After incomplete (R1-R2) resection, patients with stage I to stage III Non-Small Cell Lung Cancer (NSCLC) have a poor prognosis. With the use of preoperative patient, tumor, and treatment-related characteristics, our study attempted to create a prediction model to calculate the likelihood of an incomplete resection. Patients with NSCLC who underwent surgical surgery without neoadjuvant therapy were chosen from a Dutch national cancer database. Analysis was done on thirteen potential predictors. A prediction model was developed using multivariable logistic regression. The American National Cancer Database was used for external validation, after which the model was modified. After internal and external validation, the model's discriminatory power and calibration were established. Histology, cT and cN stages, the length of surgery, and open versus thoracoscopic technique were independent predictors. Following internal confirmation, the resultant corrected C statistic. A C statistic was produced by applying the external data set of patients with incomplete resection in patients. The nomogram in both cohorts had a satisfactory overall fit, according to calibration. The capacity to forecast a patient's specific likelihood of an incomplete resection in those with stage I to stage III NSCLC who are planning to have it is provided by a nomogram that has received worldwide validation. Alternative therapeutic approaches may be considered in cases of high predicted risks of incomplete resection, whereas low risks justify the use of surgical techniques even more.
