scholarly journals Breast microcalcifications: the UK RCR 5-point breast imaging system or BI-RADS; which is the better predictor of malignancy?

2019 ◽  
Vol 92 (1103) ◽  
pp. 20190177
Author(s):  
Linda Metaxa ◽  
Nuala A Healy ◽  
Sylvia A O’Keeffe
Keyword(s):  
Positive Predictive Value ◽  
Retrospective Review ◽  
Classification System ◽  
Predictive Value ◽  
Breast Imaging ◽  
Imaging System ◽  
System Use ◽  
The Uk

Objective: In the UK RCR 5-point breast imaging system (UKS), radiologists grade mammograms from 1 to 5 according to suspicion for malignancy, however unlike BI-RADS, no lexicon of descriptors is published. The aim of this study was to determine whether strict categorisation of microcalcifications (MCC) according to BI-RADS was a better predictor of malignancy than the UKS and whether these descriptors could be used within the UKS. Methods: A retrospective review of 241 cases, with MCC on mammography, who underwent biopsy was performed. Morphology, distribution, extent, UKS score, BI-RADS category and pathology were recorded. The positive predictive value (PPV) of each classification system for malignancy was calculated. Results: 28.6% were diagnosed with DCIS/IDC. The PPV for malignancy using the UKS was 18.9%, 69.4%, 100% for M3-5 respectively (p < 0.001) and using ΒI-RADS morphology was amorphous: 7.1%, coarse heterogeneous: 33.3%, fine pleomorphic: 48.1% and fine linear/fine linear branching: 85.2% (p < 0.001). The PPV based on distribution was grouped: 14.2%, regional: 32.3%, diffuse: 33.3% and linear/segmental: 77.8% (p < 0.001). Combining all cases of benign-appearing, amorphous and grouped coarse heterogenous and grouped fine pleomorphic MCC gave a PPV of 12.8%. Combining regional, linear or segmental coarse heterogenous and fine pleomorphic and all fine linear/branching MCC resulted in a PPV of 83.3% for malignancy. Conclusion: Combining morphology and distribution of MCC is accurate in malignancy prediction. Use of BI-RADS descriptors could help standardise reporting within the UKS and an algorithm using these within the UKS is proposed. Better prediction would enable more appropriate counselling and help to identify discrepancies. Advances in knowledge: No guidance exists on scoring of suspicious MCC in the UK breast imaging system. Use of BI-RADS morphologic/distribution descriptors can aid malignancy prediction. Findings other than morphology of MCC are important in malignancy prediction. An algorithm for use by the UK radiologist when evaluating MCC is provided.

scholarly journals Use of SNP chips to detect rare pathogenic variants: retrospective, population based diagnostic evaluation

BMJ ◽  
2021 ◽  
pp. n214
Author(s):  
Weedon MN ◽  
Jackson L ◽  
Harrison JW ◽  
Ruth KS ◽  
Tyrrell J ◽  
...  
Keyword(s):  
Positive Predictive Value ◽  
Predictive Value ◽  
Population Based ◽  
Genome Project ◽  
Personal Genome ◽  
Uk Biobank ◽  
Sequencing Data ◽  
Snp Chip ◽  
Pathogenic Variants ◽  
The Uk

Abstract Objective To determine whether the sensitivity and specificity of SNP chips are adequate for detecting rare pathogenic variants in a clinically unselected population. Design Retrospective, population based diagnostic evaluation. Participants 49 908 people recruited to the UK Biobank with SNP chip and next generation sequencing data, and an additional 21 people who purchased consumer genetic tests and shared their data online via the Personal Genome Project. Main outcome measures Genotyping (that is, identification of the correct DNA base at a specific genomic location) using SNP chips versus sequencing, with results split by frequency of that genotype in the population. Rare pathogenic variants in the BRCA1 and BRCA2 genes were selected as an exemplar for detailed analysis of clinically actionable variants in the UK Biobank, and BRCA related cancers (breast, ovarian, prostate, and pancreatic) were assessed in participants through use of cancer registry data. Results Overall, genotyping using SNP chips performed well compared with sequencing; sensitivity, specificity, positive predictive value, and negative predictive value were all above 99% for 108 574 common variants directly genotyped on the SNP chips and sequenced in the UK Biobank. However, the likelihood of a true positive result decreased dramatically with decreasing variant frequency; for variants that are very rare in the population, with a frequency below 0.001% in UK Biobank, the positive predictive value was very low and only 16% of 4757 heterozygous genotypes from the SNP chips were confirmed with sequencing data. Results were similar for SNP chip data from the Personal Genome Project, and 20/21 individuals analysed had at least one false positive rare pathogenic variant that had been incorrectly genotyped. For pathogenic variants in the BRCA1 and BRCA2 genes, which are individually very rare, the overall performance metrics for the SNP chips versus sequencing in the UK Biobank were: sensitivity 34.6%, specificity 98.3%, positive predictive value 4.2%, and negative predictive value 99.9%. Rates of BRCA related cancers in UK Biobank participants with a positive SNP chip result were similar to those for age matched controls (odds ratio 1.31, 95% confidence interval 0.99 to 1.71) because the vast majority of variants were false positives, whereas sequence positive participants had a significantly increased risk (odds ratio 4.05, 2.72 to 6.03). Conclusions SNP chips are extremely unreliable for genotyping very rare pathogenic variants and should not be used to guide health decisions without validation.

The “Death Diamond”: Death beyond Trauma

2019 ◽  
Vol 85 (7) ◽  
pp. 757-760
Author(s):  
Michael Farrell ◽  
Thomas Marconi ◽  
John Getchell ◽  
Raymond Green ◽  
Mark Cipolle ◽  
...  
Keyword(s):  
Positive Predictive Value ◽  
Retrospective Review ◽  
Predictive Value ◽  
Median Survival Time ◽  
Trauma Patients ◽  
Invasive Procedures ◽  
Multicenter Studies ◽  
Trauma Population ◽  
Futile Care ◽  
Prognostic Implications

Thromboelastography (TEG) has become a critical tool for the diagnosis, assessment, and management of hyperfibrinolysis and coagulopathy in trauma. In 2015, Chapman et al. of the Denver group coined the term “Death Diamond” (DD) to describe a TEG tracing identified in a unique trauma population. The DD was associated with a 100 per cent positive predictive value for mortality. Given the potential prognostic implications and resource savings associated with validating the DD as a marker of futile care, we sought to further evaluate DD outcomes. A retrospective review of 6850 TEGs, 34 patients (24 trauma and 10 nontrauma), displayed a DD tracing. Through invasive procedures and transfusions, nine DD tracing “normalized,” but, ultimately, this did not impact the outcome because the DD had a positive predictive value of 100 per cent for mortality in both populations. The median survival time in trauma patients was two hours compared with seven hours in nontrauma patients. Overall, this study further validates the predictive value of the DD in a trauma population while also serving as an assessment of the DD in a nontrauma population. Given these findings, a DD may prove to be an indicator of futile care. Further multicenter studies should be conducted to confirm these results.

The role of prostatic urethral biopsies as a staging tool for bladder cancer

2016 ◽  
Vol 10 (4) ◽  
pp. 359-363
Author(s):  
Adam W Nelson ◽  
Richard A Parker ◽  
Karan Wadhwa ◽  
Alexandra J Colquhoun ◽  
William H Turner
Keyword(s):  
Bladder Cancer ◽  
Positive Predictive Value ◽  
Retrospective Review ◽  
Predictive Value ◽  
Frozen Section ◽  
Prostatic Urethra ◽  
Published Data ◽  
Apical Margin ◽  
Single Centre

Objective: To determine the incidence of prostatic urethral involvement in our patient population and how prostatic urethral biopsy correlates with final cystectomy pathology. Patients and methods: We conducted a retrospective review of prostatic urethral biopsies (PUB) performed between February 2008 and April 2012 in a single centre. PUB pathology was correlated with cystectomy pathology. Results: PUB was undergone by 172 patients with a median age of 70 years (range: 37–84 years): There were 35 (20%) patients having a positive PUB and 137 (80%) who were negative. Of the 94 patients who underwent cystectomy, we found that when the entire prostatic urethra was sectioned, 20 (21%) patients had cancer in the prostatic urethra. Cancer was found in 17 (77%) of 22 patients with a positive PUB and in three (4%) out of the 72 with a negative PUB (positive predictive value (PPV) 77%, negative predictive value (NPV) 96%, sensitivity 85% and specificity 93%). In all 94 patients, the prostatic apical margin was negative. Conclusion: Disease in the prostatic urethra affected 20% of patients, consistent with published data. Prostatic urethral apical margins were all negative. Intra-operative frozen section would have missed cancer in the 20 patients with prostatic urethral cancer, whereas PUB identified 17 (85%) of the 20 patients. These data confirm the value of using PUB before cystectomy, in our UK population.

scholarly journals Positive predictive value of BIRADS (Breast imaging reporting and data system) IV lesions in detection of carcinoma breast, using histopathology as a gold standard.

2020 ◽  
Vol 27 (01) ◽  
pp. 172-179
Author(s):  
Syed Anjum Mehdi ◽  
Hassan Bukhari ◽  
Irfan Shabbir ◽  
Sobia Shabbir
Keyword(s):  
Positive Predictive Value ◽  
Predictive Value ◽  
Gold Standard ◽  
Breast Imaging ◽  
Benign Lesion ◽  
Malignant Lesion ◽  
Performance Outcomes ◽  
Duration Of Symptoms ◽  
Carcinoma Breast ◽  
The Mean

Objective: To determine the Positive Predictive Value of BIRADS IV lesions in detection of carcinoma breast, using histopathology as a gold standard. Study Design: A Cross-Sectional study. Setting: Department of Radiology Allied Hospital Faisalabad. Period: From 01-09-2015 to 01-03-2016. Material & Methods: 93 female patients referred to radiology department were included after taking consent. Data were collected on structured proforma. The final diagnosis of the BIRASDS IV lesion seen on mammography has made by consultant. Then patients were sent for biopsy. Mammographic diagnose was then compared with the histopathological diagnose by consultant. The primary performance outcomes of diagnostic mammography like sensitivity, specificity and accuracy were evaluated. Results: The mean age of patients was 45.96±7.85years. There were 19 females had subcategory A, 22 had subcategory B and 52 females had subcategory C. The mean size of lump was 3.23±0.69cm. The mean duration of symptoms was 3.97±3.43months. On BIRADS IV, malignant lesion detected in 71 (76.3%) females while 22 (23.7%) females had benign lesion. On histopathology, malignant lesions detected in 50 (53.8%) females while 43 (46.2%) females had benign lesion. Findings of BIRADS IV were compared with histopathology and the PPV was 43.7% and NPV was 13.6%. Conclusion: Through findings of this study, we concluded that in comparison to histopathology, BIRADS IV had PPV of 43.7% and NPV of 13.6%. In some cases, we can rely on BIRADS-IV and skip interventional method including biopsy for diagnosis of breast lesion.

scholarly journals Model for making diagnostic decisions in multiparametric ultrasound of breast lesions

2020 ◽  
Vol 66 (6) ◽  
pp. 653-658
Author(s):  
Ekaterina Busko ◽  
Anastasiya Goncharova ◽  
Nadezhda Rozhkova ◽  
Vladislav Semiglazov ◽  
Alena Shishova ◽  
...  
Keyword(s):  
Predictive Value ◽  
Breast Imaging ◽  
Imaging System ◽  
Optimal Algorithm ◽  
Diagnostic Methods ◽  
System Model ◽  
Diagnostic Model ◽  
Breast Lesions ◽  
Diagnostic Decision ◽  
Multiparametric Ultrasound

In order to standardize the description of the breast imaging, the BI-RADS (Breast Imaging Reporting And Data System) imaging system developed by the American College of Radiologists ACR is widely used in world practice. At the same time, numerous visual characteristics of breast lesions with different diagnostic methods complicate the adoption of diagnostic decisions while using the BI-RADS system. The greatest difficulties arise when assessing a variety of multiparametric ultrasound signs of diseases. In this regard, in order to increase the efficiency of these technologies and make fast diagnostic decisions, it becomes relevant to develop a system model based on algorithms using the BI-RADS lexicon. Materials and methods: from 2017 to 2019 on the basis of the Research Oncology Center named after N.N. Petrov 277 women with various complaints of breast disease were examined using multiparametric ultrasound with elastography and contrast enhancement (2.5 ml Sonovue) on a Hitachi Hi Vision Ascendus ultrasound scanner. The software implementation of the diagnostic decision-making model was carried out using the C # programming language using the Microsoft Visual integrated development environment. Results: The effectiveness of the developed diagnostic model using the optimal algorithm for the use of various ultrasound technologies in determining the malignancy of the formation showed Sensitivity (Se) = 90.8%, Specificity (Sp) = 95.5%, Positive Predictive Value (PPV) = 88.5%, Negative Predictive Value (NPV) = 96.4%, Accuracy (Ac) = 94.2%. The effectiveness of the developed model in grouping diseases showed Se = 84.2%, Sp = 81.1%, PPV = 62.7%, NPV = 93.1%, Ac = 81.9%. Conclusions: The proposed system model of the optimal algorithm for making a diagnostic decision based on statistically significant multiparametric ultrasound signs increases the diagnostic efficiency.

Positive predictive value of the UK clinical case definition for H1N1/09 (‘swine’) influenza

2010 ◽  
Vol 60 (5) ◽  
pp. 405-407 ◽  
Author(s):  
T. Bicanic ◽  
A.W. Solomon ◽  
N. Karunaharan ◽  
F. Chua ◽  
C. Pope ◽  
...  
Keyword(s):  
Positive Predictive Value ◽  
Predictive Value ◽  
Clinical Case ◽  
Swine Influenza ◽  
Case Definition ◽  
The Uk ◽  
Clinical Case Definition

The breast imaging reporting and data system: positive predictive value of mammographic features and final assessment categories.

1998 ◽  
Vol 171 (1) ◽  
pp. 35-40 ◽  
Author(s):  
L Liberman ◽  
A F Abramson ◽  
F B Squires ◽  
J R Glassman ◽  
E A Morris ◽  
...  
Keyword(s):  
Positive Predictive Value ◽  
Predictive Value ◽  
Breast Imaging ◽  
Data System ◽  
Final Assessment

scholarly journals Ultrasound Stratification of Hepatic Steatosis Using Hepatorenal Index

Diagnostics ◽  
2021 ◽  
Vol 11 (8) ◽  
pp. 1443
Author(s):  
Stephen I. Johnson ◽  
Daniel Fort ◽  
Kenneth J. Shortt ◽  
George Therapondos ◽  
Gretchen E. Galliano ◽  
...  
Keyword(s):  
Liver Biopsy ◽  
Positive Predictive Value ◽  
Hepatic Steatosis ◽  
Retrospective Review ◽  
Predictive Value ◽  
Liver Steatosis ◽  
Liver Biopsies ◽  
Biopsy Methods

Hepatorenal index (HRI) has been shown to be an effective, noninvasive ultrasound tool to screen patients for those with or without >5% hepatic steatosis. Objective: The aim of this study was to further refine this HRI tool in order to stratify patients according to their degree of liver steatosis and give direction as to which patients should undergo random liver biopsy. Methods: We conducted a retrospective review of 267 consecutive patients from 2015 to 2017 who had abdominal ultrasounds and a subsequent random liver biopsy within one month. The HRI was calculated and compared with the percent steatosis as assessed by histology. Results: An HRI of ≤1.17 corresponds with >95% positive predictive value of ≤5% steatosis. Between HRI values 1.18 and 1.39, performance of steatosis prediction is mixed. However, for values <1.37 there is an increased likelihood of steatosis ≤5% and likewise the opposite for values >1.37. An HRI of ≥1.4 corresponds with >95% positive predictive value of ≥10% steatosis. Conclusion: HRI is an accurate noninvasive tool to quantify degree of steatosis and guide who should undergo random liver biopsy, potentially significantly reducing the total number of necessary liver biopsies.

scholarly journals The Positive Predictive Value of Onconeural Antibody Testing: A Retrospective Review

2018 ◽  
Vol 45 (5) ◽  
pp. 577-579 ◽  
Author(s):  
Adrian Budhram ◽  
Michael W. Nicolle ◽  
Liju Yang
Keyword(s):  
Positive Predictive Value ◽  
Retrospective Review ◽  
Predictive Value ◽  
Test Interpretation ◽  
Antibody Testing ◽  
Single Centre ◽  
Neurologic Diseases ◽  
Onconeural Antibodies ◽  
Onconeural Antibody ◽  
Immune Mediated

AbstractParaneoplastic syndromes (PNS) are immune-mediated neurologic diseases that occur as an indirect effect of malignancy, and can be challenging to diagnose. Onconeural antibodies have a greater than 95% association with cancer, and their presence in a patient with neurologic symptoms is reportedly highly indicative of PNS. However, we performed a single-centre retrospective review to determine the positive predictive value of onconeural antibody testing, and found it to be concerningly low (39%). Recognising the limitations of onconeural antibody testing is critical to ensure accurate test interpretation, avoid unnecessary repeated malignancy screening and prevent the use of potentially hazardous immunotherapy.

scholarly journals Novel genotyping algorithms for rare variants significantly improve the accuracy of Applied Biosystems™ Axiom™ array genotyping calls

2021 ◽  
Author(s):  
Orna Mizrahi Man ◽  
Marcos H Woehrmann ◽  
Teresa A Webster ◽  
Jeremy Gollub ◽  
Adrian Bivol ◽  
...  
Keyword(s):  
Positive Predictive Value ◽  
Exome Sequencing ◽  
Predictive Value ◽  
Rare Variants ◽  
Uk Biobank ◽  
Sequencing Data ◽  
Data Set ◽  
Array Data ◽  
Exome Sequencing Data ◽  
The Uk

Objective: To significantly improve the positive predictive value (PPV) and sensitivity of Applied Biosystems™ Axiom™ array variant calling, by means of novel improvement to genotyping algorithms and careful quality control of array probesets. The improvement makes array genotyping more suitable for very rare variants. Design: Retrospective evaluation of UK Biobank array data re-genotyped with improved algorithms for rare variants. Participant: 488,359 people recruited to the UK Biobank with Axiom array genotyping data including 200,630 with exome sequencing data. Main Outcome Measures: A comparison of genotyping calls from array data to genotyping calls on a subset of variants with exome sequencing data. Results: Axiom genotyping [18] performed well, based on comparison to sequencing data, for over 100,000 common variants directly genotyped on the Axiom UK Biobank array and also exome sequenced by the UK Biobank Exome Sequencing Consortium. However, in a comparison to the initial exome sequencing results of the first 50K individuals, Weedon et al. [1] observed that when grouping these variants by the minor allele frequency (MAF) observed in UK Biobank, the concordance with sequencing and resulting positive predictive value (PPV) decreased with the number of heterozygous (Het) array calls per variant. An improved genotyping algorithm, Rare Heterozygous Adjustment (RHA) [16], released mid-2020 for genotyping on Axiom arrays, significantly improves PPV in all MAF ranges for the 50K data as well as when compared to the exome sequencing of 200K individuals, released after Weedon et al. [1] performed their comparison. The RHA algorithm improved PPVs in the 200K data in the lowest three frequency groups [0, 0.001%), [0.001%, 0.005%) and [0.005%, 0.01%) to 83%, 82% and 88%; respectively. PPV was above 95% for higher MAF ranges without algorithm improvement. PPVs are somewhat higher in the 200K dataset, due to a different "truth set" from exome sequencing and because monomorphic exome loci are not included in the joint genotyping calls for the 200K data set, as explained in the methods section. Sensitivity was higher in the 200K data set than in the original 50K data as well, especially for low MAF ranges. This increase is in part due to the larger data set over which sensitivity could be computed and in part due to the different WES algorithms used for the 200K data [7]. Filtering of a relatively small number of non-performing probesets (determined without reference to the exome sequencing data) significantly improved sensitivities for all MAF ranges, resulting in 70%, 88% and 94% respectively in the three lowest MAF ranges and greater than 98% and 99.9% for the two higher MAF ranges ([0.01%, 1%), [1%, 50%]). Conclusions: Improved algorithms for genotyping along with enhanced quality control of array probesets, significantly improve the positive predictive value and the sensitivity of array data, making it suitable for the detection of very rare variants. The probeset filtering methods developed have resulted in better probe designs for arrays and the new genotyping algorithm is part of the standard algorithm for all Axiom arrays since early 2020.

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