A CT-based nomogram for differentiating invasive fungal disease of the lung from bacterial pneumonia
Abstract Background There is an annual increase in the incidence of invasive fungal disease (IFD) of the lung worldwide, but it is always a challenge for physicians to make an early diagnosis of IFD of the lung. Computed tomography (CT) may play a certain role in the diagnosis of IFD of the lung, ho...
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| Format: | Article |
| Language: | English |
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BMC
2022-10-01
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| Series: | BMC Medical Imaging |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12880-022-00903-5 |
| _version_ | 1811194779172601856 |
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| author | Meilin Gong Jingmei Xu Kang Li Ke Li Yuwei Xia Yang Jing Jiafei Chen Jing Li Jing Yang Mingshan Du Wenjing Hou Yuan Ou Lian Li Wei Chen |
| author_facet | Meilin Gong Jingmei Xu Kang Li Ke Li Yuwei Xia Yang Jing Jiafei Chen Jing Li Jing Yang Mingshan Du Wenjing Hou Yuan Ou Lian Li Wei Chen |
| author_sort | Meilin Gong |
| collection | DOAJ |
| description | Abstract Background There is an annual increase in the incidence of invasive fungal disease (IFD) of the lung worldwide, but it is always a challenge for physicians to make an early diagnosis of IFD of the lung. Computed tomography (CT) may play a certain role in the diagnosis of IFD of the lung, however, there are no specific imaging signs for differentiating IFD of lung from bacterial pneumonia (BP). Methods A total of 214 patients with IFD of the lung or clinically confirmed BP were retrospectively enrolled from two institutions (171 patients from one institution in the training set and 43 patients from another institution in the test set). The features of thoracic CT images of the 214 patients were analyzed on the picture archiving and communication system by two radiologists, and these CT images were imported into RadCloud to perform radiomics analysis. A clinical model from radiologic analysis, a radiomics model from radiomics analysis and a combined model from integrating radiologic and radiomics analysis were constructed in the training set, and a nomogram based on the combined model was further developed. The area under the ROC curve (AUC) of the receiver operating characteristic (ROC) curve was calculated to assess the diagnostic performance of the three models. Decision curve analysis (DCA) was conducted to evaluate the clinical utility of the three models by estimating the net benefit at a range of threshold probabilities. Results The AUCs of the clinical model for differentiating IFD of lung from BP in the training set and test sets were 0.820 and 0.827. The AUCs of the radiomics model in the training set and test sets were 0.895 and 0.857. The AUCs of the combined model in the training set and test setswere 0.944 and 0.911. The combined model for differentiating IFD of lung from BP obtained the greatest net benefit among the three models by DCA. Conclusion Our proposed nomogram, based on a combined model integrating radiologic and radiomics analysis, has a powerful predictive capability for differentiating IFD from BP. A good clinical outcome could be obtained using our nomogram. |
| first_indexed | 2024-04-12T00:32:17Z |
| format | Article |
| id | doaj.art-a4e71c683a7740b581ca9c57ed6eb254 |
| institution | Directory Open Access Journal |
| issn | 1471-2342 |
| language | English |
| last_indexed | 2024-04-12T00:32:17Z |
| publishDate | 2022-10-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Medical Imaging |
| spelling | doaj.art-a4e71c683a7740b581ca9c57ed6eb2542022-12-22T03:55:17ZengBMCBMC Medical Imaging1471-23422022-10-0122111110.1186/s12880-022-00903-5A CT-based nomogram for differentiating invasive fungal disease of the lung from bacterial pneumoniaMeilin Gong0Jingmei Xu1Kang Li2Ke Li3Yuwei Xia4Yang Jing5Jiafei Chen6Jing Li7Jing Yang8Mingshan Du9Wenjing Hou10Yuan Ou11Lian Li12Wei Chen13Department of Radiology, Chongqing General HospitalDepartment of Radiology, The First Affiliated Hospital of Chongqing Medical UniversityDepartment of Radiology, Chongqing General HospitalDepartment of Radiology, Sichuan Provincial Corps Hospital of Chinese People’s Armed Police ForceHuiying Medical Technology Co., Ltd. Room C103, B2, Dongsheng Science and Technology ParkHuiying Medical Technology Co., Ltd. Room C103, B2, Dongsheng Science and Technology ParkDepartment of Radiology, The First Affiliated Hospital, Army Medical UniversityDepartment of Radiology, The First Affiliated Hospital, Army Medical UniversityDepartment of Radiology, The First Affiliated Hospital, Army Medical UniversityDepartment of Radiology, The First Affiliated Hospital, Army Medical UniversityDepartment of Radiology, The First Affiliated Hospital, Army Medical UniversityDepartment of Radiology, The First Affiliated Hospital, Army Medical UniversityDepartment of Radiology, The First Affiliated Hospital, Army Medical UniversityDepartment of Radiology, The First Affiliated Hospital, Army Medical UniversityAbstract Background There is an annual increase in the incidence of invasive fungal disease (IFD) of the lung worldwide, but it is always a challenge for physicians to make an early diagnosis of IFD of the lung. Computed tomography (CT) may play a certain role in the diagnosis of IFD of the lung, however, there are no specific imaging signs for differentiating IFD of lung from bacterial pneumonia (BP). Methods A total of 214 patients with IFD of the lung or clinically confirmed BP were retrospectively enrolled from two institutions (171 patients from one institution in the training set and 43 patients from another institution in the test set). The features of thoracic CT images of the 214 patients were analyzed on the picture archiving and communication system by two radiologists, and these CT images were imported into RadCloud to perform radiomics analysis. A clinical model from radiologic analysis, a radiomics model from radiomics analysis and a combined model from integrating radiologic and radiomics analysis were constructed in the training set, and a nomogram based on the combined model was further developed. The area under the ROC curve (AUC) of the receiver operating characteristic (ROC) curve was calculated to assess the diagnostic performance of the three models. Decision curve analysis (DCA) was conducted to evaluate the clinical utility of the three models by estimating the net benefit at a range of threshold probabilities. Results The AUCs of the clinical model for differentiating IFD of lung from BP in the training set and test sets were 0.820 and 0.827. The AUCs of the radiomics model in the training set and test sets were 0.895 and 0.857. The AUCs of the combined model in the training set and test setswere 0.944 and 0.911. The combined model for differentiating IFD of lung from BP obtained the greatest net benefit among the three models by DCA. Conclusion Our proposed nomogram, based on a combined model integrating radiologic and radiomics analysis, has a powerful predictive capability for differentiating IFD from BP. A good clinical outcome could be obtained using our nomogram.https://doi.org/10.1186/s12880-022-00903-5LungInvasive fungal diseaseBacterial pneumoniaRadiomicsNomogram |
| spellingShingle | Meilin Gong Jingmei Xu Kang Li Ke Li Yuwei Xia Yang Jing Jiafei Chen Jing Li Jing Yang Mingshan Du Wenjing Hou Yuan Ou Lian Li Wei Chen A CT-based nomogram for differentiating invasive fungal disease of the lung from bacterial pneumonia BMC Medical Imaging Lung Invasive fungal disease Bacterial pneumonia Radiomics Nomogram |
| title | A CT-based nomogram for differentiating invasive fungal disease of the lung from bacterial pneumonia |
| title_full | A CT-based nomogram for differentiating invasive fungal disease of the lung from bacterial pneumonia |
| title_fullStr | A CT-based nomogram for differentiating invasive fungal disease of the lung from bacterial pneumonia |
| title_full_unstemmed | A CT-based nomogram for differentiating invasive fungal disease of the lung from bacterial pneumonia |
| title_short | A CT-based nomogram for differentiating invasive fungal disease of the lung from bacterial pneumonia |
| title_sort | ct based nomogram for differentiating invasive fungal disease of the lung from bacterial pneumonia |
| topic | Lung Invasive fungal disease Bacterial pneumonia Radiomics Nomogram |
| url | https://doi.org/10.1186/s12880-022-00903-5 |
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