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BackgroundMR-Linac allows for daily online treatment adaptation to the observed geometry of tumor targets and organs at risk (OARs). Manual delineation for head and neck cancer (HNC) patients takes 45-75 minutes, making it unsuitable for online adaptive radiotherapy. This study aims to clinically and dosimetrically validate an in-house developed algorithm which automatically delineates the elective target volume and OARs for HNC patients in under a minute.MethodsAuto-contours were generated by an in-house model with 2D U-Net architecture trained and tested on 52 MRI scans via leave-one-out cross-validation. A randomized selection of 684 automated and manual contours (split half-and-half) was presented to an oncologist to perform a blind test and determine the clinical acceptability. The dosimetric impact was investigated for 13 patients evaluating the differences in dosage for all structures.ResultsAutomated contours were generated in 8 seconds per MRI scan. The blind test concluded that 114 (33%) of auto-contours required adjustments with 85 only minor and 15 (4.4%) of manual contours required adjustments with 12 only minor. Dosimetric analysis showed negligible dosimetric differences between clinically acceptable structures and structures requiring minor changes. The Dice Similarity coefficients for the auto-contours ranged from 0.66 ± 0.11 to 0.88 ± 0.06 across all structures.ConclusionMajority of auto-contours were clinically acceptable and could be used without any adjustments. Majority of structures requiring minor adjustments did not lead to significant dosimetric differences, hence manual adjustments were needed only for structures requiring major changes, which takes no longer than 10 minutes per patient.

Introduction: Diffusion-weighted image or DWI is commonly used to provide valuable and diverse information on acute stroke in tertiary care hospitals. DWI is a sensitive and accurate method for identifying the infarct core and can expose the area of cerebral infarction within a few hours of onset. This systematic review is planned to evaluate the measurement of stroke volume on DWI and correlate it with functional outcomes (modified ranking scale). Method: We have adhered to the PRISMA-P checklist to report this systematic review protocol. PubMed, Web of Science, Scopus, and TRIP (Turning Research into Practice) databases will be searched. Two independent reviewers will screen the records, extract data, and critically appraise the studies. A checklist for critical appraisal will be applied for data abstraction, and data extraction will be done using predictive modeling for systematic reviews. The risk of bias will be measured by the Prediction Model Risk of Bias Assessment Tool (PROBAST). The meta-analysis will be considered only if included studies have adequate data, and STATA statistical package version 13.1 will be used for performing a meta-analysis. A narrative synthesis will be performed if meta-analysis is not possible. Ethics and Dissemination: As this review will focus on secondary information, there is no ethical consideration required. We will disseminate our findings by publishing our analysis in a peer-reviewed journal. Protocol Registration: In Prospective Register of Systematic Reviews (CRD42019141840).

Abstract 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.

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.

Abstract Diabetes is a chronic disease or group of metabolic disease where a person suffers from an extended level of blood glucose in the body, which is either the insulin production is inadequate, or because the body’s cells do not respond properly to insulin. The constant hyperglycemia of diabetes is related to long-haul harm, brokenness, and failure of various organs, particularly the eyes, kidneys, nerves, heart, and veins. The objective of this research is to make use of significant features, design a prediction algorithm using Machine learning and find the optimal classifier to give the closest result comparing to clinical outcomes. The proposed method aims to focus on selecting the attributes that ail in early detection of Diabetes Miletus using Predictive analysis. The result shows the decision tree algorithm and the Random forest has the highest specificity of 98.20% and 98.00%, respectively holds best for the analysis of diabetic data. Naïve Bayesian outcome states the best accuracy of 82.30%. The research also generalizes the selection of optimal features from dataset to improve the classification accuracy.