Errors in prostate core biopsy diagnosis in an era of specialisation and double reporting

2020 ◽  
pp. jclinpath-2020-206726
Author(s):  
Cornelia Margaret Szecsei ◽  
Jon D Oxley
Keyword(s):  
Error Rate ◽  
False Positive ◽  
Core Biopsy ◽  
False Negative ◽  
Diagnostic Error ◽  
Error Rates ◽  
False Positive Error ◽  
Biopsy Diagnosis ◽  
False Negative Error ◽  
False Positive Error Rate

AimTo examine the effects of specialist reporting on error rates in prostate core biopsy diagnosis.MethodBiopsies were reported by eight specialist uropathologists over 3 years. New cancer diagnoses were double-reported and all biopsies were reviewed for the multidisciplinary team (MDT) meeting. Diagnostic alterations were recorded in supplementary reports and error rates were compared with a decade previously.Results2600 biopsies were reported. 64.1% contained adenocarcinoma, a 19.7% increase. The false-positive error rate had reduced from 0.4% to 0.06%. The false-negative error rate had increased from 1.5% to 1.8%, but represented fewer absolute errors due to increased cancer incidence.ConclusionsSpecialisation and double-reporting have reduced false-positive errors. MDT review of negative cores continues to identify a very low number of false-negative errors. Our data represents a ‘gold standard’ for prostate biopsy diagnostic error rates. Increased use of MRI-targeted biopsies may alter error rates and their future clinical significance.

Error Rates in Competency Testing When Test Retaking Is Permitted

1990 ◽  
Vol 15 (1) ◽  
pp. 39-52 ◽  
Author(s):  
Huynh Huynh
Keyword(s):  
Conditional Probability ◽  
False Positive ◽  
False Negative ◽  
Error Rates ◽  
Rasch Models ◽  
Positive Error ◽  
False Positive Error ◽  
Competency Testing ◽  
Negative Error ◽  
False Negative Error

False positive and false negative error rates are studied for competency testing where examinees are permitted to retake the test if they fail to pass. Formulae are provided for the beta-binomial and Rasch models, and estimates based on these two models are compared for several typical situations. Although Rasch estimates are expected to be more accurate than beta-binomial estimates, differences among them are found not to be substantial in a number of practical situations. Under relatively general conditions and when test retaking is permitted, the probability of making a false negative error is zero. Under the same situation, and given that an examinee is a true nonmaster, the conditional probability of making a false positive error for this examinee is one.

scholarly journals Feasibility of FT-NIR spectroscopy and Vis/NIR hyperspectral imaging for sorting unsound chestnuts

Italus Hortus ◽  
2020 ◽  
Vol 27 ◽  
pp. 3-18
Author(s):  
Giacomo Bedini ◽  
Giorgia Bastianelli ◽  
Swathi Sirisha Nallan Chakravartula ◽  
Carmen Morales-Rodríguez ◽  
Luca Rossini ◽  
...  
Keyword(s):  
Hyperspectral Imaging ◽  
Error Rate ◽  
False Positive ◽  
Total Error ◽  
False Negative ◽  
Nir Spectroscopy ◽  
Positive Error ◽  
False Positive Error ◽  
Negative Error ◽  
False Negative Error

Authors explored the potential use of Vis/NIR hyperspectral imaging (HSI) and Fourier-transform Near-Infrared (FT-NIR) spectroscopy to be used as in-line tools for the detection of unsound chestnut fruits (i.e. infected and/or infested) in comparison with the traditional sorting technique. For the intended purpose, a total of 720 raw fruits were collected from a local company. Chestnut fruits were preliminarily classified into sound (360 fruits) and unsound (360 fruits) batches using a proprietary floating system at the facility along with manual selection performed by expert workers. The two batches were stored at 4 ± 1 °C until use. Samples were left at ambient temperature for at least 12 h before measurements. Subsequently, fruits were subjected to non-destructive measurements (i.e. spectral analysis) immediately followed by destructive analyses (i.e. microbiological and entomological assays). Classification models were trained using the Partial Least Squares Discriminant Analysis (PLS-DA) by pairing the spectrum of each fruit with the categorical information obtained from its destructive assay (i.e., sound, Y = 0; unsound, Y = 1). Categorical data were also used to evaluate the classification performance of the traditional sorting method. The performance of each PLS-DA model was evaluated in terms of false positive error (FP), false negative error (FN) and total error (TE) rates. The best result (8% FP, 14% FN, 11% TE) was obtained using Savitzky-Golay first derivative with a 5-points window of smoothing on the dataset of raw reflectance spectra scanned from the hilum side of fruit using the Vis/NIR HSI setup. This model showed similarity in terms of False Negative error rate with the best one computed using data from the FT-NIR setup (i.e. 15% FN), which, however, had the lowest global performance (17% TE) due to the highest False Positive error rate (19%). Finally, considering that the total error rate committed by the traditional sorting system was about 14.5% with a tendency of misclassifying unsound fruits, the results indicate the feasibility of a rapid, in-line detection system based on spectroscopic measurements.

scholarly journals High resolution analysis of cervical cells--a progress report.

1977 ◽  
Vol 25 (7) ◽  
pp. 689-695 ◽  
Author(s):  
R S Poulsen ◽  
L H Oliver ◽  
R L Cahn ◽  
C Louis ◽  
G Toussaint
Keyword(s):  
High Resolution ◽  
False Positive ◽  
False Negative ◽  
Error Rates ◽  
Cell Segmentation ◽  
Negative Error ◽  
False Negative Error ◽  
High Resolution Analysis ◽  
Resolution Analysis ◽  
Cervical Cells

This paper presents preliminary results of research toward the development of a high resolution analysis stage for a dual resolution image processing-based prescreening device for cervical cytology. Experiments using both manual and automatic methods for cell segmentation are described. In both cases, 1500 cervical cells were analyzed and classified as normal or abnormal (dysplastic or malignant) using a minimum Mahalanobis distance classifier with eight subclasses of normal cells, and five subclasses of abnormal cells. With manual segmentation, false positive and false negative error rates of 2.98 and 7.73% were obtained. Similar experiments using automatic cell segmentation methods yielded false positive and false negative error rates of 3.90 and 11.56%, respectively. In both cases, independent training and testing data were used.

scholarly journals FACE RECOGNITION WITH LOW FALSE POSITIVE ERROR RATE

2019 ◽  
Vol XLII-2/W12 ◽  
pp. 11-15
Author(s):  
N. U. Bagrov ◽  
A. S. Konushin ◽  
V. S. Konushin
Keyword(s):  
Face Recognition ◽  
Error Rate ◽  
False Positive ◽  
Closed Loop ◽  
Large City ◽  
Positive Error ◽  
False Positive Error ◽  
Closed Loop Systems ◽  
False Positive Error Rate ◽  
Human Operators

<p><strong>Abstract.</strong> Nowadays face recognition systems are widely used in the world. In China these systems are used in safe cities projects in production, in Russia they are used mostly in closed-loop systems like factories, business centers with biometric access control or stadiums. Closed loop means that we need to identify people from a fixed dataset: in factory it’s a list of employees, in stadium it’s a list of ticket owners. The most challenging task is to identify people from some large city with an open dataset: we don’t have a fixed set of people in the city, it’s rapidly changing due to migration. Another limit is the accuracy of the system: we can’t make a lot of false positive errors (when a person is incorrectly recognized as another person) because number of human operators is limited and they are expensive. We propose an approach to maximize face recognition accuracy for a fixed false positive error rate using limited amount of hardware.</p>

Apophenia as the Disposition to False Positives: A Unifying Framework for the Openness-Psychoticism Dimension

2019 ◽  
Author(s):  
Scott D. Blain ◽  
Julia Longenecker ◽  
Rachael Grazioplene ◽  
Bonnie Klimes-Dougan ◽  
Colin G. DeYoung
Keyword(s):  
False Positive ◽  
Psychotic Disorders ◽  
Structural Equation Models ◽  
False Negative ◽  
Error Rates ◽  
Self Report ◽  
Auditory Signal ◽  
Positive Symptoms ◽  
Positive Error ◽  
False Positive Error

Positive symptoms of schizophrenia and its extended phenotype—often termed psychoticism or positive schizotypy—are characterized by the inclusion of novel, erroneous mental contents. One promising framework for explaining positive symptoms involves “apophenia,” conceptualized here as a disposition toward false positive errors. Apophenia and positive symptoms have shown relations to Openness to Experience, and all of these constructs involve tendencies toward pattern seeking. Nonetheless, few studies have investigated the relations between psychoticism and non-self-report indicators of apophenia, let alone the role of normal personality variation. The current research used structural equation models to test associations between psychoticism, openness, intelligence, and non-self-report indicators of apophenia comprising false positive error rates on a variety of computerized tasks. In Sample 1, 1193 participants completed digit identification, theory of mind, and emotion recognition tasks. In Sample 2, 195 participants completed auditory signal detection and semantic word association tasks. Openness and psychoticism were positively correlated. Self-reported psychoticism, openness, and their shared variance were positively associated with apophenia, as indexed by false positive error rates, whether or not intelligence was controlled for. Apophenia was not associated with other personality traits, and openness and psychoticism were not associated with false negative errors. Standardized regression paths from openness-psychoticism to apophenia were in the range of .61 to .75. Findings provide insights into the measurement of apophenia and its relation to personality and psychopathology. Apophenia and pattern seeking may be promising constructs for unifying openness with the psychosis spectrum and for providing an explanation of positive symptoms. Results are discussed in the context of possible adaptive characteristics of apophenia, as well as potential risk factors for the development of psychotic disorders.

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