Prediction of Lymph Node Metastasis in Breast Cancer by Gene Expression and Clinicopathological Models: Development and Validation within a Population-Based Cohort

Dihge, Looket; Vallon‐Christersson, Johan; Hegardt, Cecilia; Saal, Lao H.; Häkkinen, Jari; Larsson, Christer; Ehinger, Anna; Loman, Niklas; Malmberg, Martin; Bendahl, Pär‐Ola; Borg, Åke; Staaf, Johan; Rydén, Lisa

doi:10.1158/1078-0432.ccr-19-0075

Cited by 40 publications

(34 citation statements)

References 54 publications

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“…Here, the best-performing DL-CNB model yielded satisfactory predictions with an AUC of 0.816, a SENS of 81.0%, and a SPEC of 70.9% on the test set, which had superior predictive capability compared with clinical data alone. Furthermore, unlike other combined models incorporating clinical data (7,9), the DL-CNB+C model slightly improved the ACC to 0.831, which showed that our results were mainly derived from the contribution of DL-CNB model. In addition, during the subgroup analysis stratified by patient's age, our DL-CNB+C model achieved an AUC of 0.918 for patients younger than 50 years, indicating that age was the critical factor in predicting ALN status.…”

Section: Discussioncontrasting

confidence: 58%

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

Zhu

Tang

et al. 2021

Preprint

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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.

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Section: Discussioncontrasting

confidence: 58%

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

Zhu

Tang

et al. 2021

Preprint

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“…A number of predictive models have been made based on ML algorithms. Several studies have reported effective ML-based models for the prediction of LNM in other carcinomas, such as breast cancer (10,11). It was reported that radiomics could be used to predict LNM by analyzing radiological images in NSCLC (12).…”

Section: Introductionmentioning

confidence: 99%

Preoperative Prediction of Lymph Node Metastasis in Patients With Early-T-Stage Non-small Cell Lung Cancer by Machine Learning Algorithms

Liu

Han

et al. 2020

Front. Oncol.

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Background: Lymph node metastasis (LNM) is difficult to precisely predict before surgery in patients with early-T-stage non-small cell lung cancer (NSCLC). This study aimed to develop machine learning (ML)-based predictive models for LNM. Methods: Clinical characteristics and imaging features were retrospectively collected from 1,102 NSCLC ≤ 2 cm patients. A total of 23 variables were included to develop predictive models for LNM by multiple ML algorithms. The models were evaluated by the receiver operating characteristic (ROC) curve for predictive performance and decision curve analysis (DCA) for clinical values. A feature selection approach was used to identify optimal predictive factors. Results: The areas under the ROC curve (AUCs) of the 8 models ranged from 0.784 to 0.899. Some ML-based models performed better than models using conventional statistical methods in both ROC curves and decision curves. The random forest classifier (RFC) model with 9 variables introduced was identified as the best predictive model. The feature selection indicated the top five predictors were tumor size, imaging density, carcinoembryonic antigen (CEA), maximal standardized uptake value (SUV max), and age. Conclusions: By incorporating clinical characteristics and radiographical features, it is feasible to develop ML-based models for the preoperative prediction of LNM in early-T-stage NSCLC, and the RFC model performed best.

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“…The highest true validation value (AUC: 0.75) suggests that the SUS nomogram does not perfectly predict a disease-free axilla, which highlights the complexity of lymphatic spread. Although more complex prediction models may have certain advantages for estimating nodal involvement 44 , 45 , the SUS nomogram is a readily available and user-friendly predictive tool in clinical settings. Studies based on retrospective registry data may be considered unreliable, given the risks of incomplete or improperly recorded data.…”

Section: Discussionmentioning

confidence: 99%

Validation of the Skåne University Hospital nomogram for the preoperative prediction of a disease-free axilla in patients with breast cancer

et al. 2021

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Background Axillary staging via sentinel lymph node biopsy (SLNB) is performed for clinically node-negative (N0) breast cancer patients. The Skåne University Hospital (SUS) nomogram was developed to assess the possibility of omitting SLNB for patients with a low risk of nodal metastasis. Area under the receiver operating characteristic curve (AUC) was 0.74. The aim was to validate the SUS nomogram using only routinely collected data from the Swedish National Quality Registry for Breast Cancer at two breast cancer centres during different time periods. Method This retrospective study included patients with primary breast cancer who were treated at centres in Lund and Malmö during 2008–2013. Clinicopathological predictors in the SUS nomogram were age, mode of detection, tumour size, multifocality, lymphovascular invasion and surrogate molecular subtype. Multiple imputation was used for missing data. Validation performance was assessed using AUC and calibration. Results The study included 2939 patients (1318 patients treated in Lund and 1621 treated in Malmö). Node-positive disease was detected in 1008 patients. The overall validation AUC was 0.74 (Lund cohort AUC: 0.75, Malmö cohort AUC: 0.73), and the calibration was satisfactory. Accepting a false-negative rate of 5 per cent for predicting N0, a possible SLNB reduction rate of 15 per cent was obtained in the overall cohort. Conclusion The SUS nomogram provided acceptable power for predicting a disease-free axilla in the validation cohort. This tool may assist surgeons in identifying and counselling patients with a low risk of nodal metastasis on the omission of SLNB staging.

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Prediction of Lymph Node Metastasis in Breast Cancer by Gene Expression and Clinicopathological Models: Development and Validation within a Population-Based Cohort

Cited by 40 publications

References 54 publications

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

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

Preoperative Prediction of Lymph Node Metastasis in Patients With Early-T-Stage Non-small Cell Lung Cancer by Machine Learning Algorithms

Validation of the Skåne University Hospital nomogram for the preoperative prediction of a disease-free axilla in patients with breast cancer

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