The Utility of Nodule Volume in the Context of Malignancy Prediction for Small Pulmonary Nodules

Abstract

When faced with a patient with a pulmonary nodule, it is incumbent on the clinician to differentiate benign cases from cancer. Although most clinicians use clinical experience to estimate the probability of malignancy in pulmonary nodules, some rely on one or T he pulmonary nodule is a single, spherical, wellcircumscribed, radiographic opacity that measures , 3 cm in diameter and is completely surrounded by aerated lung. There is no associated atelectasis, hilar enlargement, or pleural effusion. 1 Approximately 150,000 such nodules are identifi ed each year according to dated estimates. 2,3 The incidence is likely much higher than this because of the increasing use of chest CT scan for the evaluation of a myriad of pulmonary symptoms and disorders. The National Lung Screening Trial has shown screening patients with low-dose CT (LDCT) scanning led to a relative risk reduction in death from lung cancer by 20%. 4 Over the 3-year screening period, however, 39.1% of the participants in the LDCT scanning group had a nodule discovered, of which (96.4%) were benign. 4 Currently, 7 million Americans meet the National Lung Screening Trial screening criteria. 4,5 Even if only one-fourth of those eligible are screened, a possible 680,000 new nodules could be discovered over 3 years.Background: An estimated 150,000 pulmonary nodules are identifi ed each year, and the number is likely to increase given the results of the National Lung Screening Trial. Decision tools are needed to help with the management of such pulmonary nodules. We examined whether adding any of three novel functions of nodule volume improves the accuracy of an existing malignancy prediction model of CT scan-detected nodules. Methods: Swensen's 1997 prediction model was used to estimate the probability of malignancy in CT scan-detected nodules identifi ed from a sample of 221 patients at the Medical University of South Carolina between 2006 and 2010. Three multivariate logistic models that included a novel function of nodule volume were used to investigate the added predictive value. Several measures were used to evaluate model classifi cation performance. Results: With use of a 0.5 cutoff associated with predicted probability, the Swensen model correctly classifi ed 67% of nodules. The three novel models suggested that the addition of nodule volume enhances the ability to correctly predict malignancy; 83%, 88%, and 88% of subjects were correctly classifi ed as having malignant or benign nodules, with signifi cant net improved reclassifi cation for each ( P , .0001). All three models also performed well based on Nagelkerke R 2 , discrimination slope, area under the receiver operating characteristic curve, and Hosmer-Lemeshow calibration test. Conclusions:The fi ndings demonstrate that the addition of nodule volume to existing malignancy prediction models increases the proportion of nodules correctly classifi ed. This enhanced tool will help clinicians to risk stratify pulmonary nodules more effectively.CHEST 2014; 145(3):464-472

Correlation in histological subtypes with high resolution computed tomography signatures of early stage lung adenocarcinoma

“…In univariate analysis, we found that smaller tumor size and larger GGO proportion were significantly associated with indolent and less aggressive tumors. Similarly, there were several studies also found that the size and mass of the nodule are determinants of invasive adenocarcinoma (8,(16)(17)(18). While, recently an observation found that in patients with tumors smaller than 3 cm, DFS was significantly associated with solid tumor size, but not with whole tumor size in early stage tumors (9).…”

Lung nodules: size still matters

“…MEHTA et al [36] added volumetric nodule measurement to an existing prediction model for nodule malignancy estimation, showing an increase in the number of nodules correctly classified. Notably, the study included only lesions <15 mm in diameter.…”

Thoracic malignancies and pulmonary nodules in patients under evaluation for transcatheter aortic valve implantation (TAVI): Incidence, follow up and possible impact on treatment decision

“…Next, the exact description of shape and size is important. The bigger the nodule, the higher the chance for malignant transformation [10,[27][28][29][30][31]. Likewise, information regarding location [30], growth [32] and shape [33] should be respected, too.…”