• Korean Journal of Radiology
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• v.25 no.6
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• pp.550-558
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• 2024

Hepatocellular carcinoma (HCC) is a biologically heterogeneous tumor characterized by varying degrees of aggressiveness. The current treatment strategy for HCC is predominantly determined by the overall tumor burden, and does not address the diverse prognoses of patients with HCC owing to its heterogeneity. Therefore, the prognostication of HCC using imaging data is crucial for optimizing patient management. Although some radiologic features have been demonstrated to be indicative of the biologic behavior of HCC, traditional radiologic methods for HCC prognostication are based on visually-assessed prognostic findings, and are limited by subjectivity and inter-observer variability. Consequently, artificial intelligence has emerged as a promising method for image-based prognostication of HCC. Unlike traditional radiologic image analysis, artificial intelligence based on radiomics or deep learning utilizes numerous image-derived quantitative features, potentially offering an objective, detailed, and comprehensive analysis of the tumor phenotypes. Artificial intelligence, particularly radiomics has displayed potential in a variety of applications, including the prediction of microvascular invasion, recurrence risk after locoregional treatment, and response to systemic therapy. This review highlights the potential value of artificial intelligence in the prognostication of HCC as well as its limitations and future prospects.

• Asian Pacific Journal of Cancer Prevention
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• v.14 no.2
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• pp.1111-1114
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• 2013

Background: For early detection of renal damage during the usage of cisplatin based chemotherapy, changes in renal function should be monitored carefully. In recent years, neutrophil gelatinase-associated lipocalin, a small polypeptide molecule, has shown promise as a marker of acute renal failure. The aim of this present study was to assess possible risk prediction of cisplatin-induced nephrotoxicity using serum NGAL. Materials and Methods: A total of 34 consecutive patients with documented serum creatinine at least 24 hours before every cycle of cisplatin-based chemotherapy were included in the study. Demographic and medical data including age, performance status, tumor characteristics and comorbid diseases were collected from medical charts. Renal function was evaluated at least 48 hours before the treatment and at the end of the treatment based on the Modification of Diet in Renal Disease (MDRD) formula. Before and after cisplatin infusion serum NGAL levels were measured for the first and 3rd cycles of chemotherapy. Results: The median age of the study population was 54 (32-70) years. Fifteen patients (41.1%) were treated on an adjuvant basis, whereas 19 patients (58.9%) were treated for metastatic disease. There was no correlation of serum NGAL levels with serum creatinine (r=0.20, p=0.26) and MDRD (r=-0.12, p=0.50) and creatinine clearance-Cockcroft-Gault (r=-0.22, p=0.22) after cisplatin infusion at the end of the 3rd cycle of chemotherapy. Conclusions: In our study, serum NGAL levels were not correlated with the cisplatin induced nephrotoxicity. Further prospective studies are needed to conclude that serum NGAL level is not a good surrogate marker to predict early cisplatin induced nephrotoxicity.

• Journal of Gastric Cancer
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• v.22 no.1
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• pp.24-34
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• 2022

Purpose: Total gastrectomy (TG) with lymph node (LN) dissection is recommended for early gastric cancer (EGC) but is not indicated for endoscopic resection (ER). We aimed to identify patients who could avoid TG by establishing a scoring system for predicting lymph node metastasis (LNM) in proximal EGCs. Materials and Methods: Between January 2003 and December 2017, a total of 1,025 proximal EGC patients who underwent TG with LN dissection were enrolled. Patients who met the absolute ER criteria based on pathological examination were excluded. The pathological risk factors for LNM were determined using univariate and multivariate logistic regression analyses. A scoring system for predicting LNM was developed and applied to the validation group. Results: Of the 1,025 cases, 100 (9.8%) showed positive LNM. Multivariate analysis confirmed the following independent risk factors for LNM: tumor size >2 cm, submucosal invasion, lymphovascular invasion (LVI), and perineural invasion (PNI). A scoring system was created using the four aforementioned variables, and the areas under the receiver operating characteristic curves in both the training (0.85) and validation (0.84) groups indicated excellent discrimination. The probability of LNM in mucosal cancers without LVI or PNI, regardless of size, was <2.9%. Conclusions: Our scoring system involving four variables can predict the probability of LNM in proximal EGC and might be helpful in determining additional treatment plans after ER, functioning as a good indicator of the adequacy of treatments other than TG in high surgical risk patients.

• Korean Journal of Digital Imaging in Medicine
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• v.8 no.1
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• pp.39-43
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• 2006

PACS has been run at the Kyung Hee University Medical Center since 2001, and the installation and operation of PACS have contributed to automation and quantification of center's medical environment. In order to classify tile annual number of trouble cases processed by PACS, the authorshave made a classification code system which enabled detailed statistical processes for each section. Such process method has not only shown the management efficiency to trouble management of PACS, but also raised the interests in frequently occurring troubles, and enabled the prediction of troubles that may occur hereafter. Predictable troubles lead to preventive check, and this has direct effects on medical treatment and the hospital administration. The authors intend to arouse the necessity of preventive check of PACS by analyzing trouble management processes for the last 1 year.

• Journal of Microbiology and Biotechnology
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• v.28 no.3
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• pp.454-464
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• 2018

Salivary microbiota alterations can correlate with dental caries development in children, and mechanisms mediating this association need to be studied in further detail. Our study explored salivary microbiota shifts in children and their association with the incidence of dental caries with dentine involvement. Salivary samples were collected from children with caries and their subsequently matched caries-free controls before and after caries development. The microbiota was analyzed by 16S rRNA gene-based high-throughput sequencing. The salivary microbiota was more diverse in caries-free subjects than in those with dental caries with dentine involvement (DC). Although both groups exhibited similar shifts in microbiota composition, an association with caries was found by function prediction. Analysis of potential microbiome functions revealed that Granulicatella, Streptococcus, Bulleidia, and Staphylococcus in the DC group could be associated with the bacterial invasion of epithelial cells, phosphotransferase system, and ${\text\tiny{D}}-alanine$ metabolism, whereas Neisseria, Lautropia, and Leptotrichia in caries-free subjects could be associated with bacterial motility protein genes, linoleic acid metabolism, and flavonoid biosynthesis, suggesting that functional differences in the salivary microbiota may be associated with caries formation. These results expand the current understanding of the functional significance of the salivary microbiome in caries development, and may facilitate the identification of novel biomarkers and treatment targets.

• International Journal of Computer Science & Network Security
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• v.24 no.7
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• pp.11-23
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• 2024

Triage is a practice of accurately prioritizing patients in emergency department (ED) based on their medical condition to provide them with proper treatment service. The variation in triage assessment among medical staff can cause mis-triage which affect the patients negatively. Developing ED triage system based on machine learning (ML) techniques can lead to accurate and efficient triage outcomes. This study aspires to develop a triage system using machine learning techniques to predict ED triage levels using patients' information. We conducted a retrospective study using Security Forces Hospital ED data, from 2021 through 2023 during Hajj period in Saudia Arabi. Using demographics, vital signs, and chief complaints as predictors, two machine learning models were investigated, naming gradient boosted decision tree (XGB) and deep neural network (DNN). The models were trained to predict ED triage levels and their predictive performance was evaluated using area under the receiver operating characteristic curve (AUC) and confusion matrix. A total of 11,584 ED visits were collected and used in this study. XGB and DNN models exhibit high abilities in the predicting performance with AUC-ROC scores 0.85 and 0.82, respectively. Compared to the traditional approach, our proposed system demonstrated better performance and can be implemented in real-world clinical settings. Utilizing ML applications can power the triage decision-making, clinical care, and resource utilization.

• Asian Pacific Journal of Cancer Prevention
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• v.15 no.1
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• pp.11-16
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• 2014

Accurate diagnosis and proper monitoring of cancer patients remain important obstacles for successful cancer treatment. The search for cancer biomarkers can aid in more accurate prediction of clinical outcome and may also reveal novel predictive factors and therapeutic targets. One such prognostic marker seems to be FOXA1. Many studies have shown that FOXA1 is strongly expressed in a vast majority of cancers, including breast cancer, in which high expression is associated with a good prognosis. In this review, we summarize the role of this transcription factor in the development and prognosis of breast cancer in the hope of providing insights into utility of FOXA1 as a novel biomarker.

• Journal of Digital Convergence
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• v.17 no.8
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• pp.249-255
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• 2019

Type 2 diabetes mellitus(T2DM) is included in metabolic disorders characterized by hyperglycemia, which causes many complications, and requires long-term treatment resulting in massive medical expenses each year. There have been many studies to solve this problem, but the existing studies have not been accurate by learning and predicting the data at specific time point. Thus, this study proposed a model using RNN to increase the accuracy of prediction of T2DM. This work propose a T2DM prediction model based on Korean Genome and Epidemiology study(Ansan, Anseong Korea). We trained all of the data over time to create prediction model of diabetes. To verify the results of the prediction model, we compared the accuracy with the existing machine learning methods, LR, k-NN, and SVM. Proposed prediction model accuracy was 0.92 and the AUC was 0.92, which were higher than the other. Therefore predicting the onset of T2DM by using the proposed diabetes prediction model in this study, it could lead to healthier lifestyle and hyperglycemic control resulting in lower risk of diabetes by alerted diabetes occurrence.

• Journal of Gastric Cancer
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• v.20 no.4
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• pp.385-394
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• 2020

Purpose: Patients with gastric cancer who receive neoadjuvant therapy are staged before treatment (cStage) and after treatment (ypStage). We aimed to compare the prognostic reliability of cStage and ypStage, alone and in combination. Materials and Methods: Data for all patients who received neoadjuvant therapy followed by surgery for gastric adenocarcinoma from 2004 to 2015 were extracted from the National Cancer Database. Kaplan-Meier (KM)curves were used to model overall survival based on cStage alone, ypStage alone, cStage stratified by ypStage, and ypStage stratified by cStage. P-values were generated to summarize the differences in KM curves. The discriminatory power of survival prediction was examined using Harrell's C-statistics. Results: We included 8,977 patients in the analysis. As expected, increasing cStage and ypStage were associated with worse survival. The discriminatory prognostic power provided by cStage was poor (C-statistic 0.548), while that provided by ypStage was moderate (C-statistic 0.634). Within each cStage, the addition of ypStage information significantly altered the prognosis (P<0.0001 within cStages I-IV). However, for each ypStage, the addition of cStage information generally did not alter the prognosis (P=0.2874, 0.027, 0.061, 0.049, and 0.007 within ypStages 0-IV, respectively). The discriminatory prognostic power provided by the combination of cStage and ypStage was similar to that of ypStage alone (C-statistic 0.636 vs. 0.634). Conclusions: The cStage is unreliable for prognosis, and ypStage is moderately reliable. Combining cStage and ypStage does not improve the discriminatory prognostic power provided by ypStage alone. A ypStage-based prognosis is minimally affected by the initial cStage.

• Asian Pacific Journal of Cancer Prevention
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• v.14 no.8
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• pp.4847-4851
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• 2013

Objectives: To evaluate accuracy of FDG-PET CT in prediction of persistent disease in head and neck cancer cases and to determine prognostic value of metabolic tumor response. Materials and Methods: Between 2009 and 2011, 46 patients with squamous cell carcinoma of head and neck receiving PET-CT were treated with definitive radiotherapy, with or without chemotherapy. There were 29 nasopharyngeal, 11 hypopharyngeal, 3 oropharyngeal and 3 laryngeal cancer patients, with a median age of 50.5 years (range 16-84), 32 males and 14 females. All patients were evaluated with PET-CT median 3-5 months (2.4-9.4) after completion of radiotherapy. Results: After a median 20 months of follow up, complete metabolic response was observed in 63% of patients. Suspicious residual uptake was present in 10.9% and residual metabolic uptake in 26.0% of patients. The overall sensitivity, specificity, positive predictive value and negative predictive value of FDG-PET-CT for detection of residual disease was 91% and 81%, 64% and 96% respectively. Two year LRC was 95% in complete responders while it was 34% in non-complete responders. Conclusions: FDG PET CT is a valuable tool for assessment of treatment response, especially in patients at high risk of local recurrence, and also as an indicator of prognosis. Definitely more precise criteria are required for assessment of response, there being no clear cut uptake value indicating residual disease. Futhermore, repair processes of normal tissue may consume glucose which appear as increased uptake in control FDG PET CT.