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Early differential diagnosis model for acute radiation pneumonitis based on multiple parameters.

ABSTRACT: OBJECTIVE:The present study aimed to construct a diagnosis model for the early differentiation of acute radiation pneumonitis (ARP) and infectious pneumonitis based on multiple parameters. METHODS:The present study included data of 152 patients admitted to the Department of Radiochemotherapy, Tangshan People's Hospital, who developed ARP (91 patients) or infectious pneumonia (IP; 61 patients) after radiotherapy. The radiophysical parameters, imaging characteristics, serological indicators, and other data were collected as independent variables, and ARP was considered as a dependent variable. Logistics univariate analysis and Spearman correlation analysis were used for selecting independent variables. Logistics multivariate analysis was used to fit the variables into the regression model to predict ARP. RESULTS:The univariate analysis showed that the positional relation between lesions and V20 area (PRLV), procalcitonin (PCT), C-reactive protein (CRP), mean lung dose (MLD), and lung volume receiving ?20 Gy (V20) correlated with ARP while the planning target volume (PTV) dose marginally correlated with ARP. The multivariate analysis showed that the PRLV, PCT, white blood cell (WBC), and MLD were independent diagnostic factors. The nomogram was drawn on the basis of the logistics regression model. The area under the curve (AUC) of the model was 0.849, which was significantly better than that of a single indicator and the sensitivity and specificity of the model were high (82.4 and 82.0%, respectively). These results predicted by the model were highly consistent with the actual diagnostic results. The decision curve analysis (DCA) demonstrated a satisfactory positive net benefit of the model. CONCLUSION:The diagnosis model constructed in the present study is of certain value for the differential diagnosis of ARP and IP.

SUBMITTER: Wang Z

PROVIDER: S-EPMC7167249 | biostudies-literature | 2020 Apr

REPOSITORIES: biostudies-literature

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Early differential diagnosis model for acute radiation pneumonitis based on multiple parameters.

Wang Zhiwu Z Wu Qiong Q Dong Liang L Fu Haoyu H Liu Qiwei Q

Bioscience reports 20200401 4

<h4>Objective</h4>The present study aimed to construct a diagnosis model for the early differentiation of acute radiation pneumonitis (ARP) and infectious pneumonitis based on multiple parameters.<h4>Methods</h4>The present study included data of 152 patients admitted to the Department of Radiochemotherapy, Tangshan People's Hospital, who developed ARP (91 patients) or infectious pneumonia (IP; 61 patients) after radiotherapy. The radiophysical parameters, imaging characteristics, serological in ...[more]

PMID: 32270860

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Project description:Objective The study aims to establish and validate an effective CT-based radiation pneumonitis (RP) prediction model using the multiomics method of radiomics and EQD2-based dosiomics. Materials and Methods The study performed a retrospective analysis on 91 nonsmall cell lung cancer patients who received radiotherapy from 2019 to 2021 in our hospital. The patients with RP grade ≥1 were labeled as 1, and those with RP grade < 1 were labeled as 0. The whole lung excluding clinical target volume (lung-CTV) was used as the region of interest (ROI). The radiomic and dosiomic features were extracted from the lung-CTV area's image and dose distribution. Besides, the equivalent dose of the 2 Gy fractionated radiation (EQD2) model was used to convert the physical dose to the isoeffect dose, and then, the EQD2-based dosiomic (eqd-dosiomic) features were extracted from the isoeffect dose distribution. Four machine learning (ML) models, including DVH, radiomics combined with DVH (radio + DVH), radiomics combined with dosiomics (radio + dose), and radiomics combined with eqd-dosiomics (radio + eqdose), were established to construct the prediction model via eleven different classifiers. The fivefold cross-validation was used to complete the classification experiment. The area under the curve (AUC) of the receiver operating characteristics (ROC), accuracy, precision, recall, and F1-score were calculated to assess the performance level of the prediction models. Results Compared with the DVH, radio + DVH, and radio + dose model, the value of the training AUC, accuracy, and F1-score of radio + eqdose was higher, and the difference was statistically significant (p < 0.05). Besides, the average value of the precision and recall of radio + eqdose was higher, but the difference was not statistically significant (p > 0.05). Conclusion The performance of using the ML-based multiomics method of radiomics and eqd-dosiomics to predict RP is more efficient and effective.

Project description:Radiation pneumonitis (RP) is one of the major side effects of thoracic radiotherapy. The aim of this study is to build a dose distribution based prediction model, and investigate the correlation of RP incidence and high-order features of dose distribution. A convolution 3D (C3D) neural network was used to construct the prediction model. The C3D network was pre-trained for action recognition. The dose distribution was used as input of the prediction model. With the C3D network, the convolution operation was performed in 3D space. The guided gradient-weighted class activation map (grad-CAM) was utilized to locate the regions of dose distribution which were strongly correlated with grade?2 and grade<2 RP cases, respectively. The features learned by the convolution filters were generated with gradient ascend to understand the deep network. The performance of the C3D prediction model was evaluated by comparing with three multivariate logistic regression (LR) prediction models, which used the dosimetric, normal tissue complication probability (NTCP) or dosiomics factors as input, respectively. All the prediction models were validated using 70 non-small cell lung cancer (NSCLC) patients treated with volumetric modulated arc therapy (VMAT). The area under curve (AUC) of C3D prediction model was 0.842. While the AUC of the three LR models were 0.676, 0.744 and 0.782, respectively. The guided grad-CAM indicated that the low-dose region of contralateral lung and high-dose region of ipsilateral lung were strongly correlated with the grade?2 and grade<2 RP cases, respectively. The features learned by shallow filters were simple and globally consistent, and of monotonous color. The features of deeper filters displayed more complicated pattern, which was hard or impossible to give strict mathematical definition. In conclusion, we built a C3D model for thoracic radiotherapy toxicity prediction. The results demonstrate its performance is superior over the classical LR models. In addition, CNN also offers a new perspective to further understand RP incidence.

Project description:BackgroundIn recent years, conventional coagulation (CC) and thromboelastography (TEG) parameters have been reported to be closely related to the progression of pancreatic cancer (PC). However, the potential utility of these parameters in differentiating benign and malignant pancreatic diseases is still unclear.ObjectivesA retrospective study was conducted to evaluate the efficacy of coagulation parameters in differentiating pancreatic cancer/early stage pancreatic cancer (EPC, TNM stages I and II) from benign control conditions, and to further explore whether coagulation parameters could improve the differential value of CA199.MethodsReceiver operating characteristic (ROC) curves and logistic regression analysis were used to identify the diagnostic value of each coagulation parameter or combination of parameters.ResultsCompared with benign pancreatic disease (BPD), patients with pancreatic malignant tumors had significant coagulation disorders, specifically manifested as abnormal increases or decreases in several CC and TEG parameters (such as activated partial thromboplastin time (APTT), fibrinogen (FIB), D-dimer (DD2), K time, R time, Angle, maximum amplitude (MA), coagulation index (CI), and Ly30). In the training group, ROC curve showed that FIB, DD2, Angle, MA, and CI had favorable efficacy at differentiating PC or EPC from BPD (for PC, AUC = 0.737, 0.654, 0.627, 0.602, 0.648; for EPC, AUC = 0.723, 0.635, 0.630, 0.614, 0.648). However, several combined diagnostic indicators based on FIB, DD2 and CI failed to outperform the individual coagulation indexes in diagnostic efficiency. Combinations of certain coagulation indexes with CA199 outperformed CA199 alone at identifying PC or EPC, especially FIB + CA199 (for PC, AUC = 0.904; for EPC, AUC = 0.905), FIB + DD2 + CA199 (for PC, AUC = 0.902; for EPC, AUC = 0.900), FIB + CI + CA199 (for PC, AUC = 0.906; for EPC, AUC = 0.906), and FIB + DD2 + CI + CA199 (for PC, AUC = 0.905; for EPC, AUC = 0.900). The results from a validation set also confirmed that these combinations have advantageous diagnostic value for PC and EPC.ConclusionsA significant hypercoagulable state was common in PC. Some CC and TEG parameters are valuable in the differential diagnosis of benign and malignant pancreatic diseases. In addition, coagulation indexes combined with CA199 can further enhance the differential diagnosis efficacy of CA199 in PC and EPC.

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Early differential diagnosis model for acute radiation pneumonitis based on multiple parameters.

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Early differential diagnosis model for acute radiation pneumonitis based on multiple parameters.

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