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

Wang, Xinhui; Wan, Qing; Chen, Houjin; Li, Yanfeng; Li, Xinchun

doi:10.1007/s00330-020-06768-y

Cited by 43 publications

(35 citation statements)

References 37 publications

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“…The LR classifier also showed the best performance in recognizing HRC patients in this study. Combinations of multiple sequences can yield better performance than single sequences, which was also confirmed in previous reports 16 . Although clinical information did not improve the two‐sequence models' performance in this study, since we only used patient age and visually assessed the MRI pattern as additional clinical information, the actual influence of more detailed clinical information on performance remains unclear, and studies with more clinical information are needed to address this question.…”

Section: Discussionsupporting

confidence: 80%

Prediction of High‐Risk Cytogenetic Status in Multiple Myeloma Based on Magnetic Resonance Imaging: Utility of Radiomics and Comparison of Machine Learning Methods

Liu

Zeng

Guo

et al. 2021

Magnetic Resonance Imaging

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Background Radiomics has shown promising results in the diagnosis, efficacy, and prognostic assessments of multiple myeloma (MM). However, little evidence exists on the utility of radiomics in predicting a high‐risk cytogenetic (HRC) status in MM. Purpose To develop and test a magnetic resonance imaging (MRI)‐based radiomics model for predicting an HRC status in MM patients. Study Type Retrospective. Population Eighty‐nine MM patients (HRC [n: 37] and non‐HRC [n: 52]). Field Strength/Sequence A 3.0 T; fast spin‐echo (FSE): T1‐weighted image (T1WI) and fat‐suppression T2WI (FS‐T2WI). Assessment Overall, 1409 radiomics features were extracted from each volume of interest drawn by radiologists. Three sequential feature selection steps—variance threshold, SelectKBest, and least absolute shrinkage selection operator—were repeated 10 times with 5‐fold cross‐validation. Radiomics models were constructed with the top three frequency features of T1WI/T2WI/two‐sequence MRI (T1WI and FS‐T2WI). Radiomics models, clinical data (age and visually assessed MRI pattern), or radiomics combined with clinical data were used with six classifiers to distinguish between HRC and non‐HRC statuses. Six classifiers used were support vector machine, random forest, logistic regression (LR), decision tree, k‐nearest neighbor, and XGBoost. Model performance was evaluated with area under the curve (AUC) values. Statistical Tests Mann–Whitney U‐test, Chi‐squared test, Z test, and DeLong method. Results The LR classifier performed better than the other classifiers based on different data (AUC: 0.65–0.82; P < 0.05). The two‐sequence MRI models performed better than the other data models using different classifiers (AUC: 0.68–0.82; P < 0.05). Thus, the LR two‐sequence model yielded the best performance (AUC: 0.82 ± 0.02; sensitivity: 84.1%; specificity: 68.1%; accuracy: 74.7%; P < 0.05). Conclusion The LR‐based machine learning method appears superior to other classifier methods for assessing HRC in MM. Radiomics features based on two‐sequence MRI showed good performance in differentiating HRC and non‐HRC statuses in MM. Evidence Level 3 Technical Efficacy Stage 2

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Section: Discussionsupporting

confidence: 80%

Prediction of High‐Risk Cytogenetic Status in Multiple Myeloma Based on Magnetic Resonance Imaging: Utility of Radiomics and Comparison of Machine Learning Methods

Liu

Zeng

Guo

et al. 2021

Magnetic Resonance Imaging

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“…In clinical practice, it is little known about the radiomics features based on MRI modality, mainly T2WI and DWI sequences. Using RFE with SVM, the joint model of T1WI, T2WI, and ADC showed the highest performance with AUC of 0.88 in classification of pulmonary lesions [ 33 ]. Besides, radiomics signatures extracted from ADC, DWI, T2WI can be used for predicting EGFR mutation in patients with lung adenocarcinoma [ 28 ].…”

Section: Discussionmentioning

confidence: 99%

“…Besides, radiomics signatures extracted from ADC, DWI, T2WI can be used for predicting EGFR mutation in patients with lung adenocarcinoma [ 28 ]. Compared to DWI or IVIM [ 33 ], native T1-mapping, obtained in a single breath-holding, had almost no artifacts, deformation and location shift, which is save-timing and more favorable to ensure stability and repeatability of radiomics features. And as far as we know, our team was the first to investigate the value of native T1-mapping radiomics features in differentiating malignant from benign lesions.…”

Section: Discussionmentioning

confidence: 99%

Preliminary study of 3 T-MRI native T1-mapping radiomics in differential diagnosis of non-calcified solid pulmonary nodules/masses

Yan

Shen

et al. 2021

Cancer Cell Int

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Background Cumulative CT radiation damage was positively correlated with increased tumor risks. Although it has recently been known that non-radiation MRI is alternative for pulmonary imaging. There is little known about the value of MRI T1-mapping in the diagnosis of pulmonary nodules. This article aimed to investigate the value of native T1-mapping-based radiomics features in differential diagnosis of pulmonary lesions. Methods 73 patients underwent 3 T-MRI examination in this prospective study. The 99 pulmonary lesions on native T1-mapping images were segmented twice by one radiologist at indicated time points utilizing the in-house semi-automated software, followed by extraction of radiomics features. The inter-class correlation coefficient (ICC) was used for analyzing intra-observer’s agreement. Dimensionality reduction and feature selection were performed via univariate analysis, and least absolute shrinkage and selection operator (LASSO) analysis. Then, the binary logical regression (LR), support vector machine (SVM) and decision tree classifiers with the input of optimal features were selected for differentiating malignant from benign lesions. The receiver operative characteristics (ROC) curve, area under the curve (AUC), sensitivity, specificity and accuracy were calculated. Z-test was used to compare differences among AUCs. Results 107 features were obtained, of them, 19.5% (n = 21) had relatively good reliability (ICC ≥ 0.6). The remained 5 features (3 GLCM, 1 GLSZM and 1 shape features) by dimensionality reduction were useful. The AUC of LR was 0.82(95%CI: 0.67–0.98), with sensitivity, specificity and accuracy of 70%, 85% and 80%. The AUC of SVM was 0.82(95%CI: 0.67–0.98), with sensitivity, specificity and accuracy of 70, 85 and 80%. The AUC of decision tree was 0.69(95%CI: 0.49–0.87), with sensitivity, specificity and accuracy of 50, 85 and 73.3%. Conclusions The LR and SVM models using native T1-mapping-based radiomics features can differentiate pulmonary malignant from benign lesions, especially for uncertain nodules requiring long-term follow-ups.

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“…Some investigators, on the other hand, observed that the accuracy of the radiomics model was close to radiologists when contrast CT was used [52]. In addition, MRI radiomics also demonstrated strong success (AUC = 0.88) in the differentiation of lung malignancies and benign lesions [53]. A follow-up scan is a recommended method for the management of accidental pulmonary nodules.…”

Section: Applications Of Structural Radiomic Features In Lung Cancermentioning

confidence: 99%

Structural and functional radiomics for lung cancer

Jochems

Ibrahim

et al. 2021

Eur J Nucl Med Mol Imaging

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Introduction Lung cancer ranks second in new cancer cases and first in cancer-related deaths worldwide. Precision medicine is working on altering treatment approaches and improving outcomes in this patient population. Radiological images are a powerful non-invasive tool in the screening and diagnosis of early-stage lung cancer, treatment strategy support, prognosis assessment, and follow-up for advanced-stage lung cancer. Recently, radiological features have evolved from solely semantic to include (handcrafted and deep) radiomic features. Radiomics entails the extraction and analysis of quantitative features from medical images using mathematical and machine learning methods to explore possible ties with biology and clinical outcomes. Methods Here, we outline the latest applications of both structural and functional radiomics in detection, diagnosis, and prediction of pathology, gene mutation, treatment strategy, follow-up, treatment response evaluation, and prognosis in the field of lung cancer. Conclusion The major drawbacks of radiomics are the lack of large datasets with high-quality data, standardization of methodology, the black-box nature of deep learning, and reproducibility. The prerequisite for the clinical implementation of radiomics is that these limitations are addressed. Future directions include a safer and more efficient model-training mode, merge multi-modality images, and combined multi-discipline or multi-omics to form “Medomics.”

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

Cited by 43 publications

References 37 publications

Prediction of High‐Risk Cytogenetic Status in Multiple Myeloma Based on Magnetic Resonance Imaging: Utility of Radiomics and Comparison of Machine Learning Methods

Prediction of High‐Risk Cytogenetic Status in Multiple Myeloma Based on Magnetic Resonance Imaging: Utility of Radiomics and Comparison of Machine Learning Methods

Preliminary study of 3 T-MRI native T1-mapping radiomics in differential diagnosis of non-calcified solid pulmonary nodules/masses

Structural and functional radiomics for lung cancer

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