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OBJECTIVE: Missing occult para-aortic lymph node metastasis is one of the primary concerns of sentinel lymph node biopsy in endometrial cancer. Our study aimed to evaluate the relationship between intrauterine cancer site and isolated para-aortic lymph node metastasis to tailor treatment and reduce the false negative rate of the sentinel lymph node procedure.
METHODS: A retrospective, multicenter, case control study was performed in four international centers. All patients with positive lymph nodes who had complete surgical staging with pelvic and para-aortic lymphadenectomy, between January 2013 and December 2023, were included. Detailed descriptions of the cancer location within the uterine cavity on the cranio-caudal plane and the myometrial wall involvement on the cranio-caudal and ventro-dorsal planes were collected, as were clinical data and cancer histological features. Patients with isolated para-aortic lymph node metastasis were allocated to group 1; patients with pelvic lymph node metastasis and those with both pelvic and para-aortic lymph node metastasis were allocated to group 2. The groups were compared according to the variables collected.
RESULTS: 200 preoperative early stage endometrial cancer patients with postoperative International Federation of Gynecology and Obstetrics 2009/2023 stage IIIC1/IIIC2 were included in our study: 42 patients (21%) with isolated para-aortic lymph node metastasis were allocated to group 1 and the remaining patients to group 2. The two groups had comparable clinical and pathological characteristics (p>0.05): mean age was 66.5±10.3 (group 1) and 63.5±11.9 (group 2); endometrioid histotype was the predominant one for both groups (50%); most patients had myometrial infiltration >50% (80.9% and 79.7%), grade 3 (61.9% and 63.9%), and lymph vascular space invasion (78.5% and 82.2%). Cancers involving the fundal uterine cavity, the fundal myometrial wall, or the anterior myometrial wall were 3.11 (1.04-9.27), 3.03 (1.12-8.21), and 2.12 (0.77-5.80) times more likely to metastasize only to para-aortic lymph nodes compared with cancers located in other uterine sites.
CONCLUSIONS: In this study, the intrauterine location of the cancer determined the site of lymph node metastasis. When the tumor involved the fundus (cavity or wall) and infiltrated exclusively the anterior wall, the baseline risk of spreading only into the para-aortic area increased significantly in selected patients at risk of nodal disease.

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.
CONCLUSIONS: 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.

BACKGROUND: Sentinel lymph node navigation surgery, which identifies the sentinel lymph node in early cervical cancers and omits systemic pelvic lymphadenectomy in cases where no lymph node metastasis is present, has recently gained attention. However, there are few reports on lymph node recurrence and the long-term outcomes of cervical cancer surgery performed using sentinel lymph node navigation surgery. In this study, we aimed to evaluate the long-term outcomes of sentinel node navigation surgery for early-stage cervical cancer.
METHODS: One hundred thirty-eight patients with cervical cancer were enrolled. Sentinel lymph nodes were identified by injecting 99 m Technetium-labeled phytate and indocyanine green into the uterine cervix. Surgery and survival outcomes were also analyzed.
RESULTS: The median age and body mass index of the patients were 40 years (20-78) and 21.7 kg/m2 (16.5-50.4), respectively. Open surgery, laparoscopic surgery, and robotic surgery were performed in 77 (56%), 53 (38%), and 8 (6%) patients, respectively. The overall and bilateral detection rates of the sentinel lymph node were 100% and 94%, respectively. Only one case (0.7%) exhibited lower extremity lymphedema, and pelvic lymphocele was observed in three cases (2.2%). Four cases (3%) experienced recurrence over a median follow-up of 57.5 months (range, 2-115 months), with five-year recurrence-free and overall survival rates of 97% and 97.3%, respectively.
CONCLUSIONS: Our results demonstrate that sentinel node navigation surgery may be safe and effective for early-stage cervical cancer.

BACKGROUND: 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%).
CONCLUSIONS: 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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In the management of early-stage breast cancer (BC), lymph nodes (LNs) are typically characterised using the One-Step Nucleic Acid Amplification (OSNA) assay, a standard procedure for assessing subclinical metastasis in sentinel LNs (SLNs). The pivotal role of LNs in coordinating the immune response against BC is often overlooked. Our aim was to improve prognostic information provided by the OSNA assay and explore immune-related gene signatures in SLNs. The expression of an immune gene panel was analysed in SLNs from 32 patients with Luminal A early-stage BC (cT1-T2 N0). Using an unsupervised approach based on these expression values, this study identified two clusters, regardless of the SLN invasion: one evidencing an adaptive anti-tumoral immune response, characterised by an increase in naive B cells, follicular T helper cells, and activated NK cells; and another with a more undifferentiated response, with an increase in the activated-to-resting dendritic cells (DCs) ratio. Through a protein-protein interaction (PPI) network, we identified seven immunoregulatory hub genes: CD80, CD40, TNF, FCGR3A, CD163, FCGR3B, and CCR2. This study shows that, in Luminal A early-stage BC, SLNs gene expression studies enable the identification of distinct immune profiles that may influence prognosis stratification and highlight key genes that could serve as potential targets for immunotherapy.

Lymph node metastasis (LNM) is one of the major prognostic factors in human gastrointestinal carcinomas (GICs). The lymph node-positive patients have poorer survival than node-negative patients. LNM is directly associated with the recurrence and poor survival of patients with GICs. The early detection of LNM in patients and designing effective therapies to suppress LNM may significantly impact the survival of these patients. The rapid progress made in proteomic technologies could be successfully applied to identify molecular targets for cancers at high-throughput levels. LC-MS/MS analysis enables the identification of proteins involved in LN metastasis, which can be utilized for diagnostic and therapeutic applications. This review summarizes the studies on LN metastasis in GICs using proteomic approaches to date.

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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.