Predictive Solution for Radiation Toxicity Based on Big Data

Radiotherapy is a treatment method using radiation for cancer treatment based on a patient treatment planning for each radiotherapy machine. At this time, the dose, volume, device setting information, complication, tumor control probability, etc. are considered as a single-patient treatment for each fraction during radiotherapy process. Thus, these filed-up big data for a long time and numerous patients’ cases are inevitably suitable to produce optimal treatment and minimize the radiation toxicity and complication. Thus, we are going to handle up prostate, lung, head, and neck cancer cases using machine learning algorithm in radiation oncology. And, the promising algorithms as the support vector machine, decision tree, and neural network, etc. will be introduced in machine learning. In conclusion, we explain a predictive solution of radiation toxicity based on the big data as treatment planning decision support system

Prediction of Plaque Progression in Coronary Arteries Based on a Novel Hemodynamic Index Calculated From Virtual Stenosis Method

RationalePredicting the sites in coronary arteries that are susceptible to plaque deposition is essential for the development of clinical treatment strategies and prevention. However, to date, no physiological biomarkers for this purpose have been developed. We hypothesized that the possibility of plaque deposition at a specific site in the coronary artery is associated with wall shear stress (WSS) and fractional flow reserve (FFR).Background and ObjectiveWe proposed a new biomarker called the stenosis susceptibility index (SSI) using the FFR and WSS derived using virtual stenosis method. To validate the clinical efficacy of this index, we applied the method to actual pilot clinical cases. This index non-invasively quantifies the vasodilation effects of vascular endothelial cells relative to FFR variation at a specific coronary artery site.Methods and ResultsUsing virtual stenosis method, we computed maximum WSS and FFR according to the variation in stenotic severity at each potential stenotic site and then plotted the variations of maximum WSS (y-axis) and FFR (x-axis). The slope of the graph indicated a site-specific SSI value. Then we determined the most susceptible sites for plaque deposition by comparing SSI values between the potential sites. Applying this method to seven patients revealed 71.4% in per-patient basis analysis 77.8% accuracy in per-vessel basis analysis in percutaneous coronary intervention (PCI) site prediction.ConclusionThe SSI index can be used as a predictive biomarker to identify plaque deposition sites. Patients with relatively smaller SSI values also had a higher tendency for myocardial infarction. In conclusion, sites susceptible to plaque deposition can be identified using the SSI index

Prediction of Cancer Patient Outcomes Based on Artificial Intelligence

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

Simultaneous electrochemical detection of both PSMA (+) and PSMA (-) prostate cancer cells using an RNA/peptide dual-aptamer probe

Using an RNA/peptide dual-aptamer probe, both PSMA (+) and PSMA (-) prostate cancer cells were simultaneously detected by electrochemical impedance spectroscopy. This approach can be applied as a general tool for early diagnosis of prostate cancer.CATALONA WJ, 1993, JAMA-J AM MED ASSOC, V270, P948Lupold SE, 2002, CANCER RES, V62, P4029Kue PF, 2002, INT J CANCER, V102, P572, DOI 10.1002/ijc.10734Drummond TG, 2003, NAT BIOTECHNOL, V21, P1192, DOI 10.1038/nbt873DARAIN F, 2004, BIOSENS BIOELECTRON, V20, P856Ban CG, 2004, NUCLEIC ACIDS RES, V32, DOI 10.1093/nar/gnh109Ghosh A, 2004, J CELL BIOCHEM, V91, P528, DOI 10.1002/jcb.10661LEVIN MA, 2005, J UROLOGY, V159, P475Rodriguez MC, 2005, CHEM COMMUN, P4267, DOI 10.1039/b506571bZitzmann S, 2005, CLIN CANCER RES, V11, P139Horninger W, 2001, CANCER-AM CANCER SOC, V91, P1667Lang SH, 2001, BRIT J CANCER, V85, P590Yamamoto T, 2001, UROLOGY, V58, P994Palecek E, 2002, CRIT REV ANAL CHEM, V32, P261Narain V, 2002, CANCER METAST REV, V21, P17Edwards S, 2005, BRIT J CANCER, V92, P376, DOI 10.1038/sj.bjc.6602261Postma R, 2005, EUR J CANCER, V41, P825, DOI 10.1016/j.ejca.2004.12.029Cahova-Kucharikova K, 2005, ANAL CHEM, V77, P2920Rahman MA, 2005, ANAL CHEM, V77, P4854, DOI 10.1021/ac050558vCho M, 2006, NUCLEIC ACIDS RES, V34, DOI 10.1093/nar/gkl364Farokhzad OC, 2006, P NATL ACAD SCI USA, V103, P6315, DOI 10.1073/pnas.0601755103Chu TC, 2006, CANCER RES, V66, P5989, DOI 10.1158/0008-5472.CAN-05-4583McNamara JO, 2006, NAT BIOTECHNOL, V24, P1005, DOI 10.1038/nbt1223Palecek E, 1998, BIOSENS BIOELECTRON, V13, P621Min K, 2008, BIOSENS BIOELECTRON, V23, P1819, DOI 10.1016/j.bios.2008.02.021CHO M, 2008, BMB REPORTS, V41, P119Kim D, 2007, J AM CHEM SOC, V129, P7661, DOI 10.1021/ja071471pMaalouf R, 2007, ANAL CHEM, V79, P4879, DOI 10.1021/ac070085nKRAHN MD, 1994, JAMA-J AM MED ASSOC, V272, P773

Morphological and Electrochemical Properties of Crystalline Praseodymium Oxide Nanorods

Highly crystalline Pr6O11 nanorods were prepared by a simple precipitation method of triethylamine complex at 500°C. Synthesized Pr6O11 nanorods were uniformly grown with the diameter of 12–15 nm and the length of 100–150 nm without any impurities of unstable PrO2 phase. The Pr6O11 nanorod electrodes attained a high electrical conductivity of 0.954 Scm−1 with low activation energy of 0.594 eV at 850°C. The electrochemical impedance study showed that the resistance of electrode was significantly decreased at high temperature, which resulted from its high conductivity and low activation energy. The reduced impedance and high electrical conductivity of Pr6O11 nanorod electrodes are attributed to the reduction of grain boundaries and high space charge width

Comparison of self-reported and accelerometer-assessed measurements of physical activity according to socio-demographic characteristics in Korean adults

OBJECTIVES Previous studies have shown relatively low correlations between self-reported and accelerometer-assessed physical activity (PA). However, this association differs by socio-demographic factors, and this relationship has not been fully investigated in the general population. Thus, we investigated the correlation between self-reported and accelerometer-assessed PA and whether it differed by demographic and socioeconomic factors among the Korean general population. METHODS This cross-sectional study included 623 participants (203 men and 420 women) aged 30 to 64 years, who completed a PA questionnaire and wore a wrist-worn accelerometer on the non-dominant wrist for 7 days. We examined the agreement for metabolic equivalent task minutes per week (MET-min/wk) between the 2 measures and calculated Spearman correlation coefficients according to demographic and socioeconomic factors. RESULTS The kappa coefficient between tertiles of self-reported and accelerometer-assessed total MET-min/wk was 0.16 in the total population, suggesting overall poor agreement. The correlation coefficient between the 2 measurements was 0.26 (p<0.001) in the total population, and the correlation tended to decrease with increasing age (p for trend <0.001) and depression scores (p for trend <0.001). CONCLUSIONS We found a low correlation between self-reported and accelerometer-assessed PA among healthy Korean adults, and the correlation decreased with age and depression score. When studying PA using accelerometers and/or questionnaires, age and depression need to be considered, as should differences between self-reported and accelerometer-assessed PA

Comparison of partitioned survival modeling with state transition modeling approaches with or without consideration of brain metastasis: a case study of Osimertinib versus pemetrexed-platinum

Abstract Background The partitioned survival model (PSM) and the state transition model (STM) are widely used in cost-effectiveness analyses of anticancer drugs. Using different modeling approaches with or without consideration of brain metastasis, we compared the quality-adjusted life-year (QALY) estimates of Osimertinib and pemetrexed-platinum in advanced non-small cell lung cancer with epidermal growth factor receptor mutations. Methods We constructed three economic models using parametric curves fitted to patient-level data from the National Health Insurance Review and Assessment claims database from 2009 to 2020. PSM and 3-health state transition model (3-STM) consist of three health states: progression-free, post-progression, and death. The 5-health state transition model (5-STM) has two additional health states (brain metastasis with continuing initial therapy, and with subsequent therapy). Time-dependent transition probabilities were calculated in the state transition models. The incremental life-year (LY) and QALY between the Osimertinib and pemetrexed-platinum cohorts for each modeling approach were estimated over seven years. Results The PSM and 3-STM produced similar incremental LY (0.889 and 0.899, respectively) and QALY (0.827 and 0.840, respectively). However, 5-STM, which considered brain metastasis as separate health states, yielded a slightly higher incremental LY (0.910) but lower incremental QALY (0.695) than PSM and 3-STM. Conclusions Our findings indicate that incorporating additional health states such as brain metastases into economic models can have a considerable impact on incremental QALY estimates. To ensure appropriate health technology assessment decisions, comparison and justification of different modeling approaches are recommended in the economic evaluation of anticancer drugs