• Journal of Hospice and Palliative Care
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• v.26 no.1
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• pp.1-6
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• 2023

This paper aimed to summarize the current situation of prognostication for patients with an expected survival of weeks or months, and to clarify future research priorities. Prognostic information is essential for patients, their families, and medical professionals to make end-of-life decisions. The clinician's prediction of survival is often used, but this may be inaccurate and optimistic. Many prognostic tools, such as the Palliative Performance Scale, Palliative Prognostic Index, Palliative Prognostic Score, and Prognosis in Palliative Care Study, have been developed and validated to reduce the inaccuracy of the clinician's prediction of survival. To date, there is no consensus on the most appropriate method of comparing tools that use different formats to predict survival. Therefore, the feasibility of using prognostic scales in clinical practice and the information wanted by the end users can determine the appropriate prognostic tool to use. We propose four major themes for further prognostication research: (1) functional prognosis, (2) outcomes of prognostic communication, (3) artificial intelligence, and (4) education for clinicians.

• Genomics & Informatics
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• v.16 no.4
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• pp.32.1-32.7
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• 2018

Ovarian cancer is one of the leading causes of cancer-related deaths in gynecological malignancies. Over 70% of ovarian cancer cases are high-grade serous ovarian cancers and have high death rates due to their resistance to chemotherapy. Despite advances in surgical and pharmaceutical therapies, overall survival rates are not good, and making an accurate prediction of the prognosis is not easy because of the highly heterogeneous nature of ovarian cancer. To improve the patient's prognosis through proper treatment, we present a prognostic prediction model by integrating high-dimensional RNA sequencing data with their clinical data through the following steps: gene filtration, pre-screening, gene marker selection, integrated study of selected gene markers and prediction model building. These steps of the prognostic prediction model can be applied to other types of cancer besides ovarian cancer.

• Asian Pacific Journal of Cancer Prevention
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• v.14 no.3
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• pp.1803-1806
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• 2013

Purpose: Carbohydrate antigen (CA) 242 is inversely related to prognosis in many cancers. However, few data regarding CA 242 in esophageal cancer (EC) are available. The aim of this study was to determine the prognostic value of CA 242 and propose an optimum cut-off point in predicting survival difference in patients with esophageal squamous cell carcinoma (ESCC). Methods: A retrospective analysis was conducted of 192 cases. A receiver operating characteristic (ROC) curve for survival prediction was plotted to verify the optimum cuf-off point. Univariate and multivariate analyses were performed to evaluate prognostic parameters for survival. Results: The positive rate for CA 242 was 7.3% (14/192). The ROC curve for survival prediction gave an optimum cut-off of 2.15 (U/ml). Patients with CA 242 ${\leq}$ 2.15 U/ml had significantly better 5-year survival than patients with CA 242 >2.15 U/ml (45.4% versus 22.6%; P=0.003). Multivariate analysis showed that differentiation (P=0.033), CA 242 (P=0.017), T grade (P=0.004) and N staging (P<0.001) were independent prognostic factors. Conclusions: Preoperative CA 242 is a predictive factor for long-term survival in ESCC, especially in nodal-negative patients. We conclude that 2.15 U/ml may be the optimum cuf-off point for CA 242 in predicting survival in ESCC.

• Nuclear Engineering and Technology
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• v.49 no.5
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• pp.914-919
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• 2017

While industry understands the importance of keeping equipment operational and well maintained, the importance of tracking maintenance information in reliability models is often overlooked. Prognostic models can be used to predict the failure times of critical equipment, but more often than not, these models assume that all maintenance actions are the same or do not consider maintenance at all. This study investigates the influence of integrating maintenance information on prognostic model prediction accuracy. By incorporating maintenance information to develop maintenance-dependent prognostic models, prediction accuracy was improved by more than 40% compared with traditional maintenance-independent models. This study acts as a proof of concept, showing the importance of utilizing maintenance information in modern prognostics for industrial equipment.

• Atmosphere
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• v.26 no.4
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• pp.495-508
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• 2016

In order to determine the prediction possibility of heavy rainfall, a variety of analyses was conducted by using three-dimensional data obtained from Korea Local Analysis and Prediction System (KLAPS) re-analysis data. Strong moisture convergence occurring around the time of the heavy rainfall is consistent with the results of previous studies on such continuous production. Heavy rainfall occurred in the cloud system with a thick convective clouds. The moisture convergence, temperature and potential temperature advection showed increase into the heavy rainfall occurrence area. The distribution of integrated liquid water content tended to decrease as rainfall increased and was characterized by accelerated convective instability along with increased buoyant energy. In addition, changes were noted in the various characteristics of instability indices such as K-index (KI), Showalter Stability Index (SSI), and lifted index (LI). The meteorological variables used in the analysis showed clear increases or decreases according to the changes in rainfall amount. These rapid changes as well as the meteorological variables changes are attributed to the surrounding and meteorological conditions. Thus, we verified that heavy rainfall can be predicted according to such increase, decrease, or changes. This study focused on quantitative values and change characteristics of diagnostic variables calculated by using numerical models rather than by focusing on synoptic analysis at the time of the heavy rainfall occurrence, thereby utilizing them as prognostic variables in the study of the predictability of heavy rainfall. These results can contribute to the identification of production and development mechanisms of heavy rainfall and can be used in applied research for prediction of such precipitation. In the analysis of various case studies of heavy rainfall in the future, our study result can be utilized to show the development of the prediction of severe weather.

• Annals of Clinical Neurophysiology
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• v.6 no.1
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• pp.26-30
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• 2004

Background: Electrophysiologic study accurately predicts the degree of degenerated motor axons but cannot give precise information on the type of injury that occurred in Bell's palsy. Because of these limitation for prognostic prediction in Bell's palsy, we evaluated divergence of electrophysiological time course for the purpose of presuming the type of injury in Bell's palsy. Methods: We did bilateral facial nerve conduction studies in 103 Bell's palsy patients, who visited to Han-Gang sacred heart hospital from 1998 to 2001. We compared the CMAP amplitude of disease site with that of normal site and suggested that decremental CMAP amplitude ratio (percentage) as a degree of denervation of affected facial nerve. Then we demonstrated the time course of denervation percentage. After defining normal range of CMAP amplitude difference from normal control group, we also evaluated if distinct time course of early minimal denervation is present. Results: Our results show that time course of the denervation in early stage of Bell's palsy reflect various injury type such as axonotmesis, neurotmesis or other unidentified type. We cannot identify the distinct time course of early minimal denervation. Conclusions: The time course as well as the maximal value of denervation are the best prognostic guidelines in Bell' s palsy. So repeated serial electrophysiologic test are inevitable to assess prognosis. As an another topic, early minimal denervation for prognostic prediction deserve to be evaluated as a future work up for prognostic prediction.

• Journal of Oral Medicine and Pain
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• v.26 no.2
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• pp.133-146
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• 2001

This study was performed to predict the conservative treatment outcome of TMD patients by investigating the prognostic factors ; symptom duration, history of previous treatment, history of previous medication, history of trauma, disability of daily activity, severity of pain, noise, limitation of mouth opening(LOM) and maximum comfortable opening(MCO). Two hundreds and fifty-four subjects were selected for this study among the TMD patients who had visited the Dept. of Oral Medicine BNUH and been treated conservatively with medication, physical therapy, behavioral treatment, and splint therapy from 1991 to 2000. The subjects were divided into two groups improved or unimproved according to the treatment response following six months of conservative treatment. Those who showed less than 1 on NAS for pain, TMJ noise, and opening limitation belonged to the improved group and those who showed more than 2 on NAS belonged to the unimproved group. The two groups were compared with respect to symptom severity, number of diagnosis, history of trauma, previous treatment, previous medication, and disability of daily activity. A prognostic equation with the factors revealed to be significantly related to the prognosis of conservative treatment was obtained. The obtained results were as follows ; 1. In improved group, mean duration of history was 12 months, mean treatment duration of a patient was 4 months an mean number of treatment was about 10 times. In other words, in unimproved group, mean duration of history was 27.4 months, mean treatment duration of patient was 10.5 months and mean number of treatment was 19 times. 2. In unimproved group, multiple diagnosis, chronicity, disability of daily activity were significantly greater than that of the improved group. 3. Patients in unimproved group revealed severe noise at first visit and smaller maximum comfortable opening comparatively. 4. Prognostic factors such as duration of treatment, number of treatment, multiplicity, and chronicity and disability of daily activity showed a significant relation in prediction of improvement. 5. Prognostic equation with significant variables is as follows ; Y = 1.984 - 0.251Noise + 0.068MCO - 0.673Multiplicity. - 0.958Chronicity - 0.065Disability. Classification accuracy of 70.3 %, sensitivity of 71.4% and specificity of 66.7% were shown. 6. Prognostic equation with all factors is as follows : Y = 1.599 - 0.038Pain - 0.256Noise - 0.006Limitation + 0.068MCO - 0.580Multiplicity - 1.025Chronicity - 0.720Disability - 0.329Medication - 0.087Treatment + 0.740Trauma. Classification accuracy of 70.3 %, sensitivity of 73% and specificity of 64.3% were shown. 7. Prognostic value of the improved group with significant factors was $1.0446{\pm}1.0726$ and prognostic value of the unimproved group with significant factors was $-0.013{\pm}1.0146$. Prognostic value of the improved group with all factors was $1.0465{\pm}1.0849$ and prognostic value of the unimproved group with all factors was $-0.057{\pm}1.0611$.

• Asian Pacific Journal of Cancer Prevention
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• v.13 no.4
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• pp.1281-1284
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• 2012

Background: Platinum-hased chemotherapy for advanced non-small cell lung cancer (NSCLC) is still considered the first choice, presenting a modest survival advantage. However, the patients eventually experience disease progression and require second-line therapy. While there are reliable predictors to identify patients receiving first-line chemotherapy, very little knowledge is available about the prognostic factors in patients who receive second-line treatments. The present study was therefore performed. Methods: We retrospectively reviewed 107 patients receiving second-line treatments from August 2002 to March 2012 in the Dicle University, School of Medicine, Department of Medical Oncology. Fourteen potential prognostic variables were chosen for analysis in this study. Univariate and multivariate analyses were conducted to identify prognostic factors associated with survival. Result: The results of univariate analysis for overall survival (OS) were identified to have prognostic significance: performance status (PS), stage, response to first-line chemotherapy response to second-line chemotherapy and number of metastasis. PS, diabetes mellitus (DM), response to first-line chemotherapy and response to second-line chemotherapy were identified to have prognostic significance for progression-free survival (PFS). Multivariate analysis showed that PS, response to first-line chemotherapy and response to second-line chemotherapy were considered independent prognostic factors for OS. Furthermore, PS and response to second-line chemotherapy were considered independent prognostic factors for PFS. Conclusion: In conclusion, PS, response to first and second-line chemotherapy were identified as important prognostic factors for OS in advanced NSCLC patients who were undergoing second-line palliative treatment. Furthermore, PS and response to second-line chemotherapy were considered independent prognostic factors for PFS. It may be concluded that these findings may facilitate pretreatment prediction of survival and can be used for selecting patients for the correct choice of treatment.

• Journal of Gastric Cancer
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• v.21 no.1
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• pp.1-3
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• 2021

• Journal of KIISE
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• v.45 no.1
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• pp.61-68
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• 2018

The identification of genes that contribute to the prediction of prognosis in patients with cancer is one of the challenges in providing appropriate therapies. To find the prognostic genes, several classification models using gene expression data have been proposed. However, the prediction accuracy of cancer prognosis is limited due to the heterogeneity of cancer. In this paper, we integrate microarray data with biological network data using a modified PageRank algorithm to identify prognostic genes. We also predict the prognosis of patients with 6 cancer types (including breast carcinoma) using the K-Nearest Neighbor algorithm. Before we apply the modified PageRank, we separate samples by K-Means clustering to address the heterogeneity of cancer. The proposed algorithm showed better performance than traditional algorithms for prognosis. We were also able to identify cluster-specific biological processes using GO enrichment analysis.