Preoperative Prediction of Lymph Node Metastasis from Clinical DCE MRI of the Primary Breast Tumor Using a 4D CNN

Nguyen, Son; Polat, Dogan; Karbasi, Paniz; Moser, Daniel; Wang, Liqiang; Hulsey, Keith; Çobanoğlu, Murat Can; Dogan, Basak E.; Montillo, Albert

doi:10.1007/978-3-030-59713-9_32

Cited by 12 publications

(30 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…After the title and abstract evaluation, 268 citations were omitted because they did not meet the inclusion criteria. After revision, 22 articles were excluded, leaving 13 articles for inclusion in the meta-analysis (11)(12)(13)(14)(15)(22)(23)(24)(25)(26)(27)(28)(29).…”

Section: Literature Searchmentioning

confidence: 99%

“…Of these models, the ML algorithm comprised different types of logistic regression models (12,15,22,23,28), convolutional neural network models (26,27), multiple classifier systems (11,13,14,24,25), and support vector machine models (29). In 13 studies, different phase analysis methods of DCE were used, including the strongest enhanced phase, the second postcontrast phase, the first postcontrast images, and twophase images in 4 (12,22,23,29), 3 (11,13,28), 3 (14,25,27), and 3 (15,24,26) studies, respectively. 3 T scanners were used in 8 studies (11-13, 22, 24, 25, 28, 29), and 1.5 T MR was used in 5 studies (14,15,23,26,27).…”

Section: Characteristics Of the Included Studiesmentioning

confidence: 99%

“…In 13 studies, different phase analysis methods of DCE were used, including the strongest enhanced phase, the second postcontrast phase, the first postcontrast images, and twophase images in 4 (12,22,23,29), 3 (11,13,28), 3 (14,25,27), and 3 (15,24,26) studies, respectively. 3 T scanners were used in 8 studies (11-13, 22, 24, 25, 28, 29), and 1.5 T MR was used in 5 studies (14,15,23,26,27). Seven studies (13-15, 23, 24, 28, 29) employed SLNB or ALND to serve as the reference standard, while the remaining 3 studies (12,25,26) were based on ALND.…”

Section: Characteristics Of the Included Studiesmentioning

confidence: 99%

See 2 more Smart Citations

The Diagnostic Performance of Machine Learning-Based Radiomics of DCE-MRI in Predicting Axillary Lymph Node Metastasis in Breast Cancer: A Meta-Analysis

Zhang

Zhe

et al. 2022

Front. Oncol.

View full text Add to dashboard Cite

ObjectiveThe aim of this study was to perform a meta‐analysis to evaluate the diagnostic performance of machine learning(ML)-based radiomics of dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) DCE-MRI in predicting axillary lymph node metastasis (ALNM) and sentinel lymph node metastasis(SLNM) in breast cancer.MethodsEnglish and Chinese databases were searched for original studies. The Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) and Radiomics Quality Score (RQS) were used to assess the methodological quality of the included studies. The pooled sensitivity, specificity, diagnostic odds ratio (DOR), and area under the curve (AUC) were used to summarize the diagnostic accuracy. Spearman’s correlation coefficient and subgroup analysis were performed to investigate the cause of the heterogeneity.ResultsThirteen studies (1618 participants) were included in this meta-analysis. The pooled sensitivity, specificity, DOR, and AUC with 95% confidence intervals were 0.82 (0.75, 0.87), 0.83 (0.74, 0.89), 21.56 (10.60, 43.85), and 0.89 (0.86, 0.91), respectively. The meta-analysis showed significant heterogeneity among the included studies. There was no threshold effect in the test. The result of subgroup analysis showed that ML, 3.0 T, area of interest comprising the ALN, being manually drawn, and including ALNs and combined sentinel lymph node (SLN)s and ALNs groups could slightly improve diagnostic performance compared to deep learning, 1.5 T, area of interest comprising the breast tumor, semiautomatic scanning, and the SLN, respectively.ConclusionsML-based radiomics of DCE-MRI has the potential to predict ALNM and SLNM accurately. The heterogeneity of the ALNM and SLNM diagnoses included between the studies is a major limitation.

show abstract

Section: Literature Searchmentioning

confidence: 99%

Section: Characteristics Of the Included Studiesmentioning

confidence: 99%

Section: Characteristics Of the Included Studiesmentioning

confidence: 99%

See 1 more Smart Citation

The Diagnostic Performance of Machine Learning-Based Radiomics of DCE-MRI in Predicting Axillary Lymph Node Metastasis in Breast Cancer: A Meta-Analysis

Zhang

Zhe

et al. 2022

Front. Oncol.

View full text Add to dashboard Cite

show abstract

“…Many nomograms and ANN models have been developed for predicting nodal metastasis, or the lack thereof, with (23-31) or without imaging (32, 33). The ANN model that this study aims to validate is a promising tool in the clinic because the input variables are routinely available, no extra imaging besides clinical work-up (mammography and axillary ultrasound) is necessary, and the web interface is user friendly.…”

Section: Discussionmentioning

confidence: 99%

“…The secondary endpoint is a false-negative rate of a maximum of 10% for the prediction of N0 vs. N+, compared with the histopathological assessment of excised sentinel lymph node(s). Many nomograms and ANN models have been developed for predicting nodal metastasis, or the lack thereof, with (23)(24)(25)(26)(27)(28)(29)(30)(31) or without imaging (32,33). The ANN model that this study aims to validate is a promising tool in the clinic because the input variables are routinely available, no extra imaging besides clinical work-up (mammography and axillary ultrasound) is necessary, and the web interface is user friendly.…”

Section: Secondary Endpointmentioning

confidence: 99%

The NILS study protocol - a retrospective validation study of a preoperative decision-making tool for non-invasive lymph node staging in women with primary breast cancer [ISRCTN14341750]

Skarping

Dihge

Bendahl

et al. 2022

Preprint

View full text Add to dashboard Cite

Background Routine preoperative axillary ultrasonography has proven insufficient for detecting low-burden nodal metastatic deposits. For the majority of newly diagnosed breast cancer patients presenting with clinical T1-T2 N0 disease, the standard axillary staging by sentinel lymph node biopsy is not therapeutic. The pilot non-invasive lymph node staging (NILS) artificial neural network (ANN) model to predict nodal status was published in 2019. The aim of the current study is to assess the performance measures of the model for the prediction of healthy lymph nodes in clinically N0 breast cancer patients at two breast cancer centers in Sweden. Methods This bicenter, observational, retrospective study has been designed to validate the NILS prediction model for nodal status using preoperatively collected clinicopathological and radiological data. A web-based implementation of the nodal status classifier has been developed and will be used in this study, resulting in an estimated probability of healthy lymph nodes for each study participant. Our primary endpoint is to report on the performance of the NILS prediction model to distinguish between healthy and metastatic lymph nodes (discrimination, N0 vs. N+) and compare the observed and predicted event rates of benign axillary nodal status (calibration). Discussion Internationally, there are numerous artificial intelligence projects involving non-invasive identification of N0 breast cancer. Here, we present a robust validation study based on external cohorts of our ANN model. Although validation is necessary to show generalizability, it is often overlooked. If the accuracy and discrimination reach a satisfactory level, our prediction tool can be implemented to assist medical professionals and breast cancer patients in shared decision-making on omitting sentinel node biopsy in patients predicted to be node-negative. In future, this may potentially save healthcare resources and reduce costs and adverse side effects. In addition, our study might prompt future studies of nodal metastases of malignancies in other organs, and thus might have implications beyond breast cancer. Trial registration This study has been prospectively registered in the ISRCTN registry, identification number: 14341750

show abstract

Preoperative Prediction of Axillary Lymph Node Metastasis in Breast Cancer Using CNN Based on Multiparametric MRI

Wang

Sun

et al. 2022

Magnetic Resonance Imaging

View full text Add to dashboard Cite

BackgroundMultiparametric magnetic resonance imaging (MRI) is widely used in breast cancer screening. Accurate prediction of the axillary lymph nodes metastasis (ALNM) is essential for breast cancer surgery and treatment. However, there is no mature and effective discerning method for ALNM based on multiparametric MRI.PurposeTo evaluate the ALNM using T1‐weighted imaging (T1WI), T2‐weighted imaging (T2WI), and diffusion‐weighted imaging (DWI) sequences, respectively, and construct a quantitative ALNM discerning model of integrated multiparametric MRI.Study TypeRetrospective.PopulationThree‐hundred forty‐eight breast cancer patients, 163 with ALNM (99.39% females), and 185 without ALNM (100% females). The dataset was randomly divided into the training set (315 cases) and the testing set (33 cases).Field Strength/Sequence1.5 T; T1WI (VIBRANT), T2WI (FSE), and DWI (echo planar imaging [EPI]).AssessmentThe lesion region of interest images were cropped and sent to a pretrained ResNet50 network. Then, the results of different sequences were sent to a classifier for ensemble learning to construct the ALNM model of multiparametric MRI.Statistical TestsPerformance indicators such as accuracy, the receiver operating characteristic (ROC) curve, and the area under the ROC curve (AUC) were calculated. Student's t‐test, chi‐square test, Fisher's exact test, and Delong test were performed, and P < 0.05 was considered statistically significant.ResultsT2WI performed the best among the three sequences, and achieved the accuracy and AUC of 0.933/0.989 in the testing set. Compared to T1WI with the accuracy and AUC of 0.691/0.806, the increase is significant. While compared to DWI with the accuracy and AUC of 0.800/0.910, the improvement is not significant (P = 0.126). After integrating three sequences, the accuracy and AUC improved to 0.970 and 0.996.Data ConclusionT2WI performed better than DWI and T1WI in discerning ALNM in this breast cancer dataset. The proposed quantitative model of integrated multiparametric MRI could effectively help the ALNM diagnosis.Level of Evidence1Technical Efficacy Stage2

show abstract

Preoperative Prediction of Lymph Node Metastasis from Clinical DCE MRI of the Primary Breast Tumor Using a 4D CNN

Cited by 12 publications

References 9 publications

The Diagnostic Performance of Machine Learning-Based Radiomics of DCE-MRI in Predicting Axillary Lymph Node Metastasis in Breast Cancer: A Meta-Analysis

The Diagnostic Performance of Machine Learning-Based Radiomics of DCE-MRI in Predicting Axillary Lymph Node Metastasis in Breast Cancer: A Meta-Analysis

The NILS study protocol - a retrospective validation study of a preoperative decision-making tool for non-invasive lymph node staging in women with primary breast cancer [ISRCTN14341750]

Preoperative Prediction of Axillary Lymph Node Metastasis in Breast Cancer Using CNN Based on Multiparametric MRI

Contact Info

Product

Resources

About