Rheumatoid arthritis (RA) is a systemic autoimmune disease of unknown etiology. We studied the diagnostic performances of anti-cyclic citrullinated peptides antibody (anti-CCP) assay and recombinant anti-citrullinated filaggrin antibody (AFA) assay by enzyme linked immunosorbent assay (ELISA) in patients with RA in Korea. Diagnostic performances of the anti-CCP assay and AFA assay were compared with that of rheumatoid factor (RF) latex fixation test. RF, anti-CCP, and AFA assays were performed in 324 RA patients, 251 control patients, and 286 healthy subjects. The optimal cut off values of each assay were determined at the maximal point of area under the curve by receiver-operator characteristics (ROC) curve. Sensitivity (72.8%) and specificity (92.0%) of anti-CCP were better than those of AFA (70.3%, 70.5%), respectively. The diagnostic performance of RF showed a sensitivity of 80.6% and a specificity of 78.5%. Anti-CCP and AFA showed positivity in 23.8% and 17.3% of seronegative RA patients, respectively. In conclusion, we consider that anti-CCP could be very useful serological assay for the diagnosis of RA, because anti-CCP revealed higher diagnostic specificity than RF and AFA at the optimal cut off values and could be performed by easy, convenient ELISA method.

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Knowledge-based outcome predictions are common before radiotherapy. Because there are various treatment techniques, numerous factors must be considered in predicting cancer patient outcomes. As expectations surrounding personalized radiotherapy using complex data have increased, studies on outcome predictions using artificial intelligence have also increased. Representative artificial intelligence techniques used to predict the outcomes of cancer patients in the field of radiation oncology include collecting and processing big data, text mining of clinical literature, and machine learning for implementing prediction models. Here, methods of data preparation and model construction to predict rates of survival and toxicity using artificial intelligence are described.

PurposeThe aim of this study is to develop predictive models to predict organ at risk (OAR) complication level, classification of OAR dose-volume and combination of this function with our in-house developed treatment decision support system.Materials and methodsWe analysed the support vector machine and decision tree algorithm for predicting OAR complication level and toxicity in order to integrate this function into our in-house radiation treatment planning decision support system. A total of 12 TomoTherapyTM treatment plans for prostate cancer were established, and a hundred modelled plans were generated to analyse the toxicity prediction for bladder and rectum.ResultsThe toxicity prediction algorithm analysis showed 91·0% accuracy in the training process. A scatter plot for bladder and rectum was obtained by 100 modelled plans and classification result derived. OAR complication level was analysed and risk factor for 25% bladder and 50% rectum was detected by decision tree. Therefore, it was shown that complication prediction of patients using big data-based clinical information is possible.ConclusionWe verified the accuracy of the tested algorithm using prostate cancer cases. Side effects can be minimised by applying this predictive modelling algorithm with the planning decision support system for patient-specific radiotherapy planning.

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