A Prediction Model for Pathologic N2 Disease in Lung Cancer Patients with a Negative Mediastinum by Positron Emission Tomography

• Published in: Journal of thoracic oncology Vol. 8; no. 9; pp. 1170 - 1180
• Main Authors: Farjah, Farhood, Lou, Feiran, Sima, Camelia, Rusch, Valerie W., Rizk, Nabil P.
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 01.09.2013; Copyright by the European Lung Cancer Conference and the International Association for the Study of Lung Cancer
• Subjects: Adenocarcinoma - diagnostic imaging; Adenocarcinoma - mortality; Adenocarcinoma - pathology; Adenocarcinoma, Bronchiolo-Alveolar - diagnostic imaging; Adenocarcinoma, Bronchiolo-Alveolar - mortality; Adenocarcinoma, Bronchiolo-Alveolar - pathology; Adult; Aged; Aged, 80 and over; Carcinoma, Non-Small-Cell Lung - diagnostic imaging; Carcinoma, Non-Small-Cell Lung - mortality; Carcinoma, Non-Small-Cell Lung - pathology; Carcinoma, Squamous Cell - diagnostic imaging; Carcinoma, Squamous Cell - mortality; Carcinoma, Squamous Cell - pathology; Female; Fluorodeoxyglucose F18; Follow-Up Studies; Humans; Lung cancer; Lung Neoplasms - diagnostic imaging; Lung Neoplasms - mortality; Lung Neoplasms - pathology; Lymph Nodes - diagnostic imaging; Lymph Nodes - pathology; Lymphatic Metastasis; Male; Mediastinal Neoplasms - diagnostic imaging; Mediastinal Neoplasms - mortality; Mediastinal Neoplasms - pathology; Mediastinoscopy; Middle Aged; Models, Statistical; Neoplasm Invasiveness; Neoplasm Staging; Pathologic N2 disease; Positron-Emission Tomography; Prediction model; Prognosis; Radiopharmaceuticals; Retrospective Studies; Risk Factors; Survival Rate; Mediastinoscopy; Pathologic N2 disease; Lung cancer; Prediction model
• ISSN: 1556-0864; 1556-1380
• DOI: 10.1097/JTO.0b013e3182992421

Guidance is limited for invasive staging in patients with lung cancer without mediastinal disease by positron emission tomography (PET). We developed and validated a prediction model for pathologic N2 disease (pN2), using six previously described risk factors: tumor location and size by computed tomography (CT), nodal disease by CT, maximum standardized uptake value of the primary tumor, N1 by PET, and histology. A cohort study (2004–2009) was performed in patients with T1/T2 by CT and N0/N1 by PET. Logistic regression analysis was used to develop a prediction model for pN2 among a random development set (n = 625). The model was validated in both the development set, which comprised two thirds of the patients and the validation set (n = 313), which comprised the remaining one third. Model performance was assessed in terms of discrimination and calibration. Among 938 patients, 9.9% had pN2 (9 detected by invasive staging and 84 intraoperatively). In the development set, univariate analyses demonstrated a significant association between pN2 and increasing tumor size (p < 0.001), nodal status by CT (p = 0.007), maximum standardized uptake value of the primary tumor (p = 0.027), and N1 by PET (p < 0.001); however, only N1 by PET was associated with pN2 (p < 0.001) in the multivariate prediction model. The model performed reasonably well in the development (c-statistic, 0.70; 95% confidence interval, 0.63–0.77; goodness of fit p = 0.61) and validation (c-statistic, 0.65; 95% confidence interval, 0.56–0.74; goodness-of-fit p = 0.19) sets. A prediction model for pN2 based on six previously described risk factors has reasonable performance characteristics. Observations from this study may guide prospective, multicenter development and validation of a prediction model for pN2.