جستجوی مقالات مرتبط با کلیدواژه "accuracy" در نشریات گروه "پزشکی"

Limited literature describes the accuracy of endoscopic ultrasonography–fine needle aspiration (EUS-FNA) cytology in various types of pancreatic epithelial tumors, and this underscores the usefulness of cell blocks, and highlights potential diagnostic pitfalls.  

This study included 108 patients who underwent EUS-FNA pancreatobiliary cytology followed by surgery. Age, gender, tumor location, tumor size, presence or absence of a cell block, cytologic and pathologic diagnoses, and histologic tumor grade were recorded. Cytologic and pathologic slides were examined, and the cytologic accuracy was determined by comparing cytologic with the histopathologic results as the gold standard. Additionally, the impact of cell block on the cytologic accuracy was assessed.

EUS-FNA cytology showed an overall accuracy of 80%, a sensitivity of 90%, and a false-positive rate below 1%. Pancreatic ductal adenocarcinomas (PDAs) accounted for 65% of cases, followed by neuroendocrine tumors (NETs), solid pseudopapillary neoplasms (SPNs), mucinous cystic neoplasms (MCNs), and chronic pancreatitis. Diagnostic accuracy was higher for PDA and SPN than for NET and MCN and significantly improved to 100% in cases with a cell block.

Combining pancreatobiliary cytology with a cell block significantly enhances diagnostic accuracy, reaching 100%. Moreover, poorly differentiated PDAs and well-differentiated organoid-type tumors, such as NETs and SPNs, demonstrate higher diagnostic accuracy.

Ultrasound and fine needle aspiration biopsy (FNAB) play a primary role in determining the nature of nodules.

The aim of our study is to determine the diagnostic accuracy, sensitivity, and specificity of artificial intelligence-thyroid imaging reporting and data system (AI-TIRADS) in differentiating malignant from benign nodules to establish the treatment plan for patients and avoid unnecessary FNAB.

This cross-sectional study was conducted between September 2020 and September 2021 (one year). Patients (n = 133) who had an indication for surgery due to nodular thyroid disease and were referred to the therapeutic training center of 5 Azar and Falsafi private section hospitals, Gorgan, Iran, were included. The diagnostic accuracy of FNAB and AI-TIRADS was calculated.

The mean age of patients with malignant pathology (39.65 ± 11.69) was lower than that of patients with benign pathology (44.14 ± 13.54), which was statistically significant (P = 0.042). Fifty-two out of 72 nodules less than 25 mm were malignant, demonstrating a higher prevalence of benign pathology in larger nodules. A pooled analysis of thyroid reporting (TR) = 2 and TR points ≥ 7 for their power in predicting the final pathology outcome showed figures of 0.97, 0.93, 0.97, and 0.97 for sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV), respectively, with an overall pooled accuracy of 0.97 for these two groups of sonographic results.

We concluded that AI-TIRADS has a high sensitivity and diagnostic accuracy in differentiating benign from suspicious nodules.

The importance of diffusion in prostate cancer (PCa) diagnosis has been widely proven. Several studies investigated diffusion models in PCa diagnosis.

This systematic review and meta?analysis study was performed to evaluate the ability of three diffusion models to diagnose PCa from the scientific electronic databases Embase, PubMed, Scopus, and Web of Science (ISI) for the period up to March 2022 to identify all relevant articles.

Eighteen studies were included in the systematic review section (7 diffusion kurtosis imaging [DKI] studies, 4 diffusion tensor imaging [DTI] studies, 4 intravoxel incoherent motion [IVIM] studies, and 3 IVIM?DKI studies). Pooled sensitivity, specificity, accuracy, and summary area under each diffusion model’scurve (AUC) and 95% confidence intervals (CIs) were calculated. The pooled accuracy and 95% CI on detection (differentiation of tumor from normal tissue and benign prostatic hyperplasia/prostatitis) were obtained for apparent diffusion coefficient (ADC) at 87.97% (84.56%–91.38%) for DKI parameters (Gaussian diffusion [DK] 87.94% [78.71%–97.16%] and deviation from Gaussian diffusion[K] 86.84% [81.83%–91.85%]) and IVIM parameters (true molecular diffusion [DIVIM] 81.73% [72.54%–90.91%], perfusion?related diffusion [D*] 65% [48.47%–81.53%] and perfusion fraction [f] 80.36% [64.23%–96.48%]). The AUC values and 95% CI in the detection of PCa were obtained for ADC at 0.95 (0.92–0.97), for DKI parameters (DK 0.94 [0.89–0.99] and K 0.93 [0.90–0.96]) and for IVIM parameters (DIVIM 0.85 [0.80–0.91], D* 0.60 [0.43–0.77] and f 0.73 [0.63–0.84]). Two studies showed that the DTI accuracy values were 97.34% and 85%. For IVIM–kurtosis model in PCa detection, two studies stated that the DIVIM?K and KIVIM?K accuracy values were 85% and 84.44% (the pooled accuracy; 84.64% with 95% CI 75.78%–93.50%), and 72.50% and 71.11% (the pooled accuracy, 72.10% with 95% CI 64.73%–79.48%), respectively.

Our findings showed that among the DKI, IVIM, and ADC parameters,it seems that ADC, Dk, DIVIM, and K are the most important, which can be used as an indicator to distinguish PCa from normal tissue. The DKI model probably has a higher ability to detect PCa from normal tissue than the IVIM model. DKI probably has the same diagnostic performance in PCa detection and grading compared to diffusion?weighted imaging and ADC.

Clinical decision‑making and biomedical research heavily rely on imaging techniques to visualize tissue morphology. To examine tissues in detail, it is necessary to use special histochemical stains to enhance contrast. This meta‑analysis aimed to assess the sensitivity and specificity of these stains in diagnosing oral pathologic specimens. We conducted a search in 8 databases, including EMBASE, PubMed, Web of Science, Scopus, ProQuest, Ovid, Cinahl, and Cochrane, up to June 2022. Of 87,393 studies, 41 articles were selected for inclusion in our study. The results revealed that the specificity and sensitivity of the special histochemical stains were 86% with confidence interval (CI) 95%: 80%–90% and 83% with CI 95%: 75%–89%, respectively. Among the stains evaluated, toluidine blue, Papanicolaou, silver stain, Giemsa, Gram, feulgen, and periodic acid–Schiff (PAS) were the most frequently used for the detection of malignancy, premalignant lesions, dysplasia, and candidiasis. The specificity and sensitivity of each stain were analyzed individually, considering the type of specimen. Toluidine blue was the most commonly utilized special histochemical stain, particularly effective, for detecting malignancy, with a specificity of 97% with CI 95%: 88%–99% and sensitivity of 76% with CI 95%: 56%–89%. In conclusion, special histochemical stains are effective in diagnosing oral lesions, exhibiting reasonable specificity and sensitivity, especially in cases of premalignant and malignant lesions. Based on the reviewed articles in our study, the silver stain was identified as highly sensitive, while Giemsa and Papanicolaou stain exhibited the highest specificity

Reading impairments are the most prevalent problem at school. It is vital to have a valid and reliable test to assess the student’s reading skills. This study aims to develop the KHANA test as a valid and reliable test to evaluate reading skills in Persian-speaking students and study the accuracy, rate, and reading comprehension in different grades and genders

This study included 87 students from second to seventh grade (75 typically developing students [TDS] and 12 students with reading impairments [SRI]). First, two preliminary parallel forms of student books, A and B, were prepared, both including 12 reading passages organized based on increasing difficulty as the student moved from one passage to the next. All the passages contain five comprehension questions. Then, the content and face validity, descriptive statistics, construct validity, and reliability, including test re-test, inter-rater, and the correlation between passages A and B were measured. 

Based on the results, all the passages and questions were valid. The passages can make significant differences in reading skills between different grades, while gender differences are limited. However, according to the test re-test analysis, the inter-rater reliability, and the correlation between the two passages, KHANA is a reliable tool and can discriminate SRI from TDS.

According to the results, KHANA seems a valid and reliable test to assess reading skills in Persian-speaking students from second to seventh grade.

X-rays are routinely utilized for different diagnostic purposes but there is always the risk of an inaccurate diagnosis. This systematic review was designed to investigate whether inverse grayscale mode increased diagnostic accuracy. From inception to February 2022, MEDLINE, Embase, Scopus, Web of Science, and CENTRAL were searched for studies comparing grayscale inversion diagnostic accuracy to the conventional method. Quality assessment was performed using the Quality Assessment of Diagnostic Accuracy Studies version 2 (QUADAS-2) tool. Eighteen studies were included with an overall patient population of 1704. The number of studies investigating each lesion are as follows, lung masses: 13, pneumothoraces: 4, bony lesions: 3, interstitial lung diseases: 3, orthopedic studies: 2, bullous lung disease: 1, pleural effusion: 1, urinary calculus: 1, and large vascular occlusion: 1. Two studies had an overall moderate risk of bias and the remainders had low risk. The combined mode, featuring the conventional mode with the addition of the inverse grayscale, demonstrated better performance or insignificant difference in comparison with the conventional mode in all studies except one, which showed lower sensitivity in detecting pulmonary nodules. Also, meta-analysis of 250 patients in four pulmonary nodule studies showed better area under the ROC curve (AUC) of inverse mode (0.83, 95% CI: 0.75,0.90) in comparison with conventional mode (0.80, 95% CI: 0.72,0.88). Application of inverse mode when using radiography for detection of pulmonary nodules might improve diagnostic accuracy. Also, the inverse/combined mode showed better performance for lesions other than pulmonary nodule in some studies. However, there was insufficient evidence to draw a consistent conclusion.

Heart disease is, for the most part, alluding to conditions that include limited or blocked veins that can prompt a heart attack, chest torment or stroke. Earlier identification of heart disease may reduce the death rate. The cost of medical diagnosis makes it perverse to cure it for the large amount of people early. Using machine learning models performed on dataset. This article aims to find the most efficient and accurate machine learning models for disease prediction.

Several supervised machine learning algorithms were utilized to diagnosis and prediction of heart disease such as logistic regression, decision tree, random forest and KNN. The algorithms are applied to a dataset taken from the Kaggle site including 70000 samples. In algorithms, methods such as the importance of features, hold out validation, 10-fold cross-validation, stratified 10-fold cross-validation, leave one out cross-validation are the result of effective performance and increase accuracy. In addition, feature importance scores was estimated for each feature in some algorithms. These features were ranked based on feature importance score. All the work is done in the Anaconda environment based on python programming language and Scikit-learn library.

The algorithms performance is compared to each other so that performance based on ROC curve and some criteria such as accuracy, precision, sensitivity and F1 score were evaluated for each model. As a result of evaluation, random forest algorithm with F1 score 92%, accuracy 92% and AUC ROC 95%, has better performance than other algorithms.

The area under the ROC curve and evaluating criteria related to a number of classifying algorithms of machine learning to evaluate heart disease and indeed, the diagnosis and prediction of heart disease is compared to determine the most appropriate classifier.

Breast cancer is one of the most common cancers among women compared to all other ones. Machine learning (ML) techniques can bring a large contribute on the process of prediction and early diagnosis of breast cancer, became a research hotspot and has been proved as a strong technique. Using ML models performed on multidimensional dataset, this article aims to find the most efficient and accurate ML models for tumor classification prediction.

Several supervised ML algorithms were utilized to diagnosis and prediction of cancer tumor such as Logistic Regression Decision Tree, Random Forest and KNN. The algorithms are applied to a dataset taken from the UCI repository including 699 samples. The dataset includes Breast cancer features. To enhance the algorithms’ performance, these features are analyzed, the feature importance score and cross validation are considered. In this research, ML algorithms improved coupled by limited and selective features to produce high classification accuracy in tumor classification.

As a result of evaluation, Logistic Regression algorithm with accuracy value equal to 99.14%, AUC ROC equal to 99.6%, Extra Tree algorithm with accuracy value equal to 99.14% and AUC ROC equal to 99.1% have better performance than other algorithms. Therefore, these techniques can be useful for diagnosis and prediction of cancer tumor and prescribe it correctly.

The technique of ML can be used in medicine for analyzing the related data collections to a disease and its prediction. The area under the ROC curve and evaluating criteria related to a number of classifying algorithms of ML to evaluate breast cancer and indeed, the diagnosis and prediction of breast cancer is compared to determine the most appropriate classifier.

The use of angiography is the gold standard in the diagnosis of pulmonary embolism (PE). However, the invasive side effects and being less accessible, it is rarely performed. This study sought to determine the sensitivity, specificity, accuracy and positive and negative predictive value of Wells Rule and Revised Geneva Score in identifying patients with pulmonary embolism.

This cross-sectional study was performed on 214 patients with suspected pulmonary embolism who referred to Masih Daneshvari Hospital in Tehran, Iran in 2020. The researchers recorded the CT angiographic findings of patients suspected of having pulmonary embolism. All the patients were evaluated based on each of the Wells Rule and revised Geneva score. Finally, the sensitivity and specificity of each score was evaluated in comparison with CT angiography as the gold standard method in the diagnosis of pulmonary embolism.

The overall mean scores of the Revised Geneva Score and Wells Rule were 99.2± 33.6 and 68.2± 96.3, respectively. The sensitivity, specificity and accuracy of the Revised Geneva Score were 90.6, 47.4 and 72%, respectively. The sensitivity, specificity and accuracy of the Wells Rule were 71.8, 75.3 and 77%, respectively. The positive predictive values (PPV) for the Wells Rule and the Revised Geneva Score were 67.5 and 77.8, respectively. Finally, the negative predictive values (NPV) for the Wells Rule and Revised Geneva Score were 80.7 and 68.9, respectively.

The Revised Geneva Score is highly accurate in diagnosing pulmonary embolism. The score could be used as a non-invasive clinical evaluation tool in the diagnosis of PE.

Clear cell carcinoma (CCC) is an uncommon histopathologic subtype of ovarian and endometrial carcinoma. Due to the morphologic overlapping with other subtypes of ovarian and endometrial carcinomas, an accurate diagnosis is crucial.

In this study, 31 cases of ovarian clear cell carcinoma (OCCC), 28 endometrial clear cell carcinoma (ECCC), and 80 non-CCC subtypes (33 high-grade serous carcinomas of the ovary, 2 low-grade serous carcinomas, 10 ovarian endometrioid, 3 serous carcinomas and 29 endometrioid carcinomas of the endometrium) were investigated for immunohistochemical expression of AMACR. Sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) for the distinction of OCCC and ECCC from other histopathologic subtypes were calculated.

Positive AMACR staining was seen in 18 OCCCs (58%) and 10 ECCCs (35.7%). In the non-clear cell group, 44 cases of ovarian (98%) and 25 cases of endometrial carcinoma (78%) showed negative results. Only one case of ovarian endometrioid carcinoma and 7 cases (22%) of endometrial endometrioid carcinomas revealed a positive reaction (P<0.05). Collectively, sensitivity, specificity, PPV, and NPV of AMACR expression, for the diagnosis of OCCC were calculated as  58%, 98%, 94.7%, and 77.2%, respectively. The sensitivity, specificity, PPV, and NPV were shown to be as  35.7%, 78.1%, 58.8%, and 58.1%, respectively in the endometrium.

AMACR may be  a highly specific immunohistochemical marker for the distinction of serous and clear cell carcinoma. A small percentage of endometrioid carcinoma may show positive staining. The sensitivity of this marker may not be higher than the other well-known Napsin-A IHC marker.

The measurement of mid-regional pro-atrial natriuretic peptide (MR-proANP) as a clinical biomarker has become a potent diagnostic value for identifying patients with heart failure. This study aimed to determine the diagnostic value of MR-proANP in heart failure in comparison with brain natriuretic peptide (BNP).

This prospective diagnostic accuracy study evaluated two biomarkers in consecutive patients presenting to the emergency department (ED) with acute dyspnea. Blood samples were collected immediately from the participants. A diagnostic value of MR-proANP versus BNP for the diagnosis of acute heart failure was analyzed. All patients underwent transthoracic echocardiography to classify the diagnosis as dyspnea due to heart failure or due to other causes. Patients without a subsequent diagnosis of heart failure were compared as the controls. The diagnostic performance was evaluated by receiver operating characteristics (ROC) analysis.

Overall, 100 patients with heart failure and 85 controls were included in the study. The plasma levels of BNP and MR-proANP were significantly higher in the heart failure patients than in the control patients (p<0.001). MR-proANP levels significantly correlated with BNP levels (r=0.745, p<0.001). The ROC analysis demonstrated an area under the curve (AUC) of 0.872 (95% CI: 0.815–0.917) for MR-proANP and an AUC of 0.814 (95% CI: 0.751–0.868) for BNP. The AUC of MR-proANP was significantly higher than BNP to detect heart failure in patients with dyspnea (p=0.008). The optimal cut-off point of plasma MR-proANP was 134 pmol/L (sensitivity: 88.1%; specificity: 84.5%; PPV: 87.3%; NPV: 85.5%).

MR-proANP may be a useful biomarker for the diagnosis of acute heart failure in patients with dyspnea presenting to ED. The diagnostic value of MR-proANP was superior to BNP for diagnosing heart failure.