Tracking time interval changes of pulmonary nodules on follow-up 3D CT images via image-based risk score of lung cancer

In this paper, we present a computer-aided follow-up (CAF) scheme to support physicians to track interval changes of pulmonary nodules on three dimensional (3D) CT images and to decide the treatment strategies without making any under or over treatment. Our scheme involves analyzing CT histograms to evaluate the volumetric distribution of CT values within pulmonary nodules. A variational Bayesian mixture modeling framework translates the image-derived features into an image-based risk score for predicting the patient recurrence-free survival. Through applying our scheme to follow-up 3D CT images of pulmonary nodules, we demonstrate the potential usefulness of the CAF scheme which can provide the trajectories that can characterize time interval changes of pulmonary nodules.

[1]  L. Tanoue,et al.  Subsolid Pulmonary Nodules and the Spectrum of Peripheral Adenocarcinomas of the Lung: Recommended Interim Guidelines for Assessment and Management , 2011 .

[2]  S. Sone,et al.  Prognostic significance of high-resolution CT findings in small peripheral adenocarcinoma of the lung: a retrospective study on 64 patients. , 2002, Lung cancer.

[3]  L. Tanoue.  CT Screening for Lung Cancer: Five-year Prospective Experience , 2007 .

[4]  C. Henschke.  Early lung cancer action project , 2000, Cancer.

[5]  Kenji Suzuki,et al.  Radiologic classification of small adenocarcinoma of the lung: radiologic-pathologic correlation and its prognostic impact. , 2006, The Annals of thoracic surgery.

[6]  Takashi Ohtsuka,et al.  Histogram analysis of computed tomography numbers of clinical T1 N0 M0 lung adenocarcinoma, with special reference to lymph node metastasis and tumor invasiveness. , 2003, The Journal of thoracic and cardiovascular surgery.

[7]  M. Roizen.  Reduced Lung-Cancer Mortality with Low-Dose Computed Tomographic Screening , 2012 .

[8]  Kenji Eguchi,et al.  Focal ground-glass opacity detected by low-dose helical CT. , 2002, Chest.

[9]  Kenji Eguchi,et al.  Lung Cancer with Localized Ground-Glass Attenuation Represents Early-Stage Adenocarcinoma in Nonsmokers , 2008, Journal of thoracic oncology : official publication of the International Association for the Study of Lung Cancer.

[10]  Anant Madabhushi,et al.  Computer-aided prognosis of ER+ breast cancer histopathology and correlating survival outcome with Oncotype DX assay , 2009, 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro.

[11]  Noboru Niki,et al.  Quantitative classification based on CT histogram analysis of non-small cell lung cancer: correlation with histopathological characteristics and recurrence-free survival. , 2012, Medical physics.

[12]  Noboru Niki,et al.  Image-guided decision support system for pulmonary nodule classification in 3D thoracic CT images , 2004, SPIE Medical Imaging.

[13]  M. L. R. D. Christenson,et al.  Reduced Lung-Cancer Mortality with Low-Dose Computed Tomographic Screening , 2012 .

[14]  H. Ohmatsu,et al.  Peripheral lung cancer: screening and detection with low-dose spiral CT versus radiography. , 1996, Radiology.

[15]  Feng Li,et al.  Mass screening for lung cancer with mobile spiral computed tomography scanner , 1998, The Lancet.

[16]  Hironobu Ohmatsu,et al.  Performance Evaluation of 4 Measuring Methods of Ground-Glass Opacities for Predicting the 5-Year Relapse-Free Survival of Patients With Peripheral Nonsmall Cell Lung Cancer: A Multicenter Study , 2008, Journal of computer assisted tomography.

[17]  Yoichi Kameda,et al.  Radiologic-prognostic correlation in patients with small pulmonary adenocarcinomas. , 2002, Lung cancer.

[18]  O. Miettinen,et al.  CT screening for lung cancer: frequency and significance of part-solid and nonsolid nodules. , 2002, AJR. American journal of roentgenology.

[19]  George Lee,et al.  Computer-aided prognosis: Predicting patient and disease outcome via quantitative fusion of multi-scale, multi-modal data , 2011, Comput. Medical Imaging Graph..

[20]  Setsuo Hirohashi,et al.  Small adenocarcinoma of the lung. Histologic characteristics and prognosis , 1995 .

[21]  K. Yasumoto,et al.  Peripheral lung adenocarcinoma: correlation of thin-section CT findings with histologic prognostic factors and survival. , 2001, Radiology.

[22]  Noboru Niki,et al.  Image-based computer-aided prognosis of lung cancer: predicting patient recurrent-free survival via a variational Bayesian mixture modeling framework for cluster analysis of CT histograms , 2012, Medical Imaging.

[23]  Noboru Niki,et al.  Growth-rate estimation of pulmonary nodules in three-dimensional thoracic CT images based on CT density histogram analysis and its application to nodule classification , 2006, SPIE Medical Imaging.