Imaging Phenotyping Using Radiomics to Predict Micropapillary Pattern within Lung Adenocarcinoma

Abstract

Introduction: Lung adenocarcinomas (ADCs) with a micropapillary pattern have been reported to have a poor prognosis. However, few studies have reported on the imaging-based identification of a micropapillary component, and all of them have been subjective studies dealing with qualitative computed tomography variables. We aimed to explore imaging phenotyping using a radiomics approach for predicting a micropapillary pattern within lung ADC.

Methods: We enrolled 339 patients who underwent complete resection for lung ADC. Histologic subtypes and grades of the ADC were classified. The amount of micropapillary component was determined. Clinical features and conventional imaging variables such as tumor disappearance rate and maximum standardized uptake value on positron emission tomography were assessed. Quantitative computed tomography analysis was performed on the basis of histogram, size and shape, Gray level co-occurrence matrix-based features, and intensity variance and size zone variance-based features.

Results: Higher tumor stage (OR = 3.270, 95% confidence interval [CI]: 1.483-7.212), intermediate grade (OR = 2.977, 95% CI: 1.066-8.316), lower value of the minimum of the whole pixel value (OR = 0.725, 95% CI: 0.527-0.98800), and lower value of the variance of the positive pixel value (OR = 0.961, 95% CI: 0.927-0.997) were identified as being predictive of a micropapillary component within lung ADC. On the other hand, maximum standardized uptake value and tumor disappearance rate were not significantly different in groups with a micropapillary pattern constituting at least 5% or less than 5% of the entire tumor.

Conclusion: A radiomics approach can be used to interrogate an entire tumor in a noninvasive manner. Combining imaging parameters with clinical features can provide added diagnostic value to identify the presence of a micropapillary component and thus, can influence proper treatment planning.

Keywords: Computed tomography; Lung adenocarcinoma; Micropapillary; Quantitative imaging; Radiomics.