Predicting the negative conversion time of nonsevere COVID‐19 patients using machine learning methods

Background Previous studies have shown that patients with pre-existing chronic obstructive pulmonary diseases (COPD) were more likely to be infected with coronavirus disease (COVID-19) and lead to more severe lung lesions. However, few studies have explored the severity and prognosis of COVID-19 patients with different phenotypes of COPD. Purpose The aim of this study is to investigate the value of the deep learning and radiomics features for the severity evaluation and the nucleic acid turning-negative time prediction in COVID-19 patients with COPD including two phenotypes of chronic bronchitis predominant patients and emphysema predominant patients. Methods A total of 281 patients were retrospectively collected from Hohhot First Hospital between October 2022 and January 2023. They were divided to three groups: COVID-19 group of 95 patients, COVID-19 with emphysema group of 94 patients, COVID-19 with chronic bronchitis group of 92 patients. All patients underwent chest computed tomography (CT) scans and recorded clinical data. The U-net model was pretrained to segment the pulmonary involvement area on CT images and the severity of pneumonia were evaluated by the percentage of pulmonary involvement volume to lung volume. The 107 radiomics features were extracted by pyradiomics package. The Spearman method was employed to analyze the correlation of the data and visualize it through a heatmap. Then we establish a deep learning model (model 1) and a fusion model (model 2) combined deep learning with radiomics features to predict nucleic acid turning-negative time. Results COVID-19 patients with emphysema was lowest in the lymphocyte count compared to COVID-19 patients and COVID-19 companied with chronic bronchitis, and they have the most extensive range of pulmonary inflammation. The lymphocyte count was significantly correlated with pulmonary involvement and the time for nucleic acid turning negative ( r =-0.145, P < 0.05). Importantly, our results demonstrated that model 2 achieved an accuracy of 80.9% in predicting nucleic acid turning-negative time. Conclusion The pre-existing emphysema phenotype of COPD severely aggravated the pulmonary involvement of COVID-19 patients. Deep learning and radiomics features may provide more information to accurately predict the nucleic acid turning-negative time, which is expected to play an important role in clinical practice.

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Predictive efficacy of combined tumor markers and gastrin for recurrence after endoscopic submucosal dissection in early gastric cancer patients

Zhao

2024

Am J Transl Res

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Objective: This study aims to evaluate the predictive value of tumor markers combined with gastrin for tumor recurrence after endoscopic submucosal dissection (ESD) in patients with early gastric cancer. Methods: The clinicopathological data of 169 patients with early gastric cancer treated with ESD between March 2019 and January 2021 were retrospectively analyzed. The patients were divided into a relapse group (n=45) and a nonrecurrence group (n=124). Clinical data such as carcinoembryonic antigen (CEA), cancer antigen 19-9 (CA19-9), alpha-fetoprotein (AFP), gastrin 17, pepsinogen I and pepsinogen II, as well as tumor size and degree of infiltration were examined to construct a recurrence prediction model using lasso regression. Results: The comprehensive model showed superior predictive power (AUC=0.958, C-index=0.966) over biomarker-only models (AUC=0.925), indicating a significant improvement in the prediction of recurrence risk. Decision curve analysis confirmed the clinical utility of the model with a maximum net benefit of 73.37%. Key indicators such as CEA, CA19-9, AFP, gastrin 17 and pepsinogens I and II were statistically significant in predicting recurrence with P values < 0.01. Conclusion: The comprehensive model combining tumor markers with clinical data provides a more accurate and clinically valuable tool for predicting recurrence in early gastric cancer patients after ESD. This approach facilitates personalized risk assessment and may significantly improve prognostic management, emphasizing the importance of a multifaceted strategy in the management of early gastric cancer.

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Predicting the negative conversion time of nonsevere COVID‐19 patients using machine learning methods

Cited by 3 publications

References 43 publications

A conformal regressor for predicting negative conversion time of Omicron patients

A conformal regressor for predicting negative conversion time of Omicron patients

The severity assessment and nucleic acid turning-negative-time prediction in COVID-19 patients with COPD using a fused deep learning model

Predictive efficacy of combined tumor markers and gastrin for recurrence after endoscopic submucosal dissection in early gastric cancer patients

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