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| Establishment of logistic regression diagnosis model predicting malignant solitary pulmonary nodules |
| YU Wei1, YE Bo2, XU Liyun3, Wang Zhaoyu4, Wang Shanjun5, Cao Hanbo5, Chen Zhijun1, ZHANG Yongkui1. |
| 1.Department of Cardiothoracic Surgery, Zhoushan Hospital Affiliated to Wenzhou Medical University, Zhoushan, 316021; 2.Department of Thoracic Surgery, Chest Hospital Affiliated to Shanghai Jiaotong University, Shanghai, 200030; 3.Lung Cancer Research Center of Zhoushan City, Zhoushan Hospital Affiliated to Wenzhou Medical University, Zhoushan, 316021; 4.Pathology Diagnosis Center, Zhoushan Hospital Affiliated to Wenzhou Medical University, Zhoushan, 316021; 5.Radiology Diagnosis Center, Zhoushan Hospital Affiliated to Wenzhou Medical University, Zhoushan, 316021 |
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| Cite this article: |
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YU Wei,YE Bo,XU Liyun, et al. Establishment of logistic regression diagnosis model predicting malignant solitary pulmonary nodules[J]. JOURNAL OF WEZHOU MEDICAL UNIVERSITY, 2017, 47(9): 660-665.
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Abstract Objective: To establish a logistic regression model for predicting the probability of malignancy in solitary pulmonary nodules (SPNs) and provide guidance for the diagnosis. Methods: The clinical data and computed tomography (CT) images of 212 patients with a clear pathological diagnosis of SPNs were retrospectively analyzed from Zhoushan Hospital Affiliated to Wenzhou Medical University were analyzed retrospectively, among which, benign SPNs were collected from January 2012 to December 2015, and malignant SPNs were collected from January 2013 to December 2013. To estimate the independent predictors of malignancy of SPNs, multivariate analysis was used. A logistic regression prediction model was subsequently created. Data from an additional 242 patients with pathologic diagnosis of SPNs were used to validate this logistic regression prediction model. Results: Fifty-eight percent of the nodules from 212 SPNs patients were malignant and 42% were benign. Logistic regression analysis showed that there were significant differences in nodule type, clear border, lobulation, spiculaion, pleural retraction sign between subgroups with benign and malignant SPNs (P<0.05). These factors were identified as independent predictors of malignancy in SPNs. In our model, sensitivity was 81.8%, specificity was 85.7%, positive predictive value was 88.2%, and negative predictive value was 78.3%. Conclusion: The prediction model established in this study can be used to assess the probability of malignancy in SPNs, thereby providing help for the diagnosis of SPNs.
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Received: 07 October 2016
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