Developing a new radiomics-based CT image marker to detect lymph node metastasis among cervical cancer patients

BACKGROUND AND OBJECTIVE In diagnosis of cervical cancer patients, lymph node (LN) metastasis is a highly important indicator for the following treatment management. Although CT/PET (i.e., computed tomography/positron emission tomography) examination is the most effective approach for this detection, it is limited by the high cost and low accessibility, especially for the rural areas in the U.S.A. or other developing countries. To address this challenge, this investigation aims to develop and test a novel radiomics-based CT image marker to detect lymph node metastasis for cervical cancer patients. METHODS A total of 1,763 radiomics features were first computed from the segmented primary cervical tumor depicted on one CT image with the maximal tumor region. Next, a principal component analysis algorithm was applied on the initial feature pool to determine an optimal feature cluster. Then, based on this optimal cluster, the prediction models (i.e., logistic regression or support vector machine) were trained and optimized to generate an image marker to detect LN metastasis. In this study, a retrospective dataset containing 127 cervical cancer patients were established to build and test the model. The model was trained using a leave-one-case-out (LOCO) cross-validation strategy and image marker performance was evaluated using the area under receiver operation characteristic (ROC) curve (AUC). RESULTS The results indicate that the SVM based imaging marker achieved an AUC value of 0.841 ± 0.035. When setting an operating threshold of 0.5 on model-generated prediction scores, the imaging marker yielded a positive and negative predictive value (PPV and NPV) of 0.762 and 0.765 respectively, while the total accuracy is 76.4%. CONCLUSIONS This study initially verified the feasibility of utilizing CT image and radiomics technology to develop a low-cost image marker to detect LN metastasis for assisting stratification of cervical cancer patients.

[1]  Matti Pietikäinen,et al.  A comparative study of texture measures with classification based on featured distributions , 1996, Pattern Recognit..

[2]  A. Jemal,et al.  Cancer statistics, 2020 , 2020, CA: a cancer journal for clinicians.

[3]  D. Walnut.  An Introduction to Wavelet Analysis , 2004 .

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

[5]  H. Aerts.  Semantics Features : Phenotype Quantification by a Radiologist ’ s Expert Eye , 2016 .

[6]  Antonio Torralba,et al.  Modeling the Shape of the Scene: A Holistic Representation of the Spatial Envelope , 2001, International Journal of Computer Vision.

[7]  B. S. Manjunath,et al.  Texture Features for Browsing and Retrieval of Image Data , 1996, IEEE Trans. Pattern Anal. Mach. Intell..

[8]  J. Lina,et al.  Complex Daubechies Wavelets , 1995 .

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

[10]  Hong Liu,et al.  Classification of Tumor Epithelium and Stroma by Exploiting Image Features Learned by Deep Convolutional Neural Networks , 2018, Annals of Biomedical Engineering.

[11]  Edward H. Adelson,et al.  The Laplacian Pyramid as a Compact Image Code , 1983, IEEE Trans. Commun..

[12]  I. Jolliffe.  Principal Component Analysis , 2005 .

[13]  Jun Wang,et al.  Radiomics study for differentiating gastric cancer from gastric stromal tumor based on contrast-enhanced CT images. , 2020, Journal of X-ray science and technology.

[14]  Teresa Wu,et al.  Classification of Breast Masses Using a Computer-Aided Diagnosis Scheme of Contrast Enhanced Digital Mammograms , 2018, Annals of Biomedical Engineering.

[15]  Hongmin Cai,et al.  Discrimination of Breast Cancer with Microcalcifications on Mammography by Deep Learning , 2016, Scientific Reports.

[16]  Gregory R. Pond,et al.  Effect of Positron Emission Tomography Imaging in Women With Locally Advanced Cervical Cancer , 2018, JAMA network open.

[17]  Chengjun Liu,et al.  Gabor feature based classification using the enhanced fisher linear discriminant model for face recognition , 2002, IEEE Trans. Image Process..

[18]  Javier Zamora,et al.  Diagnostic accuracy of tests for lymph node status in primary cervical cancer: a systematic review and meta-analysis , 2008, Canadian Medical Association Journal.

[19]  Yi Wang,et al.  Preoperative computed tomography-guided disease-free survival prediction in gastric cancer: a multicenter radiomics study. , 2020, Medical physics.

[20]  Zongqiong Sun,et al.  A pilot study of radiomics technology based on X-ray mammography in patients with triple-negative breast cancer. , 2019, Journal of X-ray science and technology.

[21]  Ian H. Witten,et al.  Data mining: practical machine learning tools and techniques, 3rd Edition , 1999 .

[22]  Geoffrey E. Hinton,et al.  Deep Learning , 2015, Nature.

[23]  Bin Zheng,et al.  Optimization of breast mass classification using sequential forward floating selection (SFFS) and a support vector machine (SVM) model , 2014, International Journal of Computer Assisted Radiology and Surgery.

[24]  Bill Triggs,et al.  Histograms of oriented gradients for human detection , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[25]  Yijun Tian,et al.  Plasma exosomal miRNAs-based prognosis in metastatic kidney cancer , 2017, Oncotarget.

[26]  Piotr Porwik,et al.  The Haar – Wavelet Transform in Digital Image Processing : Its Status and Achievements , 2004 .

[27]  David Gur,et al.  A method to improve visual similarity of breast masses for an interactive computer-aided diagnosis environment. , 2005, Medical physics.

[28]  Nirupam Sarkar,et al.  An Efficient Differential Box-Counting Approach to Compute Fractal Dimension of Image , 1994, IEEE Trans. Syst. Man Cybern. Syst..

[29]  B. S. Manjunath,et al.  Color and texture descriptors , 2001, IEEE Trans. Circuits Syst. Video Technol..

[30]  Morteza Heidari,et al.  Prediction of chemotherapy response in ovarian cancer patients using a new clustered quantitative image marker , 2018, Physics in medicine and biology.

[31]  M. Bernardini,et al.  Imaging of lymph node metastases in cervical cancer , 2008, Canadian Medical Association Journal.

[32]  Vladimir Vapnik,et al.  Statistical learning theory , 1998 .

[33]  Bin Zhang,et al.  Radiomic machine-learning classifiers for prognostic biomarkers of advanced nasopharyngeal carcinoma. , 2017, Cancer letters.

[34]  Fei Yang,et al.  Improvement in prediction of prostate cancer prognosis with somatic mutational signatures , 2017, Journal of Cancer.

[35]  Di Dong,et al.  2D and 3D CT Radiomics Features Prognostic Performance Comparison in Non-Small Cell Lung Cancer , 2017, Translational oncology.

[36]  Zhijie Cai,et al.  Quantitative analysis of brain optical images with 2D C 0 complexity measure , 2007, Journal of Neuroscience Methods.

[37]  Tej D. Azad,et al.  Magnetic resonance image features identify glioblastoma phenotypic subtypes with distinct molecular pathway activities , 2015, Science Translational Medicine.

[38]  James J. Chen,et al.  Assessment of performance of survival prediction models for cancer prognosis , 2012, BMC Medical Research Methodology.

[39]  Y H Chang,et al.  Feature selection for computerized mass detection in digitized mammograms by using a genetic algorithm. , 1999, Academic radiology.

[40]  Bin Zheng,et al.  Applying a new quantitative global breast MRI feature analysis scheme to assess tumor response to chemotherapy , 2016, Journal of magnetic resonance imaging : JMRI.

[41]  Bin Zheng,et al.  Fusion of quantitative imaging features and serum biomarkers to improve performance of computer‐aided diagnosis scheme for lung cancer: A preliminary study , 2018, Medical physics.

[42]  B N Bundy,et al.  Concurrent cisplatin-based radiotherapy and chemotherapy for locally advanced cervical cancer. , 1999, The New England journal of medicine.

[43]  Ce Zhang,et al.  Predicting non-small cell lung cancer prognosis by fully automated microscopic pathology image features , 2016, Nature Communications.

[44]  Stefano Trebeschi,et al.  Machine learning-based analysis of CT radiomics model for prediction of colorectal metachronous liver metastases , 2020, Abdominal Radiology.

[45]  Masoom A. Haider,et al.  Radiomics-based Prognosis Analysis for Non-Small Cell Lung Cancer , 2017, Scientific Reports.

[46]  B. Zheng,et al.  Assessment of performance improvement in content-based medical image retrieval schemes using fractal dimension. , 2009, Academic radiology.

[47]  Shouliang Qi,et al.  Deep CNN models for pulmonary nodule classification: model modification, model integration, and transfer learning. , 2019, Journal of X-ray science and technology.

[48]  J. Canales‐Vázquez,et al.  Radiomics of CT Features May Be Nonreproducible and Redundant: Influence of CT Acquisition Parameters. , 2018, Radiology.