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BACKGROUND: Axillary lymph node dissection (ALND) is a standard procedure for early-stage breast cancer (BC) patients with three or more positive sentinel lymph nodes (SLNs). However, ALND can lead to significant postoperative complications without always providing additional clinical benefits. This study aims to develop machine-learning (ML) models to predict non-sentinel lymph node (non-SLN) metastasis in Chinese BC patients with three or more positive SLNs, potentially allowing the omission of ALND. METHODS: Data from 2217 BC patients who underwent SLN biopsy at Shantou University Medical College were analyzed, with 634 having positive SLNs. Patients were categorized into those with ≤ 2 positive SLNs and those with ≥ 3 positive SLNs. We applied nine ML algorithms to predict non-SLN metastasis. Model performance was evaluated using ROC curves, precision-recall curves, and calibration curves. Decision Curve Analysis (DCA) assessed the clinical utility of the models. RESULTS: The RF model showed superior predictive performance, achieving an AUC of 0.987 in the training set and 0.828 in the validation set. Key predictive features included size of positive SLNs, tumor size, number of SLNs, and ER status. In external validation, the RF model achieved an AUC of 0.870, demonstrating robust predictive capabilities. CONCLUSION: The developed RF model accurately predicts non-SLN metastasis in BC patients with ≥ 3 positive SLNs, suggesting that ALND might be avoided in selected patients by applying additional axillary radiotherapy. This approach could reduce the incidence of postoperative complications and improve patient quality of life. Further validation in prospective clinical trials is warranted.

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Our research seeks to evaluate the utility of intraoperative frozen analysis of sentinel lymph nodes (SLNs) in the lateral cervical compartment (LCC) as a tool to inform decision-making regarding therapeutic neck dissection in patients with medullary thyroid carcinoma (MTC). This is particularly relevant due to the variability observed in guidelines regarding the indication for lateral neck dissection in this patient population. The study comprised 64 patients (25 males, 39 females) aged between 29 and 81 years, with a median age of 59, who underwent surgery for MTC at stage T1-3N0-1M0 between January 1, 2012, and December 31, 2020. A standardized surgical approach involving total thyroidectomy with central neck dissection was adopted. LCC dissection was reserved for patients with clinically apparent nodal metastases. In patients lacking clinical evidence of nodal involvement, SLNs were identified using patent blue dye, excised, and subjected to intraoperative frozen analysis. If metastasis was confirmed, LCC dissection was subsequently performed. Among the study participants, 14 individuals (21.9%) underwent therapeutic LCC dissection due to clinical lymph node (LN) metastases. This intervention resulted in clinical remission for 9 patients, while disease progression was observed in 5 cases, leading to 2 fatalities. In the remaining cohort of 50 patients clinically negative for nodal involvement, SLNs were successfully identified and examined in 38 cases, revealing metastases in 6 patients (15.8%). Among both subsets of patients with analyzed SLNs, irrespective of metastatic status, one patient each required repeat surgery due to disease recurrence; however, all patients eventually achieved clinical remission. Lymphatic mapping in the LCC plays a pivotal role in detecting early metastases, thereby aiding in the avoidance of unnecessary repeat neck surgeries, and ultimately improving the prognosis in patients with MTC.

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INTRODUCTION: Sentinel lymph node biopsy (SLNB) is a standard procedure for patients with clinically assessed negative axillary lymph nodes (cN0) during early-stage breast cancer (EBC). However, the majority of EBC patients have a negative pathological confirmation of the sentinel lymph node (SLN), and axillary surgery is inevitably associated with postoperative complications. Considering that SLNB has no therapeutic benefit, this trial aims to determine the safety of omitting SLNB in patients with cN0 early invasive breast cancer. METHODS AND ANALYSIS: The OMSLNB trial is a prospective, single-arm, non-inferiority, phase II, open-label study design involving female breast cancer patients with a tumor of ≤3 cm in diameter, who are considered axillary lymph-node-negative based on two or more radiological examinations, including axillary lymph node ultrasonography. Eligible patients will avoid axillary surgery but will undergo breast surgery, which is not limited to breast-conserving surgery. The trial begins in 2023 and is scheduled to end in 2027. The primary endpoint is 3 year invasive disease-free survival (iDFS). The secondary endpoints include the incidence of breast cancer-related lymphoedema, patient-reported outcomes, locoregional recurrence, local recurrence and regional recurrence. It is expected that the 3 year iDFS in patients undergoing SLNB is about 90%, combined with a non-inferiority cut-off of 5%, 80% power, 95% CIs, 0.05 test level, and 10% loss to follow-up rate, the planned enrollment is 311 patients. All enrolled patients will be included in the intention-to-treat analysis. ETHICS AND DISSEMINATION: This trial was approved by the Ethics Committee of the First Affiliated Hospital of Nanjing Medical University (No.2023-SR-193). All participants must provide written informed consent to be eligible. The protocol will be described in a peer-reviewed manuscript, and the results will be published in scientific journals and/or at academic conferences. TRIAL REGISTRATION NUMBER: NCT05935150.

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BACKGROUND: Risk stratification and treatment benefit prediction models are urgent to improve negative sentinel lymph node (SLN-) melanoma patient selection, thus avoiding costly and toxic treatments in patients at low risk of recurrence. To this end, the application of artificial intelligence (AI) could help clinicians to better calculate the recurrence risk and choose whether to perform adjuvant therapy. METHODS: We made use of AI to predict recurrence-free status (RFS) within 2-years from diagnosis in 94 SLN- melanoma patients. In detail, we detected quantitative imaging information from H&E slides of a cohort of 71 SLN- melanoma patients, who registered at Istituto Tumori "Giovanni Paolo II" in Bari, Italy (investigational cohort, IC). For each slide, two expert pathologists firstly annotated two Regions of Interest (ROIs) containing tumor cells alone (TUMOR ROI) or with infiltrating cells (TUMOR + INF ROI). In correspondence of the two kinds of ROIs, two AI-based models were developed to extract information directly from the tiles in which each ROI was automatically divided. This information was then used to predict RFS. Performances of the models were computed according to a 5-fold cross validation scheme. We further validated the prediction power of the two models on an independent external validation cohort of 23 SLN- melanoma patients (validation cohort, VC). RESULTS: The TUMOR ROIs have revealed more informative than the TUMOR + INF ROIs. An Area Under the Curve (AUC) value of 79.1% and 62.3%, a sensitivity value of 81.2% and 76.9%, a specificity value of 70.0% and 43.3%, an accuracy value of 73.2% and 53.4%, were achieved on the TUMOR and TUMOR + INF ROIs extracted for the IC cohort, respectively. An AUC value of 76.5% and 65.2%, a sensitivity value of 66.7% and 41.6%, a specificity value of 70.0% and 55.9%, an accuracy value of 70.0% and 56.5%, were achieved on the TUMOR and TUMOR + INF ROIs extracted for the VC cohort, respectively. CONCLUSIONS: Our approach represents a first effort to develop a non-invasive prognostic method to better define the recurrence risk and improve the management of SLN- melanoma patients.

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INTRODUCTION: Up to 88% of sentinel lymph node biopsies (SLNBs) are negative. The 31-gene expression profile (31-GEP) test can help identify patients with a low risk of SLN metastasis who can safely forego SLNB. The 31-GEP classifies patients as low (Class 1 A), intermediate (Class 1B/2A), or high risk (Class 2B) for recurrence, metastasis, and SLN positivity. The integrated 31-GEP (i31-GEP) combines the 31-GEP risk score with clinicopathologic features using a neural network algorithm to personalize SLN risk prediction. METHODS: Patients from a single surgical center with 31-GEP results were included (n = 156). An i31-GEP risk prediction < 5% was considered low risk of SLN positivity. Chi-square was used to compare SLN positivity rates between groups. RESULTS: Patients considered low risk by the i31-GEP had a 0% (0/30) SLN positivity rate compared to a 31.9% (30/94, p < 0.001) positivity rate in those with > 10% risk. Using the i31-GEP to guide SLNB decisions could have significantly reduced the number of unnecessary SLNBs by 19.2% (30/156, p < 0.001) for all patients and 33.0% (30/91, p < 0.001) for T1-T2 tumors. Patients with T1-T2 tumors and an i31-GEP-predicted SLN positivity risk > 10% had a similar SLN positivity rate (33.3%) as patients with T3-T4 tumors (31.3%). CONCLUSION: The i31-GEP identified patients with < 5% risk of SLN positivity who could safely forego SLNB. Combining the 31-GEP with clinicopathologic features for a precise risk estimate can help guide risk-aligned patient care decisions for SLNB to reduce the number of unnecessary SLNBs and increase the SLNB positivity yield if the procedure is performed.

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OBJECTIVE: Management of endometrial cancer is advancing, with accurate staging crucial for guiding treatment decisions. Understanding sentinel lymph node (SLN) involvement rates across molecular subgroups is essential. To evaluate SLN involvement in early-stage (International Federation of Gynecology and Obstetrics 2009 I-II) endometrial cancer, considering molecular subtypes and new European Society of Gynaecological Oncology (ESGO) risk classification. METHODS: The SENECA study retrospectively reviewed data from 2139 women with stage I-II endometrial cancer across 66 centers in 16 countries. Patients underwent surgery with SLN assessment following ESGO guidelines between January 2021 and December 2022. Molecular analysis was performed on pre-operative biopsies or hysterectomy specimens. RESULTS: Among the 2139 patients, the molecular subgroups were as follows: 272 (12.7%) p53 abnormal (p53abn, 1191 (55.7%) non-specific molecular profile (NSMP), 581 (27.2%) mismatch repair deficient (MMRd), 95 (4.4%) POLE mutated (POLE-mut). Tracer diffusion was detected in, at least one side, in 97.2% of the cases; with a bilateral diffusion observed in 82.7% of the cases. By ultrastaging (90.7% of the cases) or one-step nucleic acid amplification (198 (9.3%) of the cases), 205 patients were identified with affected sentinel lymph nodes, representing 9.6% of the sample. Of these, 139 (67.8%) had low-volume metastases (including micrometastases, 42.9%; and isolated tumor cells, 24.9%) while 66 (32.2%) had macrometastases. Significant differences in SLN involvement were observed between molecular subtypes, with p53abn and MMRd groups having the highest rates (12.50% and 12.40%, respectively) compared with NSMP (7.80%) and POLE-mut (6.30%), (p=0.004); (p53abn, OR=1.69 (95% CI 1.11 to 2.56), p=0.014; MMRd, OR=1.67 (95% CI 1.21 to 2.31), p=0.002). Differences were also noted among ESGO risk groups (2.84% for low-risk patients, 6.62% for intermediate-risk patients, 21.63% for high-intermediate risk patients, and 22.51% for high-risk patients; p<0.001). CONCLUSIONS: Our study reveals significant differences in SLN involvement among patients with early-stage endometrial cancer based on molecular subtypes. This underscores the importance of considering molecular characteristics for accurate staging and optimal management decisions.

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AIM OF THE STUDY: To assess the association of prevalence and size of pelvic sentinel node (SLN) metastases with risk factors in endometrial cancer (EC). PATIENTS AND METHODS: Between June 2014 and January 2024 consecutive women with a uterine confined EC undergoing robotic surgery including detection of pelvic SLNs at a University Hospital were included. An anatomically based algorithm utilizing Indocyanine green (ICG) as tracer was adhered to. Ultrastaging and immunohistochemistry (IHC) was applied on all SLNs. The prevalence and size of SLN metastases was assessed with regards to pre- and postoperative histologic types and myometrial invasion estimates. RESULTS: Of 1101 included women 72.6 % (759/1045) had low-grade, 7.6 % (79/1045) high-grade endometroid cancer and 19.8 % (207/1045) non-endometroid cancer. SLN-metastases were present in 174/1045 (16.6 %) women; 9.8 % of preoperatively presumed low-grade endometroid uterine stage 1A (6.4 % of low-grade stage 1A at final histology) and in 58.3 % and 47.8 % respectively in women with high-grade endometroid and non-endometroid uterine stage 1B cancer. In low-grade EC 45/95 (47.4 %) had only isolated tumor cells (ITC) in SLNs compared with 15/78 (19.2 %) in high-grade or non-endometroid cancer (p < .0001) CONCLUSION: This large population-based study, applying a consequent SLN-algorithm over time, provides important detailed information on the risk for, and size of, SLN metastases within risk groups of EC. The 9.8 % risk for metastases in women with presumed low grade uterine stage 1A endometrioid EC motivates detection of SLNs within this subgroup. The proportion of ITCs in SLNs was significantly lower in higher risk histologies.

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ABSTRACT: The objective of the study was to use a deep learning model to differentiate between benign and malignant sentinel lymph nodes (SLNs) in patients with breast cancer compared to radiologists' assessments.Seventy-nine women with breast cancer were enrolled and underwent lymphosonography and contrast-enhanced ultrasound (CEUS) examination after subcutaneous injection of ultrasound contrast agent around their tumor to identify SLNs. Google AutoML was used to develop image classification model. Grayscale and CEUS images acquired during the ultrasound examination were uploaded with a data distribution of 80% for training/20% for testing. The performance metric used was area under precision/recall curve (AuPRC). In addition, 3 radiologists assessed SLNs as normal or abnormal based on a clinical established classification. Two-hundred seventeen SLNs were divided in 2 for model development; model 1 included all SLNs and model 2 had an equal number of benign and malignant SLNs. Validation results model 1 AuPRC 0.84 (grayscale)/0.91 (CEUS) and model 2 AuPRC 0.91 (grayscale)/0.87 (CEUS). The comparison between artificial intelligence (AI) and readers' showed statistical significant differences between all models and ultrasound modes; model 1 grayscale AI versus readers, P = 0.047, and model 1 CEUS AI versus readers, P < 0.001. Model 2 r grayscale AI versus readers, P = 0.032, and model 2 CEUS AI versus readers, P = 0.041.The interreader agreement overall result showed κ values of 0.20 for grayscale and 0.17 for CEUS.In conclusion, AutoML showed improved diagnostic performance in balance volume datasets. Radiologist performance was not influenced by the dataset's distribution.

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BACKGROUND: Sentinel 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. METHODS: A 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. RESULTS: In 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). CONCLUSION: The 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.

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Intreduction: Melanoma is an extremely aggressive form of skin neoplasia, an important stage in the diagnostic and treatment is identifying the dissemination at the lymphatic level. For a more accurate staging, the sentinel lymph node biopsy technique is performed, which in most of the time addresses one, respectively 2 locations, but cases with sentinel nodes in 3 lymphatic basins have rarely been described. Case report: We present a case of melanoma located in the right lumbar region, which from the point of view of histopathological features has a Breslow index of 4.2 mm, classified in the pT4b stage. After the CT evaluation was performed, it was decided that there is indication for performing the sentinel lymph node technique and excision with a margin of safety. Scintigraphy revealed that sentinel lymph nodes were identified in 3 different regions, respectively the right axilla and bilateral inguinal. Conclusions: Melanoma located on the trunk can present different lymphatic routes for the sentinel lymph nodes, unlike that on the limbs where certain patterns are present. Identifying these lymph nodes in cases like this involves a challenge both from a diagnostic and surgical point of view.

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BACKGROUND: Cervical cancer's lymphatic spread primarily begins from the sentinel lymph nodes (SLNs), underlining their pivotal role in disease metastasis. However, these nodes' immune gene expression profiles and immunoregulation mechanisms have yet to be explored. METHODS: Our study aimed to elucidate the immune cell populations and their roles in the immune gene expression profile of negative SLNs compared with positive SLNs and non-SLNs using Nanostring RNA seq analysis. We performed a principal component analysis on the log2 normalized expression of 685 endogenous genes in the nCounter PanCancer Immune Profiling Panel, followed by an assessment of the differential expression of genes and immune cell type abundance. RESULTS: We found significant variations in gene expression among the groups, with negative SLNs displaying overexpression of genes related to tumor-infiltrating immune cells, specifically innate cell populations. They also demonstrated the upregulation of genes involved in antigen presentation and T-cell priming. In contrast, positive SLNs were enriched in regulatory networks, suggesting their potential role in immune evasion. A comparison of negative SLNs and non-SLNs revealed increased innate and adaptive immune cell types, underscoring the ongoing T cell response to tumor antigens. CONCLUSION: Our findings underscore a specific immunogenetic phenotype profile in negative SLNs, emphasizing their crucial role in the initial anticancer response, immunosurveillance, and the propagation of immune tolerance from the primary cervical tumor. These results highlight the potential of SLNs as a novel target for immunotherapy strategies and underscore the importance of new imaging methods for accurately identifying SLN status without removal. Future investigations are needed to understand further the immunological interplay within SLNs and their influence on cervical cancer progression.

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BACKGROUND: Sentinel lymph node biopsy (SLNB) has replaced axillary lymph node dissection (ALND) for assessing axillary lymph node status in clinically node-negative breast cancer patients. However, the approach to axillary surgery after neoadjuvant treatment is still controversial. In the present study, our objective was to predict the pathological nodal stage based on SLNB results and the clinicopathological characteristics of patients who initially presented with clinical N1 positivity but whose disease status was converted to clinical N0 after neoadjuvant chemotherapy (NAC). MATERIALS AND METHODS: After NAC, 150 clinically node-negative patients were included. The relationships between clinicopathologic parameters and the number of positive lymph nodes in SLNBs and ALNDs were assessed through binary/multivariate logistic regression analysis. RESULTS: Among 150 patients, 78 patients had negative SLNBs, and 72 patients had positive SLNBs. According to the ALND data of 21 patients with SLNB1+, there was no additional node involvement (80.8%), 1-2 lymph nodes were positive in 5 patients (19.2%), and no patient had ≥ 3 lymph nodes involved. Following the detection of SLNB1 + positivity, the rate of negative non-sentinel nodes were 75% in the luminal A/B subgroup, 100% in the HER-2-positive subgroup, and 100% in the triple-negative subgroup. Patients with a lower T stage (T1-3 vs. T4), fewer than 4 clinical nodes before NAC (< 4 vs. ≥4), and a decreased postoperative Ki-67 index (< 10% vs. stable/increase) were included. According to both univariate and multivariate analyses, being in the triple-negative or HER2-positive subgroup, compared to the luminal A/B subgroup (luminal A/B vs. HER2-positive/triple-negative), was found to be predictive of complete lymph node response. CONCLUSION: The number of SLNB-positive nodes, tumor-related parameters, and response to treatment may predict no additional nodes to be positive at ALND.

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BACKGROUND: The identification and assessment of sentinel lymph nodes (SLNs) in breast cancer is important for optimised patient management. The aim of this study was to develop an interactive 3D breast SLN atlas and to perform statistical analyses of lymphatic drainage patterns and tumour prevalence. METHODS: A total of 861 early-stage breast cancer patients who underwent preoperative lymphoscintigraphy and SPECT/CT were included. Lymphatic drainage and tumour prevalence statistics were computed using Bayesian inference, non-parametric bootstrapping, and regression techniques. Image registration of SPECT/CT to a reference patient CT was carried out on 350 patients, and SLN positions transformed relative to the reference CT. The reference CT was segmented to visualise bones and muscles, and SLN distributions compared with the European Society for Therapeutic Radiology and Oncology (ESTRO) clinical target volumes (CTVs). The SLN atlas and statistical analyses were integrated into a graphical user interface (GUI). RESULTS: Direct lymphatic drainage to the axilla level I (anterior) node field was most common (77.2%), followed by the internal mammary node field (30.4%). Tumour prevalence was highest in the upper outer breast quadrant (22.9%) followed by the retroareolar region (12.8%). The 3D atlas had 765 SLNs from 335 patients, with 33.3-66.7% of axillary SLNs and 25.4% of internal mammary SLNs covered by ESTRO CTVs. CONCLUSION: The interactive 3D atlas effectively displays breast SLN distribution and statistics for a large patient cohort. The atlas is freely available to download and is a valuable educational resource that could be used in future to guide treatment.

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PURPOSE OF REVIEW: Sentinel lymph node biopsy (SLNB) is a widely used technique in other gynaecological tumours but has not yet been implemented as the gold standard technique for nodal staging in cervical cancer. Since the majority of evidence is derived from retrospective studies, this review aims to summarize the most recent evidence on this relevant topic. RECENT FINDINGS: SLNB has demonstrated to be a well tolerated technique for lymph node staging in early-stage cervical cancer patients with promising future as exclusive lymph node assessment method avoiding full lymphadenectomy. Moreover, it allows ultrastaging and unfrequent drainage identification, which enables the detection of patients at a high risk of recurrence who would otherwise remain unnoticed. When compared with pelvic lymphadenectomy, SLNB is also associated with less intraoperative and postoperative complications, especially in terms of lymphedema formation. SUMMARY: The available evidence suggests that SLNB offers numerous advantages over the standard pelvic lymphadenectomy reducing morbidity rates and increasing diagnostic accuracy. Three ongoing prospective trials will likely answer the controversies over these questions.

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PURPOSE OF REVIEW: The most common surgical procedure associated with lymphedema formation is the regional lymphadenectomy. One of the advantages of sentinel node biopsy is the reduction of the risk of lymphedema formation. The purpose of this review is to collect and analyze the most relevant and recent evidence of the use of sentinel node biopsy and its implication on the development of postoperative lymphedema in gynecological cancer. RECENT FINDINGS: The current evidence of the use of sentinel node biopsy in cervical cancer to reduce lymphedema is heterogeneous and more data is needed to establish its role.Sentinel lymph node biopsy in endometrial cancer is a staging procedure with lower surgical complications, as well as lymphedema formation; while the results of prospective trials evaluating its impact on quality of life are still lacking.Sentinel lymph node biopsy in vulvar cancer minimizes the need for extensive dissection and reduces the incidence of complications associated with overharvesting of lymph nodes such as lymphedema without compromising oncological outcomes. SUMMARY: The prevalence of lymphedema in gynecological cancer varies based on the surgical treatment or additional therapies applied. Over the past years, one of the most important surgical modifications to decrease lymphedema formation has been implementation of sentinel lymph node technique mainly in vulvar cancer patients.

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