Background Diagnostic classification of diffuse gliomas now requires an assessment of molecular features, often including IDH-mutation and 1p19q-codeletion status. Because genetic testing requires an invasive process, an alternative noninvasive approach is attractive, particularly if resection is not recommended. The goal of this study was to evaluate the effects of training strategy and incorporation of biologically relevant images on predicting genetic subtypes with deep learning. Methods Our dataset consisted of 384 patients with newly-diagnosed gliomas who underwent preoperative MR imaging with standard anatomical and diffusion-weighted imaging, and 147 patients from an external cohort with anatomical imaging. Using tissue samples acquired during surgery, each glioma was classified into IDH-wildtype (IDHwt), IDH-mutant/1p19q-noncodeleted (IDHmut-intact), and IDH-mutant/1p19q-codeleted (IDHmut-codel) subgroups. After optimizing training parameters, top performing convolutional neural network (CNN) classifiers were trained, validated, and tested using combinations of anatomical and diffusion MRI with either a 3-class or tiered structure. Generalization to an external cohort was assessed using anatomical imaging models. Results The best model used a 3-class CNN containing diffusion-weighted imaging as an input, achieving 85.7% (95% CI:[77.1,100]) overall test accuracy and correctly classifying 95.2%, 88.9%, 60.0% of the IDHwt, IDHmut-intact, and IDHmut-codel tumors. In general, 3-class models outperformed tiered approaches by 13.5-17.5%, and models that included diffusion-weighted imaging were 5-8.8% more accurate than those that used only anatomical imaging. Conclusion Training a classifier to predict both IDH-mutation and 1p19q-codeletion status outperformed a tiered structure that first predicted IDH-mutation, then1p19q-codeletion. Including ADC, a surrogate marker of cellularity, more accurately captured differences between subgroups.
Background Differentiating treatment-induced injury from recurrent high-grade glioma is an ongoing challenge in neuro-oncology, in part due to lesion heterogeneity. This study aimed to determine whether different MR features were relevant for distinguishing recurrent tumor from the effects of treatment in contrast-enhancing lesions (CEL) and non-enhancing lesions (NEL). Methods This prospective study analyzed 291 tissue samples (222 recurrent tumor, 69 treatment-effect) with known coordinates on imaging from 139 patients who underwent preoperative 3T MRI and surgery for a suspected recurrence. 8 MR parameter values were tested from perfusion-weighted, diffusion-weighted, and MR spectroscopic imaging at each tissue sample location for association with histopathological outcome using generalized estimating equation models for CEL and NEL tissue samples. Individual cutoff values were evaluated using receiver operating characteristic curve analysis with 5-fold cross-validation. Results In tissue samples obtained from CEL, elevated relative cerebral blood volume (rCBV) was associated with the presence of recurrent tumor pathology (P < 0.03), while increases in normalized choline (nCho) and choline-to-NAA index (CNI) were associated with the presence of recurrent tumor pathology in NEL tissue samples (P < 0.008). A mean CNI cutoff value of 2.7 had the highest performance, resulting in mean sensitivity and specificity of 0.61 and 0.81 for distinguishing treatment-effect from recurrent tumor within the NEL. Conclusion Although our results support prior work that underscores the utility of rCBV in distinguishing the effects of treatment from recurrent tumor within the contrast enhancing lesion, we found that metabolic parameters may be better at differentiating recurrent tumor from treatment-related changes in the NEL of high-grade gliomas. Key Points 1. MR signatures that distinguish treatment effect and high-grade tumor vary spatially. 2. MR spectroscopic metrics within nonenhancing regions are the most predictive.
The psychological, emotional and social well-being of an individual determines their ability to contribute and function as a social member. Several studies over the years have proven that an alarming number of people live with mental illnesses, of which only a fraction is documented. Studies conducted by Open Sourcing Mental Illness (OSMI) organization have indicated that these figures are much higher in the tech industry. We present an analysis of patterns and infer contributory factors for mental illness in the tech industry, to aid in the early detection and assess employees’ risk of diagnosis. Towards this end, the study comprises a detailed analysis, models for prediction of diagnosis, risk-based clustering and investigation into existing literature on factors contributing to mental illness. In addition to this, we have attempted to understand the impact of Covid-19 through analyzing trends of the factors influencing mental health, pre- and post-pandemic. We conclude with an insight to the impact of the COVID-19 pandemic on global mental health and the actions taken in the workplace to mitigate this.
We present the Snehagram model, an applied psychosocial intervention for adolescents living with HIV/AIDS (ALWH), delivered through service learning (SL). The model is a synthesised blueprint of the ongoing intervention adopting a multi-component structure, addressing the overall development of ALWH through the integration of educational support, psychological interventions and research. Intervention outcomes include psychosocial skills, mental health and holistic education using research- and evidence-based practice to support ALWH with skills needed for re-integration and functioning in society post intra-community residence. The SL delivery framework parallelly benefits student trainees and the community, resulting in active participation via experiential learning and professional development. This unique approach to resource utilisation also makes it a viable and sustainable model in developing countries where resources are limited.
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