MRI-Based Machine Learning in Differentiation Between Benign and Malignant Breast Lesions

Zhao, Yanjie; Chen, Rong; Zhang, Ting; Chen, Chaoyue; Muhetaer, Muhelisa; Huang, Jen‐Wei; Xu, Yan; Ma, Xuelei

doi:10.3389/fonc.2021.552634

Cited by 10 publications

(9 citation statements)

References 37 publications

(42 reference statements)

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“…The AUC values of the proposed model in the training and the validation sets were 0.979 and 0.977, respectively, indicating good reliability and diagnostic performance. In addition, the diagnostic performance of the model was better than what was reported in previous research 34–36 . Although the model of this study shows a high sensitivity of 90% and a high specificity of 96%, however, we know that for our research—that is, in a specific diagnostic environment, 10% of our research results have missed diagnosis of cancer, which is a rather high value.…”

Section: Discussioncontrasting

confidence: 72%

“…In addition, the diagnostic performance of the model was better than what was reported in previous research. [34][35][36] Although the model of this study shows a high sensitivity of 90% and a high specificity of 96%, however, we know that for our research-that is, in a specific diagnostic environment, 10% of our research results have missed diagnosis of cancer, which is a rather high value. It will bring many problems to the clinical screening process.…”

Section: Discussionmentioning

confidence: 59%

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Ultrasound Radiomics‐Based Logistic Regression Model to Differentiate Between Benign and Malignant Breast Nodules

Shi¹,

An²,

Li³

2022

J of Ultrasound Medicine

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Objectives-To explore the potential value of ultrasound radiomics in differentiating between benign and malignant breast nodules by extracting the radiomic features of two-dimensional (2D) grayscale ultrasound images and establishing a logistic regression model. Methods-The clinical and ultrasound data of 1000 female patients (500 pathologically benign patients, 500 pathologically malignant patients) who underwent breast ultrasound examinations at our hospital were retrospectively analyzed. The cases were randomly divided into training and validation sets at a ratio of 7:3. Once the region of interest (ROI) of the lesion was manually contoured, Spearman's rank correlation, least absolute shrinkage and selection operator (LASSO) regression, and the Boruta algorithm were adopted to determine optimal features and establish a logistic regression classification model. The performance of the model was assessed using the area under the receiver operating characteristic curve (AUC), and calibration and decision curves (DCA).Results-Eight ultrasound radiomic features were selected to establish the model. The AUC values of the model were 0.979 and 0.977 in the training and validation sets, respectively (P = .0029), indicating good discriminative ability in both datasets. Additionally, the calibration and DCA suggested that the model's calibration efficiency and clinical application value were both superior. Conclusions-The proposed logistic regression model based on 2D grayscale ultrasound images could facilitate differential diagnosis of benign and malignant breast nodules. The model, which was constructed using ultrasound radiomic features identified in this study, demonstrated good diagnostic performance and could be useful in helping clinicians formulate individualized treatment plans for patients.

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Section: Discussioncontrasting

confidence: 72%

Section: Discussionmentioning

confidence: 59%

Ultrasound Radiomics‐Based Logistic Regression Model to Differentiate Between Benign and Malignant Breast Nodules

Shi¹,

An²,

Li³

2022

J of Ultrasound Medicine

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“…The ML model has been used to differentiate benign and malignant breast nodules in some studies (31)(32)(33). However, few studies used traditional ML methods combined with imaging features to predict NSLN metastasis in patients with breast cancer.…”

Section: Discussionmentioning

confidence: 99%

A machine-learning approach based on multiparametric MRI to identify the risk of non-sentinel lymph node metastasis in patients with early-stage breast cancer

Yu,

Li,

Xie

et al. 2023

Acta Radiol

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Background It has been reported that patients with early breast cancer with 1–2 positive sentinel lymph nodes have a lower risk of non-sentinel lymph node (NSLN) metastasis and cannot benefit from axillary lymph node dissection. Purpose To develop the potential of machine learning based on multiparametric magnetic resonance imaging (MRI) and clinical factors for predicting the risk of NSLN metastasis in breast cancer. Material and Methods This retrospective study included 144 patients with 1–2 positive sentinel lymph node breast cancer. Multiparametric MRI morphologic findings and the detailed demographical characteristics of the primary tumor and axillary lymph node were extracted. The logistic regression, support vector classification, extreme gradient boosting, and random forest algorithm models were established to predict the risk of NSLN metastasis. The prediction efficiency of a machine-learning–based model was evaluated. Finally, the relative importance of each input variable was analyzed for the best model. Results Of the 144 patients, 80 (55.6%) developed NSLN metastasis. A total of 24 imaging features and 14 clinicopathological features were analyzed. The extreme gradient boosting algorithm had the strongest prediction efficiency with an area under curve of 0.881 and 0.781 in the training set and test set, respectively. Five main factors for the metastasis of NSLN were found, including histological grade, cortical thickness, fatty hilum, short axis of lymph node, and age. Conclusion The machine-learning model incorporating multiparametric MRI features and clinical factors can predict NSLN metastasis with high accuracy for breast cancer and provide predictive information for clinical protocol.

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“…Finally, the gradient boosting decision tree (GBDT) algorithm was applied to determine the top-ranked and most valuable features for predicting the tumor response. GBDT was proposed as a tree-based algorithm based on a greedy strategy (called gradient boosting) that evaluates the importance of a texture feature through the time it used as branching point for the tree [19] . After those steps, TR, PTR (3 mm), PTR (5 mm), and PTR (10 mm) radiomics models were separately established using logistic regression algorithm with 5-fold cross-validation.…”

Section: Feature Selection and Radiomics Model Constructionmentioning

confidence: 99%

“…This approach would capture the three-dimensional (3D) information of the entire tumor and has been proved effective in characterizing the heterogeneity of the tumor by acting as a whole tumor virtual biopsy [17,18] . Numerous studies have demonstrated that radiomics-based models effectively identify the diagnosis and pathological characteristics or predict therapeutic e cacy and prognosis of cancer patients for clinical decisionmaking [19][20][21][22] . Recently, there has been increasing interest in evaluating radiomics patterns of the region surrounding the visible tumor [20,23,24] .…”

Section: Introductionmentioning

confidence: 99%

Intratumoral and Peritumoral Radiomics Based on Contrast-enhanced MRI for Preoperatively Predicting Treatment Response of Transarterial Chemoembolization in Hepatocellular Carcinoma

Zhao

Zhang

Wang

et al. 2022

Preprint

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Background Noninvasive and precise methods to estimate treatment response and identify hepatocellular carcinoma (HCC) patients who could benefit from transarterial chemoembolization (TACE) are urgently required. The present study aimed to investigate the ability of intratumoral and peritumoral radiomics based on contrast-enhanced magnetic resonance imaging (CE-MRI) to preoperatively predict tumor response to TACE in HCC patients. Methods This retrospective study involved 138 HCC patients (objective response, n = 73 vs. non-response, n = 65) who were divided into the training (n = 96) and validation (n = 42) cohorts. Total 1206 radiomics features were extracted from arterial, venous, and delayed phases images. Radiomics models on intratumoral region (TR) and peritumoral region (PTR) (3 mm, 5 mm, and 10 mm) were established using logistic regression. Three integrated radiomics models, including intratumoral and peritumoral region (T-PTR) (3 mm), T-PTR (5 mm), and T-PTR (10 mm) models, were constructed by using TR and PTR radiomics scores. A clinical-radiological model and a combined model incorporating the optimal radiomics score and selected clinical-radiological predictors were constructed, and the combined model was presented as a nomogram. The discrimination, calibration, and clinical utilities were evaluated by receiver operating characteristic curve, calibration curve, and decision curve analysis, respectively. Results The (T-PTR) (3 mm) radiomics model demonstrated the best performance among all radiomics models with an area under the curve (AUC) of 0.911 (95% confidence interval(CI), 0.825–0.975) in the validation cohort. The (T-PTR) (3 mm) radiomics score, alkaline phosphatase, tumor size, and satellite nodule were combined to construct a combined nomogram. The combined nomogram outperformed the clinical-radiological model with the AUCs of 0.918 (95%CI, 0.831–0.986) and 0.782 (95%CI, 0.660–0.902) and achieved good calibration capability and clinical utility. Conclusions CE-MRI-based intratumoral and peritumoral radiomics approach can provide an effective tool for the precise and individualized estimation of treatment response for HCC patients treated with TACE.

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MRI-Based Machine Learning in Differentiation Between Benign and Malignant Breast Lesions

Cited by 10 publications

References 37 publications

Ultrasound Radiomics‐Based Logistic Regression Model to Differentiate Between Benign and Malignant Breast Nodules

Ultrasound Radiomics‐Based Logistic Regression Model to Differentiate Between Benign and Malignant Breast Nodules

A machine-learning approach based on multiparametric MRI to identify the risk of non-sentinel lymph node metastasis in patients with early-stage breast cancer

Intratumoral and Peritumoral Radiomics Based on Contrast-enhanced MRI for Preoperatively Predicting Treatment Response of Transarterial Chemoembolization in Hepatocellular Carcinoma

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