Management options for node-positive muscle-invasive bladder cancer.

Localized node-positive bladder cancer is characterized by a high degree of heterogeneity, leading to significant variability in overall survival outcomes among affected individuals. The absence of standardized treatment guidelines presents a critical challenge in managing these patients effectively. This comprehensive review article delves into the pathophysiology, clinical significance, and management of node-positive bladder cancer. It critically evaluates the current therapeutic landscape and explores emerging treatment strategies, including novel drugs currently undergoing clinical trials. By synthesizing the latest research findings, the review aims to provide valuable insights into the optimal management of node-positive urothelial cell carcinoma, ultimately contributing to improved patient outcomes and quality of life.

Para-aortic and pelvic lymphadenectomy in locally advanced cervical cancer with pelvic lymph node metastasis.

OBJECTIVE: This study sought to explore the efficiency of para-aortic and pelvic lymphadenectomy in the treatment of locally advanced cervical cancer (LACC) with pelvic lymph node (PLN) metastasis. METHODS: A total of 171 LACC patients with imaging-confirmed pelvic lymph node metastasis were included in this study. These patients were divided into two groups: the surgical staging group, comprising 58 patients who had received para-aortic and pelvic lymphadenectomy (surgical staging) along with concurrent chemoradiation therapy (CCRT), and the imaging staging group, comprising 113 patients who had received only CCRT. The two groups' progression-free survival (PFS), overall survival (OS) and treatment-related complications were compared. RESULTS: The surgical staging group started radiotherapy 10.2 days (range 9-12 days) later than the imaging staging group. The overall incidence of lymphatic cysts was 9.30%. In the surgical staging group, para-aortic lymph node metastasis was identified in 34.48% (20/58) of patients, while pathology-negative PLN was observed in 12.07% (7/58). Over a median follow-up period of 52 months, no significant differences in PFS and OS rates were found between the two groups (p > 0.05). Subgroup analysis of patients with lymph node diameters of ≥ 1.5 cm revealed a five-year PFS rate of 75.0% and an OS rate of 80.0% in the surgical staging group, compared to 41.5% and 50.1% in the imaging staging group, respectively, showing statistically significant differences (p = 0.022, HR:0.34 [0.13, 0.90] and p = 0.038, HR: 0.34 [0.12,0.94], respectively for PFS and OS). Additionally, in patients with two or more metastatic lymph nodes, the five-year PFS and OS rates were 69.2% and 73.1% in the surgical staging group, versus 41.0% and 48.4% in the imaging staging group, with these differences also being statistically significant (p = 0.025, HR: 0.41[0.19,0.93] and p = 0.046, HR: 0.42[0.18,0.98], respectively). CONCLUSION: Performing surgical staging before CCRT is safe and delivers accurate lymph node details crucial for tailoring radiotherapy. This approach merits further investigation, particularly in women with pelvic lymph nodes measuring 1.5 cm or more in diameter or patients with two or more imaging-positive PLNs.

Machine learning to predict distant metastasis and prognostic analysis of moderately differentiated gastric adenocarcinoma patients: a novel focus on lymph node indicators.

Background: Moderately differentiated gastric adenocarcinoma (MDGA) has a high risk of metastasis and individual variation, which strongly affects patient prognosis. Using large-scale datasets and machine learning algorithms for prediction can improve individualized treatment. The specific efficacy of several lymph node indicators in predicting distant metastasis (DM) and patient prognosis in MDGA remains obscure. Methods: We collected data from MDGA patients from the SEER database from 2010 to 2019. Additionally, we collected data from MDGA patients in China. We used nine machine learning algorithms to predict DM. Subsequently, we used Cox regression analysis to determine the risk factors affecting overall survival (OS) and cancer-specific survival (CSS) in DM patients and constructed nomograms. Furthermore, we used logistic regression and Cox regression analyses to assess the specific impact of six lymph node indicators on DM incidence and patient prognosis. Results: We collected data from 5,377 MDGA patients from the SEER database and 109 MDGC patients from hospitals. T stage, N stage, tumor size, primary site, number of positive lymph nodes, and chemotherapy were identified as independent risk factors for DM. The random forest prediction model had the best overall predictive performance (AUC = 0.919). T stage, primary site, chemotherapy, and the number of regional lymph nodes were identified as prognostic factors for OS. Moreover, T stage, number of regional lymph nodes, primary site, and chemotherapy were also influential factors for CSS. The nomograms showed good predictive value and stability in predicting the 1-, 3-, and 5-year OS and CSS in DM patients. Additionally, the log odds of a metastatic lymph node and the number of negative lymph nodes may be risk factors for DM, while the regional lymph node ratio and the number of regional lymph nodes are prognostic factors for OS. Conclusion: The random forest prediction model accurately identified high-risk populations, and we established OS and CSS survival prediction models for MDGA patients with DM. Our hospital samples demonstrated different characteristics of lymph node indicators in terms of distant metastasis and prognosis.

Outcomes and failure patterns after chemoradiotherapy for locally advanced rectal cancer with positive lateral pelvic lymph nodes: a propensity score-matched analysis.

PURPOSE: This study aimed to use propensity score matching (PSM) to explore the long-term outcomes and failure patterns in locally advanced rectal cancer (LARC) patients with positive versus negative lateral pelvic lymph node (LPLN). MATERIALS AND METHODS: Patients with LARC were retrospectively divided into LPLN-positive and LPLN-negative groups. Clinical characteristics were compared between the groups using the chi-square test. PSM was applied to balance these differences. Progression-free survival (PFS) and overall survival (OS), and local-regional recurrence (LRR) and distant metastasis (DM) rates were compared between the groups using the Kaplan-Meier method and log-rank tests. RESULTS: A total of 651 LARC patients were included, 160 (24.6%) of whom had positive LPLN and 491 (75.4%) had negative LPLN. Before PSM, the LPLN-positive group had higher rates of lower location (53.1% vs. 43.0%, P = 0.025), T4 stage (37.5% vs. 23.2%, P = 0.002), mesorectal fascia (MRF)-positive (53.9% vs. 35.4%, P < 0.001) and extramural venous invasion (EMVI)-positive (51.2% vs. 27.2%, P < 0.001) disease than the LPLN-negative group. After PSM, there were 114 patients for each group along with the balanced clinical factors, and both groups had comparable surgery, pathologic complete response (pCR), and ypN stage rates. The median follow-up was 45.9 months, 3-year OS (88.3% vs. 92.1%, P = 0.276) and LRR (5.7% vs. 2.8%, P = 0.172) rates were comparable between LPLN-positive and LPLN-negative groups. Meanwhile, despite no statistical difference, 3-year PFS (78.8% vs. 85.9%, P = 0.065) and DM (20.4% vs. 13.3%, P = 0.061) rates slightly differed between the groups. 45 patients were diagnosed with DM, 11 (39.3%) LPLN-positive and 3 (17.6%) LPLN-negative patients were diagnosed with oligometastases (P = 0.109). CONCLUSIONS: Our study indicates that for LPLN-positive patients, there is a tendency of worse PFS and DM than LPLN-negative patients, and for this group patients, large samples are needed to further confirm our conclusion.

Automatic segmentation of esophageal cancer, metastatic lymph nodes and their adjacent structures in CTA images based on the UperNet Swin network.

OBJECTIVE: To create a deep-learning automatic segmentation model for esophageal cancer (EC), metastatic lymph nodes (MLNs) and their adjacent structures using the UperNet Swin network and computed tomography angiography (CTA) images and to improve the effectiveness and precision of EC automatic segmentation and TN stage diagnosis. METHODS: Attention U-Net, UperNet Swin, UNet++ and UNet were used to train the EC segmentation model to automatically segment the EC, esophagus, pericardium, aorta and MLN from CTA images of 182 patients with postoperative pathologically proven EC. The Dice similarity coefficient (DSC), sensitivity, and positive predictive value (PPV) were used to assess their segmentation effectiveness. The volume of EC was calculated using the segmentation results, and the outcomes and times of automatic and human segmentation were compared. All statistical analyses were completed using SPSS 25.0 software. RESULTS: Among the four EC autosegmentation models, the UperNet Swin had the best autosegmentation results with a DSC of 0.7820 and the highest values of EC sensitivity and PPV. The esophagus, pericardium, aorta and MLN had DSCs of 0.7298, 0.9664, 0.9496 and 0.5091. The DSCs of the UperNet Swin were 0.6164, 0.7842, 0.8190, and 0.7259 for T1-4 EC. The volume of EC and its adjacent structures between the ground truth and UperNet Swin model were not significantly different. CONCLUSIONS: The UperNet Swin showed excellent efficiency in autosegmentation and volume measurement of EC, MLN and its adjacent structures in different T stage, which can help to T and N stage diagnose EC and will save clinicians time and energy.

A metabolic-inflammatory-nutritional score (MINS) is associated with lymph node metastasis and prognostic stratification for endometrial cancer patients.

Objective: This study aims to propose a personalized cancer prediction model based on the metabolic-inflammatory-nutritional score (MINS) for predicting lymph node metastasis (LNM) in endometrial cancer (EC) and validated prediction of survival probability in patients with a family history of Lynch syndrome-associated cancers (LSAC). Methods: A total of 676 patients diagnosed with EC were enrolled in this study. We calculated the optimal cutoff value using restricted cubic splines (RCS) analysis or the mean value. Our feature selection process for constructing the MINS involved using the LASSO regression model. MINS were evaluated for LNM using logistic regression analysis. To assess the prognostic value of the MINS, we generated survival curves using the Kaplan-Meier method with a log-rank test. Furthermore, we constructed a nomogram to validate the prognostic significance of the MINS. The predictive accuracy of nomogram was evaluated using the concordance index (C-index) and calibration plot. Results: LNM risk was associated with family history of LSAC and MINS group (all adjusted p<0.05). Patients in the high-risk MINS group or patients with a family history of LSAC exhibited poorer overall survival (p=0.038, p=0.001, respectively). Additionally, a nomogram was demonstrated effective predictive performance with a C-index of 0.778 (95% CI: 0.725-0.832). Conclusion: Preoperative MINS has been determined to be associated with the risk of LNM in EC patients. Utilizing MINS as a basis, the development of a prognostic nomogram holds promise as an effective tool for risk stratification in clinical settings among EC patients with a family history of LSAC.

Individualized prediction of non-sentinel lymph node metastasis in Chinese breast cancer patients with ≥ 3 positive sentinel lymph nodes based on machine-learning algorithms.

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.

Risk factors and distribution pattern of lateral lymph node recurrence after central neck dissection for cN1a papillary thyroid carcinoma.

BACKGROUND: The indication and extent of selective lateral neck dissection (LND) for cN1a papillary thyroid carcinoma (PTC) remain uncertain. The present study aimed to identify potential predictors and distribution pattern of lateral lymph node recurrence (LLNR) after central neck dissection in cN1a PTC patients. METHODS: The cN1a PTC patients who underwent initial central neck dissection at our centre were retrospectively reviewed, and the median follow-up period was 6.8 years. Reoperation with LND was performed when LLNR was confirmed. Risk factors for LLNR were identified, and the metastatic status of each lateral level was recorded. RESULTS: Of the 310 patients enrolled in the present study, fifty-eight patients (18.7%) presented with LLNR. Six independent factors, including tumour diameter, pathological T4 stage, number of involved central lymph nodes, pTNM stage, extrathyroidal extension, and I131 treatment (P values < 0.05) were identified via multivariate analysis. LLNR was found at level II in 26 patients (44.8%), level III in 38 patients (65.5%), level IV in 30 patients (51.7%), and level V in 8 patients (13.8%). The number of positive lateral lymph nodes at levels II, III, IV and V was 44 (22.9%), 76 (39.6%), 63 (32.8%), and 9 (4.9%), respectively. CONCLUSIONS: For cN1a PTC patients who underwent central neck dissection, tumour diameter ≥ 2 cm, pathological T4 stage, number of involved central lymph nodes ≥ 3, pTNM stage III-IV, extrathyroidal extension, and failure to receive I131 treatment were independent predictors of LLNR, which was more likely to occur at levels III and IV.

Comparative yield of EBUS-TBNA with EBUS-IFBTLP for diagnosis of mediastinal lymphadenopathy.

BACKGROUND: Patients with mediastinal lymph node enlargement (MLNE) are diagnosed depending on lymph node biopsy. Whereas, how to obtain larger tissue masses from mediastinal lymph nodes and improve the diagnostic yield of the disease remains to be investigated. OBJECTIVES: Aiming to assess the diagnostic value of endobronchial ultrasound-guided intranodal forceps biopsy via transbronchial laser photoablation (EBUS-IFB-TLP) in patients with MLNE. DESIGN: A prospective, self-controlled study. METHODS: This study was conducted on 67 MLNE patients requiring a lymph node biopsy for diagnosis at the Henan Provincial People's Hospital and the Fuwai Central China Cardiovascular Hospital in China, from January 2020 to December 2022. Each patient underwent endobronchial ultrasound-guided transbronchial needle aspiration (EBUS-TBNA group) and EBUS-IFB-TLP (EBUS-IFB-TLP group) on the same mediastinal lymph node for biopsies. The operation time, diagnostic efficiency, and complication rates of the two biopsy methods were compared. RESULTS: The number of diagnosed patients in the EBUS-IFB-TLP and the EBUS-TBNA groups was 65 (97.0%) and 57 (85.1%), respectively (p = 0.021). In the EBUS-IFB-TLP group, 28 cases (96.6%) were diagnosed with lung cancer and were classified into different epithelial types. In the EBUS-TBNA group, there were 27 cases (93.1%) diagnosed with lung cancer, of which 26 (89.7%) were classified into different epithelial types. There were 37 (97.4%) and 30 (78.9%) non-lung cancer patients diagnosed in the EBUS-IFB-TLP and EBUS-TBNA groups, respectively (p = 0.039), while 27 cases (96.4%) of sarcoidosis in the EBUS-IFB-TLP group and 20 cases (71.4%) of sarcoidosis in the EBUS-TBNA group were diagnosed (p = 0.016). The percentages of intraoperative mild to moderate bleeding complications were 23.9% (16/67) and 14.9% (10/67) in the EBUS-IFB-TLP and in the EBUS-TBNA groups, respectively (p = 0.109). CONCLUSION: This study demonstrated that EBUS-IFB-TLP could be a feasible and effective method in the diagnosis of patients with MLNE, presenting an analogous safety profile compared with EBUS-TBNA. Further studies are needed to verify the diagnostic performance of EBUS-IFB-TLP for MLNE.

A new way of obtaining a larger biopsy sample in patients with enlarged lymph nodes in the chestWhy was the study done?Endobronchial ultrasound-guided transbronchial needle aspiration (EBUS-TBNA) allows doctors to look at a patient's lungs using a tiny camera (called a bronchoscope). A needle is found at the tip of the bronchoscope and is used to take samples (biopsies) from the lymph nodes in the chest. Lymph nodes are small structures that help filter foreign substances in the body, for example cancer cells. The enlarged (big) lymph nodes are often caused by cancer. Researchers are still trying to work out how to obtain large samples from the lymph nodes which could lead to a better diagnosis.What did the researchers do?We explored a new method called endobronchial ultrasound-guided intranodal forceps biopsy based on transbronchial laser photoablation (EBUS-IFB-TLP) to be used in diagnosing patients who have enlarged lymph nodes. EBUS-IFB-TLP is performed under the guidance of endndobronchial ultrasound, the laser fiber is inserted through the bronchoscope to act on the airway wall, creating a hole in the target lymph node, a biopsy forcep was inserted into the lymph node through the biopsy hole. We used both methods on each patient in this study and compared them.What did the researchers find?More patients were diagnosed with enlarged lymph nodes when using the EBUS-IFB-TLP method, but there were milder to moderate bleeding complications.What do the findings mean?This study shows that EBUS-IFB-TLP could be use in the diagnosis of enlarged lymph nodes.

Lymph node ratio as an indicator of nodal status in the assessment of survival and recurrence in vulvar cancer: A cohort study.

BACKGROUND: Inguinal lymph node (LN) metastasis and particularly the number of metastatic lymph nodes (NMLN) represent a determinant prognostic factor in vulvar squamous cell carcinoma (VSCC). However, the NMLN may be related to the number of removed LNs. Therefore, the lymph node ratio (LNR) reflects not only the burden of LN involvement but also the quality and extent of lymphadenectomy. OBJECTIVES: To investigate the value of the LNR and the count of LN on overall survival (OS) and recurrence-free survival (RFS). DESIGN: This study is a retrospective, longitudinal, institution-based study. METHODS: This study included 192 patients treated for VSCC at the Salah Azaiez Institute between 1994 and 2022. Clinical, pathological, and evolutionary data were reported. Survival curves were generated by the Kaplan-Meier method, and predictive factors of outcome were analyzed using Cox proportional hazards models. RESULTS: Surgery consisted of a radical vulvectomy, hemivulvectomy, and pelvic exenteration in, respectively, 96.4%, 2.1%, and 1.6% of cases followed by adjuvant radiotherapy in 38.5% of cases. LN dissection was bilateral in 88.5% of cases. LNR = 0, LNR = 0-0.2, and LNR ⩾0.2 were recorded in, respectively, 64.7%, 22.1%, and 13.2% of cases. With a mean follow-up time of 35 ± 42.06 months, the 5-year OS was 52.5% and the 5-year RFS was 55.8%. On multivariate analysis, the independent prognostic factors of OS were the LNR (hazard ratio (HR) = 5.702; 95% confidence interval (CI) = 2.282-14.245; p < 0.0001), Federation of Gynecology and Obstetrics (FIGO) stage (HR = 2.089; 95% CI = 1.028-4.277; p = 0.042), and free margins (HR = 2.247; 95% CI = 1.215-4.155; p = 0.01). Recurrences were recorded in 37.5% of cases. Independent prognostic factors of RFS were the LNR (HR = 2.911; 95% CI = 1.468-5.779; p = 0.002), FIGO stage (HR = 1.835; 95% CI = 1.071-3.141; p = 0.027), and free margins (HR = 2.091; 95% CI = 1.286-3.999; p = 0.003). CONCLUSION: Surgical margin, FIGO stage, and LNR represent the independent prognostic factors of survival and LNR showed superior prognostic predictive accuracy compared with the revised 2021 FIGO staging system for predicting OS and RFS in VSCC.

Prediction of non-sentinel lymph node metastases in T1-2 sentinel lymph node-positive breast cancer patients undergoing mastectomy following neoadjuvant therapy.

BACKGROUND: Axillary lymph node dissection (ALND) is the standard axillary management for breast cancer patients with positive sentinel lymph node biopsy (SLNB) after neoadjuvant therapy. Nevertheless, when that happens, the frequency of additional positive nodes is not properly evaluated. We aim to develop a prediction model to assess the frequency of additional nodal disease after a positive sentinel lymph node following neoadjuvant therapy. METHODS: We retrospectively analyzed the ultrasound and clinicopathological characteristics of breast cancer patients with 1-3 positive sentinel lymph nodes (SLN) undergoing mastectomy after neoadjuvant therapy (NAT) at our institution, and performed univariate and multivariate logistic analyses to confirm the factors affecting non-SLN metastasis. These factors were included to establish a nomogram, and the area under receiver operating characteristic curve (AUC) and decision curve analysis (DCA) were utilized to assess the validity of this model. RESULTS: A total of 126 breast cancer patients were ultimately included in our study, 38 (53.5%) patients were diagnosed with non-SLN metastases of all 71 patients in training set. The results of multifactorial logistic analysis suggested that lymph node metastasis ratio (LNR), short axis of lymph node and progesterone receptor (PR) were strongly associated with non-SLN metastasis. We established a nomogram using the above three variables as predictors, which yielded an area under the curve of 0.795, and validated with a favorable AUC of 0.876. CONCLUSION: The nomogram we constructed can accurately predict the likelihood of non-SLN metastasis in our patients with 1-3 positive SLN after NAT, which may help guide decision making regarding axillary management.

Feasibility of indocyanine green (ICG) fluorescence in ex vivo pathological dissection of colorectal lymph nodes-a pilot study.

Accurate lymph node (LN) retrieval during colorectal carcinoma resection is pivotal for precise N-staging and the determination of adjuvant therapy. Current guidelines recommend the examination of at least 12 mesocolic or mesorectal lymph nodes for accurate staging. Traditional histological processing techniques, reliant on visual inspection and palpation, are time-consuming and heavily dependent on the examiner's expertise and availability. Various methods have been documented to enhance LN retrieval from colorectal specimens, including intra-arterial ex vivo methylene blue injection. Recent studies have explored the utility of indocyanine green (ICG) fluorescence imaging for visualizing pericolic lymph nodes and identifying sentinel lymph nodes in colorectal malignancies. This study included 10 patients who underwent colon resection for malignant tumors. During surgery, intravenous ICG dye and an endoscopic camera were employed to assess intestinal perfusion. Post-resection, ex vivo intra-arterial administration of ICG dye was performed on the specimens, followed by routine histological processing and an ICG-assisted lymph node dissection. The objective was to evaluate whether ICG imaging could identify additional lymph nodes compared to routine manual dissection and to assess the clinical relevance of these findings. For each patient, a minimum of 12 lymph nodes (median = 25.5, interquartile range = 12.25, maximum = 33) were examined. ICG imaging facilitated the detection of a median of three additional lymph nodes not identified during routine processing. Metastatic lymph nodes were found in four patients however no additional metastatic nodes were detected with ICG assistance. Our findings suggest that ex vivo intra-arterial administration of indocyanine green dye can augment lymph node dissection, particularly in cases where the number of lymph nodes retrieved is below the recommended threshold of 12.

RATIO OF METASTATIC LYMPH NODES VS. RESECTED LYMPH NODES (N-RATIO) HAS PROGNOSTIC IMPLICATIONS IN GASTRIC CANCER.

BACKGROUND: Lymph node status is vital for gastric cancer (GC) prognosis, but the conventional pN stage may be limited by variations in lymphadenectomy and stage migration. The N-Ratio, which assesses the ratio of metastatic to resected lymph nodes, emerges as a promising prognostic tool. AIMS: To assess N-Ratios prognostic value in GC, particularly in patients with <25 resected lymph nodes. METHODS: Patients who underwent gastrectomy with curative intent for GC were retrospectively evaluated. The N-Ratio categories were determined using the ROC curve method, and the area under the curve (AUC) was used as a measure of performance in predicting recurrence/death. RESULTS: A total of 561 GC patients were included in the study, 57% had pN+ status, and 17.5% had <25 resected lymph nodes. N-Ratio, with a mean of 0.12, predicted survival with 74% accuracy (AUC=0.74; 95%CI 0.70-0.78, p<0.001). N-Ratio categories included: N-Ratio 0 (43%); N-Ratio 1 (12.3%); N-Ratio 2 (31.6%); and N-Ratio 3 (13.2%). Disease-free survival (DFS) varied among all N-Ratio groups, with N-Ratio 3 showing worse survival than pN3 cases (DFS=21.8 vs. 11 months, p=0.022, p<0.05). In cases with <25 resected lymph nodes, DFS was not significantly worse in N-Ratio 0 (68.8 vs. 81.9%, p=0.061, p>0.05) and N-Ratio 1 (66.2 vs. 50%, p=0.504, p>0.05) groups. The DFS of N-Ratio-0 cases with <25 lymph nodes was similar to N-Ratio 1 cases. CONCLUSIONS: N-Ratio influenced survival in GC patients, especially in advanced lymph node disease (N-Ratio 3). Considering that N-Ratio does not impact pN0 cases, individualized prognosis assessment is essential for patients with <25 resected lymph nodes.

Single-center external validation and reconstruction of multiple predictive models for skip lateral lymph node metastasis in papillary thyroid carcinoma.

Objective: The unique metastatic pattern of skip lateral lymph node metastasis (SLLNM) in PTC patients may lead to missed diagnosis of lateral cervical metastatic lymph nodes. Therefore, many different SLLNM prediction models were constructed. In this study, partially eligible models (Hu 2020, Wang 2020, and Zhao 2023 nomograms) were selected for external validation, and then new variables were incorporated for model reconstruction to extend clinical applicability. Methods: 576 PTC patients from our center were selected to evaluate the performance of the three nomograms using the receiver operating characteristic curve (ROC), calibration curves, and decision curve analyses (DCA). Three new variables were added to calibrate the model, including assessment of LN status on ultrasound (US-SLLNM), the distance from the tumor to the capsule (Capsular distance), and the number of central lymph node dissections (CLND number). Univariate and multivariate logistic regression analyses were used to screen independent predictors to reconstruct the model, and 1000 Bootstrap internal validations were performed. Results: SLLNM were present in 69/576 patients (12.0%). In external validation, the area under the ROC curves (AUCs) for Hu 2020, Wang 2020, and Zhao 2023 nomograms were 0.695 (95% CI:0.633-0.766), 0.792 (95% CI=0.73-0.845), and 0.769 (95% CI:0.713-0.824), respectively. The calibration curves for the three models were overall poorly fitted; DCA showed some net clinical benefit. Model differentiation and net clinical benefit improved by adding three new variables. Based on multivariate analysis, female, age, and maximum tumor diameter ≤ 10 mm, located at the upper pole, Capsular distance < 0mm, US-SLLNM, CLND number ≤ 5 were identified as independent predictors of SLLNM and were used to construct the new model. After 1000 Bootstrap internal validations, the mean AUC of the model was 0.870 (95% CI:0.839-0.901), the calibration curve was close to the ideal curve, and the net clinical benefit was significant. Conclusion: Overall, these nomograms were well differentiated and provided some net clinical benefit, but with varying degrees of underestimation or overestimation of the actual risk and high false-negative rates. New dynamic nomogram was constructed based on the addition of new variables and larger samples, showing better performance.

Neu1 deficiency and fibrotic lymph node microenvironment lead to imbalance in M1/M2 macrophage polarization.

Macrophages play a pivotal role in tissue homeostasis, pathogen defense, and inflammation resolution. M1 and M2 macrophage phenotypes represent two faces in a spectrum of responses to microenvironmental changes, crucial in both physiological and pathological conditions. Neuraminidase 1 (Neu1), a lysosomal and cell surface sialidase responsible for removing terminal sialic acid residues from glycoconjugates, modulates several macrophage functions, including phagocytosis and Toll-like receptor (TLR) signaling. Current evidence suggests that Neu1 expression influences M1/M2 macrophage phenotype alterations in the context of cardiovascular diseases, indicating a potential role for Neu1 in macrophage polarization. For this reason, we investigated the impact of Neu1 deficiency on macrophage polarization in vitro and in vivo. Using bone marrow-derived macrophages (BMDMs) and peritoneal macrophages from Neu1 knockout (Neu1-/- ) mice and wild-type (WT) littermate controls, we demonstrated that Neu1-deficient macrophages exhibit an aberrant M2-like phenotype, characterized by elevated macrophage mannose receptor 1 (MMR/CD206) expression and reduced responsiveness to M1 stimuli. This M2-like phenotype was also observed in vivo in peritoneal and splenic macrophages. However, lymph node (LN) macrophages from Neu1-/- mice exhibited phenotypic alterations with reduced CD206 expression. Further analysis revealed that peripheral LNs from Neu1-/- mice were highly fibrotic, with overexpression of transforming growth factor-beta 1 (TGF-ß1) and hyperactivated TGF-ß signaling in LN macrophages. Consistently, TGF-ß1 was found to alter M1/M2 macrophage polarization in vitro. Our findings showed that Neu1 deficiency prompts macrophages towards an M2 phenotype and that microenvironmental changes, particularly increased TGF-ß1 in fibrotic tissues such as peripheral LNs in Neu1-/- mice, further influence M1/M2 macrophage polarization, highlighting its sensitivity to the local microenvironment. Therapeutic interventions targeting Neu1 or TGF-ß signaling pathways may offer the potential to regulate macrophage behavior across different diseases.

Explainable machine learning model for predicting paratracheal lymph node metastasis in cN0 papillary thyroid cancer.

Prophylactic dissection of paratracheal lymph nodes in clinically lymph node-negative (cN0) papillary thyroid carcinoma (PTC) remains controversial. This study aims to integrate preoperative and intraoperative variables to compare traditional nomograms and machine learning (ML) models, developing and validating an interpretable predictive model for paratracheal lymph node metastasis (PLNM) in cN0 PTC patients. We retrospectively selected 3213 PTC patients treated at the First Affiliated Hospital of Chongqing Medical University from 2016 to 2020. They were randomly divided into the training and test datasets with a 7:3 ratio. The 533 PTC patients treated at the Guangyuan Central Hospital from 2019 to 2022 were used as an external test sets. We developed and validated nine ML models using 10-fold cross-validation and grid search for hyperparameter tuning. The predictive performance was evaluated using ROC curves, decision curve analysis (DCA), calibration curves, and precision-recall curves. The best model was compared to a traditional logistic regression-based nomogram. The XGBoost model achieved AUC values of 0.935, 0.857, and 0.775 in the training, validation, and test sets, respectively, significantly outperforming the traditional nomogram model with AUCs of 0.85, 0.844, and 0.769, respectively. SHapley Additive exPlanations (SHAP)-based visualization identified the top 10 predictive features of the XGBoost model, and a web-based calculator was created based on these features. ML is a reliable tool for predicting PLNM in cN0 PTC patients. The SHAP method provides valuable insights into the XGBoost model, and the resultant web-based calculator is a clinically useful tool to assist in the surgical planning for paratracheal lymph node dissection.