scholarly journals Automated detection of cribriform growth patterns in prostate histology images

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Pierre Ambrosini ◽  
Eva Hollemans ◽  
Charlotte F. Kweldam ◽  
Geert J. L. H. van Leenders ◽  
Sjoerd Stallinga ◽  
...  
Keyword(s):  
Deep Learning ◽  
False Positive ◽  
Clinical Decision Making ◽  
False Negative ◽  
Area Under The Curve ◽  
High Sensitivity ◽  
False Positives ◽  
Growth Patterns ◽  
Learning Method ◽  
Method Performance

Abstract Cribriform growth patterns in prostate carcinoma are associated with poor prognosis. We aimed to introduce a deep learning method to detect such patterns automatically. To do so, convolutional neural network was trained to detect cribriform growth patterns on 128 prostate needle biopsies. Ensemble learning taking into account other tumor growth patterns during training was used to cope with heterogeneous and limited tumor tissue occurrences. ROC and FROC analyses were applied to assess network performance regarding detection of biopsies harboring cribriform growth pattern. The ROC analysis yielded a mean area under the curve up to 0.81. FROC analysis demonstrated a sensitivity of 0.9 for regions larger than $${0.0150}\,\hbox {mm}^{2}$$ 0.0150 mm 2 with on average 7.5 false positives. To benchmark method performance for intra-observer annotation variability, false positive and negative detections were re-evaluated by the pathologists. Pathologists considered 9% of the false positive regions as cribriform, and 11% as possibly cribriform; 44% of the false negative regions were not annotated as cribriform. As a final experiment, the network was also applied on a dataset of 60 biopsy regions annotated by 23 pathologists. With the cut-off reaching highest sensitivity, all images annotated as cribriform by at least 7/23 of the pathologists, were all detected as cribriform by the network and 9/60 of the images were detected as cribriform whereas no pathologist labelled them as such. In conclusion, the proposed deep learning method has high sensitivity for detecting cribriform growth patterns at the expense of a limited number of false positives. It can detect cribriform regions that are labelled as such by at least a minority of pathologists. Therefore, it could assist clinical decision making by suggesting suspicious regions.

Evaluation of Positive T- and B-Cell Gene Rearrangement Studies Among Patients Without a Definitive Diagnosis by Other Assays

2019 ◽  
Vol 152 (Supplement_1) ◽  
pp. S35-S36
Author(s):  
Hadrian Mendoza ◽  
Christopher Tormey ◽  
Alexa Siddon
Keyword(s):  
T Cell ◽  
False Positive ◽  
Gene Rearrangement ◽  
Hematologic Malignancy ◽  
False Negative ◽  
False Positives ◽  
False Negatives ◽  
True Negative ◽  
Flow Cytometric ◽  
Pathology Reports

Abstract In the evaluation of bone marrow (BM) and peripheral blood (PB) for hematologic malignancy, positive immunoglobulin heavy chain (IG) or T-cell receptor (TCR) gene rearrangement results may be detected despite unrevealing results from morphologic, flow cytometric, immunohistochemical (IHC), and/or cytogenetic studies. The significance of positive rearrangement studies in the context of otherwise normal ancillary findings is unknown, and as such, we hypothesized that gene rearrangement studies may be predictive of an emerging B- or T-cell clone in the absence of other abnormal laboratory tests. Data from all patients who underwent IG or TCR gene rearrangement testing at the authors’ affiliated VA hospital between January 1, 2013, and July 6, 2018, were extracted from the electronic medical record. Date of testing; specimen source; and morphologic, flow cytometric, IHC, and cytogenetic characterization of the tissue source were recorded from pathology reports. Gene rearrangement results were categorized as true positive, false positive, false negative, or true negative. Lastly, patient records were reviewed for subsequent diagnosis of hematologic malignancy in patients with positive gene rearrangement results with negative ancillary testing. A total of 136 patients, who had 203 gene rearrangement studies (50 PB and 153 BM), were analyzed. In TCR studies, there were 2 false positives and 1 false negative in 47 PB assays, as well as 7 false positives and 1 false negative in 54 BM assays. Regarding IG studies, 3 false positives and 12 false negatives in 99 BM studies were identified. Sensitivity and specificity, respectively, were calculated for PB TCR studies (94% and 93%), BM IG studies (71% and 95%), and BM TCR studies (92% and 83%). Analysis of PB IG gene rearrangement studies was not performed due to the small number of tests (3; all true negative). None of the 12 patients with false-positive IG/TCR gene rearrangement studies later developed a lymphoproliferative disorder, although 2 patients were later diagnosed with acute myeloid leukemia. Of the 14 false negatives, 10 (71%) were related to a diagnosis of plasma cell neoplasms. Results from the present study suggest that positive IG/TCR gene rearrangement studies are not predictive of lymphoproliferative disorders in the context of otherwise negative BM or PB findings. As such, when faced with equivocal pathology reports, clinicians can be practically advised that isolated positive IG/TCR gene rearrangement results may not indicate the need for closer surveillance.

scholarly journals Real-bogus classification for the Zwicky Transient Facility using deep learning

2019 ◽  
Vol 489 (3) ◽  
pp. 3582-3590 ◽  
Author(s):  
Dmitry A Duev ◽  
Ashish Mahabal ◽  
Frank J Masci ◽  
Matthew J Graham ◽  
Ben Rusholme ◽  
...  
Keyword(s):  
Deep Learning ◽  
False Positive ◽  
Large Scale ◽  
Moving Objects ◽  
False Negative ◽  
Efficient Manner ◽  
Astronomical Surveys ◽  
Comparable Performance ◽  
Cost Efficient ◽  
Initial Results

ABSTRACT Efficient automated detection of flux-transient, re-occurring flux-variable, and moving objects is increasingly important for large-scale astronomical surveys. We present braai, a convolutional-neural-network, deep-learning real/bogus classifier designed to separate genuine astrophysical events and objects from false positive, or bogus, detections in the data of the Zwicky Transient Facility (ZTF), a new robotic time-domain survey currently in operation at the Palomar Observatory in California, USA. Braai demonstrates a state-of-the-art performance as quantified by its low false negative and false positive rates. We describe the open-source software tools used internally at Caltech to archive and access ZTF’s alerts and light curves (kowalski ), and to label the data (zwickyverse). We also report the initial results of the classifier deployment on the Edge Tensor Processing Units that show comparable performance in terms of accuracy, but in a much more (cost-) efficient manner, which has significant implications for current and future surveys.

Pearls and Pitfalls of Interpretation in CT Colonography

2020 ◽  
Vol 71 (2) ◽  
pp. 140-148
Author(s):  
Michael Schonberger ◽  
Philippe Lefere ◽  
Abraham H. Dachman
Keyword(s):  
Computed Tomography ◽  
Sensitivity And Specificity ◽  
False Positive ◽  
Ct Colonography ◽  
False Negative ◽  
High Sensitivity ◽  
Negative Results ◽  
False Negative Results ◽  
The Common

The accuracy of computed tomography (CT) colonography (CTC) requires that the radiologist be well trained in the recognition of pitfalls of interpretation. In order to achieve a high sensitivity and specificity, the interpreting radiologist must be well versed in the causes of both false-positive and false-negative results. In this article, we review the common and uncommon pitfalls of interpretation in CTC.

scholarly journals Hyperglycemia Identification Using ECG in Deep Learning Era

Sensors ◽  
2021 ◽  
Vol 21 (18) ◽  
pp. 6263
Author(s):  
Renato Cordeiro ◽  
Nima Karimian ◽  
Younghee Park
Keyword(s):  
Deep Learning ◽  
Blood Glucose Concentration ◽  
Area Under The Curve ◽  
High Sensitivity ◽  
Physiological Signals ◽  
Ecg Signals ◽  
Non Invasive ◽  
Learning Classifier ◽  
Electrocardiogram Ecg ◽  
Smart Wearable

A growing number of smart wearable biosensors are operating in the medical IoT environment and those that capture physiological signals have received special attention. Electrocardiogram (ECG) is one of the physiological signals used in the cardiovascular and medical fields that has encouraged researchers to discover new non-invasive methods to diagnose hyperglycemia as a personal variable. Over the years, researchers have proposed different techniques to detect hyperglycemia using ECG. In this paper, we propose a novel deep learning architecture that can identify hyperglycemia using heartbeats from ECG signals. In addition, we introduce a new fiducial feature extraction technique that improves the performance of the deep learning classifier. We evaluate the proposed method with ECG data from 1119 different subjects to assess the efficiency of hyperglycemia detection of the proposed work. The result indicates that the proposed algorithm is effective in detecting hyperglycemia with a 94.53% area under the curve (AUC), 87.57% sensitivity, and 85.04% specificity. That performance represents an relative improvement of 53% versus the best model found in the literature. The high sensitivity and specificity achieved by the 10-layer deep neural network proposed in this work provide an excellent indication that ECG possesses intrinsic information that can indicate the level of blood glucose concentration.

Methylene Blue as a Diagnosis and Screening Tool for Oral Cancer and Precancer

2020 ◽  
pp. 019459982094768
Author(s):  
Se Hwan Hwang ◽  
Sung Won Kim ◽  
Eun A. Song ◽  
Junuk Lee ◽  
Do Hyun Kim
Keyword(s):  
Methylene Blue ◽  
Oral Cancer ◽  
Diagnostic Accuracy ◽  
False Positive ◽  
Characteristic Curve ◽  
False Positive Rate ◽  
False Negative ◽  
High Sensitivity ◽  
Summary Receiver Operating Characteristic ◽  
True Negative

Objectives To evaluate the accuracy of methylene blue (MB) for diagnosing oral cancer and precancer. Data Sources PubMed, Cochrane Database, Embase, Web of Science, SCOPUS, and Google Scholar. Review Methods Two authors working independently reviewed 6 databases from their dates of inception until April 2020. Studies exploring oral mucosal disorders as detected by MB were assessed. True-positive, true-negative, false-positive, and false-negative data were extracted for each study. Methodological quality was evaluated with the Quality Assessment of Diagnostic Accuracy Studies tool (v 2). Results Seven prospective and retrospective studies (N = 493) were included. The diagnostic odds ratio of MB was 20.017 (95% CI, 10.65-37.63, I2 = 23%). The area under the summary receiver operating characteristic curve was 0.699. Sensitivity was 0.903 (95% CI, 0.84-0.94, I2 = 54%), and specificity was 0.68 (95% CI, 0.60-0.75, I2 = 0%). The correlation between the sensitivity and the false-positive rate was –0.17, indicating an absence of heterogeneity. Conclusions Regarding diagnostic accuracy, MB had high sensitivity but low specificity, suggesting that it cannot be recommended as a replacement for the currently used standard of a scalpel biopsy with histologic assessment. Instead, it should be used as an adjunct to conventional assessment because of its low toxicity and price.

scholarly journals Assessing the Clinical Impact of Prostate-Specific Antigen Assay Variability and Nonequimolarity: A Simulation Study Based on the Population of the United Kingdom

2004 ◽  
Vol 50 (6) ◽  
pp. 1012-1016 ◽  
Author(s):  
Andrew W Roddam ◽  
Christopher P Price ◽  
Naomi E Allen ◽  
Anthony Milford Ward ◽  
Keyword(s):  
United Kingdom ◽  
Prostate Specific Antigen ◽  
False Positive ◽  
False Negative ◽  
Specific Antigen ◽  
False Positives ◽  
False Negatives ◽  
Worst Case ◽  
The United Kingdom ◽  
Analytical Imprecision

Abstract Background: Prostate-specific antigen (PSA) is the most widely used serum biomarker to differentiate between malignant and benign prostate disease. Assays that measure PSA can be biased and/or nonequimolar and hence report significantly different PSA values for samples with the same nominal amount. This report investigates the effects of biased and nonequimolar assays on the decision to recommend a patient for a prostate biopsy based on age-specific PSA values. Methods: A simulation model, calibrated to the distribution of PSA values in the United Kingdom, was developed to estimate the effects of bias, nonequimolarity, and analytical imprecision in terms of the rates of men who are recommended to have a biopsy on the basis of their assay-reported PSA values when their true PSA values are below the threshold (false positives) or vice versa (false negatives). Results: False recommendation rates for a calibrated equimolar assay are 0.5–0.9% for analytical imprecision between 5% and 10%. Positive bias leads to significant increases in false positives and significant decreases in false negatives, whereas negative bias has the opposite effect. False-positive rates for nonequimolar assays increase from 0.5% to 13% in the worst-case scenario, whereas false-negative rates are almost always 0%. Conclusions: Biased and nonequimolar assays can have major detrimental effects on both false-negative and false-positive rates for recommending biopsy. PSA assays should therefore be calibrated to the International Standards and be unbiased and equimolar in response to minimize the likelihood of incorrect clinical decisions, which are potentially detrimental for both patient and healthcare provider.

scholarly journals CORP: Minimizing the chances of false positives and false negatives

2017 ◽  
Vol 122 (1) ◽  
pp. 91-95 ◽  
Author(s):  
Douglas Curran-Everett
Keyword(s):  
Hypothesis Testing ◽  
Experimental Technique ◽  
False Positive ◽  
Scientific Discovery ◽  
False Negative ◽  
False Positives ◽  
Experimental Result ◽  
False Negatives ◽  
Scientific Equipment ◽  
Recent Initiative

Statistics is essential to the process of scientific discovery. An inescapable tenet of statistics, however, is the notion of uncertainty which has reared its head within the arena of reproducibility of research. The Journal of Applied Physiology’s recent initiative, “Cores of Reproducibility in Physiology,” is designed to improve the reproducibility of research: each article is designed to elucidate the principles and nuances of using some piece of scientific equipment or some experimental technique so that other researchers can obtain reproducible results. But other researchers can use some piece of equipment or some technique with expert skill and still fail to replicate an experimental result if they neglect to consider the fundamental concepts of statistics of hypothesis testing and estimation and their inescapable connection to the reproducibility of research. If we want to improve the reproducibility of our research, then we want to minimize the chance that we get a false positive and—at the same time—we want to minimize the chance that we get a false negative. In this review I outline strategies to accomplish each of these things. These strategies are related intimately to fundamental concepts of statistics and the inherent uncertainty embedded in them.

scholarly journals Predictors of dementia misclassification when using brief cognitive assessments

2018 ◽  
Vol 9 (2) ◽  
pp. 109-117 ◽  
Author(s):  
Janice M. Ranson ◽  
Elżbieta Kuźma ◽  
William Hamilton ◽  
Graciela Muniz-Terrera ◽  
Kenneth M. Langa ◽  
...  
Keyword(s):  
Nursing Home ◽  
False Positive ◽  
Polynomial Regression ◽  
False Negative ◽  
False Positives ◽  
Cognitive Assessments ◽  
False Negatives ◽  
Cut Point ◽  
Home Residency ◽  
Poor Memory

BackgroundBrief cognitive assessments can result in false-positive and false-negative dementia misclassification. We aimed to identify predictors of misclassification by 3 brief cognitive assessments; the Mini-Mental State Examination (MMSE), Memory Impairment Screen (MIS) and animal naming (AN).MethodsParticipants were 824 older adults in the population-based US Aging, Demographics and Memory Study with adjudicated dementia diagnosis (DSM-III-R and DSM-IV criteria) as the reference standard. Predictors of false-negative, false-positive and overall misclassification by the MMSE (cut-point <24), MIS (cut-point <5) and AN (cut-point <9) were analysed separately in multivariate bootstrapped fractional polynomial regression models. Twenty-two candidate predictors included sociodemographics, dementia risk factors and potential sources of test bias.ResultsMisclassification by at least one assessment occurred in 301 (35.7%) participants, whereas only 14 (1.7%) were misclassified by all 3 assessments. There were different patterns of predictors for misclassification by each assessment. Years of education predicts higher false-negatives (odds ratio [OR] 1.23, 95% confidence interval [95% CI] 1.07–1.40) and lower false-positives (OR 0.77, 95% CI 0.70–0.83) by the MMSE. Nursing home residency predicts lower false-negatives (OR 0.15, 95% CI 0.03–0.63) and higher false-positives (OR 4.85, 95% CI 1.27–18.45) by AN. Across the assessments, false-negatives were most consistently predicted by absence of informant-rated poor memory. False-positives were most consistently predicted by age, nursing home residency and non-Caucasian ethnicity (all p < 0.05 in at least 2 models). The only consistent predictor of overall misclassification across all assessments was absence of informant-rated poor memory.ConclusionsDementia is often misclassified when using brief cognitive assessments, largely due to test specific biases.

Drug screening in urine by cloned enzyme donor immunoassay (CEDIA) and kinetic interaction of microparticles in solution (KIMS): a comparative study

2006 ◽  
Vol 44 (4) ◽  
Author(s):  
Lutz Schwettmann ◽  
Wolf-Rüdiger Külpmann ◽  
Christian Vidal
Keyword(s):  
Gas Chromatography ◽  
Drug Screening ◽  
False Positive ◽  
False Negative ◽  
False Positives ◽  
Gas Chromatography Mass Spectrometry ◽  
Electron Capture Detection ◽  
False Negatives ◽  
Negative Results ◽  
Kinetic Interaction

AbstractTwo commercially available drug-screening assays were evaluated: the Roche kinetic interaction of microparticles in solution (KIMS) assay and the Microgenics cloned enzyme donor immunoassay (CEDIA). Urine samples from known drug-abuse patients were analyzed for amphetamines, barbiturates, benzodiazepines, benzoylecgonine, cannabinoids, LSD, methadone and opiates. Samples with discordant findings for the two assays were analyzed by gas chromatography/mass spectrometry (GC/MS) or gas chromatography/electron capture detection (GC/ECD). Amphetamines showed 96.0% concordant results, with two false positive findings by CEDIA, three by KIMS and a further two false negatives by KIMS. Barbiturates showed 99.4% concordant results, with one false negative by KIMS. Benzodiazepines showed 97.4% concordant results, with two false negatives by KIMS (cutoff 100μg/L, CEDIA cutoff 300 μg/L). Benzoylecgonine showed 17.8% concordant positive and 82.2% concordant negative results and no false finding by either assay. Cannabinoids showed 99.3% concordant results, with one sample negative by KIMS at a cutoff of 50μg/L and positive by CEDIA (cutoff 25μg/L). For LSD, 6.7% of findings were not in agreement. Methadone showed 97.5% concordant results, with two false positives by CEDIA, and one false positive and one false negative by KIMS. Opiates showed 96.9% concordant results, with no false KIMS results, but four false positives by CEDIA. The results indicate that the agreement of the CEDIA and KIMS results for the eight drugs is rather good (93.3–100%).

Design And Analysis Of Qualitative Collaborative Studies: Minimum Collaborative Program

1990 ◽  
Vol 73 (6) ◽  
pp. 953-960 ◽  
Author(s):  
Foster D Mcclure
Keyword(s):  
False Positive ◽  
Performance Indicator ◽  
False Negative ◽  
Test Sample ◽  
Method Performance ◽  
Quantitative Studies ◽  
Collaborative Program ◽  
Collaborative Studies ◽  
Detailed Method ◽  
Sensitivity Specificity

Abstract Collaborative studies Involving qualitative data are usually conducted under design constraints to fulfill the requirements for quantitative studies. The data from these qualitative studies are often analyzed In a manner that ignores the fact that collaborative studies Involve matching (I.e., each laboratory analyzes a portion of each test sample). This report presents some design considerations and analysis procedures for qualitative collaborative studies that take into account that the design Involves matching. Suggestions are offered as to the number of laboratories and test samples to use in the minimum collaborative program, and analysis procedures for outlier screening are detailed. Method performance Is assessed through such Indicators as sensitivity, specificity, false positive, and false negative rates. Methods for estimating the error of the performance indicator rates are explained, and procedures are given for estimating false positive and false negative rates for lot defect rates that may occur In practice.

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