Predicting Sentinel Lymph Node Metastasis Research Articles

Molybdenum target mammography-based prediction model for metastasis of axillary sentinel lymph node in early-stage breast cancer.

Sentinel lymph node (SLN) status is closely related to axillary lymph node metastasis in breast cancer. However, SLN biopsy has certain limitations due to invasiveness and diagnostic efficiency. This study aimed to develop a model to predict the risk of axillary SLN metastasis in early-stage breast cancer based on mammography, a noninvasive, cost-effective, and potential complementary way. Herein, 649 patients with early-stage breast cancer (cT1-T2) who received SLN biopsy were assigned to the training cohort (n = 487) and the validation cohort (n = 162). A prediction model based on specific characteristics of tumor mass in mammography was developed and validated with R software. The performance of model was evaluated by receiver operating characteristic curve, calibration plot, and decision curve analysis. Tumor margins, spicular structures, calcification, and tumor size were independent predictors of SLN metastasis (all P < .05). A nomogram showed a satisfactory performance with an AUC of 0.829 (95% CI = 0.792-0.865) in the training cohort and an AUC of 0.825 (95% CI = 0.763-0.888) in validation cohort. The consistency between model-predicted results and actual observations showed great Hosmer-Lemeshow goodness-of-fit (P = .104). Patients could benefit from clinical decisions guided by the present model within the threshold probabilities of 6% to 84%. The prediction model for axillary SLN metastasis showed satisfactory discrimination, calibration abilities, and wide clinical practicability. These findings suggest that our prediction model based on mammography characteristics is a reliable tool for predicting SLN metastasis in patients with early-stage breast cancer.

Ultrasound-based radiomics combined with immune status to predict sentinel lymph node metastasis in primary breast cancer

In the past few years, the axillary lymph node dissection technique has been steadily replaced by sentinel lymph node biopsy for treating and diagnosing breast cancer, thereby minimizing the complications and sequelae of the patients. Nevertheless, sentinel lymph node biopsy still presents limitations, such as high operation requirements, prolonged surgical duration, and adverse reactions to tracer agents. This study developed a novel non-invasive method to predict sentinel lymph node metastasis in breast cancer by analyzing the ultrasound imaging characteristics of the primary tumor, combined with the analysis of peripheral blood T-cell subsets that reflect the immune status of the body. The radiomic features analyzed in this study were extracted from preoperative ultrasound images of 199 solitary breast cancer patients, who were undergoing surgery and were pathologically diagnosed at the Yancheng First People's Hospital. All cases were randomly categorized in a 4:1 ratio to the training (n = 159) and validation (n = 40) cohorts. The extracted radiomics features were subjected to dimensional reduction with the help of the least absolute shrinkage and selection operator technique, resulting in the inclusion of 19 radiomics features. Four classifiers, including naïve Bayesian, logistic regression, classification decision tree, and support vector machine, were utilized to model the radiomics features, conventional ultrasound features, and peripheral blood T cell subsets in the training dataset, and validated using the validation dataset. The best-performing model was chosen for constructing the combined model. The radiomics model constructed using the logistic regression showed the best performance, with the training and validation cohorts showing areas under the curve (AUCs) of 0.77 and 0.68, respectively. The conventional ultrasound and peripheral blood T cell models constructed using the classification decision tree showed the best performance, wherein the training cohort presented AUCs of 0.71 and 0.81, respectively, while the validation cohort presented AUCs of 0.68 and 0.69, respectively. The combined model constructed by logistic regression showed AUCs of 0.91 and 0.79 in the training and validation datasets, respectively. The resulting combined model can be considered a simple, non-invasive method with strong reproducibility and clinical significance. Thus, it can be utilized to predict sentinel lymph node metastasis in breast cancer. Furthermore, the combined model can be effectively used to guide clinical decisions related to the selection of surgical procedures in breast surgery.

A novel nomogram for the preoperative prediction of sentinel lymph node metastasis in breast cancer.

A practical noninvasive method to identify sentinel lymph node (SLN) status in breast cancer patients, who had a suspicious axillary lymph node (ALN) at ultrasound (US), but a negative clinical physical examination is needed. To predict SLN metastasis using a nomogram based on US and biopsy-based pathological features, this retrospective study investigated associations between clinicopathological features and SLN status. Patients treated with SLN dissection at four centers were apportioned to training, internal, or external validation sets (n=472, 175, and 81). Lymph node ultrasound and pathological characteristics were compared using chi-squared and t-tests. A nomogram predicting SLN metastasis was constructed using multivariate logistic regression models. In the training set, statistically significant factors associated with SLN+ were as follows: histology type (p < 0.001); progesterone receptor (PR: p=0.003); Her-2 status (p=0.049); and ALN-US shape (p=0.034), corticomedullary demarcation (CMD: p < 0.001), and blood flow (p=0.001). With multivariate analysis, five independent variables (histological type, PR status, ALN-US shape, CMD, and blood flow) were integrated into the nomogram (C-statistic 0.714 [95% CI: 0.688-0.740]) and validated internally (0.816 [95% CI: 0.784-0.849]) and externally (0.942 [95% CI: 0.918-0.966]), with good predictive accuracy and clinical applicability. This nomogram could be a direct and reliable tool for individual preoperative evaluation of SLN status, and therefore aids decisions concerning ALN dissection and adjuvant treatment.

Is the Platelet-Lymphocyte Ratıo a Useful Tool for Predicting Sentinel Lymph Node Metastasıs in Breast Cancer Patients Receiving Neoadjuvant Therapy?

Aim: Breast cancer is one of the cancers with the highest morbidity and mortality in women. Surgical excision of axillary lymph nodes facilitates staging and provides regional control in those with axillary metastases. Although SLNB is routinely performed in the management of patients with cN0 disease who underwent primary surgery, it is controversial when to perform SLNB in patients receiving neoadjuvant chemotherapy. In our study, we evaluated the success of the platelet/lymphocyte ratio before and after neoadjuvant therapy in predicting sentinel lymph node metastases in breast cancer patients receiving neoadjuvant therapy.&#x0D; &#x0D; Materials-Methods: Patients who received neoadjuvant chemotherapy for locally advanced breast cancer in our clinic were evaluated. Among these patients, patients with histopathologically proven axilla metastases but no pathological lymph nodes in clinical examination and imaging methods after neoadjuvant therapy were evaluated.&#x0D; &#x0D; Results: An average of 3.81 lymph node excisions were performed for sentinel lymph node sampling. We studied the PLR cut-off value with Roc-curve analysis. We found the cut-off value of 138.88 with a standard error of 0.061 (p=0.001). Patients with high PLR are more likely to have sentinel lymph node metastases than patients with low PLR (OR= 1.013, 95%Cl: 1.005-1.021, p=0.002). We also found a significant positive correlation between PLR and the number of metastatic sentinel lymph nodes (p=0.005). Each unit increase in PLR can cause an increase of 0.004 units in the number of metastatic sentinel lymph nodes.&#x0D; Conclusion: In patients receiving neoadjuvant chemotherapy, PLR plays an important role in predicting sentinel lymph node metastasis as a practical, simple, and inexpensive hematological indicator and may facilitate the selection of an appropriate treatment plan before surgery.&#x0D; Keywords: Sentinel lymph node, Breast cancer, inflammatory parameters

A model for the early identification of sentinel lymph node metastasis in patients with breast cancer based on contrast-enhanced ultrasound and clinical features.

The present study was designed to establish a model for the early identification of sentinel lymph node (SLN) metastasis in patients with breast cancer (BC). The SLN metastasis predictive model was established with a retrospective training set of 365 patients with BC and was re-evaluated using a prospective validation set of 402 patients with BC. The multivariable analysis indicated that the tumor diameter [odds ratio (OR), 1.189; 95% confidence interval (CI), 1.124-1.257; P<0.001], menopause (OR, 1.011; 95% CI, 0.603-1.436; P<0.001), estrogen receptor (ER) expression (OR, 3.199; 95% CI, 1.077-6.567; P=0.043) and contrast-enhanced ultrasonography (CEUS) type (OR, 10.563; 95% CI, 6.890-28.372; P<0.001) were independent predictors of SLN status in patients with BC. The SLN metastasis predictive model was as follows: (0.173 × tumor diameter)-(4.490 × menopause) + (2.322 × ER) + (5.445 × CEUS type)-1.9521. In the training set, the model was highly sensitive (83.6%) and specific (94.3%) for the early identification of SLN metastasis. Similarly, in the validation set, the model was highly sensitive (70.4%) and specific (89.5%) for the early identification of SLN metastasis in patients with BC. Overall, in the present study, a model was successfully established to predict SLN metastasis in patients with BC that includes tumor diameter, menopausal status, ER expression and CEUS detection.

A machine learning model based on ultrasound image features to assess the risk of sentinel lymph node metastasis in breast cancer patients: Applications of scikit-learn and SHAP.

BackgroundThis study aimed to determine an optimal machine learning (ML) model for evaluating the preoperative diagnostic value of ultrasound signs of breast cancer lesions for sentinel lymph node (SLN) status.MethodThis study retrospectively analyzed the ultrasound images and postoperative pathological findings of lesions in 952 breast cancer patients. Firstly, the univariate analysis of the relationship between the ultrasonographic features of breast cancer morphological features and SLN metastasis. Then, based on the ultrasound signs of breast cancer lesions, we screened ten ML models: support vector machine (SVM), extreme gradient boosting (XGBoost), random forest (RF), linear discriminant analysis (LDA), logistic regression (LR), naive bayesian model (NB), k-nearest neighbors (KNN), multilayer perceptron (MLP), long short-term memory (LSTM), and convolutional neural network (CNN). The diagnostic performance of the model was evaluated using the area under the receiver operating characteristic (ROC) curve (AUC), Kappa value, accuracy, F1-score, sensitivity, and specificity. Then we constructed a clinical prediction model which was based on the ML algorithm with the best diagnostic performance. Finally, we used SHapley Additive exPlanation (SHAP) to visualize and analyze the diagnostic process of the ML model.ResultsOf 952 patients with breast cancer, 394 (41.4%) had SLN metastasis, and 558 (58.6%) had no metastasis. Univariate analysis found that the shape, orientation, margin, posterior features, calculations, architectural distortion, duct changes and suspicious lymph node of breast cancer lesions in ultrasound signs were associated with SLN metastasis. Among the 10 ML algorithms, XGBoost had the best comprehensive diagnostic performance for SLN metastasis, with Average-AUC of 0.952, Average-Kappa of 0.763, and Average-Accuracy of 0.891. The AUC of the XGBoost model in the validation cohort was 0.916, the accuracy was 0.846, the sensitivity was 0.870, the specificity was 0.862, and the F1-score was 0.826. The diagnostic performance of the XGBoost model was significantly higher than that of experienced radiologists in some cases (P<0.001). Using SHAP to visualize the interpretation of the ML model screen, it was found that the ultrasonic detection of suspicious lymph nodes, microcalcifications in the primary tumor, burrs on the edge of the primary tumor, and distortion of the tissue structure around the lesion contributed greatly to the diagnostic performance of the XGBoost model.ConclusionsThe XGBoost model based on the ultrasound signs of the primary breast tumor and its surrounding tissues and lymph nodes has a high diagnostic performance for predicting SLN metastasis. Visual explanation using SHAP made it an effective tool for guiding clinical courses preoperatively.

Toward Exempting from Sentinel Lymph Node Biopsy in T1 Breast Cancer Patients: A Retrospective Study.

Background and ObjectiveSentinel lymph node biopsy (SLNB) is used to assess the status of axillary lymph node (ALN), but it causes many adverse reactions. Considering the low rate of sentinel lymph node (SLN) metastasis in T1 breast cancer, this study aims to identify the characteristics of T1 breast cancer without SLN metastasis and to select T1 breast cancer patients who avoid SLNB through constructing a nomogram.MethodsA total of 1,619 T1 breast cancer patients with SLNB in our hospital were enrolled in this study. Through univariate and multivariate logistic regression analysis, we analyzed the tumor anatomical and clinicopathological factors and constructed the Heilongjiang Medical University (HMU) nomogram. We selected the patients exempt from SLNB by using the nomogram.ResultsIn the training cohort of 1,000 cases, the SLN metastasis rate was 23.8%. Tumor volume, swollen axillary lymph nodes, pathological types, and molecular subtypes were found to be independent predictors for SLN metastasis in multivariate regression analysis. Distance from nipple or surface and position of tumor have no effect on SLN metastasis. A regression model based on the results of the multivariate analysis was developed to predict the risk of SLN metastasis, indicating an AUC of 0.798. It showed excellent diagnostic performance (AUC = 0.773) in the validation cohort.ConclusionThe HMU nomogram for predicting SLN metastasis incorporates four variables, including tumor volume, swollen axillary lymph nodes, pathological types, and molecular subtypes. The SLN metastasis rates of intraductal carcinoma and HER2 enriched are 2.05% and 6.67%. These patients could be included in trials investigating the SLNB exemption.

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

PurposeTo 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 MethodsThe 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.ResultsModels 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.ConclusionALN radiomics feature extraction on DCE-MRI is a potential method to evaluate SLN status in BC patients.

Role of Hormonal Receptor in Predicting Sentinel Lymph Node Metastasis in Early Breast Cancer.

Background:Sentinel lymph node biopsy is one of the minimally invasive techniques that can confirm the presence of metastasis of regional lymph nodes in cancer. Sentinel lymph node biopsy can be done with a lymph mapping technique using blue-dye, radiotracer, or a combination of both. In developing countries, sentinel lymph node biopsy is often done with a single agent, which is the blue dye. The limitation of conducting SLNB in Indonesia is the availability of patent blue dye and radioisotope tracer. To overcome that, a hormonal receptor is expected to aid in predicting sentinel lymph node metastasis.Objective:The aim of this study was to investigate the hormonal receptor as a prognostic factor of sentinel lymph node metastasis in breast cancer.Methods:This study was conducted in Universitas Sumatera Utara Teaching Hospital with the acknowledgment from the Ethics Committee of the respected hospital by the number of 116/KEP/USU/2020. Total of 51 patients participated in this research.Results:Statistically, the p-value in each immunohistochemistry group is > 0.05 in all ER (+) / PR (+); ER (+) / PR (-); ER (-) / PR (+) groups. This shows that there is no significant relationship between hormonal receptors on sentinel lymph node metastases.Conclusion:The statistical evaluation showed that there is no significant correlation between the hormonal receptor and sentinel lymph node metastasis (p>0.05), but is found clinically significant. Therefore, hormonal receptors should be considered as a predicting factor for sentinel lymph node metastasis.

Does failed mapping predict sentinel lymph node metastasis in cN0 breast cancer?

Aims: The clinical significance of nonvisualized sentinel lymph nodes (non-vSLNs) is unknown. The authors sought to determine the incidence of non-vSLNs on lymphoscintigraphy, the identification rate during surgery, factors associated with non-vSLNs and related axillary management. Patients & methods:A total of30,508 consecutive SLN proceduresperformed at a single institutionfrom 2000 to 2017were retrospectively studied. Associations between clinicopathological factors and the identification of SLNs during surgery were assessed. Results: Non-vSLN occurred in 525 of the procedures (1.7%). In 73.3%, at least one SLN was identified intraoperatively. Nodal involvement was only significantly associated with SLN nonidentification (p<0.001). Conclusion: Patients with non-vSLN had an increased risk for SLN metastasis. The detection rate during surgery was consistent, reducing the amount of unnecessary axillary dissection.

MelRisk: Using neutrophil-to-lymphocyte ratio to improve risk prediction models for metastatic cutaneous melanoma in the sentinel lymph node

Identifying metastatic melanoma in the sentinel lymph node (SLN) is important because 80% of SLN biopsies are negative and 11% of patients develop complications. The neutrophil-to-lymphocyte ratio (NLR), a biomarker of micrometastatic disease, could improve prediction models for SLN status. We externally validated existing models and developed 'MelRisk' prognostic score to better predict SLN metastasis. The models were externally validated using data from a multicenter cohort study of 1,251 adults. Additionally, we developed and internally validated a new prognostic score `MelRisk', using candidate predictors derived from the extant literature. The Karakousis model had a C-statistic of 0.58 (95% CI, 0.54-0.62). The Sondak model had a C-statistic of 0.57 (95% CI 0.53-0.61). The MIA model had a C-statistic of 0.60 (95% CI. 0.56-0.64). Our 'MelRisk' model (which used Breslow thickness, ulceration, age, anatomical site, and the NLR) showed an adjusted C-statistic of 0.63 (95% CI, 0.56-0.64). Our prediction tool is freely available in the Google Play Store and Apple App Store, and we invite colleagues to externally validate its performance .

Establishment of a model for predicting sentinel lymph node metastasis in early breast cancer based on contrast-enhanced ultrasound and clinicopathological features.

Sentinel lymph node (SLN) biopsy (SLNB) is the standard procedure for axillary staging in clinically node-negative (cN0) breast cancer patients. However, the positive rate of SLNs among cN0 stage patients is 26-35%. The identification of appropriate candidates for SLNB is quite challenging. This study aimed to establish and verify a predictive model of SLN metastasis using contrast-enhanced ultrasound (CEUS) and other clinicopathological indicators. The clinicopathological data of 224 patients who had undergone SLNB at the Affiliated Cancer Hospital of Zhengzhou University from June 2018 to July 2019 were analyzed retrospectively. The risk prediction model of SLN metastasis was established by logistic regression analysis. According to the β value of each variable in the model, a risk score system of SLN metastasis was established and verified using the internal population. The predictive model was prospectively applied to 73 patients from July 2019 to September 2019 to evaluate the clinical value of the model in patients with early breast cancer. Multivariate analysis confirmed that body mass index (BMI), SLN aspect ratio of CEUS mode, SLN aspect ratio of mammography, lympho-vascular invasion, and cytokeratin (CK)5/6 were independent risk factors for SLN metastasis. A scoring system was established according to the above risk factors, and a receiver operating characteristic (ROC) curve was drawn. After internal- and external verification, a corrected ROC curve was drawn, respectively. The ROC curve of the modeling group, internal verification group, and external verification group was 0.9075 (95% CI: 0.8616-0.9534), 0.8766 (95% CI: 0.8192-0.9341), and 0.8505 (95% CI: 0.7333-0.9676), respectively. We constructed and verified a prediction model of SLN metastasis in early breast cancer. The model has a specific predictive value for preoperative evaluation of SLN status.

Validation of CP‐GEP (Merlin Assay) for predicting sentinel lymph node metastasis in primary cutaneous melanoma patients: A U.S. cohort study

BackgroundApproximately 85% of melanoma patients who undergo a sentinel lymph node biopsy (SLNB) are node‐negative. Melanoma incidence is highest in patients ≥65 years, but their SLNB positivity rate is lower than in younger patients. CP‐GEP, a model combining clinicopathologic and gene expression variables, identifies primary cutaneous melanoma (CM) patients who may safely forgo SLNB due to their low risk for nodal metastasis. Here, we validate CP‐GEP in a U.S. melanoma patient cohort.MethodsA cohort of 208 adult patients with primary CM from the Mayo Clinic and West Virginia University was used. Patients were stratified according to their risk for nodal metastasis: CP‐GEP High Risk and CP‐GEP Low Risk. The main performance measures were SLNB reduction rate (RR) and negative predictive value (NPV).ResultsSLNB positivity rate for the entire cohort was 21%. Most patients had a T1b (34%) or T2a (31%) melanoma. In the T1‐T2 group (153 patients), CP‐GEP achieved an SLNB RR of 41.8% (95% CI: 33.9‐50.1) at an NPV of 93.8% (95% CI: 84.8‐98.3). Subgroup analysis showed similar performance in T1‐T2 patients ≥65 years of age (51 patients; SLNB positivity rate, 9.8%): SLNB RR of 43.1% (95% CI: 29.3‐57.8) at an NPV of 95.5% (95% CI: 77.2‐99.9).ConclusionWe confirmed the potential of CP‐GEP to reduce negative SLNB in all relevant age groups. Our findings are especially relevant to patients ≥65 years, where surgery is often elective. CP‐GEP may guide SLNB decision‐making in clinical practice.

[AI-assisted Prediction of Lymph Node Metastasis of Breast Cancer: Current and Prospective Research].

One of the most important application of artificial intelligence (AI) in pathology is prediction, using morphological features, of patient prognosis and response to specific treatments. As one of the most common kinds of malignancies in the world and the crucial important cause of death due to malignant tumor among women, breast cancer has become the center of attention in clinical services. Axillary lymph node metastasis is an important prognostic factor in breast cancer. The accuracy of the assessment of axillary lymph node metastasis bears heavily on clinical diagnosis and treatment. At present, based on the principle of non-invasive procedures, many studies have been done to develop models that can be used to predict sentinel lymph node metastasis of breast cancer. However, different clinical and pathological parameters are used in these predictive models. How to analyze the clinical and pathological data of breast cancer patients in a more comprehensive way and how to establish a prediction model with better precision have become the future direction of development. In this paper, we describe the research progress of AI in pathology and the current status of its use in breast cancer research. We have conducted in-depth reflection and looked into the future of ways to predict effectively breast cancer lymph node metastasis and to establish more accurate and effective deep-learning algorithm based on AI assistance so as to continuously improve the diagnosis and treatment of breast cancer.

Preoperative ultrasound-based radiomics score can improve the accuracy of the Memorial Sloan Kettering Cancer Center nomogram for predicting sentinel lymph node metastasis in breast cancer

PurposeTo develop a combined nomogram by incorporating the Memorial Sloan Kettering Cancer Center (MSKCC) nomogram and ultrasound (US)-based radiomics score (Radscore) for predicting sentinel lymph node (SLN) metastasis in invasive breast cancer. Materials and MethodsThis retrospective study was approved by the ethics committee of our institution, and written informed consent was waived. A total of 452 patients with invasive breast cancer who received SLN Biopsy in a single center were included between January 2016 and December 2019. The patients were divided into a training set (n = 318) and a validation set (n = 134). A total of 1216 features were extracted from the regions of interest (ROIs) of the tumors on conventional ultrasound. The maximum relevance minimum redundancy (mRMR) and the least absolute shrinkage and selection operator (LASSO) algorithm were used to build the Radscore. Afterward, the diagnostic performance was assessed and validated. Comparison of receiver operating characteristic (ROC) curves and decision curve analysis (DCA) were performed to evaluate the incremental value of the combined model. ResultsObtained from 18 features, the Radscore indicated a favorable discriminatory capability in the training set with an area under the curve (AUC) of 0.834, whereas a value of 0.770 was observed in the validation set. The AUC of the combined model was 0.901 (95 % confidence interval (95 % CI): 0.865−0.938) in the training set and 0.833 (95 % CI: 0.788−0.878) in the validation set. Both of them were superior to MSKCC or imaging Radscore alone (P < 0.05). DCA demonstrated that the combined model was superior to the others in terms of clinical practicability. ConclusionPreoperative US-based Radscore can improve the accuracy of clinical MSKCC nomogram for SLN metastasis prediction in breast cancer.

Prediction of Sentinel Lymph Node Metastasis in Breast Ductal Carcinoma In Situ Diagnosed by Preoperative Core Needle Biopsy.

PurposeThe positivity of sentinel lymph node (SLN) metastasis is relatively low in ductal carcinoma in situ (DCIS) patients. The aim of this study was to investigate factors associated with SLN metastasis and build a model to predict the potential risk of SLN metastasis in patients with a preoperative diagnosis of DCIS.Patients and MethodsCore needle biopsy-proved DCIS patients who underwent SLN biopsy and breast surgery were retrospectively reviewed and selected. Univariate analysis was used to identify the variables correlated with SLN metastasis. A model to predict SLN metastasis was developed using a multivariate logistic regression in the training set and then validated in an internal set.ResultsA total of 407 patients with a preoperative diagnosis of DCIS were included. Upstaging to invasive/microinvasive cancer occurred in 225 patients after surgery. SLN metastasis was found in 42 patients, including 32 patients upstaging to invasive disease, 8 to microinvasive disease, and 2 pure DCIS. Tumor size based on US examination, axillary ultrasound finding, multifocality, surgery, upstaging, and Ki-67 expression were significantly related to SLN metastasis. The model incorporating tumor size, axillary ultrasound finding and multifocality yielded an AUC of 0.805 (95% CI: 0.715–0.895, p<0.001) in the training set, and 0.729 (95% CI: 0.547–0.911, p=0.013) in the testing set.ConclusionA simple model was developed to predict SLN metastasis in patients with a preoperative diagnosis of DCIS. With good discriminatory power, this model should be helpful for surgeons to decide if SLN biopsy could be safely avoided in certain patients.

Optimizing the Peritumoral Region Size in Radiomics Analysis for Sentinel Lymph Node Status Prediction in Breast Cancer

Peritumoral features have been suggested to be useful in improving the prediction performance of radiomic models. The aim of this study is to systematically investigate the prediction performance improvement for sentinel lymph node (SLN) status in breast cancer from peritumoral features in radiomic analysis by exploring the effect of peritumoral region sizes. This retrospective study was performed using dynamic contrast-enhanced MRI scans of 162 breast cancer patients. The effect of peritumoral features was evaluated in a radiomics pipeline for predicting SLN metastasis in breast cancer. Peritumoral regions were generated by dilating the tumor regions-of-interest (ROIs) manually annotated by two expert radiologists, with thicknesses of 2 mm, 4 mm, 6 mm, and 8 mm. The prediction models were established in the training set (∼67% of cases) using the radiomics pipeline with and without peritumoral features derived from different peritumoral thicknesses. The prediction performance was tested in an independent validation set (the remaining ∼33%). For this specific application, the accuracy in the validation set when using the two radiologists' ROIs could be both improved from 0.704 to 0.796 by incorporating peritumoral features. The choice of the peritumoral size could affect the level of improvement. This study systematically investigates the effect of peritumoral region sizes in radiomic analysis for prediction performance improvement. The choice of the peritumoral size is dependent on the ROI drawing and would affect the final prediction performance of radiomic models, suggesting that peritumoral features should be optimized in future radiomics studies.

Prediction of lymph node metastasis by tumor-infiltrating lymphocytes in T1 breast cancer

BackgroundLymph node metastasis is more likely in early-stage breast cancer with lower tumor-infiltrating lymphocyte (TIL) density. Therefore, we investigated the correlation between TILs and lymph node metastasis in cT1 breast cancer patients undergoing surgery and the usefulness of TILs in predicting sentinel lymph node metastasis (SLNM) in cT1N0M0 breast cancer.MethodsWe investigated 332 breast cancer patients who underwent surgery as the first-line treatment after preoperative diagnosis of cT1. A positive diagnosis of SLNM as an indication for axillary clearance was defined as macrometastasis in the sentinel lymph node (SLN) (macrometastasis: tumor diameter > 2 mm). Semi-quantitative evaluation of lymphocytes infiltrating the peritumoral stroma as TILs in primary tumor biopsy specimens prior to treatment was conducted.ResultsFor SLN biopsy (SLNB), a median of 2 (range, 1–8) SLNs were pathologically evaluated. Sixty cases (19.4%) of SLNM (macrometastasis: 46, micrometastasis: 16) were observed. Metastasis was significantly greater in breast cancers with tumor diameter > 10 mm than in those with diameter ≤ 10 mm (p = 0.016). Metastasis was significantly associated with lymphatic invasion (p < 0.001). These two clinicopathological factors correlated with SLNM even in patients diagnosed with cN0 (tumor size; p = 0.017, lymphatic invasion; p = 0.002). Multivariate analysis for SLNM predictors revealed lymphatic invasion (p = 0.008, odds ratio [OR] = 2.522) and TILs (p < 0.001, OR = 0.137) as independent factors.ConclusionsOur results suggest a correlation between lymph node metastasis and tumor immune-microenvironment in cT1 breast cancer. TIL density may be a predictor of SLNM in breast cancer without lymph node metastasis on preoperative imaging.

Prediction of Sentinel Lymph Node Metastasis Using the Platelet-to-lymphocyte Ratio in T1 Breast Cancer.

The host's systemic inflammatory response is thought to affect the progression of cancer and the antitumor effects of chemotherapy. Meta-analyses have reported that the peripheral blood platelet-to-lymphocyte ratio (PLR) is a prognostic indicator of this effect. Therefore, we hypothesized that PLR may differ, depending on sentinel lymph node metastasis (SLNM) in patients diagnosed with cT1N0M0 breast cancer by preoperative imaging. This study investigated the ability of preoperative PLR to predict SLNM in patients diagnosed with cT1N0M0 breast cancer. This study included 475 patients with cT1N0M0 breast cancer diagnosed by preoperative imaging. Peripheral blood was obtained at diagnosis, i.e., before surgery. PLR was calculated from preoperative blood tests, by dividing the absolute platelet count by the absolute lymphocyte count. The probability of SLNM was significantly higher (p=0.002) in cases where the tumor diameter was larger than 10 mm. The incidence of SLNM was significantly high in the high (preoperative) PLR group (p=0.031). Multivariate analysis revealed that high PLR [compared to low PLR, p=0.021, odds ratio (OR)=1.815, 95% confidence interval (CI)=1.093-3.090] and large tumor size (compared to small tumor size, p=0.001, OR=2.688, 95%CI=1.524-4.997) were independent factors influencing SLNM. PLR may act as a predictor of SLNM in cT1N0M0 breast cancer.

Deep Learning Signature Based on Staging CT for Preoperative Prediction of Sentinel Lymph Node Metastasis in Breast Cancer

To evaluate the noninvasive predictive performance of deep learning features based on staging CT for sentinel lymph node (SLN) metastasis of breast cancer. A total of 348 breast cancer patients were enrolled in this study, with their SLN metastases pathologically confirmed. All patients received contrast-enhanced CT preoperative examinations and CT images were segmented and analyzed to extract deep features. After the feature selection, deep learning signature was built with the selected key features. The performance of the deep learning signatures was assessed with respect to discrimination, calibration, and clinical usefulness in the primary cohort (184 patients from January 2016 to March 2017) and then validated in the independent validation cohort (164 patients from April 2017 to December 2018). Ten deep learning features were automatically selected in the primary cohort to establish the deep learning signature of SLN metastasis. The deep learning signature shows favorable discriminative ability with an area under curve of 0.801 (95% confidence interval: 0.736-0.867) in primary cohort and 0.817 (95% confidence interval: 0.751-0.884) in validation cohort. To further distinguish the number of metastatic SLNs (1-2 or more than two metastatic SLN), another deep learning signature was constructed and also showed moderate performance (area under curve 0.770). We developed the deep learning signatures for preoperative prediction of SLN metastasis status and numbers (1-2 or more than two metastatic SLN) in patients with breast cancer. The deep learning signature may potentially provide a noninvasive approach to assist clinicians in predicting SLN metastasis in patients with breast cancer.