scholarly journals Temporal Stability and Prognostic Biomarker Potential of the Prostate Cancer Urine miRNA Transcriptome

2019 ◽  
Vol 112 (3) ◽  
pp. 247-255 ◽  
Author(s):  
Jouhyun Jeon ◽  
Ekaterina Olkhov-Mitsel ◽  
Honglei Xie ◽  
Cindy Q Yao ◽  
Fang Zhao ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Machine Learning ◽  
Confidence Interval ◽  
Receiver Operating Characteristic ◽  
Operating Characteristic ◽  
Validation Cohort ◽  
Machine Learning Techniques ◽  
Training Cohort ◽  
Learning Techniques ◽  
Receiver Operating

Abstract Background The development of noninvasive tests for the early detection of aggressive prostate tumors is a major unmet clinical need. miRNAs are promising noninvasive biomarkers: they play essential roles in tumorigenesis, are stable under diverse analytical conditions, and can be detected in body fluids. Methods We measured the longitudinal stability of 673 miRNAs by collecting serial urine samples from 10 patients with localized prostate cancer. We then measured temporally stable miRNAs in an independent training cohort (n = 99) and created a biomarker predictive of Gleason grade using machine-learning techniques. Finally, we validated this biomarker in an independent validation cohort (n = 40). Results We found that each individual has a specific urine miRNA fingerprint. These fingerprints are temporally stable and associated with specific biological functions. We identified seven miRNAs that were stable over time within individual patients and integrated them with machine-learning techniques to create a novel biomarker for prostate cancer that overcomes interindividual variability. Our urine biomarker robustly identified high-risk patients and achieved similar accuracy as tissue-based prognostic markers (area under the receiver operating characteristic = 0.72, 95% confidence interval = 0.69 to 0.76 in the training cohort, and area under the receiver operating characteristic curve = 0.74, 95% confidence interval = 0.55 to 0.92 in the validation cohort). Conclusions These data highlight the importance of quantifying intra- and intertumoral heterogeneity in biomarker development. This noninvasive biomarker may usefully supplement invasive or expensive radiologic- and tissue-based assays.

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 Lesion-to-background ratio threshold value of SUVmax of simultaneous [68Ga]Ga-PSMA-11 PET/MRI imaging in patients with prostate cancer

2020 ◽  
Vol 11 (1) ◽  
Author(s):  
Jing Zhao ◽  
Bernd Hamm ◽  
Winfried Brenner ◽  
Marcus R. Makowski
Keyword(s):  
Prostate Cancer ◽  
Receiver Operating Characteristic ◽  
Operating Characteristic ◽  
Absolute Threshold ◽  
Threshold Value ◽  
Imaging Findings ◽  
Characteristic Analysis ◽  
Psma Pet ◽  
The Absolute ◽  
Receiver Operating

Abstract Purpose This study aimed to calculate an applicable relative ratio threshold value instead of the absolute threshold value for simultaneous 68Ga prostate-specific membrane antigen/positron emission tomography ([68Ga]Ga-PSMA-11 PET) in patients with prostate cancer (PCa). Materials and methods Our study evaluated thirty-two patients and 170 focal prostate lesions. Lesions are classified into groups according to Prostate Imaging Reporting and Data System (PI-RADS). Standardized uptake values maximum (SUVmax), corresponding lesion-to-background ratios (LBRs) of SUVmax, and LBR distributions of each group were measured based on regions of interest (ROI). We examined LBR with receiver operating characteristic analysis to determine threshold values for differentiation between multiparametric magnetic resonance imaging (mpMRI)-positive and mpMRI-negative lesions. Results We analyzed a total of 170 focal prostate lesions. Lesions number of PI-RADS 2 to 5 was 70, 16, 46, and 38. LBR of SUVmax of each PI-RADS scores was 1.5 (0.9, 2.4), 2.5 (1.6, 3.4), 3.7 (2.6, 4.8), and 6.7 (3.5, 12.7). Based on an optimal threshold ratio of 2.5 to be exceeded, lesions could be classified into MRI-positive lesion on [68Ga]Ga-PSMA PET with a sensitivity of 85.2%, a specificity of 72.0%, with the corresponding area under the receiver operating characteristic curve (AUC) of 0.83, p < 0.001. This value matches the imaging findings better. Conclusion The ratio threshold value of SUVmax, LBR, has improved clinical and research applicability compared with the absolute value of SUVmax. A higher threshold value than the background’s uptake can dovetail the imaging findings on MRI better. It reduces the bias from using absolute background uptake value as the threshold value.

scholarly journals Prediction of Core and Lower Extremity Strains and Sprains in Collegiate Football Players: A Preliminary Study

2012 ◽  
Vol 47 (3) ◽  
pp. 264-272 ◽  
Author(s):  
Gary B. Wilkerson ◽  
Jessica L. Giles ◽  
Dustin K. Seibel
Keyword(s):  
Risk Factors ◽  
Confidence Interval ◽  
Lower Extremity ◽  
Receiver Operating Characteristic ◽  
Injury Risk ◽  
Operating Characteristic ◽  
Football Players ◽  
Low Back ◽  
Collegiate Football ◽  
Receiver Operating

Context: Poor core stability is believed to increase vulnerability to uncontrolled joint displacements throughout the kinetic chain between the foot and the lumbar spine. Objective: To assess the value of preparticipation measurements as predictors of core or lower extremity strains or sprains in collegiate football players. Design: Cohort study. Setting: National Collegiate Athletic Association Division I Football Championship Subdivision football program. Patients or Other Participants: All team members who were present for a mandatory physical examination on the day before preseason practice sessions began (n  =  83). Main Outcome Measure(s): Preparticipation administration of surveys to assess low back, knee, and ankle function; documentation of knee and ankle injury history; determination of body mass index; 4 different assessments of core muscle endurance; and measurement of step-test recovery heart rate. All injuries were documented throughout the preseason practice period and 11-game season. Receiver operating characteristic analysis and logistic regression analysis were used to identify dichotomized predictive factors that best discriminated injured from uninjured status. The 75th and 50th percentiles were evaluated as alternative cutpoints for dichotomization of injury predictors. Results: Players with ≥2 of 3 potentially modifiable risk factors related to core function had 2 times greater risk for injury than those with &lt;2 factors (95% confidence interval  =  1.27, 4.22), and adding a high level of exposure to game conditions increased the injury risk to 3 times greater (95% confidence interval  =  1.95, 4.98). Prediction models that used the 75th and 50th percentile cutpoints yielded results that were very similar to those for the model that used receiver operating characteristic-derived cutpoints. Conclusions: Low back dysfunction and suboptimal endurance of the core musculature appear to be important modifiable football injury risk factors that can be identified on preparticipation screening. These predictors need to be assessed in a prospective manner with a larger sample of collegiate football players.

scholarly journals Machine Learning Strategy for Gut Microbiome-Based Diagnostic Screening of Cardiovascular Disease

Hypertension ◽  
2020 ◽  
Vol 76 (5) ◽  
pp. 1555-1562
Author(s):  
Sachin Aryal ◽  
Ahmad Alimadadi ◽  
Ishan Manandhar ◽  
Bina Joe ◽  
Xi Cheng
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Gut Microbiota ◽  
Receiver Operating Characteristic ◽  
Gut Microbiome ◽  
Operating Characteristic ◽  
Diagnostic Screening ◽  
Testing Area ◽  
Receiver Operating Characteristic Curves ◽  
Receiver Operating

Cardiovascular disease (CVD) is the number one leading cause for human mortality. Besides genetics and environmental factors, in recent years, gut microbiota has emerged as a new factor influencing CVD. Although cause-effect relationships are not clearly established, the reported associations between alterations in gut microbiota and CVD are prominent. Therefore, we hypothesized that machine learning (ML) could be used for gut microbiome–based diagnostic screening of CVD. To test our hypothesis, fecal 16S ribosomal RNA sequencing data of 478 CVD and 473 non-CVD human subjects collected through the American Gut Project were analyzed using 5 supervised ML algorithms including random forest, support vector machine, decision tree, elastic net, and neural networks. Thirty-nine differential bacterial taxa were identified between the CVD and non-CVD groups. ML modeling using these taxonomic features achieved a testing area under the receiver operating characteristic curve (0.0, perfect antidiscrimination; 0.5, random guessing; 1.0, perfect discrimination) of ≈0.58 (random forest and neural networks). Next, the ML models were trained with the top 500 high-variance features of operational taxonomic units, instead of bacterial taxa, and an improved testing area under the receiver operating characteristic curves of ≈0.65 (random forest) was achieved. Further, by limiting the selection to only the top 25 highly contributing operational taxonomic unit features, the area under the receiver operating characteristic curves was further significantly enhanced to ≈0.70. Overall, our study is the first to identify dysbiosis of gut microbiota in CVD patients as a group and apply this knowledge to develop a gut microbiome–based ML approach for diagnostic screening of CVD.

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