Predicting Sentinel Lymph Node Research Articles

Predictive Value of Dynamic Contrast-Enhanced Breast Magnetic Resonance Imaging and Diffusion-Weighted Imaging Findings for Sentinel Lymph Node Metastasis in Early-Stage Invasive Breast Cancer.

This retrospective study aimed to evaluate the predictive value of the preoperative dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and diffusion-weighted imaging (DWI) findings of mass lesions for predicting sentinel lymph node (SLN) metastasis in early breast cancer. A total of 310 patients with suspicious mass lesions detected in preoperative MRI who subsequently underwent surgery and SLN biopsy (SLNB) between September 2015 and September 2022 were analyzed. The relationship between DCE-MRI and DWI findings and SLNB positivity was analyzed. SLNB was positive for SLN metastasis in 108 of 310 lesions. Younger age (p = 0.001) and larger lesion size (p < 0.001) were found to be associated with SLNB positivity. Findings associated with SLN metastasis included peritumoral edema in 53%, adjacent vessel sign (AVS) in 81%, and increased whole-breast vascularity (WBV) in 58% of patients with positive SLNB (p < 0.001). The SLNB positivity rate was higher in mass lesions with DCE-MRI findings of heterogenous enhancement pattern (p = 0.003), medium or rapid initial phase enhancement (p = 0.001), and washout delayed phase kinetic curve (p = 0.001). It was found that lower tumoral apparent diffusion coefficient (ADC) values (p = 0.003) and higher peritumoral/tumoral ADC ratios (p = 0.018) increased the probability of encountering SLN metastasis. Patient age, presence of peritumoral edema, presence of AVS, increased WBV, and initial phase kinetic curve of the lesions on MRI were found to be associated with SLN metastasis. We found that younger age and MR findings obtained from the perilesional area of breast cancer may be helpful in the preoperative prediction of SLN metastasis.

Declining Clinical Utility of Tools for Predicting Sentinel Lymph Node Biopsy Status: A Single Institution Experience from 2000 to 2021.

Clinicopathologic data-based sentinel lymph node (SLN) prediction models are used to select patients with melanoma for sentinel lymph node biopsy (SLNB). However, the temporal performance of these models is unknown. Therefore, we investigated whether the performance and clinical utility of the Melanoma Institute of Australia, Memorial Sloan Kettering Cancer Center, and Friedman et al. models changed over time. Primary cutaneous melanoma cases that underwent SLNB at a single tertiary-care cancer center from 2000 to 2021 were identified from a prospectively maintained database. Calibration plots were generated. Values for estimated risks of SLN positivity and area under the receiver operator curve (AUC) were calculated. Clinical utility was assessed at thresholds between 5 and 10% using decision curve analysis. In total, 2977 SLNB cases were included. The estimated risk of SLN positivity and AUCs were similar across periods for all models. However, calibration decreased over time for all models, with progressive underprediction of SLN positivity. Clinical utility also declined over time; in the most recent period investigated (2018-2021), no model offered clinical utility at risk thresholds ≤ 8%, and only the Friedman model provided clinical utility at risk thresholds of 9-10%. The calibration and clinical utility of three predominant models for SLN prediction declined over time. There is a need to periodically reassess the performance of SLN prognostic tools as they are applied to contemporary cohorts. Future studies are needed to determine whether findings are generalizable outside of this study cohort.

Are Nomograms Useful for Predicting Sentinel Lymph Node Status in Melanoma Patients?

ABSTRACTBackground and ObjectivesClinical nomograms have been developed to predict sentinel lymph node (SLN) status in early‐stage melanoma patients, but the clinical utility of these tools remains debatable. We created and validated a nomogram using data from a randomized clinical trial and assessed its accuracy against the well‐validated Melanoma Institute Australia (MIA) nomogram.MethodsWe developed our model to predict SLN status using logistic regression on clinicopathological patient data from the Multicenter Selective Lymphadenectomy Trial‐I. The model was externally validated using the National Cancer Database (NCDB) data set, and its performance was compared to that of the MIA nomogram.ResultsOur model had good discrimination between positive and negative SLNs, with a training set area under the curve (AUC) of 0.706 (0.661–0.751). Our model achieved an AUC of 0.715 (0.706–0.724) compared to 0.723 (0.715–0.731) with the MIA model, using the NCDB set.ConclusionOur model performed similarly to the MIA model, confirming that despite using different clinical features and data sets, no clinical nomogram is currently accurate enough for clinical use.

Society of Surgical Oncology Consensus Statement: Assessing the Evidence for and Utility of Gene Expression Profiling of Primary Cutaneous Melanoma.

Gene expression profiling (GEP) of primary cutaneous melanoma aims to offer prognostic and predictive information to guide clinical care. Despite limited evidence of clinical utility, these tests are increasingly incorporated into clinical care. A panel of melanoma experts from the Society of Surgical Oncology convened to develop recommendations regarding the use of GEP to guide management of patients with melanoma. The use of currently available GEP tests were evaluated in three clinical scenarios: (1) the utility in patient selection for sentinel lymph node biopsy; (2) the utility to guide surveillance; and (3) the utility to inform adjuvant therapy. As a basis for these recommendations, the panel performed a systematic review of the literature, including articles published from January 2012 until August 2023. After review of 137 articles,50met the inclusion criteria. These articles included evidence related to three available GEP tests: 31-GEP, CP-GEP, and 11-GEP. The consensus recommendations were finalized using a modified Delphi process. The panel found that current evidence often fails to account for known clinicopathologic risk factors and lacks high-level data. The panel recognizes that thestudy of GEP tests is still evolving. The integration of GEP into routine clinical practice for predicting sentinel lymph node status and patient prognosis in melanoma is therefore not currently recommended. At present, GEP should be considered primarily an investigational tool, ideally used in the context of clinical trials or specialized research settings.

Can we skip invasive biopsy of sentinel lymph nodes? A preliminary investigation to predict sentinel lymph node status using PET/CT-based radiomics

BackgroundSentinel lymph node (SLN) biopsy (SLNB) is considered the gold standard for detecting SLN metastases in patients with invasive ductal breast cancer (IDC). However, SLNB is invasive and associated with several complications. Thus, this study aimed to evaluate the diagnostic performance of a non-invasive radiomics analysis utilizing 2-deoxy-2-[18F]fluoro-d-glucose positron emission tomography/computed tomography (18F-FDG-PET/CT) for assessing SLN metastasis in IDC patients.MethodsThis retrospective study included 132 patients with biopsy-confirmed IDC, who underwent 18F-FDG PET/CT scans prior to mastectomy or breast-conserving surgery with SLNB. Tumor resection or SLNB was conducted within one-week post-scan. Clinical data and metabolic parameters were analyzed to identify independent SLN metastasis predictors. Radiomic features were extracted from each PET volume of interest (VOI) and CT-VOI. Feature selection involved univariate and multivariate logistic regression analysis, and the least absolute shrinkage and selection operator (LASSO) method. Three models were developed to predict SLN status using the random forest (RF), decision tree (DT), and k-Nearest Neighbors (KNN) classifiers. Model performance was assessed using the area under the receiver operating characteristic curve (AUC).ResultsThe study included 91 cases (32 SLN-positive and 59 SLN-negative patients) in the training cohort and 41 cases (29 SLN-positive and 12 SLN-negative patients) in the validation cohort. Multivariate logistic regression analysis identified Ki 67 and TLG as independent predictors of SLN status. Five PET-derived features, three CT-derived features, and two clinical variables were selected for model development. The AUC values of the RF, KNN, and DT models for the training cohort were 0.887, 0.849, and 0.824, respectively, and for the validation cohort were 0.856, 0.830, and 0.819, respectively. The RF model demonstrated the highest accuracy for the preoperative prediction of SLN metastasis in IDC patients.ConclusionThe PET-CT radiomics approach may offer robust and non-invasive predictors for SLN status in IDC patients, potentially aiding in the planning of personalized treatment strategies for IDC patients.

54814 Mitotic Rate is More Important than Breslow Thickness for Predicting Sentinel Lymph Node Biopsy Positivity and Melanoma-Associated Survival for T1 Cutaneous Malignant Melanoma Cases

54814 Mitotic Rate is More Important than Breslow Thickness for Predicting Sentinel Lymph Node Biopsy Positivity and Melanoma-Associated Survival for T1 Cutaneous Malignant Melanoma Cases

Sentinel lymph node positivity in melanoma: Which risk prediction tool is most accurate?

BackgroundSentinel lymph node biopsy for melanoma determines treatment and prognostic factors and improves disease-specific survival. To risk-stratify patients for sentinel lymph node biopsy consideration, Memorial Sloan Kettering Cancer Center and Melanoma Institute Australia developed nomograms to predict sentinel lymph node positivity. We aimed to compare the accuracy of these 2 nomograms. MethodsA multi-institutional study of patients with melanoma receiving sentinel lymph node biopsy between September 2018 and December 2022 was performed. The accuracy of the 2 risk prediction tools in determining a positive sentinel lymph node biopsy was analyzed using receiver operating characteristic curves and area under the curve. ResultsIn total, 532 patients underwent sentinel lymph node biopsy for melanoma; 98 (18.4%) had positive sentinel lymph node. Increasing age was inversely related to sentinel lymph node positivity (P < .01); 35.7% of patients ≤30 years had positive sentinel lymph node compared with 9.7% of patients ≥75 years. When we analyzed the entire study population, accuracy of the 2 risk prediction tools was equal (area under the curveMemorial Sloan Kettering Cancer Center: 0.693; area under the curveMIA: 0.699). However, Memorial Sloan Kettering Cancer Center tool was a better predictor in patients aged ≥75 years (area under the curveMemorial Sloan Kettering Cancer Center: 0.801; area under the curveMelanoma Institute Australia: 0.712, P < .01) but Melanoma Institute Australia tool performed better in patients with a higher mitotic index (mitoses/mm2 ≥2; area under the curveMemorial Sloan Kettering Cancer Center: 0.659; area under the curveMelanoma Institute Australia: 0.717, P = .027). Both models were poor predictors of sentinel lymph node positivity in young patients (age ≤30 years; area under the curveMemorial Sloan Kettering Cancer Center: 0.456; area under the curveMelanoma Institute Australia: 0.589, P = .283). ConclusionThe current study suggests that the 2 risk stratification tools differ in their abilities to predict sentinel lymph node positivity in specific populations: Memorial Sloan Kettering Cancer Center tool is a better predictor for older patients, whereas Melanoma Institute Australia tool is more accurate in patients with a higher mitotic index. Both nomograms performed poorly in predicting sentinel lymph node positivity in young patients.

Evaluating the added value of synthetic magnetic resonance imaging in predicting sentinel lymph node status in breast cancer

Evaluating the added value of synthetic magnetic resonance imaging in predicting sentinel lymph node status in breast cancer

Classification of breast edema on T2-weighted imaging for predicting sentinel lymph node metastasis and biological behavior in breast cancer

To determine whether preoperative classification of breast edema on T2-weighted imaging (T2WI) is useful for predicting sentinel lymph node (SLN) metastasis and biological behavior in patients with early-stage breast cancer. This retrospective study involved 341 women with breast cancer who underwent breast MRI from January 2019 to March 2022. Breast edema was scored on a scale of 1-4 on T2WI (1, no edema; 2, peritumoral edema; 3, prepectoral edema; and 4, subcutaneous edema). A logistic regression model was employed for univariate and multivariate analyses. A clinicopathological model was established using independent influencing factors identified in the multivariate analyses, excluding breast edema score (BES). Subsequently, BES was incorporated into this model to establish a combined BES model. The AUC and Delong test were used to examine the additional predictive value of the BES. Logistic regression analysis showed that breast edema was an independent risk factor for SLN metastasis. The combined BES model significantly improved the predictive performance of SLN metastasis compared with the clinicopathological model alone (AUC, 0.77 vs. 0.71; p=0.005). In addition, the BES was significantly positively correlated with the tumor diameter (p<0.001), histologic grade (p=0.001), Ki-67 index (p<0.001), and non-luminal subtypes (p<0.001). The BES on T2WI is useful for predicting SLN metastasis. A higher grade of breast edema is associated with breast cancer aggressiveness and increases the probability of SLN metastasis.

Population-Based Validation of the MIA and MSKCC Tools for Predicting Sentinel Lymph Node Status

Patients with melanoma are selected for sentinel lymph node biopsy (SLNB) based on their risk of a positive SLN. To improve selection, the Memorial Sloan Kettering Cancer Center (MSKCC) and Melanoma Institute Australia (MIA) developed predictive models, but the utility of these models remains to be tested. To determine the clinical utility of the MIA and MSKCC models. This was a population-based comparative effectiveness research study including 10 089 consecutive patients with cutaneous melanoma undergoing SLNB from the Swedish Melanoma Registry from January 2007 to December 2021. Data were analyzed from May to August 2023. The predicted probability of SLN positivity was calculated using the MSKCC model and a limited MIA model (using mitotic rate as absent/present instead of count/mm2 and excluding the optional variable lymphovascular invasion) for each patient. The operating characteristics of the models were assessed and compared. The clinical utility of each model was assessed using decision curve analysis and compared with a strategy of performing SLNB on all patients. Among 10 089 included patients, the median (IQR) age was 64.0 (52.0-73.0) years, and 5340 (52.9%) were male. The median Breslow thickness was 1.8 mm, and 1802 patients (17.9%) had a positive SLN. Both models were well calibrated across the full range of predicted probabilities and had similar external area under the receiver operating characteristic curves (AUC; MSKCC: 70.8%; 95% CI, 69.5-72.1 and limited MIA: 69.7%; 95% CI, 68.4-71.1). At a risk threshold of 5%, decision curve analysis indicated no added net benefit for either model compared to performing SLNB for all patients. At risk thresholds of 10% or higher, both models added net benefit compared to SLNB for all patients. The greatest benefit was observed in patients with T2 melanomas using a threshold of 10%; in that setting, the use of the nomograms led to a net reduction of 8 avoidable SLNBs per 100 patients for the MSKCC nomogram and 7 per 100 patients for the limited MIA nomogram compared to a strategy of SLNB for all. This study confirmed the statistical performance of both the MSKCC and limited MIA models in a large, nationally representative data set. However, decision curve analysis demonstrated that using the models only improved selection for SLNB compared to biopsy in all patients when a risk threshold of at least 7% was used, with the greatest benefit seen for T2 melanomas at a threshold of 10%. Care should be taken when using these nomograms to guide selection for SLNB at the lowest thresholds.

Pathological characteristics predicting sentinel lymph node metastasis in early breast cancer patients.

In this study, we aimed to identify the predicting pathological factors affecting sentinel lymph node biopsy (SLNB) in patients with clinically node-negative breast cancer. Our single institution retrospective study was conducted at the Cancer Research Center of Shahid Beheshti University of Medical Sciences from 2018 to 2021. Data were imported into and analyzed using SPSS Version 28 for Windows (IBM Corp., Armonk, NY, USA). Of the 76 patients who underwent SLNB, 43 (56.6%) had negative SLNB and 33 (43.4%) had positive SLNB which led to axillary lymph node dissection (ALND). The relationship between hormone receptor status (ER/PR/Her2), pathology type (IDC, ILC, DCIS, LCIS), tumor size, and Ki67 expression was assessed. According to the results, axillary lymph node involvement can be predicted based on the scores and results of the three variables: IDC tumor type, lympho vascular invasion (LVI), and Ki67 expression. The positive relationship between IDC tumor type and LVI with SLNB indicates that with positive IDC tumor type and LVI, there is a higher probability of positive axillary lymph nodes (3.88 times higher probability for IDC tumor type and 6.75 times higher probability for the LVI factor). However, when the Ki67 expression is lower, the probability of positive axillary lymph nodes is higher (3.58 times higher probability). IDC tumor type, LVI, and lower Ki67 expression are independent predictive factors of positive SLNB.

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.

Can inflammatory indices predict sentinel lymph node status in patients with early-stage breast cancer?

Breast cancer research has focused on the early detection and treatment of breast cancer. Axillary lymph node status is essential for primary breast cancer staging, recurrence, and survival. The current quest for precision medicine is to identify predictive markers that offer the advantage of individualized treatment options. This study aimed to investigate the value of inflammatory indices in predicting positive sentinel nodes in breast cancer. We studied 602 patients with early-stage breast cancer who underwent sentinel lymph node biopsies (SLNB) at the Bülent Ecevit University General Surgery Clinic. We obtained data, including the clinical and demographic characteristics of the patients, such as age, histological type, and sentinel lymph nodes. Neutrophil, lymphocyte, platelet, and monocyte counts were obtained from preoperative complete blood count test data from the patient registry. The neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), lymphocyte-to-monocyte ratio (LMR), systemic inflammatory index (SII), and sentinel lymph node biopsy were analyzed. Sentinel LAP was negative in 391 (65%) patients and positive in 211 (35%). In the receiver operating characteristic curve analysis, no significant difference was found between SLNB positivity and negativity in terms of NLR, PLR, LMR, or SII. In contrast to previous research, NLR, PLR, LMR, or SII did not affect SLNB positivity prediction in our study.

Predictors of Sentinel Lymph Node Metastasis in Very Thin Invasive Melanomas.

Melanomas <0.8 mm in Breslow depth have less than 5% risk for nodal positivity. Nonetheless, nodal positivity is prognostic for this group. Early identification of nodal positivity may improve outcomes for these patients. To determine the degree to which ulceration and other high-risk features predict sentinel lymph node (SLN) positivity for very thin melanomas. The National Cancer Database was reviewed from 2012 to 2018 for melanoma patients with Breslow thickness < 0.8 mm. Data were analyzed between July 7, 2022, through February 25, 2023. Patients were excluded if data regarding their ulceration status or sentinel lymph node biopsy (SLNB) performance were unknown. We analyzed patient, tumor, and health system factors for their effect on SLN positivity. Data were analyzed using chi-square tests and logistic regressions. Overall survival (OS) was compared by Kaplan-Meier analyses. Positive nodal metastases were seen in 876 (5.0%) of patients who underwent sentinel lymph node biopsy (17,692). Factors significantly associated with nodal positivity on multivariable analysis include: lymphovascular invasion (Odds ratio (OR)= 4.5, p< 0.001), ulceration (OR= 2.6, p < 0.001), mitoses (OR= 2.1, p< 0.001), and nodular subtype (OR= 2.1, p< 0.001). Five-year overall survival was 75% and 92% for patients with positive and negative SLN, respectively. Nodal positivity has prognostic significance for very thin melanomas. In our cohort, the rate of nodal positivity was 5% overall in these patients who underwent SLNB. Specific tumor factors (e.g. lymphovascular invasion, ulceration, mitoses, nodular subtype) were associated with higher rates of SLN metastases and should be used to guide clinicians in choosing which patients will benefit from sentinel lymph node biopsy.

Online risk calculator and nomogram for predicting sentinel lymph node positivity in Merkel cell carcinoma

Online risk calculator and nomogram for predicting sentinel lymph node positivity in Merkel cell carcinoma

167P Deep learning-based whole slide image analysis to predict sentinel node status in the INSEMA cohort

As further de-escalation of axillary surgery is ongoing, new biomarkers that convey the same prognostic information as sentinel node status are called for. To predict sentinel node status, we trained a deep learning (DL) image analysis model on H&E-stained whole slide images (WSIs) of primary tumors. For training, we used cases from the INSEMA standard arm (n=762, ca. 94 % HR+/HER2-, 3.5 % G3, less than 1% pT3/4, 13 % SLN-positive) and a cohort from the Women’s Clinic in Mannheim, Germany (n=150, all HR+/HER2-, G2, pT1/2, ca. 16 % SLN positive). We also fitted a logistic regression with clinical data (pT stage, Ki-67) for this task. Models were tested on a holdout INSEMA set (n=381), and the image model also on the higher risk TCGA BRCA cohort (n=650, ca. 72% HR+, ca. 55% SLN+). Vice versa, we trained a model on TCGA WSIs and tested it on the other cohorts. During training, the image and the clinical model yielded Areas under the Receiver Operating Characteristic Curve (AUROCs) of 0.62 and of 0.77 on the Mannheim WSIs, respectively. However, performance of the image model was random on the INSEMA (determined by blinded assessment) and TCGA BRCA test sets. The clinical classifier retained an AUROC of 0.62 on the INSEMA set. Inclusion of the image classifier output in the logistic regression did not improve performance on INSEMA. The image model trained on TCGA also yielded random performance on the INSEMA and Mannheim cohorts. Image analysis algorithms trained on H&E stains of the primary tumors from INSEMA or TCGA using established techniques were unable to predict sentinel status, which may suggest a lack of systematic histological differences by lymph node status in these cohorts. Thus, DL-based WSI analysis may not be a good strategy to replace sentinel node assessment, especially in low- to intermediate-risk, hormone receptor-positive breast cancer.

200 Deep learning model to predict sentinel lymph node status in melanoma patients

200 Deep learning model to predict sentinel lymph node status in melanoma patients

Abstract CT053: Merlin_001: a prospective registry study of a primary melanoma gene-signature to predict sentinel node (SN) status and determine its prognostic value for more accurate staging of SN-negative melanoma patients

Abstract Background: Sentinel lymph node biopsy (SLNB) provides important staging and prognostic information that guides surveillance and adjuvant systemic therapy decisions for patients with cutaneous melanoma. At most centers, SLNB is indicated for patients with cutaneous melanoma with at least a 5% risk of having nodal metastases, typically melanomas ≥0.8 mm in thickness or selected thinner lesions with high-risk features such as elevated mitotic rate and/or ulceration. However, up to 85% of patients who undergo SLNB have a negative result. Currently, there is an unmet clinical need to better identify patients with a low risk of nodal metastasis who may safely forgo SLNB, but otherwise meet established criteria for undergoing SLNB. Previously, a model using primary tumor gene expression profile (GEP) data combined with clinicopathological features (CP) was developed to identify melanoma patients with a low risk of having a positive SLN. The model has been externally validated in multiple retrospective studies. The aim of the MERLIN_001 observational registry study is to prospectively validate the CP-GEP model in an independent multicenter cohort of primary cutaneous melanoma patients, who undergo lymphatic mapping and SLNB per current clinical guidelines. Methods: A total of 9 centers across the US are included in the study with planned enrollment of 2,340 patients, allowing for a loss of up to 30% due to screen failure, tissue loss, low RNA yield, no gene expression profile or failure to perform the planned SLNB. Patients with clinically node-negative cutaneous melanoma and planned SLNB using current guideline indications are eligible for the study and will be followed for five years. Both patients and investigators are blinded to the results (NCT04759781). FFPE material from the initial melanoma biopsy is collected and the GEP of the primary melanoma is assessed. Subsequently, CP-GEP probability scores are calculated and expressed as a binary classification (Low Risk or High Risk for nodal metastasis), which will be compared to SLNB pathology results. Performance metrics for CP-GEP will be evaluated and will include: SLNB Reduction Rate based on Negative Predictive Value, Positive Predictive Value, Sensitivity and Specificity, and the corresponding 95% confidence intervals. Finally, the performance of CP-GEP to stratify patients according to risk of recurrence (recurrence-free survival, distant metastasis-free survival, overall survival) will also be assessed, based on five-year outcomes data. Additional analyses will be performed using the data collected throughout the study. Enrollment of patients started in September 2021 and is ongoing. As of January 2023, 890 patients have been enrolled, representing 46% of the targeted number of patients with a successful SLNB and CP-GEP test result. Citation Format: Tina J. Hieken, Michael E. Egger, Christina V. Angeles, Erin E. Burke, Michael C. Lowe, John R. Hyngstrom, Georgia M. Beasley, Edmund K. Bartlett, Vernon K. Sondak. Merlin_001: a prospective registry study of a primary melanoma gene-signature to predict sentinel node (SN) status and determine its prognostic value for more accurate staging of SN-negative melanoma patients [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 2 (Clinical Trials and Late-Breaking Research); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(8_Suppl):Abstract nr CT053.

Tumor width and calculated tumor area do not outperform Breslow thickness in predicting sentinel lymph node biopsy positivity

Tumor width and calculated tumor area do not outperform Breslow thickness in predicting sentinel lymph node biopsy positivity

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