Validation of the Memorial Sloan Kettering Cancer Center nomogram for predicting non-sentinel lymph node metastasis in sentinel lymph node-positive breast-cancer patients

Individualized prediction of non-sentinel lymph node metastasis in Chinese breast cancer patients with ≥ 3 positive sentinel lymph nodes based on machine-learning algorithms

BACKGROUND

Axillary lymph node dissection (ALND) is a standard procedure for early-stage breast cancer (BC) patients with three or more positive sentinel lymph nodes (SLNs). However, ALND can lead to significant postoperative complications without always providing additional clinical benefits. This study aims to develop machine-learning (ML) models to predict non-sentinel lymph node (non-SLN) metastasis in Chinese BC patients with three or more positive SLNs, potentially allowing the omission of ALND.

METHODS

Data from 2217 BC patients who underwent SLN biopsy at Shantou University Medical College were analyzed, with 634 having positive SLNs. Patients were categorized into those with ≤ 2 positive SLNs and those with ≥ 3 positive SLNs. We applied nine ML algorithms to predict non-SLN metastasis. Model performance was evaluated using ROC curves, precision-recall curves, and calibration curves. Decision Curve Analysis (DCA) assessed the clinical utility of the models.

RESULTS

The RF model showed superior predictive performance, achieving an AUC of 0.987 in the training set and 0.828 in the validation set. Key predictive features included size of positive SLNs, tumor size, number of SLNs, and ER status. In external validation, the RF model achieved an AUC of 0.870, demonstrating robust predictive capabilities.

CONCLUSION

The developed RF model accurately predicts non-SLN metastasis in BC patients with ≥ 3 positive SLNs, suggesting that ALND might be avoided in selected patients by applying additional axillary radiotherapy. This approach could reduce the incidence of postoperative complications and improve patient quality of life. Further validation in prospective clinical trials is warranted.

Association between postoperative pathological results and non-sentinel nodal metastasis in breast cancer patients with sentinel lymph node-positive breast cancer

OBJECTIVE

For patients with 1-2 positive sentinel lymph nodes (SLN) identified by biopsy, the necessity of axillary lymph node dissection (ALND) remains a matter of debate. The primary aim of this study was to investigate the association between postoperative pathological factors and non-sentinel lymph node (NSLN) metastases in Chinese patients diagnosed with sentinel node-positive breast cancer.

METHODS

This research involved a total of 280 individuals with SLN-positive breast cancer. The relationship between postoperative pathological variables and non-sentinel lymph node metastases was scrutinized using univariate, multivariate, and stratified analysis.

RESULTS

Among the 280 patients with a complete count of SLN positives, 126 (45.0%) exhibited NSLN metastasis. Within this group, 45 cases (35.71%) had 1 SLN positive, while 81 cases (64.29%) demonstrated more than 1 SLN positive. Multivariate logistic regression analysis revealed that HER2 expression status (OR 2.25, 95% CI 1.10-4.60, P = 0.0269), LVI (OR 6.08, 95% CI 3.31-11.14, P < 0.0001), and the number of positive SLNs (OR 4.17, 95% CI 2.35-7.42, P < 0.0001) were positively correlated with NSLNM.

CONCLUSION

In our investigation, the risk variables for NSLN metastasis included LVI, HER2 expression, and the quantity of positive sentinel lymph nodes. However, further validation is imperative, including this institution, distinct institutions, and diverse patient populations.

A Radiomics Model for Preoperative Predicting Sentinel Lymph Node Metastasis in Breast Cancer Based on Dynamic Contrast-Enhanced MRI

Purpose

To develop a radiomics model based on preoperative dynamic contrast-enhanced MRI (DCE-MRI) to identify sentinel lymph node (SLN) metastasis in breast cancer (BC) patients.

Materials and Methods

The MRI images and clinicopathological data of 142 female primary BC patients from January 2017 to December 2018 were included in this study. The patients were randomly divided into the training and testing cohorts at a ratio of 7:3. Four types of radiomics models were built: 1) a radiomics model based on the region of interest (ROI) of breast tumor; 2) a radiomics model based on the ROI of intra- and peri-breast tumor; 3) a radiomics model based on the ROI of axillary lymph node (ALN); 4) a radiomics model based on the ROI of ALN and breast tumor. Receiver operating characteristic (ROC) curve analysis and decision curve analysis (DCA) were used to assess the performance of the three radiomics models. The technique for order of preference by similarity to ideal solution (TOPSIS) through decision matrix analysis was used to select the best model.

Results

Models 1, 2, 3, and 4 yielded AUCs of 0.977, 0.999, 0.882, and 1.000 in the training set and 0.699, 0.817, 0.906, and 0.696 in the testing set, respectively, in terms of predicting SLN metastasis. Model 3 had the highest AUC in the testing cohort, and only the difference from Model 1 was statistically significant (p = 0.022). DCA showed that Model 3 yielded a greater net benefit to predict SLN metastasis than the other three models in the testing cohort. The best model analyzed by TOPSIS was Model 3, and the method's names for normalization, dimensionality reduction, feature selection, and classification are mean, principal component analysis (PCA), ANOVA, and support vector machine (SVM), respectively.

Conclusion

ALN radiomics feature extraction on DCE-MRI is a potential method to evaluate SLN status in BC patients.

Preoperative prediction of lymph node metastasis using deep learning-based features

Lymph node involvement increases the risk of breast cancer recurrence. An accurate non-invasive assessment of nodal involvement is valuable in cancer staging, surgical risk, and cost savings. Radiomics has been proposed to pre-operatively predict sentinel lymph node (SLN) status; however, radiomic models are known to be sensitive to acquisition parameters. The purpose of this study was to develop a prediction model for preoperative prediction of SLN metastasis using deep learning-based (DLB) features and compare its predictive performance to state-of-the-art radiomics. Specifically, this study aimed to compare the generalizability of radiomics vs DLB features in an independent test set with dissimilar resolution. Dynamic contrast-enhancement images from 198 patients (67 positive SLNs) were used in this study. Of these subjects, 163 had an in-plane resolution of 0.7 × 0.7 mm², which were randomly divided into a training set (approximately 67%) and a validation set (approximately 33%). The remaining 35 subjects with a different in-plane resolution (0.78 × 0.78 mm²) were treated as independent testing set for generalizability. Two methods were employed: (1) conventional radiomics (CR), and (2) DLB features which replaced hand-curated features with pre-trained VGG-16 features. The threshold determined using the training set was applied to the independent validation and testing dataset. Same feature reduction, feature selection, model creation procedures were used for both approaches. In the validation set (same resolution as training), the DLB model outperformed the CR model (accuracy 83% vs 80%). Furthermore, in the independent testing set of the dissimilar resolution, the DLB model performed markedly better than the CR model (accuracy 77% vs 71%). The predictive performance of the DLB model outperformed the CR model for this task. More interestingly, these improvements were seen particularly in the independent testing set of dissimilar resolution. This could indicate that DLB features can ultimately result in a more generalizable model.

Development of a prediction model based on LASSO regression to evaluate the risk of non-sentinel lymph node metastasis in Chinese breast cancer patients with 1-2 positive sentinel lymph nodes

This study aimed to develop an intraoperative prediction model to evaluate the risk of non-sentinel lymph node (NSLN) metastasis in Chinese breast cancer patients with 1–2 positive sentinel lymph nodes (SLNs). The clinicopathologic data of 714 patients with 1–2 positive SLNs were investigated. Univariate and multivariate analyses were performed to identify the risk factors of NSLN metastasis. A new mathematical prediction model was developed based on LASSO and validated in an independent cohort of 131 patients. The area under the receiver operating characteristic curve (AUC) was used to quantify performance of the model. Patients with NSLN metastasis accounted for 37.3% (266/714) and 34.3% (45/131) of the training and validation cohorts, respectively. A LASSO regression-based prediction model was developed and included the 13 most powerful factors (age group, clinical tumour stage, histologic type, number of positive SLNs, number of negative SLNs, number of SLNs dissected, SLN metastasis ratio, ER status, PR status, HER2 status, Ki67 staining percentage, molecular subtype and P53 status). The AUCs of training and validation cohorts were 0.764 (95% CI 0.729–0.798) and 0.777 (95% CI 0.692–0.862), respectively. We presented a new prediction model with excellent clinical applicability and diagnostic performance for use by clinicians as an intraoperative clinical tool to predict risk of NSLN metastasis in Chinese breast cancer patients with 1–2 positive SLNs and make the final decisions regarding axillary lymph node dissection.

Establishing a prediction model of axillary nodal burden based on the combination of CT and ultrasound findings and the clinicopathological features in patients with early-stage breast cancer

Background

Axillary lymph node (ALN) management in early-stage breast cancer (ESBC) patients has become less invasive during the past decades. Here, we tried to explore whether high nodal burden (HNB) in ESBC patients could be predicted preoperatively, so as to avoid unnecessary sentinel lymph node biopsy (SLNB).

Methods

The clinicopathological and imaging data of patients with early invasive breast cancer (cT_1-2N₀M₀) were analyzed retrospectively. Univariate and multivariate analyses were performed for the risk factors of axillary HNB in ESBC patients, and a risk prediction model of HNB was established.

Results

HNB was identified in 105 (8.0%) of 1,300 ESBC patients. Multivariate analysis showed that estrogen receptors (ER) status, human epidermal growth factor receptor 2 (HER2) status, number of abnormal lymph nodes (LNs) on computed tomography (CT), and axillary score on ultrasound (US) were the risk factors of HNB (all P<0.05). The area under the receiver operating characteristic (ROC) curve in the prediction model was 0.914, with the sensitivity being 85.7% and the specificity being 82.4%. The calibration curve showed that the prediction model had good performance.

Conclusions

As a valuable tool for predicting HNB in ESBC patients, this newly established model helps clinicians to make reasonable axillary surgery decisions and thus avoid unnecessary SLNB.

Validation of Breast Cancer Models for Predicting the Nonsentinel Lymph Node Metastasis After a Positive Sentinel Lymph Node Biopsy in a Chinese Population

Objectives:

Over the years, completion axillary lymph node dissection is recommended for the patients with breast cancer if sentinel lymph node metastasis is found. However, not all of these patients had nonsentinel lymph node metastasis on final histology. Some predicting models have been developed for calculating the risk of nonsentinel lymph node metastasis. The aim of our study was to validate some of the predicting models in a Chinese population.

Method:

Two hundred thirty-six patients with positive sentinel lymph node and complete axillary lymph node dissection were included. Patients were applied to 6 models for evaluation of the risk of nonsentinel lymph node involvement. The receiver–operating characteristic curves were shown in our study. The calculation of area under the curves and false negative rate was done for each model to assess the discriminative power of the models.

Results:

There are 105 (44.5%) patients who had metastatic nonsentinel lymph node(s) in our population. Primary tumor size, the number of metastatic sentinel lymph node, and the proportion of metastatic sentinel lymph nodes/total sentinel lymph nodes were identified as the independent predictors of nonsentinel lymph node metastasis. The Seoul National University Hospital and Louisville scoring system outperformed the others, with area under the curves of 0.706 and 0.702, respectively. The area under the curve values were 0.677, 0.673, 0.432, and 0.674 for the Memorial Sloan-Kettering Cancer Center, Tenon, Stanford, and Shanghai Cancer Hospital models, respectively. With adjusted cutoff points, the Louisville scoring system outperformed the others by classifying 26.51% of patients with breast cancer to the low-risk group.

Conclusion:

The Louisville and Seoul National University Hospital scoring system were found to be more predictive among the 6 models when applied to the Chinese patients with breast cancer in our database. Models developed at other institutions should be used cautiously for decision-making regarding complete axillary lymph node dissection after a positive biopsy in sentinel lymph node.

Management of the Axilla in the Era of Breast Cancer Heterogeneity

Systemic cancer therapies take into account breast cancer (BC) heterogeneity by targeting pathways specifically involved in some BC subtypes. On the other hand, BC intrinsic radiosensitivity is poorly understood and studied. Hence, radiotherapy personalization in BC is still “work in progress”. In this review, we will summarize the existing data on the management of axillary lymph nodes in BC, the impact of BC radiotherapy on axillary management, the indications for axillary radiotherapy, and biomarkers to predict patients’ outcome (tumor control and late toxicities) after axillary irradiation.

Role of number of sentinel nodes in predicting non-sentinel node metastasis in breast cancer

Objective

The aim of the present study was to determine how many sentinel lymph nodes (SLNs) are appropriate for predicting non-SLN metastasis in breast cancer.

Methods

The association between clinicopathological features and non-SLN metastasis was retrospectively analyzed in 472 patients who underwent axillary lymph node dissection (ALND) following SLN biopsy. Another 251 patients who underwent only SLN biopsy without ALND were analyzed and followed up for 2 years.

Results

A large tumor size, positive SLN, and HER-2 positivity were independent predictors of non-SLN metastasis. There were significant differences in non-SLN metastasis between patients with one negative SLN and patients with an absence of negative SLNs. There was no significant difference in non-SLN metastasis between patients with one negative SLN and two or more negative SLNs. The recurrence-free survival rate for patients who did not undergo ALND was 99.6% (245/246).

Conclusion

Surgeons should ensure that the number of SLNs obtained is appropriate. The presence of one negative SLN is enough in SLN biopsy. Considering the invasiveness of the surgery, two or more negative SLNs may be unnecessary.

Preoperative prediction of sentinel lymph node metastasis in breast cancer based on radiomics of T2-weighted fat-suppression and diffusion-weighted MRI

OBJECTIVES

To predict sentinel lymph node (SLN) metastasis in breast cancer patients using radiomics based on T₂-weighted fat suppression (T₂-FS) and diffusion-weighted MRI (DWI).

METHODS

We enrolled 146 patients with histologically proven breast cancer. All underwent pretreatment T₂-FS and DWI MRI scan. In all, 10,962 texture and four non-texture features were extracted for each patient. The 0.623 + bootstrap method and the area under the curve (AUC) were used to select the features. We constructed ten logistic regression models (orders of 1-10) based on different combination of image features using stepwise forward method.

RESULTS

For T₂-FS, model 10 with ten features yielded the highest AUC of 0.847 in the training set and 0.770 in the validation set. For DWI, model 8 with eight features reached the highest AUC of 0.847 in the training set and 0.787 in the validation set. For joint T₂-FS and DWI, model 10 with ten features yielded an AUC of 0.863 in the training set and 0.805 in the validation set.

CONCLUSIONS

Full utilisation of breast cancer-specific textural features extracted from anatomical and functional MRI images improves the performance of radiomics in predicting SLN metastasis, providing a non-invasive approach in clinical practice.

KEY POINTS

• SLN biopsy to access breast cancer metastasis has multiple complications. • Radiomics uses features extracted from medical images to characterise intratumour heterogeneity. • We combined T ₂ -FS and DWI textural features to predict SLN metastasis non-invasively.

Prognostic nomogram for previously untreated patients with esophageal squamous cell carcinoma after esophagectomy followed by adjuvant chemotherapy

OBJECTIVE

The aim of the study was to establish an effective prognostic nomogram for esophageal squamous cell carcinoma after radical esophagectomy followed by adjuvant chemotherapy in those previously untreated patients.

METHODS

The clinicopathological data from 328 patients who underwent radical esophagectomy followed by adjuvant chemotherapy or not at the Tianjin Medical University Cancer Institute and Hospital between 2006 and 2010 were retrospectively studied. Nomograms which predicted survival of esophageal squamous cell carcinoma were established based on the Cox proportional hazards regression model. To determine its predictive accuracy and discriminatory capacity, the concordance index and calibration curve were calculated after bootstrapping in the internal validation. An external validation of 76 patients in 2011 was prospectively studied at the same institution. To verify the performance of the nomogram, the comparison between the nomogram and Tumor-Node-Metastasis staging system was conducted.

RESULTS

The 5-year overall survival was 43.1% in the primary cohort. Based on multivariate analyses, five independent prognostic variables including gender, tumor length, T stage, N stage and chemotherapy cycles were selected to build the nomograms to predict disease-free survival and overall survival. The concordance index of the nomogram to predict overall survival was 0.71 (95% confidence interval, 0.63-0.79), which was superior to the predictive power of Tumor-Node-Metastasis staging system (0.64) in the primary cohort. Meanwhile, the calibration curve showed good accuracy between predictive and actual overall survival. In the validation cohort, the concordance index (0.77) and calibration plot displayed favorable performances. The other nomogram to predict disease-free survival also performed well.

CONCLUSIONS

The prognostic nomogram provided individualized risk estimate of survival in patients after esophagectomy followed by adjuvant chemotherapy.

Nanomaterials-Tools, Technology and Methodology of Nanotechnology Based Biomedical Systems for Diagnostics and Therapy

Nanomedicine helps to fight diseases at the cellular and molecular level by utilizing unique properties of quasi-atomic particles at a size scale ranging from 1 to 100 nm. Nanoparticles are used in therapeutic and diagnostic approaches, referred to as theranostics. The aim of this review is to illustrate the application of general principles of nanotechnology to select examples of life sciences, molecular medicine and bio-assays. Critical aspects relating to those examples are discussed.