Purpose The current study provides national cancer statistics and their secular trends in Korea, including incidence, mortality, survival, and prevalence in 2018.Materials and Methods Incidence, survival, and prevalence rates of cancer were calculated using the Korea National Cancer Incidence Database, from 1999 to 2018, with survival follow-up until December 31, 2019. Deaths from cancer were assessed using causes-of-death data obtained from Statistics Korea. Crude and age-standardized rates (ASRs) for incidence, mortality, prevalence, and 5-year relative survival rates were calculated, and temporal trends for incidence and mortality rates were evaluated, with annual percentage changes.Results In 2018, newly diagnosed cancer cases and deaths from cancer were reported as 243,837 (ASR, 270.4 per 100,000) and 79,153 (ASR, 73.3 per 100,000), respectively. The overall cancer incidence rates increased by 3.3% annually from 1999 to 2012, and decreased by 5.4% annually from 2012 to 2015, thereafter, followed by nonsignificant changes. Cancer mortality rates have been decreasing since 2002, with more rapid decline in recent years (annual decrease of 2.7% from 2007 to 2014; 3.7% from 2014 to 2018). The 5-year relative survival between 2014 and 2018 was 70.3%, which contributed to prevalent cases reaching over 2 million by the end of 2018.Conclusion Cancer statistics have improved significantly during the past two decades. However, there remain important challenges to be solved, such as controlling cancers with low survival rates. Cancer statistics can be used to discover blind spots in cancer control, and as evidence for developing and implementing future cancer control strategies.

A facile one-pot synthesis of d-glucuronic acid-coated ultrasmall Ln(2)O(3) (Ln = Eu, Gd, Dy, Ho, and Er) nanoparticles is presented. Their water proton relaxivities were studied to address their possibility as a new potential MRI contrast agent. We focused on the d-glucuronic acid-coated ultrasmall Dy(2)O(3) nanoparticle because it showed the highest r(2) relaxivity among studied nanoparticles. Its performance as a T(2) MRI contrast agent was for the first time proved in vivo through its 3 T T(2) MR images of a mouse, showing that it can be further exploited for the rational design of a new T(2) MRI contrast agent at high MR fields.

The current study provides national cancer statistics and their secular trends in Korea, including incidence, mortality, survival, and prevalence in 2019. Materials and MethodsIncidence, survival, and prevalence rates of cancer were calculated using the Korea National

FGF19 protein expression might be an effective predictor of early recurrence and a marker for poor prognosis of hepatocellular carcinoma after curative hepatectomy, indicating that FGF19 might be a potential preventive target in hepatocellular carcinoma patients.

Background Visual inspection, lesion detection, and differentiation between malignant and benign features are key aspects of an endoscopist’s role. The use of machine learning for the recognition and differentiation of images has been increasingly adopted in clinical practice. This study aimed to establish convolutional neural network (CNN) models to automatically classify gastric neoplasms based on endoscopic images. Methods Endoscopic white-light images of pathologically confirmed gastric lesions were collected and classified into five categories: advanced gastric cancer, early gastric cancer, high grade dysplasia, low grade dysplasia, and non-neoplasm. Three pretrained CNN models were fine-tuned using a training dataset. The classifying performance of the models was evaluated using a test dataset and a prospective validation dataset. Results A total of 5017 images were collected from 1269 patients, among which 812 images from 212 patients were used as the test dataset. An additional 200 images from 200 patients were collected and used for prospective validation. For the five-category classification, the weighted average accuracy of the Inception-Resnet-v2 model reached 84.6 %. The mean area under the curve (AUC) of the model for differentiating gastric cancer and neoplasm was 0.877 and 0.927, respectively. In prospective validation, the Inception-Resnet-v2 model showed lower performance compared with the endoscopist with the best performance (five-category accuracy 76.4 % vs. 87.6 %; cancer 76.0 % vs. 97.5 %; neoplasm 73.5 % vs. 96.5 %; P  < 0.001). However, there was no statistical difference between the Inception-Resnet-v2 model and the endoscopist with the worst performance in the differentiation of gastric cancer (accuracy 76.0 % vs. 82.0 %) and neoplasm (AUC 0.776 vs. 0.865). Conclusion The evaluated deep-learning models have the potential for clinical application in classifying gastric cancer or neoplasm on endoscopic white-light images.

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A facile one-pot synthesis of a water-soluble MnO nanocolloid (i.e., D-glucuronic acid-coated MnO nanoparticle) is presented. The MnO nanoparticle in the MnO nanocolloid was coated with a biocompatible and hydrophilic D-glucuronic acid, and its particle diameter was nearly monodisperse and ranged from 2 to 3 nm. The average hydrodynamic diameter of the MnO nanocolloid was estimated to be 5 nm. The MnO nanoparticle was nearly paramagnetic down to T=3 K. The MnO nanocolloid showed a high longitudinal water proton relaxivity of r1=7.02 s(-1) mM(-1) with the r2/r1 ratio of 6.83 due to five unpaired S-state electrons of Mn(II) ion (S=5/2) as well as a high surface to volume ratio of the MnO nanoparticle. High contrast in vivo T1 MR images were obtained for various organs, showing the capability of the MnO nanocolloid as a sensitive T1 MRI contrast agent. The suggested three key-parameters which control the r1 and r2 relaxivities of nanocolloids (i.e., the S value of a metal ion, the spin structure, and the surface to volume ratio of a nanoparticle) successfully accounted for the observed r1 and r2 relaxivities of the MnO nanocolloid.

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