Predicting adenocarcinoma recurrence using computational texture models of nodule components in lung CT

Authors

  • Depeursinge Adrien,

    1. Department of Radiology, Stanford University School of Medicine, Stanford, California 94305 and Business Information Systems, University of Applied Sciences Western Switzerland (HES-SO), Sierre 3960, Switzerland
    Search for more papers by this author
  • Yanagawa Masahiro,

    1. Department of Radiology, Stanford University School of Medicine, Stanford, California 94305 and Department of Radiology, Osaka University Graduate School of Medicine, Suita, Osaka 565-0871, Japan
    Search for more papers by this author
  • Leung Ann N.,

    1. Department of Radiology, Stanford University School of Medicine, Stanford, California 94305
    Search for more papers by this author
  • Rubin Daniel L.

    1. Department of Radiology, Stanford University School of Medicine, Stanford, California 94305
    Search for more papers by this author

Errata

This article is corrected by:

  1. Errata: Erratum: “Predicting adenocarcinoma recurrence using computational texture models of nodule components in lung CT” [Med. Phys. 42, 2054 (10pp.) (2015)] Volume 42, Issue 5, 2653, Article first published online: 1 May 2015

Abstract

Purpose:

To investigate the importance of presurgical computed tomography (CT) intensity and texture information from ground-glass opacities (GGO) and solid nodule components for the prediction of adenocarcinoma recurrence.

Methods:

For this study, 101 patients with surgically resected stage I adenocarcinoma were selected. During the follow-up period, 17 patients had disease recurrence with six associated cancer-related deaths. GGO and solid tumor components were delineated on presurgical CT scans by a radiologist. Computational texture models of GGO and solid regions were built using linear combinations of steerable Riesz wavelets learned with linear support vector machines (SVMs). Unlike other traditional texture attributes, the proposed texture models are designed to encode local image scales and directions that are specific to GGO and solid tissue. The responses of the locally steered models were used as texture attributes and compared to the responses of unaligned Riesz wavelets. The texture attributes were combined with CT intensities to predict tumor recurrence and patient hazard according to disease-free survival (DFS) time. Two families of predictive models were compared: LASSO and SVMs, and their survival counterparts: Cox-LASSO and survival SVMs.

Results:

The best-performing predictive model of patient hazard was associated with a concordance index (C-index) of 0.81 ± 0.02 and was based on the combination of the steered models and CT intensities with survival SVMs. The same feature group and the LASSO model yielded the highest area under the receiver operating characteristic curve (AUC) of 0.8 ± 0.01 for predicting tumor recurrence, although no statistically significant difference was found when compared to using intensity features solely. For all models, the performance was found to be significantly higher when image attributes were based on the solid components solely versus using the entire tumors (p < 3.08 × 10−5).

Conclusions:

This study constitutes a novel perspective on how to interpret imaging information from CT examinations by suggesting that most of the information related to adenocarcinoma aggressiveness is related to the intensity and morphological properties of solid components of the tumor. The prediction of adenocarcinoma relapse was found to have low specificity but very high sensitivity. Our results could be useful in clinical practice to identify patients for which no recurrence is expected with a very high confidence using a presurgical CT scan only. It also provided an accurate estimation of the risk of recurrence after a given duration t from surgical resection (i.e., C-index = 0.81 ± 0.02).

Ancillary