. Author manuscript; available in PMC: 2021 Aug 13.

Published in final edited form as: J Thorac Oncol. 2020 Jun 17;15(10):1599–1610. doi: 10.1016/j.jtho.2020.06.001

Table 2.

Selection of Variables for Histologic Patterns in the Training Set

	Recurrence		Death
Variables in the Model	C-Index	AUC	C-Index	AUC

Baseline	0.65	0.68	0.68	0.673
Baseline + predominant pattern	0.698	0.719	0.727	0.729
Baseline + predominant pattern + secondary pattern	0.742	0.765	0.764	0.760
Baseline + predominant pattern + high-grade pattern	0.740	0.749	0.758	0.741
Baseline + predominant pattern + high-grade pattern (20% cutoff)	0.732	0.749	0.732	0.787
Baseline + weighted average	0.698	0.719	0.742	0.733
Baseline + pattern 1 + pattern 2 + pattern 3 (binary)^a	0.726	0.734	0.732	0.714
Baseline + pattern 1 + pattern 2 + pattern 3 (numeric)^a	0.733	0.742	0.746	0.735
Baseline + all 7 patterns (binary)^a	0.744	0.756	0.754	0.757
Baseline + all 7 patterns (numeric)^a	0.745	0.755	0.759	0.747

Baseline model represent clinical characteristics only.

Patterns 1 to 3 indicate the following three-tiered classification of predominant pattern in the tumor: pattern 1, lepidic; pattern 2, acinar or papillary; pattern 3, micropapillary, solid, or complex glandular pattern (cribriform and fused glands). All seven patterns indicate that all patterns present in the tumor are individually counted. Binary represents presence or absence of patterns but assigned a number (1–3) as described previously. Numeric indicates that the numerical proportion of that pattern was taken into consideration.

AUC, area under the ROC curve; C-index, concordance index; ROC, receiver operating characteristic.