Yangwei Xiang1, Yifeng Sun1, Yuan Liu2, Baohui Han3, Qunhui Chen4, Xiaodan Ye4, Li Zhu4, Wen Gao1,5, Wentao Fang1. 1. Department of Thoracic Surgery, Shanghai Chest Hospital, Shanghai Jiaotong University, Shanghai 200030, China. 2. Department of Statistics Cente, Shanghai Chest Hospital, Shanghai Jiaotong University, Shanghai 200030, China. 3. Department of Pulmonary Medicine, Shanghai Chest Hospital, Shanghai Jiaotong University, Shanghai 200030, China. 4. Department of Radiology, Shanghai Chest Hospital, Shanghai Jiaotong University, Shanghai 200030, China. 5. Department of Thoracic Surgery, Shanghai Huadong Hospital, Fudan University School of Medicine, Shanghai 200030, China.
Abstract
BACKGROUND: The purpose of this study is to develop a predictive model to accurately predict the malignancy of solid solitary pulmonary nodule (SPN) by data mining methods. METHODS: A training cohort of 388 consecutive patients with solid SPNs was used to develop a predictive model to evaluate the malignancy of solid SPNs. By using SPSS Modeler, we utilized logistic regression (LR), artificial neural network (ANN), k-nearest neighbor (KNN), random forest (RF), and support vector machines (SVM) classifiers to build predictive models. Another cohort of 200 consecutive patients with solid SPNs was used to verify the accuracy of the predictive model. Predictive performance was evaluated using the area under the receiver operating characteristic curve (AUC). RESULTS: There was no significant difference in patients' characteristics between the training cohort and the validation cohort. The AUCs of LR, ANN, KNN, RF, and SVM models for the validation cohort were 0.874±0.0280 (P=0.605), 0.833±0.0351 (P=0.104), 0.792±0.0418 (P=0.014), 0.775±0.0400 (P=0.013), and 0.890±0.0323 (reference), respectively. The SVM algorithm had the highest AUC, and the best sensitivity (90.3%), specificity (80.4%), positive predictive value (93.9%), negative predictive value (71.2%) and accuracy (88.0%) for the validation cohort among the five models. CONCLUSIONS: Data mining by SVM might be a useful auxiliary algorithm in predicting malignancy of solid SPNs.
BACKGROUND: The purpose of this study is to develop a predictive model to accurately predict the malignancy of solid solitary pulmonary nodule (SPN) by data mining methods. METHODS: A training cohort of 388 consecutive patients with solid SPNs was used to develop a predictive model to evaluate the malignancy of solid SPNs. By using SPSS Modeler, we utilized logistic regression (LR), artificial neural network (ANN), k-nearest neighbor (KNN), random forest (RF), and support vector machines (SVM) classifiers to build predictive models. Another cohort of 200 consecutive patients with solid SPNs was used to verify the accuracy of the predictive model. Predictive performance was evaluated using the area under the receiver operating characteristic curve (AUC). RESULTS: There was no significant difference in patients' characteristics between the training cohort and the validation cohort. The AUCs of LR, ANN, KNN, RF, and SVM models for the validation cohort were 0.874±0.0280 (P=0.605), 0.833±0.0351 (P=0.104), 0.792±0.0418 (P=0.014), 0.775±0.0400 (P=0.013), and 0.890±0.0323 (reference), respectively. The SVM algorithm had the highest AUC, and the best sensitivity (90.3%), specificity (80.4%), positive predictive value (93.9%), negative predictive value (71.2%) and accuracy (88.0%) for the validation cohort among the five models. CONCLUSIONS: Data mining by SVM might be a useful auxiliary algorithm in predicting malignancy of solid SPNs.
Entities:
Keywords:
Lung cancer; data mining; solitary pulmonary nodule (SPN)
Authors: Denise R Aberle; Amanda M Adams; Christine D Berg; William C Black; Jonathan D Clapp; Richard M Fagerstrom; Ilana F Gareen; Constantine Gatsonis; Pamela M Marcus; JoRean D Sicks Journal: N Engl J Med Date: 2011-06-29 Impact factor: 91.245
Authors: Claudia I Henschke; David F Yankelevitz; Rosna Mirtcheva; Georgeann McGuinness; Dorothy McCauley; Olli S Miettinen Journal: AJR Am J Roentgenol Date: 2002-05 Impact factor: 3.959