Tumors of the central nervous system (CNS) constitute approximately 2% of all malignancies. Although relatively rare, the associated morbidity and mortality and the significant proportion of affected young and middle-aged individuals has a major bearing on the death-adjusted life years compared to other malignancies. CNS tumors encompass a very broad spectrum with regards to age, location, histology, and clinical outcomes. Advances in diagnostic imaging, surgical techniques, radiotherapy equipment, and generation of newer chemotherapeutic and targeted agents over the past few years have helped improving treatment outcome. Further insights into the molecular pathways leading to the development of tumors made in the past decade are being incorporated into routine clinical practice. Several focused groups within India have been working on a range of topics related to CNS tumors, and a significant body of work from India, in the recent years, is being increasingly recognized throughout the world. The present article summarizes key published work with particular emphasis on gliomas and medulloblastoma, the two commonly encountered tumors.

Background: This study was conducted in order to develop a model for predicting response to neoadjuvant chemotherapy (NAC) in patients with locally advanced breast cancer (LABC) using pretreatment quantitative ultrasound (QUS) radiomics.Methods: This was a multicenter study involving four sites across North America, and appropriate approval was obtained from the individual ethics committees. Eighty‐two patients with LABC were included for final analysis. Primary tumors were scanned using a clinical ultrasound system before NAC was started. The tumors were contoured, and radiofrequency data were acquired and processed from whole tumor regions of interest. QUS spectral parameters were derived from the normalized power spectrum, and texture analysis was performed based on six QUS features using a gray level co‐occurrence matrix. Patients were divided into responder or nonresponder classes based on their clinical‐pathological response. Classification analysis was performed using machine learning algorithms, which were trained to optimize classification accuracy. Cross‐validation was performed using a leave‐one‐out cross‐validation method.Results: Based on the clinical outcomes of NAC treatment, there were 48 responders and 34 nonresponders. A K‐nearest neighbors (K‐NN) approach resulted in the best classifier performance, with a sensitivity of 91%, a specificity of 83%, and an accuracy of 87%.Conclusion: QUS‐based radiomics can predict response to NAC based on pretreatment features with acceptable accuracy.

Novel biological insights have led to consensus classification of medulloblastoma into 4 distinct molecular subgroups—wingless (WNT), sonic hedgehog (SHH), Group 3, and Group 4. We aimed to predict molecular subgrouping in medulloblastoma based on preoperative multiparametric magnetic resonance imaging (MRI) characteristics. A set of 19 MRI features were evaluated in 111 patients with histologic diagnosis of medulloblastoma for prediction of molecular subgrouping. MRI characteristics were correlated with molecular subgroups derived from tissue samples in 111 patients (WNT = 17, SHH = 44, Group 3 = 27, and Group 4 = 23). Multinomial logistic regression of imaging parameters was performed on a training cohort (TC) of 76 patients, representing two-thirds of randomly selected patients from each of 4 molecular subgroups, to generate binary nomograms. Validation of these nomograms was performed on the remaining 35 patients as the validation cohort (VC). Medulloblastoma subgroups could be accurately predicted by preoperative MRI features in 74% of cases. Predictive accuracy was excellent for SHH (95%), acceptably high for Group 4 (78%), modest for Group 3 (56%) and worst for WNT (41%) subgroup medulloblastoma. SHH-specific nomogram was associated with excellent correlation between TC and VC, with area under the curve (AUC) of 0.939 and 0.991, respectively. AUC for Group 4 was acceptable at 0.851 and 0.788 in TC and VC, respectively; however, these values were consistently suboptimal in WNT and Group 3 medulloblastoma. The predictive accuracy of MRI-based nomograms was excellent for SHH and encouraging for Group 4 medulloblastoma. Further work is needed for Group 3 and WNT-pathway medulloblastoma.

Neoadjuvant chemotherapy (NAC) is the standard of care for patients with locally advanced breast cancer (LABC). The study was conducted to investigate the utility of quantitative ultrasound (QUS) carried out during NAC to predict the final tumour response in a multi-institutional setting. Fifty-nine patients with LABC were enrolled from three institutions in North America (Sunnybrook Health Sciences Centre (Toronto, Canada), MD Anderson Cancer Centre (Texas, USA), and Princess Margaret Cancer Centre (Toronto, Canada)). QUS data were collected before starting NAC and subsequently at weeks 1 and 4 during chemotherapy. Spectral tumour parametric maps were generated, and textural features determined using grey-level co-occurrence matrices. Patients were divided into two groups based on their pathological outcomes following surgery: responders and non-responders. Machine learning algorithms using Fisher’s linear discriminant (FLD), K-nearest neighbour (K-NN), and support vector machine (SVM-RBF) were used to generate response classification models. Thirty-six patients were classified as responders and twenty-three as non-responders. Among all the models, SVM-RBF had the highest accuracy of 81% at both weeks 1 and week 4 with area under curve (AUC) values of 0.87 each. The inclusion of week 1 and 4 features led to an improvement of the classifier models, with the accuracy and AUC from baseline features only being 76% and 0.68, respectively. QUS data obtained during NAC reflect the ongoing treatment-related changes during chemotherapy and can lead to better classifier performances in predicting the ultimate pathologic response to treatment compared to baseline features alone.

At our institution, we have historically treated brain metastasis (BM) ≤2 cm in eloquent brain with a radiosurgery (SRS) lower prescription dose (PD) to reduce the risk of radionecrosis (RN). We sought to evaluate the impact of this practice on outcomes. We analyzed a prospective registry of BM patients treated with SRS between 2008 and 2017. Incidences of local failure (LF) and RN were determined and Cox regression was performed for univariate and multivariate analyses (MVAs). We evaluated 1533 BM ≤2 cm. Median radiographic follow-up post SRS was 12.7 months (1.4–100). Overall, the 2-year incidence of LF was lower for BM treated with PD ≥21 Gy (9.3%) compared with PD ≤15 Gy (19.5%) (sub–hazard ratio, 2.3; 95% CI: 1.4–3.7; P = 0.0006). The 2-year incidence of RN was not significantly higher for the group treated with PD ≥21 Gy (9.5%) compared with the PD ≤15 Gy group (7.5%) (P = 0.16). MVA demonstrated that PD (≤15 Gy) and tumor size (>1 cm) were significantly correlated (P < 0.05) with higher rates of LF and RN, respectively. For tumors ≤1 cm, when comparing PD ≤15 Gy with ≥21 Gy, the risks of LF and RN are equivalent. However, for lesions >1 cm, PD ≥21 Gy is associated with a lower incidence of LF without significantly increasing the risk of RN. Our results indicate that rates of LF or RN following SRS for BM are strongly correlated with size and PD. Based on our results, we now, depending upon the clinical context, consider increasing PD to 21 Gy for BM in eloquent brain, excluding the brainstem.