A random forest algorithm predicting model combining intraoperative frozen section analysis and clinical features guides surgical strategy for peripheral solitary pulmonary nodules

Liqiang Qian; Yinjie Zhou; Wanqin Zeng; Xiaoke Chen; Zhengping Ding; Yujia Shen; Yifeng Qian; Davide Tosi; Mario Silva; Yuchen Han; Xiaolong Fu

doi:10.21037/tlcr-22-395

A random forest algorithm predicting model combining intraoperative frozen section analysis and clinical features guides surgical strategy for peripheral solitary pulmonary nodules

Transl Lung Cancer Res. 2022 Jun;11(6):1132-1144. doi: 10.21037/tlcr-22-395.

Authors

Liqiang Qian^#¹, Yinjie Zhou^#², Wanqin Zeng³, Xiaoke Chen¹, Zhengping Ding¹, Yujia Shen³, Yifeng Qian⁴, Davide Tosi⁵, Mario Silva⁶, Yuchen Han⁷, Xiaolong Fu³

Affiliations

¹ Shanghai Lung Cancer Center, Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai, China.
² Department of Thoracic Surgery, Hwa Mei Hospital, University of Chinese Academy of Science (Ningbo No. 2 Hospital), Ningbo, China.
³ Department of Radiation Oncology, Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai, China.
⁴ National Clinical Research Center for Oral Disease, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University, Shanghai, China.
⁵ Thoracic Surgery and Lung Transplant Unit, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy.
⁶ Scienze Radiologiche, Department of Medicine and Surgery (DiMeC), University of Parma, Parma, Italy.
⁷ Department of Pathology, Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai, China.

^# Contributed equally.

Abstract

Background: Intraoperative frozen section (FS) analysis has been used to guide the extent of resection in patients with solitary pulmonary nodules (SPNs), but its accuracy varies greatly among different hospitals. Artificial intelligence (AI) and multidimensional data technology are developing rapidly these years, meanwhile, surgeons need better methods to guide the surgical strategy of SPNs. We established predicting models combining FS results with multidimensional perioperative clinical features using logistic regression analysis and the random forest (RF) algorithm to get more accurate extent of SPN resection.

Methods: Patients with peripheral SPNs who underwent FS-guided surgical resection at the Shanghai Chest Hospital (January 2017-December 2018) were retrospectively examined (N=3,089). The accuracy of intraoperative FS-guided resection extent was analyzed and used as Model 1. The clinical features (sex, age, CT features, tumor markers, smoking history, lesion size and nodule location) of patients were collected, and Models 2 and 3 were established using logistic regression and RF algorithms to combine the FS with clinical features. We confirmed the performance of these models in an external validation cohort of 117 patients from Hwa Mei Hospital, University of Chinese Academy of Science (Ningbo No. 2 Hospital). We compared the effectiveness in classifying low/high-risk groups of SPN among them.

Results: The accuracy of FS analysis was 61.3%. Model 3 exhibited the best diagnostic accuracy and had an area under the curve of 0.903 in n the internal validation cohort and 0.919 in the external validation cohort. The calibration plots and net reclassification index (NRI) of Model 3 also exhibited significantly better performance than the other models. Improved diagnostic accuracy was observed in in both internal and external validation cohort.

Conclusions: Using an RF algorithm, clinical characteristics can be combined with intraoperative FS analysis to significantly improve intraoperative judgment accuracy for low- and high-risk tumors, and may serve as a reliable complementary method when FS evaluation is equivocal, improving the accuracy of the extent of surgical resection.

Keywords: Solitary pulmonary nodule (SPN); diagnostic accuracy; frozen section (FS); random forest (RF); surgical resection.