The Growth Rate of Subsolid Lung Adenocarcinoma Nodules at Chest CT.

Background Confirming that subsolid adenocarcinomas show exponential growth is important because it would justify using volume doubling time to assess their growth. Purpose To test whether the growth of lung adenocarcinomas manifesting as subsolid nodules at chest CT is accurately represented by an exponential model. Materials and Methods Patients with lung adenocarcinomas manifesting as subsolid nodules surgically resected between January 2005 and May 2018, with three or more longitudinal CT examinations before resection, were retrospectively included. Overall volume (for all nodules) and solid component volume (for part-solid nodules) were measured over time. A linear mixed-effects model was used to identify the growth pattern (linear, exponential, quadratic, or power law) that best represented growth. The interactions between nodule growth and clinical, CT morphologic, and pathologic parameters were studied. Results Sixty-nine patients (mean age, 70 years ± 9 [standard deviation]; 48 women) with 74 lung adenocarcinomas were evaluated. Overall growth and solid component growth were better represented by an exponential model (adjusted R2 = 0.89 and 0.95, respectively) than by a quadratic model (r2 = 0.88 and 0.93, respectively), a linear model (r2 = 0.87 and 0.92, respectively), or a power law model (r2 = 0.82 and 0.93, respectively). Faster overall volume growth was associated with a history of lung cancer (P < .001), a baseline nodule volume less than 500 mm3 (P = .03), and histologic findings of invasive adenocarcinoma (P < .001). The median volume doubling time of noninvasive adenocarcinoma was significantly longer than that of invasive adenocarcinoma (939 days [interquartile range, 588-1563 days] vs 678 days [interquartile range, 392-916 days], respectively; P = .01). Conclusion The overall volume growth of adenocarcinomas manifesting as subsolid nodules at chest CT was best represented by an exponential model compared with the other tested models. This justifies the use of volume doubling time for the growth assessment of these nodules. © RSNA, 2020 Online supplemental material is available for this article. See also the editorial by Kuriyama and Yanagawa in this issue.

Preoperative bronchial cytology for the assessment of tumor spread through air spaces in lung adenocarcinoma resection specimens.

BACKGROUND: Tumor spread through air spaces (STAS), a significant prognostic indicator, has been described recently as a pattern of invasion in pulmonary carcinomas. However, questions remain regarding preoperative identification of STAS and whether it represents an in vivo phenomenon versus an ex vivo artifact. METHODS: We retrospectively reviewed 67 paired preoperative bronchoalveolar lavage (BAL) or bronchial washing (BW) cytology specimens with the subsequent lung adenocarcinoma surgical resection specimen to determine whether preoperative cytology could predict STAS. Other clinical, radiologic, and pathologic features of the resected lesions were also correlated with preoperative bronchial cytology results. RESULTS: Positive bronchial cytology was observed in 28 cases (41.8%), 24 of which had STAS (85.7%); however, negative BAL/BW cytology was observed in 39 cases (58.2%), 29 of which had STAS (74.4%) (x2  = 1.27, P = .26, not significant). High-STAS burden was observed in 44 cases (83.0%), 21 (47.7%) with negative BAL/BW and 23 (52.3%) with positive BAL/BW. Low-STAS burden was observed in 9 cases (17.0%), 8 (88.9%) with negative BAL/BW and only 1 (11.1%) with positive BAL/BW (x2  = 5.11, P = .024, significant). For tumors with STAS, a statistically significant difference was identified in the maximal STAS distance from the main tumor edge between BAL/BW-positive and BAL/BW-negative groups (P = .007). Of the remaining clinicopathologic and radiologic features, only visceral pleural invasion was significantly associated with BAL/BW positivity. CONCLUSION: Presurgical bronchial cytology alone cannot adequately predict tumor STAS; however, it may provide useful information regarding the extent and overall burden of STAS on the subsequent resection specimen.

Assessing invasiveness of subsolid lung adenocarcinomas with combined attenuation and geometric feature models.

The aim of this study was to develop and test multiclass predictive models for assessing the invasiveness of individual lung adenocarcinomas presenting as subsolid nodules on computed tomography (CT). 227 lung adenocarcinomas were included: 31 atypical adenomatous hyperplasia and adenocarcinomas in situ (class H1), 64 minimally invasive adenocarcinomas (class H2) and 132 invasive adenocarcinomas (class H3). Nodules were segmented, and geometric and CT attenuation features including functional principal component analysis features (FPC1 and FPC2) were extracted. After a feature selection step, two predictive models were built with ordinal regression: Model 1 based on volume (log) (logarithm of the nodule volume) and FPC1, and Model 2 based on volume (log) and Q.875 (CT attenuation value at the 87.5% percentile). Using the 200-repeats Monte-Carlo cross-validation method, these models provided a multiclass classification of invasiveness with discriminative power AUCs of 0.83 to 0.87 and predicted the class probabilities with less than a 10% average error. The predictive modelling approach adopted in this paper provides a detailed insight on how the value of the main predictors contribute to the probability of nodule invasiveness and underlines the role of nodule CT attenuation features in the nodule invasiveness classification.