scholarly journals Bayesian Tragedy and Categorical Medical Error in Prostate Cancer

2019 ◽  
pp. 20-28
Author(s):  
Nery Lamothe ◽  
Mara Lamothe ◽  
Alejandro Alonso-Altamirano ◽  
Pedro A Lamothe
Keyword(s):  
Prostate Cancer ◽  
Sensitivity And Specificity ◽  
Medical Errors ◽  
Medical Error ◽  
Diagnostic Techniques And Procedures ◽  
Diagnostic Techniques ◽  
Iatrogenic Disease ◽  
Urinary Urgency ◽  
Urinary Frequency ◽  
Practice Of Medicine

The Bayesian concept is complex and contrary to intuition, but it is fundamental in the practice of medicine as well as in biotechnological development. We expect that a tragedy of ignoring the Bayesian concept will result clear to the reader who should acquire the tools to recognize its meaning, as well as, its use; but especially, to visualize the serious consequences of ignoring it. The physician is not mystically fascinated for knowing what percentage of patients who has prostate cancer have no symptoms (47%), urinary frequency (38%), urinary urgency (10%), decreased urine stream (23%) or hematuria (1.4%). What the physician and every patient want and vitally need to know is the probability of having prostate cancer, giving that he has one or more of those symptoms. Keywords: Medical Errors; Iatrogenic disease; Sensitivity and specificity; Diagnostic Techniques and procedures

Improved Sensitivity and Specificity for Detection of Prostate Cancer

2007 ◽  
Author(s):  
Rao P. Gullapalli ◽  
Michael Naslund ◽  
John Papasdimitrou ◽  
Elliot Siegel
Keyword(s):  
Prostate Cancer ◽  
Sensitivity And Specificity

scholarly journals Diagnosis of transition zone prostate cancer by multiparametric MRI: added value of MR spectroscopic imaging with sLASER volume selection

2021 ◽  
Vol 28 (1) ◽  
Author(s):  
Neda Gholizadeh ◽  
Peter B. Greer ◽  
John Simpson ◽  
Jonathan Goodwin ◽  
Caixia Fu ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
High Risk ◽  
Transition Zone ◽  
Sensitivity And Specificity ◽  
Cancer Detection ◽  
Diagnostic Performance ◽  
Spectroscopic Imaging ◽  
Multiparametric Mri ◽  
Low Risk ◽  
Intermediate Risk

Abstract Background Current multiparametric MRI (mp-MRI) in routine clinical practice has poor-to-moderate diagnostic performance for transition zone prostate cancer. The aim of this study was to evaluate the potential diagnostic performance of novel 1H magnetic resonance spectroscopic imaging (MRSI) using a semi-localized adiabatic selective refocusing (sLASER) sequence with gradient offset independent adiabaticity (GOIA) pulses in addition to the routine mp-MRI, including T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI) and quantitative dynamic contrast enhancement (DCE) for transition zone prostate cancer detection, localization and grading. Methods Forty-one transition zone prostate cancer patients underwent mp-MRI with an external phased-array coil. Normal and cancer regions were delineated by two radiologists and divided into low-risk, intermediate-risk, and high-risk categories based on TRUS guided biopsy results. Support vector machine models were built using different clinically applicable combinations of T2WI, DWI, DCE, and MRSI. The diagnostic performance of each model in cancer detection was evaluated using the area under curve (AUC) of the receiver operating characteristic diagram. Then accuracy, sensitivity and specificity of each model were calculated. Furthermore, the correlation of mp-MRI parameters with low-risk, intermediate-risk and high-risk cancers were calculated using the Spearman correlation coefficient. Results The addition of MRSI to T2WI + DWI and T2WI + DWI + DCE improved the accuracy, sensitivity and specificity for cancer detection. The best performance was achieved with T2WI + DWI + MRSI where the addition of MRSI improved the AUC, accuracy, sensitivity and specificity from 0.86 to 0.99, 0.83 to 0.96, 0.80 to 0.95, and 0.85 to 0.97 respectively. The (choline + spermine + creatine)/citrate ratio of MRSI showed the highest correlation with cancer risk groups (r = 0.64, p < 0.01). Conclusion The inclusion of GOIA-sLASER MRSI into conventional mp-MRI significantly improves the diagnostic accuracy of the detection and aggressiveness assessment of transition zone prostate cancer.

scholarly journals Precise Identification of Prostate Cancer from DWI Using Transfer Learning

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3664
Author(s):  
Islam R. Abdelmaksoud ◽  
Ahmed Shalaby ◽  
Ali Mahmoud ◽  
Mohammed Elmogy ◽  
Ahmed Aboelfetouh ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Transfer Learning ◽  
Sensitivity And Specificity ◽  
Benign Tumors ◽  
B Values ◽  
Cad System ◽  
Average Accuracy ◽  
Apparent Diffusion ◽  
Invasive Techniques ◽  
Fine Tune

Background and Objective: The use of computer-aided detection (CAD) systems can help radiologists make objective decisions and reduce the dependence on invasive techniques. In this study, a CAD system that detects and identifies prostate cancer from diffusion-weighted imaging (DWI) is developed. Methods: The proposed system first uses non-negative matrix factorization (NMF) to integrate three different types of features for the accurate segmentation of prostate regions. Then, discriminatory features in the form of apparent diffusion coefficient (ADC) volumes are estimated from the segmented regions. The ADC maps that constitute these volumes are labeled by a radiologist to identify the ADC maps with malignant or benign tumors. Finally, transfer learning is used to fine-tune two different previously-trained convolutional neural network (CNN) models (AlexNet and VGGNet) for detecting and identifying prostate cancer. Results: Multiple experiments were conducted to evaluate the accuracy of different CNN models using DWI datasets acquired at nine distinct b-values that included both high and low b-values. The average accuracy of AlexNet at the nine b-values was 89.2±1.5% with average sensitivity and specificity of 87.5±2.3% and 90.9±1.9%. These results improved with the use of the deeper CNN model (VGGNet). The average accuracy of VGGNet was 91.2±1.3% with sensitivity and specificity of 91.7±1.7% and 90.1±2.8%. Conclusions: The results of the conducted experiments emphasize the feasibility and accuracy of the developed system and the improvement of this accuracy using the deeper CNN.

scholarly journals Can mean ADC value and ADC ratio of benign prostate tissue to prostate cancer assist in the prediction of clinically significant prostate cancer within the PI-RADSv2 scoring system?

2020 ◽  
Vol 51 (1) ◽  
Author(s):  
Samar Ramzy Ragheb ◽  
Reem Hassan Bassiouny
Keyword(s):  
Prostate Cancer ◽  
Sensitivity And Specificity ◽  
Gleason Score ◽  
Treatment Strategies ◽  
Serum Levels ◽  
Prostatic Tissue ◽  
Histopathological Analysis ◽  
Adc Value ◽  
The Mean ◽  
Clinically Significant

Abstract Background The aim of this study is to investigate whether quantitative DW metrics can provide additive value to the reliable categorization of lesions within existing PI-RADSv2 guidelines. Fifty-eight patients with clinically suspicious prostate cancer who underwent PR examination, PSA serum levels, sextant TRUS-guided biopsies, and bi-parametric MR imaging were included in the study. Results Sixty-six lesions were detected by histopathological analysis of surgical specimens. The mean ADC values were significantly lower in tumor than non-tumor tissue. The mean ADC value inversely correlated with Gleason score of tumors with a significant p value < 0.001.Conversely, a positive relationship was found between the ADC ratio (ADC of benign prostatic tissue to prostate cancer) and the pathologic Gleason score with a significant elevation of the ADC ratio along with an increase of the pathologic Gleason score (p < 0.001). ROC curves constructed for the tumor ADC and ADC ratio helped to distinguish pathologically aggressive (Gleason score ≥ 7) from non-aggressive (Gleason score ≤ 6) tumors and to correlate it with PIRADSv2 scoring to predict the presence of clinically significant PCA (PIRADSv2 DW ≥ 4). The ability of the tumor ADC and ADC ratio to predict highly aggressive tumors (GS> 7) was high (AUC for ADC and ADC ratio, 0.946 and 0.897; p = 0.014 and 0.039, respectively). The ADC cut-off value for GS ≥ 7 was < 0.7725 and for GS ≤ 6 was > 0.8620 with sensitivity and specificity 97 and 94%. The cutoff ADC ratio for predicting (GS > 7) was 1.42 and for GS ≤ 6 was > 1.320 with sensitivity and specificity 97 and 92%. By applying this ADC ratio cut-off value the sensitivity and specificity of reader 1 for correct categorization of PIRADSv2 DW > 4 increased from 90 and 68% to 95 and 90% and that of reader 2 increased from 94 and 88% to 97 and 92%, respectively. Conclusion Estimation of DW metrics (ADC and ADC ratio between benign prostatic tissue and prostate cancer) allow the non-invasive assessment of biological aggressiveness of prostate cancer and allow reliable application of the PIRADSv2 scoring to determine clinically significant cancer (DW score > 4) which may contribute in planning initial treatment strategies.

scholarly journals Clinical diagnosis of prostate cancer using digital rectal examination and prostate-specific antigen tests: a systematic review and meta-analysis of sensitivity and specificity

2021 ◽  
Vol 27 (1) ◽  
Author(s):  
Nelson C. Okpua ◽  
Simon I. Okekpa ◽  
Stanley Njaka ◽  
Augusta N. Emeh
Keyword(s):  
Prostate Cancer ◽  
Prostate Specific Antigen ◽  
Sensitivity And Specificity ◽  
Specific Antigen ◽  
Digital Rectal Examination ◽  
Antigen Test ◽  
Rectal Examination ◽  
Prostate Specific Antigen Test ◽  
Prostate Cancers ◽  
Antigen Tests

Abstract Background Being diagnosed with cancer, irrespective of type initiates a serious psychological concern. The increasing rate of detection of indolent prostate cancers is a source of worry to public health. Digital rectal examination and prostate-specific antigen tests are the commonly used prostate cancer screening tests. Understanding the diagnostic accuracies of these tests may provide clearer pictures of their characteristics and values in prostate cancer diagnosis. This review compared the sensitivities and specificities of digital rectal examination and prostate-specific antigen test in detection of clinically important prostate cancers using studies from wider population. Main body We conducted literature search in PubMed, Medline, Science Direct, Wiley Online, CINAHL, Scopus, AJOL and Google Scholar, using key words and Boolean operators. Studies comparing the sensitivity and specificity of digital rectal examination and prostate-specific antigen tests in men 40 years and above, using biopsy as reference standard were retrieved. Data were extracted and analysed using Review manager (RevMan 5.3) statistical software. The overall quality of the studies was good, and heterogeneity was observed across the studies. The result comparatively shows that prostate-specific antigen test has higher sensitivity (P < 0.00001, RR 0.74, CI 0.67–0.83) and specificity (P < 0.00001, RR 1.81, CI 1.54–2.12) in the detection of prostate cancers than digital rectal examination. Conclusion Prostate-specific antigen test has higher sensitivity and specificity in detecting prostate cancers from men of multiple ethnic origins. However, combination of prostate-specific antigen test and standardized digital rectal examination procedure, along with patients history, may improve the accuracy and minimize over-diagnoses of indolent prostate cancers.

Multivariable Logistic Regression Equation to Predict Prostate Cancer

2021 ◽  
Author(s):  
Lu Ma ◽  
Dong Cheng ◽  
Qinghua Li ◽  
Jingbo Zhu ◽  
Yu Wang ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Logistic Regression ◽  
Regression Analysis ◽  
Prediction Model ◽  
Prostate Specific Antigen ◽  
Sensitivity And Specificity ◽  
Logistic Regression Analysis ◽  
Roc Curve ◽  
Specific Antigen ◽  
Clinical Feature

Abstract Objective: To explore the predictive value of white blood cell (WBC), monocyte (M), neutrophil-to-lymphocyte ratio (NLR), fibrinogen (FIB), free prostate-specific antigen (fPSA) and free prostate-specific antigen/prostate-specific antigen (f/tPSA) in prostate cancer (PCa).Materials and methods: Retrospective analysis of 200 cases of prostate biopsy and collection of patients' systemic inflammation indicators, biochemical indicators, PSA and fPSA. First, the dimensionality of the clinical feature parameters is reduced by the Lass0 algorithm. Then, the logistic regression prediction model was constructed using the reduced parameters. The cut-off value, sensitivity and specificity of PCa are predicted by the ROC curve analysis and calculation model. Finally, based on Logistic regression analysis, a Nomogram for predicting PCa is obtained.Results: The six clinical indicators of WBC, M, NLR, FIB, fPSA, and f/tPSA were obtained after dimensionality reduction by Lass0 algorithm to improve the accuracy of model prediction. According to the regression coefficient value of each influencing factor, a logistic regression prediction model of PCa was established: logit P=-0.018-0.010×WBC+2.759×M-0.095×NLR-0.160×FIB-0.306×fPSA-2.910×f/tPSA. The area under the ROC curve is 0.816. When the logit P intercept value is -0.784, the sensitivity and specificity are 72.5% and 77.8%, respectively.Conclusion: The establishment of a predictive model through Logistic regression analysis can provide more adequate indications for the diagnosis of PCa. When the logit P cut-off value of the model is greater than -0.784, the model will be predicted to be PCa.

Narrowing the mindware gap in medicine

Diagnosis ◽  
2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Pat Croskerry
Keyword(s):  
United States ◽  
Cognitive Performance ◽  
Clinical Reasoning ◽  
Medical Errors ◽  
Medical Error ◽  
Causes Of Death ◽  
The United States ◽  
Morbidity And Mortality ◽  
Medical Educators ◽  
High Degree

Abstract Medical error is now recognized as one of the leading causes of death in the United States. Of the medical errors, diagnostic failure appears to be the dominant contributor, failing in a significant number of cases, and associated with a high degree of morbidity and mortality. One of the significant contributors to diagnostic failure is the cognitive performance of the provider, how they think and decide about the process of diagnosis. This thinking deficit in clinical reasoning, referred to as a mindware gap, deserves the attention of medical educators. A variety of specific approaches are outlined here that have the potential to close the gap.

scholarly journals EVALUATION OF ELEVEN IMMUNOCHROMATOGRAPHIC ASSAYS FOR SARS-CoV-2 DETECTION: INVESTIGATING DENGUE CROSS-REACTION

2020 ◽  
Author(s):  
Beatriz Araujo Oliveira ◽  
Lea Campos de Oliveira ◽  
Franciane Mendes de Oliveira ◽  
Geovana Maria Pereira ◽  
Regina Maia de Souza ◽  
...  
Keyword(s):  
Sensitivity And Specificity ◽  
High Sensitivity ◽  
Diagnostic Techniques ◽  
Cross Reactivity ◽  
Cross Reaction ◽  
List Type ◽  
Rt Pcr ◽  
Serum Samples ◽  
Igm Antibodies ◽  
Good Agreement

AbstractBackgroundCOVID-19 disease (Coronavirus disease 2019) caused by SARS-CoV-2 (Severe acute respiratory syndrome coronavirus 2) is widespread worldwide, affecting more than 11 million people globally (July 6th, 2020). Diagnostic techniques have been studied in order to contain the pandemic. Immunochromatographic (IC) assays are feasible and low cost alternative for monitoring the spread of COVID-19 in the population.MethodsHere we evaluate the sensitivity and specificity of eleven different immunochromatographic tests in 98 serum samples from confirmed cases of COVID-19 through RT-PCR and 100 negative serum samples from blood donors collected in February 2019. Considering the endemic situation of Dengue in Brazil, we also evaluated the cross-reactivity with Dengue using 20 serum samples from patients with confirmed diagnosis for Dengue collected in early 2019 through four different tests.ResultsOur results demonstrated agreement between immunochromatographic assays and RT-PCR, especially after 10 days since the onset of symptoms. The evaluation of IgG and IgM antibodies combined demonstrated a strong level of agreement (0.85) of IC assays and RT-PCR. It was observed cross-reactivity between Dengue and COVID-19 using four different IC assays for COVID-19 diagnosis. The specificity of IC assays to detected COVID-19 IgM antibodies using Dengue serum samples varied from 80% to 85%; the specificity of IgG detection was 100% and total antibody was 95%.ConclusionsWe found high sensitivity, specificity and good agreement of IC assays, especially after 10 days onset of symptoms. However, we detected cross-reactivity between Dengue and COVID-19 mainly with IgM antibodies demonstrating the need for better studies about diagnostic techniques for these diseases.HighlightsImmunochromatographic assays demonstrated high sensitivity and specificity and good agreement with the gold-standard RT-PCR;Increase in sensitivity and specificity of assays using samples collected after the 10th day of symptoms;Cross-reaction with Dengue serology in evaluation of IgM.

scholarly journals Novel Blood Biomarkers for an Earlier Diagnosis of Alzheimer’s Disease: A Literature Review

2020 ◽  
Author(s):  
Shiavax Rao ◽  
Andrew J. Boileau
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Literature Review ◽  
Clinical Diagnosis ◽  
Sensitivity And Specificity ◽  
Memory Loss ◽  
High Sensitivity ◽  
Diagnostic Techniques ◽  
Pubmed Database ◽  
Diagnostic Approaches

Alzheimer’s disease is a neurodegenerative condition associated with neurofibrillary tangles and cortical deposition of amyloid plaques. Clinical presentation of the disease involves manifestations such as memory loss, cognitive decline and dementia with some of the earliest reported deficits being episodic memory impairment and olfactory dysfunction. Current diagnostic approaches rely on autopsy characterization of gross brain pathology, or brain imaging of biomarkers late in the disease course. The aim of this literature review is to identify and compare newly emerging and novel CSF, serum and mucosal biomarkers, with the potential of making an earlier clinical diagnosis of Alzheimer’s disease. Utilizing such techniques may allow for earlier therapeutic intervention, reduction of disability and enhancement of quality of life. Literature review and analysis was performed by screening the PubMed database for relevant studies within the past 5 years. All studies showed statistically significant (P < 0.05) differences in testing between AD patients and controls. Two categories of serum biomarkers (redox-reactive antiphospholipid antibodies and microRNAs) and an olfactory mucosal marker (microRNA-206) could discriminate between early AD patients and controls with high sensitivity and specificity. In conclusion, certain studies have shown promising results with high sensitivity and specificity, high discriminative potential for Alzheimer’s disease early in its progression, and statistically significant results in larger study samples. Utilization of such diagnostic techniques should increase the efficacy of making an earlier clinical diagnosis of Alzheimer’s disease.

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