Comparison of diagnostic accuracy of endoscopic diagnosis (EC, pit pattern, NBI) in low grade adenoma
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ABSTRACT:
DISEASE(S): Colorectal Adenoma
PROVIDER: 2653008 | ecrin-mdr-crc |
REPOSITORIES: ECRIN MDR
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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
Project description:BackgroundAn educational and training program is required for generalization of Japan NBI Expert Team (JNET) classification. However, there is no detailed report on the learning curve of the diagnostic accuracy of endoscopists using JNET classification. We examined the effect of an educational lecture on beginners and less experienced endoscopists for improving their diagnostic accuracy of colorectal lesions by JNET classification.MethodsSeven beginners with no endoscopy experience (NEE group), 7 less experienced endoscopists (LEE group), and 3 highly experienced endoscopists (HEE group) performed diagnosis using JNET classification for randomized NBI images of colorectal lesions from 180 cases (Type 1: 22 cases, Type 2A: 105 cases, Type 2B: 33 cases, and Type 3: 20 cases). Next, the NEE and LEE groups received a lecture on JNET classification, and all 3 groups repeated the diagnostic process. We compared the correct diagnosis rate and interobserver agreement before and after the lecture comprehensively and for each JNET type.ResultsIn the HEE group, the correct diagnosis rate was more than 90% with good interobserver agreements (kappa value: 0.78-0.85). In the NEE and LEE groups, the correct diagnosis rate (NEE: 60.2 → 68.0%, P < 0.01; LEE: 66.4 → 86.7%, P < 0.01), high-confidence correct diagnosis rate (NEE: 19.6 → 37.2%, P < 0.01; LEE: 43.6 → 61.1%, P < 0.01), and interobserver agreement (kappa value, NEE: 0.32 → 0.43; LEE: 0.39 → 0.75) improved after the lecture. In the examination by each JNET type, the specificity and positive predictive value in the NEE and LEE groups generally improved after the lecture.ConclusionAfter conducting an appropriate lecture, the diagnostic ability using JNET classification was improved in beginners or endoscopists with less experience in NBI magnifying endoscopy.
| S-EPMC7934459 | biostudies-literature
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.
| 2642573 | ecrin-mdr-crc
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
| 2661544 | ecrin-mdr-crc
Project description:The Cancer Genome Atlas (TCGA) identified 4 groups of endometrial carcinomas based on an integrated genomic characterization: POLE ultramutated (POLE), microsatellite instability-high, copy number-low (CN-L), and copy number-high (CN-H). In that study, CN-H comprised all of the serous carcinoma cases and 25% of all International Federation of Gynecology and Obstetrics (FIGO) Grade 3 endometrioid carcinoma cases. In this study, 2 expert gynecologic pathologists undertook a morphologic reassessment of the FIGO Grade 3 endometrioid carcinoma subset of the TCGA study cohort, including an analysis for evidence of serous differentiation. Interobserver variability ?values are reported for the histologic evaluation of all 4 genomic clusters, and diagnostic discrepancies are discussed. Overall, there were 55 agreements, 6 disagreements, and 14 deferrals. Of the 75 cases analyzed, 6 cases had a consensus morphologic diagnosis of serous carcinoma, but only 2 of these cases had a serous carcinoma genotype, whereas the remaining 4 cases were genotypically endometrioid carcinoma. For the CN-H group, 2 of 15 cases were serous carcinoma by morphology and genotype, whereas at least 1 pathologist interpreted the remaining 13 cases as endometrioid carcinoma. The interobserver agreement rate was highest in the CN-L group (90%; ?=0.9), compared with the other genomic groups (POLE: 62%, ?=0.55; microsatellite instability-high: 78%, ?=0.74; and CN-H: 53%, ?=0.48). Our review confirms that most high-grade endometrial carcinomas diagnosed by TCGA as FIGO Grade 3 endometrioid carcinoma are indeed endometrioid carcinomas by morphology and genotype, and that the reproducibility of histologic diagnosis between pathologists varies between the TCGA-integrated genomic clusters.
| S-EPMC4934379 | biostudies-literature
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
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
Project description:BackgroundClinical practice of Chinese medicine requires little information for differentiation of Zang-fu patterns. This study is to test the impact of information amount on the diagnostic accuracy of pattern differentiation algorithm (PDA) using stochastic simulation of cases.MethodsA dataset with 69 Zang-fu single patterns was used with manifestations according to the Four Examinations, namely inspection (Ip), auscultation and olfaction (AO), inquiry (Iq) and palpation (P). A variable quantity of available information (N(%)) was randomly sampled to generate 100 true positive and 100 true negative manifestation profiles per pattern to which PDA was applied. Four runs of simulations were used according to the Four Examinations: Ip, Ip+AO, Ip+AO+Iq and Ip+AO+Iq+P. The algorithm performed pattern differentiation by ranking a list of diagnostic hypotheses by the amount of explained information F(%). Accuracy, sensitivity, specificity and negative and positive predictive values were calculated.ResultsUse the Four Examinations resulted in the best accuracy with the smallest cutoff value (N(%)= 28.5%), followed by Ip+AO+Iq (33.5%), Ip+AO (51.5%) and Ip (52.0%). All tested combinations provided concave-shaped curves for accuracy, indicating an optimal value subject to N(%-cutoff). Use of N(%-cutoff) as a secondary criterion resulted in 94.7% (94.3; 95.1) accuracy, 89.8% (89.1; 90.6) sensitivity, and 99.5% (99.3; 99.7) specificity with the Four Examinations.ConclusionPattern differentiation based on both explained and optimum available information (F(%) and N(%-cutoff)) is more accurate than using explained and available information without cutoff (F(%) and N(%)). Both F(%) and N(%-cutoff) should be used as PDA's objective criteria to perform Zang-fu single pattern differentiation.
| S-EPMC2806360 | biostudies-literature
Project description:BackgroundFractional flow reserve (FFR) and instantaneous wave-free ratio (iFR) disagree in about 20% of intermediate coronary lesions. As the physiological pattern of coronary artery disease has a significant influence on FFR-iFR discordance, we sought to assess it may impact on the diagnostic accuracy of quantitative flow reserve (QFR).MethodsOne hundred and ninety-four patients with 224 intermediate coronary lesions were investigated with iFR, FFR, and QFR. The physiological pattern of disease was assessed with iFR Scout pullback and QFR virtual pullback in all the cases.ResultsA predominantly physiologically focal pattern was observed in 81 (36.2%) lesions, whereas a predominantly physiologically diffuse was observed in 143 (63.8%) cases. QFR demonstrated a significant correlation (r = 0.581, p < 0.001) and a substantial agreement with iFR, both in diffuse (AUC = 0.798) and in focal (AUC = 0.812) pattern of disease. Discordance between QFR and iFR was observed in 51 (22.8%) lesions, consisting of iFR+/QFR- (64.7%) and iFR-/QFR+ (35.3%). Notably, the physiological pattern of disease was the only variable significantly associated with iFR/QFR discordance. QFR virtual pullback demonstrated an excellent agreement (83.9%) with iFR Scout pullback in classifying the physiological pattern of disease.ConclusionsQFR has a good diagnostic accuracy in assessing myocardial ischemia independently of the pattern of coronary disease. However, the physiological pattern of disease has an influence on the QFR/iFR discordance, which occurs in ~20% of the cases. The QFR virtual pullback correctly defined the physiological pattern of disease in the majority of the cases using the iFR pullback as reference.
| S-EPMC9544909 | biostudies-literature
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