Study cohort
This single-center observational retrospective study was approved by the Institutional Review Board of St. Luke’s International Hospital, Tokyo, Japan (approval no. 19-R026; issued April 9, 2020). The need for written informed consent was waived. A total of 201,275 patients underwent periodic health examinations between January 2014 and 2019. After 51,136 patients were examined using chest CT, 309 were suspected of having malignancies and were referred to the outpatient clinic for close surgical investigation. Among these patients, 115 surgeries were performed for nodules suspected to be malignant. We excluded patients with (i) no available data on annual LD-CT in the last 5 years (n = 53) to minimize measurement bias and (ii) a pathological diagnosis, except for lung adenocarcinoma (n = 15). Finally, 47 patients (47 × 6 = 282 subsolid nodules) were enrolled (Fig. 1). The study was conducted in accordance with the Declaration of Helsinki.
Ld-ct Image Acquisition
LD-CT was performed using a 64-detector row scanner (Aquilion ONE, Toshiba Medical Systems, Tokyo, Japan; Revolution and Optima 660, GE Healthcare Japan, Tokyo, Japan) with the following standard parameters: 120 kV, automatically set for amplification, and bone reconstruction algorithm. A 2.5-mm slice thickness was acquired for all images using standard reconstruction kernels with lung window settings (window level, − 500 Hounsfield units [HU]; window width, 1500 HU). The acquired images were evaluated by expert radiologists (DY with 5 years of experience, MM and YK with > 20 years of experience each).
Assessment Of Followed-up Nodules At Multiple Medical Checks
Data of 282 pulmonary lesions (47 nodules per year) detected on LD-CT were extracted semi-automatically. The volume of interest (VOI) was estimated using a three-dimensional image analysis software (SYNAPSE VINCENT; Fujifilm Medical, Tokyo, Japan). This was equivalent to the overall tumor volume determined using voxels (mm3) and CT values (HU). We set the ratio of the solid component (within − 300 HU) expressed as the volume percentage (% solid), and the area of ground-glass opacity (ranging from − 1000 HU to − 300 HU) had a border of − 300 HU (Fig. 2). Subsequently, a voxel-based histogram analysis (VHA) was conducted for the VOI by calculating the following five parameters, as previously described [5]: mean CT value, variance, skewness, kurtosis, and entropy. We focused on changes in the tumor from the initial detection until the most recent check-up after 5 years.
Therefore, the growth rate or change rate (Δ) of seven radiological parameters (X) based on VHA was calculated and evaluated using the following formula:
ΔX per year = value at the time of surgery - mean value for four years from the initial detection to one year prior to surgery (mean of five times)
X = tumor volume (cm3), solid volume percentage (% solid, %), mean CT value (HU), variance (×104), kurtosis, skewness, or entropy.
Clinicopathological Findings
The 8th edition of the Tumor, Node, Metastasis staging system was applied for this study [6]. According to the International Association for the Study of Lung Cancer, American Thoracic Society, and European Respiratory Society classification of lung adenocarcinoma, 47 resected specimens were classified into the following two groups: the less-invasive group (adenocarcinoma in situ [AIS] and minimally invasive adenocarcinoma [MIA]) and invasive group (invasive adenocarcinoma [IA]) [6]. The measurements of the tumor dimensions (maximum and solid sizes in diameter) for all 282 lesions performed by expert radiologists were applied as a reference to assess the clinical T factors. The consolidation-to-tumor ratio (CTR) was also determined based on the findings.
Indication Of Segmentectomy
Segmentectomy was performed under the following conditions: i) nodules < 2 cm in size, ii) pure ground-glass (CTR = 0) or part-solid nodules on CT (CTR < 0.5), and iii) suspected metastases from other organ cancers [7].
Statistical Analyses
We calculated descriptive statistics for patient characteristics and continuous data in terms of the growth rate and change rate (Δ) of the seven radiological parameters. Categorical variables were summarized as numbers and proportions and continuous variables as medians and interquartile ranges (IQR). To compare each continuous variable between the less-invasive and invasive groups, we performed a univariate analysis based on the Mann–Whitney U test. All data were analyzed using two-sided hypothesis tests. Statistical significance was set at p < 0.05. To provide a visual analysis of how each parameter changed over time, we plotted all patient data per year for each parameter and applied locally weighted scatterplot smoothing (LOWESS) to capture the features of the changing trends. We proposed a new approach in terms of the prediction of increasing invasiveness based on Δ. Therefore, to validate the results derived from our new method, we performed a visualization analysis using LOWESS.
We also performed multiple logistic regression analysis (adjusted) and identified the essential factors needed to detect the invasive group by exploiting the differences (Δ) in each of the seven variables between the two groups. We performed stepwise variable selection for the logistic regression model based on the seven variables. Finally, we estimated the most sophisticated logistic regression model after variable selection.
Using the final logistic regression model, we calculated the area under the receiver operating characteristic curve (AUC) to determine the threshold score. A decision tree of the classification and regression tree (CART) model was constructed to predict the IAs and estimate the cut-off values of the critical variables. To conduct the CART analysis, we used the Gini index criterion and set the tuning parameter for the decision-tree complexity to 0.01. All descriptive statistical analyses, univariate analysis, LOWESS, multiple logistic regression, and CART analyses were conducted using R (version 3.4.3; R Foundation for Statistical Computing, Vienna, Austria).