glioma (DIPG) remain dismal. This is a retrospective study to understand the pattern of care and its impact on DIPG patients diagnosed over 5 years in a single institute. Subjects and Methods: DIPGs diagnosed between 2015 and 2019 were retrospectively reviewed to understand the demographics, clinical features, patterns of care, and outcomes. The usage of steroids and response to treatment were analyzed as per the available records and criteria. The re-irradiation cohort was propensity matched with patients with a progression-free survival (PFS) >6 months treated with supportive care alone based on PFS and age as a continuous variable. Survival analysis was performed using the Kaplan-Meier method, and Cox regression model was used to identify any potential prognostic factors. Results: One hundred and eighty-four patients were identified with demographic profiles similar to western population-based data in the literature. Of them, 42.4% were residents from outside the state of the institution. About 75.2% of patients completed their first radiotherapy treatment, of which only 5% and 6% had worsening clinical symptoms and persistent need for steroids 1 month posttreatment. On multivariate analysis, Lansky performance status <60 (P = 0.028) and cranial nerve IX and X (P = 0.026) involvement were associated with poor survival outcomes while receiving radiotherapy with better survival (P < 0.001). In the cohort of patients receiving radiotherapy, only re-irradiation (reRT) was associated with improved survival (P = 0.002). Conclusion: Many patient families still do not choose radiotherapy treatment, although it has a consistent and significant positive association with survival and steroid usage. reRT further improves outcomes in the selective cohorts. Involvement of cranial nerves IX and X needs improved care.

CT-Based Screening for Pulmonary Metastases in Head and Neck Squamous Cell Cancers: Diagnostic Accuracy and Cost Comparison with PET-CECT.

This study's objective was to compare detection rates of radiograph, computed tomography (CT), and positron emission tomography-contrast-enhanced computed tomography (PET-CECT) for pulmonary metastasis/synchronous primary lung tumors in head and neck squamous cell cancer (HNSCC) and its association with clinico-radio-pathological factors. Our retrospective study included 837 HNSCC patients from January 2012 to December 2017. Lung nodules were characterized on CT as benign, indeterminate, and metastatic. The true detection rate and statistical significance of associated risk factors were calculated. Risk factors for metastasis were determined using univariate and multivariate logistic regression models. Seventy-five (8.9%) patients had pulmonary metastasis and 3 (0.3%) had second lung primary. Detection rate of pulmonary metastasis by CT was higher (sensitivity-97.3%, specificity-97.2%) as compared to radiograph (sensitivity 49% and specificity 89%). Correlation was found between pulmonary and extra-pulmonary metastasis and N classification (P = 0.01, P = 0.02) and positive low jugular node (P = 0.001, P = 0.001). Using PET-CECT in place of CT costed an extra outlay of 7,033,805 INR (95,551.85 USD) while detecting distant metastasis in only 4 (0.47%) extra cases. Chest CT is a useful pulmonary metastases screening tool in advanced HNSCC patients with reasonable imaging cost as compared to PET-CT.

Predicting IDH subtype of grade 4 astrocytoma and glioblastoma from tumor radiomic patterns extracted from multiparametric magnetic resonance images using a machine learning approach.

Background and purpose: Semantic imaging features have been used for molecular subclassification of high-grade gliomas. Radiomics-based prediction of molecular subgroups has the potential to strategize and individualize therapy. Using MRI texture features, we propose to distinguish between IDH wild type and IDH mutant type high grade gliomas. Methods: Between 2013 and 2020, 100 patients were retrospectively analyzed for the radiomics study. Immunohistochemistry of the pathological specimen was used to initially identify patients for the IDH mutant/wild phenotype and was then confirmed by Sanger's sequencing. Image texture analysis was performed on contrast-enhanced T1 (T1C) and T2 weighted (T2W) MR images. Manual segmentation was performed on MR image slices followed by single-slice multiple sampling image augmentation. Both whole tumor multislice segmentation and single-slice multiple sampling approaches were used to arrive at the best model. Radiomic features were extracted, which included first-order features, second-order (GLCM-Grey level co-occurrence matrix), and shape features. Feature enrichment was done using LASSO (Least Absolute Shrinkage and Selection Operator) regression, followed by radiomic classification using Support Vector Machine (SVM) and a 10-fold cross-validation strategy for model development. The area under the Receiver Operator Characteristic (ROC) curve and predictive accuracy were used as diagnostic metrics to evaluate the model to classify IDH mutant and wild-type subgroups. Results: Multislice analysis resulted in a better model compared to the single-slice multiple-sampling approach. A total of 164 MR-based texture features were extracted, out of which LASSO regression identified 14 distinctive GLCM features for the endpoint, which were used for further model development. The best model was achieved by using combined T1C and T2W MR images using a Quadratic Support Vector Machine Classifier and a 10-fold internal cross-validation approach, which demonstrated a predictive accuracy of 89% with an AUC of 0.89 for each IDH mutant and IDH wild subgroup. Conclusion: A machine learning classifier of radiomic features extracted from multiparametric MRI images (T1C and T2w) provides important diagnostic information for the non-invasive prediction of the IDH mutant or wild-type phenotype of high-grade gliomas and may have potential use in either escalating or de-escalating adjuvant therapy for gliomas or for using targeted agents in the future.

Unleashing the Mystery of a Treated Case of Medulloblastoma.

Medulloblastoma (MB) is the most common malignant brain tumor in children. Despite advancement in treatment modalities, recurrence remains common, even among those treated with a combination of neurosurgery, craniospinal irradiation, and chemotherapy. The diagnosis of recurrence is usually not difficult in these cases. However, it may pose a challenge in cases with unusual clinical presentation and imaging. Imaging findings on magnetic resonance imaging, with application of perfusion, in conjunction with positron emission tomography-computed tomography can help in clinching the diagnosis in such cases. MB subgroups show consistent patterns even in cases of recurrence, and sonic hedgehog group MB may present as local recurrence showing enhancement with no diffusion restriction, as demonstrated in this case.