Cancer Management and Research (Nov 2019)

Nomogram For The Prediction Of Malignancy In Small (8–20 mm) Indeterminate Solid Solitary Pulmonary Nodules In Chinese Populations

  • Chen XB,
  • Yan RY,
  • Zhao K,
  • Zhang DF,
  • Li YJ,
  • Wu L,
  • Dong XX,
  • Chen Y,
  • Gao DP,
  • Ding YY,
  • Wang XC,
  • Li ZH

Journal volume & issue
Vol. Volume 11
pp. 9439 – 9448

Abstract

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Xiao-Bo Chen,1,* Rui-Ying Yan,2,* Ke Zhao,3,4,* Da-Fu Zhang,2 Ya-Jun Li,3,4 Lin Wu,5 Xing-Xiang Dong,2 Ying Chen,1 De-Pei Gao,2 Ying-Ying Ding,2 Xi-Cai Wang,6 Zhen-Hui Li2 1First Department of Thoracic Surgery, The Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Yunnan Cancer Center, Kunming 650118, People’s Republic of China; 2Department of Radiology, The Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Yunnan Cancer Center, Kunming 650118, People’s Republic of China; 3Department of Radiology, Guangdong General Hospital, Guangdong Academy of Medical Sciences, Guangzhou 510080, People’s Republic of China; 4School of Medicine, South China University of Technology, Guangzhou 510641, People’s Republic of China; 5Department of Pathology, The Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Yunnan Cancer Center, Kunming 650118, People’s Republic of China; 6Cancer Research Institute, The Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Yunnan Cancer Center, Kunming 650118, People’s Republic of China*These authors contributed equally to this workCorrespondence: Xi-Cai WangCancer Research Institute, The Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Yunnan Cancer Center, No.519 Kun Zhou Road, Xi shan District, Kunming 650118, People’s Republic of ChinaTel +86 13888087351Email [email protected] LiDepartment of Radiology, The Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Yunnan Cancer Center, No.519 Kun Zhou Road, Xi shan District, Kunming 650118, People’s Republic of ChinaTel +86 13698736132Email [email protected]: This study aimed to develop and validate a nomogram for predicting the malignancy of small (8–20 mm) solid indeterminate solitary pulmonary nodules (SPNs) in a Chinese population by using routine clinical and computed tomography data.Methods: The prediction model was developed using a retrospective cohort that comprised 493 consecutive patients with small indeterminate SPNs who were treated between December 2012 and December 2016. The model was independently validated using a second retrospective cohort comprising 216 consecutive patients treated between January 2017 and May 2018. The investigated variables included patient characteristics (e.g., age and smoking history), nodule parameters (e.g., marginal spiculation and significant enhancement), and tumor biomarker levels (e.g., carcinoembryonic antigen). A prediction model was developed by using multivariable logistic regression analysis, and the model’s performance was presented as a nomogram. The model was evaluated based on its discriminative ability, calibration, and clinical usefulness.Results: The developed nomogram was ultimately based on age, marginal spiculation, significant enhancement, and pleural indentation. The Harrell concordance index values were 0.869 in the training cohort (95% confidence interval: 0.837–0.901) and 0.847 in the validation cohort (95% confidence interval: 0.792–0.902). The Hosmer-Lemeshow test revealed good calibration in each of the training and validation cohorts. Decision curve analysis confirmed that the nomogram was clinically useful (risk threshold from 0.10 to 0.85).Conclusion: Patient age, marginal spiculation, significant enhancement, and pleural indentation are independent predictors of malignancy in small indeterminate solid SPNs. The developed nomogram is easy-to-use and may allow the accurate prediction of malignancy in small indeterminate solid SPNs among Chinese patients.Keywords: lung cancer, solitary pulmonary nodule, solid nodule, nomogram, China

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