title: Predicting adenocarcinoma recurrence using computational texture models of nodule components in lung CT.
abstract: OBJECTIVE
  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).
authors.affiliation: 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.
authors.forename: Adrien
authors.initials: A
authors.lastname: Depeursinge
authors.affiliation: 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.
authors.forename: Masahiro
authors.initials: M
authors.lastname: Yanagawa
authors.affiliation: Department of Radiology, Stanford University School of Medicine, Stanford, California 94305.
authors.forename: Ann N
authors.initials: AN
authors.lastname: Leung
authors.affiliation: Department of Radiology, Stanford University School of Medicine, Stanford, California 94305.
authors.forename: Daniel L
authors.initials: DL
authors.lastname: Rubin
mesh_terms: D000230:Adenocarcinoma / Q000175:diagnosis / Q000000981:diagnostic imaging* / Q000401:mortality / Q000601:surgery; D019540:Area Under Curve; D003198:Computer Simulation; D018572:Disease-Free Survival; D005260:Female; D005500:Follow-Up Studies; D006801:Humans; D016014:Linear Models; D008168:Lung / Q000000981:diagnostic imaging / Q000601:surgery; D008175:Lung Neoplasms / Q000175:diagnosis / Q000000981:diagnostic imaging* / Q000401:mortality / Q000601:surgery; D008297:Male; D008875:Middle Aged; D009364:Neoplasm Recurrence, Local / Q000175:diagnosis / Q000000981:diagnostic imaging* / Q000401:mortality; D009367:Neoplasm Staging; D011379:Prognosis; D012372:ROC Curve; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D012189:Retrospective Studies; D012680:Sensitivity and Specificity; D060388:Support Vector Machine; D014057:Tomography, X-Ray Computed / Q000379:methods*; D058067:Wavelet Analysis
references.citation: Lung Cancer. 2000 Sep;29(3):179-88
references.pmid: 10996420
references.citation: J Comput Assist Tomogr. 2001 Jan-Feb;25(1):23-7
references.pmid: 11176288
references.citation: Lung Cancer. 2001 Jul;33(1):17-25
references.pmid: 11429192
references.citation: AJR Am J Roentgenol. 2002 May;178(5):1053-7
references.pmid: 11959700
references.citation: J Comput Assist Tomogr. 2002 Jul-Aug;26(4):573-8
references.pmid: 12218822
references.citation: Br J Cancer. 2002 Sep 23;87(7):694-701
references.pmid: 12232748
references.citation: Ann Thorac Surg. 2002 Nov;74(5):1635-9
references.pmid: 12440622
references.citation: J Comput Assist Tomogr. 2003 Jan-Feb;27(1):56-61
references.pmid: 12544244
references.citation: Eur J Cardiothorac Surg. 2004 Jun;25(6):1102-6
references.pmid: 15145016
references.citation: Radiology. 2005 Nov;237(2):395-400
references.pmid: 16244247
references.citation: Ann Thorac Surg. 2006 Feb;81(2):413-9
references.pmid: 16427823
references.citation: Artif Intell Med. 2007 Sep;41(1):25-37
references.pmid: 17624744
references.citation: Bioinformatics. 2008 Jul 15;24(14):1632-8
references.pmid: 18515276
references.citation: IEEE Trans Biomed Eng. 2008 Jul;55(7):1822-30
references.pmid: 18595800
references.citation: Lung Cancer. 2010 Dec;70(3):286-94
references.pmid: 20392516
references.citation: Med Image Comput Comput Assist Interv. 2009;12(Pt 2):715-23
references.pmid: 20426175
references.citation: Cancer Imaging. 2010 Jul 06;10:137-43
references.pmid: 20605762
references.citation: J Stat Softw. 2010;33(1):1-22
references.pmid: 20808728
references.citation: J Thorac Oncol. 2011 Feb;6(2):244-85
references.pmid: 21252716
references.citation: IEEE Trans Image Process. 2011 Oct;20(10):2705-21
references.pmid: 21478076
references.citation: Med Image Comput Comput Assist Interv. 2011;14(Pt 3):215-22
references.pmid: 22003702
references.citation: Eur Radiol. 2012 Apr;22(4):796-802
references.pmid: 22086561
references.citation: IEEE Trans Pattern Anal Mach Intell. 2012 Nov;34(11):2274-82
references.pmid: 22641706
references.citation: Med Phys. 2012 Sep;39(9):5405-18
references.pmid: 22957608
references.citation: Radiology. 2013 Jan;266(1):304-17
references.pmid: 23070270
references.citation: Radiology. 2013 Jan;266(1):326-36
references.pmid: 23169792
references.citation: Med Image Comput Comput Assist Interv. 2012;15(Pt 3):517-24
references.pmid: 23286170
references.citation: Ann Thorac Surg. 2013 Jun;95(6):1878-84
references.pmid: 23618519
references.citation: Radiology. 2013 Oct;269(1):8-15
references.pmid: 24062559
references.citation: J Thorac Oncol. 2014 Jan;9(1):74-82
references.pmid: 24346095
references.citation: Med Phys. 2014 Mar;41(3):033502
references.pmid: 24593744
references.citation: Radiology. 2014 Aug;272(2):557-67
references.pmid: 24708191
references.citation: IEEE Trans Image Process. 2014 Feb;23(2):898-908
references.pmid: 26270926
references.citation: J Stat Softw. 2011 Mar;39(5):1-13
references.pmid: 27065756
references.citation: Cancer. 1994 Feb 15;73(4):1163-70
references.pmid: 8313318
references.citation: Stat Med. 1996 Feb 28;15(4):361-87
references.pmid: 8668867