Enhancing survival predictions in lung cancer with cystic airspaces: a multimodal approach combining clinical and radiomic features
ObjectiveTo enhance the prognostic assessment and management of lung cancer with cystic airspaces (LCCA) by integrating temporal clinical and phenotypic dimensions of tumor growth.Patients and methodsA retrospective analysis was conducted on LCCA patients treated at two hospitals. Clinical and imagi...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2025-04-01
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| Series: | Frontiers in Oncology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fonc.2025.1524212/full |
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| author | Liang Yin Jing Wang Pingyou Fu Lu Xing Yuan Liu Zongchang Li Jie Gan |
| author_facet | Liang Yin Jing Wang Pingyou Fu Lu Xing Yuan Liu Zongchang Li Jie Gan |
| author_sort | Liang Yin |
| collection | DOAJ |
| description | ObjectiveTo enhance the prognostic assessment and management of lung cancer with cystic airspaces (LCCA) by integrating temporal clinical and phenotypic dimensions of tumor growth.Patients and methodsA retrospective analysis was conducted on LCCA patients treated at two hospitals. Clinical and imaging characteristics were analyzed using the independent samples t-test, Mann-Whitney U test, and χ2 test. Features with significant differences were further analyzed using multivariate Cox regression to identify independent prognostic factors. Radiomic features were extracted from CT images, and volume doubling time (VDT) was calculated from two follow-up scans. Separate predictive models were constructed based on radiomic features and VDT. A fusion model integrating radiomic features, VDT, and independent clinical prognostic factors was developed. Model performance was evaluated using receiver operating characteristic curve and the area under the curve, with DeLong’s test used for comparison.ResultsA total of 193 patients were included, with an average survival time of 48.5 months. Significant differences were found between survivors and non-survivors in age, smoking status, chronic obstructive pulmonary disease, and tumor volume (P < 0.05). Multivariate Cox analysis identified smoking and chronic obstructive pulmonary disease as independent risk factors (P = 0.028 and P = 0.013). The VDT for survivors was 421 (298 582.5) days compared to 334.5 ± 106.1 days for non-survivors (Z = -3.330, P = 0.001). In the validation set, the area under the curve for the VDT model was 0.805, for the radiomic model 0.717, and for the fusion model 0.895, demonstrating the highest predictive performance (P < 0.05).ConclusionIntegrating VDT, radiomics, and clinical imaging features into a fusion model improves the accuracy of predicting the five-year survival rate for LCCA patients, enhancing personalized and precise cancer treatment. |
| format | Article |
| id | doaj-art-850961366c8f45e2ba5c90be863e91be |
| institution | OA Journals |
| issn | 2234-943X |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Oncology |
| spelling | doaj-art-850961366c8f45e2ba5c90be863e91be2025-08-20T02:16:44ZengFrontiers Media S.A.Frontiers in Oncology2234-943X2025-04-011510.3389/fonc.2025.15242121524212Enhancing survival predictions in lung cancer with cystic airspaces: a multimodal approach combining clinical and radiomic featuresLiang Yin0Jing Wang1Pingyou Fu2Lu Xing3Yuan Liu4Zongchang Li5Jie Gan6Medical Imaging, Shandong Provincial Third Hospital, Jinan, Shandong, ChinaMedical Imaging, Shandong Provincial Third Hospital, Jinan, Shandong, ChinaRadiology Department, Shandong Yellow River Hospital, Jinan, ChinaRadiology Department, Shandong Yellow River Hospital, Jinan, ChinaRadiology Department, Shandong Yellow River Hospital, Jinan, ChinaMedical Imaging, Shandong Provincial Third Hospital, Jinan, Shandong, ChinaMedical Imaging, Shandong Provincial Third Hospital, Jinan, Shandong, ChinaObjectiveTo enhance the prognostic assessment and management of lung cancer with cystic airspaces (LCCA) by integrating temporal clinical and phenotypic dimensions of tumor growth.Patients and methodsA retrospective analysis was conducted on LCCA patients treated at two hospitals. Clinical and imaging characteristics were analyzed using the independent samples t-test, Mann-Whitney U test, and χ2 test. Features with significant differences were further analyzed using multivariate Cox regression to identify independent prognostic factors. Radiomic features were extracted from CT images, and volume doubling time (VDT) was calculated from two follow-up scans. Separate predictive models were constructed based on radiomic features and VDT. A fusion model integrating radiomic features, VDT, and independent clinical prognostic factors was developed. Model performance was evaluated using receiver operating characteristic curve and the area under the curve, with DeLong’s test used for comparison.ResultsA total of 193 patients were included, with an average survival time of 48.5 months. Significant differences were found between survivors and non-survivors in age, smoking status, chronic obstructive pulmonary disease, and tumor volume (P < 0.05). Multivariate Cox analysis identified smoking and chronic obstructive pulmonary disease as independent risk factors (P = 0.028 and P = 0.013). The VDT for survivors was 421 (298 582.5) days compared to 334.5 ± 106.1 days for non-survivors (Z = -3.330, P = 0.001). In the validation set, the area under the curve for the VDT model was 0.805, for the radiomic model 0.717, and for the fusion model 0.895, demonstrating the highest predictive performance (P < 0.05).ConclusionIntegrating VDT, radiomics, and clinical imaging features into a fusion model improves the accuracy of predicting the five-year survival rate for LCCA patients, enhancing personalized and precise cancer treatment.https://www.frontiersin.org/articles/10.3389/fonc.2025.1524212/fulllung cancer with cystic airspacessurvivalvolume doubling timeradiomicspredictive model |
| spellingShingle | Liang Yin Jing Wang Pingyou Fu Lu Xing Yuan Liu Zongchang Li Jie Gan Enhancing survival predictions in lung cancer with cystic airspaces: a multimodal approach combining clinical and radiomic features Frontiers in Oncology lung cancer with cystic airspaces survival volume doubling time radiomics predictive model |
| title | Enhancing survival predictions in lung cancer with cystic airspaces: a multimodal approach combining clinical and radiomic features |
| title_full | Enhancing survival predictions in lung cancer with cystic airspaces: a multimodal approach combining clinical and radiomic features |
| title_fullStr | Enhancing survival predictions in lung cancer with cystic airspaces: a multimodal approach combining clinical and radiomic features |
| title_full_unstemmed | Enhancing survival predictions in lung cancer with cystic airspaces: a multimodal approach combining clinical and radiomic features |
| title_short | Enhancing survival predictions in lung cancer with cystic airspaces: a multimodal approach combining clinical and radiomic features |
| title_sort | enhancing survival predictions in lung cancer with cystic airspaces a multimodal approach combining clinical and radiomic features |
| topic | lung cancer with cystic airspaces survival volume doubling time radiomics predictive model |
| url | https://www.frontiersin.org/articles/10.3389/fonc.2025.1524212/full |
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