A Model Based on Artificial Intelligence Algorithm for Monitoring Recurrence of HCC after Hepatectomy

2021 ◽  
pp. 000313482110635
Author(s):  
Li-Yue Sun ◽  
Qing Ouyang ◽  
Wen-Jian Cen ◽  
Fang Wang ◽  
Wen-Ting Tang ◽  
...  
Keyword(s):  
Operating Characteristic ◽  
Validation Cohort ◽  
Tnm Staging ◽  
Resonance Imaging ◽  
Diagnostic Efficacy ◽  
Receiver Operating Characteristic Curves ◽  
Dynamic Variations ◽  
Included Patient ◽  
Artificial Intelligence Algorithm ◽  
Adaboost Classifier

Background There is no satisfactory indicator for monitoring recurrence after resection of hepatocellular carcinoma (HCC). This retrospective study aimed to design and validate an HCC monitor recurrence (HMR) model for patients without metastasis after hepatectomy. Methods A training cohort was recruited from 1179 patients with HCC without metastasis after hepatectomy between February 2012 and December 2015. An HMR model was developed using an AdaBoost classifier algorithm. The factors included patient age, TNM staging, tumor size, and pre/postoperative dynamic variations of alpha-fetoprotein (AFP). The diagnostic efficacy of the model was evaluated based on the area under the receiver operating characteristic curves (AUCs). The model was validated using a cohort of 695 patients. Results In preoperative patients with positive or negative AFP, the AUC of the validation cohort in the HMR model was .8877, which indicated better diagnostic efficacy than that of serum AFP (AUC, .7348). The HMR model predicted recurrence earlier than computed tomography/magnetic resonance imaging did by 191.58 ± 165 days. In addition, the HMR model can predict the prognosis of patients with HCC after resection. Conclusions The HMR model established in this study is more accurate than serum AFP for monitoring recurrence after hepatectomy for HCC and can be used for real-time monitoring of the postoperative status in patients with HCC without metastasis.

scholarly journals Usefulness of sialic acid for diagnosis of sepsis in critically ill patients: a retrospective study

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Bo Yao ◽  
Wen-juan Liu ◽  
Di Liu ◽  
Jin-yan Xing ◽  
Li-juan Zhang
Keyword(s):  
Sialic Acid ◽  
Critically Ill ◽  
Critically Ill Patients ◽  
Incomplete Data ◽  
Acid Level ◽  
Operating Characteristic ◽  
Area Under The Curve ◽  
Diagnostic Efficacy ◽  
Receiver Operating Characteristic Curves ◽  
Sensitivity Specificity

Abstract Background Early diagnosis of sepsis is very important. It is necessary to find effective and adequate biomarkers in order to diagnose sepsis. In this study, we compared the value of sialic acid and procalcitonin for diagnosing sepsis. Methods Newly admitted intensive care unit patients were enrolled from January 2019 to June 2019. We retrospectively collected patient data, including presence of sepsis or not, procalcitonin level and sialic acid level. Receiver operating characteristic curves for the ability of sialic acid, procalcitonin and combination of sialic acid and procalcitonin to diagnose sepsis were carried out. Results A total of 644 patients were admitted to our department from January 2019 to June 2019. The incomplete data were found in 147 patients. Finally, 497 patients data were analyzed. The sensitivity, specificity and area under the curve for the diagnosis of sepsis with sialic acid, procalcitonin and combination of sialic acid and procalcitonin were 64.2, 78.3%, 0.763; 67.9, 84.0%, 0.816 and 75.2, 84.6%, 0.854. Moreover, sialic acid had good values for diagnosing septic patients with viral infection, with 87.5% sensitivity, 82.2% specificity, and 0.882 the area under the curve. Conclusions Compared to procalcitonin, sialic acid had a lower diagnostic efficacy for diagnosing sepsis in critically ill patients. However, the combination of sialic acid and procalcitonin had a higher diagnostic efficacy for sepsis. Moreover, sialic acid had good value for diagnosing virus-induced sepsis.

Ultrasound grayscale ratio: a reliable parameter for differentiating papillary thyroid microcarcinoma from micronodular goiter

2020 ◽  
Author(s):  
Chenke Xu ◽  
Lifang Yu ◽  
Jianhua Fang ◽  
Zhijiang Han ◽  
Dingcun Luo ◽  
...  
Keyword(s):  
Operating Characteristic ◽  
Roc Curves ◽  
Mean Value ◽  
Papillary Thyroid ◽  
Diagnostic Efficacy ◽  
Receiver Operating Characteristic Curves ◽  
Thyroid Microcarcinoma ◽  
Reliable Parameter ◽  
The Mean ◽  
Reading System

Abstract Background: To evaluate the reliability and diagnostic efficacy of the ultrasound grayscale ratio (UGSR) for differentiating papillary thyroid microcarcinomas (PTMC) from micronodular goiters (diameter ≤ 1.0cm).Methods: The ultrasound data of 241 pathologically-confirmed cases of patients with 265 PTMC and 141 patients with 168 micronodular goiters were retrospectively reviewed. All patients underwent outpatient ultrasonic examination and preoperative ultrasonic positioning. The RADinfo radiograph reading system (Zhejiang RAD Information Technology Co., Ltd., China) was used to measure and calculated the UGSR of PTMC, micronodular goiters. Patients were divided into the outpatient examination, preoperative positioning, and mean value groups, and the receiver operating characteristic curves (ROC) were calculated to obtain the optimal UGSR threshold for distinguishing PTMC from micronodular goiters.Results: The UGSR values of the PTMC and micronodular goiters were 0.56±0.14 and 0.80±0.19 (t=5.84, P<0.01) in the outpatient examination group, 0.55±0.14 and 0.80±0.19 (t=18.74, P<0.01) in the preoperative positioning group, and 0.56±0.12 and 0.80±0.18 (t=16.49, P<0.01) in the mean value group. The areas under the ROC curves in the outpatient examination, preoperative positioning, and mean value groups were 0.860, 0.856, and 0.875, respectively. When the cut-off UGSR values for the outpatient examination, preoperative positioning, and mean value groups were 0.649, 0.646, and 0.657, the sensitivity and specificity for predicting PTMC were 78.9% and 86.9%, 79.2% and 83.9%, 82.6%, and 85.7%, respectively. A reliable UGSR value was obtained between the outpatient examination and preoperative positioning groups (ICC=0.79, P=0.68).Conclusion: The UGSR is an accurate and feasible tool for differentiating PTMC from micronodular goiters with better diagnostic efficacy.

scholarly journals Computer Aided-Diagnosis of Prostate Cancer on Multiparametric MRI: A Technical Review of Current Research

2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
Shijun Wang ◽  
Karen Burtt ◽  
Baris Turkbey ◽  
Peter Choyke ◽  
Ronald M. Summers
Keyword(s):  
Prostate Cancer ◽  
Operating Characteristic ◽  
State Of The Art ◽  
The United States ◽  
Computer Aided Diagnosis ◽  
Resonance Imaging ◽  
Receiver Operating Characteristic Curves ◽  
Computer Aided ◽  
Detection And Diagnosis ◽  
Aided Diagnosis

Prostate cancer (PCa) is the most commonly diagnosed cancer among men in the United States. In this paper, we survey computer aided-diagnosis (CADx) systems that use multiparametric magnetic resonance imaging (MP-MRI) for detection and diagnosis of prostate cancer. We review and list mainstream techniques that are commonly utilized in image segmentation, registration, feature extraction, and classification. The performances of 15 state-of-the-art prostate CADx systems are compared through the area under their receiver operating characteristic curves (AUC). Challenges and potential directions to further the research of prostate CADx are discussed in this paper. Further improvements should be investigated to make prostate CADx systems useful in clinical practice.

Diagnostic efficiency of four lactate dehydrogenase isoenzyme-1 ratios in serum after myocardial infarction.

1988 ◽  
Vol 34 (10) ◽  
pp. 1960-1965 ◽  
Author(s):  
J F Loughlin ◽  
P M Krijnen ◽  
G Jablonsky ◽  
F Y Leung ◽  
A R Henderson
Keyword(s):  
Myocardial Infarction ◽  
Chest Pain ◽  
Operating Characteristic ◽  
Decision Threshold ◽  
Diagnostic Efficiency ◽  
Threshold Values ◽  
Likelihood Ratios ◽  
Diagnostic Efficacy ◽  
Receiver Operating Characteristic Curves ◽  
Optimum Decision

Abstract We compared the diagnostic efficacy of the ratios LD-1/LD-2, LD-1/LD-3, LD-1/LD-4, and LD-1/LD-5 in 69 documented cases of myocardial infarction. We used 149 patients with congestive heart failure and 67 patients with nonmyocardial infarct as controls. We used a computer program to produce receiver-operating characteristic curves, decision threshold plots, and likelihood ratios for these LD ratios at 6-h intervals up to 108 h after the onset of chest pain or hospital admission. All ratios in the myocardial infarction cases peaked around 36 h after the onset of chest pain, while those for the nonmyocardial and congestive cardiac failure cases did not change over the 108-h period. In all patients with infarctions, LD-1/LD-4 and LD-1/LD-5 increased by 1.7 times (when LD-1 was less than 40%) and 3.4 times (when LD-1 was greater than 40%), respectively, over control values. Optimum decision threshold values were obtained at 13-24 h (LD-1/LD-5), 31-36 h (LD-1/LD-4 and LD-1/LD-3), and 55-60 h (LD-1/LD-2) after onset of symptoms. The highest likelihood ratio was obtained with the LD-1/LD-4 ratio; therefore, we suggest that this is a better diagnostic test for myocardial infarction than LD-1/LD-2.

scholarly journals Artificial intelligence algorithm for detecting myocardial infarction using six-lead electrocardiography

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Younghoon Cho ◽  
Joon-myoung Kwon ◽  
Kyung-Hee Kim ◽  
Jose R. Medina-Inojosa ◽  
Ki-Hyun Jeon ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Myocardial Infarction ◽  
Operating Characteristic ◽  
External Validation ◽  
Receiver Operating Characteristic Curves ◽  
Treatment And Prevention ◽  
Intelligence Algorithm ◽  
Variational Autoencoder ◽  
Artificial Intelligence Algorithm ◽  
Life Type

AbstractRapid diagnosis of myocardial infarction (MI) using electrocardiography (ECG) is the cornerstone of effective treatment and prevention of mortality; however, conventional interpretation methods has low reliability for detecting MI and is difficulty to apply to limb 6-lead ECG based life type or wearable devices. We developed and validated a deep learning-based artificial intelligence algorithm (DLA) for detecting MI using 6-lead ECG. A total of 412,461 ECGs were used to develop a variational autoencoder (VAE) that reconstructed precordial 6-lead ECG using limb 6-lead ECG. Data from 9536, 1301, and 1768 ECGs of adult patients who underwent coronary angiography within 24 h from each ECG were used for development, internal and external validation, respectively. During internal and external validation, the area under the receiver operating characteristic curves of the DLA with VAE using a 6-lead ECG were 0.880 and 0.854, respectively, and the performances were preserved by the territory of the coronary lesion. Our DLA successfully detected MI using a 12-lead ECG or a 6-lead ECG. The results indicate that MI could be detected not only with a conventional 12 lead ECG but also with a life type 6-lead ECG device that employs our DLA.

A validated novel preoperative index to predict the extent of intraperitoneal contamination in patients with acute abdominal pathology: A cohort study

2019 ◽  
Vol 30 (7-8) ◽  
pp. 221-228
Author(s):  
Shahab Hajibandeh ◽  
Shahin Hajibandeh ◽  
Nicholas Hobbs ◽  
Jigar Shah ◽  
Matthew Harris ◽  
...  
Keyword(s):  
Receiver Operating Characteristic Curve ◽  
Receiver Operating Characteristic ◽  
Operating Characteristic ◽  
Validation Cohort ◽  
Characteristic Curve ◽  
Curve Analysis ◽  
Emergency Laparotomy ◽  
Contamination Index ◽  
Operating Characteristic Curve ◽  
Receiver Operating

Aims To investigate whether an intraperitoneal contamination index (ICI) derived from combined preoperative levels of C-reactive protein, lactate, neutrophils, lymphocytes and albumin could predict the extent of intraperitoneal contamination in patients with acute abdominal pathology. Methods Patients aged over 18 who underwent emergency laparotomy for acute abdominal pathology between January 2014 and October 2018 were randomly divided into primary and validation cohorts. The proposed intraperitoneal contamination index was calculated for each patient in each cohort. Receiver operating characteristic curve analysis was performed to determine discrimination of the index and cut-off values of preoperative intraperitoneal contamination index that could predict the extent of intraperitoneal contamination. Results Overall, 468 patients were included in this study; 234 in the primary cohort and 234 in the validation cohort. The analyses identified intraperitoneal contamination index of 24.77 and 24.32 as cut-off values for purulent contamination in the primary cohort (area under the curve (AUC): 0.73, P < 0.0001; sensitivity: 84%, specificity: 60%) and validation cohort (AUC: 0.83, P < 0.0001; sensitivity: 91%, specificity: 69%), respectively. Receiver operating characteristic curve analysis also identified intraperitoneal contamination index of 33.70 and 33.41 as cut-off values for feculent contamination in the primary cohort (AUC: 0.78, P < 0.0001; sensitivity: 87%, specificity: 64%) and validation cohort (AUC: 0.79, P < 0.0001; sensitivity: 86%, specificity: 73%), respectively. Conclusions As a predictive measure which is derived purely from biomarkers, intraperitoneal contamination index may be accurate enough to predict the extent of intraperitoneal contamination in patients with acute abdominal pathology and to facilitate decision-making together with clinical and radiological findings.

scholarly journals Evaluation of alpha-l-fucosidase for the diagnosis of hepatocellular carcinoma based on meta-analysis

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Lei Xi ◽  
Chunqing Yang
Keyword(s):  
Hepatocellular Carcinoma ◽  
Operating Characteristic ◽  
Meta Analysis ◽  
Area Under The Curve ◽  
Diagnostic Odds Ratio ◽  
Diagnostic Value ◽  
Receiver Operating Characteristic Curves ◽  
Sensitivity Specificity ◽  
Review Manager

AbstractObjectivesThe main aim of the present study was to assess the diagnostic value of alpha-l-fucosidase (AFU) for hepatocellular carcinoma (HCC).MethodsStudies that explored the diagnostic value of AFU in HCC were searched in EMBASE, SCI, and PUBMED. The sensitivity, specificity, and DOR about the accuracy of serum AFU in the diagnosis of HCC were pooled. The methodological quality of each article was evaluated with QUADAS-2 (quality assessment for studies of diagnostic accuracy 2). Receiver operating characteristic curves (ROC) analysis was performed. Statistical analysis was conducted by using Review Manager 5 and Open Meta-analyst.ResultsEighteen studies were selected in this study. The pooled estimates for AFU vs. α-fetoprotein (AFP) in the diagnosis of HCC in 18 studies were as follows: sensitivity of 0.7352 (0.6827, 0.7818) vs. 0.7501 (0.6725, 0.8144), and specificity of 0.7681 (0.6946, 0.8283) vs. 0.8208 (0.7586, 0.8697), diagnostic odds ratio (DOR) of 7.974(5.302, 11.993) vs. 13.401 (8.359, 21.483), area under the curve (AUC) of 0.7968 vs. 0.8451, respectively.ConclusionsAFU is comparable to AFP for the diagnosis of HCC.

A conditional approach for the receiver operating characteristic curve construction to evaluate diagnostic test performance in a family-matched case–control design

2021 ◽  
pp. 096228022199595
Author(s):  
Yalda Zarnegarnia ◽  
Shari Messinger
Keyword(s):  
Receiver Operating Characteristic Curve ◽  
Receiver Operating Characteristic ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
Case Control ◽  
Paired Data ◽  
Characteristic Curves ◽  
Receiver Operating Characteristic Curves ◽  
Operating Characteristic Curve ◽  
Receiver Operating

Receiver operating characteristic curves are widely used in medical research to illustrate biomarker performance in binary classification, particularly with respect to disease or health status. Study designs that include related subjects, such as siblings, usually have common environmental or genetic factors giving rise to correlated biomarker data. The design could be used to improve detection of biomarkers informative of increased risk, allowing initiation of treatment to stop or slow disease progression. Available methods for receiver operating characteristic construction do not take advantage of correlation inherent in this design to improve biomarker performance. This paper will briefly review some developed methods for receiver operating characteristic curve estimation in settings with correlated data from case–control designs and will discuss the limitations of current methods for analyzing correlated familial paired data. An alternative approach using conditional receiver operating characteristic curves will be demonstrated. The proposed approach will use information about correlation among biomarker values, producing conditional receiver operating characteristic curves that evaluate the ability of a biomarker to discriminate between affected and unaffected subjects in a familial paired design.

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