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

An Intraoperative Ultrasound Evaluation of Axillary Lymph Nodes: Cassandra Predictive Models in Patients with Breast Cancer-A Multicentric Study

Background and Objectives: Axillary lymph node (ALN) staging is crucial for the management of invasive breast cancer (BC). Although various radiological investigations are available, ultrasound (US) is the preferred tool for evaluating ALNs. Despite its immediacy, widespread use, and good predictive value, US is limited by intra- and inter-operator variability. This study aims to evaluate US and Elastosonography Shear Wave (SW-ES) parameters for ALN staging to develop a predictive model, named the Cassandra score (CS), to improve the interpretation of findings and standardize staging. Materials and Methods: Sixty-three women diagnosed with BC and treated at two Italian hospitals were enrolled in the study. A total of 529 lymph nodes were surgically removed, underwent intraoperative US examination, and were individually sent for a final histological analysis. The study aimed to establish a direct correlation between eight US-SWES features (margins, vascularity, roundness index (RI), loss of hilum fat, cortical thickness, shear-wave elastography hardness (SWEH), peripheral infiltration (PI), and hypoechoic appearance) and the histological outcome (benign vs. malignant). Results: Several statistical models were compared. PI was strongly correlated with malignant ALNs. An ROC analysis for Model A revealed an impressive AUC of 0.978 (S.E. = 0.007, p < 0.001), while in Model B, the cut-offs of SWEH and RI were modified to minimize the risk of false negatives (AUC of 0.973, S.E. = 0.009, p < 0.001). Model C used the same cut-offs as Model B, but excluded SWEH from the formula, to make the Cassandra model usable even if the US machine does not have SW-ES capability (AUC of 0.940, S.E. = 0.015, p < 0.001). A two-tiered model was finally set up, leveraging the strong predictive capabilities of SWEH and RI. In the first tier, only SWES and RI were evaluated: a positive result was predicted if both hardness and roundness were present (SWES > 137 kPa and RI < 1.55), and conversely, a negative result was predicted if both were absent (SWES < 137 kPa and RI > 1.55). In the second tier, if there was a mix of the results (SWES > 137 kPa and RI > 1.55 or SWES < 137 kPa and RI < 1.55), the algorithm in Model B was applied. The model demonstrated an overall prediction accuracy of 90.2% in the training set, 87.5% in the validation set, and 88.9% across the entire dataset. The NPV was notably high at 99.2% in the validation set. This model was named the Cassandra score (CS) and is proposed for the clinical management of BC patients. Conclusion: CS is a simple, non-invasive, fast, and reliable method that showed a PPV of 99.1% in the malignancy prediction of ALNs, potentially being also well suited for young sonographers.

Characteristics and risk factors of axillary lymph node metastasis of microinvasive breast cancer

PURPOSE

To select patients who would benefit most from sentinel lymph node biopsy (SLNB) by investigating the characteristics and risk factors of axillary lymph node metastasis (ALNM) in microinvasive breast cancer (MIBC).

METHODS

This retrospective study included 1688 patients with MIBC who underwent breast surgery with axillary staging at the Asan Medical Center from 1995 to 2020.

RESULTS

Most patients underwent SLNB alone (83.5%). Seventy (4.1%) patients were node-positive, and the majority had positive lymph nodes < 10 mm, with micro-metastases occurring frequently (n = 37; 55%). Node-positive patients underwent total mastectomy and axillary lymph node dissection (ALND) more than breast-conserving surgery (BCS) and SLNB compared with node-negative patients (p < 0.001). In the multivariate analysis, independent predictors of ALNM included young age [odds ratio (OR) 0.959; 95% confidence interval (CI) 0.927-0.993; p = 0.019], ALND (OR 11.486; 95% CI 5.767-22.877; p < 0.001), number of lymph nodes harvested (≥ 5) (OR 3.184; 95% CI 1.555-6.522; p < 0.001), lymphovascular invasion (OR 6.831; 95% CI 2.386-19.557; p < 0.001), presence of multiple microinvasion foci (OR 2.771; 95% CI 1.329-5.779; p = 0.007), prominent lymph nodes in preoperative imaging (OR 2.675; 95% CI 1.362-5.253; p = 0.004), and hormone receptor positivity (OR 2.491; 95% CI 1.230-5.046; p = 0.011).

CONCLUSION

Low ALNM rate (4.1%) suggests that routine SLNB for patients with MIBC is unnecessary but can be valuable for patients with specific risk factors. Ongoing trials for omitting SLNB in early breast cancer, and further subanalyses focusing on rare populations with MIBC are necessary.

Ultrasound-based radiomics nomogram for predicting axillary lymph node metastasis in invasive breast cancer

OBJECTIVE

To develop a nomogram for predicting axillary lymph node metastasis (ALNM) in patients with invasive breast cancer.

METHODS

We included 307 patients with clinicopathologically confirmed invasive breast cancer. The cohort was divided into a training group (n=215) and a validation group (n=92). Ultrasound images were used to extract radiomics features. The least absolute shrinkage and selection operator (LASSO) algorithm helped select pertinent features, from which Radiomics Scores (Radscores) were calculated using the LASSO regression equation. We developed three logistic regression models based on Radscores and 2D image features, and assessed the models' performance in the validation group. A nomogram was created from the best-performing model.

RESULTS

In the training set, the area under the curve (AUC) for the Radscore model, 2D feature model, and combined model were 0.76, 0.85, and 0.88, respectively. In the validation set, the AUCs were 0.71, 0.78, and 0.83, respectively. The combined model demonstrated good calibration and promising clinical utility.

CONCLUSION

Our ultrasound-based radiomics nomogram can accurately and non-invasively predict ALNM in breast cancer, suggesting potential clinical applications to optimize surgical and medical strategies.

Ultrasound Pretreatment Lymph Node Evaluation in Early-Stage Breast Cancer: Should We Biopsy High Suspicion Nodes?

BACKGROUND

With the growing incidence of breast cancer, efficient and correct staging is essential for further treatment decisions. Axillary ultrasound (US) remains the most common method for regional nodal involvement assessment. The aim of this study was to evaluate whether high-risk US features can accurately predict axillary lymph node metastasis.

METHODS

A total of 150 early-stage breast cancer patients (T1 or T2) were prospectively included in the study. Based on axillary US, patients were classified as normal, low-risk, or high-risk, with all patients in the low-risk and high-risk groups undergoing fine-needle aspiration (FNAB) and core-needle biopsies.

RESULTS

For the low-risk US group, a lower prediction rate of axillary nodal metastasis was achieved than for the group with high-risk features, recording a sensitivity of 66.6% vs. 89.2%, a specificity of 57.1% vs. 100%, a positive predictive value (PPV) of 26.6% vs. 100%, a negative predictive value (NPV) of 88% for both groups, and an accuracy of 58.9% vs. 94%, respectively. FNAB resulted in more false-negative results compared to core-needle biopsy in both low-risk and high-risk US groups.

CONCLUSIONS

Our findings suggest that high-risk US features can predict axillary lymph node metastasis with high accuracy.

Noninvasive Staging of Lymph Node Status in Breast Cancer Using Machine Learning: External Validation and Further Model Development

BACKGROUND

Most patients diagnosed with breast cancer present with a node-negative disease. Sentinel lymph node biopsy (SLNB) is routinely used for axillary staging, leaving patients with healthy axillary lymph nodes without therapeutic effects but at risk of morbidities from the intervention. Numerous studies have developed nodal status prediction models for noninvasive axillary staging using postoperative data or imaging features that are not part of the diagnostic workup. Lymphovascular invasion (LVI) is a top-ranked predictor of nodal metastasis; however, its preoperative assessment is challenging.

OBJECTIVE

This paper aimed to externally validate a multilayer perceptron (MLP) model for noninvasive lymph node staging (NILS) in a large population-based cohort (n=18,633) and develop a new MLP in the same cohort. Data were extracted from the Swedish National Quality Register for Breast Cancer (NKBC, 2014-2017), comprising only routinely and preoperatively available documented clinicopathological variables. A secondary aim was to develop and validate an LVI MLP for imputation of missing LVI status to increase the preoperative feasibility of the original NILS model.

METHODS

Three nonoverlapping cohorts were used for model development and validation. A total of 4 MLPs for nodal status and 1 LVI MLP were developed using 11 to 12 routinely available predictors. Three nodal status models were used to account for the different availabilities of LVI status in the cohorts and external validation in NKBC. The fourth nodal status model was developed for 80% (14,906/18,663) of NKBC cases and validated in the remaining 20% (3727/18,663). Three alternatives for imputation of LVI status were compared. The discriminatory capacity was evaluated using the validation area under the receiver operating characteristics curve (AUC) in 3 of the nodal status models. The clinical feasibility of the models was evaluated using calibration and decision curve analyses.

RESULTS

External validation of the original NILS model was performed in NKBC (AUC 0.699, 95% CI 0.690-0.708) with good calibration and the potential of sparing 16% of patients with node-negative disease from SLNB. The LVI model was externally validated (AUC 0.747, 95% CI 0.694-0.799) with good calibration but did not improve the discriminatory performance of the nodal status models. A new nodal status model was developed in NKBC without information on LVI (AUC 0.709, 95% CI: 0.688-0.729), with excellent calibration in the holdout internal validation cohort, resulting in the potential omission of 24% of patients from unnecessary SLNBs.

CONCLUSIONS

The NILS model was externally validated in NKBC, where the imputation of LVI status did not improve the model's discriminatory performance. A new nodal status model demonstrated the feasibility of using register data comprising only the variables available in the preoperative setting for NILS using machine learning. Future steps include ongoing preoperative validation of the NILS model and extending the model with, for example, mammography images.

Noninvasive prediction of node-positive breast cancer response to presurgical neoadjuvant chemotherapy therapy based on machine learning of axillary lymph node ultrasound

OBJECTIVES

To explore an optimal model to predict the response of patients with axillary lymph node (ALN) positive breast cancer to neoadjuvant chemotherapy (NAC) with machine learning using clinical and ultrasound-based radiomic features.

METHODS

In this study, 1014 patients with ALN-positive breast cancer confirmed by histological examination and received preoperative NAC in the Affiliated Hospital of Qingdao University (QUH) and Qingdao Municipal Hospital (QMH) were included. Finally, 444 participants from QUH were divided into the training cohort (n = 310) and validation cohort (n = 134) based on the date of ultrasound examination. 81 participants from QMH were used to evaluate the external generalizability of our prediction models. A total of 1032 radiomic features of each ALN ultrasound image were extracted and used to establish the prediction models. The clinical model, radiomics model, and radiomics nomogram with clinical factors (RNWCF) were built. The performance of the models was assessed with respect to discrimination and clinical usefulness.

RESULTS

Although the radiomics model did not show better predictive efficacy than the clinical model, the RNWCF showed favorable predictive efficacy in the training cohort (AUC, 0.855; 95% CI 0.817-0.893), the validation cohort (AUC, 0.882; 95% CI 0.834-0.928), and the external test cohort (AUC, 0.858; 95% CI 0.782-0.921) compared with the clinical factor model and radiomics model.

CONCLUSIONS

The RNWCF, a noninvasive, preoperative prediction tool that incorporates a combination of clinical and radiomics features, showed favorable predictive efficacy for the response of node-positive breast cancer to NAC. Therefore, the RNWCF could serve as a potential noninvasive approach to assist personalized treatment strategies, guide ALN management, avoiding unnecessary ALND.