Supplementary methods
Scan specifications
High-resolution Computed Tomography (HRCT) scans of the patients prior to surgery
were retrieved for analysis. The scans with collimation width ranging from 1.25 to 3 mm were
included. The axial in-plane resolution was sub-millimeter and isotropic. CT scans obtained with
intravenous contrast or images reconstructed with highly edge-enhancing kernels (such as GE
Lung or Siemens B70f) were excluded from the analysis. All HRCT were obtained within 3
months of surgical resection.
Similarity metric
This is a mathematical measure of the similarities between pairs of nodules. This measure
has two components: a symmetric Cramer-von-Mises (CVM) distance metric and an asymmetric
ratio of volumes of the nodules. The overall metric defining the mathematical distance, D,
between two nodules A and B is given by:
D( A, B) 
volume( A)
CVM ( A, B)
volume( B)
Where, CVM   ( ACDF  BCDF ) 2 ; ACDF and BCDF are the cumulative density functions of the
normalized distribution of the nine labels of nodule A and B, respectively.
Cluster Analysis
The quantitative efficacy of the stratification was assessed for statistical significance
using Analysis of Similarity (ANOSIM). This method computes pair-wise inter-cluster R values
based on the distance measures; R value of 0 indicates no different between clusters and a value
1
of 1 suggests the clusters are completely different. A combined R value is average of the
pairwise inter-cluster R values.
Internal Validation of Stratification
We validated the consistency of categorization using leave-one-out (LOO) and k-fold (k
= 10) cross validation (CV) techniques. In LOO CV, iteratively one nodule was excluded and
clustering was performed to identify new exemplars. Subsequently, the excluded nodule was
categorized vis-à-vis the exemplars. Similarly, in 10-fold CV, ten nodules were simultaneously
excluded.
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