Marnix J A Rasing1, Max Peters1, Amy C Moreno2, Erik F N Hofman3, Gerarda J M Herder4, Pim W N Welvaart5, Franz M N H Schramel6, Joyce E Lodeweges1, Steven H Lin2, Joost J C Verhoeff7, Peter S N van Rossum1. 1. University Medical Center Utrecht. Heidelberglaan 100, 3584 CX Utrecht, The Netherlands. Department of Radiation Oncology. 2. The University of Texas MD Anderson Cancer Center. 1515 Holcombe Blvd, Houston Texas 77030, United States of America. Department of Radiation Oncology. 3. St. Antonius Hospital. Koekoekslaan 1, 3430 EM Nieuwegein, The Netherlands. Department of Cardiothoracic surgery. 4. Meander Medical Center. Maatweg 3, 3800 BM Amersfoort, The Netherlands. Department of Pulmonology. 5. Meander Medical Center. Maatweg 3, 3800 BM Amersfoort, The Netherlands. Department of Surgery. 6. St. Antonius Hospital. Koekoekslaan 1, 3430 EM Nieuwegein, The Netherlands. Department of Pulmonology. 7. University Medical Center Utrecht. Heidelberglaan 100, 3584 CX Utrecht, The Netherlands. Department of Radiation Oncology. Electronic address: j.j.c.verhoeff-10@umcutrecht.nl.
Abstract
BACKGROUND: Patients who are surgically treated for stage I-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 surgery 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 7,156 patients, 511 had an incomplete resection (7.1%). Independent predictors were histology, cT-stage, cN-stage, extent of surgery and open versus thoracoscopic approach. After internal validation, the corrected c-statistic of the resulting nomogram was 0.72. Application of the nomogram to an external dataset of 85,235 patients with incomplete resection in 2,485 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-III NSCLC planned for surgery. 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 surgery.
BACKGROUND:Patients who are surgically treated for stage I-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 NSCLCpatients who had surgery 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 7,156 patients, 511 had an incomplete resection (7.1%). Independent predictors were histology, cT-stage, cN-stage, extent of surgery and open versus thoracoscopic approach. After internal validation, the corrected c-statistic of the resulting nomogram was 0.72. Application of the nomogram to an external dataset of 85,235 patients with incomplete resection in 2,485 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-III NSCLC planned for surgery. 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 surgery.
Authors: W Hugo van Joolingen; Marnix J A Rasing; Max Peters; Anne S R van Lindert; Linda M de Heer; Mieke J Aarts; Joost J C Verhoeff; Peter S N van Rossum Journal: Ann Surg Oncol Date: 2021-10-30 Impact factor: 5.344