Radiomics model based on shear-wave elastography in the assessment of axillary lymph node status in early-stage breast cancer

Zhao

Technol Cancer Res Treat

et al. 2023

Background: Axillary lymph node (ALN) metastatic load is very important in the diagnosis and treatment of breast cancer (BC). We aimed to construct a model for predicting ALN metastatic load using deep learning radiomics (DLR) techniques based on the preoperative ultrasound and clinicopathologic information of patients with stage T1-2 BC. Methods: Retrospective analysis was performed on 176 patients with pathologically confirmed BC in our hospital from February 2018 to April 2020. ALN metastases were divided into a low-load group (< 3 lymph node metastases) and a high-load group (≥ 3 lymph node metastases) according to pathological results. Pyradiomics and pre-trained ResNet50 were used to extract radiomics and deep learning features, respectively. Independent sample T-test, random forest recursive elimination, and Lasso were used to screen the features to construct the deep learning radiomics signature (DLRS). Based on single/multivariate logistic regression analysis results, a DLR nomogram (DLRN) model was constructed by combining valuable clinical features and DLRS. Results: The DLRS was composed of 3 radiomics features and 14 deep learning features and combined with the maximum diameter of lesions to construct the DLRN. The AUCs of the training and test sets were 0.900 (95% CI: 0.853-0.931) and 0.821 (95% CI: 0.769-0.868), respectively. The calibration curve and Hosmer–Lemeshow test confirmed that the DLRN model has a good consistency. The decision curve also confirmed its good clinical practicality. Conclusion: Ultrasound-based DLRN has an excellent performance in predicting ALN load in patients with BC.

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Prediction of Axillary Lymph Node Metastatic Load of Breast Cancer Based on Ultrasound Deep Learning Radiomics Nomogram

Zhao

Technol Cancer Res Treat

et al. 2023

“…Further research focused on validation will extend the generalized use of the nomogram. At last, studies had shown that elasticity was associated with ALN status [ 38 ], however, our study was a retrospective study and lacked data on elastography, more prospective studies may be needed in the future to further explore.…”

Section: Discussionmentioning

confidence: 99%

Ultrasonography and clinicopathological features of breast cancer in predicting axillary lymph node metastases

Xiong

Zuo

et al. 2022

BMC Cancer

Background Early identification of axillary lymph node metastasis (ALNM) in breast cancer (BC) is still a clinical difficulty. There is still no good method to replace sentinel lymph node biopsy (SLNB). The purpose of our study was to develop and validate a nomogram to predict the probability of ALNM preoperatively based on ultrasonography (US) and clinicopathological features of primary tumors. Methods From September 2019 to April 2022, the preoperative US) and clinicopathological data of 1076 T1-T2 BC patients underwent surgical treatment were collected. Patients were divided into a training set (875 patients from September 2019 to October 2021) and a validation set (201 patients from November 2021 to April 2022). Patients were divided into positive and negative axillary lymph node (ALN) group according pathology of axillary surgery. Compared the US and clinicopathological features between the two groups. The risk factors for ALNM were determined using multivariate logistic regression analysis, and a nomogram was constructed. AUC and calibration were used to assess its performance. Results By univariate and multivariate logistic regression analysis, age (p = 0.009), histologic grades (p = 0.000), molecular subtypes (p = 0.000), tumor location (p = 0.000), maximum diameter (p = 0.000), spiculated margin (p = 0.000) and distance from the skin (p = 0.000) were independent risk factors of ALNM. Then a nomogram was developed. The model was good discriminating with an AUC of 0.705 and 0.745 for the training and validation set, respectively. And the calibration curves demonstrated high agreement. However, in further predicting a heavy nodal disease burden (> 2 nodes), none of the variables were significant. Conclusion This nomogram based on the US and clinicopathological data can predict the presence of ALNM good in T1-T2 BC patients. But it cannot effectively predict a heavy nodal disease burden (> 2 nodes).

“…A metastatic ALN is commonly characterized by a cortical thickness of ≥2.5–3.0 mm ( 16 ), a lobulated cortex, loss of the hilum, a round shape, and an abnormal cortical blood flow ( 17 ). Moreover, Jiang et al ( 18 ) found that shear-wave elastography and ultrasound image features were associated with ALN metastasis. Also, the experimental results that were reported by Fu et al ( 19 ) indicated that magnetic resonance imaging (MRI) is worthy of clinical promotion and usage, based on its ability to diagnose the ALN metastasis of BC.…”

Section: Introductionmentioning

confidence: 99%

Development and validation of an ultrasonography and clinicopathological features-based nomogram for non-sentinel lymph node metastasis

Chen¹,

Dai²,

Chen³

et al. 2023

Gland Surg

Background: With the development of precise treatment for breast cancer, the current trend of clinical treatment aims to limit axillary surgery as much as possible. At present, there is an unmet need to predict the probability of patients with a low risk of non-sentinel lymph node (SLN) metastasis and determine whether the omission of axillary lymph node dissection (ALND) is appropriate. Methods: We retrospectively analyzed the data of patients with breast cancer who underwent sentinel lymph node biopsy (SLNB) and ALND. The patients were randomly assigned to training and validation sets. The associations between non-SLN metastasis (NSLNM) and ultrasonography and clinicopathological characteristics were assessed by multivariate logistic regression. Then, a nomogram model was constructed and validated using the calibration curve and the receiver operating characteristic curve. Results: Vascular infiltration, positive SLN number, negative SLN number, human epidermal growth factor receptor 2 (HER2) status, and lymph node shape were identified as independent predictive factors for positive NSLNM. The areas under the curve of the nomogram model to predict NSLNM were 0.793 and 0.780 in the training and validation sets, respectively, and P=0.161 and P=0.768 in the Hosmer-Lemeshow goodness of fit test, respectively. Conclusions: A nomogram model based on ultrasonography and clinicopathological features predicting NSLNM was established in our study, which is helpful for accurately assessing the risk of NSLNM in invasive breast cancer and providing evidence for individual surgical procedures involving axillary lymph nodes.