Development of nomograms to predict axillary lymph node status in breast cancer patients

Photoacoustic Imaging with Attention-Guided Deep Learning for Predicting Axillary Lymph Node Status in Breast Cancer

RATIONALE AND OBJECTIVES

Preoperative assessment of axillary lymph node (ALN) status is essential for breast cancer management. This study explores the use of photoacoustic (PA) imaging combined with attention-guided deep learning (DL) for precise prediction of ALN status.

MATERIALS AND METHODS

This retrospective study included patients with histologically confirmed early-stage breast cancer from 2022 to 2024, randomly divided (8:2) into training and test cohorts. All patients underwent preoperative dual modal photoacoustic-ultrasound (PA-US) examination, were treated with surgery and sentinel lymph node biopsy or ALN dissection, and were pathologically examined to determine the ALN status. Attention-guided DL model was developed using PA-US images to predict ALN status. A clinical model, constructed via multivariate logistic regression, served as the baseline for comparison. Subsequently, a nomogram incorporating the DL model and independent clinical parameters was developed. The performance of the models was evaluated through discrimination, calibration, and clinical applicability.

RESULTS

A total of 324 patients (mean age ± standard deviation, 51.0 ± 10.9 years) were included in the study and were divided into a development cohort (n = 259 [79.9%]) and a test cohort (n = 65 [20.1%]). The clinical model incorporating three independent clinical parameters yielded an area under the curve (AUC) of 0.775 (95% confidence interval [CI], 0.711-0.829) in the training cohort and 0.783 (95% CI, 0.654-0.897) in the test cohort for predicting ALN status. In comparison, the nomogram showed superior predictive performance, with an AUC of 0.906 (95% CI, 0.867-0.940) in the training cohort and 0.868 (95% CI, 0.769-0.954) in the test cohort. Decision curve analysis further confirmed the nomogram's clinical applicability, demonstrating a better net benefit across relevant threshold probabilities.

CONCLUSION

This study highlights the effectiveness of attention-guided PA imaging in breast cancer patients, providing novel nomograms for individualized clinical decision-making in predicting ALN node status.

Sustainability in radiation oncology: opportunities for enhancing patient care and reducing CO2 emissions in breast cancer radiotherapy at selected German centers

BACKGROUND AND OBJECTIVE

Radiotherapy often entails a substantial travel burden for patients accessing radiation oncology centers. The total travel distance for such treatments is primarily influenced by two factors: fractionation schedules and the distances traveled. Specific data on these aspects are not well documented in Germany. This study aims to quantify the travel distances for routine breast cancer patients of five radiation oncology centers located in metropolitan, urban, and rural areas of Germany and to record the CO2 emissions resulting from travel.

METHODS

We analyzed the geographic data of breast cancer patients attending their radiotherapy treatments and calculated travelling distances using Google Maps. Carbon dioxide emissions were estimated assuming a standard 40-miles-per-gallon petrol car emitting 0.168 kg of CO2 per kilometer.

RESULT

Addresses of 4198 breast cancer patients treated between 2018 and 2022 were analyzed. Our sample traveled an average of 37.2 km (minimum average: 14.2 km, maximum average: 58.3 km) for each radiation fraction. This yielded an estimated total of 6.2 kg of CO2 emissions per visit, resulting in 156.2 kg of CO2 emissions when assuming 25 visits (planning, treatment, and follow-up).

CONCLUSION

Our study highlights the environmental consequences associated with patient commutes for external-beam radiotherapy, indicating that reducing the number of treatment fractions can notably decrease CO2 emissions. Despite certain assumptions such as the mode of transport and possible inaccuracies in patient addresses, optimizing fractionation schedules not only reduces travel requirements but also achieves greater CO2 reductions while keeping improved patient outcomes as the main focus.

Breast-conserving surgery versus modified radical mastectomy in T1-2N3M0 stage breast cancer: a propensity score matching analysis

PURPOSE

Breast-conserving surgery (BCS) plus radiotherapy and mastectomy exhibit highly comparable prognoses for early-stage breast cancer; however, the safety of BCS for T1-2N3M0 breast cancer remains unclear. This study compared long-term survival for BCS versus (vs.) modified radical mastectomy (MRM) among patients with T1-2N3M0 breast cancer.

METHODS

Data of patients with T1-2N3M0 breast cancer were extracted from the Surveillance, Epidemiology, and End Results database. Eligible patients were divided into 2 groups, BCS and MRM; Pearson's chi-squared test was used to estimate differences in clinicopathological features. Propensity score matching (PSM) was used to balance baseline characteristics. Univariate and multivariate analyses were performed to investigate the effects of surgical methods and other factors on breast cancer-specific survival (BCSS) and overall survival (OS).

RESULTS

In total, 2124 patients were included; after PSM, 596 patients were allocated to each group. BCS exhibited the same 5-year BCSS (77.9% vs. 77.7%; P = 0.814) and OS (76.1% vs. 74.6%; P = 0.862) as MRM in the matched cohorts. Multivariate survival analysis revealed that BCS had the same BCSS and OS as MRM (hazard ratios [HR] 0.899 [95% confidence intervals (CI) 0.697-1.160], P = 0.413 and HR 0.858 [95% CI 0.675-1.089], P = 0.208, respectively); this was also seen in most subgroups. BCS demonstrated better BCSS (HR 0.558 [95% CI 0.335-0.929]; P = 0.025) and OS (HR 0.605 [95% CI 0.377-0.972]; P = 0.038) than MRM in those with the triple-negative subtype.

CONCLUSIONS

BCS has the same long-term survival as MRM in T1-2N3M0 breast cancer and may be a better choice for triple-negative breast cancer.

Assessment of the axilla in women with early-stage breast cancer undergoing primary surgery: a review

Sentinel node biopsy (SNB) is routinely performed in people with node-negative early breast cancer to assess the axilla. SNB has no proven therapeutic benefit. Nodal status information obtained from SNB helps in prognostication and can influence adjuvant systemic and locoregional treatment choices. However, the redundancy of the nodal status information is becoming increasingly apparent. The accuracy of radiological assessment of the axilla, combined with the strong influence of tumour biology on systemic and locoregional therapy requirements, has prompted many to consider alternative options for SNB. SNB contributes significantly to decreased quality of life in early breast cancer patients. Substantial improvements in workflow and cost could accrue by removing SNB from early breast cancer treatment. We review the current viewpoints and ideas for alternative options for assessing and managing a clinically negative axilla in patients with early breast cancer (EBC). Omitting SNB in selected cases or replacing SNB with a non-invasive predictive model appear to be viable options based on current literature.

Multiparametric MRI-Based Radiomics Signature with Machine Learning for Preoperative Prediction of Prognosis Stratification in Pediatric Medulloblastoma

RATIONALE AND OBJECTIVES

Despite advances in risk-stratified treatment strategies for children with medulloblastoma (MB), the prognosis for MB with short-term recurrence is extremely poor, and there is still a lack of evaluation of short-term recurrence risk or short-term survival. This study aimed to construct and validate a radiomics model for predicting the outcome of MB based on preoperative multiparametric magnetic resonance images (MRIs) and to provide an objective for clinical decision-making.

MATERIALS AND METHODS

The clinical and imaging data of 64 patients with MB admitted to Shenzhen Children's Hospital from December 2012 to December 2021 and confirmed by pathology were retrospectively collected. According to the 18-month progression-free survival, the cases were classified into a good prognosis group and a poor prognosis group, and all cases were divided into training group (70%) and validation group (30%) randomly. Radiomics features were extracted from MRI of each child. The consistency test, t-test, and the least absolute shrinkage and selection operator were used for feature selection. The support vector machine (SVM) and receiver operator characteristic were used to evaluate the distinguishing ability of the selected features to the prognostic groups. RAD score was calculated based on the selected features. The clinical characteristics and RAD score were included in the multivariate logistic regression, and prediction models were constructed by screening out independent influences. The radiomics nomogram was constructed, and its clinical significance was evaluated.

RESULTS

A total of 1930 radiomic features were extracted from the images of each patient, and 11 features were included in the construction of radiomics score after selected. The area under the curve (AUC) values of the SVM model in the training and validation groups were 0.946 and 0.797, respectively. The radiomics nomogram was constructed based on the training cohort, and the AUC values in the training group and the validation group were 0.926 and 0.835, respectively. The results of clinical decision curve analysis showed that a good net benefit could be obtained from the nomogram.

CONCLUSION

The radiomics nomogram established based on MRI can be used as a noninvasive predictive tool to evaluate the prognosis of children with MB, which is expected to help neurosurgeons better conduct preoperative planning and patient follow-up management.

Retrospective validation study of an artificial neural network-based preoperative decision-support tool for noninvasive lymph node staging (NILS) in women with primary breast cancer (ISRCTN14341750)

BACKGROUND

Surgical sentinel lymph node biopsy (SLNB) is routinely used to reliably stage axillary lymph nodes in early breast cancer (BC). However, SLNB may be associated with postoperative arm morbidities. For most patients with BC undergoing SLNB, the findings are benign, and the procedure is currently questioned. A decision-support tool for the prediction of benign sentinel lymph nodes based on preoperatively available data has been developed using artificial neural network modelling.

METHODS

This was a retrospective geographical and temporal validation study of the noninvasive lymph node staging (NILS) model, based on preoperatively available data from 586 women consecutively diagnosed with primary BC at two sites. Ten preoperative clinicopathological characteristics from each patient were entered into the web-based calculator, and the probability of benign lymph nodes was predicted. The performance of the NILS model was assessed in terms of discrimination with the area under the receiver operating characteristic curve (AUC) and calibration, that is, comparison of the observed and predicted event rates of benign axillary nodal status (N0) using calibration slope and intercept. The primary endpoint was axillary nodal status (discrimination, benign [N0] vs. metastatic axillary nodal status [N+]) determined by the NILS model compared to nodal status by definitive pathology.

RESULTS

The mean age of the women in the cohort was 65 years, and most of them (93%) had luminal cancers. Approximately three-fourths of the patients had no metastases in SLNB (N0 74% and 73%, respectively). The AUC for the predicted probabilities for the whole cohort was 0.6741 (95% confidence interval: 0.6255-0.7227). More than one in four patients (n = 151, 26%) were identified as candidates for SLNB omission when applying the predefined cut-off for lymph node-negative status from the development cohort. The NILS model showed the best calibration in patients with a predicted high probability of healthy axilla.

CONCLUSION

The performance of the NILS model was satisfactory. In approximately every fourth patient, SLNB could potentially be omitted. Considering the shift from postoperatively to preoperatively available predictors in this validation study, we have demonstrated the robustness of the NILS model. The clinical usability of the web interface will be evaluated before its clinical implementation.

TRIAL REGISTRATION

Registered in the ISRCTN registry with study ID ISRCTN14341750. Date of registration 23/11/2018.

Nomogram based on multiparametric analysis of early-stage breast cancer: Prediction of high burden metastatic axillary lymph nodes

BACKGROUND

The Z0011 and AMAROS trials found that axillary lymph node dissection (ALND) was no longer mandatory for early-stage breast cancer patients who had one or two metastatic axillary lymph nodes (mALNs). The aim of our study was to establish a nomogram which could be used to quantitatively predict the individual likelihood of high burden mALN (≥3 mALN).

METHODS

We retrospectively analyzed 564 women with early breast cancer who had all undergone both ultrasound (US) and magnetic resonance imaging (MRI) to examine axillary lymph nodes before radical surgery. All the patients were divided into training (n = 452) and validation (n = 112) cohorts by computer-generated random numbers. Their clinicopathological features and preoperative imaging associated with high burden mALNs were evaluated by logistic regression analysis to develop a nomogram for predicting the probability of high burden mALNs.

RESULTS

Multivariate analysis showed that high burden mALNs were significantly associated with replaced hilum and the shortest diameter >10 mm on MRI, with cortex thickness >3 mm on US (p < 0.05 each). These imaging criteria plus higher grade (grades II and III) and quadrant of breast tumor were used to develop a nomogram calculating the probability of high burden mALNs. The AUC of the nomogram was 0.853 (95% CI: 0.790-0.908) for the training set and 0.783 (95% CI: 0.638-0.929) for the validation set. Both internal and external validation evaluated the accuracy of nomogram to be good.

CONCLUSION

A well-discriminated nomogram was developed to predict the high burden mALN in early-stage breast patients, which may assist the breast surgeon in choosing the appropriate surgical approach.

Multi-center study on predicting breast cancer lymph node status from core needle biopsy specimens using multi-modal and multi-instance deep learning

The objective of our study is to develop a deep learning model based on clinicopathological data and digital pathological image of core needle biopsy specimens for predicting breast cancer lymph node metastasis. We collected 3701 patients from the Fourth Hospital of Hebei Medical University and 190 patients from four medical centers in Hebei Province. Integrating clinicopathological data and image features build multi-modal and multi-instance (MMMI) deep learning model to obtain the final prediction. For predicting with or without lymph node metastasis, the AUC was 0.770, 0.709, 0.809 based on the clinicopathological features, WSI and MMMI, respectively. For predicting four classification of lymph node status (no metastasis, isolated tumor cells (ITCs), micrometastasis, and macrometastasis), the prediction based on clinicopathological features, WSI and MMMI were compared. The AUC for no metastasis was 0.770, 0.709, 0.809, respectively; ITCs were 0.619, 0.531, 0.634, respectively; micrometastasis were 0.636, 0.617, 0.691, respectively; and macrometastasis were 0.748, 0.691, 0.758, respectively. The MMMI model achieved the highest prediction accuracy. For prediction of different molecular types of breast cancer, MMMI demonstrated a better prediction accuracy for any type of lymph node status, especially in the molecular type of triple negative breast cancer (TNBC). In the external validation sets, MMMI also showed better prediction accuracy in the four classification, with AUC of 0.725, 0.757, 0.525, and 0.708, respectively. Finally, we developed a breast cancer lymph node metastasis prediction model based on a MMMI model. Through all cases tests, the results showed that the overall prediction ability was high.

Novel nomograms for predicting survival for immediate breast reconstruction patients diagnosed with invasive breast cancer-a single-center 15-year experience

Background

Immediate breast reconstruction is widely accepted following oncologic mastectomy. This study aimed to build a novel nomogram predicting the survival outcome for Chinese patients undergoing immediate reconstruction following mastectomy for invasive breast cancer.

Methods

A retrospective review of all patients undergoing immediate reconstruction following treatment for invasive breast cancer was performed from May 2001 to March 2016. Eligible patients were assigned to a training set or a validation set. Univariate and multivariate Cox proportional hazard regression models were used to select associate variables. Two nomograms were developed based on the training cohort for breast cancer-specific survival (BCSS) and disease-free survival (DFS). Internal and external validations were performed, and the C-index and calibration plots were generated to evaluate the performance (discrimination and accuracy) of the models.

Results

The 10-year estimated BCSS and DFS were 90.80% (95% CI: 87.30%-94.40%) and 78.40% (95% CI: 72.50%-84.70%), respectively, in the training cohort. In the validation cohort, they were and 85.60% (95% CI, 75.90%-96.50%) and 84.10% (95% CI, 77.80%-90.90%), respectively. Ten independent factors were used to build a nomogram for prediction of 1-, 5- and 10-year BCSS, while nine were used for DFS. The C-index was 0.841 for BCSS and 0.737 for DFS in internal validation, and the C-index was 0.782 for BCSS and 0.700 for DFS in external validation. The calibration curve for both BCSS and DFS demonstrated acceptable agreement between the predicted and actual observation in the training and the validation cohorts.

Conclusion

The nomograms provided valuable visualization of factors predicting BCSS and DFS in invasive breast cancer patients with immediate breast reconstruction. The nomograms may have tremendous potential in guiding individualized decision-making for physicians and patients in choosing the optimized treatment methods.

Predictive nomogram based on serum tumor markers and clinicopathological features for stratifying lymph node metastasis in breast cancer

BACKGROUND

This study was aimed to establish the nomogram to predict patients' axillary node status by using patients' clinicopathological and tumor characteristic factors.

METHODS

A total of 705 patients with breast cancer were enrolled in this study. All patients were randomly divided into a training group and a validation group. Univariate and multivariate ordered logistic regression were used to determine the predictive ability of each variable. A nomogram was performed based on the factors selected from logistic regression results. Receiver operating characteristic curve (ROC) analysis, calibration plots and decision curve analysis (DCA) were used to evaluate the discriminative ability and accuracy of the models.

RESULTS

Logistic regression analysis demonstrated that CEA, CA125, CA153, tumor size, vascular-invasion, calcification, and tumor grade were independent prognostic factors for positive ALNs. Integrating all the predictive factors, a nomogram was successfully developed and validated. The C-indexes of the nomogram for prediction of no ALN metastasis, positive ALN, and four and more ALN metastasis were 0.826, 0.706, and 0.855 in training group and 0.836, 0.731, and 0.897 in validation group. Furthermore, calibration plots and DCA demonstrated a satisfactory performance of our nomogram.

CONCLUSION

We successfully construct and validate the nomogram to predict patients' axillary node status by using patients' clinicopathological and tumor characteristic factors.

Nomogram models for stratified prediction of axillary lymph node metastasis in breast cancer patients (cN0)

Objectives

To determine the predictors of axillary lymph node metastasis (ALNM), two nomogram models were constructed to accurately predict the status of axillary lymph nodes (ALNs), mainly high nodal tumour burden (HNTB, > 2 positive lymph nodes), low nodal tumour burden (LNTB, 1-2 positive lymph nodes) and negative ALNM (N0). Accordingly, more appropriate treatment strategies for breast cancer patients without clinical ALNM (cN0) could be selected.

Methods

From 2010 to 2015, a total of 6314 patients with invasive breast cancer (cN0) were diagnosed in the Surveillance, Epidemiology, and End Results (SEER) database and randomly assigned to the training and internal validation groups at a ratio of 3:1. As the external validation group, data from 503 breast cancer patients (cN0) who underwent axillary lymph node dissection (ALND) at the Second Affiliated Hospital of Chongqing Medical University between January 2011 and December 2020 were collected. The predictive factors determined by univariate and multivariate logistic regression analyses were used to construct the nomograms. Receiver operating characteristic (ROC) curves and calibration plots were used to assess the prediction models' discrimination and calibration.

Results

Univariate analysis and multivariate logistic regression analyses showed that tumour size, primary site, molecular subtype and grade were independent predictors of both ALNM and HNTB. Moreover, histologic type and age were independent predictors of ALNM and HNTB, respectively. Integrating these independent predictors, two nomograms were successfully developed to accurately predict the status of ALN. For nomogram 1 (prediction of ALNM), the areas under the receiver operating characteristic (ROC) curve in the training, internal validation and external validation groups were 0.715, 0.688 and 0.876, respectively. For nomogram 2 (prediction of HNTB), the areas under the ROC curve in the training, internal validation and external validation groups were 0.842, 0.823 and 0.862. The above results showed a satisfactory performance.

Conclusion

We established two nomogram models to predict the status of ALNs (N0, 1-2 positive ALNs or >2 positive ALNs) for breast cancer patients (cN0). They were well verified in further internal and external groups. The nomograms can help doctors make more accurate treatment plans, and avoid unnecessary surgical trauma.

Accurate Evaluation of Feature Contributions for Sentinel Lymph Node Status Classification in Breast Cancer

The current guidelines recommend the sentinel lymph node biopsy to evaluate the lymph node involvement for breast cancer patients with clinically negative lymph nodes on clinical or radiological examination. Machine learning (ML) models have significantly improved the prediction of lymph nodes status based on clinical features, thus avoiding expensive, time-consuming and invasive procedures. However, the classification of sentinel lymph node status represents a typical example of an unbalanced classification problem. In this work, we developed a ML framework to explore the effects of unbalanced populations on the performance and stability of feature ranking for sentinel lymph node status classification in breast cancer. Our results indicate state-of-the-art AUC (Area under the Receiver Operating Characteristic curve) values on a hold-out set (67%) while providing particularly stable features related to tumor size, histological subtype and estrogen receptor expression, which should therefore be considered as potential biomarkers.

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

Background

This study aimed at constructing a nomogram to predict axillary lymph node metastasis (ALNM) based on axillary ultrasound and tumor clinicopathological features.

Methods

A 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.

Results

In 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.

Conclusions

This 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.

Analysis on factors behind sentinel lymph node metastasis in breast cancer by color ultrasonography, molybdenum target, and pathological detection

BACKGROUND

This study aimed to identify the factors underlying the metastasis of breast cancer and sentinel lymph nodes and to screen and analyze the risk factors of sentinel lymph node metastasis to provide a reference and basis for clinical work.

METHODS

A total of 99 patients with breast cancer were enrolled in this study. These patients received treatment in our hospital between May 2017 and May 2020. The general information, characteristics of the color Doppler echocardiography, molybdenum, conventional pathology, and molecular pathology of the patients were collected. Factors influencing sentinel lymph node metastasis in breast cancer patients were retrospectively analyzed.

RESULTS

In this study, age, tumor diameter, BI-RADS category, pathology type, expression profiles of CK5/6, EGFR, and CK19, and TP53 and BRAC1/2 mutations were independent risk factors for sentinel lymph node metastasis in breast cancer (P < 0.05). The number and locations of tumors, quadrant of tumors, regularity of tumor margins, presence of blood flow signals, presence of posterior echo attenuation, presence of calcification, histological grade, molecular typing, and mutations of BRAF, ATM, and PALB2 were irrelevant factors (P > 0.05).

CONCLUSIONS

In conclusion, age, tumor diameter, BI-RADS category, invasive type, expression of CK5/6, EGFR, and CK19, and mutations in TP53 and BRAC1/2 were positively correlated with sentinel lymph node metastasis. These independent risk factors should be given more attention in clinical studies to strengthen the management and control of sentinel lymph node metastasis in high-risk breast cancer and support early chemotherapy or targeted therapy.

Sentinel Lymph Node Metastasis on Clinically Negative Patients: Preliminary Results of a Machine Learning Model Based on Histopathological Features

The reported incidence of node metastasis at sentinel lymph node biopsy is generally low, so that the majority of women underwent unnecessary invasive axilla surgery. Although the sentinel lymph node biopsy is time consuming and expensive, it is still the intra-operative exam with the highest performance, but sometimes surgery is achieved without a clear diagnosis and also with possible serious complications. In this work, we developed a machine learning model to predict the sentinel lymph nodes positivity in clinically negative patients. Breast cancer clinical and immunohistochemical features of 907 patients characterized by a clinically negative lymph node status were collected. We trained different machine learning algorithms on the retrospective collected data and selected an optimal subset of features through a sequential forward procedure. We found comparable performances for different classification algorithms: on a hold-out training set, the logistics regression classifier with seven features, i.e., tumor diameter, age, histologic type, grading, multiplicity, in situ component and Her2-neu status reached an AUC value of 71.5% and showed a better trade-off between sensitivity and specificity (69.4 and 66.9%, respectively) compared to other two classifiers. On the hold-out test set, the performance dropped by five percentage points in terms of accuracy. Overall, the histological characteristics alone did not allow us to develop a support tool suitable for actual clinical application, but it showed the maximum informative power contained in the same for the resolution of the clinical problem. The proposed study represents a starting point for future development of predictive models to obtain the probability for lymph node metastases by using histopathological features combined with other features of a different nature.

Are Breast Cancer Nomograms Still Valid to Predict the Need for Axillary Dissection?

BACKGROUND

Nomograms can help in estimating the nodal status among clinically node-negative patients. Yet their validity in external cohorts over time is unknown. If the nodal stage can be estimated preoperatively, the need for axillary dissection can be decided.

OBJECTIVES

The aim of this study was to validate three existing nomograms predicting 4 or more axillary lymph node metastases.

METHOD

The risk for ≥4 lymph node metastases was calculated for n = 529 eligible breast cancer patients using the nomograms of Chagpar et al. [Ann Surg Oncol. 2007;14:670-7], Katz et al. [J Clin Oncol. 2008;26(13):2093-8], and Meretoja et al. [Breast Cancer Res Treat. 2013;138(3):817-27]. Discrimination and calibration were calculated for each nomogram to determine their validity.

RESULTS

In this cohort, the AUC values for the Chagpar, Katz, and Meretoja models were 0.79 (95% CI 0.74-0.83), 0.87 (95% CI 0.83-0.91), and 0.82 (95% CI 0.76-0.86), respectively, showing good discrimination between patients with and without high nodal burdens.

CONCLUSION

This study presents support for the use of older breast cancer nomograms and confirms their current validity in an external population.

Predicting of Sentinel Lymph Node Status in Breast Cancer Patients with Clinically Negative Nodes: A Validation Study

Simple Summary

Sentinel lymph node biopsy procedure is time consuming and expensive, but it is still the intra-operative exam capable of the best performance. However, sometimes, surgery is achieved without a clear diagnosis, so clinical decision support systems developed with artificial intelligence techniques are essential to assist current diagnostic procedures. In this work, we evaluated the usefulness of a CancerMath tool in the sentinel lymph nodes positivity prediction for clinically negative patients. We tested it on 993 patients referred to our institute characterized by sentinel lymph node status, tumor size, age, histologic type, grading, expression of estrogen receptor, progesterone receptor, HER2, and Ki-67. By training the CancerMath (CM) model on our dataset, we reached a sensitivity value of 72%, whereas the online one was 46%, despite a specificity reduction. It was found the addiction of the prognostic factors Her2 and Ki67 could help improve performances on the classification of particular types of patients.

Abstract

In the absence of lymph node abnormalities detectable on clinical examination or imaging, the guidelines provide for the dissection of the first axillary draining lymph nodes during surgery. It is not always possible to arrive at surgery without diagnostic doubts, and machine learning algorithms can support clinical decisions. The web calculator CancerMath (CM) allows you to estimate the probability of having positive lymph nodes valued on the basis of tumor size, age, histologic type, grading, expression of estrogen receptor, and progesterone receptor. We collected 993 patients referred to our institute with clinically negative results characterized by sentinel lymph node status, prognostic factors defined by CM, and also human epidermal growth factor receptor 2 (HER2) and Ki-67. Area Under the Curve (AUC) values obtained by the online CM application were comparable with those obtained after training its algorithm on our database. Nevertheless, by training the CM model on our dataset and using the same feature, we reached a sensitivity median value of 72%, whereas the online one was equal to 46%, despite a specificity reduction. We found that the addition of the prognostic factors Her2 and Ki67 could help improve performances on the classification of particular types of patients with the aim of reducing as much as possible the false positives that lead to axillary dissection. As showed by our experimental results, it is not particularly suitable for use as a support instrument for the prediction of metastatic lymph nodes on clinically negative patients.

Delay in surgery is associated with axillary upstaging of clinically node negative breast cancer patients

BACKGROUND AND OBJECTIVES

Most breast cancer (BC) patients present with early disease and clinically negative lymph nodes (cN0). Timing of surgery has not been standardized. We hypothesized that surgical delay results in an increased likelihood of nodal metastasis.

METHODS

Patients diagnosed with cN0 BC undergoing surgery with sentinel lymph node biopsy as initial therapy between 2006 and 2014 were identified in the NCDB and divided into four groups based on time intervals between diagnosis and surgery (<4 weeks, 4-8 weeks, 8-12 weeks, and >12 weeks). Regression analysis evaluated the independent impact of surgical timing on axillary upstaging and survival.

RESULTS

Of 355,443 patients with cN0 BC, 39.6% had surgery within 4 weeks and 5.4% more than 12 weeks from diagnosis. After controlling for relevant factors, a month delay in surgery was associated with an increased likelihood of nodal positivity (odds ratio: 1.04; 95% confidence interval [CI]: 1.04-1.05; p < .001) and decreased overall survival (hazard ratio: 1.03; 95% CI: 1.02-1.04; p < .001). When compared to patients who underwent surgery less than 4 weeks from diagnosis, the absolute increase in nodal positivity and relative risks were 5.3% (95% CI: 0.047-0.059) and 1.34 (95% CI: 1.30-1.38), respectively, in the more than 12 weeks group.

CONCLUSIONS

Delay in BC surgery in cN0 patients was associated with an increased likelihood of axillary upstaging and decreased survival.

A negative binomial regression model for risk estimation of 0-2 axillary lymph node metastases in breast cancer patients

Extensive clinical trials indicate that patients with negative sentinel lymph node biopsy do not need axillary lymph node dissection (ALND). However, the ACOSOG Z0011 trial indicates that patients with clinically negative axillary lymph nodes (ALNs) and 1-2 positive sentinel lymph nodes having breast conserving surgery with whole breast radiotherapy do not benefit from ALND. The aim of this study is therefore to identify those patients with 0-2 positive nodes who might avoid ALND. A total of 486 patients were eligible for the study with 212 patients in the modeling group and 274 patients in the validation group, respectively. Clinical lymph node status, histologic grade, estrogen receptor status, and human epidermal growth factor receptor 2 status were found to be significantly associated with ALN metastasis. A negative binomial regression (NBR) model was developed to predict the probability of having 0-2 ALN metastases with the area under the curve of 0.881 (95% confidence interval 0.829-0.921, P < 0.001) in the modeling group and 0.758 (95% confidence interval 0.702-0.807, P < 0.001) in the validation group. Decision curve analysis demonstrated that the model was clinically useful. The NBR model demonstrated adequate discriminative ability and clinical utility for predicting 0-2 ALN metastases.

Development and external validation of a nomogram to predict four or more positive nodes in breast cancer patients with one to three positive sentinel lymph nodes

Objective

To develop a nomogram for predicting the possibility of four or more positive nodes in breast cancer patients with 1–3 positive sentinel lymph nodes (SLN).

Materials and methods

Retrospective analysis of data of patients from two institutions was conducted. The inclusion criteria were: invasive breast cancer; clinically node negative; received lumpectomy or mastectomy plus SLN biopsy followed by axillary lymph node dissection (ALND); and pathologically confirmed T1-2 tumor, with 1–3 positive SLNs. Patients from one institution formed the training group and patients from the other the validation group. Univariate and multivariate analyses were performed to identify the predictors of four or more positive nodes. These predictors were used to build the nomogram. The area under the receiver operating characteristic curve (AUC) was calculated to assess the accuracy of the model.

Results

Of the 1480 patients (966 patients in the training group, 514 in the validation group), 306 (20.7%) had four or more positive nodes. Multivariate stepwise logistic regression showed number of positive (p < .001) and negative SLN (p < .001), extracapsular extension (p < .001), pT stage (p = .016), and tumor location in outer upper quadrant (p = .031) to be independent predictors of four or more positive nodes. The nomogram was built using these five factors. The AUC was 0.845 in the training group and 0.804 in the validation group.

Conclusion

The proposed nomogram appears to accurately estimate the likelihood of four or more positive nodes and could help radiation oncologists to decide on use of regional nodal irradiation (RNI) for breast cancer patients with 1–3 positive nodes but no ALND.

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Five predictors of four or more positive nodes in breast cancer patients were identified.

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A nomogram was built using these five factors.

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The nomogram was validated on an external cohort.

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The proposed nomogram predicts four or more positive nodes with high accuracy.

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The nomogram can help in decision making on use of regional nodal irradiation.

Can a machine-learning model improve the prediction of nodal stage after a positive sentinel lymph node biopsy in breast cancer?

Background: The current standard for evaluating axillary nodal burden in clinically node negative breast cancer is sentinel lymph node biopsy (SLNB). However, the accuracy of SLNB to detect nodal stage N2-3 remains debatable. Nomograms can help the decision-making process between axillary treatment options. The aim of this study was to create a new model to predict the nodal stage N2-3 after a positive SLNB using machine learning methods that are rarely seen in nomogram development.Material and methods: Primary breast cancer patients who underwent SLNB and axillary lymph node dissection (ALND) between 2012 and 2017 formed cohorts for nomogram development (training cohort, N = 460) and for nomogram validation (validation cohort, N = 70). A machine learning method known as the gradient boosted trees model (XGBoost) was used to determine the variables associated with nodal stage N2-3 and to create a predictive model. Multivariate logistic regression analysis was used for comparison.Results: The best combination of variables associated with nodal stage N2-3 in XGBoost modeling included tumor size, histological type, multifocality, lymphovascular invasion, percentage of ER positive cells, number of positive sentinel lymph nodes (SLN) and number of positive SLNs multiplied by tumor size. Indicating discrimination, AUC values for the training cohort and the validation cohort were 0.80 (95%CI 0.71-0.89) and 0.80 (95%CI 0.65-0.92) in the XGBoost model and 0.85 (95%CI 0.77-0.93) and 0.75 (95%CI 0.58-0.89) in the logistic regression model, respectively.Conclusions: This machine learning model was able to maintain its discrimination in the validation cohort better than the logistic regression model. This indicates advantages in employing modern artificial intelligence techniques into nomogram development. The nomogram could be used to help identify nodal stage N2-3 in early breast cancer and to select appropriate treatments for patients.

Artificial neural network models to predict nodal status in clinically node-negative breast cancer

BACKGROUND

Sentinel lymph node biopsy (SLNB) is standard staging procedure for nodal status in breast cancer, but lacks therapeutic benefit for patients with benign sentinel nodes. For patients with positive sentinel nodes, individualized surgical strategies are applied depending on the extent of nodal involvement. Preoperative prediction of nodal status is thus important for individualizing axillary surgery avoiding unnecessary surgery. We aimed to predict nodal status in clinically node-negative breast cancer and identify candidates for SLNB omission by including patient-related and pathological characteristics into artificial neural network (ANN) models.

METHODS

Patients with primary breast cancer were consecutively included between January 1, 2009 and December 31, 2012 in a prospectively maintained pathology database. Clinical- and radiological data were extracted from patient's files and only clinically node-negative patients constituted the final study cohort. ANN-based models for nodal prediction were constructed including 15 risk variables for nodal status. Area under the receiver operating characteristic curve (AUC) and Hosmer-Lemeshow goodness-of-fit test (HL) were used to assess performance and calibration of three predictive ANN-based models for no lymph node metastasis (N0), metastases in 1-3 lymph nodes (N1) and metastases in ≥ 4 lymph nodes (N2). Linear regression models for nodal prediction were calculated for comparison.

RESULTS

Eight hundred patients (N0, n = 514; N1, n = 232; N2, n = 54) were included. Internally validated AUCs for N0 versus N+ was 0.740 (95% CI = 0.723-0.758); median HL was 9.869 (P = 0.274), for N1 versus N0, 0.705 (95% CI = 0.686-0.724; median HL: 7.421; P = 0.492) and for N2 versus N0 and N1, 0.747 (95% CI = 0.728-0.765; median HL: 9.220; P = 0.324). Tumor size and vascular invasion were top-ranked predictors of all three end-points, followed by estrogen receptor status and lobular cancer for prediction of N2. For each end-point, ANN models showed better discriminatory performance than multivariable logistic regression models. Accepting a false negative rate (FNR) of 10% for predicting N0 by the ANN model, SLNB could have been abstained in 27.25% of patients with clinically node-negative axilla.

CONCLUSIONS

In this retrospective study, ANN showed promising result as decision-supporting tools for estimating nodal disease. If prospectively validated, patients least likely to have nodal metastasis could be spared SLNB using predictive models.

TRIAL REGISTRATION

Registered in the ISRCTN registry with study ID ISRCTN14341750 . Date of registration 23/11/2018. Retrospectively registered.

Facilitating validation of prediction models: a comparison of manual and semi-automated validation using registry-based data of breast cancer patients in the Netherlands

Background

Clinical prediction models are not routinely validated. To facilitate validation procedures, the online Evidencio platform (https://www.evidencio.com) has developed a tool partly automating this process. This study aims to determine whether semi-automated validation can reliably substitute manual validation.

Methods

Four different models used in breast cancer care were selected: CancerMath, INFLUENCE, Predicted Probability of Axillary Metastasis, and PREDICT v.2.0. Data were obtained from the Netherlands Cancer Registry according to the inclusion criteria of the original development population. Calibration (intercepts and slopes) and discrimination (area under the curve (AUC)) were compared between semi-automated and manual validation.

Results

Differences between intercepts and slopes of all models using semi-automated validation ranged from 0 to 0.03 from manual validation, which was not clinically relevant. AUCs were identical for both validation methods.

Conclusions

This easy to use semi-automated validation option is a good substitute for manual validation and might increase the number of validations of prediction models used in clinical practice. In addition, the validation tool was considered to be user-friendly and to save a lot of time compared to manual validation. Semi-automated validation will contribute to more accurate outcome predictions and treatment recommendations in the target population.

Electronic supplementary material

The online version of this article (10.1186/s12874-019-0761-5) contains supplementary material, which is available to authorized users.

A Nomogram Predicting Lymph Node Metastasis in T1 Breast Cancer based on the Surveillance, Epidemiology, and End Results Program

Background: Patients with early stage breast cancer with lymph nodes metastasis were proven to have more aggressive biologically phenotypes. This study aimed to build a nomogram to predict lymph node metastasis in patients with T1 breast cancer.

Methods: We identified female patients with T1 breast cancer diagnosed between 2010 and 2014 in the Surveillance, Epidemiology and End Results database. The patients were randomized into training and validation sets. Univariate and multivariate logistic regressions were carried out to assess the relationships between lymph node metastasis and clinicopathological characteristics. A nomogram was developed and validated by a calibration curve and receptor operating characteristic curve analysis.

Result: Age, race, tumour size, tumour primary site, pathological grade, oestrogen receptor (ER) status, progesterone receptor (PR) status and human epidermal growth factor receptor 2 (HER2) status were independent predictive factors of positive lymph node metastasis in T1 breast cancer. Increasing age, tumour size and pathological grade were positively correlated with the risk of lymph node metastasis. We developed a nomogram to predict lymph node metastasis and further validated it in a validation set, with areas under the receiver operating characteristic curves of 0.733 and 0.741 in the training and validation sets, respectively.

Conclusions: A better understanding of the clinicopathological characteristics of T1 breast cancer patients might important for assessing their lymph node status. The nomogram developed here, if further validated in other large cohorts, might provide additional information regarding lymph node metastasis. Together with sentinel lymph node biopsy, this nomogram can help comprehensively predict lymph node metastasis.

Effect of Primary Breast Tumor Location on Axillary Nodal Positivity

BACKGROUND

Variables such as tumor size, histology, and grade, tumor biology, presence of lymphovascular invasion, and patient age have been shown to impact likelihood of nodal positivity. The aim of this study is to determine whether primary location of invasive disease within the breast is associated with nodal positivity.

PATIENTS AND METHODS

Patients with invasive breast cancer undergoing axillary staging from 2010 to 2014 were identified from the National Cancer Data Base. Rates of axillary nodal positivity by primary tumor locations were compared, and multivariable analysis performed using logistic regression to control for factors known to impact nodal positivity.

RESULTS

A total of 599,722 patients met inclusion criteria. Likelihood of nodal positivity was greatest with primary tumors located in the nipple (43.8%), followed by multicentric disease (40.8%), central breast lesions (39.4%), and axillary tail lesions (38.4%). Tumor location remained independently associated with nodal positivity on multivariable analysis adjusting for variables known to affect nodal positivity with odds ratio 2.8 for tumors in the nipple [95% confidence interval (CI) 2.5-3.1], 2.2 for central breast (95% CI: 2.2-2.3), and 2.7 for axillary tail (95% CI: 2.4-2.9). When restricted to patients with clinically negative nodes (n = 430,949), a similar association was seen.

CONCLUSION

Patients with invasive breast cancer located in the nipple, central breast, and axillary tail have the highest risk of positive axillary lymph nodes independent of patient age, tumor grade, biologic subtype, histology, and size. This should be considered along with other factors in preoperative counseling and decision-making regarding plans for axillary lymph node staging.