Diagnostic accuracy of functional imaging modalities for chondrosarcoma: A systematic review and meta-analysis.
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SUBMITTER: Jo I
PROVIDER: S-EPMC6811998 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Obtain summary estimates of sensitivity and specificity for imaging modalities for chronic pancreatitis (CP) assessment.A systematic search was performed in Cochrane Library, MEDLINE, Embase and CINAHL databases for studies evaluating imaging modalities for the diagnosis of CP up to September 2016. A bivariate random-effects modeling was used to obtain summary estimates of sensitivity and specificity.We included 43 studies evaluating 3460 patients. Sensitivity of endoscopic retrograde cholangiopancreatography (ERCP) (82%; 95%CI: 76%-87%) was significant higher than that of abdominal ultrasonography (US) (67%; 95%CI: 53%-78%; P=0.018). The sensitivity estimates of endoscopic ultrasonography (EUS), magnetic resonance imaging (MRI), and computed tomography (CT) were 81% (95%CI: 70%-89%), 78% (95%CI: 69%-85%), and 75% (95%CI: 66%-83%), respectively, and did not differ significantly from each other. Estimates of specificity were comparable for EUS (90%; 95%CI: 82%-95%), ERCP (94%; 95%CI: 87%-98%), CT (91%; 95% CI: 81%-96%), MRI (96%; 95%CI: 90%-98%), and US (98%; 95%CI: 89%-100%).EUS, ERCP, MRI and CT all have comparable high diagnostic accuracy in the initial diagnosis of CP. EUS and ERCP are outperformers and US has the lowest accuracy. The choice of imaging modality can therefore be made based on invasiveness, local availability, experience and costs.• EUS, ERCP, MRI and CT have high diagnostic sensitivity for chronic pancreatitis • Diagnostic specificity is comparable for all imaging modalities • EUS and ERCP are outperformers and US has the lowest accuracy • The choice of imaging can be made based on clinical considerations.
| S-EPMC5544812 | biostudies-other
Project description:BackgroundWith the rise of artificial intelligence (AI) in ophthalmology, the need to define its diagnostic accuracy is increasingly important. The review aims to elucidate the diagnostic accuracy of AI algorithms in screening for all ophthalmic conditions in patient care settings that involve digital imaging modalities, using the reference standard of human graders.MethodsThis is a systematic review and meta-analysis. A literature search will be conducted on Ovid MEDLINE, Ovid EMBASE, and Wiley Cochrane CENTRAL from January 1, 2000, to December 20, 2021. Studies will be selected via screening the titles and abstracts, followed by full-text screening. Articles that compare the results of AI-graded ophthalmic images with results from human graders as a reference standard will be included; articles that do not will be excluded. The systematic review software DistillerSR will be used to automate part of the screening process as an adjunct to human reviewers. After the full-text screening, data will be extracted from each study via the categories of study characteristics, patient information, AI methods, intervention, and outcomes. Risk of bias will be scored using Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) by two trained independent reviewers. Disagreements at any step will be addressed by a third adjudicator. The study results will include summary receiver operating characteristic (sROC) curve plots as well as pooled sensitivity and specificity of artificial intelligence for detection of any ophthalmic conditions based on imaging modalities compared to the reference standard. Statistics will be calculated in the R statistical software.DiscussionThis study will provide novel insights into the diagnostic accuracy of AI in new domains of ophthalmology that have not been previously studied. The protocol also outlines the use of an AI-based software to assist in article screening, which may serve as a reference for improving the efficiency and accuracy of future large systematic reviews.Trial registrationPROSPERO, CRD42021274441.
| S-EPMC9281030 | biostudies-literature
Project description:Deep learning (DL) has the potential to transform medical diagnostics. However, the diagnostic accuracy of DL is uncertain. Our aim was to evaluate the diagnostic accuracy of DL algorithms to identify pathology in medical imaging. Searches were conducted in Medline and EMBASE up to January 2020. We identified 11,921 studies, of which 503 were included in the systematic review. Eighty-two studies in ophthalmology, 82 in breast disease and 115 in respiratory disease were included for meta-analysis. Two hundred twenty-four studies in other specialities were included for qualitative review. Peer-reviewed studies that reported on the diagnostic accuracy of DL algorithms to identify pathology using medical imaging were included. Primary outcomes were measures of diagnostic accuracy, study design and reporting standards in the literature. Estimates were pooled using random-effects meta-analysis. In ophthalmology, AUC's ranged between 0.933 and 1 for diagnosing diabetic retinopathy, age-related macular degeneration and glaucoma on retinal fundus photographs and optical coherence tomography. In respiratory imaging, AUC's ranged between 0.864 and 0.937 for diagnosing lung nodules or lung cancer on chest X-ray or CT scan. For breast imaging, AUC's ranged between 0.868 and 0.909 for diagnosing breast cancer on mammogram, ultrasound, MRI and digital breast tomosynthesis. Heterogeneity was high between studies and extensive variation in methodology, terminology and outcome measures was noted. This can lead to an overestimation of the diagnostic accuracy of DL algorithms on medical imaging. There is an immediate need for the development of artificial intelligence-specific EQUATOR guidelines, particularly STARD, in order to provide guidance around key issues in this field.
| S-EPMC8027892 | biostudies-literature
Project description:ObjectivesAccurate subtype classification in primary aldosteronism (PA) is critical in assessing the optimal treatment options. This study aimed to evaluate the diagnostic accuracy of adrenal imaging for unilateral PA classification.MethodsSystematic searches of PubMed, EMBASE and the Cochrane databases were performed from 1 January 2000 to 1 February 2020, for all studies that used CT or MRI in determining unilateral PA and validated the results against invasive adrenal vein sampling (AVS). Summary diagnostic accuracies were assessed using a bivariate random-effects model. Subgroup analyses, meta-regression and sensitivity analysis were performed to explore the possible sources of heterogeneity.ResultA total of 25 studies, involving a total of 4669 subjects, were identified. The overall analysis revealed a pooled sensitivity of 68% (95% CI: 61% to 74%) and specificity of 57% (95% CI 50% to 65%) for CT/MRI in identifying unilateral PA. Sensitivity was higher in the contrast-enhanced (CT) group versus the traditional CT group (77% (95% CI 66% to 85%) vs 58% (95% CI 50% to 66%). Subgroup analysis stratified by screening test for PA showed that the sensitivity of the aldosterone-to-renin ratio (ARR) group was higher than that of the non-ARR group (78% (95% CI 69% to 84%) vs 66% (95% CI 58% to 72%)). The diagnostic accuracy of PA patients aged ≤40 years was reported in four studies, and the overall sensitivity was 71%, with 79% specificity. Meta-regression revealed a significant impact of sample size on sensitivity and of age and study quality on specificity.ConclusionCT/MRI is not a reliable alternative to invasive AVS without excellent sensitivity or specificity for correctly identifying unilateral PA. Even in young patients (≤40 years), 21% of patients would have undergone unnecessary adrenalectomy based on imaging results alone.
| S-EPMC7780716 | biostudies-literature
Project description:ObjectivesTo review the diagnostic performance of contemporary imaging modalities for determining local disease extent and nodal metastasis in patients with newly diagnosed cervical cancer.MethodsPubmed and Embase databases were searched for studies published from 2000 to 2019 that used ultrasound (US), CT, MRI, and/or PET for evaluating various aspects of local extent and nodal metastasis in patients with newly diagnosed cervical cancer. Sensitivities and specificities from the studies were meta-analytically pooled using bivariate and hierarchical modeling.ResultsOf 1311 studies identified in the search, 115 studies with 13,999 patients were included. MRI was the most extensively studied modality (MRI, CT, US, and PET were evaluated in 78, 12, 9, and 43 studies, respectively). Pooled sensitivities and specificities of MRI for assessing all aspects of local extent ranged between 0.71-0.88 and 0.86-0.95, respectively. In assessing parametrial invasion (PMI), US demonstrated pooled sensitivity and specificity of 0.67 and 0.94, respectively-performance levels comparable with those found for MRI. MRI, CT, and PET performed comparably for assessing nodal metastasis, with low sensitivity (0.29-0.69) but high specificity (0.88-0.98), even when stratified to anatomical location (pelvic or paraaortic) and level of analysis (per patient vs. per site).ConclusionsMRI is the method of choice for assessing any aspect of local extent, but where not available, US could be of value, particularly for assessing PMI. CT, MRI, and PET all have high specificity but poor sensitivity for the detection of lymph node metastases.Key points• Magnetic resonance imaging is the method of choice for assessing local extent. • Ultrasound may be helpful in determining parametrial invasion, especially in lower-resourced countries. • Computed tomography, magnetic resonance imaging, and positron emission tomography perform similarly for assessing lymph node metastasis, with high specificity but low sensitivity.
| S-EPMC8353650 | biostudies-literature
Project description:Although there has been major progress in gout imaging, no gout classification criteria currently include advanced imaging techniques.To examine the usefulness of imaging modalities in the classification of gout when compared to monosodium urate (MSU) crystal confirmation as the gold standard, in order to inform development of new gout classification criteria.We systematically reviewed the published literature concerning the diagnostic performance of plain film radiography, MRI, ultrasound (US), conventional CT and dual energy CT (DECT). Only studies with MSU crystal confirmation as the gold standard were included. When more than one study examined the same imaging feature, the data were pooled and summary test characteristics were calculated.11 studies (9 manuscripts and 2 meeting abstracts) satisfied the inclusion criteria. All were set in secondary care, with mean gout disease duration of at least 7 years. Three features were examined in more than one study: the double contour sign (DCS) on US, tophus on US, and MSU crystal deposition on DECT. The pooled (95% CI) sensitivity and specificity of US DCS were 0.83 (0.72 to 0.91) and 0.76 (0.68 to 0.83), respectively; of US tophus, were 0.65 (0.34 to 0.87) and 0.80 (0.38 to 0.96), respectively; and of DECT, were 0.87 (0.79 to 0.93) and 0.84 (0.75 to 0.90), respectively.US and DECT show promise for gout classification but the few studies to date have mostly been in patients with longstanding, established disease. The contribution of imaging over clinical features for gout classification criteria requires further examination.
| S-EPMC4869978 | biostudies-literature
Project description:BackgroundHaving a penicillin allergy label associates with a higher risk for antibiotic resistance and increased health care use.ObjectiveWe sought to assess the accuracy of skin tests and specific IgE quantification in the diagnostic evaluation of patients reporting a penicillin/β-lactam allergy.MethodsWe performed a systematic review and diagnostic accuracy meta-analysis, searching on MEDLINE, Scopus, and Web of Science. We included studies conducted in patients reporting a penicillin allergy and in whom skin tests and/or specific IgE quantification were performed and compared with drug challenge results. We quantitatively assessed the accuracy of diagnostic tests with bivariate random-effects meta-analyses. Meta-regression and subgroup analyses were performed to explore causes of heterogeneity. Studies' quality was evaluated using QUADAS-2 criteria.ResultsWe included 105 primary studies, assessing 31,761 participants. Twenty-seven studies were assessed by bivariate meta-analysis. Skin tests had a summary sensitivity of 30.7% (95% CI, 18.9%-45.9%) and a specificity of 96.8% (95% CI, 94.2%-98.3%), with a partial area under the summary receiver-operating characteristic curve of 0.686 (I2 = 38.2%). Similar results were observed for subanalyses restricted to patients reporting nonimmediate maculopapular exanthema or urticaria/angioedema. Specific IgE had a summary sensitivity of 19.3% (95% CI, 12.0%-29.4%) and a specificity of 97.4% (95% CI, 95.2%-98.6%), with a partial area under the summary receiver-operating characteristic curve of 0.420 (I2 = 8.5%). Projected predictive values mainly reflect the low frequency of true penicillin allergy.ConclusionsSkin tests and specific IgE quantification appear to have low sensitivity and high specificity. Because current evidence is insufficient for assessing the role of these tests in stratifying patients for delabeling, we identified key requirements needed for future studies.
| S-EPMC8019189 | biostudies-literature
Project description:RATIONALE AND OBJECTIVES:There have been a large number of case-control studies using diffusion tensor imaging (DTI) in amyotrophic lateral sclerosis (ALS). The objective of this study was to perform an individual patient data (IPD) meta-analysis for the estimation of the diagnostic accuracy measures of DTI in the diagnosis of ALS using corticospinal tract data. MATERIALS AND METHODS:MEDLINE, EMBASE, CINAHL, and Cochrane databases (1966-April 2011) were searched. Studies were included if they used DTI region of interest or tractography techniques to compare mean cerebral corticospinal tract fractional anisotropy values between ALS subjects and healthy controls. Corresponding authors from the identified articles were contacted to collect individual patient data. IPD meta-analysis and meta-regression were performed using Stata. Meta-regression covariate analysis included age, gender, disease duration, and Revised Amyotrophic Lateral Sclerosis Functional Rating Scale scores. RESULTS:Of 30 identified studies, 11 corresponding authors provided IPD and 221 ALS patients and 187 healthy control subjects were available for study. Pooled area under the receiver operating characteristic curve (AUC) was 0.75 (95% CI: 0.66-0.83), pooled sensitivity was 0.68 (95% CI: 0.62-0.75), and pooled specificity was 0.73 (95% CI: 0.66-0.80). Meta-regression showed no significant differences in pooled AUC for each of the covariates. There was moderate to high heterogeneity of pooled AUC estimates. Study quality was generally high. Data from 19 of the 30 eligible studies were not ascertained, raising possibility of selection bias. CONCLUSION:Using corticospinal tract individual patient data, the diagnostic accuracy of DTI appears to lack sufficient discrimination in isolation. Additional research efforts and a multimodal approach that also includes ALS mimics will be required to make neuroimaging a critical component in the workup of ALS.
| S-EPMC4384461 | biostudies-literature
Project description:Artificial intelligence (AI) systems have become critical in support of decision-making. This systematic review summarizes all the data currently available on the AI-assisted CT-Scan prediction accuracy for COVID-19. The ISI Web of Science, Cochrane Library, PubMed, Scopus, CINAHL, Science Direct, PROSPERO, and EMBASE were systematically searched. We used the revised Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool to assess all included studies' quality and potential bias. A hierarchical receiver-operating characteristic summary (HSROC) curve and a summary receiver operating characteristic (SROC) curve have been implemented. The area under the curve (AUC) was computed to determine the diagnostic accuracy. Finally, 36 studies (a total of 39,246 image data) were selected for inclusion into the final meta-analysis. The pooled sensitivity for AI was 0.90 (95% CI, 0.90-0.91), specificity was 0.91 (95% CI, 0.90-0.92) and the AUC was 0.96 (95% CI, 0.91-0.98). For deep learning (DL) method, the pooled sensitivity was 0.90 (95% CI, 0.90-0.91), specificity was 0.88 (95% CI, 0.87-0.88) and the AUC was 0.96 (95% CI, 0.93-0.97). In case of machine learning (ML), the pooled sensitivity was 0.90 (95% CI, 0.90-0.91), specificity was 0.95 (95% CI, 0.94-0.95) and the AUC was 0.97 (95% CI, 0.96-0.99). AI in COVID-19 patients is useful in identifying symptoms of lung involvement. More prospective real-time trials are required to confirm AI's role for high and quick COVID-19 diagnosis due to the possible selection bias and retrospective existence of currently available studies.
| S-EPMC8099790 | biostudies-literature
Project description:Aim:To assess the diagnostic performance of lateral radiograph of the neck for supraglottitis in adults and children. Methods:Electronic database searches (including PubMed, EMBASE, CINAHL, Web of Science, and WHO International Clinical Trials Registry Platform) were carried out through July 2014. Citations of included studies and recent narrative reviews were searched. Studies that compared lateral radiograph of the neck with a reference standard of direct/indirect laryngoscopy were included. Two reviewers independently assessed the methodological quality of included studies by Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2). Results:Of 2,088 potentially relevant citations, two single-gate (cohort) studies and six two-gate (case-control) studies were identified. These included studies of design are at the moderate or high risk of bias in QUADAS-2. The pooled sensitivity from bivariate random-effects regression was 92.9% (95% confidence interval [CI], 88.5-95.9%) and the pooled specificity was 89.2% (95% CI, 85.9-91.9%), but the diagnostic value would be overestimated because of selection bias in the six two-gate studies. The sensitivity and specificity of the single-gate studies were 100.0% (95% CI, 92.2-100.0%) and 30.6% (95% CI, 15.5-35.6%) in children and 81.0% (95% CI, 78.2-93.2%) and 85.7% specificity (95% CI, 78.2-93.2%) in adults. Conclusion:This study determines that there are insufficient studies of lateral neck radiograph for detecting supraglottitis. Lateral radiograph of the neck seems to have moderate accuracy for detecting supraglottitis. Further approximately unbiased studies are needed to obtain more valid and reliable estimates of test accuracy.
| S-EPMC5667265 | biostudies-literature