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Comparison of diagnostic accuracy of endoscopic diagnosis (EC, pit pattern, NBI) in low grade adenoma

ABSTRACT: Primary outcome(s): The diagnostic accuracy of low grade adenoma using EC normal pit (NP) sign in endoscopic images

DISEASE(S): Colorectal Adenoma

PROVIDER: 2653008 | ecrin-mdr-crc |

REPOSITORIES: ECRIN MDR

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Diagnostic accuracy of liquid biopsy in endometrial cancer

Project description:INTRODUCTION. Liquid biopsies are a minimally invasive collection of a patient body fluid sample. In oncology, they offer several advantages compared to traditional tissue biopsies. However, potential of this method in endometrial cancer (EC) remains poorly explored. We studied the utility of tumor educated platelets (TEPs) and circulating tumor DNA (ctDNA) for preoperative EC diagnosis, including histology determination. MATERIALS AND METHODS. TEPs from 297 subjects (53 EC patients, 40 patients with benign gynecologic conditions and 204 healthy women) were RNA-sequenced. DNA sequencing was performed in 519 primary tumor tissue samples and in16 plasma samples. Artificial intelligence was applied to sample classification. RESULTS. Platelet-dedicated classifier yielded AUC of 93.1% in test set when discriminating between healthy subjects and cancer patients. However, the discrimination between endometrial cancer and benign gynecologic conditions was relatively low, with AUC of 60.7%. ctDNA-dedicated classifier discriminated primary tumor tissue samples with AUC of 91.4% and ctDNA blood samples with AUC of 87.5%. CONCLUSIONS Liquid biopsies show potential in EC diagnosis. Both TEPs and ctDNA profiles coupled with artificial intelligence constitute a source of useful information. Further work, involving more cases, is warranted.

2021-12-01 | GSE184904 | GEO

Effect of educational lecture on the diagnostic accuracy of Japan NBI Expert Team classification for colorectal lesions.

Project description: Not available

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Prospective study on clinical practice using ultra magnifying endoscope and artificial intelligence -regarding diagnostic accuracy and image acquisition rate of colorectal tumor and cancer-

Project description:Primary outcome(s): This trial will be triple primary endpoint clinical trial. 1.When EC-CAD was used under methylene blue staining for polyps of 5 mm or less in diameter in the sigmoid colon from the rectum, it was verified that the result diagnosed with high confidence (probability 90% or more) was NPV 90% or more. 2.When observing a polyp of 5 mm or less in diameter with magnifying narrow band imaging (NBI) and NBI combined endocytoscopy (EC-NBI), verify that the ratio at which trainee can acquire a focused endoscope video for 3 seconds or more at EC-NBI. 3.When EC-CAD is used under methylene blue staining for colon lesions of 20 mm or more in diameter, we verify that the diagnostic ability for invasive cancer is over 90% PPV.

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Usefulness of acetic acid NBI magnification endoscopy for colorectal tumors

Project description:Primary outcome(s): Inter-methodological diagnostic agreement and inter-observer diagnostic agreement between NBI magnification endoscopic pit pattern diagnosis under acetic acid spraying and crystal violet stained magnification endoscopic pit pattern diagnosis of colorectal neoplasm

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The Genomic Heterogeneity of FIGO Grade 3 Endometrioid Carcinoma Impacts Diagnostic Accuracy and Reproducibility.

Project description: Not available

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Multiomic neuropathology improves diagnostic accuracy in pediatric neuro-oncology

Project description:To assess their utility in routine neuropathology, we prospectively integrated DNA methylation-based CNS tumor classification and targeted gene panel sequencing of tumor and constitutional DNA with blinded neuropathological reference diagnostics for a population-based cohort of > 1,200 newly-diagnosed pediatric patients.

2023-01-30 | GSE215240 | GEO

An integrated molecular diagnostic report for heart transplant biopsies using an ensemble of diagnostic algorithms

Project description:We previously reported a microarray-based diagnostic system for heart transplant endomyocardial biopsies (EMBs), using either 3-archetype (3AA) or 4-archetype (4AA) unsupervised algorithms to estimate rejection. The present study aimed to examine the stability of machine-learning algorithms in new biopsies, compare 3AA vs. 4AA algorithms, assess supervised binary classifiers trained on histologic or molecular diagnoses, create a report combining many scores into an ensemble of estimates, and examine possible automated sign-outs. Algorithms derived in cohort 331 performed similarly in new biopsies despite differences in case mix. In the combined cohort, the 4AA model, including a parenchymal injury score, retained correlations with histologic rejection and DSA similar to the 3AA model. Supervised molecular classifiers predicted molecular rejection (AUCs>0.87) better than histologic rejection (AUCs<0.78), even when trained on histology diagnoses. A report incorporating many AA and binary classifier scores interpreted by one expert showed highly significant agreement with histology (p<0.001), but with many discrepancies as expected from the known noise in histology. An automated random forest score closely predicted expert diagnoses, confirming potential for automated sign-outs.Molecular algorithms are stable in new populations and can be assembled into an ensemble that combines many supervised and unsupervised estimates of the molecular disease states. (ClinicalTrials.gov NCT02670408).

2019-08-16 | GSE124897 | GEO

Diagnostic accuracy of pattern differentiation algorithm based on Chinese medicine theory: a stochastic simulation study.

Project description: Not available

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A diagnostic host response biosignature for COVID-19 from RNA profiling of nasal swabs and blood

Project description:To elucidate key pathways in the host transcriptome of patients infected with SARS-CoV-2, we used RNA sequencing (RNA Seq) to analyze nasopharyngeal (NP) swab and whole blood (WB) samples from 333 COVID-19 patients and controls, including patients with other viral and bacterial infections. Analyses of differentially expressed genes (DEGs) and pathways was performed relative to other infections (e.g. influenza, other seasonal coronaviruses, bacterial sepsis) in both NP swabs and WB. Comparative COVID-19 host responses between NP swabs and WB were examined. Both hospitalized patients and outpatients exhibited upregulation of interferon-associated pathways, although heightened and more robust inflammatory and immune responses were observed in hospitalized patients with more clinically severe disease. A two-layer machine learning-based classifier, run on an independent test set of NP swab samples, was able to discriminate between COVID-19 and non-COVID-19 infectious or non-infectious acute respiratory illness using complete (>1,000 genes), medium (<100) and small (<20) gene biomarker panels with 85.1%-86.5% accuracy, respectively. These findings demonstrate that SARS-CoV-2 infection has a distinct biosignature that differs between NP swabs and WB and can be leveraged for differential diagnosis of COVID-19 disease.

2020-12-15 | GSE163151 | GEO

Proteomic analysis of endometrioid and high-grade serous ovarian tumour samples

Project description:Precision medicine approaches in ovarian cancer require the accurate diagnosis of histotypes. In particular, there is a critical need to distinguish endometroid (EC) from high grade serous (HGSC) carcinomas, which differ greatly in outcomes, genetic predisposition and optimal treatment approaches. Herein, we performed label-free quantitative proteomics on freshly frozen tumour tissues to discover biomarkers that may distinguish EC from HGSC, and then used IHC to validate these using a contemporarily classified cohort of EC and HGSC samples.

2019-03-13 | PXD012998 | Pride

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