This study aimed to describe the landscape of Immune checkpoint inhibitors (ICIs)-related adverse events (AEs) in a predominantly Chinese cohort. We searched electronic datasets including PubMed, Web of Science and Embase to identify and recruit relevant trials up to September 2, 2019. Clinical trials focusing on ICIs in Chinese patients or a predominantly Chinese population were included. Incidences of treatment-related AEs (TRAEs) and immune-related AEs (irAEs) were pooled and compared. In total, we recruited 13 trials consisting of 1063 patients, with 922 (86.7%) receiving ICI monotherapy and 141 (13.3%) receiving combination of ICI with chemotherapy or anti-angiogenesis. The pooled incidence of any grade TRAEs, grade 1–2, grade 3–5 TRAEs, any grade irAEs, grade 1–2 irAEs and grade 3–5 irAEs in all 1063 patients were 84.1%, 63.3%, 20.9%, 43.3%, 40.0% and 3.0%, respectively. Moreover, 4.3% (44/1018) of patients experienced treatment discontinuation and only 8 (0.8%) patients experienced treatment-related death. Compared to ICI monotherapy, combination significantly increased grade 3–5 TRAEs (46.1% vs. 17.0%, P < 0.001) and grade 3–5 irAEs (7.1% vs. 2.0%, P = 0.015). By comparing the toxicity profiles between different ICIs, we found some drug-specific AEs such as reactive capillary haemangiomas for camrelizumab (58.6%), hyperglycemia for toripalimab (55.6%) and pyrexia for tislelizumab (54.3%). Additionally, nivolumab has the lowest incidence of any grade (64.1%) and grade 3–5 (11.8%) TRAEs. ICI-related AEs were generally mild and tolerable for a predominantly Chinese cohort. However, we should pay attention to the combination of ICI with chemotherapy as it could increase grade 3–5 TRAEs and irAEs.
Background Radiotherapy is frequently used to treat head and neck Squamous cell carcinomas (HNSCC). Treatment outcomes being highly uncertain, there is a significant need for robust predictive tools to improvise treatment decision-making and better understand HNSCC by recognizing hidden patterns in data. We conducted this study to identify if Machine Learning (ML) could accurately predict outcomes and identify new prognostic variables in HNSCC. Method Retrospective data of 311 HNSCC patients treated with radiotherapy between 2013 and 2018 at our center and having a follow-up of at least three months' duration were collected. Binary-classification prediction models were developed for: Choice of Initial Treatment, Residual disease, Locoregional Recurrence, Distant Recurrence, and Development of New Primary. Clinical data were pre-processed using Imputation, Feature selection, Minority Oversampling, and Feature scaling algorithms. A method to retain original characteristics of dataset in testing samples while performing minority oversampling is illustrated. The classification comparison was performed using Random Forest (RF), Kernel Support Vector Machine (KSVM), and XGBoost classification algorithms for each model. Results For the choice of the initial treatment model, the testing accuracy was 84.58% using RF. The distant recurrence, locoregional recurrence, new-primary, and residual models had a testing accuracy (using KSVM) of 95.12%, 77.55%, 98.61%, and 92.25%, respectively. The important clinical determinants were identified using Shapely Values for each classification model, and the mean area under the curve (AUC) for the receiver operating curve was plotted. Conclusion ML was able to predict several clinically relevant outcomes, and with additional clinical validation, could facilitate recognition of novel prognostic factors in HNSCC.
A database management system is a constant application of science that provides a platform for the creation, movement, and use of voluminous data. The area has witnessed a series of developments and technological advancements from its conventional structured database to the recent buzzword, bigdata. This paper aims to provide a complete model of a relational database that is still being widely used because of its well known ACID properties namely, atomicity, consistency, integrity and durability. Specifically, the objective of this paper is to highlight the adoption of relational model approaches by bigdata techniques. Towards addressing the reason for this in corporation, this paper qualitatively studied the advancements done over a while on the relational data model. First, the variations in the data storage layout are illustrated based on the needs of the application. Second, quick data retrieval techniques like indexing, query processing and concurrency control methods are revealed. The paper provides vital insights to appraise the efficiency of the structured database in the unstructured environment, particularly when both consistency and scalability become an issue in the working of the hybrid transactional and analytical database management system.
Background Radiomics involves the extraction of quantitative information from annotated Computed-Tomography (CT) images, and has been used to predict outcomes in Head and Neck Squamous Cell Carcinoma (HNSCC). Subjecting combined Radiomics and Clinical features to Machine Learning (ML) could offer better predictions of clinical outcomes. This study is a comparative performance analysis of ML models with Clinical, Radiomics, and Clinico-Radiomic datasets for predicting four outcomes of HNSCC treated with Curative Radiation Therapy (RT): Distant Metastases, Locoregional Recurrence, New Primary, and Residual Disease. Methodology The study used retrospective data of 311 HNSCC patients treated with radiotherapy between 2013–2018 at our centre. Binary prediction models were developed for the four outcomes with Clinical-only, Clinico-Radiomic, and Radiomics-only datasets, using three different ML classification algorithms namely, Random Forest (RF), Kernel Support Vector Machine (KSVM), and XGBoost. The best-performing ML algorithms of the three dataset groups was then compared. Results The Clinico-Radiomic dataset using KSVM classifier provided the best prediction. Predicted mean testing accuracy for Distant Metastases, Locoregional Recurrence, New Primary, and Residual Disease was 97%, 72%, 99%, and 96%, respectively. The mean area under the receiver operating curve (AUC) was calculated and displayed for all the models using three dataset groups. Conclusion Clinico-Radiomic dataset improved the predictive ability of ML models over clinical features alone, while models built using Radiomics performed poorly. Radiomics data could therefore effectively supplement clinical data in predicting outcomes.
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