Noninvasive characterization of the histopathologic features of pulmonary nodules of the lung adenocarcinoma spectrum using computer-aided nodule assessment and risk yield (CANARY)--a pilot study

Abstract

Introduction: Pulmonary nodules of the adenocarcinoma spectrum are characterized by distinctive morphological and radiologic features and variable prognosis. Noninvasive high-resolution computed tomography-based risk stratification tools are needed to individualize their management.

Methods: Radiologic measurements of histopathologic tissue invasion were developed in a training set of 54 pulmonary nodules of the adenocarcinoma spectrum and validated in 86 consecutively resected nodules. Nodules were isolated and characterized by computer-aided analysis, and data were analyzed by Spearman correlation, sensitivity, and specificity and the positive and negative predictive values.

Results: Computer-aided nodule assessment and risk yield (CANARY) can noninvasively characterize pulmonary nodules of the adenocarcinoma spectrum. Unsupervised clustering analysis of high-resolution computed tomography data identified nine unique exemplars representing the basic radiologic building blocks of these lesions. The exemplar distribution within each nodule correlated well with the proportion of histologic tissue invasion, Spearman R = 0.87, p < 0.0001 and 0.89 and p < 0.0001 for the training and the validation set, respectively. Clustering of the exemplars in three-dimensional space corresponding to tissue invasion and lepidic growth was used to develop a CANARY decision algorithm that successfully categorized these pulmonary nodules as "aggressive" (invasive adenocarcinoma) or "indolent" (adenocarcinoma in situ and minimally invasive adenocarcinoma). Sensitivity, specificity, positive predictive value, and negative predictive value of this approach for the detection of aggressive lesions were 95.4, 96.8, 95.4, and 96.8%, respectively, in the training set and 98.7, 63.6, 94.9, and 87.5%, respectively, in the validation set.

Conclusion: CANARY represents a promising tool to noninvasively risk stratify pulmonary nodules of the adenocarcinoma spectrum.

Figure 1

CANARY workflow outlining the procedural steps involved in categorizing the HRCT based nodules into one of AIS, MIA and IA. The first step involves the automated lung parenchymal isolation and classification using CALIPER. Subsequently, each nodule is identified by the operator using seed placement. This is followed the automated volumetric extraction of the nodule and the characterization of each voxel within a given nodule based on the 9 exemplars. This distribution of the exemplars within an individual lesion is than used to categorize each nodule.

Figure 2

Natural clustering of HRCT regions of interest (ROI) of lung nodules. Panel A shows representative ROIs selected from different nodules in the training set. Each of the 774 ROI were compared with each other to derive the pairwise 774×774 similarity matrix and color coded (Panel B) such that the darkness is proportional to the similarity. Panel C shows the similarity matrix in the Affinity Propagation based clustered space wherein the arbitrarily color-coded diagonal sub-blocks show the automatically computed natural clusters.

Figure 3

Representative signatures for invasive adenocarcinoma, minimally invasive adenocarcinoma and adenocarcinoma in situ (Panel A). Representative CT images with the superimposed distribution of the 9 adenocarcinoma exemplars are show for IA, MIA and AIS. Panel B shows the three-dimensional distribution of the 9 adenocarcinoma exemplars using Multi-Dimensional Scaling. It demonstrates the secondary clustering of the exemplars, violet-indigo-red-orange (V-I-R-O) representing invasion, yellow-pink (Y-P) and blue-green-cyan (B-G-C) representing lepidic growth.

Figure 4. Radiological-histopathologic correlation of tissue invasion between CANARY based nodule assessment and consensus histopathology

Examples of representative CT images with superimposed CANARY “signatures” (distinctive combinations of exemplars within one nodule) associated with nodules with varying degrees of histological invasion (%, 100 – consensus histopathology lepidic growth %) (Panel A.). Correlation between CANARY and consensus histopathology for pulmonary nodules of the adenocarcinoma spectrum, Training Set, excluding 16 cases used to develop CANARY (n=38), Panel B. and Validation Set (n=86), Panel C. Spearman’s correlation (p<0.0001), line represents linear regression (Panels B. and C.).

Figure 5

Rule-based CANARY decision algorithm based on the distribution of exemplar clusters (%): violet-red-orange (VIRO), yellow-pink (YP) and blue-green-cyan (BGC) within each lesion.

Figure 6

Two by two contingency table of CANARY’s diagnostic performance (rows) to predict consensus histopathological tissue invasion (columns). Panel A. Training Set (n=54) and Panel B. Validation Set (n=86)

Figure 7

Lung cancer specific post-operative survival. Panel A. Consensus histopathology and Panel B. CANARY.

Figure 8

Representative nodules with discrepant radiologic and histologic classification. Nodules in panels A and B were categorized differently by the expert radiologists (IA versus MIA) but correctly identified by CANARY (histologic consensus MIA). Histology confirmed MIA nodules in (C) and (D) were categorized by both radiologists as IA. The histologically confirmed IA nodule in (E) was categorized by both the radiologists as MIA. The rule-based CANARY categorization of these nodules was the same as histology consensus.