Major clinical feature summary statistics.

A misdiagnosis of pulmonary embolism (PE) can have severe consequences such as disability or death. It’s crucial to accurately identify key clinical features of PE in clinical practice to promptly identify potential PE patients who may present asymptomatically, and to prevent misdiagnosing PE as asthma exacerbation in patients with symptoms like dyspnea or chest pain. However, reliably identifying these important features can be challenging due to many factors influencing the likelihood of PE development in complex fashions (e.g., the interactions among these factors). To address this difficulty, we presented an effective framework using the deep neural network (DNN) model and the permutation-based feature importance test (PermFIT) procedure, i.e., PermFIT-DNN. We applied the PermFIT-DNN framework to the analysis of data from a PE study for asthma exacerbation patients. Our analysis results show that the PermFIT-DNN framework can robustly identify key features for classifying PE status. The important features identified can also aid in accurately predicting the PE risk.</div

Identified important clinical features.

A misdiagnosis of pulmonary embolism (PE) can have severe consequences such as disability or death. It’s crucial to accurately identify key clinical features of PE in clinical practice to promptly identify potential PE patients who may present asymptomatically, and to prevent misdiagnosing PE as asthma exacerbation in patients with symptoms like dyspnea or chest pain. However, reliably identifying these important features can be challenging due to many factors influencing the likelihood of PE development in complex fashions (e.g., the interactions among these factors). To address this difficulty, we presented an effective framework using the deep neural network (DNN) model and the permutation-based feature importance test (PermFIT) procedure, i.e., PermFIT-DNN. We applied the PermFIT-DNN framework to the analysis of data from a PE study for asthma exacerbation patients. Our analysis results show that the PermFIT-DNN framework can robustly identify key features for classifying PE status. The important features identified can also aid in accurately predicting the PE risk.</div

PE prediction performance comparison.

A misdiagnosis of pulmonary embolism (PE) can have severe consequences such as disability or death. It’s crucial to accurately identify key clinical features of PE in clinical practice to promptly identify potential PE patients who may present asymptomatically, and to prevent misdiagnosing PE as asthma exacerbation in patients with symptoms like dyspnea or chest pain. However, reliably identifying these important features can be challenging due to many factors influencing the likelihood of PE development in complex fashions (e.g., the interactions among these factors). To address this difficulty, we presented an effective framework using the deep neural network (DNN) model and the permutation-based feature importance test (PermFIT) procedure, i.e., PermFIT-DNN. We applied the PermFIT-DNN framework to the analysis of data from a PE study for asthma exacerbation patients. Our analysis results show that the PermFIT-DNN framework can robustly identify key features for classifying PE status. The important features identified can also aid in accurately predicting the PE risk.</div

S1 Data -

A misdiagnosis of pulmonary embolism (PE) can have severe consequences such as disability or death. It’s crucial to accurately identify key clinical features of PE in clinical practice to promptly identify potential PE patients who may present asymptomatically, and to prevent misdiagnosing PE as asthma exacerbation in patients with symptoms like dyspnea or chest pain. However, reliably identifying these important features can be challenging due to many factors influencing the likelihood of PE development in complex fashions (e.g., the interactions among these factors). To address this difficulty, we presented an effective framework using the deep neural network (DNN) model and the permutation-based feature importance test (PermFIT) procedure, i.e., PermFIT-DNN. We applied the PermFIT-DNN framework to the analysis of data from a PE study for asthma exacerbation patients. Our analysis results show that the PermFIT-DNN framework can robustly identify key features for classifying PE status. The important features identified can also aid in accurately predicting the PE risk.</div

Confusion matrix via PermFIT-DNN.

A misdiagnosis of pulmonary embolism (PE) can have severe consequences such as disability or death. It’s crucial to accurately identify key clinical features of PE in clinical practice to promptly identify potential PE patients who may present asymptomatically, and to prevent misdiagnosing PE as asthma exacerbation in patients with symptoms like dyspnea or chest pain. However, reliably identifying these important features can be challenging due to many factors influencing the likelihood of PE development in complex fashions (e.g., the interactions among these factors). To address this difficulty, we presented an effective framework using the deep neural network (DNN) model and the permutation-based feature importance test (PermFIT) procedure, i.e., PermFIT-DNN. We applied the PermFIT-DNN framework to the analysis of data from a PE study for asthma exacerbation patients. Our analysis results show that the PermFIT-DNN framework can robustly identify key features for classifying PE status. The important features identified can also aid in accurately predicting the PE risk.</div

Summery odds ratios on the relation of the <i>XRCC1</i> Arg399Gln polymorphisms to HNSCC risk.

<p>Summery odds ratios on the relation of the <i>XRCC1</i> Arg399Gln polymorphisms to HNSCC risk.</p

Forest plot of odds ratio for Arg/Gln vs. Arg/Arg of <i>XRCC1</i> Arg399Gln variants associated with HNSCC risk.

<p>Forest plot of odds ratio for Arg/Gln vs. Arg/Arg of <i>XRCC1</i> Arg399Gln variants associated with HNSCC risk.</p

Association of X-ray Repair Cross Complementing Group 1 Arg399Gln Polymorphisms with the Risk of Squamous Cell Carcinoma of the Head and Neck: Evidence from an Updated Meta-Analysis

<div><p>Background</p><p>Epidemiologic studies have reported the association of X-ray repair cross-complementary group 1 (<i>XRCC1</i>) Arg399Gln polymorphisms with susceptibility to squamous cell carcinoma of the head and neck (HNSCC). However, the results were conflictive rather than conclusive. The purpose of this study was to clarify the association of <i>XRCC1</i> Arg399Gln variants with HNSCC risk.</p><p>Methods</p><p>Systematic searches were performed through the search engines of PubMed, Elsevier, Science Direct, CNKI and Chinese Biomedical Literature Database. Summary odds ratio (OR) with 95% confidence intervals (CI) was computed to estimate the strength association.</p><p>Results</p><p>Overall, we did not observe any association of <i>XRCC1</i> Arg399Gln polymorphisms with HNSCC risk in total population (OR = 0.95, 95% CI: 0.76–1.19 for Gln/Gln vs. Arg/Arg, OR = 1.05, 95% CI: 0.92–1.20 for Arg/Gln vs. Arg/Arg, and OR = 1.03, 95% CI: 0.90–1.18 for Gln/Gln+Arg/Gln vs. Arg/Arg) based on 18 studies including 3917 cases and 4560 controls. In subgroup analyses, we observed an increased risk of <i>XRCC1</i> 399 Arg/Gln genotype for HNSCC in Caucasians (OR = 1.20, 95% CI: 1.00–1.44) and Gln/Gln genotype for larynx squamous cell carcinoma (OR = 1.63, 95% CI: 1.10–2.40). We did not observe any association between <i>XRCC1</i> Arg399Gln variants and HNSCC risk in additional subgroup analyses.</p><p>Conclusion</p><p>The results from this present meta-analysis suggest that <i>XRCC1</i> Arg399Gln variants may contribute to HNSCC risk among Caucasians and to the risk of larynx squamous cell carcinoma. Further, well-designed studies with larger sample sizes are required to verify our findings.</p></div

Forest plot of odds ratio for Gln/Gln vs. Arg/Arg of <i>XRCC1</i> Arg399Gln variants associated with HNSCC risk.

<p>Forest plot of odds ratio for Gln/Gln vs. Arg/Arg of <i>XRCC1</i> Arg399Gln variants associated with HNSCC risk.</p

Forest plot of odds ratio for Gln/Gln+Arg/Gln vs. Arg/Arg of <i>XRCC1</i> Arg399Gln variants associated with HNSCC risk.

<p>Forest plot of odds ratio for Gln/Gln+Arg/Gln vs. Arg/Arg of <i>XRCC1</i> Arg399Gln variants associated with HNSCC risk.</p