Dataset Information

Radiomics Nomogram Analyses for Differentiating Pneumonia and Acute Paraquat Lung Injury.

ABSTRACT: Paraquat poisoning has become a serious public health problem in some Asian countries because of misuse or suicide. We sought to develop and validate a radiomics nomogram incorporating radiomics signature and laboratory bio-markers, for differentiating bacterial pneumonia and acute paraquat lung injury. 180 patients with pneumonia and acute paraquat who underwent CT examinations between December 2014 and October 2017 were retrospectively evaluated for testing and validation. Clinical information including demographic data, clinical symptoms and laboratory test were also recorded. A prediction model was built by using backward logistic regression and presented on a nomogram. The radiomics-based features yielded areas under the receiver operating characteristic curve of 0.870 (95% CI 0.757-0.894), sensitivity of 0.857, specificity of 0.804, positive predictive value of 83.3%, negative predictive value of 0.818 in the primary cohort, while in the validation cohort the model showed similar results (0.865 (95% CI 0.686-0.907), 0.833, 0.792, 81.5%, respectively). The individualized nomogram included radiomics signature, body temperature, nausea and vomiting, and aspartate transaminase. We have developed a radiomics nomogram that combination of the radiomics features and clinical risk factors to differentiate paraquat lung injury and pneumonia for patients with an unclear medical history of exposure to paraquat poisoning, providing appropriate therapy decision support.

SUBMITTER: Yanling W

PROVIDER: S-EPMC6803642 | biostudies-literature | 2019 Oct

REPOSITORIES: biostudies-literature

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Radiomics Nomogram Analyses for Differentiating Pneumonia and Acute Paraquat Lung Injury.

Yanling Wang W Duo Gao G Zuojun Geng G Zhongqiang Shi S Yankai Wu W Shan Lu L Hongying Cui C

Scientific reports 20191021 1

Paraquat poisoning has become a serious public health problem in some Asian countries because of misuse or suicide. We sought to develop and validate a radiomics nomogram incorporating radiomics signature and laboratory bio-markers, for differentiating bacterial pneumonia and acute paraquat lung injury. 180 patients with pneumonia and acute paraquat who underwent CT examinations between December 2014 and October 2017 were retrospectively evaluated for testing and validation. Clinical information ...[more]

PMID: 31636276

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Project description:ObjectivesTo develop and validate a radiomics model for predicting 2019 novel coronavirus (COVID-19) pneumonia.MethodsFor this retrospective study, a radiomics model was developed on the basis of a training set consisting of 136 patients with COVID-19 pneumonia and 103 patients with other types of viral pneumonia. Radiomics features were extracted from the lung parenchyma window. A radiomics signature was built on the basis of reproducible features, using the least absolute shrinkage and selection operator method (LASSO). Multivariable logistic regression model was adopted to establish a radiomics nomogram. Nomogram performance was determined by its discrimination, calibration, and clinical usefulness. The model was validated in 90 consecutive patients, of which 56 patients had COVID-19 pneumonia and 34 patients had other types of viral pneumonia.ResultsThe radiomics signature, consisting of 3 selected features, was significantly associated with COVID-19 pneumonia (p?<?0.05) in both training and validation sets. The multivariable logistic regression model included the radiomics signature and distribution; maximum lesion, hilar, and mediastinal lymph node enlargement; and pleural effusion. The individualized prediction nomogram showed good discrimination in the training sample (area under the receiver operating characteristic curve [AUC], 0.959; 95% confidence interval [CI], 0.933-0.985) and in the validation sample (AUC, 0.955; 95% CI, 0.899-0.995) and good calibration. The mixed model achieved better predictive efficacy than the clinical model. Decision curve analysis demonstrated that the radiomics nomogram was clinically useful.ConclusionsThe radiomics model derived has good performance for predicting COVID-19 pneumonia and may help in clinical decision-making.Key points• A radiomics model showed good performance for prediction 2019 novel coronavirus pneumonia and favorable discrimination for other types of pneumonia on CT images. • A central or peripheral distribution, a maximum lesion range?>?10 cm, the involvement of all five lobes, hilar and mediastinal lymph node enlargement, and no pleural effusion is associated with an increased risk of 2019 novel coronavirus pneumonia. • A radiomics model was superior to a clinical model in predicting 2019 novel coronavirus pneumonia.

Project description:Paraquat (PQ), one of the most widely used herbicides worldwide, causes severe toxic effects in humans and animals. 1-methylhydantoin (MH) is an active ingredient of Ranae Oviductus, which has broad pharmacological activities, e.g., eliminating reactive oxygen species and inhibiting inflammation. This study investigated the effects of MH on lung injury induced by PQ. A PQ poisoning model was established by intragastric infusion of PQ (25 mg/kg), and the control group was simultaneously gavaged with the same dose of saline. The MH group was intraperitoneally injected with 100 mg/kg once per day after intragastric infusion of PQ (25 mg/kg) for five consecutive days. All animals were sacrificed on the sixth day, and the lung tissues were dissected for metabolomics analysis. The lactate dehydrogenase (LDH) activity, superoxide dismutase (SOD) activity, TNF-? and malondialdehyde (MDA) content were determined according to the instructions of the detection kit. Compared with that in the control group, the content of LDH, TNF-? and MDA in the lung tissue of the PQ group was significantly higher, and the activity of SOD in the lung tissue was significantly lower (all p<0.05). Compared with that in the control group, the content of LDH, TNF-? and MDA in the MH group was significantly higher, and the activity of SOD was significantly lower (all p<0.05). However, the differences in SOD activity, LDH activity between the PQ and MH groups were not statistically significant (all p > 0.05). There were significant differences in MDA and TNF-? content between the PQ group and MH group (all p<0.05). MH decreased the production of malondialdehyde and TNF-? to protect against the lung injury caused by PQ poisoning, but it had no significant effect on the activity of LDH and SOD. There were significant differences in metabolomics between the MH group and the PQ poisoning group, primarily in bile acid biosynthesis and metabolism of cholesterol, nicotinate, nicotinamide, alanine, aspartate, glutamate, glycine, threonine, serine, phenylalanine and histidine. Therefore, this study highlights that MH has non-invasive mechanisms and may be a promising tool to treat lung injury induced by PQ poisoning.

Project description:BackgroundLung cancer is the most commonly diagnosed cancer worldwide. Its survival rate can be significantly improved by early screening. Biomarkers based on radiomics features have been found to provide important physiological information on tumors and considered as having the potential to be used in the early screening of lung cancer. In this study, we aim to establish a radiomics model and develop a tool to improve the discrimination between benign and malignant pulmonary nodules.MethodsA retrospective study was conducted on 875 patients with benign or malignant pulmonary nodules who underwent computed tomography (CT) examinations between June 2013 and June 2018. We assigned 612 patients to a training cohort and 263 patients to a validation cohort. Radiomics features were extracted from the CT images of each patient. Least absolute shrinkage and selection operator (LASSO) was used for radiomics feature selection and radiomics score calculation. Multivariate logistic regression analysis was used to develop a classification model and radiomics nomogram. Radiomics score and clinical variables were used to distinguish benign and malignant pulmonary nodules in logistic model. The performance of the radiomics nomogram was evaluated by the area under the curve (AUC), calibration curve and Hosmer-Lemeshow test in both the training and validation cohorts.ResultsA radiomics score was built and consisted of 20 features selected by LASSO from 1288 radiomics features in the training cohort. The multivariate logistic model and radiomics nomogram were constructed using the radiomics score and patients' age. Good discrimination of benign and malignant pulmonary nodules was obtained from the training cohort (AUC, 0.836; 95% confidence interval [CI]: 0.793-0.879) and validation cohort (AUC, 0.809; 95% CI: 0.745-0.872). The Hosmer-Lemeshow test also showed good performance for the logistic regression model in the training cohort (P = 0.765) and validation cohort (P = 0.064). Good alignment with the calibration curve indicated the good performance of the nomogram.ConclusionsThe established radiomics nomogram is a noninvasive preoperative prediction tool for malignant pulmonary nodule diagnosis. Validation revealed that this nomogram exhibited excellent discrimination and calibration capacities, suggesting its clinical utility in the early screening of lung cancer.

Dataset Information

Radiomics Nomogram Analyses for Differentiating Pneumonia and Acute Paraquat Lung Injury.

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Radiomics Nomogram Analyses for Differentiating Pneumonia and Acute Paraquat Lung Injury.

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