A nomogram for predicting lymphovascular invasion in lung adenocarcinoma: a retrospective study
Abstract Backgroud Lymphovascular invasion (LVI) was histological factor that was closely related to prognosis of lung adenocarcinoma (LAC).The primary aim was to investigate the value of a nomogram incorporating clinical and computed tomography (CT) factors to predict LVI in LAC, and validating the...
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BMC
2024-11-01
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| Series: | BMC Pulmonary Medicine |
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| Online Access: | https://doi.org/10.1186/s12890-024-03400-3 |
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| author | Miaomaio Lin Xiang Zhao Haipeng Huang Huashan Lin Kai Li |
| author_facet | Miaomaio Lin Xiang Zhao Haipeng Huang Huashan Lin Kai Li |
| author_sort | Miaomaio Lin |
| collection | DOAJ |
| description | Abstract Backgroud Lymphovascular invasion (LVI) was histological factor that was closely related to prognosis of lung adenocarcinoma (LAC).The primary aim was to investigate the value of a nomogram incorporating clinical and computed tomography (CT) factors to predict LVI in LAC, and validating the predictive efficacy of a clinical model for LVI in patients with lung adenocarcinoma with lesions ≤ 3 cm. Methods A total of 450 patients with LAC were retrospectively enrolled. Clinical data and CT features were analyzed to identify independent predictors of LVI. A nomogram incorporating the independent predictors of LVI was built. The performance of the nomogram was evaluated by assessing its discriminative ability and clinical utility.We took 321 patients with tumours ≤ 3 cm in diameter to continue constructing the clinical prediction model, which was labelled subgroup clinical model. Results Carcinoembryonic antigen (CEA) level, maximum tumor diameter, spiculation, and vacuole sign were independent predictors of LVI. The LVI prediction nomogram showed good discrimination in the training set [area under the curve (AUC), 0.800] and the test set (AUC, 0.790), the subgroup clinical model also owned the stable predictive efficacy for preoperative prediction of LVI in lung adenocarcinoma patients, and both training and test set AUC reached 0.740. Conclusions The nomogram developed in this study could predict the risk of LVI in LAC patients, facilitate individualized risk-stratification, and help inform treatment decision-makin, and the subgroup clinical model also had good predictive performance for lung cancer patients with lesion ≤ 3 cm in diameter. |
| format | Article |
| id | doaj-art-3fac3de8bcc54548bd5ee155a4a0d308 |
| institution | Kabale University |
| issn | 1471-2466 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Pulmonary Medicine |
| spelling | doaj-art-3fac3de8bcc54548bd5ee155a4a0d3082024-12-01T12:08:05ZengBMCBMC Pulmonary Medicine1471-24662024-11-0124111210.1186/s12890-024-03400-3A nomogram for predicting lymphovascular invasion in lung adenocarcinoma: a retrospective studyMiaomaio Lin0Xiang Zhao1Haipeng Huang2Huashan Lin3Kai Li4Department of Radiology, The First Affiliated Hospital of Guangxi Medical UniversityDepartment of Radiology, The First Affiliated Hospital of Guangxi Medical UniversityDepartment of Radiology, People’s Hospital of Guangxi Zhuang AutonomousDepartment of Pharmaceutical Diagnosis, GE HealthcareDepartment of Radiology, The First Affiliated Hospital of Guangxi Medical UniversityAbstract Backgroud Lymphovascular invasion (LVI) was histological factor that was closely related to prognosis of lung adenocarcinoma (LAC).The primary aim was to investigate the value of a nomogram incorporating clinical and computed tomography (CT) factors to predict LVI in LAC, and validating the predictive efficacy of a clinical model for LVI in patients with lung adenocarcinoma with lesions ≤ 3 cm. Methods A total of 450 patients with LAC were retrospectively enrolled. Clinical data and CT features were analyzed to identify independent predictors of LVI. A nomogram incorporating the independent predictors of LVI was built. The performance of the nomogram was evaluated by assessing its discriminative ability and clinical utility.We took 321 patients with tumours ≤ 3 cm in diameter to continue constructing the clinical prediction model, which was labelled subgroup clinical model. Results Carcinoembryonic antigen (CEA) level, maximum tumor diameter, spiculation, and vacuole sign were independent predictors of LVI. The LVI prediction nomogram showed good discrimination in the training set [area under the curve (AUC), 0.800] and the test set (AUC, 0.790), the subgroup clinical model also owned the stable predictive efficacy for preoperative prediction of LVI in lung adenocarcinoma patients, and both training and test set AUC reached 0.740. Conclusions The nomogram developed in this study could predict the risk of LVI in LAC patients, facilitate individualized risk-stratification, and help inform treatment decision-makin, and the subgroup clinical model also had good predictive performance for lung cancer patients with lesion ≤ 3 cm in diameter.https://doi.org/10.1186/s12890-024-03400-3Lymphovascular invasionLung adenocarcinomaComputed TomographyNomogram |
| spellingShingle | Miaomaio Lin Xiang Zhao Haipeng Huang Huashan Lin Kai Li A nomogram for predicting lymphovascular invasion in lung adenocarcinoma: a retrospective study BMC Pulmonary Medicine Lymphovascular invasion Lung adenocarcinoma Computed Tomography Nomogram |
| title | A nomogram for predicting lymphovascular invasion in lung adenocarcinoma: a retrospective study |
| title_full | A nomogram for predicting lymphovascular invasion in lung adenocarcinoma: a retrospective study |
| title_fullStr | A nomogram for predicting lymphovascular invasion in lung adenocarcinoma: a retrospective study |
| title_full_unstemmed | A nomogram for predicting lymphovascular invasion in lung adenocarcinoma: a retrospective study |
| title_short | A nomogram for predicting lymphovascular invasion in lung adenocarcinoma: a retrospective study |
| title_sort | nomogram for predicting lymphovascular invasion in lung adenocarcinoma a retrospective study |
| topic | Lymphovascular invasion Lung adenocarcinoma Computed Tomography Nomogram |
| url | https://doi.org/10.1186/s12890-024-03400-3 |
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