Deep learning prediction of axillary lymph node status using ultrasound images

Ha, Richard; Nemer, John S.; Sun, Shen-Su; Mutasa, Simukayi; Sun, Mary; Siddique, Maham; Jambawalikar, Sachin; Liu, Mike; Desperito, Elise

doi:10.1016/j.compbiomed.2022.105250

Cited by 29 publications

(19 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Sun et al. reported an AUC of 0.72 (SD ± 0.08) in predicting ALNM from US images using a deep learning technique in the test dataset ( 28 ). However, practical applications of AI are still being implemented in daily radiology practice ( 29 ).…”

Section: Discussionmentioning

confidence: 99%

Elaboration and Validation of a Nomogram Based on Axillary Ultrasound and Tumor Clinicopathological Features to Predict Axillary Lymph Node Metastasis in Patients With Breast Cancer

Liu

Wang

et al. 2022

Front. Oncol.

View full text Add to dashboard Cite

BackgroundThis study aimed at constructing a nomogram to predict axillary lymph node metastasis (ALNM) based on axillary ultrasound and tumor clinicopathological features.MethodsA retrospective analysis of 281 patients with pathologically confirmed breast cancer was performed between January 2015 and March 2018. All patients were randomly divided into a training cohort (n = 197) and a validation cohort (n = 84). Univariate and multivariable logistic regression analyses were performed to identify the clinically important predictors of ALNM when developin1 g the nomogram. The area under the curve (AUC), calibration plots, and decision curve analysis (DCA) were used to assess the discrimination, calibration, and clinical utility of the nomogram.ResultsIn univariate and multivariate analyses, lymphovascular invasion (LVI), axillary lymph node (ALN) cortex thickness, and an obliterated ALN fatty hilum were identified as independent predictors and integrated to develop a nomogram for predicting ALNM. The nomogram showed favorable sensitivity for ALNM with AUCs of 0.87 (95% confidence interval (CI), 0.81–0.92) and 0.84 (95% CI, 0.73–0.92) in the training and validation cohorts, respectively. The calibration plots of the nomogram showed good agreement between the nomogram prediction and actual ALNM diagnosis (P > 0.05). Decision curve analysis (DCA) revealed the net benefit of the nomogram.ConclusionsThis study developed a nomogram based on three daily available clinical parameters, with good accuracy and clinical utility, which may help the radiologist in decision-making for ultrasound-guided fine needle aspiration cytology/biopsy (US-FNAC/B) according to the nomogram score.

show abstract

Section: Discussionmentioning

confidence: 99%

Elaboration and Validation of a Nomogram Based on Axillary Ultrasound and Tumor Clinicopathological Features to Predict Axillary Lymph Node Metastasis in Patients With Breast Cancer

Liu

Wang

et al. 2022

Front. Oncol.

View full text Add to dashboard Cite

show abstract

“…Thus, complementary methods may be used to increase sensitivity and for a complete preoperative assessment of the axillary nodal status [ 27 , 28 , 29 ]. Utility of deep learning models for prediction of axillary lymph node metastasis has already been proven in ultrasound images and also in breast MRI [ 30 , 31 , 32 , 33 ]. In these studies, deep learning models were used for binary classification to determine if axillary lymph node metastasis was present.…”

Section: Discussionmentioning

confidence: 99%

Detecting Abnormal Axillary Lymph Nodes on Mammograms Using a Deep Convolutional Neural Network

et al. 2022

View full text Add to dashboard Cite

The purpose of this study was to determine the feasibility of a deep convolutional neural network (dCNN) to accurately detect abnormal axillary lymph nodes on mammograms. In this retrospective study, 107 mammographic images in mediolateral oblique projection from 74 patients were labeled to three classes: (1) “breast tissue”, (2) “benign lymph nodes”, and (3) “suspicious lymph nodes”. Following data preprocessing, a dCNN model was trained and validated with 5385 images. Subsequently, the trained dCNN was tested on a “real-world” dataset and the performance compared to human readers. For visualization, colored probability maps of the classification were calculated using a sliding window approach. The accuracy was 98% for the training and 99% for the validation set. Confusion matrices of the “real-world” dataset for the three classes with radiological reports as ground truth yielded an accuracy of 98.51% for breast tissue, 98.63% for benign lymph nodes, and 95.96% for suspicious lymph nodes. Intraclass correlation of the dCNN and the readers was excellent (0.98), and Kappa values were nearly perfect (0.93–0.97). The colormaps successfully detected abnormal lymph nodes with excellent image quality. In this proof-of-principle study in a small patient cohort from a single institution, we found that deep convolutional networks can be trained with high accuracy and reliability to detect abnormal axillary lymph nodes on mammograms.

show abstract

“…A comparison between CNNs and traditional ML methods (random forests) was made in another study performed on 479 breast cancer patients with 2395 breast ultrasound images [ 37 ]. Moreover, the study focused on different parts of US images, where intratumoral, peritumoral and combined regions were utilized to train and evaluate the models.…”

Section: Studies Using Radiomics For Breast Cancer Lymph Node Predictionmentioning

confidence: 99%

“…Moreover, the study focused on different parts of US images, where intratumoral, peritumoral and combined regions were utilized to train and evaluate the models. CNNs performed better than random forests in all modalities ( p < 0.05), and combining intratumoral and peritumoral regions yielded the best result (AUC = 0.912 [0.834–99.0]) ( Table 2 ) [ 37 ]. Confidence intervals were reported, but it was not stated by which method they were obtained.…”

Section: Studies Using Radiomics For Breast Cancer Lymph Node Predictionmentioning

confidence: 99%

The Role of AI in Breast Cancer Lymph Node Classification: A Comprehensive Review

Vrdoljak

Krešo

Kumrić

et al. 2023

Cancers

View full text Add to dashboard Cite

Breast cancer is a significant health issue affecting women worldwide, and accurately detecting lymph node metastasis is critical in determining treatment and prognosis. While traditional diagnostic methods have limitations and complications, artificial intelligence (AI) techniques such as machine learning (ML) and deep learning (DL) offer promising solutions for improving and supplementing diagnostic procedures. Current research has explored state-of-the-art DL models for breast cancer lymph node classification from radiological images, achieving high performances (AUC: 0.71–0.99). AI models trained on clinicopathological features also show promise in predicting metastasis status (AUC: 0.74–0.77), whereas multimodal (radiomics + clinicopathological features) models combine the best from both approaches and also achieve good results (AUC: 0.82–0.94). Once properly validated, such models could greatly improve cancer care, especially in areas with limited medical resources. This comprehensive review aims to compile knowledge about state-of-the-art AI models used for breast cancer lymph node metastasis detection, discusses proper validation techniques and potential pitfalls and limitations, and presents future directions and best practices to achieve high usability in real-world clinical settings.

show abstract

Deep learning prediction of axillary lymph node status using ultrasound images

Cited by 29 publications

References 32 publications

Elaboration and Validation of a Nomogram Based on Axillary Ultrasound and Tumor Clinicopathological Features to Predict Axillary Lymph Node Metastasis in Patients With Breast Cancer

Elaboration and Validation of a Nomogram Based on Axillary Ultrasound and Tumor Clinicopathological Features to Predict Axillary Lymph Node Metastasis in Patients With Breast Cancer

Detecting Abnormal Axillary Lymph Nodes on Mammograms Using a Deep Convolutional Neural Network

The Role of AI in Breast Cancer Lymph Node Classification: A Comprehensive Review

Contact Info

Product

Resources

About