Multi-modality radiomics nomogram based on DCE-MRI and ultrasound images for benign and malignant breast lesion classification

Liu, Xinmiao; Zhang, Ji; Zhou, Jiejie; He, Yun; Xu, Ye; Zhang, Zhenhua; Cao, Guoquan; Miao, Haiwei; Chen, Zhongwei; Zhao, Youfan; Jin, Xiance; Wang, Meihao

doi:10.3389/fonc.2022.992509

Cited by 10 publications

(9 citation statements)

References 51 publications

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“…Xinmiao Liu et al. found that combining MRI, B‐mode ultrasound (BMUS), and strain elastography (SE) images improved the accuracy of predicting breast lesion classification to an AUC of 0.941, compared with that of MRI (0.877), BMUS (0.819) and SE (0.880) alone 34 . These studies highlighted the importance of using multi‐modality imaging in radiomic feature analysis to improve the accuracy of predicting treatment responses in patients with head‐and‐neck cancer.…”

Section: Discussionmentioning

confidence: 99%

A novel loss function to reproduce texture features for deep learning‐based MRI‐to‐CT synthesis

Yuan,

Liu,

Wei

et al. 2023

Medical Physics

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BackgroundStudies on computed tomography (CT) synthesis based on magnetic resonance imaging (MRI) have mainly focused on pixel‐wise consistency, but the texture features of regions of interest (ROIs) have not received appropriate attention.PurposeThis study aimed to propose a novel loss function to reproduce texture features of ROIs and pixel‐wise consistency for deep learning‐based MRI‐to‐CT synthesis. The method was expected to assist the multi‐modality studies for radiomics.MethodsThe study retrospectively enrolled 127 patients with nasopharyngeal carcinoma. CT and MRI images were collected for each patient, and then rigidly registered as pre‐procession. We proposed a gray‐level co‐occurrence matrix (GLCM)‐based loss function to improve the reproducibility of texture features. This novel loss function could be embedded into the present deep learning‐based framework for image synthesis. In this study, a typical image synthesis model was selected as the baseline, which contained a Unet trained mean square error (MSE) loss function. We embedded the proposed loss function and designed experiments to supervise different ROIs to prove its effectiveness. The concordance correlation coefficient (CCC) of the GLCM feature was employed to evaluate the reproducibility of GLCM features, which are typical texture features. Besides, we used a publicly available dataset of brain tumors to verify our loss function.ResultsCompared with the baseline, the proposed method improved the pixel‐wise image quality metrics (MAE: 107.5 to 106.8 HU; SSIM: 0.9728 to 0.9730). CCC values of the GLCM features in GTVnx were significantly improved from 0.78 ± 0.12 to 0.82 ± 0.11 (p < 0.05 for paired t‐test). Generally, > 90% (22/24) of the GLCM‐based features were improved compared with the baseline, where the Informational Measure of Correlation feature was improved the most (CCC: 0.74 to 0.83). For the public dataset, the loss function also shows its effectiveness. With our proposed loss function added, the ability to reproduce texture features was improved in the ROIs.ConclusionsThe proposed method reproduced texture features for MRI‐to‐CT synthesis, which would benefit radiomics studies based on image multi‐modality synthesis.

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Section: Discussionmentioning

confidence: 99%

A novel loss function to reproduce texture features for deep learning‐based MRI‐to‐CT synthesis

Yuan,

Liu,

Wei

et al. 2023

Medical Physics

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“…In addition, the radiomic logistic regression model could noninvasively predict the likelihood of malignancy of breast lesions amenable to breast biopsy. Although in the medical literature many studies are devoted to assessing the possibility of differentiating between benign and malignant lesions for MRI data [ 7 , 8 , 9 , 10 , 11 , 12 , 13 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 ], there are currently no public and widely accepted radiomics-based guidelines for the pre-operative prediction of malignancy likelihood in patients amenable to MR-VABB. Some recent studies have paved the way to a radiomics-driven exploratory research phase [ 33 , 34 ], and much effort should be made to realize translation into clinical settings.…”

Section: Discussionmentioning

confidence: 99%

Freehand 1.5T MR-Guided Vacuum-Assisted Breast Biopsy (MR-VABB): Contribution of Radiomics to the Differentiation of Benign and Malignant Lesions

et al. 2023

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Radiomics and artificial intelligence have been increasingly applied in breast MRI. However, the advantages of using radiomics to evaluate lesions amenable to MR-guided vacuum-assisted breast biopsy (MR-VABB) are unclear. This study includes patients scheduled for MR-VABB, corresponding to subjects with MRI-only visible lesions, i.e., with a negative second-look ultrasound. The first acquisition of the multiphase dynamic contrast-enhanced MRI (DCE-MRI) sequence was selected for image segmentation and radiomics analysis. A total of 80 patients with a mean age of 55.8 years ± 11.8 (SD) were included. The dataset was then split into a training set (50 patients) and a validation set (30 patients). Twenty out of the 30 patients with a positive histology for cancer were in the training set, while the remaining 10 patients with a positive histology were included in the test set. Logistic regression on the training set provided seven features with significant p values (<0.05): (1) ‘AverageIntensity’, (2) ‘Autocorrelation’, (3) ‘Contrast’, (4) ‘Compactness’, (5) ‘StandardDeviation’, (6) ‘MeanAbsoluteDeviation’ and (7) ‘InterquartileRange’. AUC values of 0.86 (95% C.I. 0.73–0.94) for the training set and 0.73 (95% C.I. 0.54–0.87) for the test set were obtained for the radiomics model. Radiological evaluation of the same lesions scheduled for MR-VABB had AUC values of 0.42 (95% C.I. 0.28–0.57) for the training set and 0.4 (0.23–0.59) for the test set. In this study, a radiomics logistic regression model applied to DCE-MRI images increased the diagnostic accuracy of standard radiological evaluation of MRI suspicious findings in women scheduled for MR-VABB. Confirming this performance in large multicentric trials would imply that using radiomics in the assessment of patients scheduled for MR-VABB has the potential to reduce the number of biopsies, in suspicious breast lesions where MR-VABB is required, with clear advantages for patients and healthcare resources.

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“…Additionally, Liu et al. employed radiomics features extracted from SE images for breast cancer prediction, yielding a Radscore with an AUC of 0.866 in the test set ( 18 ). Besides, Ma et al.…”

Section: Discussionmentioning

confidence: 99%

A clinical-radiomics nomogram based on multimodal ultrasound for predicting the malignancy risk in solid hypoechoic breast lesions

Shiyan,

Liqing,

Yueqiong

et al. 2023

Front. Oncol.

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BackgroundIn routine clinical examinations, solid hypoechoic breast lesions are frequently encountered, but accurately distinguishing them poses a challenge. This study proposed a clinical-radiomics nomogram based on multimodal ultrasound that enhances the diagnostic accuracy for solid hypoechoic breast lesions.MethodThis retrospective study analyzed ultrasound strain elastography (SE) and automated breast volume scanner images (ABVS) of 423 solid hypoechoic breast lesions from 423 female patients in our hospital between August 2019 and May 2022. They were assigned to the training (n=296) and validation (n=127) groups in a 7:3 ratio by generating random numbers. Radiomics features were extracted and screened from ABVS and SE images, followed by the calculation of the radiomics score (Radscore) based on these features. Subsequently, a nomogram was constructed through multivariate logistic regression to assess the malignancy risk in breast lesions by combining Radscore with Breast Imaging Reporting and Data System (BI-RADS) scores and clinical risk factors associated with breast malignant lesions. The diagnostic performance, calibration performance, and clinical usefulness of the nomogram were assessed by the area under the curve (AUC) of the receiver operating characteristic curve, the calibration curve, and the decision analysis curve, respectively.ResultsThe diagnostic performance of the nomogram is significantly superior to that of both the clinical diagnostic model (BI-RADS model) and the multimodal radiomics model (SE+ABVS radiomics model) in training (AUC: 0.972 vs 0.930 vs 0.941) and validation group (AUC:0.964 vs 0.916 vs 0.933). In addition, the nomogram also exhibited a favorable goodness-of-fit and could lead to greater net benefits for patients.ConclusionThe nomogram enables a more effective assessment of the malignancy risk of solid hypoechoic breast lesions; therefore, it can serve as a new and efficient diagnostic tool for clinical diagnosis.

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Multi-modality radiomics nomogram based on DCE-MRI and ultrasound images for benign and malignant breast lesion classification

Cited by 10 publications

References 51 publications

A novel loss function to reproduce texture features for deep learning‐based MRI‐to‐CT synthesis

A novel loss function to reproduce texture features for deep learning‐based MRI‐to‐CT synthesis

Freehand 1.5T MR-Guided Vacuum-Assisted Breast Biopsy (MR-VABB): Contribution of Radiomics to the Differentiation of Benign and Malignant Lesions

A clinical-radiomics nomogram based on multimodal ultrasound for predicting the malignancy risk in solid hypoechoic breast lesions

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