• 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.

• Journal of Korea Multimedia Society
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• v.13 no.5
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• pp.722-729
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• 2010

Prediction problems are widely used in medical domains. For example, computer aided diagnosis or prognosis is a key component in a CDSS (Clinical Decision Support System). SVMs with nonlinear kernels like RBF kernels, have shown superior accuracy in prediction problems. However, they are not preferred by physicians for medical prediction problems because nonlinear SVMs are difficult to visualize, thus it is hard to provide intuitive interpretation of prediction results to physicians. Nomogram was proposed to visualize SVM classification models. However, it cannot visualize nonlinear SVM models. Localized Radial Basis Function (LRBF) was proposed which shows comparable accuracy as the RBF kernel while the LRBF kernel is easier to interpret since it can be linearly decomposed. This paper presents a new tool named VRIFA, which integrates the nomogram and LRBF kernel to provide users with an interactive visualization of nonlinear SVM models, VRIFA visualizes the internal structure of nonlinear SVM models showing the effect of each feature, the magnitude of the effect, and the change at the prediction output. VRIFA also performs nomogram-based feature selection while training a model in order to remove noise or redundant features and improve the prediction accuracy. The area under the ROC curve (AUC) can be used to evaluate the prediction result when the data set is highly imbalanced. The tool can be used by biomedical researchers for computer-aided diagnosis and risk factor analysis for diseases.

• Asian Pacific Journal of Cancer Prevention
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• v.14 no.2
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• pp.929-934
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• 2013

Aim: To analyze the significance of different clinical factors for prognostic prediction in diffuse large B-cell lymphoma (DLBCL) patients. Methods: Two hundred and twenty-seven DLBCL patients were retrospectively reviewed. Patients were managed with cyclophosphamide, doxorubicin, vincristine, and prednisone (CHOP) regimen or rituximab plus the CHOP (RCHOP) regimen. Results: Lactate dehydrogenase (LDH), ${\beta}2$-microglobulin (${\beta}2$-M), B symptoms, Ann Arbor stage and genetic subtypes were statistically relevant in predicting the prognosis of the overall survival (OS). In the CHOP group, the OS in patients with germinal center B-cell-like (GCB)(76.2%) was significantly higher than that of the non-GCB group (51.9%, P=0.032). With RCHOP management, there was no statistical difference in OS between the GCB (88.4%) and non-GCB groups (81.9%, P=0.288). Conclusion: Elevated LDH and ${\beta}2$-M levels, positive B symptoms, Ann Arbor stage III/IV, and primary nodal lymphoma indicate an unfavorable prognosis of DLBCL patients. Patients with GCB-like DLBCL have a better prognosis than those with non-GCB when treated with the CHOP regimen. The RCHOP treatment with the addition of rituximab can improve the prognosis of patients with DLBCL.

• 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 the Korean Burn Society
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• v.23 no.1
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• pp.1-6
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• 2020

Purpose: PL, creatinine and urine output are biomarkers of the suitability and prognosis of fluid therapy in severe burn patients. The purpose of this study is to evaluate the usefulness of predicting mortality by biomarkers and its change during initial fluid therapy for severe burn patients. Methods: A retrograde review was performed on 733 patients from January 2014 to December 2018 who were admitted as severe burn patients to our burn intensive care unit (BICU). Plasma lactate, serum creatinine and urine output were measured at the time of admission to the BICU and after 48 hours. ABSI score, Hangang score, APACHEII, revised Baux index and TBSA were collected after admission. Results: 733 patients were enrolled. PL was the most useful indicators for predicting mortality in burn patients at the time of admission (AUC: 0.813) and after 48 hours (AUC: 0.698). On the other hand, mortality prediction from initial fluid therapy for 48 hours showed different results. Only creatinine showed statistical differences (P<0.05) in mortality prediction. But there were no statistical differences in mortality prediction with PL and UO (P>0.05). Conclusion: In this study, PL was most useful predictor among biomarkers for predicting mortality. Improvement in creatinine levels during the first 48 hours is associated with improved mortality. Therefore, efforts are needed to improve creatinine levels.

• Journal of Korean Neurosurgical Society
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• v.61 no.3
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• pp.376-385
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• 2018

Recent discoveries of brain tumor-related genes and fast advances in genomic testing technologies have led to the era of molecular diagnosis of brain tumor. Molecular profiling of brain tumor became the significant step in the diagnosis, the prediction of prognosis and the treatment of brain tumor. Because traditional molecular testing methods have limitations in time and cost for multiple gene tests, next-generation sequencing technologies are rapidly introduced into clinical practice. Targeted sequencing panels using these technologies have been developed for brain tumors. In this article, focused on pediatric brain tumor, key discoveries of brain tumor-related genes are reviewed and cancer panels used in the molecular profiling of brain tumor are discussed.

• Asian Pacific Journal of Cancer Prevention
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• v.15 no.3
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• pp.1067-1074
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• 2014

In Morocco, breast cancer is the most prevalent cancer in women and a major public health problem. Several Moroccan studies have focused on studying this disease, but more are needed, especially at the genetic and molecular levels. It is therefore interesting to establish the genetic and molecular profile of Moroccan patients with breast cancer. In this paper, we will highlight some pertinent hypotheses that may enhance breast cancer care in Moroccan patients. This review will give a precise description of breast cancer in Morocco and propose some new markers for detection and prediction of breast cancer prognosis.

• Journal of Biomedical Engineering Research
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• v.38 no.5
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• pp.271-276
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• 2017

Cancer biomarkers are using in the diagnosis, staging, prognosis and prediction of disease progression. But, there are not sufficiently profiled and validated in early detection and risk classification of prostate cancer. In this study, we have devoted to finding a panel of serum biomarkers that are able to detect the diagnosis of prostate cancer. The serum samples were consisted of 111 prostate cancer and 343 control samples and examined. Eleven biomarkers were constructed in this study, and then nine biomarkers were relevant to candidate biomarkers by using t test. Finally, four biomarkers, PSA, ApoA2, CYFRA21.1 and TTR, were selected as the prostate cancer biomarker panel, logistic regression was used to identify algorithms for diagnostic biomarker combinations(AUC = 0.9697). A panel of combination biomarkers is less invasive and could supplement clinical diagnostic accuracy.

• Journal of Biomedical Engineering Research
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• v.12 no.4
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• pp.295-302
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• 1991

In this paper, the inter- and intra-frame distortions in the gray levels of a series of fluorescein ocular fundus photographs are corrected. For doing this, the background images are extracted from original images using the image blurring effect by decimation, and then shading corrected images are obtained by subtracting the background images from the original images pixel by pixel. In a series of fluorescein ocular fundus photographs, after the gray scale distoriton is corrected, the intensity volumes of dye leakage are measured and represented by a graph. These data may be useful for the prediction of prognosis and the therapeutic management.

• Journal of Digestive Cancer Research
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• v.11 no.3
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• pp.130-140
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• 2023

Gastric cancer (GC) is one of the most common malignant tumors worldwide, with a 5-year survival rate of < 40%. The diagnosis and treatment decisions of GC rely on human experts' judgments on medical images; therefore, the accuracy can be hindered by image condition, objective criterion, limited experience, and interobserver discrepancy. In recent years, several applications of artificial intelligence (AI) have emerged in the GC field based on improvement of computational power and deep learning algorithms. AI can support various clinical practices in endoscopic examination, pathologic confirmation, radiologic staging, and prognosis prediction. This review has systematically summarized the current status of AI applications after a comprehensive literature search. Although the current approaches are challenged by data scarcity and poor interpretability, future directions of this field are likely to overcome the risk and enhance their accuracy and applicability in clinical practice.