Visualizing Glioma Infiltration by the Combination of Multimodality Imaging and Artificial Intelligence, a Systematic Review of the Literature

The aim of this study was to systematically review the literature concerning the integration of multimodality imaging with artificial intelligence methods for visualization of tumor cell infiltration in glioma patients. The review was performed in accordance with the preferred reporting items for systematic reviews and meta-analysis (PRISMA) guidelines. The literature search was conducted in PubMed, Embase, The Cochrane Library and Web of Science and yielded 1304 results. 14 studies were included in the qualitative analysis. The reference standard for tumor infiltration was either histopathology or recurrence on image follow-up. Critical assessment was performed according to the Quality Assessment of Diagnostic Accuracy Studies (QUADAS2). All studies concluded their findings to be of significant value for future clinical practice. Diagnostic test accuracy reached an area under the curve of 0.74–0.91 reported in six studies. There was no consensus with regard to included image modalities, models or training and test strategies. The integration of artificial intelligence with multiparametric imaging shows promise for visualizing tumor cell infiltration in glioma patients. This approach can possibly optimize surgical resection margins and help provide personalized radiotherapy planning.

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Prediction of Chronic Atrophic Gastritis and Gastric Neoplasms by Serum Pepsinogen Assay: A Systematic Review and Meta-Analysis of Diagnostic Test Accuracy

Journal of Clinical Medicine ◽

10.3390/jcm8050657 ◽

2019 ◽

Vol 8 (5) ◽

pp. 657 ◽

Cited By ~ 13

Author(s):

Chang Seok Bang ◽

Jae Jun Lee ◽

Gwang Ho Baik

Keyword(s):

Atrophic Gastritis ◽

Meta Analysis ◽

Area Under The Curve ◽

High Specificity ◽

Chronic Atrophic Gastritis ◽

Diagnostic Odds Ratio ◽

Gastric Neoplasms ◽

Cochrane Library ◽

Test Accuracy ◽

Serum Pepsinogen

Serum pepsinogen assay (sPGA), which reveals serum pepsinogen (PG) I concentration and the PG I/PG II ratio, is a non-invasive test for predicting chronic atrophic gastritis (CAG) and gastric neoplasms. Although various cut-off values have been suggested, PG I ≤70 ng/mL and a PG I/PG II ratio of ≤3 have been proposed. However, previous meta-analyses reported insufficient systematic reviews and only pooled outcomes, which cannot determine the diagnostic validity of sPGA with a cut-off value of PG I ≤70 ng/mL and/or PG I/PG II ratio ≤3. We searched the core databases (MEDLINE, Cochrane Library, and Embase) from their inception to April 2018. Fourteen and 43 studies were identified and analyzed for the diagnostic performance in CAG and gastric neoplasms, respectively. Values for sensitivity, specificity, diagnostic odds ratio, and area under the curve with a cut-off value of PG I ≤70 ng/mL and PG I/PG II ratio ≤3 to diagnose CAG were 0.59, 0.89, 12, and 0.81, respectively and for diagnosis of gastric cancer (GC) these values were 0.59, 0.73, 4, and 0.7, respectively. Methodological quality and ethnicity of enrolled studies were found to be the reason for the heterogeneity in CAG diagnosis. Considering the high specificity, non-invasiveness, and easily interpretable characteristics, sPGA has potential for screening of CAG or GC.

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Artificial Intelligence for the Prediction of Acute Kidney Injury During the Perioperative Period: Systematic Review and Meta-Analysis of Diagnostic Test Accuracy

10.21203/rs.3.rs-1209770/v1 ◽

2022 ◽

Author(s):

hanfei zhang ◽

Amanda Y Wang ◽

Shukun Wu ◽

Johnathan Ngo ◽

Yunlin Feng ◽

...

Keyword(s):

Artificial Intelligence ◽

Acute Kidney Injury ◽

Early Recognition ◽

Meta Analysis ◽

Kidney Injury ◽

Perioperative Period ◽

Cochrane Library ◽

Test Accuracy ◽

Operating Characteristics ◽

Wide Range

Abstract Background: Acute kidney injury (AKI) is independently associated with morbidity and mortality in a wide range of surgical settings. Nowadays, with the increasing use of electronic health records (EHR), advances in patient information retrieval, and cost reduction in clinical informatics, artificial intelligence is increasingly being used to improve early recognition and management for perioperative AKI. However, there is no quantitative synthesis of the performance of these methods.Objective: To estimate the sensitivity and specificity of artificial intelligence for the prediction of acute kidney injury during the perioperative period.Methods: Pubmed, Embase, and Cochrane Library were searched to 2nd October 2021. Studies presenting diagnostic performance of artificial intelligence in the early detection of perioperative acute kidney injury were included. Two independent evaluators extracted data. The risk of bias of eligible studies was assessed using the PROBAST tool.Results: Nineteen studies involving 304,076 patients were included. Quantitative random-effects meta-analysis using the Rutter and Gatsonis hierarchical summary receiver operating characteristics (HSROC) model revealed pooled sensitivity, specificity, and diagnostic odds ratio of 0.77 (95% CI: 0.73 to 0.81),0.75 (95% CI: 0.71 to 0.80), and 10.7 (95% CI 8.5 to 13.5), respectively. Threshold effect was found to be the only source of heterogeneity, and there was no evidence of publication bias.Conclusions: Our review demonstrates the promising performance of artificial intelligence for early prediction of perioperative AKI. Further studies should focus on the improvement of existing models, novel biomarkers, and clinical effectiveness.

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Diagnostic and prognostic values of circular RNAs for lung cancer: a meta-analysis

Postgraduate Medical Journal ◽

10.1136/postgradmedj-2019-137178 ◽

2020 ◽

pp. postgradmedj-2019-137178

Author(s):

Qian Yang ◽

Lizhen Chen ◽

Li Yang ◽

Yuanshuai Huang

Keyword(s):

Lung Cancer ◽

Cancer Patients ◽

Meta Analysis ◽

Area Under The Curve ◽

High Sensitivity ◽

Cochrane Library ◽

Clinicopathological Parameters ◽

Circular Rnas ◽

Lung Cancer Patients ◽

Diagnostic Values

Circular RNAs (circRNAs) may serve as potential biomarkers for patients with lung cancer. The aim of this meta-analysis was to analyse the diagnostic, prognostic and clinicopathological values of circRNAs in lung cancer patients. A systematic search of PubMed, Embase, Web of Science, Scopus and the Cochrane Library databases was performed for relevant articles from inception to 29 January 2020. Pooled parameters including sensitivity, specificity and area under the curve (AUC) were used to assess the diagnostic performance, HRs and 95% CIs were used to evaluate overall survival (OS) and ORs were used to estimate clinicopathological parameters. 52 studies from 45 articles were enrolled in this study, including 17 on diagnosis and 35 on prognosis. For diagnostic values, circRNAs could discriminate lung cancer patients from the controls, with AUC of 0.83 (95% CI: 0.79 to 0.86), a relatively high sensitivity of 0.77 (95% CI: 0.73 to 0.81) and speciﬁcity of 0.75 (95% CI: 0.71 to 0.79). For prognostic significances, overexpression of 23 upregulated circRNAs was relevant to a poor prognosis (OS: HR=2.21, 95% CI: 1.96 to 2.49, p<0.001), and overexpression of 9 downregulated circRNAs was correlated with a favourable prognosis (OS: HR=0.62, 95% CI: 0.53 to 0.73, p<0.001). As for clinicopathological parameters, high expression of 23 upregulated circRNAs was associated with unfavourable clinicopathological features while 9 downregulated circRNAs proved the contrary. In conclusion, this study confirmed that circRNAs might serve as important biomarkers for diagnostic and prognostic values of lung cancer.

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Waist height ratio predicts chronic kidney disease: a systematic review and meta-analysis, 1998–2019

Archives of Public Health ◽

10.1186/s13690-019-0379-4 ◽

2019 ◽

Vol 77 (1) ◽

Cited By ~ 1

Author(s):

Ling Liu ◽

Yanqiu Wang ◽

Wanjun Zhang ◽

Weiwei Chang ◽

Yuelong Jin ◽

...

Keyword(s):

Chronic Kidney Disease ◽

Kidney Disease ◽

Statistical Difference ◽

Meta Analysis ◽

Area Under The Curve ◽

Cochrane Library ◽

Height Ratio ◽

Physical Measurements ◽

Significant Difference ◽

The Difference

Abstract Background The incidence of chronic kidney disease (CKD) increases each year, and obesity is an important risk factor for CKD. The main anthropometric indicators currently reflecting obesity are body mass index (BMI), waist circumference (WC), waist-to-hip ratio (WHR) and waist-to-height ratio (WHtR), but the rationality and merits of various indicators vary. This article aims to find whether the WHtR is a more suitable physical measurement that can predict CKD. Methods Pubmed, embase, the cochrane library, and web of science were systematically searched for articles published between 1998 and 2019 screening CKD through physical indicators. Two reviewers independently screened the literature according to the inclusion and exclusion criteria, extracted the data, and evaluated the quality of the methodology included in the study. Meta-analysis used the Stata 12.0 software. Results Nine studies were included, with a total of 202,283 subjects. Meta-analysis showed that according to the analysis of different genders in 6 studies, regardless of sex, WHtR was the area with the largest area under the curve (AUC). Except WHtR and visceral fat index (VFI) in women which showed no statistical difference, WHtR and other indicators were statistically different. In three studies without gender-based stratification, the area under the curve AUC for WHtR remained the largest, but only the difference between WHtR and BMI was statistically significant. When the Chinese population was considered as a subgroup, the area under the curve AUC for WHtR was the largest. Except for WHtR and VFI which showed no statistical difference in women, there was a statistically significant difference between WHtR and other indicators in men and women. Conclusion WHtR could be better prediction for CKD relative to other physical measurements. It also requires higher-quality prospective studies to verify the clinical application of WHtR.

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Associations and prognostic accuracy of electrolyte imbalances in predicting poor COVID-19 outcome: a systematic review and meta-analysis of 28 observational studies

10.1101/2021.11.19.21266563 ◽

2021 ◽

Author(s):

Harris Jun Jie Muhammad Danial Song ◽

Alys Zhi Qin Chia ◽

Benjamin Kye Jyn Tan ◽

Chong Boon Teo ◽

Horng Ruey Chua ◽

...

Keyword(s):

Clinical Outcome ◽

Observational Studies ◽

Inflammatory Marker ◽

Meta Analysis ◽

Area Under The Curve ◽

Cochrane Library ◽

Likelihood Ratios ◽

Predictive Values ◽

Poor Outcome ◽

Prognostic Accuracy

BACKGROUND: Serum electrolyte imbalances are highly prevalent in COVID-19 patients. However, their associations with COVID-19 outcomes are inconsistent, and of unknown prognostic value. OBJECTIVES: To systematically clarify the associations and prognostic accuracy of electrolyte imbalances (sodium, calcium, potassium, magnesium, chloride and phosphate) in predicting poor COVID-19 clinical outcome. METHODS: PubMed, Embase and Cochrane Library were searched. Odds of poor clinical outcome (a composite of mortality, intensive-care unit (ICU) admission, need for respiratory support and acute respiratory distress syndrome) were pooled using mixed-effects models. The associated prognostic sensitivity, positive and negative likelihood ratios (LR+, LR-) and predictive values (PPV, NPV; assuming 25% pre-test probability), and area under the curve (AUC) were computed. RESULTS: We included 28 observational studies from 953 records with low to moderate risk-of-bias. Hyponatremia (OR=2.08, 95%CI=1.48-2.94, I2=93%, N=8), hypernatremia (OR=4.32, 95%CI=3.17-5.88, I2=45%, N=7) and hypocalcemia (OR=3.31, 95%CI=2.24-4.88, I2=25%, N=6) were associated with poor COVID-19 outcome. These associations remained significant on adjustment for covariates such as demographics and comorbidities. Hypernatremia was 97% specific in predicting poor outcome (LR+ 4.0, PPV=55%, AUC=0.80) despite no differences in CRP and IL-6 levels between hypernatremic and normonatremic patients. Hypocalcemia was 76% sensitive in predicting poor outcome (LR- 0.44, NPV=87%, AUC=0.71). Overall quality of evidence ranged from very low to moderate. CONCLUSION: Hyponatremia, hypernatremia and hypocalcemia are associated with poor COVID-19 clinical outcome. Hypernatremia is 97% specific for a poor outcome and the association is independent of inflammatory marker levels. Further studies should evaluate if correcting these imbalances help improve clinical outcome.

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Artificial intelligence for pre-operative lymph node staging in colorectal cancer: a systematic review and meta-analysis

BMC Cancer ◽

10.1186/s12885-021-08773-w ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Sergei Bedrikovetski ◽

Nagendra N. Dudi-Venkata ◽

Hidde M. Kroon ◽

Warren Seow ◽

Ryash Vather ◽

...

Keyword(s):

Artificial Intelligence ◽

Colorectal Cancer ◽

Systematic Review ◽

Deep Learning ◽

Lymph Node ◽

Lymph Node Metastasis ◽

Meta Analysis ◽

Cochrane Library ◽

Learning Models ◽

Node Metastasis

Abstract Background Artificial intelligence (AI) is increasingly being used in medical imaging analysis. We aimed to evaluate the diagnostic accuracy of AI models used for detection of lymph node metastasis on pre-operative staging imaging for colorectal cancer. Methods A systematic review was conducted according to PRISMA guidelines using a literature search of PubMed (MEDLINE), EMBASE, IEEE Xplore and the Cochrane Library for studies published from January 2010 to October 2020. Studies reporting on the accuracy of radiomics models and/or deep learning for the detection of lymph node metastasis in colorectal cancer by CT/MRI were included. Conference abstracts and studies reporting accuracy of image segmentation rather than nodal classification were excluded. The quality of the studies was assessed using a modified questionnaire of the QUADAS-2 criteria. Characteristics and diagnostic measures from each study were extracted. Pooling of area under the receiver operating characteristic curve (AUROC) was calculated in a meta-analysis. Results Seventeen eligible studies were identified for inclusion in the systematic review, of which 12 used radiomics models and five used deep learning models. High risk of bias was found in two studies and there was significant heterogeneity among radiomics papers (73.0%). In rectal cancer, there was a per-patient AUROC of 0.808 (0.739–0.876) and 0.917 (0.882–0.952) for radiomics and deep learning models, respectively. Both models performed better than the radiologists who had an AUROC of 0.688 (0.603 to 0.772). Similarly in colorectal cancer, radiomics models with a per-patient AUROC of 0.727 (0.633–0.821) outperformed the radiologist who had an AUROC of 0.676 (0.627–0.725). Conclusion AI models have the potential to predict lymph node metastasis more accurately in rectal and colorectal cancer, however, radiomics studies are heterogeneous and deep learning studies are scarce. Trial registration PROSPERO CRD42020218004.

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Diagnostic and prognostic significance of dysregulated expression of circular RNAs in osteosarcoma

10.21203/rs.3.rs-19087/v1 ◽

2020 ◽

Author(s):

Chenghao Zhang ◽

Jieyu He ◽

Lin Qi ◽

Zhixi Duan ◽

Lu Wan ◽

...

Keyword(s):

Meta Analysis ◽

Prognostic Significance ◽

Area Under The Curve ◽

Disease Free Survival ◽

Diagnostic Odds Ratio ◽

Cochrane Library ◽

Clinicopathological Parameters ◽

Circular Rnas ◽

Diagnostic Significance ◽

Enneking Stage

Abstract Background Circular RNAs (circRNAs) have emerged as pivotal regulators in osteosarcoma tumorigenesis and progression, but their prognostic and diagnostic significance remain unclear. Herein, we aimed to perform an updated meta-analysis to explore the clinical, diagnostic and prognostic values of circRNAs in osteosarcoma. Methods Several databases, including PubMed, Web of Science, EMBASE, Scopus and Cochrane Library, were systematically searched up to Mar 10, 2020. Eligible studies regarding the relationship between circRNAs levels and clinicopathological, diagnostic and prognostic values in osteosarcoma patients were included in this study. Pooled odds ratios (ORs) with corresponding 95% confidence intervals (CIs) were used to measure clinical characteristics, while hazard ratios (HRs) with 95% CIs were adopted to assess overall survival (OS) and disease-free survival (DFS). Results Overall, 26 relevant studies involving 1,652 patients with osteosarcoma were enrolled, with eighteen studies on clinicopathological parameters, ten on diagnosis and eighteen on prognosis. For clinical parameters, overexpression of oncogenic circRNAs was intimately correlated with larger tumor size (P <0.00001), advanced Enneking stage (P <0.00001), poor differentiation (P =0.0001), and distant metastasis (DM) (P <0.00001). In contrast, the downregulated circRNAs showed negative correlation with Enneking stage (P=0.002) and DM (P<0.0001). For the diagnostic values, the summary area under the curve (AUC) of circRNA for the discriminative efficacy between osteosarcoma patients and non-cancer counterparts was estimated to be 0.86 (95% CI: 0.83-0.89), with a weighted sensitivity of 0.80 (95% CI: 0.74-0.84), specificity of 0.80 (95%: 0.75-0.84), and diagnostic odds ratio (DOR) of 15.48 (10.85-22.10), respectively. For the prognostic significance, oncogenic circRNAs had poor OS (HR=1.92, 95% CI: 1.68-2.19) and DFS (HR=2.65, 95% CI: 2.02-3.49), while elevated expression of tumor-suppressor circRNAs were closely related to longer OS (HR=0.44, 95% CI: 0.28-0.69). Conclusions Taken together, our study showed that aberrantly expressed circRNA signatures could serve as potential predictive indicators in diagnosis and prognosis in patients with osteosarcoma.

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Prognostic and diagnostic value of circRNA expression in colorectal carcinoma: a meta-analysis

10.21203/rs.2.16194/v2 ◽

2020 ◽

Author(s):

Jinpeng Yuan ◽

Dongming Guo ◽

Xinxin Li ◽

Juntian Chen

Keyword(s):

Colorectal Cancer ◽

Meta Analysis ◽

Area Under The Curve ◽

Diagnostic Value ◽

Cochrane Library ◽

Circular Rnas ◽

Control Factors ◽

Potential Biomarker ◽

Negative Effect ◽

Newcastle Ottawa Scale

Abstract Background: Circular RNAs (circRNAs) are new stars in the network of noncoding RNAs and are regarded as key control factors in numerous tumours. The purpose of our study was to evaluate the clinical, prognostic and diagnostic role of circRNAs in colorectal cancer. The quality of all the articles were assessed by the Newcastle‐Ottawa Scale. Methods: An online search in electronic databases, including the PubMed, Cochrane Library and Web of Science online databases, was conducted to identify as many relevant papers as possible. Nineteen relevant studies were enrolled in this meta-analysis, with seven on diagnosis, eight on prognosis and 11 on clinicopathological features. Results: For the diagnostic value of circRNAs, the pooled results showed that the area under the curve (AUC) was 0.82 for identifying patients with colorectal cancer, with a sensitivity of 83% and a specificity of 72%. In terms of prognosis, carcinogenic circRNAs have a negative effect on overall survival (OS: HR = 2.29, 95% CI: 1.50-3.52), and increases in tumour suppressor circRNA expression are associated with longer survival (OS: HR = 0.37, 95% CI: 0.22-0.64). And the elevated expression of oncogenic circRNAs is associated with poor clinical features while tumor suppressor circRNAs are the complete opposite. Conclusions: These results suggest that circRNAs may be a potential biomarker for the diagnosis and prognosis of colorectal cancer.

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Reliability of circulating fibrinogen in the diagnosis of prosthesis-related infections: a systematic review and meta-analysis

Journal of Orthopaedic Surgery and Research ◽

10.1186/s13018-020-02171-x ◽

2021 ◽

Vol 16 (1) ◽

Author(s):

Xingyang Zhu ◽

Haitao Zhang ◽

Xiaobo Sun ◽

Yijin Li ◽

Jiahao Li ◽

...

Keyword(s):

Likelihood Ratio ◽

Meta Analysis ◽

Joint Infection ◽

Area Under The Curve ◽

Positive Likelihood Ratio ◽

Negative Likelihood Ratio ◽

Diagnostic Odds Ratio ◽

Small Sample ◽

Cochrane Library ◽

Sensitivity Specificity

Abstract Background Fibrinogen (FIB) has recently been used as a biomarker to diagnose periprosthetic joint infection (PJI), but its reliability is still questionable. The aim of this study was to investigate the accuracy of FIB in the diagnosis of PJI after joint replacement. Methods We searched for literatures published in PubMed, EMBASE, and the Cochrane Library from the time of database inception to September 2020 and screened the studies according to the inclusion criteria. Then, we calculated the diagnostic parameters of FIB, including the pooled sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), area under the curve (AUC), and diagnostic odds ratio (DOR). In addition, we implemented subgroup analyses to identify the sources of heterogeneity. Results Seven studies including 1341 patients were selected in our meta-analysis. The pooled sensitivity, specificity, PLR, NLR, and DOR of FIB for PJI diagnosis were 0.78 (95% confidence interval [CI], 0.73–0.82), 0.83 (95% CI, 0.81–0.86), 4.60 (95% CI, 3.30–6.42), 0.24 (95% CI, 0.18–0.34), and 20.13 (95% CI, 14.80–27.36), respectively, while the AUC was 0.896. Conclusion The present study indicated that FIB was a reliable detection method and might be introduced into the diagnostic criteria for PJI. However, more robust studies are still needed to confirm the current findings, because most of the included studies were retrospective and had small sample sizes.

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Circulating long non-coding RNAs as potential biomarkers for stomach cancer: A systematic review and meta-analysis

10.21203/rs.3.rs-81358/v1 ◽

2020 ◽

Author(s):

Fang Cao ◽

Yongwei Hu ◽

Zaichang Chen ◽

Wei Han ◽

Weijie Lu ◽

...

Keyword(s):

Stomach Cancer ◽

Meta Analysis ◽

Area Under The Curve ◽

Cochrane Library ◽

Clinical Value ◽

Summary Receiver Operating Characteristic ◽

Non Coding Rna ◽

Diagnostic Values ◽

Meta Regression Analysis ◽

Characteristic Area

Abstract Background: Recent researches have suggested that long non-coding RNA (lncRNA) is involved in the tumorigenesis and development of stomach cancer (SC). This meta-analysis aimed to identify the diagnostic performance of circulating lncRNAs in SC.Methods: All relevant studies were systematically searched through PubMed, Web of Science, Cochrane Library and EMBASE databases. The diagnostic values of lncRNAs were mainly assessed by pooled sensitivity (SEN), specificity (SPE), and summary receiver operating characteristic area under the curve (SROC AUC). Meta-DiSc 1.4, Review Manager 5.3 and STATA 12.0 were used for statistical analysis. Results: A total of 42 eligible studies were included in this meta-analysis. The pooled SEN, SPE, and AUC were 0.78 (95%CI: 0.75-0.81), 0.75 (95%CI: 0.71-0.78), and 0.83 (95%CI: 0.80-0.86) respectively, suggesting that the lncRNAs test had a high accuracy for the diagnosis of SC. Obvious heterogeneity might come from the type of lncRNA through subgroup and meta-regression analysis. Fagan diagram showed the clinical value of lncRNAs test in SC. Conclusions: Abnormal expression of circulating lncRNAs exhibits a high efficacy for diagnosing SC, which is promising in clinical application.

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