Skip to main content
Key Specialties
Cardiology Diabetology Endocrinology Gastroenterology Geriatrics and Gerontology Gynecology and Obstetrics Hematology Infectious Disease Internal Medicine Nephrology Neurology Oncology Pediatrics Respiratory Medicine Rheumatology Surgery
Congress Coverage
2024 ACC Congress 2023 ESMO Congress 2023 EASD Congress 2023 ERS Congress 2023 ESC Congress 2023 EHA Congress 2023 ASCO Annual Meeting
Clinical Collections
Advances in Alzheimer’s

At a glance: The ONWARDS insulin icodec trials

A round-up of the ONWARDS phase 3 clinical trials evaluating the novel weekly insulin icodec in people with diabetes.
ADVANCED SEARCH
Log in
Top
European Radiology
Published in: European Radiology 8/2020

01-08-2020 | Magnetic Resonance Imaging | Magnetic Resonance

Classification of pulmonary lesion based on multiparametric MRI: utility of radiomics and comparison of machine learning methods

Authors: Xinhui Wang, Qi Wan, Houjin Chen, Yanfeng Li, Xinchun Li

Published in: European Radiology | Issue 8/2020

Login to get access

Abstract

Objectives

We develop and validate a radiomics model based on multiparametric magnetic resonance imaging (MRI) in the classification of the pulmonary lesion and identify optimal machine learning methods.

Materials and methods

This retrospective analysis included 201 patients (143 malignancies, 58 benign lesions). Radiomics features were extracted from multiparametric MRI, including T2-weighted imaging (T2WI), T1-weighted imaging (TIWI), and apparent diffusion coefficient (ADC) map. Three feature selection methods, including recursive feature elimination (RFE), t test, and least absolute shrinkage and selection operator (LASSO), and three classification methods, including linear discriminate analysis (LDA), support vector machine (SVM), and random forest (RF) were used to distinguish benign and malignant pulmonary lesions. Performance was compared by AUC, sensitivity, accuracy, precision, and specificity. Analysis of performance differences in three randomly drawn cross-validation sets verified the stability of the results.

Results

For most single MR sequences or combinations of multiple MR sequences, RFE feature selection method with SVM classifier had the best performance, followed by RFE with RF. The radiomics model based on multiple sequences showed a higher diagnostic accuracy than single sequence for every machine learning method. Using RFE with SVM, the joint model of T1WI, T2WI, and ADC showed the highest performance with AUC = 0.88 ± 0.02 (sensitivity 83%; accuracy 82%; precision 91%; specificity 79%) in test set.

Conclusion

Quantitative radiomics features based on multiparametric MRI have good performance in differentiating lung malignancies and benign lesions. The machine learning method of RFE with SVM is superior to the combination of other feature selection and classifier methods.

Key Points

• Radiomics approach has the potential to distinguish between benign and malignant pulmonary lesions.
• Radiomics model based on multiparametric MRI has better performance than single-sequence models.
• The machine learning methods RFE with SVM perform best in the current cohort.
Appendix
Available only for authorised users
Literature
1.
Siegel RL, Miller KD, Jemal A (2018) Cancer statistics, 2018. CA Cancer J Clin 68:7–30CrossRef
2.
Wan Q, Deng YS, Lei Q et al (2019) Differentiating between malignant and benign solid solitary pulmonary lesions: are intravoxel incoherent motion and diffusion kurtosis imaging superior to conventional diffusion-weighted imaging? Eur Radiol 29:1607–1615CrossRef
3.
Meier-Schroers M, Homsi R, Gieseke J, Schild HH, Thomas D (2019) Lung cancer screening with MRI: evaluation of MRI for lung cancer screening by comparison of LDCT- and MRI-derived lung-RADS categories in the first two screening rounds. Eur Radiol 29:898–905CrossRef
4.
Brea TP, Ravina AR, Villamor JMC, Gomez AG, de Alegria AM, Valdes L (2019) Use of magnetic resonance imaging for N-staging in patients with non-small cell lung cancer. A systematic review. Arch Bronconeumol 55:9–16CrossRef
5.
Guan HX, Pan YY, Wang YJ, Tang DZ, Zhou SC, Xia LM (2018) Comparison of various parameters of DWI in distinguishing solitary pulmonary nodules. Curr Med Sci 38:920–924CrossRef
6.
7.
Yuan M, Zhang YD, Zhu C et al (2016) Comparison of intravoxel incoherent motion diffusion-weighted MR imaging with dynamic contrast-enhanced MRI for differentiating lung cancer from benign solitary pulmonary lesions. J Magn Reson Imaging 43:669–679CrossRef
8.
Le Bihan D, Iima M (2015) Diffusion magnetic resonance imaging: what water tells us about biological tissues. PLoS Biol 13:e1002203CrossRef
9.
Shen G, Hu S, Deng H, Kuang A (2016) Performance of DWI in the nodal characterization and assessment of lung cancer: a meta-analysis. AJR Am J Roentgenol 206:283–290CrossRef
10.
Shen G, Jia Z, Deng H (2016) Apparent diffusion coefficient values of diffusion-weighted imaging for distinguishing focal pulmonary lesions and characterizing the subtype of lung cancer: a meta-analysis. Eur Radiol 26:556–566CrossRef
11.
Bi WL, Hosny A, Schabath MB et al (2019) Artificial intelligence in cancer imaging: clinical challenges and applications. CA Cancer J Clin 69:127–157PubMedPubMedCentral
12.
van Griethuysen JJM, Fedorov A, Parmar C et al (2017) Computational radiomics system to decode the radiographic phenotype. Cancer Res 77:e104–e107CrossRef
13.
Kniep HC, Madesta F, Schneider T et al (2019) Radiomics of brain MRI: utility in prediction of metastatic tumor type. Radiology 290:479–487CrossRef
14.
Lin M, Chen W, Zhao M et al (2018) Prostate lesion delineation from multiparametric magnetic resonance imaging based on locality alignment discriminant analysis. Med Phys 45:4607–4618CrossRef
15.
Yang R, Wu J, Sun L et al (2019) Radiomics of small renal masses on multiphasic CT: accuracy of machine learning-based classification models for the differentiation of renal cell carcinoma and angiomyolipoma without visible fat. Eur Radiol. https://​doi.​org/​10.​1007/​s00330-019-06384-5
16.
Yin P, Mao N, Zhao C et al (2019) Comparison of radiomics machine-learning classifiers and feature selection for differentiation of sacral chordoma and sacral giant cell tumour based on 3D computed tomography features. Eur Radiol 29:1841–1847CrossRef
17.
Maniruzzaman M, Jahanur Rahman M, Ahammed B et al (2019) Statistical characterization and classification of colon microarray gene expression data using multiple machine learning paradigms. Comput Methods Programs Biomed 176:173–193CrossRef
18.
19.
Zhang X, Yan LF, Hu YC et al (2017) Optimizing a machine learning based glioma grading system using multi-parametric MRI histogram and texture features. Oncotarget 8:47816–47830CrossRef
20.
21.
Peng Y, Jiang Y, Antic T, Giger ML, Eggener SE, Oto A (2014) Validation of quantitative analysis of multiparametric prostate MR images for prostate cancer detection and aggressiveness assessment: a cross-imager study. Radiology 271:461–471CrossRef
22.
Peng Y, Jiang Y, Yang C et al (2013) Quantitative analysis of multiparametric prostate MR images: differentiation between prostate cancer and normal tissue and correlation with Gleason score--a computer-aided diagnosis development study. Radiology 267:787–796CrossRef
23.
Chatterjee S, Dey D, Munshi S (2019) Integration of morphological preprocessing and fractal based feature extraction with recursive feature elimination for skin lesion types classification. Comput Methods Programs Biomed 178:201–218CrossRef
24.
Fan M, Liu Z, Xie S et al (2019) Integration of dynamic contrast-enhanced magnetic resonance imaging and T2-weighted imaging radiomic features by a canonical correlation analysis-based feature fusion method to predict histological grade in ductal breast carcinoma. Phys Med Biol. https://​doi.​org/​10.​1088/​1361-6560/​ab3fd3
25.
Liu Y, Shi H, Huang S et al (2019) Early prediction of acute xerostomia during radiation therapy for nasopharyngeal cancer based on delta radiomics from CT images. Quant Imaging Med Surg 9:1288–1302CrossRef
26.
Shen TX, Liu L, Li WH et al (2019) CT imaging-based histogram features for prediction of EGFR mutation status of bone metastases in patients with primary lung adenocarcinoma. Cancer Imaging 19:34CrossRef
27.
28.
29.
Garapati SS, Hadjiiski L, Cha KH et al (2017) Urinary bladder cancer staging in CT urography using machine learning. Med Phys 44:5814–5823CrossRef
30.
Chen L, Pan X, Zhang YH et al (2019) Primary tumor site specificity is preserved in patient-derived tumor xenograft models. Front Genet 10:738CrossRef
31.
Chen X, Zargari A, Hollingsworth AB, Liu H, Zheng B, Qiu Y (2019) Applying a new quantitative image analysis scheme based on global mammographic features to assist diagnosis of breast cancer. Comput Methods Programs Biomed 179:104995CrossRef
32.
Geetha R, Sivasubramanian S, Kaliappan M, Vimal S, Annamalai S (2019) Cervical cancer identification with synthetic minority oversampling technique and PCA analysis using random forest classifier. J Med Syst 43:286CrossRef
33.
Chen CH, Chang CK, Tu CY et al (2018) Radiomic features analysis in computed tomography images of lung nodule classification. PLoS One 13:e0192002CrossRef
34.
Choi W, Oh JH, Riyahi S et al (2018) Radiomics analysis of pulmonary nodules in low-dose CT for early detection of lung cancer. Med Phys 45:1537–1549CrossRef
35.
Wu W, Parmar C, Grossmann P et al (2016) Exploratory study to identify radiomics classifiers for lung cancer histology. Front Oncol 6:71PubMedPubMedCentral
36.
Mei D, Luo Y, Wang Y, Gong J (2018) CT texture analysis of lung adenocarcinoma: can radiomic features be surrogate biomarkers for EGFR mutation statuses. Cancer Imaging 18:52CrossRef
37.
Coroller TP, Agrawal V, Narayan V et al (2016) Radiomic phenotype features predict pathological response in non-small cell lung cancer. Radiother Oncol 119:480–486CrossRef
38.
Gillies RJ, Kinahan PE, Hricak H (2016) Radiomics: images are more than pictures, they are data. Radiology 278:563–577CrossRef
Metadata
Title
Classification of pulmonary lesion based on multiparametric MRI: utility of radiomics and comparison of machine learning methods
Authors
Xinhui Wang
Qi Wan
Houjin Chen
Yanfeng Li
Xinchun Li
Publication date
01-08-2020
Publisher
Springer Berlin Heidelberg
Published in
European Radiology / Issue 8/2020
Print ISSN: 0938-7994
Electronic ISSN: 1432-1084
DOI
https://doi.org/10.1007/s00330-020-06768-y

Other articles of this Issue 8/2020

European Radiology 8/2020 Go to the issue

Correction

Correction to: CT compared to MRI for functional evaluation of the right ventricle: a systematic review and meta-analysis

Contrast Media

The anterior eye chamber: entry of the natural excretion pathway of gadolinium contrast agents?

Musculoskeletal

CT texture analysis of acetabular subchondral bone can discriminate between normal and cam-positive hips

Cardiac

Association between myocardial hypoxia and fibrosis in hypertrophic cardiomyopathy: analysis by T2* BOLD and T1 mapping MRI

Urogenital

MRI of female genital tract congenital anomalies: European Society of Urogenital Radiology (ESUR) guidelines

Interventional

Radiomics of cholangiocarcinoma on pretreatment CT can identify patients who would best respond to radioembolisation