Computer Aided Nodule Analysis and Risk Yield (CANARY) characterization of adenocarcinoma: radiologic biopsy, risk stratification and future directions

Abstract

The majority of incidentally and screen-detected lung cancers are adenocarcinomas. Optimal management of these tumors is clinically challenging due to variability in tumor histopathology and behavior. Invasive adenocarcinoma (IA) is generally aggressive while adenocarcinoma in situ (AIS) and minimally invasive adenocarcinoma (MIA) may be extremely indolent. Computer Aided Nodule Analysis and Risk Yield (CANARY) is a quantitative computed tomography (CT) analysis tool that allows non-invasive assessment of tumor characteristics. This analysis may obviate the need for tissue biopsy and facilitate the risk stratification of adenocarcinoma of the lung. CANARY was developed by unsupervised machine learning techniques using CT data of histopathologically-characterized adenocarcinomas of the lung. This technique identified 9 distinct exemplars that constitute the spectrum of CT features found in adenocarcinoma of the lung. The distributions of these features in a nodule correlate with histopathology. Further automated clustering of CANARY nodules defined three distinct groups that have distinctly different post-resection disease free survival (DFS). CANARY has been validated within the NLST cohort and multiple other cohorts. Using semi-automated segmentation as input to CANARY, there is excellent repeatability and interoperator correlation of results. Confirmation and longitudinal tracking of indolent adenocarcinoma with CANARY may ultimately add decision support in nuanced cases where surgery may not be in the best interest of the patient due to competing comorbidity. Currently under investigation is CANARY's role in detecting differing driver mutations and tumor response to targeted chemotherapeutics. Combining the results from CANARY analysis with clinical information and other quantitative techniques such as analysis of the tumor-free surrounding lung may aid in building more powerful predictive models. The next step in CANARY investigation will be its prospective application, both in selecting low-risk stage 1 adenocarcinoma for active surveillance and investigation in selecting high-risk early stage adenocarcinoma for adjuvant therapy.

Lung cancer screening: the path forward

“…Kadir and Gleeson then further explore radiomics/texture analysis and deep machine learning approaches to characterizing indeterminate nodules as either benign or malignant (21). Dr. Clay and his team describe their quantitative analysis tool, Computer Aided Nodule Analysis and Risk Yield (CANARY), that allows non-invasive assessment of tumor characteristics, and potentially risk stratification of pulmonary adenocarcinomas (22).…”

Quantifying emphysema in lung screening computed tomography with robust automated lobe segmentation

“…Within the first goal, identifying novel risk factors of lung cancer works toward an individualized approach to lung cancer risk and further elucidates radiologic manifestations that are correlated with lung cancer development 2 – 6 As a related second goal, annual lung screenings have served as encounters to communicate lung cancer risk and promote health behavior change such as smoking cessation. As a common abnormality seen on lung screening CT that correlates with tobacco use and the development of lung cancer, emphysema may be a useful biomarker for both goals.…”

Assessment of interobserver concordance in radiomic tools for lung nodule classification, with a focus on BRODERS and SILA

“…In addition, we have previously developed and validated which analyzes lung nodules based on the distribution of 9 texture based, color coded exemplars. CANARY analysis allows the “virtual biopsy” and post-surgical outcome-based risk stratification of pulmonary nodules of the lung adenocarcinoma spectrum 9 . The distribution of the 9 CANARY exemplars can be summarized numerically by the continuous Score Indicative of Lung cancer Aggression (SILA score).…”