Predictors of non-sentinel lymph node metastasis in clinical early stage (cT1-2N0) breast cancer patients with 1-2 metastatic sentinel lymph nodes

Clinical study on the prediction of ALN metastasis based on intratumoral and peritumoral DCE-MRI radiomics and clinico-radiological characteristics in breast cancer

Objective

To investigate the value of predicting axillary lymph node (ALN) metastasis based on intratumoral and peritumoral dynamic contrast-enhanced MRI (DCE-MRI) radiomics and clinico-radiological characteristics in breast cancer.

Methods

A total of 473 breast cancer patients who underwent preoperative DCE-MRI from Jan 2017 to Dec 2020 were enrolled. These patients were randomly divided into training (n=378) and testing sets (n=95) at 8:2 ratio. Intratumoral regions (ITRs) of interest were manually delineated, and peritumoral regions of 3 mm (3 mmPTRs) were automatically obtained by morphologically dilating the ITR. Radiomics features were extracted, and ALN metastasis-related radiomics features were selected by the Mann-Whitney U test, Z score normalization, variance thresholding, K-best algorithm and least absolute shrinkage and selection operator (LASSO) algorithm. Clinico-radiological risk factors were selected by logistic regression and were also used to construct predictive models combined with radiomics features. Then, 5 models were constructed, including ITR, 3 mmPTR, ITR+3 mmPTR, clinico-radiological and combined (ITR+3 mmPTR+ clinico-radiological) models. The performance of models was assessed by sensitivity, specificity, accuracy, F1 score and area under the curve (AUC) of receiver operating characteristic (ROC), calibration curves and decision curve analysis (DCA).

Results

A total of 2264 radiomics features were extracted from each region of interest (ROI), 3 and 10 radiomics features were selected for the ITR and 3 mmPTR, respectively. 5 clinico-radiological risk factors were selected, including lesion size, human epidermal growth factor receptor 2 (HER2) expression, vascular cancer thrombus status, MR-reported ALN status, and time-signal intensity curve (TIC) type. In the testing set, the combined model showed the highest AUC (0.839), specificity (74.2%), accuracy (75.8%) and F1 Score (69.3%) among the 5 models. DCA showed that it had the greatest net clinical benefit compared to the other models.

Conclusion

The intra- and peritumoral radiomics models based on DCE-MRI could be used to predict ALN metastasis in breast cancer, especially for the combined model with clinico-radiological characteristics showing promising clinical application value.

Lymphovascular invasion is a significant risk factor for non-sentinel nodal metastasis in breast cancer patients with sentinel lymph node (SLN)-positive breast cancer: a cross-sectional study

BACKGROUND

A connection between lymphovascular invasion and axillary lymph node metastases in breast cancer has been observed, but the findings are inconsistent and primarily based on research in Western populations. We investigated the association between lymphovascular invasion and non-sentinel lymph node (non-SLN) metastasis in breast cancer patients with sentinel lymph node (SLN) metastasis in western China.

METHODS

This study comprised 280 breast cancer patients who tested positive for SLN through biopsy and subsequently underwent axillary lymph node dissection (ALND) at The People's Hospital of Guangxi Zhuang Autonomous Region between March 2013 and July 2022. We used multivariate logistic regression analyses to assess the association between clinicopathological characteristics and non-SLN metastasis. Additionally, we conducted further stratified analysis.

RESULTS

Among the 280 patients with positive SLN, only 126 (45%) exhibited non-SLN metastasis. Multivariate logistic regression demonstrated that lymphovascular invasion was an independent risk factor for non-SLN in breast cancer patients with SLN metastasis (OR = 6.11; 95% CI, 3.62-10.32, p < 0.05). The stratified analysis yielded similar results.

CONCLUSIONS

In individuals with invasive breast cancer and 1-2 positive sentinel lymph nodes, lymphovascular invasion is the sole risk factor for non-SLN metastases. This finding aids surgeons and oncologists in devising a plan for local axillary treatment, preventing both over- and undertreatment.

Non-sentinel lymph node metastases risk factors in patients with breast cancer with one or two sentinel lymph node macro-metastases

Approximately 59 % of patients with breast cancer with one or two sentinel lymph nodes (1-2 SLN) macrometastases do not benefit from axillary lymph node dissection (ALND), which may also incur morbidities. It is necessary to evaluate the association between various clinicopathological characteristics and non-sentinel lymph node metastases (non-SLNM) in patients with breast cancer with 1-2 SLN macrometastases, and determine whether they 1-2 should avoid ALND. Eight electronic literature databases (PubMed, Embase, Web of Science, Cochrane Library, China National Knowledge Infrastructure, Chinese Scientific Journal, Wanfang, and Chinese Biomedical Literature) were searched from their inception to June 30, 2023, and two reviewers independently extracted the data and assessed the risk of bias. Association strength was summarized using odds ratios (OR) and 95 % confidence intervals (CI). Heterogeneity was accounted for using a subgroup analysis. Publication bias was evaluated using funnel plots and Egger's test. There were 25 studies with 8021 participants, and 27 potential risk factors were evaluated. The risk factors for non-SLNM in patients with 1-2 SLN macrometastatic breast cancer include the following: factors of primary tumor: multifocality (OR (95 % CI (2.63 (1.96, 3.54))), tumor size ≥ T2 (2.64 (2.22, 3.14)), tumor localization (upper outer quad) (2.06 (1.23, 3.43)), histopathological grade (G3) (2.45 (1.70, 3.52)), vascular invasion (VI) (2.60 (1.35, 4.98)), lymphovascular invasion (LVI) (2.87 (1.80, 4.56)), perineural invasion (PNI) (3.16 (1.18,8.43)). Factors of lymph nodes: method of SLNs detected (blue dye) (3.85 (1.54, 9.60)), SLN metastasis ratio ≥0.5 (2.79 (2.24, 3.48)), two positive SLNs (3.55, (2.08, 6.07)), zero negative SLN (3.72 (CI 2.50, 4.29)), extranodal extension (ENE) (4.69 (2.16, 10.18)). Molecular typing: Her-2 positive (2.08 (1.26, 3.43)), Her-2 over-expressing subtype (1.83 (1.22, 2.73)). Factors of examination/inspection: axillary lymph nodes (ALNs) positive on imaging (3.18 (1.68, 6.00)), cancer antigen 15-3 (CA15-3) (4.01 (2.33,6.89)), carcinoembryonic antigen (CEA) (2.13 (1.32-3.43)). This review identified the risk factors for non-SLNM in patients with 1-2 SLN macrometastatic breast cancer. However, additional studies are needed to confirm the above findings owing to the limited number and types of studies included.

Identifying clinicopathological risk factors for regional lymph node metastasis in Chinese patients with T1 breast cancer: a population-based study

Objectives

To analyze clinicopathological risk factors and regular pattern of regional lymph node metastasis (LNM) in Chinese patients with T1 breast cancer and the effect on overall survival (OS) and disease-free survival (DFS).

Materials and methods

Between 1999 and 2020, breast cancer patients meeting inclusion criteria of unilateral, no distant metastatic site, and T1 invasive ductal carcinoma were reviewed. Clinical pathology characteristics were retrieved from medical records. Survival analysis was performed using Kaplan-Meier methods and an adjusted Cox proportional hazards model.

Results

We enrolled 11,407 eligible patients as a discovery cohort to explore risk factors for LNM and 3484 patients with stage T1N0 as a survival analysis cohort to identify the effect of those risk factors on OS and DFS. Compared with patients with N- status, patients with N+ status had a younger age, larger tumor size, higher Ki67 level, higher grade, higher HR+ and HER2+ percentages, and higher luminal B and HER2-positive subtype percentages. Logistic regression indicated that age was a protective factor and tumor size/higher grade/HR+ and HER2+ risk factors for LNM. Compared with limited LNM (N1) patients, extensive LNM (N2/3) patients had larger tumor sizes, higher Ki67 levels, higher grades, higher HR- and HER2+ percentages, and lower luminal A subtype percentages. Logistic regression indicated that HR+ was a protective factor and tumor size/higher grade/HER2+ risk factors for extensive LNM. Kaplan-Meier analysis indicated that grade was a predictor of both OS and DFS; HR was a predictor of OS but not DFS. Multivariate survival analysis using the Cox regression model demonstrated age and Ki67 level to be predictors of OS and grade and HER2 status of DFS in stage T1N0 patients.

Conclusion

In T1 breast cancer patients, there were several differences between N- and N+ patients, limited LNM and extensive LNM patients. Besides, HR+ plays a dual role in regional LNM. In patients without LNM, age and Ki67 level are predictors of OS, and grade and HER2 are predictors of DFS.

Anatomical site and size of sentinel lymph node metastasis predicted additional axillary tumour burden and breast cancer survival

AIMS

Sentinel lymph node (SLN) biopsy is the current standard assessment for tumour burden in axillary lymph node (ALN). However, not all SLN+ patients have ALN metastasis. The prognostic implication of SLN features is not clear. We aimed to evaluate predictive factors for ALN metastasis and the clinical value of SLN features.

METHODS AND RESULTS

A total of 228 SLN+ and 228 SLN- (with matched year and grade) cases were included. Clinicopathological features in SLN, ALN and primary tumours, treatment data and survival data were analysed according to ALN status and outcome. Except for larger tumour size and the presence of LVI (both P < 0.001), no significant differences were found in SLN- and SLN+ cases. Only 31.8% of SLN+ cases with ALN dissection had ALN metastasis. The presence of macrometastases (MaM), extranodal extension (ENE), deeper level of tumour invasion in SLN and more SLN+ nodes were associated with ALN metastasis (P ≤ 0.025). Moreover, isolated tumour cells (ITC) and level of tumour invasion in SLN were independent adverse prognostic features for disease-free survival and breast cancer-specific survival, respectively. Interestingly, cases with ITC located in the subcapsular region have better survival than those in cortex (OS: χ²  = 4.046, P = 0.044).

CONCLUSIONS

Our study identified features in SLN, i.e. the level of tumour invasion at SLN and tumour size in SLN as useful predictors for both ALN metastasis and breast cancer outcome. The presence of ITC, particularly those with a deeper invasion in SLN, portended a worse prognosis. Proper attention should be taken for their management.

A novel nomogram for decision-making assistance on exemption of axillary lymph node dissection in T1–2 breast cancer with only one sentinel lymph node metastasis

Background

T1–2 breast cancer patients with only one sentinel lymph node (SLN) metastasis have an extremely low non-SLN (NSLN) metastatic rate and are favorable for axillary lymph node dissection (ALND) exemption. This study aimed to construct a nomogram-based preoperative prediction model of NSLN metastasis for such patients, thereby assisting in preoperatively selecting proper surgical procedures.

Methods

A total of 729 T1–2 breast cancer patients with only one SLN metastasis undergoing sentinel lymph node biopsy and ALND were retrospectively selected from Harbin Medical University Cancer Hospital between January 2013 and December 2020, followed by random assignment into training (n=467) and validation cohorts (n=262). A nomogram-based prediction model for NSLN metastasis risk was constructed by incorporating the independent predictors of NSLN metastasis identified from multivariate logistic regression analysis in the training cohort. The performance of the nomogram was evaluated by the calibration curve and the receiver operating characteristic (ROC) curve. Finally, decision curve analysis (DCA) was used to determine the clinical utility of the nomogram.

Results

Overall, 160 (21.9%) patients had NSLN metastases. Multivariate analysis in the training cohort revealed that the number of negative SLNs (OR: 0.98), location of primary tumor (OR: 2.34), tumor size (OR: 3.15), and lymph-vascular invasion (OR: 1.61) were independent predictors of NSLN metastasis. The incorporation of four independent predictors into a nomogram-based preoperative estimation of NSLN metastasis demonstrated a satisfactory discriminative capacity, with a C-index and area under the ROC curve of 0.740 and 0.689 in the training and validation cohorts, respectively. The calibration curve showed good agreement between actual and predicted NSLN metastasis risks. Finally, DCA revealed the clinical utility of the nomogram.

Conclusion

The nomogram showed a satisfactory discriminative capacity of NSLN metastasis risk in T1–2 breast cancer patients with only one SLN metastasis, and it could be used to preoperatively estimate NSLN metastasis risk, thereby facilitating in precise clinical decision-making on the selective exemption of ALND in such patients.

A Pilot Study of Prognostic Value of Metastatic Lymph Node Count and Size in Patients with Different Stages of Gastric Carcinoma

Background

The correlation between the preoperative lymph node count (LNC) on multidetector computed tomography (MDCT) and the prognosis of gastric carcinoma (GC) remains to be defined. This research aims to evaluate the prognostic value of LNC on MDCT in GC patients based on tumor-node-metastasis (TNM) staging, using different size criteria for counting.

Methods

The clinical data of 126 patients with gastric adenocarcinoma undergoing gastrectomy were retrospectively analyzed. Lymph nodes greater than 8mm and 5mm on MDCT were counted and recorded. The prognostic implications of LNC on MDCT for patient survival were analyzed according to different size criteria for counting and tumor TNM staging.

Results

When 8mm was used as the counting criterion, LNC on MDCT had no significant effect on the overall survival (OS) of the entire cohort. In addition, the OS of T1–T2 GC patients with LNC on MDCT ≥1 was significantly worse than that of patients with LNC on MDCT <1. When 5mm was used as the counting criterion, LNC on MDCT was found to be significantly associated with the OS of the entire cohort. In the subgroup analysis, patients with relatively advanced (T3-T4, N+ and III) GC with LNC on MDCT >7 showed a significantly worse OS than those with LNC on MDCT ≤7. LNC on MDCT >7 with 5mm as the counting criterion and Stage III were independent risk factors for adverse prognosis.

Conclusion

The prognostic value of LNC on MDCT based on different size criteria varies in patients with different stages of GC. LNC of a smaller size (5mm) on MDCT may be a prognostic factor for patients with relatively advanced GC.

Application of the Machine-Learning Model to Improve Prediction of Non-Sentinel Lymph Node Metastasis Status Among Breast Cancer Patients

Background

Performing axillary lymph node dissection (ALND) is the current standard option after a positive sentinel lymph node (SLN). However, whether 1–2 metastatic SLNs require ALND is debatable. The probability of metastasis in non-sentinel lymph nodes (NSLNs) can be calculated using nomograms. In this study, we developed an individualized model using machine-learning (ML) methods to select potential variables, which influence NSLN metastasis.

Materials and Methods

Cohorts of patients with early breast cancer who underwent SLN biopsy and ALND between 2012 and 2021 were created (training cohort, N 157 and validation cohort, N 58) for the development of the nomogram. Three ML methods were trained in the training set to create a strong predictive model. Finally, the multiple iterations of the least absolute shrinkage and selection operator regression method were used to determine the variables associated with NSLN status.

Results

Four independent variables (positive SLN number, absence of lymph node hilum, lymphovascular invasion (LVI), and total number of SLNs harvested) were combined to generate the nomogram. The area under the receiver operating characteristic curve (AUC) value of 0.759 was obtained in the entire set. The AUC values for the training set and the test set were 0.782 and 0.705, respectively. The Hosmer-Lemeshow test of the model fit accuracy was identified with p = 0.759.

Conclusion

This study developed a nomogram that incorporates ultrasound (US)-related variables using the ML method and serves to clinically predict the non-metastatic status of NSLN and help in the selection of the appropriate treatment option.

Perineural invasion as a predictive biomarker of groin metastases and survival outcomes in vulvar cancer: a meta-analysis

We searched international databases to identify evidence that refer to the impact of perineural invasion on survival outcomes of patients with squamous cell vulvar cancer. We identified six retrospective cohort studies that investigated 887 patients. Of those, 234 (26.4%) had perineural invasion in the pathology analysis. Women with perineural invasion were more likely to have inguinal lymph node metastases (HR 3.45, 95% CI 1.12, 10.67). The impact of perineural invasion on progression-free survival rates was significant (HR 1.61, 95% CI 1.21, 2.15) as well as its impact on overall survival rates (HR 2.73, 95% CI 1.94, 3.84).

Artificial Intelligence Algorithm-Based Ultrasound Image Segmentation Technology in the Diagnosis of Breast Cancer Axillary Lymph Node Metastasis

This paper aimed to investigate the application of ultrasound image segmentation technology based on the back propagation neural network (BPNN) artificial intelligence algorithm in the diagnosis of breast cancer axillary lymph node metastasis, thereby providing a theoretical basis for clinical diagnosis. In this study, 90 breast cancer patients with axillary lymph node metastasis were selected as the research objects and rolled randomly into an experimental group and a control group. Besides, all of them were examined by ultrasound. The BPNN algorithm for the ultrasound image segmentation diagnosis method was applied to the patiens from the experimental group, while the control group was given routine ultrasound diagnosis. Thus, the value of this algorithm in ultrasonic diagnosis was compared and explored. The results showed that when the number of hidden layer nodes based on the BPNN artificial intelligence algorithm was 2, 3, 4, 5, 6, 7, and 8, the corresponding segmentation accuracy was 97.3%, 96.5%, 94.8%, 94.8%, and 94.1% in turn. Among them, the segmentation accuracy was the highest when the number of hidden layer nodes was 2. The correlation of independent variable bubble plot analysis showed that the presence or absence of capsules, the presence of crab feet or burrs in breast cancer lesions was critical influencing factors for the occurrence of axillary lymph node metastasis, and the standardized importance was 99.7% and 70.8%, respectively. Besides, the area under the two-dimensional receiver operating characteristic (ROC) curve of the BPNN artificial intelligence algorithm model classification was always greater than the area under the curve of manual segmentation, and the segmentation accuracy was 90.31%, 94.88%, 95.48%, 95.44%, and 97.65% in sequence. In addition, the segmentation specificity of different running times was higher than that of manual segmentation. In conclusion, the BPNN artificial intelligence algorithm had high accuracy, sensitivity, and specificity for ultrasound image segmentation, with a better segmentation effect. Therefore, it had a better diagnostic effect for breast cancer axillary lymph node metastasis.