Tata Memorial Centre Evidence Based Management of Breast cancer.

ABSTRACT: The incidence of breast cancer is increasing rapidly in urban India due to the changing lifestyle and exposure to risk factors. Diagnosis at an advanced stage and in younger women are the most concerning issues of breast cancer in India. Lack of awareness and social taboos related to cancer diagnosis make women feel hesitant to seek timely medical advice. As almost half of women develop breast cancer at an age younger than 50 years, breast cancer diagnosis poses a huge financial burden on the household and impacts the entire family. Moreover, inaccessibility, unaffordability, and high out-of-pocket expenditure make this situation grimmer. Women find it difficult to get quality cancer care closer to their homes and end up traveling long distances for seeking treatment. Significant differences in the cancer epidemiology compared to the west make the adoption of western breast cancer management guidelines challenging for Indian women. In this article, we intend to provide a comprehensive review of the management of breast cancer from diagnosis to treatment for both early and advanced stages from the perspective of low-middle-income countries. Starting with a brief introduction to epidemiology and guidelines for diagnostic modalities (imaging and pathology), treatment has been discussed for early breast cancer (EBC), locally advanced, and MBC. In-depth information on loco-regional and systemic therapy has been provided focusing on standard treatment protocols as well as scenarios where treatment can be de-escalated or escalated.

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.

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.

Primary Neuroendocrine Tumor of Prostate in a Case of Metastatic Adenocarcinoma of Lung: Rare Entity with Histopathological and Gallium 68 DOTANOC Positron Emission Tomography Correlation.

Neuroendocrine tumor (NET) of the prostate is an extremely rare entity which represents <1% of the prostatic cancers, but with increasing incidence. Its spectrum encompasses several histological variants ranging from well-differentiated tumor which are often indolent in nature; to aggressive neuroendocrine carcinoma which portends aggressive management. Hence, such rare entities are to be characterized and treated accordingly. We report an unusual case of well-differentiated NET of prostate which was flagged on fluorodeoxyglucose positron emission tomography computed tomography (PET/CT) performed for other indication and confirmed on Gallium-68 DOTANOC PET/CT. Histopathology and immunohistochemistry confirmed the findings subsequently.

Validation of Dual-Color Dual In Situ Hybridization for HER2/neu Gene in Breast Cancer.

CONTEXT: Human epidermal growth factor (HER2/neu) gene amplification, a poor prognostic factor in invasive breast cancer, has shown substantial utility as a predictive marker, with significantly improved survival following anti-HER2 therapies like trastuzumab. Dual-color dual in situ hybridization (D-DISH), a recently introduced fully automated assay for HER2/neu evaluation on light microscopy, has several advantages over fluorescence in situ hybridization (FISH). OBJECTIVE: To standardize and validate the D-DISH assay using FISH as the gold standard and assess interobserver reproducibility in interpreting the D-DISH assay. DESIGN: D-DISH was performed using the latest HER2 Dual ISH DNA Probe Cocktail assay (Ventana Medical Systems Inc, Tucson, Arizona) in 148 cases of invasive breast cancer. The same block was used for performing immunohistochemistry by Ventana PATHWAY anti-HER2/neu (4B5) antibody and FISH assay by ZytoLight SPEC ERBB2/CEN17 Dual Color Probe. D-DISH was separately interpreted by 4 pathologists blinded to FISH results. RESULTS: Concordance of 98.65% and a Cohen κ value of 0.97 were observed between FISH and D-DISH. Intraclass correlation coefficient (0.93-0.97) and κ values (0.98-1.0) for interobserver reproducibility showed almost perfect agreement by D-DISH. Interobserver reproducibility was also evaluated for genomic heterogeneity, HER2 group categorization, and polysomy (κ values 0.42-0.74, 0.89-0.93, and 0.98-1.0, respectively). CONCLUSIONS: We successfully validated the latest version of D-DISH assay as a substitute for FISH in predicting HER2 gene status with significant interobserver reproducibility, concluding that this D-DISH assay may be introduced in routine diagnostic services as a reflex test to ascertain HER2 gene status.

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.

Validation of Remote Digital Pathology based diagnostic reporting of Frozen Sections from home.

Background: Despite the promising applications of whole-slide imaging (WSI) for frozen section (FS) diagnosis, its adoption for remote reporting is limited. Objective: To assess the feasibility and performance of home-based remote digital consultation for FS diagnosis. Material & Method: Cases accessioned beyond regular working hours (5 pm-10 pm) were reported simultaneously using optical microscopy (OM) and WSI. Validation of WSI for FS diagnosis from a remote site, i.e. home, was performed by 5 pathologists. Cases were scanned using a portable scanner (Grundium Ocus®40) and previewed on consumer-grade computer devices through a web-based browser (http://grundium.net). Clinical data and diagnostic reports were shared through a google spreadsheet. The diagnostic concordance, inter- and intra-observer agreement for FS diagnosis by WSI versus OM, and turnaround time (TAT), were recorded. Results: The overall diagnostic accuracy for OM and WSI (from home) was 98.2% (range 97%-100%) and 97.6% (range 95%-99%), respectively, when compared with the reference standard. Almost perfect inter-observer (k = 0.993) and intra-observer (k = 0.987) agreement for WSI was observed by 4 pathologists. Pathologists used consumer-grade laptops/desktops with an average screen size of 14.58 inches (range = 12.3-17.7 inches) and a network speed of 64 megabits per second (range: 10-90 Mbps). The mean diagnostic assessment time per case for OM and WSI was 1:48 min and 5:54 min, respectively. Mean TAT of 27.27 min per case was observed using WSI from home. Seamless connectivity was observed in approximately 75% of cases. Conclusion: This study validates the role of WSI for remote FS diagnosis for its safe and efficient adoption in clinical use.

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.

Sporadic Cerebellar Hemangioblastoma With Strong SSTR Expression on 68 Ga-DOTANOC PET/CT.

ABSTRACT: Sporadic cerebellar hemangioblastomas are rare with majority of these tumors presenting as a part of von Hippel-Lindau syndrome. We demonstrate an unusual case of a symptomatic sporadic cerebellar hemangioblastoma mimicking a meningioma on MRI and 68 Ga-DOTANOC PET imaging.

Synchronous and Metachronous Breast and Ovarian Cancers: Experience from a Single Tertiary Care Cancer Centre in India.

Women with either breast cancer (BC) or ovarian cancer (OC) have a 1.5-2 times higher risk of developing the other. Discerning discrete primaries versus metastases from either can be challenging. Clinico-pathological and outcome details of patients diagnosed with both BC and OC from December 1994 to August 2018 were retrospectively evaluated at a single tertiary cancer centre. We report the pattern of presentation and recurrences with case-based illustrations. Out of 139 patients, presentation was BC-first in 66.2%, OC-first in 24.5% and synchronous cancers (SC) in 9.3% of women. The median age at diagnosis in BC-first, OC-first and SC was 42 years, 48 years and 49 years, respectively. The most common histological subtype was invasive breast carcinoma-no special type (74.8%) in BC and serous cystadenocarcinoma (81.3%) in OC. BC presented at an early stage in 67.6% while OC presented at an advanced stage in 48.2% of patients. Germline mutation results were available in 82% with 61.4% of the cohort exhibiting a mutation- BRCA1 mutation being the most common. The median time to development of second cancer was 77.4 months and 39.4 months in BC-first and OC-first, respectively. At a median follow-up of 9.47 years, disease-free survival was 32.6%, 32.4% and 30.8% in BC-first, OC-first and SC, respectively (p < 0.001). In hereditary breast and ovarian cancer, BC-first patients have a better prognosis while synchronous malignancies have worse oncological outcomes. Deaths are mainly due to OC progression. Appropriate surveillance and prophylactic intervention in young patients with breast cancer may improve overall outcomes.

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.

Paediatric type diffuse high grade gliomas in the WHO CNS5 classification: What the pathologist needs to know?

Pediatric-type of diffuse high-grade gliomas (HGG) are classified as a distinct group in the current fifth edition of WHO classification. This group of high-grade tumors is no more called as glioblastoma (GBM), which has been reserved for adult isocitrate dehydrogenase (IDH)-wild type HGG. These tumors are uncommon as compared to embryonal tumors and low-grade gliomas (LGG). Pediatric-type of diffuse HGG biologically differs from their adult counterparts in that they are therapeutically less sensitive to alkylating chemotherapies. They comprise a heterogeneous group of molecularly defined tumors - predominantly histone gene altered, less common receptor tyrosine kinase (RTK)-mediated, and syndrome-associated. This review provides an overview of these uncommon tumors and discusses the diagnostic approach of this heterogeneous group of tumors.

Machine-learning approach to predict molecular subgroups of medulloblastoma using multiparametric MRI-based tumor radiomics.

OBJECTIVE: Image-based prediction of molecular subgroups of Medulloblastoma (MB) has the potential to optimize and personalize therapy. The objective of the study is to distinguish between broad molecular subgroups of MB using MR-Texture analysis. METHODS: Thirty-eight MB patients treated between 2007 and 2020 were retrospectively analyzed. Texture analysis was performed on contrast enhanced T1(T1C) and T2 weighted (T2W) MR images. Manual segmentation was performed on all slices and radiomic features were extracted which included first order, second order (GLCM - Grey level co-occurrence matrix) and shape features. Feature enrichment was done using LASSO (Least Absolute Shrinkage and Selection Operator) regression and thereafter Support Vector Machine (SVM) and a 10-fold cross-validation strategy was used for model development. The area under Receiver Operator Characteristic (ROC) curve was used to evaluate the model. RESULTS: A total of 174 and 170 images were obtained for analysis from the Axial T1C and T2W image datasets. One hundred and sixty-four MR based texture features were extracted. The best model was arrived at by using a combination of 30 GLCM and six shape features on T1C MR sequence. A 10-fold cross-validation demonstrated an AUC of 0.93, 0.9, 0.93, and 0.93 in predicting WNT, SHH, Group 3, and Group 4 MB subgroups, respectively. CONCLUSION: Radiomic analysis of MR images in MB can predict molecular subgroups with acceptable degree of accuracy. The strategy needs further validation in an external dataset for its potential use in ab initio management paradigms of MBs. ADVANCES IN KNOWLEDGE: Medulloblastoma can be classified into four distinct molecular subgroups using radiomic feature classifier from non-invasive Multiparametric Magnetic resonance imaging. This can have future ramifications in the extent of surgical resection of Medulloblastoma which can ultimately result in reduction of morbidity.

Phyllodes Tumor as a Secondary Neoplasm in Survivors of Childhood Cancer: A Tertiary Care Cancer Centre Experience.

Secondary neoplasms (SNs) are being increasingly identified in long-term survivors of childhood cancer. Phyllodes tumor (PT) form a distinctly uncommon SN. We report a series of 6 female childhood cancer survivors who developed PT as SN. The median age at primary diagnosis was 13 years. Their primary tumors were bone sarcoma (4) and acute leukemia (2), and all were treated with chemotherapy, predominantly with alkylating agents and/or anthracyclines. None had received direct radiotherapy to the chest wall. Subsequently, PT were detected after a median interval of 7.5 years, with 2 patients developing bilateral and malignant PT. The series highlights a rare SN in childhood cancer survivors, underscoring the importance of regular long-term follow-up.

Optimizing Postoperative Adjuvant Therapy in Elderly Patients with Newly Diagnosed Glioblastoma: Single-Institution Audit of Clinical Outcomes from a Tertiary-Care Comprehensive Cancer Center in India.

BACKGROUND: There is lack of consensus regarding optimal adjuvant therapy in elderly glioblastoma (GBM). We have been treating elderly (≥60 years) GBM patients with normofractionated or hypofractionated radiotherapy (RT) plus temozolomide (TMZ) based on Karnofsky performance status (KPS). Herein we report clinical outcomes in this cohort treated at our institute using this approach. METHODS: Medical records of elderly GBM patients (≥60 years) treated between 2013 and 2017 with either normofractionated RT (59.4-60 Gy/30-33 fractions/6-6.5 weeks) or hypofractionated RT (35 Gy/10 fractions/2 weeks) plus TMZ were reviewed retrospectively. Outcomes of interest included progression-free survival (PFS), overall survival (OS), and ≥grade 3 myelotoxicity. Time-to-event outcomes were analyzed with Kaplan-Meier methods, compared using log-rank test, and reported as point estimates with 95% confidence interval (CI). RESULTS: The normofractionated cohort (n = 126) was characterized by a higher proportion of patients younger than age 65 years, KPS ≥70, methylated O6-methylguanine DNA methyltransferase (MGMT), and receiving adjuvant TMZ including extended adjuvant TMZ (>6 cycles) compared with the hypofractionated cohort (n = 20), confirming selection bias. At a median follow-up of 13 months, 1-year Kaplan-Meier estimates of PFS and OS were 43% (95% CI: 36%-52%) and 56% (95% CI: 48%-64%), yielding median PFS and OS of 11.0 months and 13.1 months, respectively. Higher KPS, methylated MGMT, normofractionated RT, and extended adjuvant TMZ emerged as favorable prognostic factors. TMZ was well tolerated with a low risk of ≥grade 3 myelotoxicity. CONCLUSIONS: Our single-institution clinical audit confirms poor survival in elderly GBM with suboptimal performance status but demonstrates acceptably fair outcomes in patients with preserved KPS comparable with the nonelderly cohort.

Prognostic impact of semantic MRI features on survival outcomes in molecularly subtyped medulloblastoma.

PURPOSE: Imaging features are known to reflect inherent disease biology in various cancers including brain tumors. We report on the prognostic impact of magnetic resonance imaging (MRI) features on survival in patients with medulloblastoma treated between 2007 and 2018 at our institute. METHODS: Sixteen semantic imaging features (with predefined categories) were extracted from pre- and postcontrast T1-weighted and T2-weighted MRI by consensus. Univariate analysis and multivariate Cox regression analysis were performed to assess the correlation of semantic features with relapse-free survival (RFS) and overall survival (OS). RESULTS: The study cohort comprised 171 medulloblastoma patients (median age 9 years) treated with maximal safe resection followed by risk-stratified adjuvant radio(chemo)therapy. A total of 55 patients experienced recurrent/progressive disease (commonly neuraxial metastases) resulting in 44 deaths, including one treatment-related death. At a median follow-up of 45 months (interquartile range 19-65 months), 5­year Kaplan-Meier estimates of RFS and OS were 64% and 71%, respectively. Semantic MRI features such as non-central tumor location on vertical axis, absence of brainstem involvement, ≤ 80% solid tumor area with contrast uptake, heterogenous pattern of contrast enhancement, necrosis, calcification, and T2-weighted heterogeneity were associated with significantly worse RFS and/or OS in univariate analysis. Cox regression analysis identified tumor location on the vertical axis, brainstem involvement, and calcification as independent prognostic factors impacting outcomes. Distinctive MRI features correlated with survival even within individual molecular subgroups of medulloblastoma. CONCLUSION: Distinctive semantic MRI features correlate significantly with survival outcomes in medulloblastoma, also within individual molecular subgroups, reflecting their prognostic impact.