Nonsentinel node metastasis in breast cancer patients: assessment of an existing and a new predictive nomogram

doi:10.1016/j.amjsurg.2005.06.008

The American Journal of Surgery

Volume 190, Issue 4, October 2005, Pages 543-550

Presented at the Sixth Annual Meeting of the American Society of Breast Surgeons, Los Angeles, California, March 16–20, 2005

https://doi.org/10.1016/j.amjsurg.2005.06.008 Get rights and content

Abstract

Background

The accurate prediction of nonsentinel node (NSN) metastasis in breast cancer patients remains uncertain.

Methods

The medical records of 574 breast cancer patients from 2 different institutions (Mayo Clinic and University of Michigan) with sentinel lymph node biopsy examination and completion axillary lymph node dissection were reviewed for multiple clinicopathologic variables. The Memorial Sloan Kettering Cancer Center nomogram performance for prediction of NSN metastases was assessed. A new model was developed with clinically relevant variables and possible advantages.

Results

The Memorial Sloan Kettering Cancer Center nomogram predicted the likelihood of NSN metastasis with an area under the receiver operating characteristic curve of .72 and .86. For predicted probability cut-off points of 5% and 10%, the false-negative rates were 0% and 14% (Mayo), and 17% and 11% (Michigan). A new model was developed with similar area under the curve but lower false-negative rates for low-probability subgroups.

Conclusions

Predictive models for NSN tumor burden are imperfect.

Section snippets

Materials and Methods

Medical records were reviewed for 574 consecutive breast cancer patients with a positive SLNB who underwent CALND from October 1997 to November 2002 at the University of Michigan (n = 109) and from November 1997 to June 2004 at the Mayo Clinic (n = 465). Patients with histopathologically diagnosed primary invasive breast cancer with a clinically negative axilla, successful SLNB with metastatic disease, and subsequent CALND were included in the study. The study was reviewed and approved by

Results

Our study group consisted of 574 patients; 109 from the University of Michigan and 465 from the Mayo Clinic. The mean age of the patients in the Mayo dataset was 57 years (range, 26–88 years), and the mean age of the patients in the Michigan dataset was 53 years (range, 31–78 years). Among the Mayo patients, 203 (44%) had additional positive NSNs. Among the Michigan patients, this number was 36 (33%). Table 1 shows the descriptive patient and tumor characteristics, including missing frequencies

Comments

SLNB examination has become the standard of care for assessing nodal status in breast cancer. However, the surgical management of patients with metastatic disease in the SNs is controversial. In approximately 40% to 60% of patients, the SN is the sole site of regional node metastasis, and these patients would not be expected to benefit from ALND [2]. The American College of Surgeons Oncology Group Z0011 trial, which randomized SN-positive patients to CALND or to no further axillary surgery, has

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Machine Learning Radiomics-Based Prediction of Non-sentinel Lymph Node Metastasis in Chinese Breast Cancer Patients with 1-2 Positive Sentinel Lymph Nodes: A Multicenter Study
2024, Academic Radiology
This study aimed to construct a machine learning radiomics-based model using dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) images to evaluate non-sentinel lymph node (NSLN) metastasis in Chinese breast cancer (BC) patients who underwent total mastectomy (TM) and had 1–2 positive sentinel lymph nodes (SLNs).
In total, 494 patients were retrospectively enrolled from two hospitals, and were divided into the training (n = 286), internal validation (n = 122), and external validation (n = 86) cohorts. Features were extracted from DCE-MRI images for each patient and screened. Six ML classifies were trained and the best classifier was evaluated to calculate radiomics (Rad)-scores. A combined model was developed based on Rad-scores and clinical risk factors, then the calibration, discrimination, reclassification, and clinical usefulness were evaluated.
14 radiomics features were ultimately selected. The random forest (RF) classifier showed the best performance, with the highest average area under the curve (AUC) of 0.833 in the validation cohorts. The combined model incorporating RF-based Rad-scores, tumor size, lymphovascular invasion, and proportion of positive SLNs resulted in the best discrimination ability, with AUCs of 0.903, 0.890, and 0.836 in the training, internal validation, and external validation cohorts, respectively. Furthermore, the combined model significantly improved the classification accuracy and clinical benefit for NSLN metastasis prediction.
A RF-based combined model using DCE-MRI images exhibited a promising performance for predicting NSLN metastasis in Chinese BC patients who underwent TM and had 1–2 positive SLNs, thereby aiding in individualized clinical treatment decisions.
Sentinel Lymph Node Positive Rate Predicts Non-Sentinel Lymph Node Metastasis in Breast Cancer
2022, Journal of Surgical Research
To investigate retrospectively an association between the number of metastatic sentinel lymph nodes (SLNs) per total number of SLNs per patient (i.e., the SLN positive rate, or SLN-PR) and non-SLN metastasis in breast cancer.
A large population (n = 2250) underwent SLN dissection from January 1, 2014 to January 1, 2020; 627 (27.87%) had at least one positive SLN (SLN⁺). Among these, 283 underwent axillary lymph node (ALN) dissection, and formed the test group. Four external validation groups comprised 43 patients treated in 2019. SLN mappings were examined using methylene blue and indocyanine green. Lymph node ultrasound, SLN-PR, and pathological characteristics were compared between patients with and without non-SLN metastasis. An SLN-PR cutoff value was calculated using receiver operating characteristic (ROC) curves. Associations between clinicopathological variables and SLN-PR with non-SLN metastasis were analyzed by multivariate logistic regression model.
The median age was 47 years (IQR: 42-56 y). The median number of resected SLNs was 4. Patients with positive non-SLNs (126/283, 44.52%) had a median of 2 positive node. SLN-PR > 0.333 was a risk factor for non-SLN positivity (area under the ROC curve, 0.726); and carried significantly higher risk of non-SLN metastasis (P < 0.001). This was validated in the external group.
SLN-PR > 0.333 was associated with greater risk of non-SLN metastasis. This provides a reference to non-SLN metastasis in patients with SLN metastasis, an indication for ALN dissection and choice of adjuvant treatment.
Predictive factors of non-sentinel lymph node disease in breast cancer patients with positive sentinel lymph node
2022, Cirugia Espanola
El manejo del ganglio centinela positivo en cáncer de mama sigue siendo un tema de debate. El objetivo es evaluar la incidencia e identificar los factores predictivos de metástasis en ganglios no centinela.
Revisión retrospectiva de los pacientes con cáncer de mama con axila clínicamente negativa (cN0) tratados entre enero del 2013 y diciembre del 2017, con biopsia de ganglio centinela (BGC) positiva a quienes se les realizó linfadenectomía axilar (LA).
De los 328 pacientes incluidos, la mayoría tenía tumores cT1 o cT2, con invasión linfovascular en el 58,4% de casos. La media de ganglios detectados en BGC fue 2,7, con una media de 1,6 ganglios positivos, el 60,7% con extensión extracapsular. En LA, una media de 13,9 ganglios fueron detectados, con media de 2,1 ganglios positivos. No se observó metástasis en LA en el 50,9% de los pacientes y el 18,9% tenía ≥ cuatro ganglios positivos. En análisis multivariado, la invasión linfovascular, la extensión extracapsular, la dimensión de mayor metástasis (>10 mm) y la ratio de ganglios centinela positivos (> 50%) fueron factores predictivos independientes de metástasis en ganglios no centinela. Estos factores fueron usados para construir un score para predecir la posibilidad de LA positiva después de BGC positiva. El área bajo la curva ROC (AUC) del modelo fue 0,69 y el 81% de los pacientes con score = 0, y el 65,6% con score = 1 no tenían metástasis en la LA.
La ausencia de metástasis en ganglios no centinela en la mayoría de los casos con uno a dos ganglios positivos en la BGC con score de bajo riesgo cuestiona la necesidad de hacer LA en estos pacientes. Los factores predictivos identificados pueden ayudar a seleccionar pacientes para omitir la LA.
Management of positive sentinel lymph node biopsy (SLNB) in breast cancer remains a matter of debate. Our aim was to evaluate the incidence and identify predictive factors of non-sentinel lymph node metastases.
Retrospective review of all cN0 breast cancer patients treated between January 2013 and December 2017, with positive SLNB that were submitted to ALND.
Of the 328 patients included, the majority of tumors were cT1 or cT2, with lymphovascular invasion in 58.4% of cases. The mean isolated nodes in SLNB was 2.7, with a mean of 1.6 positive nodes, 60.7% with extracapsular extension. Regarding ALND, a mean of 13.9 nodes were isolated, with a mean of 2.1 positive nodes. There was no residual disease in the ALND in 50.9% of patients, with 18.9% having ≥ four positive nodes. In the multivariate analysis, lymphovascular invasion, extracapsular extension in SLN, largest SLN metastases size (>10 mm) and ratio of positive SNL (> 50%) were independent predictors of non-sentinel lymph node metastases. These four factors were used to build a non-pondered score to predict the probability of a positive ALND after a positive SLNB. The AUC of the model was 0.69 and 81% of patients with score = 0 and 65.6% with score = 1 had no additional disease in ALND.
The absence of non-sentinel lymph node metastases in the majority of patients with 1-2 positive SLN with low risk score questions the need of ALND in this population. The identified predictive factors may help select patients in which ALND can be omitted.
Locoregional Management of the Axilla in Mastectomy Patients with One or Two Positive Sentinel Nodes: The Role of Intraoperative Pathology
2021, Clinical Breast Cancer
Introduction: Controversy exists regarding optimal management of the axilla in clinically node-negative (cN0) mastectomy patients with one or two positive sentinel lymph nodes (+SLNs). We evaluated the influence of frozen-section pathology on axillary management and recurrence. Patients and Methods: We studied cN0 breast cancer patients treated from 2008 to 2018 with mastectomy and SLN surgery with one or two +SLNs. Patients with one or two +SLNs identified on frozen-section intraoperatively (FS+SLN) were compared to those with one or two +SLNs not detected by frozen section (FS–SLN). Recurrence rates were estimated using the Kaplan–Meier method. Results: Of 2295 cN0 mastectomy patients, 338 patients had one or two +SLNs: 108 (32%) FS–SLN and 230 (68%) FS+SLN. In the FS+SLN cases, completion axillary lymph node dissection (cALND) was more frequent (97% vs. 39%; P < .001), and median SLN metastasis size (5 vs. 1.3 mm; P < .001) and likelihood of positive non-SLNs (31% vs. 14%; P = .02) were greater compared with FS–SLN cases. Across all 338 patients, 40% had SLN surgery alone, and 47% of cALND patients received post-mastectomy radiation therapy (PMRT). At a median follow-up of 61 months, no axillary recurrences were observed among FS–SLN patients. Among FS+SLN patients, 97% proceeded to cALND but 49% avoided PMRT; three regional nodal recurrences were observed (all in patients treated with cALND, of whom two received PMRT). Conclusion: Mastectomy patients with one or two FS+SLNs have a higher nodal disease burden than FS–SLN patients. The majority of FS+SLN patients underwent cALND, and 51% received PMRT with very low 5-year regional nodal recurrence rates. A substantial proportion of FS–SLN patients successfully avoided both cALND and PMRT. Frozen-section pathology analysis can guide de-escalation of axillary management.
Predicting Nonsentinel Lymph Node Metastasis in Breast Cancer: A Multicenter Retrospective Study
2021, Journal of Surgical Research
Sentinel lymph node (SLN) biopsy has been the standard modality for breast cancer patients with clinically node negative disease. In patients who undergo axillary lymph node dissection (ALND) due to SLN metastasis, the harvested nodes (non-SLNs) often contain no metastasis. Here, we evaluated the predictive factors associated with non-SLN metastasis in breast cancer patients.
This was a retrospective study of patients with operable cT1-3, cN0 invasive breast cancer who underwent SLN biopsy followed by ALND due to SLN metastasis. The clinicopathologic factors and predictive factors of non-SLN metastasis were analyzed. The optimal cutoff for the Ki67 index and the number of positive and negative SLNs that were predictive of non-SLN metastasis were evaluated using receiver operating characteristic curves.
The median number of SLN and non-SLN was 3 and 11, respectively. Of the 150 patients, 52 (35.0%) had metastases in non-SLNs. The optimal cutoffs for the Ki67 index and the number of positive and negative SLNs were of 12%, 2, and 1, respectively. In the univariate analysis, the Ki67 index and the number of positive SLNs≥2 and negative SLNs≤1 were higher in the non-SLN + group than that in the non-SLN – group. The number of negative SLNs was as a predictive factor for non-SLNs metastasis in the multivariate analysis.
The number of negative SLNs predicts the risk of non-SLN metastasis in breast cancer. When deciding on whether to omit ALND, the number of positive and negative SLNs should be considered.
Deep learning radiomics of ultrasonography: Identifying the risk of axillary non-sentinel lymph node involvement in primary breast cancer
2020, EBioMedicine
Completion axillary lymph node dissection is overtreatment for patients with sentinel lymph node (SLN) metastasis in whom the metastatic risk of residual non-SLN (NSLN) is low. However, the National Comprehensive Cancer Network panel posits that none of the previous studies has successfully identified such subset patients. Here, we develop a multicentre deep learning radiomics of ultrasonography model (DLRU) to predict the risk of SLN and NSLN metastasis.
In total, 937 eligible breast cancer patients with ultrasound images were enrolled from two hospitals as the training set (n = 542) and independent test set (n = 395) respectively. Using the images, we developed and validated a prediction model combined with deep learning radiomics and axillary ultrasound to sequentially identify the metastatic risk of SLN and NSLN, thereby, classifying patients to relevant axillary management groups.
In the test set, the DLRU yields the best performance in identifying patients with metastatic disease in SLNs (sensitivity=98.4%, 95% CI 96.6–100) and NSLNs (sensitivity=98.4%, 95% CI 95.6–99.9). The DLRU also accurately stratifies patients without metastasis in SLN or NSLN into the corresponding low-risk (LR)-SLN and high-risk (HR)-SLN&LR-NSLN category with the negative predictive value of 97% (95% CI 94.2–100) and 91.7% (95% CI 88.8–97.9), respectively. Moreover, compared with the current clinical management, DLRU appropriately assigned 51% (39.6%/77.4%) of overtreated patients in the entire study cohort into the LR group, perhaps avoiding overtreatment.
The performance of the DLRU indicates that it may offer a simple preoperative tool to promote personalized axillary management of breast cancer.
The National Nature Science Foundation of China; The National Outstanding Youth Science Fund Project of National Natural Science Foundation of China; The Scientific research project of Heilongjiang Health Committee; The Postgraduate Research &Practice Innovation Program of Harbin Medical University.

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PresentationNonsentinel node metastasis in breast cancer patients: assessment of an existing and a new predictive nomogram

Abstract

Background

Methods

Results

Conclusions

Section snippets

Materials and Methods

Results

Comments

Eur J Surg Oncol

Eur J Cancer

Breast

Prerandomization surgical training for the NSABP-B32 trial—a randomization phase III clinical trial to compare sentinel node resection to conventional axillary dissection in clinically node negative breast cancer

Ann Surg

Predictors of positive axillary lymph nodes after sentinel lymph node biopsy in breast cancer

Am J Surg

Breast

Sentinel node metastasis in patients with breast carcinoma accurately predicts immunohistochemically detectable nonsentinel node metastasis

Ann Surg Oncol

Sentinel lymph node biopsy with metastasiscan axillary dissection be avoided in some patients with breast cancer

J Clin Oncol

Ten year result of a randomized clinical trial comparing radical mastectomy and total mastectomy with or without radiation

N Engl J Med

Physical and psychological morbidity after axillary lymph node dissection for breast cancer

J Clin Oncol

Presentation
Nonsentinel node metastasis in breast cancer patients: assessment of an existing and a new predictive nomogram