Added Value of Radiomics on Mammography for Breast Cancer Diagnosis: A Feasibility Study

Mao, Ning; Yin, Ping; Wang, Qinglin; Liu, Meijie; Dong, Jianjun; Zhang, Xuexi; Xie, Haizhu; Hong, Nan

doi:10.1016/j.jacr.2018.09.041

Cited by 67 publications

(45 citation statements)

References 36 publications

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“…Similarly, Lang et al (16) found that the accuracy of radiomics analysis and convolutional neural network (CNN) was similar in the identification of spinal metastases originated from the lung and other tumors. LR is one of the most commonly used algorithms in radiomics analysis and has been proved to be effective (27)(28)(29)(30). Despite nomogram's visualization, it has limited power for future big data era.…”

Section: Discussionmentioning

confidence: 99%

Machine and Deep Learning Based Radiomics Models for Preoperative Prediction of Benign and Malignant Sacral Tumors

et al. 2020

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Purpose: To assess the performance of deep neural network (DNN) and machine learning based radiomics on 3D computed tomography (CT) and clinical characteristics to predict benign or malignant sacral tumors. Materials and methods: This single-center retrospective analysis included 459 patients with pathologically proven sacral tumors. After semi-automatic segmentation, 1,316 hand-crafted radiomics features of each patient were extracted. All models were built on training set (321 patients) and tested on validation set (138 patients). A DNN model and four machine learning classifiers (logistic regression [LR], random forest [RF], support vector machine [SVM] and k-nearest neighbor [KNN]) based on CT features and clinical characteristics were built, respectively. The area under the receiver operating characteristic curve (AUC) and accuracy (ACC) were used to evaluate different models. Results: In total, 459 patients (255 males, 204 females; mean age of 42.1 ± 17.8 years, range 4-82 years) were enrolled in this study, including 206 cases of benign tumor and 253 cases of malignant tumor. The sex, age and tumor size had significant differences between the benign tumors and malignant tumors (c 2 sex = 10.854, Z age = −6.616, Z size = 2.843, P < 0.05). The radscore, sex, and age were important indicators for differentiating benign and malignant sacral tumors (odds ratio [OR]1 = 2.492, OR2 = 2.236, OR3 = 1.037, P < 0.01). Among the four clinical-radiomics models (RMs), clinical-LR had the best performance in the validation set (AUC = 0.84, ACC = 0.81). The clinical-DNN model also achieved a high performance (an AUC of 0.83 and an ACC of 0.76 in the validation set) in identifying benign and malignant sacral tumors. Conclusions: Both the clinical-LR and clinical-DNN models would have a high impact on assisting radiologists in their clinical diagnosis of sacral tumors.

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Section: Discussionmentioning

confidence: 99%

Machine and Deep Learning Based Radiomics Models for Preoperative Prediction of Benign and Malignant Sacral Tumors

et al. 2020

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“…Radiomics can extract massive image features; transform medical images into high-dimensional and exploitable data; and use artificial intelligence to combine medical images, genes, and huge clinical data to establish a model that supports clinical decision-making and quantify tumor heterogeneity (5)(6)(7)(8)(9). This method has good clinical prospects (9)(10)(11). The combined analysis of multiple features including clinical ones is the most promising approach, especially for the clinical management of tumors (12)(13)(14)(15)(16).…”

Section: Introductionmentioning

confidence: 99%

Radiomics Nomogram of DCE-MRI for the Prediction of Axillary Lymph Node Metastasis in Breast Cancer

et al. 2020

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This study aimed to establish and validate a radiomics nomogram based on dynamic contrast-enhanced (DCE)-MRI for predicting axillary lymph node (ALN) metastasis in breast cancer. Method: This retrospective study included 296 patients with breast cancer who underwent DCE-MRI examinations between July 2017 and June 2018. A total of 396 radiomics features were extracted from primary tumor. In addition, the least absolute shrinkage and selection operator (LASSO) algorithm was used to select the features. Radiomics signature and independent risk factors were incorporated to build a radiomics nomogram model. Calibration and receiver operator characteristic (ROC) curves were used to confirm the performance of the nomogram in the training and validation sets. The clinical usefulness of the nomogram was evaluated by decision curve analysis (DCA). Results: The radiomics signature consisted of three ALN-status-related features, and the nomogram model included the radiomics signature and the MR-reported lymph node (LN) status. The model showed good calibration and discrimination with areas under the ROC curve (AUC) of 0.92 [95% confidence interval (CI), 0.87-0.97] in the training set and 0.90 (95% CI, 0.85-0.95) in the validation set. In the MR-reported LN-negative (cN0) subgroup, the nomogram model also exhibited favorable discriminatory ability (AUC, 0.79; 95% CI, 0.70-0.87). DCA findings indicated that the nomogram model was clinically useful. Conclusions: The MRI-based radiomics nomogram model could be used to preoperatively predict the ALN metastasis of breast cancer.

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“…[ 34 ] Zhang et al produced a radiomics signature built with 11 features that outperformed conventional clinical variables in predicting local recurrence-free survival in patients with non-metastatic T4 NPC. [ 33 ] In a study of 120 NPC patients, Wang et al showed that the radiomics model could predict early response to induction chemotherapy.…”

Section: Discussionmentioning

confidence: 99%

“…[30,31] Magnetic resonance imaging (MRI) is the imaging of choice in the diagnosis and local staging in NPC due to its superior soft tissue contrast and allows for accurate delineation of target volumes for purposes of radiotherapy. [32][33][34][35][36][37] Whilst some research has been carried out on the application of radiomics in nasopharyngeal cancer, an approach that utilizes MRI radiomics as a predictive signature for intra-tumoral radio-resistance has not yet been developed. Comprehensive image analysis using radiomics that can identify radio-resistant tumor sub-volumes from pre-treatment MRI scans could guide individualized radiation therapy by suggesting target volumes in which a higher dose of radiation is needed for better tumor control.…”

Section: Introductionmentioning

confidence: 99%

Exploring MRI based radiomics analysis of intratumoral spatial heterogeneity in locally advanced nasopharyngeal carcinoma treated with intensity modulated radiotherapy

et al. 2020

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Background We hypothesized that spatial heterogeneity exists between recurrent and non-recurrent regions within a tumor. The aim of this study was to determine if there is a difference between radiomics features derived from recurrent versus non recurrent regions within the tumor based on pre-treatment MRI. Methods A total of 14 T4NxM0 NPC patients with histologically proven "in field" recurrence in the post nasal space following curative intent IMRT were included in this study. Pretreatment MRI were co-registered with MRI at the time of recurrence for the delineation of gross tumor volume at diagnosis(GTV) and at recurrence(GTVr). A total of 7 histogram features and 40 texture features were computed from the recurrent(GTVr) and non-recurrent region(GTV-GTVr). Paired ttests and Wilcoxon signed-rank tests were carried out on the 47 quantified radiomics features. Results A total of 7 features were significantly different between recurrent and non-recurrent regions. Other than the variance from intensity-based histogram, the remaining six significant features were either from the gray-level size zone matrix (GLSZM) or the neighbourhood gray-tone difference matrix (NGTDM). Conclusions The radiomic features extracted from pre-treatment MRI can potentially reflect the difference between recurrent and non-recurrent regions within a tumor and has a potential role in pre-treatment identification of intra-tumoral radio-resistance for selective dose escalation.

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Added Value of Radiomics on Mammography for Breast Cancer Diagnosis: A Feasibility Study

Cited by 67 publications

References 36 publications

Machine and Deep Learning Based Radiomics Models for Preoperative Prediction of Benign and Malignant Sacral Tumors

Machine and Deep Learning Based Radiomics Models for Preoperative Prediction of Benign and Malignant Sacral Tumors

Radiomics Nomogram of DCE-MRI for the Prediction of Axillary Lymph Node Metastasis in Breast Cancer

Exploring MRI based radiomics analysis of intratumoral spatial heterogeneity in locally advanced nasopharyngeal carcinoma treated with intensity modulated radiotherapy

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