Predicting Incomplete Resection in Non-Small Cell Lung Cancer Preoperatively: A Validated Nomogram

Abstract

Background: Patients who are surgically treated for stage I to III non-small cell lung cancer (NSCLC) have dismal prognosis after incomplete (R1-R2) resection. Our study aimed to develop a prediction model to estimate the chance of incomplete resection based on preoperative patient-, tumor-, and treatment-related factors.

Methods: From a Dutch national cancer database, NSCLC patients who had surgical treatment without neoadjuvant therapy were selected. Thirteen possible predictors were analyzed. Multivariable logistic regression was used to create a prediction model. External validation was applied in the American National Cancer Database, whereupon the model was adjusted. Discriminatory ability and calibration of the model was determined after internal and external validation. The prediction model was presented as nomogram.

Results: Of 7156 patients, 511 had an incomplete resection (7.1%). Independent predictors were histology, cT stage, cN stage, extent of surgery, and open vs thoracoscopic approach. After internal validation, the corrected C statistic of the resulting nomogram was 0.72. Application of the nomogram to an external data set of 85,235 patients with incomplete resection in 2485 patients (2.9%) resulted in a C statistic of 0.71. Calibration revealed good overall fit of the nomogram in both cohorts.

Conclusions: An internationally validated nomogram is presented providing the ability to predict the individual chance of incomplete resection in patients with stage I to III NSCLC planned for resection. In case of a high predicted risk of incomplete resection, alternative treatment strategies could be considered, whereas a low risk further supports the use of surgical procedures.