Combined model of radiomics and clinical features for differentiating pneumonic-type mucinous adenocarcinoma from lobar pneumonia: An exploratory study
PurposeThe purpose of this study was to distinguish pneumonic-type mucinous adenocarcinoma (PTMA) from lobar pneumonia (LP) by pre-treatment CT radiological and clinical or radiological parameters.MethodsA total of 199 patients (patients diagnosed with LP = 138, patients diagnosed with PTMA = 61) we...
| Main Authors: | , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2023-01-01
|
| Series: | Frontiers in Endocrinology |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fendo.2022.997921/full |
| _version_ | 1797952059882864640 |
|---|---|
| author | Huijun Ji Qianqian Liu Yingxiu Chen Mengyao Gu Qi Chen Shaolan Guo Shangkun Ning Juntao Zhang Wan-Hu Li |
| author_facet | Huijun Ji Qianqian Liu Yingxiu Chen Mengyao Gu Qi Chen Shaolan Guo Shangkun Ning Juntao Zhang Wan-Hu Li |
| author_sort | Huijun Ji |
| collection | DOAJ |
| description | PurposeThe purpose of this study was to distinguish pneumonic-type mucinous adenocarcinoma (PTMA) from lobar pneumonia (LP) by pre-treatment CT radiological and clinical or radiological parameters.MethodsA total of 199 patients (patients diagnosed with LP = 138, patients diagnosed with PTMA = 61) were retrospectively evaluated and assigned to either the training cohort (n = 140) or the validation cohort (n = 59). Radiomics features were extracted from chest CT plain images. Multivariate logistic regression analysis was conducted to develop a radiomics model and a nomogram model, and their clinical utility was assessed. The performance of the constructed models was assessed with the receiver operating characteristic (ROC) curve and the area under the curve (AUC). The clinical application value of the models was comprehensively evaluated using decision curve analysis (DCA).ResultsThe radiomics signature, consisting of 14 selected radiomics features, showed excellent performance in distinguishing between PTMA and LP, with an AUC of 0.90 (95% CI, 0.83–0.96) in the training cohort and 0.88 (95% CI, 0.79–0.97) in the validation cohort. A nomogram model was developed based on the radiomics signature and clinical features. It had a powerful discriminative ability, with the highest AUC values of 0.94 (95% CI, 0.90–0.98) and 0.91 (95% CI, 0.84–0.99) in the training cohort and validation cohort, respectively, which were significantly superior to the clinical model alone. There were no significant differences in calibration curves from Hosmer–Lemeshow tests between training and validation cohorts (p = 0.183 and p = 0.218), which indicated the good performance of the nomogram model. DCA indicated that the nomogram model exhibited better performance than the clinical model.ConclusionsThe nomogram model based on radiomics signatures of CT images and clinical risk factors could help to differentiate PTMA from LP, which can provide appropriate therapy decision support for clinicians, especially in situations where differential diagnosis is difficult. |
| first_indexed | 2024-04-10T22:41:33Z |
| format | Article |
| id | doaj.art-4a0957f918e849ccb028b15226ee2aa7 |
| institution | Directory Open Access Journal |
| issn | 1664-2392 |
| language | English |
| last_indexed | 2024-04-10T22:41:33Z |
| publishDate | 2023-01-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Endocrinology |
| spelling | doaj.art-4a0957f918e849ccb028b15226ee2aa72023-01-16T04:44:57ZengFrontiers Media S.A.Frontiers in Endocrinology1664-23922023-01-011310.3389/fendo.2022.997921997921Combined model of radiomics and clinical features for differentiating pneumonic-type mucinous adenocarcinoma from lobar pneumonia: An exploratory studyHuijun Ji0Qianqian Liu1Yingxiu Chen2Mengyao Gu3Qi Chen4Shaolan Guo5Shangkun Ning6Juntao Zhang7Wan-Hu Li8Department of Radiology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, Shandong, ChinaDepartment of Radiology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, Shandong, ChinaDepartment of Radiology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, Shandong, ChinaDepartment of Radiology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, Shandong, ChinaDepartment of Radiology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, Shandong, ChinaDepartment of Radiology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, Shandong, ChinaDepartment of Radiology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, Shandong, ChinaGE Healthcare, Precision Health Institution, Shanghai, ChinaDepartment of Radiology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, Shandong, ChinaPurposeThe purpose of this study was to distinguish pneumonic-type mucinous adenocarcinoma (PTMA) from lobar pneumonia (LP) by pre-treatment CT radiological and clinical or radiological parameters.MethodsA total of 199 patients (patients diagnosed with LP = 138, patients diagnosed with PTMA = 61) were retrospectively evaluated and assigned to either the training cohort (n = 140) or the validation cohort (n = 59). Radiomics features were extracted from chest CT plain images. Multivariate logistic regression analysis was conducted to develop a radiomics model and a nomogram model, and their clinical utility was assessed. The performance of the constructed models was assessed with the receiver operating characteristic (ROC) curve and the area under the curve (AUC). The clinical application value of the models was comprehensively evaluated using decision curve analysis (DCA).ResultsThe radiomics signature, consisting of 14 selected radiomics features, showed excellent performance in distinguishing between PTMA and LP, with an AUC of 0.90 (95% CI, 0.83–0.96) in the training cohort and 0.88 (95% CI, 0.79–0.97) in the validation cohort. A nomogram model was developed based on the radiomics signature and clinical features. It had a powerful discriminative ability, with the highest AUC values of 0.94 (95% CI, 0.90–0.98) and 0.91 (95% CI, 0.84–0.99) in the training cohort and validation cohort, respectively, which were significantly superior to the clinical model alone. There were no significant differences in calibration curves from Hosmer–Lemeshow tests between training and validation cohorts (p = 0.183 and p = 0.218), which indicated the good performance of the nomogram model. DCA indicated that the nomogram model exhibited better performance than the clinical model.ConclusionsThe nomogram model based on radiomics signatures of CT images and clinical risk factors could help to differentiate PTMA from LP, which can provide appropriate therapy decision support for clinicians, especially in situations where differential diagnosis is difficult.https://www.frontiersin.org/articles/10.3389/fendo.2022.997921/fullpneumonic-type mucinous adenocarcinomalung cancerlobar pneumoniaadenocarcinomainflammationcomputed tomography |
| spellingShingle | Huijun Ji Qianqian Liu Yingxiu Chen Mengyao Gu Qi Chen Shaolan Guo Shangkun Ning Juntao Zhang Wan-Hu Li Combined model of radiomics and clinical features for differentiating pneumonic-type mucinous adenocarcinoma from lobar pneumonia: An exploratory study Frontiers in Endocrinology pneumonic-type mucinous adenocarcinoma lung cancer lobar pneumonia adenocarcinoma inflammation computed tomography |
| title | Combined model of radiomics and clinical features for differentiating pneumonic-type mucinous adenocarcinoma from lobar pneumonia: An exploratory study |
| title_full | Combined model of radiomics and clinical features for differentiating pneumonic-type mucinous adenocarcinoma from lobar pneumonia: An exploratory study |
| title_fullStr | Combined model of radiomics and clinical features for differentiating pneumonic-type mucinous adenocarcinoma from lobar pneumonia: An exploratory study |
| title_full_unstemmed | Combined model of radiomics and clinical features for differentiating pneumonic-type mucinous adenocarcinoma from lobar pneumonia: An exploratory study |
| title_short | Combined model of radiomics and clinical features for differentiating pneumonic-type mucinous adenocarcinoma from lobar pneumonia: An exploratory study |
| title_sort | combined model of radiomics and clinical features for differentiating pneumonic type mucinous adenocarcinoma from lobar pneumonia an exploratory study |
| topic | pneumonic-type mucinous adenocarcinoma lung cancer lobar pneumonia adenocarcinoma inflammation computed tomography |
| url | https://www.frontiersin.org/articles/10.3389/fendo.2022.997921/full |
| work_keys_str_mv | AT huijunji combinedmodelofradiomicsandclinicalfeaturesfordifferentiatingpneumonictypemucinousadenocarcinomafromlobarpneumoniaanexploratorystudy AT qianqianliu combinedmodelofradiomicsandclinicalfeaturesfordifferentiatingpneumonictypemucinousadenocarcinomafromlobarpneumoniaanexploratorystudy AT yingxiuchen combinedmodelofradiomicsandclinicalfeaturesfordifferentiatingpneumonictypemucinousadenocarcinomafromlobarpneumoniaanexploratorystudy AT mengyaogu combinedmodelofradiomicsandclinicalfeaturesfordifferentiatingpneumonictypemucinousadenocarcinomafromlobarpneumoniaanexploratorystudy AT qichen combinedmodelofradiomicsandclinicalfeaturesfordifferentiatingpneumonictypemucinousadenocarcinomafromlobarpneumoniaanexploratorystudy AT shaolanguo combinedmodelofradiomicsandclinicalfeaturesfordifferentiatingpneumonictypemucinousadenocarcinomafromlobarpneumoniaanexploratorystudy AT shangkunning combinedmodelofradiomicsandclinicalfeaturesfordifferentiatingpneumonictypemucinousadenocarcinomafromlobarpneumoniaanexploratorystudy AT juntaozhang combinedmodelofradiomicsandclinicalfeaturesfordifferentiatingpneumonictypemucinousadenocarcinomafromlobarpneumoniaanexploratorystudy AT wanhuli combinedmodelofradiomicsandclinicalfeaturesfordifferentiatingpneumonictypemucinousadenocarcinomafromlobarpneumoniaanexploratorystudy |