Delta-radiomics features for the prediction of patient outcomes in non–small cell lung cancer

Radiomics is the use of quantitative imaging features extracted from medical images to characterize tumor pathology or heterogeneity. Features measured at pretreatment have successfully predicted patient outcomes in numerous cancer sites. This project was designed to determine whether radiomics features measured from non–small cell lung cancer (NSCLC) change during therapy and whether those features (delta-radiomics features) can improve prognostic models. Features were calculated from pretreatment and weekly intra-treatment computed tomography images for 107 patients with stage III NSCLC. Pretreatment images were used to determine feature-specific image preprocessing. Linear mixed-effects models were used to identify features that changed significantly with dose-fraction. Multivariate models were built for overall survival, distant metastases, and local recurrence using only clinical factors, clinical factors and pretreatment radiomics features, and clinical factors, pretreatment radiomics features, and delta-radiomics features. All of the radiomics features changed significantly during radiation therapy. For overall survival and distant metastases, pretreatment compactness improved the c-index. For local recurrence, pretreatment imaging features were not prognostic, while texture-strength measured at the end of treatment significantly stratified high- and low-risk patients. These results suggest radiomics features change due to radiation therapy and their values at the end of treatment may be indicators of tumor response.

[1]  L. Schwartz,et al.  New response evaluation criteria in solid tumours: revised RECIST guideline (version 1.1). , 2009, European journal of cancer.

[2]  Huan Wang,et al.  Multilevel binomial logistic prediction model for malignant pulmonary nodules based on texture features of CT image. , 2010, European journal of radiology.

[3]  Kenneth A. Miles,et al.  How to use CT texture analysis for prognostication of non-small cell lung cancer , 2016, Cancer Imaging.

[4]  Dushyant V. Sahani,et al.  Response assessment to neoadjuvant therapy in soft tissue sarcomas: using CT texture analysis in comparison to tumor size, density, and perfusion , 2015, Abdominal Imaging.

[5]  Mary M. Galloway,et al.  Texture analysis using gray level run lengths , 1974 .

[6]  K. Miles,et al.  Tumour heterogeneity in non-small cell lung carcinoma assessed by CT texture analysis: a potential marker of survival , 2012, European Radiology.

[7]  William N. Venables,et al.  Modern Applied Statistics with S , 2010 .

[8]  M.,et al.  Statistical and Structural Approaches to Texture , 2022 .

[9]  M. V. van Herk,et al.  Precise and real-time measurement of 3D tumor motion in lung due to breathing and heartbeat, measured during radiotherapy. , 2002, International journal of radiation oncology, biology, physics.

[10]  Y. Benjamini,et al.  Controlling the false discovery rate: a practical and powerful approach to multiple testing , 1995 .

[11]  Pedro M. Valero-Mora,et al.  ggplot2: Elegant Graphics for Data Analysis , 2010 .

[12]  Beau Dabbs,et al.  Summary and discussion of : “ Controlling the False Discovery Rate : A Practical and Powerful Approach to Multiple Testing , 2014 .

[13]  Raymond H Mak,et al.  Radiomic phenotype features predict pathological response in non-small cell lung cancer. , 2016, Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology.

[14]  Robert M. Haralick,et al.  Textural Features for Image Classification , 1973, IEEE Trans. Syst. Man Cybern..

[15]  Samuel H. Hawkins,et al.  Reproducibility and Prognosis of Quantitative Features Extracted from CT Images. , 2014, Translational oncology.

[16]  Robert J. Gillies,et al.  Developing a classifier model for lung tumors in CT-scan images , 2011, 2011 IEEE International Conference on Systems, Man, and Cybernetics.

[17]  Lei Dong,et al.  Reduce in variation and improve efficiency of target volume delineation by a computer-assisted system using a deformable image registration approach. , 2007, International journal of radiation oncology, biology, physics.

[18]  Richard M. Simon,et al.  Using cross-validation to evaluate predictive accuracy of survival risk classifiers based on high-dimensional data , 2011, Briefings Bioinform..

[19]  Jinzhong Yang,et al.  Measuring Computed Tomography Scanner Variability of Radiomics Features , 2015, Investigative radiology.

[20]  O. Mawlawi,et al.  Prognostic value and reproducibility of pretreatment CT texture features in stage III non-small cell lung cancer. , 2014, International journal of radiation oncology, biology, physics.

[21]  P. Lambin,et al.  Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach , 2014, Nature Communications.

[22]  Jinzhong Yang,et al.  Preliminary investigation into sources of uncertainty in quantitative imaging features , 2015, Comput. Medical Imaging Graph..

[23]  Andre Dekker,et al.  Radiomics: the process and the challenges. , 2012, Magnetic resonance imaging.

[24]  Robert King,et al.  Textural features corresponding to textural properties , 1989, IEEE Trans. Syst. Man Cybern..

[25]  Balaji Ganeshan,et al.  Texture analysis of non-small cell lung cancer on unenhanced computed tomography: initial evidence for a relationship with tumour glucose metabolism and stage , 2010, Cancer imaging : the official publication of the International Cancer Imaging Society.

[26]  Hiroto Hatabu,et al.  New Response Evaluation Criteria in Solid Tumors (RECIST) guidelines for advanced non-small cell lung cancer: comparison with original RECIST and impact on assessment of tumor response to targeted therapy. , 2010, AJR. American journal of roentgenology.

[27]  Benjamin Haibe-Kains,et al.  Radiomic feature clusters and Prognostic Signatures specific for Lung and Head & Neck cancer , 2015, Scientific Reports.

[28]  P. Lambin,et al.  CT-based radiomic signature predicts distant metastasis in lung adenocarcinoma. , 2015, Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology.

[29]  E. Mohammadi,et al.  Barriers and facilitators related to the implementation of a physiological track and trigger system: A systematic review of the qualitative evidence , 2017, International journal for quality in health care : journal of the International Society for Quality in Health Care.

[30]  Peter A Balter,et al.  Impact of image preprocessing on the volume dependence and prognostic potential of radiomics features in non-small cell lung cancer , 2016 .

[31]  Tadayoshi Fushiki,et al.  Estimation of prediction error by using K-fold cross-validation , 2011, Stat. Comput..

[32]  George Starkschall,et al.  Assessing respiration-induced tumor motion and internal target volume using four-dimensional computed tomography for radiotherapy of lung cancer. , 2007, International journal of radiation oncology, biology, physics.

[33]  G. Clark,et al.  Practical p-value adjustment for optimally selected cutpoints. , 1996, Statistics in medicine.

[34]  Patrick Granton,et al.  Radiomics: extracting more information from medical images using advanced feature analysis. , 2012, European journal of cancer.

[35]  R. Korn,et al.  Noninvasive Image Texture Analysis Differentiates K-ras Mutation from Pan-Wildtype NSCLC and Is Prognostic , 2014, PloS one.

[36]  P. Grambsch,et al.  A Package for Survival Analysis in S , 1994 .

[37]  P. Lambin,et al.  Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach , 2014, Nature Communications.

[38]  Steven E Schild,et al.  Non-small cell lung cancer: epidemiology, risk factors, treatment, and survivorship. , 2008, Mayo Clinic proceedings.

[39]  V. Goh,et al.  Assessment of response to tyrosine kinase inhibitors in metastatic renal cell cancer: CT texture as a predictive biomarker. , 2011, Radiology.

[40]  F. Harrell,et al.  Evaluating the yield of medical tests. , 1982, JAMA.

[41]  Olivier Gevaert,et al.  Non-small cell lung cancer: identifying prognostic imaging biomarkers by leveraging public gene expression microarray data--methods and preliminary results. , 2012, Radiology.

[42]  Simon Wan,et al.  Tumor Heterogeneity and Permeability as Measured on the CT Component of PET/CT Predict Survival in Patients with Non–Small Cell Lung Cancer , 2013, Clinical Cancer Research.

[43]  S. Armato,et al.  Lung texture in serial thoracic computed tomography scans: correlation of radiomics-based features with radiation therapy dose and radiation pneumonitis development. , 2015, International journal of radiation oncology, biology, physics.

[44]  Hadley Wickham,et al.  ggplot2 - Elegant Graphics for Data Analysis (2nd Edition) , 2017 .

[45]  W. McGuire,et al.  Why do so many prognostic factors fail to pan out? , 2005, Breast Cancer Research and Treatment.

[46]  P. Marsden,et al.  False Discovery Rates in PET and CT Studies with Texture Features: A Systematic Review , 2015, PloS one.

[47]  J R Anderson,et al.  Analysis of survival by tumor response. , 1983, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[48]  Jinzhong Yang,et al.  IBEX: an open infrastructure software platform to facilitate collaborative work in radiomics. , 2015, Medical physics.

[49]  D. Bates,et al.  Fitting Linear Mixed-Effects Models Using lme4 , 2014, 1406.5823.

[50]  U. Dafni.  Landmark Analysis at the 25-Year Landmark Point , 2011, Circulation. Cardiovascular quality and outcomes.

[51]  Philippe Lambin,et al.  Early variation of FDG-PET radiomics features in NSCLC is related to overall survival - the “delta radiomics” concept , 2016 .

[52]  C. Jaffe.  Measures of response: RECIST, WHO, and new alternatives. , 2006, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[53]  Walter H Backes,et al.  CT texture analysis in colorectal liver metastases: A better way than size and volume measurements to assess response to chemotherapy? , 2016, United European gastroenterology journal.

[54]  Radhe Mohan,et al.  Implementation and validation of a three-dimensional deformable registration algorithm for targeted prostate cancer radiotherapy. , 2004, International journal of radiation oncology, biology, physics.