Quality Assessment Of Diagnostic Accuracy Studies Research Articles

Radiomics-based machine learning models for prediction of medulloblastoma subgroups: a systematic review and meta-analysis of the diagnostic test performance.

Medulloblastomas are a major cause of cancer-related mortality in the pediatric population. Four molecular groups have been identified, and these molecular groups drive risk stratification, prognostic modeling, and the development of novel treatment modalities. It has been demonstrated that radiomics-based machine learning (ML) models are effective at predicting the diagnosis, molecular class, and grades of CNS tumors. To assess radiomics-based ML models' diagnostic performance in predicting medulloblastoma subgroups and the methodological quality of the studies. A comprehensive literature search was performed on PubMed; the last search was conducted on 1 May 2022. Studies that predicted all four medulloblastoma subgroups in patients with histopathologically confirmed medulloblastoma and reporting area under the curve (AUC) values were included in the study. The quality assessments were conducted according to the Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) and Checklist for Artificial Intelligence in Medical Imaging (CLAIM). A meta-analysis of radiomics-based ML studies' diagnostic performance for the preoperative evaluation of medulloblastoma subgrouping was performed. Five studies were included in this meta-analysis. Regarding patient selection, two studies indicated an unclear risk of bias according to the QUADAS-2. The five studies had an average CLAIM score and compliance score of 23.2 and 0.57, respectively. The meta-analysis showed pooled AUCs of 0.88, 0.82, 0.83, and 0.88 for WNT, SHH, group 3, and group 4 for classification, respectively. Radiomics-based ML studies have good classification performance in predicting medulloblastoma subgroups, with AUCs >0.80 in every subgroup. To be applied to clinical practice, they need methodological quality improvement and stability.

Current status and quality of radiomic studies for predicting KRAS mutations in colorectal cancer patients: A systematic review and meta‑analysis

PurposeThe purpose of this study was to evaluate the methodological quality of radiomics-based studies for noninvasive, preoperative prediction of Kirsten rat sarcoma (KRAS) mutations in patients with colorectal cancer; furthermore, we systematically evaluate the diagnostic accuracy of predicting models. MethodsWe systematically searched PubMed, Embase, Cochrane Library and Web of Science databases up to 20 April 2022 for eligible studies. The methodological quality of included studies was assessed using the Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) and Radiomics Quality Score (RQS) tools. A meta-analysis of studies on the prediction of KRAS status in colorectal cancer patients was performed. ResultTwenty-nine studies were identified in the systematic review, including three studies on the prediction of KRAS status in colorectal cancer liver metastases. All studies had an average RQS score of 9.55 (26.5% of the total score), ranging from 3 to 17. Most studies demonstrated a low or unclear risk of bias in the domains of QUADAS-2. Nineteen studies were included in the meta-analysis, mostly imaged with magnetic resonance imaging (MRI), followed by computed tomography (CT), positron emission tomography-CT (PET/CT). With pooled sensitivity, specificity and area under the curve (AUC) of the training cohorts were 0.80(95% confidence interval(CI), 0.75–0.84), 0.80(95% CI, 0.74–0.85) and 0.87(95% CI, 0.84–0.90),respectively. The pooled sensitivity, specificity, and AUC for the validation cohorts (13 studies) were 0.78(95% CI, 0.71–0.84), 0.84(95% CI, 0.74–0.90), and 0.86(95% CI, 0.83–0.89), respectively. ConclusionRadiomics is a potential noninvasive technology that has a moderate preoperative diagnosis and prediction effect on KRAS mutations. However, it has not been implemented as a clinical decision-making tool. Future researchers should pay more attention to the methodological quality of the study and further externally validate the model using multicenter datasets.

Brain natriuretic peptide and N-terminal brain natriuretic peptide for the diagnosis of haemodynamically significant patent ductus arteriosus in preterm neonates.

Brain natriuretic peptide and N-terminal brain natriuretic peptide for the diagnosis of haemodynamically significant patent ductus arteriosus in preterm neonates.

Diagnostic performance of magnetic resonance imaging-based machine learning in Alzheimer's disease detection: a meta-analysis.

Advanced machine learning (ML) algorithms can assist rapid medical image recognition and realize automatic, efficient, noninvasive, and convenient diagnosis. We aim to further evaluate the diagnostic performance of ML to distinguish patients with probable Alzheimer's disease (AD) from normal older adults based on structural magnetic resonance imaging (MRI). The Medline, Embase, and Cochrane Library databases were searched for relevant literature published up until July 2021. We used the Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) tool and Checklist for Artificial Intelligence in Medical Imaging (CLAIM) to evaluate all included studies' quality and potential bias. Random-effects models were used to calculate pooled sensitivity and specificity, and the Deeks' test was used to assess publication bias. We included 24 models based on different brain features extracted by ML algorithms in 19 papers. The pooled sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, diagnostic odds ratio, and area under the summary receiver operating characteristic curve for ML in detecting AD were 0.85 (95%CI 0.81-0.89), 0.88 (95%CI 0.84-0.91), 7.15 (95%CI 5.40-9.47), 0.17 (95%CI 0.12-0.22), 43.34 (95%CI 26.89-69.84), and 0.93 (95%CI 0.91-0.95). ML using structural MRI data performed well in diagnosing probable AD patients and normal elderly. However, more high-quality, large-scale prospective studies are needed to further enhance the reliability and generalizability of ML for clinical applications before it can be introduced into clinical practice.

Deep learning applications in coronary anatomy imaging: a systematic review and meta-analysis.

The application of deep learning on medical imaging is growing in prevalence in the recent literature. One of the most studied areas is coronary artery disease (CAD). Imaging of coronary artery anatomy is fundamental, which has led to a high number of publications describing a variety of techniques. The aim of this systematic review is to review the evidence behind the accuracy of deep learning applications in coronary anatomy imaging. The search for the relevant studies, which applied deep learning on coronary anatomy imaging, was performed in a systematic approach on MEDLINE and EMBASE databases, followed by reviewing of abstracts and full texts. The data from the final studies was retrieved using data extraction forms. A meta-analysis was performed on a subgroup of studies, which looked at fractional flow reserve (FFR) prediction. Heterogeneity was tested using tau2, I2 and Q tests. Finally, a risk of bias was performed using Quality Assessment of Diagnostic Accuracy Studies (QUADAS) approach. A total of 81 studies met the inclusion criteria. The most common imaging modality was coronary computed tomography angiography (CCTA) (58%) and the most common deep learning method was convolutional neural network (CNN) (52%). The majority of studies demonstrated good performance metrics. The most common outputs were focused on coronary artery segmentation, clinical outcome prediction, coronary calcium quantification and FFR prediction, and most studies reported area under the curve (AUC) of ≥80%. The pooled diagnostic odds ratio (DOR) derived from 8 studies looking at FFR prediction using CCTA was 12.5 using the Mantel-Haenszel (MH) method. There was no significant heterogeneity amongst studies according to Q test (P=0.2496). Deep learning has been used in many applications on coronary anatomy imaging, most of which are yet to be externally validated and prepared for clinical use. The performance of deep learning, especially CNN models, proved to be powerful and some applications have already translated into medical practice, such as computed tomography (CT)-FFR. These applications have the potential to translate technology into better care of CAD patients.

Performance of 68Ga-labeled prostate-specific membrane antigen ligand positron emission tomography/computed tomography in the diagnosis of primary prostate cancer: a systematic review and meta-analysis.

To explore the feasibility of 68Ga-PSMA PET/CT in diagnosing primary prostate cancer. Embase, PubMed and Cochrane Library databases were searched for studies published before July 2020. The studies that used 68Ga-PSMA PET/CT for detecting primary prostate cancer, and pathological biopsy as the reference standard were included. The selecting process used preferred reporting items for systematic reviews and meta-analyses (PRISMA). The quality of enrolled studies was assessed by the Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) tool. According to our search strategy, 9 studies were included for analysis. A total of 547 patients with primary prostate cancer and 443 lesion segments that underwent 68Ga-PSMA PET/CT scans were included and their pathological biopsies were compared. The results of these studies showed some differences. For instance, the lowest sensitivity of 68Ga-PSMA PET/CT in diagnosing primary prostate cancer was 67%, while the highest sensitivity recorded was 97%. Compared with conventional imaging examinations, 68Ga-PSMA PET/CT had higher sensitivity and specificity in detecting primary prostate cancer. At present, most of the studies that used 68Ga-PSMA PET/CT for detecting prostate cancer are retrospective studies. Based on its advantage of high detection rate, the use of 68Ga-PSMA PET/CT in the detection of primary prostate cancer is worthy of promotion.

The added value of S-detect in the diagnostic accuracy of breast masses by senior and junior radiologist groups: a systematic review and meta-analysis.

S-detect is an emerging computer-aided diagnosis (CAD) technique that provides a reference for radiologists to identify breast cancer. Some studies have shown that US (ultrasound) + S-detect can improve the diagnostic accuracy of junior radiologists more than senior radiologists, but the results are inconsistent in various studies. Therefore, this meta-analysis aimed to assess the value of S-detect combined with the US outcomes from senior and junior radiologists for the diagnosis of breast cancer. We searched the PubMed, Cochrane Library, Embase, Web of Science, and Wanfang databases, China Biology Medicine disc, China National Knowledge Infrastructure (CNKI), and VIP database for trials on the diagnostic accuracy of US + S-detect for the diagnosis of breast masses. The search time frame was from the date of establishment of the database to August 20, 2022. Two researchers independently screened the literature, extracted the information, and evaluated the quality of the included literature using the Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) scale. StataSE 15.1 software was utilized to assess pooled metrics, including sensitivity, specificity, and the area under the curve (AUC). A total of 19 articles with 3,349 patients and 3,895 breast masses were included in this meta-analysis. Of these, seventeen articles evaluated the diagnostic performance of senior radiologists' US + S-detect for breast cancer, while twelve articles reported junior radiologists' diagnostic performance. The risk of bias was primarily attributed to patient selection, flow and timing. In the senior radiologist group, the pooled sensitivity and specificity of US + S-detect were 0.93 [95% confidence interval (CI): 0.89-0.95] and 0.86 (95% CI: 0.80-0.90), respectively, with an AUC of 0.96. As for the junior radiologist group, the pooled sensitivity and specificity of US + S-detect were 0.89 (95% CI: 0.83-0.93) and 0.79 (95% CI: 0.72-0.84), respectively, and the AUC was 0.91. The results of this meta-analysis showed that the pooled sensitivity and the AUC of both the senior and junior radiologist groups were high, with good diagnostic efficacy and high clinical application. However, the results of this study are highly heterogeneous and need to be validated by collecting more high-quality studies and accumulating a larger sample size.

Yield of Surveillance Imaging After Mastectomy With or Without Reconstruction for Patients With Prior Breast Cancer

A discrepancy on current guidelines and clinical practice exists regarding routine imaging surveillance after mastectomy, mainly regarding the lack of adequate evidence for imaging in this setting. To investigate the usefulness of imaging surveillance in terms of cancer detection and interval cancer rates after mastectomy with or without reconstruction for patients with prior breast cancer. A comprehensive literature search was conducted in 3 electronic databases-PubMed, ISI Web of Science, and Scopus-without year restriction. References from relevant reviews and eligible studies were also manually searched. Eligible studies were defined as those conducting surveillance imaging (mammography, ultrasonography, or magnetic resonance imaging [MRI]) of patients with prior breast cancer after mastectomy with or without reconstruction that presented adequate data to calculate cancer detection rates for each surveillance method. Independent data extraction by 2 investigators with consensus on discrepant results was performed. A quality assessment of studies was performed using the QUADAS-2 (Quality Assessment of Diagnostic Accuracy Studies-2) template. The generalized linear mixed model framework with both fixed-effects and random-effects models was used to meta-analyze the proportion of cases across studies including 3 variables: surveillance method, reconstruction after mastectomy, and surveillance measure. Three outcome measures were calculated for each eligible study and each surveillance imaging method within studies: overall cancer detection (defined as ipsilateral cancer, both palpable and nonpalpable) rate per 1000 examinations, clinically occult (nonpalpable) cancer detection rate per 1000 examinations, and interval cancer rate per 1000 examinations. In total, 16 studies were eligible for the meta-analysis. The pooled overall cancer detection rates per 1000 examinations were 1.86 (95% CI, 1.05-3.30) for mammography, 2.66 (95% CI, 1.48-4.76) for ultrasonography, and 5.17 (95% CI, 1.49-17.75) for MRI. For mastectomy without reconstruction, the rate of clinically occult (nonpalpable) cancer per 1000 examinations (2.96; 95% CI, 1.38-6.32) and the interval cancer rate per 1000 examinations (3.73; 95% CI, 0.84-3.98) were lower than the overall cancer detection rate (including both palpable and nonpalpable lesions) per 1000 examinations (6.41; 95% CI, 3.09-13.25) across all imaging modalities. The interval cancer rate per 1000 examinations for mastectomy with reconstruction (3.73; 95% CI, 0.41-2.73) was comparable to the pooled cancer detection rate per 1000 examinations (4.73; 95% CI, 2.32-9.63) across all imaging modalities. In all clinical scenarios and imaging modalities, lower rates of clinically occult cancer compared with cancer detection rates were observed. Lower detection rates of clinically occult-compared with overall-cancer across all 3 imaging modalities challenge the use of imaging surveillance after mastectomy, with or without reconstruction. Findings suggest that imaging surveillance in this context is unnecessary in clinical practice, at least until further studies demonstrate otherwise. Future studies should consider using the clinically occult cancer detection rate as a more clinically relevant measure in this setting.

Application of deep learning models for detection of subdural hematoma: a systematic review and meta-analysis

BackgroundThis study aimed to investigate the application of deep learning (DL) models for the detection of subdural hematoma (SDH).MethodsWe conducted a comprehensive search using relevant keywords. Articles extracted were original...

Detection of cerebral aneurysms using artificial intelligence: a systematic review and meta-analysis

BackgroundSubarachnoid hemorrhage from cerebral aneurysm rupture is a major cause of morbidity and mortality. Early aneurysm identification, aided by automated systems, may improve patient outcomes. Therefore, a systematic review and...

Predictive Role of Neutrophil to Lymphocyte Ratio in Adnexal Torsion: A Systematic Review and Meta-Analysis.

The goal of this systematic review and meta-analysis was to consolidate the available data on the role of the neutrophil to lymphocyte ratio (NLR) in predicting adnexal torsion (AT), to help guide clinical decision-making and outcomes. We used Web of Science, PubMed, and Scopus to conduct a systematic search for relevant publications published before June 26, 2022. We reported standardized mean difference (SMD) with a 95% confidence interval (CI). Because a significant level of heterogeneity was found, we used the random-effects model to calculate pooled effects. We used the Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) too for quality assessment. Overall, 15 articles were included in the analysis. A random-effects model revealed that patients with AT had elevated levels of NLR compared to those with other adnexal masses (SMD = 1.06, 95%CI = 0.67 to 1.45, p < 0.001). So, NLR had diagnostic value. In the subgroup analysis according to ethnicity, we found that Caucasian patients with AT had elevated levels of NLR compared to patients who were operated due to adnexal mass and reported as having a benign ovarian cyst, without torsion (SMD = 1.12, 95%CI = 0.71 to 1.54, p < 0.001). However, in the case of East Asian patients, there was no difference between cases and controls (SMD = 0.86, 95%CI = -0.21 to 1.94, p = 0.11). The pooled sensitivity of NLR was 0.79 (95%CI = 0.72-0.85), and the pooled specificity was 0.84 (95% CI, 0.74-0.91). In conclusion, there has been an interest in the use of NLR as a diagnostic marker for AT.

Three-dimensional transvaginal ultrasound vs magnetic resonance imaging for preoperative staging of deep myometrial and cervical invasion in patients with endometrial cancer: systematic review and meta-analysis.

To evaluate and compare the diagnostic test accuracy (DTA) of three-dimensional transvaginal ultrasound (3D-TVS) and magnetic resonance imaging (MRI) for deep myometrial infiltration (DMI) and cervical invasion for preoperative staging and surgery planning in patients with endometrial cancer (EC). This systematic review and meta-analysis investigated the DTA of MRI and 3D-TVS for DMI and cervical invasion in patients with EC. A literature search was performed using MEDLINE, Scopus, EMBASE, ScienceDirect, The Cochrane library, ClinicalTrials.gov, Cochrane Central Register of Controlled Trials, EU Clinical Trials Register and World Health Organization International Clinical Trials Registry Platform to identify relevant studies published between January 2000 and December 2021. Study quality was assessed using the Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) tool. Five studies, including a total of 450 patients, were included in the systematic review. All five studies compared the DTA of 3D-TVS vs MRI for DMI, and three studies compared the DTA of 3D-TVS vs MRI for cervical invasion. Pooled sensitivity, positive likelihood ratio and negative likelihood ratio for detecting DMI using 3D-TVS were 77% (95% CI, 66-85%), 4.57 and 0.31, respectively. The respective values for detecting DMI on MRI were 80% (95% CI, 73-86%), 4.22 and 0.24. Bivariate metaregression indicated a similar DTA of 3D-TVS and MRI (P = 0.80) for the correct identification of DMI. Pooled ln diagnostic odds ratio for detecting cervical invasion was 3.11 (95% CI, 2.09-4.14) for 3D-TVS and 2.36 (95% CI, 0.90-3.83) for MRI. The risk of bias was low for most of the four domains assessed in QUADAS-2. 3D-TVS demonstrated good diagnostic accuracy in terms of sensitivity and specificity for the evaluation of DMI and cervical invasion, with results comparable with those of MRI. Thus, we confirmed the potential role of 3D-TVS in the preoperative staging and surgery planning in patients with EC. © 2022 The Authors. Ultrasound in Obstetrics & Gynecology published by John Wiley & Sons Ltd on behalf of International Society of Ultrasound in Obstetrics and Gynecology.

The Diagnostic Accuracy of Serum and Urine Human Epididymis Protein 4 (HE4) in Ovarian Cancer in 15,394 Subjects: An Updated Meta-Analysis

Background We aim to determine the diagnostic accuracy of both serum and urinary human epididymis protein 4 (HE4) in the diagnosis of ovarian cancer. Methods Electronic databases and search engines such as PubMed, Cochrane Library, and Google Scholar were searched systematically by two independent reviewers to retrieve articles published from inception to June 11, 2022. The diagnostic accuracy of serum and urinary HE4 was computed using the random-effects model in terms of pooled sensitivity, pooled specificity, and diagnostic odds ratio (DOR) with 95% confidence interval (CI). To explain any source of possible heterogeneity, meta-regression and subgroup analyses were performed. Risk of bias assessment was conducted using the Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) tools recommended by the Cochrane Library. Result and conclusion This meta-analysis included a total of 38 studies of serum HE4 involving 14,745 subjects and five studies for urinary HE4 involving 649 subjects. We observed acceptable pooled sensitivity, specificity, summary receiver operating characteristics (SROC), and diagnostic odds ratio (DOR) at 0.79 (95% CI: 0.75-0.82), 0.92 (95% CI: 0.87-0.95), 0.88 (95% CI: 0.85-0.91), and 43 (95% CI: 25-72), respectively, for serum HE4 for discriminating ovarian cancer. For urine HE4, the pooled sensitivity, specificity, SROC, and DOR were 0.80 (95% CI: 0.64-0.90), 0.93 (95% CI: 0.83-0.98), 0.94 (95% CI: 0.91-0.95), and 55 (95% CI: 15-198), respectively. Therefore, HE4 is a promising biomarker with a high degree of specificity and acceptable sensitivity for the diagnosis of ovarian cancer. Registration number This meta-analysis was performed after the registration of the protocol in the PROSPERO database with registration numberCRD42022324947.

Mammography diagnosis of breast cancer screening through machine learning: a systematic review and meta-analysis.

Breast cancer was the fourth leading cause of cancer-related death worldwide, and early mammography screening could decrease the breast cancer mortality. Artificial intelligence (AI)-assisted diagnose system based on machine learning (ML) methods can help improve the screening accuracy and efficacy. This study aimed to systematically review and make a meta-analysis on the diagnostic accuracy of mammography diagnosis of breast cancer through various ML methods. Springer Link, Science Direct (Elsevier), IEEE Xplore, PubMed and Web of Science were searched for relevant studies published from January 2000 to September 2021. The study was registered with the PROSPERO International Prospective Register of Systematic Reviews (protocol no. CRD42021284227). A Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) was used to assess the included studies, and reporting was evaluated using the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA). The pooled summary estimates for sensitivity, specificity, the area under the receiver operating characteristic curve (AUC) for three ML methods (convolutional neural network [CNN], artificialneuralnetwork [ANN], supportvectormachine [SVM]) were calculated. A total of 32 studies with 23,804 images were included in the meta-analysis. The overall pooled estimate for sensitivity, specificity and AUC was 0.914 [95% CI 0.868-0.945], 0.916 [95% CI 0.873-0.945] and 0.945 for mammography diagnosis of breast cancer through three ML methods. The pooled sensitivity, specificity and AUC of CNN were 0.961 [95% CI 0.886-0.988], 0.950 [95% CI 0.924-0.967] and 0.974. The pooled sensitivity, specificity and AUC of ANN were 0.837 [95% CI 0.772-0.886], 0.894 [95% CI 0.764-0.957] and 0.881. The pooled sensitivity, specificity and AUC of SVM were 0.889 [95% CI 0.807-0.939], 0.843 [95% CI 0.724-0.916] and 0.913. Machine learning methods (especially CNN) show excellent performance in mammography diagnosis of breast cancer screening based on retrospective studies. More rigorous prospective studies are needed to evaluate the longitudinal performance of AI.

Do COVID-19 CT features vary between patients from within and outside mainland China? Findings from a meta-analysis.

BackgroundChest computerized tomography (CT) plays an important role in detecting patients with suspected coronavirus disease 2019 (COVID-19), however, there are no systematic summaries on whether the chest CT findings of patients within mainland China are applicable to those found in patients outside.MethodsRelevant studies were retrieved comprehensively by searching PubMed, Embase, and Cochrane Library databases before 15 April 2022. Quality assessment of diagnostic accuracy studies (QUADAS) was used to evaluate the quality of the included studies, which were divided into two groups according to whether they were in mainland China or outside. Data on diagnostic performance, unilateral or bilateral lung involvement, and typical chest CT imaging appearances were extracted, and then, meta-analyses were performed with R software to compare the CT features of COVID-19 pneumonia between patients from within and outside mainland China.ResultsOf the 8,258 studies screened, 19 studies with 3,400 patients in mainland China and 14 studies with 554 outside mainland China were included. Overall, the risk of quality assessment and publication bias was low. The diagnostic value of chest CT is similar between patients from within and outside mainland China (93, 91%). The pooled incidence of unilateral lung involvement (15, 7%), the crazy-paving sign (31, 21%), mixed ground-glass opacities (GGO) and consolidations (51, 35%), air bronchogram (44, 25%), vascular engorgement (59, 33%), bronchial wall thickening (19, 12%), and septal thickening (39, 26%) in patients from mainland China were significantly higher than those from outside; however, the incidence rates of bilateral lung involvement (75, 84%), GGO (78, 87%), consolidations (45, 58%), nodules (12, 17%), and pleural effusion (9, 15%) were significantly lower.ConclusionConsidering that the chest CT features of patients in mainland China may not reflect those of the patients abroad, radiologists and clinicians should be familiar with various CT presentations suggestive of COVID-19 in different regions.

Artificial intelligence with magnetic resonance imaging for prediction of pathological complete response to neoadjuvant chemoradiotherapy in rectal cancer: A systematic review and meta-analysis

PurposeThe purpose of this study was to evaluate the diagnostic accuracy of artificial intelligence (AI) models with magnetic resonance imaging(MRI) in predicting pathological complete response(pCR) to neoadjuvant chemoradiotherapy (nCRT) in patients with rectal cancer. Furthermore, assessed the methodological quality of the models.MethodsWe searched PubMed, Embase, Cochrane Library, and Web of science for studies published before 21 June 2022, without any language restrictions. The Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) and Radiomics Quality Score (RQS) tools were used to assess the methodological quality of the included studies. We calculated pooled sensitivity and specificity using random-effects models, I2 values were used to measure heterogeneity, and subgroup analyses to explore potential sources of heterogeneity.ResultsWe selected 21 papers for inclusion in the meta-analysis from 1562 retrieved publications, with a total of 1873 people in the validation groups. The meta-analysis showed that AI models based on MRI predicted pCR to nCRT in patients with rectal cancer: a pooled area under the curve (AUC) 0.91 (95% CI, 0.88-0.93), sensitivity of 0.82(95% CI,0.71-0.90), pooled specificity 0.86(95% CI,0.80-0.91). In the subgroup analysis, the pooled AUC of the deep learning(DL) model was 0.97, the pooled AUC of the radiomics model was 0.85; the pooled AUC of the combined model with clinical factors was 0.92, and the pooled AUC of the radiomics model alone was 0.87. The mean RQS score of the included studies was 10.95, accounting for 30.4% of the total score.ConclusionsRadiomics is a promising noninvasive method with high value in predicting pathological response to nCRT in patients with rectal cancer. DL models have higher predictive accuracy than radiomics models, and combined models incorporating clinical factors have higher diagnostic accuracy than radiomics models alone. In the future, prospective, large-scale, multicenter investigations using radiomics approaches will strengthen the diagnostic power of pCR.Systematic Review Registration https://www.crd.york.ac.uk/prospero/, identifier CRD42021285630.

Circulating Tumor DNA, Imaging, and Carcinoembryonic Antigen: Comparison of Surveillance Strategies Among Patients Who Underwent Resection of Colorectal Cancer-A Systematic Review and Meta-analysis.

Almost one-third of colorectal cancer (CRC) patients experience recurrence after resection; nevertheless, follow-up strategies remain controversial. We sought to systematically assess and compare the accuracy of carcinoembryonic antigen (CEA), imaging [positron emission tomography (PET) and computed tomography (CT) scans], and circulating tumor DNA (CtDNA) as surveillance strategies. PubMed, Medline, Embase, Scopus, Cochrane, Web of Science, and CINAHL were systematically searched. The Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) was used to assess methodological quality. We performed a bivariate random-effects meta-analysis and reported pooled sensitivity, specificity, and diagnostic odds ratio (DOR) values for each surveillance strategy. Thirty studies were included in the analysis. PET scans had the highest sensitivity to detect recurrence (0.95; 95%CI 0.91-0.97), followed by CT scans (0.77; 95%CI 0.67-0.85). CtDNA positivity had the highest specificity to detect recurrence (0.95; 95%CI 0.91-0.97), followed by increased CEA levels (0.88; 95%CI 0.82-0.92). Furthermore, PET scans had the highest DOR to detect recurrence (DOR 120.7; 95%CI 48.9-297.9) followed by CtDNA (DOR 37.6; 95%CI 20.8-68.0). PET scans had the highest sensitivity and DOR to detect recurrence, while CtDNA had the highest specificity and second highest DOR. Combinations of traditional cross-sectional/functional imaging and newer platforms such as CtDNA may result in optimized surveillance of patients following resection of CRC.

Can serum autoantibodies be a potential early detection biomarker for breast cancer in women? A diagnostic test accuracy review and meta-analysis

BackgroundThe increasing incidence of breast cancer necessitates the need to explore alternate screening strategies that circumvent the setbacks of conventional techniques especially among population that report earlier age at diagnosis. Serum autoantibodies is one such potential area of interest. However, their ubiquitous presence across cancer types limits its applicability to any one specific type of cancer. This review was therefore carried out to explore and consolidate available evidence on autoantibodies for early detection of breast cancer and to identify those that demonstrated a higher sensitivity.MethodsA diagnostic test accuracy (DTA) review was carried out to ascertain serum autoantibodies that could be used for early detection of breast cancer among women. All relevant articles that investigated the role of autoantibodies in early detection of breast cancer were included for the review. MEDLINE, Scopus, ProQuest, Ovid SP, and Cochrane Library were searched extensively for eligible studies. Quality of the included studies was assessed using Quality Assessment of Diagnostic Accuracy Studies (QUADAS)-2 tool. RevMan 5.3 was used for exploratory and MetaDTA 2019 for hierarchical analyses. The review helped identify the most frequently investigated autoantibodies and a meta-analysis further consolidated the findings.ResultsA total of 53 articles were included for the final analysis that reported over a 100 autoantibodies that were studied for early detection of breast cancer in women. P53, MUC1, HER2, HSP60, P16, Cyclin B1, and c-Myc were the most frequently investigated autoantibodies. Of these P53, MUC1, HER2, and HSP60 exhibited higher summary sensitivity measures. While the individual pooled sensitivity estimates ranged between 10 and 56%, the panel sensitivity values reported across studies were higher with an estimated range of 60–87%.ConclusionFindings from the review indicate a higher sensitivity for an autoantibody panel in comparison to individual assays. A panel comprising of P53, MUC1, HER2, and HSP60 autoantibodies has the potential to be investigated as an early detection biomarker for breast cancer.

A systematic review of validated case-finding definitions to identify hypertensive disorders of pregnancy in administrative healthcare databases

Abstract Background Administrative healthcare data are frequently used to study cardiovascular disease (CVD) risk in women with hypertensive disorders of pregnancy (HDP); however, little is known about the validity of case-finding definitions (CFDs, e.g., International Classification of Disease codes/algorithms) designed to identify these conditions in administrative databases. Purpose To systematically identify and summarize available evidence on the validity of administrative CFDs for HDP. Methods Four bibliographic databases and grey literature sources were searched for eligible studies. The titles/abstracts of all records were independently screened for eligibility by two reviewers, then assessed at full text. Study data (design and participant characteristics, validation statistics) were extracted by two independent reviewers and discrepancies resolved through consensus. Quality of reporting was assessed using checklists; risk of bias was assessed using a modified version of the Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) tool, adapted for administrative validation studies. Results Twenty-five studies, published between 1998 and 2021, met the eligibility criteria. Nearly half (48%) were conducted in the United States. Ten studies validated CFDs for ≥1 HDP as a primary aim; most (60%) validated several maternal and infant morbidities, including ≥1 HDP. Preeclampsia (any, mild, moderate, serious, severe, superimposed) was the most validated HDP subtype. Only six studies reported gold standard definitions for all HDPs validated; definitions were heterogeneous with respect to blood pressure thresholds and timing of diagnosis. Seven studies (∼25%) reported all 2x2 table values (true positives/negatives, false positives/negatives) for ≥1 CFD, or they were calculable. The majority of CFDs reported in primary analyses (n=23) were highly specific (≥98%); however, sensitivity varied widely (3.2% to 100%; Figure 1). Nearly all (n=20, 87%) had a positive predictive value (PPV) of ≥70%, 13 of which had a PPV of &amp;gt;80% in combination with high specificity. Across studies, HDP prevalence ranged from 0.1% (eclampsia) to 37% (any maternal hypertensive disorder). Quality of reporting was generally poor to moderate, and all studies were judged to be at unclear or high risk of bias on ≥1 QUADAS-2 domain. Five studies were judged to be of “low concern” regarding study applicability (Figure 2). Conclusions Clinical understanding of CVD risk in women with HDP could be drastically impacted if there is low confidence that these conditions have been correctly identified from administrative data. Researchers should quantitatively explore the extent to which CVD risk estimates may be impacted by CFDs with low sensitivity and artificially inflated PPVs, influenced by greater study prevalence of HDP than would be expected in the general population. Higher quality validation studies that employ more rigorous methodology and improved reporting are needed. Funding Acknowledgement Type of funding sources: None.

Diagnostic accuracy of mac-2-binding protein glycosylation isomer for diagnosing hepatitis B-related fibrosis: A meta-analysis.

Mac-2-binding protein glycosylation isomer (M2BPGi) is a novel biomarker for liver fibrosis. The aim of this meta-analysis was to evaluate the accuracy of M2BPGi for predicting hepatitis B virus (HBV)-related liver fibrosis. EMBASE, PubMed, Web of Science, China National Knowledge Infrastructure, SinoMED, VIP Database for Chinese Technical Periodicals databases were searched comprehensively for articles published up to March 2022. Quality assessment was carried out in accordance with the Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2). The pooled diagnostic estimates including sensitivity, specificity, the area under the summary receiver operating characteristic curve (AUROC) were calculated. Before pooling the estimates, the threshold effect was assessed. Subgroup analysis was performed as well. In all, 11 studies including 1836 patients were included. None of the 11 studies met all the criteria of QUADAS-2. The threshold effect was found (r= 0.757, P= 0.011) for predicting HBV-related severe fibrosis. The sensitivity, specificity and AUROC of M2BPGi for the prediction of significant fibrosis were 0.68 (0.65-0.71), 0.67 (0.64-0.70) and 0.741, respectively, while those for predicting cirrhosis were 0.65 (0.57-0.72), 0.79 (0.77-0.81) and 0.792. Additionally, the AUROC of M2BPGi for predicting severe fibrosis reached 0.766. No publication bias was observed. The results of subgroup analyses were similar to the overall results. M2BPGi has moderate diagnostic accuracy for predicting HBV-related significant fibrosis, severe fibrosis, and cirrhosis. Further studies stratified by etiology, liver inflammation, treatment, etc, are urgently needed.