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Background: Glioblastoma (GBM) and primary central nervous system lymphoma (PCNSL) are common in elderly yet difficult to differentiate on MRI. Their management and prognosis are quite different. Recent surge of interest in predictive analytics, using machine learning (ML) from radiomic features and deep learning (DL) for diagnosing, predicting response and prognosticating disease has evinced interest among radiologists and clinicians. The objective of this systematic review and meta-analysis was to evaluate the deep learning & ML algorithms in classifying PCNSL from GBM. Methods: The authors performed a systematic review of the literature from MEDLINE, EMBASE and the Cochrane central trials register for the search strategy in accordance with PRISMA guidelines to select and evaluate studies that included themes of ML, DL, AI, GBM, PCNSL. All studies reporting on ML algorithms or DL that for differentiating PCNSL from GBM on MR imaging were included. These studies were further narrowed down to focus on works published between 2018 and 2021. Two researchers independently conducted the literature screening, database extraction and risk bias assessment. The extracted data was synthesised and analysed by forest plots. Outcomes assessed were test characteristics such as accuracy, sensitivity, specificity and balanced accuracy. Results: Ten articles meeting the eligibility criteria were identified addressing use of ML and DL in training and validation classifiers to distinguish PCNSL from GBM on MR imaging. The total sample size was 1311 in the included studies. ML approach was used in 6 studies while DL in 4 studies. The lowest reported sensitivity was 80%, while the highest reported sensitivity was 99% in studies in which ML and DL was directly compared with the gold standard histopathology. The lowest reported specificity was 87% while the highest reported specificity was 100%. The highest reported balanced accuracy was 100% and the lowest was 84%. Conclusions: Extensive search of the database revealed a limited number of studies that have applied ML or DL to differentiate PCNSL from GBM. Of the currently published studies, Both DL & ML algorithms have demonstrated encouraging results and certainly have the potential to aid neurooncologists in taking preoperative decisions in the future leading to not only reduction in morbidities but also be cost effective.
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INTRODUCTION: Management of a clinical condition is patient centric. Interdisciplinary coordination plays an influential role in patient management. Pharmacology deals with the study of drugs. Clinical pharmacology deals with applied aspects of pharmacology in addition to clinical research. METHODOLOGY: We set up a survey to assess the perceptions of clinical pharmacologists (CPs) regarding their roles and about clinical pharmacology courses in India. The survey was administered via a Google questionnaire sent via LinkedIn, Email, or WhatsApp to 100 CP's working in India. RESULTS: Respondents to the questionnaire were working as CPs. They were either postgraduate in pharmacology (MD pharmacology (doctor of medicine in pharmacology) 60.2%) or had a super-specialization degree in clinical pharmacology (DM clinical pharmacology (doctorate in medicine in clinical pharmacology) (34.7%)) or other pharma postgraduates. They were working in pharmaceutical companies (41.8%), hospitals (26.5%), or academic institutions (30.6%). When the responses from the respondents were stratified by qualification or experience, they showed that most of the CPs felt that the CPs played a significant role in academia, pharmaceutical organizations, hospitals, and drug regulatory bodies. CONCLUSIONS: All the CPs opined that training during the postgraduation course was not sufficient to be qualified as CP. There was no consensus among the CPs on the benefit of existing certification courses in clinical research. However, they felt that the centres offering these courses should be accredited, and the curriculum should be uniform. Respondents opined that CPs' patient management role could be improved by collaborating with clinicians and organizing workshops and conferences.