Integrating radiomics features and CT semantic characteristics for predicting visceral pleural invasion in clinical stage Ia peripheral lung adenocarcinoma
Abstract Objectives The aim of this study was to non-invasively predict the visceral pleural invasion (VPI) of peripheral lung adenocarcinoma (LA) highly associated with pleura of clinical stage Ia based on preoperative chest computed tomography (CT) scanning. Methods A total of 537 patients diagnos...
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| Format: | Article |
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Springer
2025-05-01
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| Series: | Discover Oncology |
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| Online Access: | https://doi.org/10.1007/s12672-025-02548-6 |
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| author | Fengnian Zhao Yunqing Zhao Zhaoxiang Ye Qingna Yan Haoran Sun Guiming Zhou |
| author_facet | Fengnian Zhao Yunqing Zhao Zhaoxiang Ye Qingna Yan Haoran Sun Guiming Zhou |
| author_sort | Fengnian Zhao |
| collection | DOAJ |
| description | Abstract Objectives The aim of this study was to non-invasively predict the visceral pleural invasion (VPI) of peripheral lung adenocarcinoma (LA) highly associated with pleura of clinical stage Ia based on preoperative chest computed tomography (CT) scanning. Methods A total of 537 patients diagnosed with clinical stage Ia LA underwent resection and were stratified into training and validation cohorts at a ratio of 7:3. Radiomics features were extracted using PyRadiomics software following tumor lesion segmentation and were subsequently filtered through spearman correlation analysis, minimum redundancy maximum relevance, and least absolute shrinkage and selection operator regression analysis. Univariate and multivariable logistic regression analyses were conducted to identify independent predictors. A predictive model was established with visual nomogram and independent sample validation, and evaluated in terms of area under the receiver operating characteristic curve (AUC). Results The independent predictors of VPI were identified: pleural attachment (p < 0.001), pleural contact angle (p = 0.019) and Rad-score (p < 0.001). The combined model showed good calibration with an AUC of 0.843 (95% confidence intervals (CI 0.796, 0.882), in contrast to 0.757 (95% CI 0.724, 0.785; DeLong’s test P < 0.001) and 0.715 (95% CI 0.688, 0.746; DeLong’s test P < 0.001) when only radiomics or CT semantic features were utilized separately. For validation group, the accuracy of combined prediction model was reasonable with an AUC of 0.792 (95% CI 0.765, 0.824). Conclusion Our predictive model, which integrated radiomics features of primary tumors and peritumoral CT semantic characteristics, offers a non-invasive method for evaluating VPI in patients with clinical stage Ia LA. Additionally, it provides prognostic information and supports surgeons in making more personalized treatment decisions. |
| format | Article |
| id | doaj-art-1824530caeed42a89607edd38e7f1085 |
| institution | OA Journals |
| issn | 2730-6011 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Springer |
| record_format | Article |
| series | Discover Oncology |
| spelling | doaj-art-1824530caeed42a89607edd38e7f10852025-08-20T02:25:15ZengSpringerDiscover Oncology2730-60112025-05-0116111410.1007/s12672-025-02548-6Integrating radiomics features and CT semantic characteristics for predicting visceral pleural invasion in clinical stage Ia peripheral lung adenocarcinomaFengnian Zhao0Yunqing Zhao1Zhaoxiang Ye2Qingna Yan3Haoran Sun4Guiming Zhou5Department of Ultrasound, Tianjin Medical University General HospitalDepartment of Ultrasound, Tianjin Medical University General HospitalDepartment of Ultrasound, Tianjin Medical University General HospitalDepartment of Ultrasound, Tianjin Medical University General HospitalDepartment of Ultrasound, Tianjin Medical University General HospitalDepartment of Ultrasound, Tianjin Medical University General HospitalAbstract Objectives The aim of this study was to non-invasively predict the visceral pleural invasion (VPI) of peripheral lung adenocarcinoma (LA) highly associated with pleura of clinical stage Ia based on preoperative chest computed tomography (CT) scanning. Methods A total of 537 patients diagnosed with clinical stage Ia LA underwent resection and were stratified into training and validation cohorts at a ratio of 7:3. Radiomics features were extracted using PyRadiomics software following tumor lesion segmentation and were subsequently filtered through spearman correlation analysis, minimum redundancy maximum relevance, and least absolute shrinkage and selection operator regression analysis. Univariate and multivariable logistic regression analyses were conducted to identify independent predictors. A predictive model was established with visual nomogram and independent sample validation, and evaluated in terms of area under the receiver operating characteristic curve (AUC). Results The independent predictors of VPI were identified: pleural attachment (p < 0.001), pleural contact angle (p = 0.019) and Rad-score (p < 0.001). The combined model showed good calibration with an AUC of 0.843 (95% confidence intervals (CI 0.796, 0.882), in contrast to 0.757 (95% CI 0.724, 0.785; DeLong’s test P < 0.001) and 0.715 (95% CI 0.688, 0.746; DeLong’s test P < 0.001) when only radiomics or CT semantic features were utilized separately. For validation group, the accuracy of combined prediction model was reasonable with an AUC of 0.792 (95% CI 0.765, 0.824). Conclusion Our predictive model, which integrated radiomics features of primary tumors and peritumoral CT semantic characteristics, offers a non-invasive method for evaluating VPI in patients with clinical stage Ia LA. Additionally, it provides prognostic information and supports surgeons in making more personalized treatment decisions.https://doi.org/10.1007/s12672-025-02548-6Computed tomographyRadiomicsAdenocarcinomaNon-small cell lung cancerVisceral pleural invasion |
| spellingShingle | Fengnian Zhao Yunqing Zhao Zhaoxiang Ye Qingna Yan Haoran Sun Guiming Zhou Integrating radiomics features and CT semantic characteristics for predicting visceral pleural invasion in clinical stage Ia peripheral lung adenocarcinoma Discover Oncology Computed tomography Radiomics Adenocarcinoma Non-small cell lung cancer Visceral pleural invasion |
| title | Integrating radiomics features and CT semantic characteristics for predicting visceral pleural invasion in clinical stage Ia peripheral lung adenocarcinoma |
| title_full | Integrating radiomics features and CT semantic characteristics for predicting visceral pleural invasion in clinical stage Ia peripheral lung adenocarcinoma |
| title_fullStr | Integrating radiomics features and CT semantic characteristics for predicting visceral pleural invasion in clinical stage Ia peripheral lung adenocarcinoma |
| title_full_unstemmed | Integrating radiomics features and CT semantic characteristics for predicting visceral pleural invasion in clinical stage Ia peripheral lung adenocarcinoma |
| title_short | Integrating radiomics features and CT semantic characteristics for predicting visceral pleural invasion in clinical stage Ia peripheral lung adenocarcinoma |
| title_sort | integrating radiomics features and ct semantic characteristics for predicting visceral pleural invasion in clinical stage ia peripheral lung adenocarcinoma |
| topic | Computed tomography Radiomics Adenocarcinoma Non-small cell lung cancer Visceral pleural invasion |
| url | https://doi.org/10.1007/s12672-025-02548-6 |
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