Deep Learning vs. Radiomics for Predicting Axillary Lymph Node Metastasis of Breast Cancer Using Ultrasound Images: Don't Forget the Peritumoral Region

Abstract

Objective: Axillary lymph node (ALN) metastasis status is important in guiding treatment in breast cancer. The aims were to assess how deep convolutional neural network (CNN) performed compared with radiomics analysis in predicting ALN metastasis using breast ultrasound, and to investigate the value of both intratumoral and peritumoral regions in ALN metastasis prediction. Methods:We retrospectively enrolled 479 breast cancer patients with 2,395 breast ultrasound images. Based on the intratumoral, peritumoral, and combined intra-and peritumoral regions, three CNNs were built using DenseNet, and three radiomics models were built using random forest, respectively. By combining the molecular subtype, another three CNNs and three radiomics models were built. All models were built on training cohort (343 patients 1,715 images) and evaluated on testing cohort (136 patients 680 images) with ROC analysis. Another prospective cohort of 16 patients was enrolled to further test the models.Results: AUCs of image-only CNNs in both training/testing cohorts were 0.957/0.912 for combined region, 0.944/0.775 for peritumoral region, and 0.937/0.748 for intratumoral region, which were numerically higher than their corresponding radiomics models with AUCs of 0.940/0.886, 0.920/0.724, and 0.913/0.693. The overall performance of image-molecular CNNs in terms of AUCs on training/testing cohorts slightly increased to 0.962/0.933, 0.951/0.813, and 0.931/0.794, respectively. AUCs of both CNNs and radiomics models built on combined region were significantly better than those on either intratumoral or peritumoral region on the testing cohort (p < 0.05). In the prospective study, the CNN model built on combined region achieved the highest AUC of 0.95 among all image-only models. Sun et al. Ultrasound-CNN Predicted Breast Cancer MetastasisConclusions: CNNs showed numerically better overall performance compared with radiomics models in predicting ALN metastasis in breast cancer. For both CNNs and radiomics models, combining intratumoral, and peritumoral regions achieved significantly better performance.

Assessment of Intratumoral and Peritumoral Computed Tomography Radiomics for Predicting Pathological Complete Response to Neoadjuvant Chemoradiation in Patients With Esophageal Squamous Cell Carcinoma

“…There is emerging evidence that predictive models should not be limited to mere tumor areas. Recent studies 24 , 25 , 26 , 37 , 38 , 39 , 40 have shown that the surrounding regions may provide complementary information on tumor heterogeneity in other cancers. Here we proposed a noninvasive, CT-based radiomics model with favorable predictive value using both intratumoral and peritumoral radiomics features to predict the possibility of pCR in patients with ESCC before receiving nCRT.…”

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

“…Previous studies have compared the performance of deep learning and radiomics in differentiating benign and malignant breast lesions (13,15), predicting lymph node metastases of breast cancer (14), identifying of spinal metastases originated from the lung and other cancers (16), predicting of survival of patients with high-grade gliomas (17), and predicting the (24) found that their DNN model was 80% accurate in predicting complete response after neoadjuvant chemoradiotherapy in locally advanced rectal cancer, which was better than LR and SVM models. Due to the rarity of primary sacral tumors, only a few previous studies have identified sacral tumor types using machine learning methods (1,5,10).…”

“…Previous studies have compared the performance of deep learning and radiomics in differentiating benign and malignant breast lesions ( 13 , 15 ), predicting lymph node metastases of breast cancer ( 14 ), identifying of spinal metastases originated from the lung and other cancers ( 16 ), predicting of survival of patients with high-grade gliomas ( 17 ), and predicting the invasiveness risk of Stage-I lung adenocarcinomas ( 18 ). Dong et al.…”

“…Deep neural network (DNN), as a deep architecture, has shown excellent performance in classification tasks and is increasingly being used in various areas of cancer research (11,12). Early studies on the application of deep learning to the detection or classification of lesions have shown that it performs better than traditional techniques and even better than radiologists on some tasks (13)(14)(15)(16)(17)(18). Ren et al (19) proposed a novel manifold regularized classification DNN to enhance CT image-based lung nodule classification.…”

Technology trends and applications of deep learning in ultrasonography: image quality enhancement, diagnostic support, and improving workflow efficiency

“…Features obtained from deep learning-based radiomics were combined with clinical parameters such as patient age, size of the lesion, Breast Imaging-Reporting and Data System (BI-RADS) category, tumor type, estrogen receptor (ER) status, progesterone receptor status, human epidermal growth factor receptor 2 (HER2), Ki-67 proliferation index and others. Sun et al [61] included additional molecular subtype information like HER2 positive, triple-negative.…”