A True Positive and a False Negative? The Dilemma of Negative Colonoscopy After a Positive Fecal Occult Blood Test

Hunt, James S.; Cock, Charles; Symonds, Erin L.

doi:10.1007/s10620-021-06986-4

Cited by 5 publications

(3 citation statements)

References 22 publications

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“…Multiple factors may underlie the increased likelihood of FOBT false-negative results in patients with smaller or right-sided AA. The size of the lesion, to a certain extent, determines the extent of blood loss 17 , with smaller lesions usually resulting in less bleeding 21 that may not meet the fixed threshold for quantitative FOBT. In addition, hemoglobin produced from left-sided tumors is less prone to degradation compared to tumors on the right-sided 22 .…”

Section: Discussionmentioning

confidence: 99%

Adenoma location, size, and morphology are risk factors for FOBT false-negative results in inpatients with advanced colorectal adenoma

Cao,

Meng,

Liu

et al. 2024

Sci Rep

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Recently, advanced adenoma (AA) has been recognized as a target for colorectal cancer (CRC) screening. However, the fecal occult blood test (FOBT), the primary non-invasive screening method, shows limited sensitivity in detecting AA. This study investigates the relationship between adenoma characteristics and FOBT false-negative results. In a retrospective cohort study conducted from 2015 to 2022, we examined 342 inpatients with AA who underwent colonoscopy and received qualitative FOBT. FOBT sensitivity was analyzed about various adenoma characteristics, and logistic regression models were employed to investigate the relationship between adenoma features and FOBT false-negative outcomes. FOBT sensitivity in AA inpatients was 52.63%. Significant differences in sensitivity were observed based on adenoma location (left vs. right), morphology (with or without pedunculation), and size (≤ 10 mm vs. > 10 mm). After adjusting for several potential confounders, FOBT showed a reduced false-negative rate in AA with large-sized (OR, 0.49; 95% CI 0.31–0.77), left-sided location (OR, 0.53; 95% CI 0.31–0.89), and pedunculated morphology (OR, 0.73; 95% CI 0.43–1.24). AA with large size, left-sided location, and pedunculated morphology independently contribute to a decreased rate of FOBT false-negative results. However, these adenoma characteristics are not actively modifiable. Therefore, novel non-invasive methods are needed to improve AA detection accuracy.

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Section: Discussionmentioning

confidence: 99%

Adenoma location, size, and morphology are risk factors for FOBT false-negative results in inpatients with advanced colorectal adenoma

Cao,

Meng,

Liu

et al. 2024

Sci Rep

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“…The total prediction possible in the dataset is 32 values. Hence the limit of the confusion matrix [37] is restricted to 32 values in four criterions viz., 1) True Positive, 2) True negative, 3) False positive and 4) False Negative respectively. According to the Predictive Analytics of NSCLC datasets, True Positive and False Negative [38] are correct predictions whereas the True Negatives and False Positives [39] are wrong predictions.…”

Section: Nsclc Dataset Competency Analyticsmentioning

confidence: 99%

Augmentation of Predictive Competence of Non-Small Cell Lung Cancer Datasets through Feature Pre-Processing Techniques

Sumalatha

Parthiban

2022

EAI Endorsed Trans Perv Health Tech

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The major Objective of the Study is to augment the predictive analytics of Non-Small Cell Lung Cancer (NSCLC) datasets with Feature Pre-Processing (FPP) technique in three stages viz. Remove base errors with common analytics on emptiness or non-numerical or missing values in the dataset, remove repeated features through regression analysis and eliminate irrelevant features through clustering methods. The FPP Model is validated using classifiers like simple and complex Tree, Linear and Gaussian SVM, Weighted KNN and Boosted Trees in terms of accuracy, sensitivity, specificity, kappa, positive and negative likelihood. The result showed that the NSCLC dataset formed after FPP outperformed the raw NSCLC dataset in all performance levels and showed good augmentation in predictive analytics of NSCLC datasets. The research proved that preprocessing is essential for better prediction of complex medical datasets.

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“…A supervised machine learning model was constructed with the purpose of predicting the presence of COVID-19 in a person (Villavicencio et al 2021). By efficiently identifying at-risk COVID-19 patients early, the suggested methodology can aid decision-makers and healthcare professionals (Aljameel et al 2021) (Hunt, Cock, and Symonds 2021).…”

Section: Introductionmentioning

confidence: 99%

Analysis and Comparison for Innovative Prediction Technique of COVID-19 using Decision Tree Algorithm over the Support Vector Machine Algorithm with Improved Accuracy

Charan¹,

Kumar²

2023

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Aim: The primary goal of this research is to increase the accuracy of COVID-19 prediction and its analysis. Materials and Method: This study relied on data collected from Kaggle’s website and samples are divided into two groups, GROUP 1 (N=20) for the Decision tree and GROUP 2 (N=20) for the Support Vector Machine (SVM) in accordance with the total sample size calculated using clinical.com by keeping alpha error-threshold value 0.05, 95% confidence interval, enrolment ratio as 0:1, and G power at 80%. It involves the software implementation program in MatLab 2021a validating with 20 validations. Results: The accuracy, sensitivity, and precision rates are compared using Statistical Package for the Social Sciences (SPSS) software and an Independent sample T-Test. In comparison to the two, the Decision tree 93.91% accuracy, 94.33% sensitivity, 92% precision with P=0.001 ((p<0.05) produces a superior outcome to the Support Vector Machine 91.25% accuracy, 93.93% sensitivity, 86.11% precision (P<0.001)). Conclusion: The decision tree algorithm produces better results compared to the Support Vector Machine.

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A True Positive and a False Negative? The Dilemma of Negative Colonoscopy After a Positive Fecal Occult Blood Test

Cited by 5 publications

References 22 publications

Adenoma location, size, and morphology are risk factors for FOBT false-negative results in inpatients with advanced colorectal adenoma

Adenoma location, size, and morphology are risk factors for FOBT false-negative results in inpatients with advanced colorectal adenoma

Augmentation of Predictive Competence of Non-Small Cell Lung Cancer Datasets through Feature Pre-Processing Techniques

Analysis and Comparison for Innovative Prediction Technique of COVID-19 using Decision Tree Algorithm over the Support Vector Machine Algorithm with Improved Accuracy

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