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

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.

Blood ◽  
2019 ◽  
Vol 134 (Supplement_1) ◽  
pp. 5223-5223
Author(s):  
Hadrian Mendoza ◽  
Christopher Tormey ◽  
Alexa J. Siddon

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 two 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. Disclosures No relevant conflicts of interest to declare.


2004 ◽  
Vol 50 (6) ◽  
pp. 1012-1016 ◽  
Author(s):  
Andrew W Roddam ◽  
Christopher P Price ◽  
Naomi E Allen ◽  
Anthony Milford Ward ◽  

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.


2017 ◽  
Vol 122 (1) ◽  
pp. 91-95 ◽  
Author(s):  
Douglas Curran-Everett

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.


2018 ◽  
Vol 9 (2) ◽  
pp. 109-117 ◽  
Author(s):  
Janice M. Ranson ◽  
Elżbieta Kuźma ◽  
William Hamilton ◽  
Graciela Muniz-Terrera ◽  
Kenneth M. Langa ◽  
...  

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.


Author(s):  
Lutz Schwettmann ◽  
Wolf-Rüdiger Külpmann ◽  
Christian Vidal

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%).


Author(s):  
Heinrich A. Backmann ◽  
Marthe Larsen ◽  
Anders S. Danielsen ◽  
Solveig Hofvind

Abstract Objective To analyze the association between radiologists’ performance and image position within a batch in screen reading of mammograms in Norway. Method We described true and false positives and true and false negatives by groups of image positions and batch sizes for 2,937,312 screen readings performed from 2012 to 2018. Mixed-effects models were used to obtain adjusted proportions of true and false positive, true and false negative, sensitivity, and specificity for different image positions. We adjusted for time of day and weekday and included the individual variation between the radiologists as random effects. Time spent reading was included in an additional model to explore a possible mediation effect. Result True and false positives were negatively associated with image position within the batch, while the rates of true and false negatives were positively associated. In the adjusted analyses, the rate of true positives was 4.0 per 1000 (95% CI: 3.8–4.2) readings for image position 10 and 3.9 (95% CI: 3.7–4.1) for image position 60. The rate of true negatives was 94.4% (95% CI: 94.0–94.8) for image position 10 and 94.8% (95% CI: 94.4–95.2) for image position 60. Per 1000 readings, the rate of false negative was 0.60 (95% CI: 0.53–0.67) for image position 10 and 0.62 (95% CI: 0.55–0.69) for image position 60. Conclusion There was a decrease in the radiologists’ sensitivity throughout the batch, and although this effect was small, our results may be clinically relevant at a population level or when multiplying the differences with the number of screen readings for the individual radiologists. Key Points • True and false positive reading scores were negatively associated with image position within a batch. • A decreasing trend of positive scores indicated a beneficial effect of a certain number of screen readings within a batch. • False negative scores increased throughout the batch but the association was not statistically significant.


2021 ◽  
Vol 19 (9) ◽  
pp. 1072-1078
Author(s):  
Changyu Shen ◽  
Enrico G. Ferro ◽  
Huiping Xu ◽  
Daniel B. Kramer ◽  
Rushad Patell ◽  
...  

Background: Statistical testing in phase III clinical trials is subject to chance errors, which can lead to false conclusions with substantial clinical and economic consequences for patients and society. Methods: We collected summary data for the primary endpoints of overall survival (OS) and progression-related survival (PRS) (eg, time to other type of event) for industry-sponsored, randomized, phase III superiority oncology trials from 2008 through 2017. Using an empirical Bayes methodology, we estimated the number of false-positive and false-negative errors in these trials and the errors under alternative P value thresholds and/or sample sizes. Results: We analyzed 187 OS and 216 PRS endpoints from 362 trials. Among 56 OS endpoints that achieved statistical significance, the true efficacy of experimental therapies failed to reach the projected effect size in 33 cases (58.4% false-positives). Among 131 OS endpoints that did not achieve statistical significance, the true efficacy of experimental therapies reached the projected effect size in 1 case (0.9% false-negatives). For PRS endpoints, there were 34 (24.5%) false-positives and 3 (4.2%) false-negatives. Applying an alternative P value threshold and/or sample size could reduce false-positive errors and slightly increase false-negative errors. Conclusions: Current statistical approaches detect almost all truly effective oncologic therapies studied in phase III trials, but they generate many false-positives. Adjusting testing procedures in phase III trials is numerically favorable but practically infeasible. The root of the problem is the large number of ineffective therapies being studied in phase III trials. Innovative strategies are needed to efficiently identify which new therapies merit phase III testing.


2021 ◽  
Author(s):  
Rodrigo Ferrer

Background: The quantification of the change is crucial to correctly estimate the effect of a treatment and, for to distinguish random or non-systematic from substantive changes. The objective of the present study was to learn about the performance of two distribution-based methods (the Jacobson-Truax Reliable Change Index [RCI] and the Hageman-Arrindell [HA] approach) designed to evaluate individual change (reliable change).Methods: A pre-post design was simulated with the purpose to evaluate the false positive and false negative rates of RCI and HA methods. In this design, a first measurement is obtained before treatment and a second measurement is obtained after treatment, in the same group of subjects.Results: The rate of false positives, only the HA statistic provided acceptable results. Regarding the rate of false negatives, both statistics offered similar results and both could claim to offer acceptable rates when Ferguson’s stringent criteria were used to define effect sizes as opposed to when the conventional criteria advanced by Cohen were employed. Conclusions: Since the HA statistic appeared to be a better option than the RCI statistic, we have developed and presented an Excel macro so that the greater complexity of calculating HA would not represent an obstacle for the non-expert user.Key words: Individual reliable change, false positives, false negatives, assessment of change, effect size.


2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Pierre Ambrosini ◽  
Eva Hollemans ◽  
Charlotte F. Kweldam ◽  
Geert J. L. H. van Leenders ◽  
Sjoerd Stallinga ◽  
...  

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.


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