scholarly journals Predicting Axillary Lymph Node Metastasis in Early Breast Cancer Using Deep Learning on Primary Tumor Biopsy Slides

2021 ◽  
Vol 11 ◽  
Author(s):  
Feng Xu ◽  
Chuang Zhu ◽  
Wenqi Tang ◽  
Ying Wang ◽  
Yu Zhang ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Deep Learning ◽  
Lymph Node ◽  
Early Breast Cancer ◽  
Primary Tumor ◽  
Axillary Lymph Node ◽  
Axillary Lymph Node Metastasis ◽  
Multiple Instance Learning ◽  
Axillary Lymph ◽  
Tumor Biopsy

ObjectivesTo develop and validate a deep learning (DL)-based primary tumor biopsy signature for predicting axillary lymph node (ALN) metastasis preoperatively in early breast cancer (EBC) patients with clinically negative ALN.MethodsA total of 1,058 EBC patients with pathologically confirmed ALN status were enrolled from May 2010 to August 2020. A DL core-needle biopsy (DL-CNB) model was built on the attention-based multiple instance-learning (AMIL) framework to predict ALN status utilizing the DL features, which were extracted from the cancer areas of digitized whole-slide images (WSIs) of breast CNB specimens annotated by two pathologists. Accuracy, sensitivity, specificity, receiver operating characteristic (ROC) curves, and areas under the ROC curve (AUCs) were analyzed to evaluate our model.ResultsThe best-performing DL-CNB model with VGG16_BN as the feature extractor achieved an AUC of 0.816 (95% confidence interval (CI): 0.758, 0.865) in predicting positive ALN metastasis in the independent test cohort. Furthermore, our model incorporating the clinical data, which was called DL-CNB+C, yielded the best accuracy of 0.831 (95%CI: 0.775, 0.878), especially for patients younger than 50 years (AUC: 0.918, 95%CI: 0.825, 0.971). The interpretation of DL-CNB model showed that the top signatures most predictive of ALN metastasis were characterized by the nucleus features including density (p = 0.015), circumference (p = 0.009), circularity (p = 0.010), and orientation (p = 0.012).ConclusionOur study provides a novel DL-based biomarker on primary tumor CNB slides to predict the metastatic status of ALN preoperatively for patients with EBC.

scholarly journals Predicting Axillary Lymph Node Metastasis in Early Breast Cancer Using Deep Learning on Primary Tumor Biopsy Slides

2021 ◽  
Author(s):  
Feng Xu ◽  
Chuang Zhu ◽  
Wenqi Tang ◽  
Ying Wang ◽  
Yu Zhang ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Deep Learning ◽  
Lymph Node ◽  
Receiver Operating Characteristic ◽  
Early Breast Cancer ◽  
Primary Tumor ◽  
Axillary Lymph Node ◽  
Operating Characteristic ◽  
Axillary Lymph ◽  
Tumor Biopsy

Objectives: To develop and validate a deep learning (DL) based primary tumor biopsy signature for predicting axillary lymph node (ALN) metastasis preoperatively in early breast cancer (EBC) patients with clinically negative ALN. Methods: A total of 1058 EBC patients with pathologically confirmed ALN status were enrolled from May 2010 to August 2020. A deep learning core-needle biopsy (DL-CNB) model was built on the attention based multiple instance learning (AMIL) framework to predict ALN status utilizing the deep learning features, which were extracted from the cancer areas of digitized whole-slide images (WSIs) of breast CNB specimens annotated by two pathologists. Accuracy, sensitivity, specificity, receiver operating characteristic (ROC) curves, and areas under the receiver operating characteristic curve (AUCs) were analyzed to evaluate our model. Results: The best performing DL-CNB model with VGG16_BN as the feature extractor achieved an AUC of 0.816 (95% confidence interval (CI): 0.758, 0.865) in predicting positive ALN metastasis in the independent test cohort. Furthermore, our model incorporating the clinical data, which was called DL-CNB+C, yielded the best accuracy of 0.831 (95%CI: 0.775, 0.878), especially for patients younger than 50 years (AUC: 0.918, 95%CI: 0.825, 0.971). The interpretation of DL-CNB model showed that the top signatures most predictive of ALN metastasis were characterized by the nuclei features including density (p=0.015), circumference (p=0.009), circularity (p=0.010), and orientation (p=0.012). Conclusion: Our study provides a novel deep learning-based biomarker on primary tumor CNB slides to predict the metastatic status of ALN preoperatively for patients with early breast cancer.

Developing and validating deep learning-based heterogeneous model to improve diagnostic performance of ultrasound elastography for axillary lymph node metastasis of early breast cancer.

2021 ◽  
Vol 39 (15_suppl) ◽  
pp. e12583-e12583
Author(s):  
Jian Li ◽  
Cai Nian ◽  
Xie Ze-Ming ◽  
Zhou Jingwen ◽  
Huang Kemin
Keyword(s):  
Breast Cancer ◽  
Deep Learning ◽  
Lymph Node ◽  
Early Breast Cancer ◽  
Predictive Value ◽  
Axillary Lymph Node ◽  
Diagnostic Performance ◽  
Ultrasound Elastography ◽  
Axillary Lymph ◽  
Heterogeneous Model

e12583 Background: To improve the performance of ultrasound (US) for diagnosing metastatic axillary lymph node (ALN), machine learning was used to reveal the inherently medical hints from ultrasonic images and assist pre-treatment evaluation of ALN for patients with early breast cancer. Methods: A total of 214 eligible patients with 220 breast lesions, from whom 220 target ALNs of ipsilateral axillae underwent ultrasound elastography (UE), were prospectively recruited. Based on feature extraction and fusion of B-mode and shear wave elastography (SWE) images of 140 target ALNs using radiomics and deep learning, with reference to the axillary pathological evaluation from training cohort, a proposed deep learning-based heterogeneous model (DLHM) was established and then validated by a collection of B-mode and SWE images of 80 target ALNs from testing cohort. Performance was compared between UE based on radiological criteria and DLHM in terms of areas under the receiver operating characteristics curve (AUC), sensitivity, specificity, accuracy, negative predictive value, and positive predictive value for diagnosing ALN metastasis. Results: DLHM achieved an excellent performance for both training and validation cohorts. In the prospectively testing cohort, DLHM demonstrated the best diagnostic performance with AUC of 0.911(95% confidence interval [CI]: 0.826, 0.963) in identifying metastatic ALN, which significantly outperformed UE in terms of AUC (0.707, 95% CI: 0.595, 0.804, P<0.001). Conclusions: DLHM provides an effective, accurate and non-invasive preoperative method for assisting the diagnosis of ALN metastasis in patients with early breast cancer.[Table: see text]

Construction and Validation of a Risk Prediction Model for Clinical Axillary Lymph Node Metastasis in T1–2 Breast Cancer

2021 ◽  
Author(s):  
Na Luo ◽  
Ying Wen ◽  
Dengjie Ouyang ◽  
Qitong Chen ◽  
Liyun Zeng ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Lymph Node ◽  
Lymph Node Metastasis ◽  
Prediction Model ◽  
Primary Tumor ◽  
Axillary Lymph Node ◽  
Axillary Lymph Node Metastasis ◽  
Diagnostic Model ◽  
Axillary Lymph ◽  
Node Metastasis

Abstract The current diagnostic technologies for assessing the axillary lymph node metastasis (ALNM) status accurately in breast cancer (BC) remain unsatisfactory. Here, we developed a diagnostic model for evaluating the ALNM status using a combination of mRNAs and the T stage of the primary tumor as a novel biomarker. We collected relevant information on T1-2 BC from public databases. An ALNM prediction model was developed by logistic regression based on the screened signatures and then internally and externally validated. Calibration curves and the area under the curve (AUC) were employed as performance metrics. The prognostic value and tumor immune infiltration of the model were also determined. An optimal diagnostic model was created using a combination of 11 mRNAs and T stage of the primary tumor and showed high discrimination, with AUCs of 0.828 and 0.746 in the training sets. AUCs of 0.671 and 0.783 were achieved in the internal validation cohorts. The mean external AUC value was 0.686 and ranged between 0.644 and 0.742. Moreover, the new model has good specificity in T1 and hormone receptor-negative/human epidermal growth factor receptor 2- negative (HR-/HER2-) BC and good sensitivity in T2 BC. In addition, the risk of ALNM and 11 mRNAs were correlated with the infiltration of M2 macrophages, as well as the prognosis of BC. This novel prediction model is a useful tool to identify the risk of ALNM in T1-2 BC patients, particularly given that it can be used to adjust surgical options in the future.

scholarly journals Clinical predictors of axillary lymph node metastasis in early breast cancer in Indian patients

2019 ◽  
Vol 7 (3) ◽  
pp. 739
Author(s):  
Raghunandan Gorantlu Chowdappa ◽  
Suhaildeen Kajamohideen
Keyword(s):  
Breast Cancer ◽  
Lymph Node ◽  
Positive Predictive Value ◽  
Early Breast Cancer ◽  
Predictive Value ◽  
Axillary Lymph Node ◽  
Primary Objective ◽  
Axillary Lymph Node Metastasis ◽  
Axillary Lymph ◽  
Clinical Predictors

Background: Surgical dissection is the accepted mode of staging the axilla in breast cancer. Proper prediction of axillary node positivity can help towards stratifying patients. The primary objective of the study was to assess the clinical factors influencing pathological axillary lymph node positivity in early carcinoma breast.Methods: This was a retrospective study, conducted at a tertiary cancer centre. Case records of all the patients with invasive breast cancer which are clinical T1 and T2 and either N0 or NI, from January 2011 to October 2014 were analysed. Clinical profile of the patient including age, BMI, comorbid, menstrual history, family history, symptoms, site of the lesion, size, single or multi centric origin were analysed.Results: Total of 608 patients of early breast cancer analysed of which 248 had pathological nodal positivity. The age group of 51 to 75 years, BMI ≥30, pre-menopausal patients had significant positive predictive value when compared to post-menopausal. Tumours in lower outer quadrant, central sector and multiple tumours also had positive predictive value. Clinical T2 when compared to clinical T1 stage and MRM when compared to BCS had significant positive predictive value.Conclusions: To conclude in present study age of the patient and clinical location of the tumour and surgery performed emerged as significant independent predictive factors of positive lymph node. Prospective studies are required to further prove the significance of these factors.

scholarly journals Construction and validation of a risk prediction model for clinical axillary lymph node metastasis in T1–2 breast cancer

2022 ◽  
Vol 12 (1) ◽  
Author(s):  
Na Luo ◽  
Ying Wen ◽  
Qiongyan Zou ◽  
Dengjie Ouyang ◽  
Qitong Chen ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Lymph Node ◽  
Lymph Node Metastasis ◽  
Prediction Model ◽  
Primary Tumor ◽  
Axillary Lymph Node ◽  
Axillary Lymph Node Metastasis ◽  
Diagnostic Model ◽  
Axillary Lymph ◽  
Node Metastasis

AbstractThe current diagnostic technologies for assessing the axillary lymph node metastasis (ALNM) status accurately in breast cancer (BC) remain unsatisfactory. Here, we developed a diagnostic model for evaluating the ALNM status using a combination of mRNAs and the T stage of the primary tumor as a novel biomarker. We collected relevant information on T1–2 BC from public databases. An ALNM prediction model was developed by logistic regression based on the screened signatures and then internally and externally validated. Calibration curves and the area under the curve (AUC) were employed as performance metrics. The prognostic value and tumor immune infiltration of the model were also determined. An optimal diagnostic model was created using a combination of 11 mRNAs and T stage of the primary tumor and showed high discrimination, with AUCs of 0.828 and 0.746 in the training sets. AUCs of 0.671 and 0.783 were achieved in the internal validation cohorts. The mean external AUC value was 0.686 and ranged between 0.644 and 0.742. Moreover, the new model has good specificity in T1 and hormone receptor-negative/human epidermal growth factor receptor 2- negative (HR−/HER2−) BC and good sensitivity in T2 BC. In addition, the risk of ALNM and 11 mRNAs were correlated with the infiltration of M2 macrophages, as well as the prognosis of BC. This novel prediction model is a useful tool to identify the risk of ALNM in T1–2 BC patients, particularly given that it can be used to adjust surgical options in the future.

scholarly journals Dedicated Axillary MRI-Based Radiomics Analysis for the Prediction of Axillary Lymph Node Metastasis in Breast Cancer

Cancers ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 757
Author(s):  
Sanaz Samiei ◽  
Renée W. Y. Granzier ◽  
Abdalla Ibrahim ◽  
Sergey Primakov ◽  
Marc B. I. Lobbes ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Lymph Node ◽  
Lymph Node Metastasis ◽  
Lymph Nodes ◽  
Axillary Lymph Node ◽  
Axillary Lymph Node Metastasis ◽  
Axillary Lymph Nodes ◽  
Axillary Lymph ◽  
Mr Images ◽  
Node Metastasis

Radiomics features may contribute to increased diagnostic performance of MRI in the prediction of axillary lymph node metastasis. The objective of the study was to predict preoperative axillary lymph node metastasis in breast cancer using clinical models and radiomics models based on T2-weighted (T2W) dedicated axillary MRI features with node-by-node analysis. From August 2012 until October 2014, all women who had undergone dedicated axillary 3.0T T2W MRI, followed by axillary surgery, were retrospectively identified, and available clinical data were collected. All axillary lymph nodes were manually delineated on the T2W MR images, and quantitative radiomics features were extracted from the delineated regions. Data were partitioned patient-wise to train 100 models using different splits for the training and validation cohorts to account for multiple lymph nodes per patient and class imbalance. Features were selected in the training cohorts using recursive feature elimination with repeated 5-fold cross-validation, followed by the development of random forest models. The performance of the models was assessed using the area under the curve (AUC). A total of 75 women (median age, 61 years; interquartile range, 51–68 years) with 511 axillary lymph nodes were included. On final pathology, 36 (7%) of the lymph nodes had metastasis. A total of 105 original radiomics features were extracted from the T2W MR images. Each cohort split resulted in a different number of lymph nodes in the training cohorts and a different set of selected features. Performance of the 100 clinical and radiomics models showed a wide range of AUC values between 0.41–0.74 and 0.48–0.89 in the training cohorts, respectively, and between 0.30–0.98 and 0.37–0.99 in the validation cohorts, respectively. With these results, it was not possible to obtain a final prediction model. Clinical characteristics and dedicated axillary MRI-based radiomics with node-by-node analysis did not contribute to the prediction of axillary lymph node metastasis in breast cancer based on data where variations in acquisition and reconstruction parameters were not addressed.

Sign in / Sign up

Export Citation Format

Share Document