Independent Predictor Of Lymph Node Metastasis Research Articles

Interpretable machine learning-based clinical prediction model for predicting lymph node metastasis in patients with intrahepatic cholangiocarcinoma

ObjectivePrediction of lymph node metastasis (LNM) for intrahepatic cholangiocarcinoma (ICC) is critical for the treatment regimen and prognosis. We aim to develop and validate machine learning (ML)-based predictive models for LNM in patients with ICC.MethodsA total of 345 patients with clinicopathological characteristics confirmed ICC from Jan 2007 to Jan 2019 were enrolled. The predictors of LNM were identified by the least absolute shrinkage and selection operator (LASSO) and logistic analysis. The selected variables were used for developing prediction models for LNM by six ML algorithms, including Logistic regression (LR), Gradient boosting machine (GBM), Extreme gradient boosting (XGB), Random Forest (RF), Decision tree (DT), Multilayer perceptron (MLP). We applied 10-fold cross validation as internal validation and calculated the average of the areas under the receiver operating characteristic (ROC) curve to measure the performance of all models. A feature selection approach was applied to identify importance of predictors in each model. The heat map was used to investigate the correlation of features. Finally, we established a web calculator using the best-performing model.ResultsIn multivariate logistic regression analysis, factors including alcoholic liver disease (ALD), smoking, boundary, diameter, and white blood cell (WBC) were identified as independent predictors for LNM in patients with ICC. In internal validation, the average values of AUC of six models ranged from 0.820 to 0.908. The XGB model was identified as the best model, the average AUC was 0.908. Finally, we established a web calculator by XGB model, which was useful for clinicians to calculate the likelihood of LNM.ConclusionThe proposed ML-based predicted models had a good performance to predict LNM of patients with ICC. XGB performed best. A web calculator based on the ML algorithm showed promise in assisting clinicians to predict LNM and developed individualized medical plans.

Abstract 5859: PLK1 predicts aggressive behavior in CRC patients and its inhibition reverse chemoresistance in CRC cells

Abstract Polo-like kinase 1 (PLK1) is a serine/threonine-protein kinase, that is involved in multiple stages of cell cycle progression in mammals. Dysregulation of PLK1 has been found in a variety of human cancers and was associated with poor prognoses in various cancers. However, its role in the pathogenesis of CRC from Middle Eastern ethnicity has not yet been explored. Therefore, we examined the expression of PLK1 protein in a large cohort of over 1000 Middle Eastern CRC cases by immunohistochemistry. Correlation with clinico-pathological parameters were performed. PLK1 overexpression was noted in 60.3% (693/1149) CRC cases and was significantly associated with larger tumor size (T3/T4 tumors), lymphnode metastasis, Stage III tumors, MSI-H status and p-ERK1/2 over-expression. Interestingly, on multivariate logistic regression analysis, we found that PLK1 was an independent predictor of lymphnode metastasis. However, no association was noted between PLK1 expression and clinical outcomes (DFS, RFS, OS, CSS). Our in vitro experimentation revealed that CRC cells expressing high levels of PLK1 exhibited resistance to 5-Fu treatment, whereas cells with low PLK1 expression demonstrated sensitivity to the treatment. To explore the role of PLK1 on chemoresistance, we inhibited PLK1 using specific inhibitor, volasertib and we showed that volasertib effectively reversed 5-Fu resistance in these cells. Furthermore, knockdown of PLK1 reduced the progression of epithelial-to-mesenchymal transition (EMT) and the stem cell-like properties in 5-Fu resistant cells, showing the role of PLK1 on inducing EMT and stemness in CRC. Interestingly, the forced expression of PLK1 triggered the activation of the CRAF-MEK-ERK-ZEB1 signaling cascade, whereas its inhibition led to the suppression of this cascade. In addition, we showed that silencing of ERK1/2 reversed the chemoresistance, EMT and stemness of PLK1 expressing CRC cell lines. Our findings underscore the significant role of PLK1 in conferring chemoresistance, and targeting PLK1 could represent a viable therapeutic approach for the treatment of patients with an aggressive subtype of CRC. Citation Format: Divya Padmaja, Maha Al-Rasheed, Pratheesh Kumar Poyil, Abdul K. Siraj, Sandeep Kumar Parvathareddy, Wael Haqawi, Fouad Al-Dayel, Khawla S. Al-Kuraya. PLK1 predicts aggressive behavior in CRC patients and its inhibition reverse chemoresistance in CRC cells [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 5859.

Prediction of lymph node metastasis in advanced gastric adenocarcinoma based on dual-energy CT radiomics: focus on the features of lymph nodes with a short axis diameter ≥6 mm.

To explore the value of the features of lymph nodes (LNs) with a short-axis diameter ≥6 mm in predicting lymph node metastasis (LNM) in advanced gastric adenocarcinoma (GAC) based on dual-energy CT (DECT) radiomics. Data of patients with GAC who underwent radical gastrectomy and LN dissection were retrospectively analyzed. To ensure the correspondence between imaging and pathology, metastatic LNs were only selected from patients with pN3, nonmetastatic LNs were selected from patients with pN0, and the short-axis diameters of the enrolled LNs were all ≥6 mm. The traditional features of LNs were recorded, including short-axis diameter, long-axis diameter, long-to-short-axis ratio, position, shape, density, edge, and the degree of enhancement; univariate and multivariate logistic regression analyses were used to establish a clinical model. Radiomics features at the maximum level of LNs were extracted in venous phase equivalent 120 kV linear fusion images and iodine maps. Intraclass correlation coefficients and the Boruta algorithm were used to screen significant features, and random forest was used to build a radiomics model. To construct a combined model, we included the traditional features with statistical significance in univariate analysis and radiomics scores (Rad-score) in multivariate logistic regression analysis. Receiver operating curve (ROC) curves and the DeLong test were used to evaluate and compare the diagnostic performance of the models. Decision curve analysis (DCA) was used to evaluate the clinical benefits of the models. This study included 114 metastatic LNs from 36 pN3 cases and 65 nonmetastatic LNs from 28 pN0 cases. The samples were divided into a training set (n=125) and a validation set (n=54) at a ratio of 7:3. Long-axis diameter and LN shape were independent predictors of LNM and were used to establish the clinical model; 27 screened radiomics features were used to build the radiomics model. LN shape and Rad-score were independent predictors of LNM and were used to construct the combined model. Both the radiomics model (area under the curve [AUC] of 0.986 and 0.984) and the combined model (AUC of 0.970 and 0.977) outperformed the clinical model (AUC of 0.772 and 0.820) in predicting LNM in both the training and validation sets. DCA showed superior clinical benefits from radiomics and combined models. The models based on DECT LN radiomics features or combined traditional features have high diagnostic performance in determining the nature of each LN with a short-axis diameter of ≥6 mm in advanced GAC.

Assessment of risk factors of lymph node metastasis and prognosis of Siewert II/III adenocarcinoma of esophagogastric junction: A retrospective study.

Adenocarcinoma of the esophagogastric junction (AEG) has a high incidence, and the extent of lymph node dissection (LND) and its impact on prognosis remain controversial. This study aimed to explore the risk factors for lymph node metastasis (LNM) and prognosis in Siewert II/III AEG patients. A retrospective review of 239 Siewert II/III AEG patients surgically treated at Beijing Friendship Hospital from July 2013 to December 2022 was conducted. Preoperative staging was conducted via endoscopy, ultrasound gastroscopy, CT, and biopsy. Depending on the stage, patients received radical gastrectomy with LND and chemotherapy. Clinicopathological data were collected, and survival was monitored semiannually until November 2023. Utilizing logistic regression for data analysis and Cox regression for survival studies, multivariate analysis identified infiltration depth (OR = 0.038, 95% CI: 0.011-0.139, P < .001), tumor deposit (OR = 0.101, 95% CI: 0.011-0.904, P = .040), and intravascular cancer embolus (OR = 0.234, 95% CI: 0.108-0.507, P < .001) as independent predictors of LNM. Lymph nodes No. 1, 2, 3, 4, 7, 10, and 11 were more prone to metastasis in the abdominal cavity. Notably, Siewert III AEG patients showed a higher metastatic rate in nodes No. 5 and No. 6 compared to Siewert II. Mediastinal LNM was predominantly found in nodes No. 110 and No. 111 for Siewert II AEG, with rates of 5.45% and 3.64%, respectively. A 3-year survival analysis underscored LNM as a significant prognostic factor (P = .001). Siewert II AEG patients should undergo removal of both celiac and mediastinal lymph nodes, specifically nodes No. 1, 2, 3, 4, 7, 10, 11, 110, and 111. Dissection of nodes No. 5 and No. 6 is not indicated for these patients. In contrast, Siewert III AEG patients do not require mediastinal LND, but pyloric lymphadenectomy for nodes No. 5 and No. 6 is essential. The presence of LNM is associated with poorer long-term prognosis. Perioperative chemotherapy may offer a survival advantage for AEG patients.

Differentiation grade as a risk factor for lymph node metastasis in T1 colorectal cancer.

Japanese guidelines include high-grade (poorly differentiated) tumors as a risk factor for lymph node metastasis (LNM) in T1 colorectal cancer (CRC). However, whether the grading is based on the least or most predominant component when the lesion consists of two or more levels of differentiation varies among institutions. This study aimed to investigate which method is optimal for assessing the risk of LNM in T1 CRC. We retrospectively evaluated 971 consecutive patients with T1 CRC who underwent initial or additional surgical resection from 2001 to 2021 at our institution. Tumor grading was divided into low-grade (well- to moderately differentiated) and high-grade based on the least or predominant differentiation analyses. We investigated the correlations between LNM and these two grading analyses. LNM was present in 9.8% of patients. High-grade tumors, as determined by least differentiation analysis, accounted for 17.0%, compared to 0.8% identified by predominant differentiation analysis. A significant association with LNM was noted for the least differentiation method (p < 0.05), while no such association was found for predominant differentiation (p = 0.18). In multivariate logistic regression, grading based on least differentiation was an independent predictor of LNM (p = 0.04, odds ratio 1.68, 95% confidence interval 1.00-2.83). Sensitivity and specificity for detecting LNM were 27.4% and 84.1% for least differentiation, and 2.1% and 99.3% for predominant differentiation, respectively. Tumor grading via least differentiation analysis proved to be a more reliable measure for assessing LNM risk in T1 CRC compared to grading by predominant differentiation.

A Retrospective Multicenter Study of Risk Factors, Stratification, and Prognosis of Lymph Node Metastasis in T1 and T2 Colorectal Cancer.

The objective of this study was to compare the long-term prognosis of patients with T1 and T2 colorectal cancer (CRC) according to lymph node metastasis (LNM) and to identify risk factors for LNM. We retrospectively reviewed patients who underwent curative resection for T1 or T2 CRC at five University-affiliated hospitals between January 2012 and December 2021. The patients were divided into several groups depending on the presence of LNM or the number of risk factors. Of the total 765 patients, 87 (11.3%) patients had LNM. These patients had poorer recurrence-free survival (RFS) than patients without LNM (72.6% vs. 88.6%). The multivariable analysis showed that high-grade tumors (p = 0.003), lymphovascular invasion (p < 0.001), and rectal location (p = 0.049) were independent predictors of LNM. When divided into groups according to the number of the three risk factors, the risk of LNM increased from 5.4% (ultralow-risk group; no risk factor) to 60.0% (high-risk group; all three risk factors) and the 5-year RFS rate decreased from 96.3% in the ultralow-risk group to 60% in the high-risk group (p < 0.001). Radical surgery should be considered for T1 and T2 CRC patients with these risk factors.

The ratio of lymphocyte/red blood cells and platelets/lymphocytes are predictive biomarkers for lymph node metastasis in patients with breast cancer.

Axillary lymph node metastasis (LNM) affects the progression of breast cancer. However, it is difficult to preoperatively diagnose axillary lymph node status with high sensitivity. Therefore, we hypothesized that platelets/lymphocytes ratio (PLR) and lymphocytes/ red blood cells ratio (LRR) might help in the prognosis of lymph node metastasis in T1-T2 breast cancer. 166 patients (Chang Ning Maternity & Infant Health Institute) were included in our study, and the associations of PLR and LPR with lymph node metastasis were investigated. Peripheral blood was collected one week before the surgery, and the patients were divided into different categories based on their PLR and LRR. The incidence of LNM was significantly increased in the high PLR group (p= 0.002) compared with the low PLR group; LNM was also significantly increased in the low LRR group (p= 0.036) compared with the high LPR group. Further, our study revealed that high PLR (p< 0.001, OR = 4.397, 95% CI = 2.005-9.645), low LRR (p= 0.017, OR = 0.336, 95%CI = 0.136-0.825) and high clinical T stage (p< 0.001, OR = 3.929, 95%CI = 1.913-8.071) are independent predictors of LNM. PLR and LRR could be identified as predictors of LNM in patients with T1/T2 breast cancer.

Risk factors associated with lymph node metastasis in early-stage distal gastric cancer.

This study was designed to explore the risk factors of lymph node metastasis (LNM) in distal gastric cancer with early stage, and to provide reference for the choice of treatment protocols. In this retrospective observational study, 824 early distal gastric cancer (EDGC) cases who treated at our unit from 2010 to 2020 were selected as research objects. Subsequently, univariate and multivariate logistic regression analyses were conducted to investigate the associations between LNM and clinicopathological features. Of these 824 EDGC cases, 140 (17.0%) developed LNM, including 72 N1 stage and 68 N2-3 stage LNM. Multivariate logistic regression analysis identified the tumor diameter ≥1.75cm (odds ratio (OR)=2.361, p<0.001), tumor location (OR=1.552, p=0.046), histological classification (p=0.004), tumor infiltration depth (OR=2.154, p=0.001), and vascular infiltration (OR=4.354, p<0.001) as independent predictors for LNM. Logistic regression analyses based on 756 N0-1 LNM cases identified the smoking history (OR=0.507, p=0.043), tumor diameter ≥1.75cm (OR=2.265, p=0.010), tumor location (OR=1.834, p=0.036), histological classification (p=0.018), tumor infiltration depth (OR=1.939, p=0.034), and vascular infiltration (OR=3.225, p<0.001) as independent predictors for N1 LNM. Moreover, preoperative hypoalbuminemia (OR=7.087, p=0.015), significant preoperative weight loss (OR=2.724, p=0.023), tumor diameter ≥1.75cm (OR=5.484, p=0.001), multiple tumors (OR=9.986, p=0.038), histological classification (p=0.029), and vascular infiltration (OR=33.704, p<0.001) were proved to be associated with LNM for T1a tumors. The tumor diameter, location and infiltration depth, histological classification, and vascular infiltration were expected to be used as predictors of LNM in EDGC, and preoperative hypoalbuminemia, significant weight loss, tumor diameter and number, histological classification, and vascular infiltration were associated with LNM for T1a tumors.

Development and validation of a predictive model associated with lymph node metastasis of gastric signet ring carcinoma patients.

The risk factors for lymph node metastasis (LNM) in patients with gastric signet ring cell carcinoma (GSRC) have not been well-defined. This study was designed to prognosticate LNM in patients with GSRC by constructing and verifying a nomogram. A total of 2789 patients with GSRC from the Surveillance, Epidemiology, and End Results (SEER) database and Yijishan Hospital of Wannan Medical College (YJS) were retrospectively reviewed. A predictive model was established using logistic regression based on the SEER cohort. The performance of the model was evaluated using the concordance index (C-index) and decision curve analysis (DCA). In addition, its robustness was validated using the YJS cohort. Four independent predictors of LNM were identified in the SEER cohort. Next, a nomogram was constructed by incorporating these predictors. The C-index were 0.800 (95% confidence interval [CI] = 0.781-0.819) and 0.837 (95% CI = 0.784-0.890) in the training and external validation cohorts, respectively. The outcomes of DCA supported good clinical benefits. The proposed model for evaluating the LNM in patients with GSRC can help to avoid the misdiagnosis risk of N-stage, assist to screen the population suitable for neoadjuvant therapy and help clinicians to optimize clinical decisions.

Risk factor analysis and prediction model for papillary thyroid carcinoma with lymph node metastasis.

We aimed to identify the clinical factors associated with lymph node metastasis (LNM) based on ultrasound characteristics and clinical data, and develop a nomogram for personalized clinical decision-making. A retrospective analysis was performed on 252 patients with papillary thyroid carcinoma (PTC). The patient's information was subjected to univariate and multivariate logistic regression analyses to identify risk factors. A nomogram to predict LNM was established combining the risk factors. The performance of the nomogram was evaluated using receiver operating characteristic (ROC) curve, calibration curve, cross-validation, decision curve analysis (DCA), and clinical impact curve. There are significant differences between LNM and non-LNM groups in terms of age, sex, tumor size, hypoechoic halo around the nodule, thyroid capsule invasion, lymph node microcalcification, lymph node hyperechoic area, peak intensity of contrast (PI), and area under the curve (AUC) of the time intensity curve of contrast (P<0.05). Age, sex, thyroid capsule invasion, lymph node microcalcification were independent predictors of LNM and were used to establish the predictive nomogram. The ROC was 0.800, with excellent discrimination and calibration. The predictive accuracy of 0.757 and the Kappa value was 0.508. The calibration curve, DCA and calibration curve demonstrated that the prediction model had excellent net benefits and clinical practicability. Age, sex, thyroid capsule invasion, and lymph node microcalcification were identified as significant risk factors for predicting LNM in patients with PTC. The visualized nomogram model may assist clinicians in predicting the likelihood of LNM in patients with PTC prior to surgery.

Loss of CDH16 expression is a strong independent predictor for lymph node metastasis in Middle Eastern papillary thyroid cancer

Papillary Thyroid Cancer (PTC) is the most common type of thyroid cancer. The membrane-associated glycoprotein cadherin-16 (CDH16) plays a significant role in the embryonal development of thyroid follicles and cell adhesion. Previous studies have indicated a substantial downregulation of CDH16 in PTC. However, its role in Middle Eastern PTC has not been elucidated. We analyzed a tissue microarray comprising 1606 PTC and 240 normal thyroid tissues using immunohistochemistry to assess CDH16 expression and determine its clinico-pathological associations. We also conducted BRAF and TERT mutations analyses through Sanger sequencing. Disease-free survival (DFS) was assessed using Kaplan–Meier curves. CDH16 immunostaining was seen in 100% of normal thyroid tissues but only in 9.4% of PTC tissues (p < 0.0001). The loss of CDH16 expression was associated with aggressive PTC characteristics including bilaterality, multifocality, extrathyroidal extension, tall cell variant, lymph node metastasis (LNM) and distant metastasis. Additionally a correlation between loss of CDH16 expression and BRAF and TERT mutations was identified. Intriguingly, upon conducting multivariate logistic regression analysis, CDH16 was determined to be an independent predictor for LNM (Odds ratio = 2.46; 95% confidence interval = 1.60–3.79; p < 0.0001). Furthermore, CDH16 loss was associated with a shorter DFS (p = 0.0015). However, when we further subdivided CDH16 negative patients based on the co-existence of TERT and/or BRAF mutations, we found that patients with both CDH16 negative expression and TERT mutation exhibited the shortest DFS (p < 0.0001). In conclusion, our results suggest that CDH16 protein expression could serve as a valuable diagnostic tool for PTC. Furthermore, these findings demonstrate that the loss of CDH16 expression is an independent predictor of LNM and may contribute to the aggressiveness of PTC. Therefore, downregulation of CDH16 in PTC might be a potential target for designing novel therapeutic strategies to treat PTC.

WDR4 promotes the progression and lymphatic metastasis of bladder cancer via transcriptional down-regulation of ARRB2

Lymph node (LN) metastasis is one of the key prognostic factors in bladder cancer, but its underlying mechanisms remain unclear. Here, we found that elevated expression of WD repeat domain 4 (WDR4) in bladder cancer correlated with worse prognosis. WDR4 can promote the LN metastasis and proliferation of bladder cancer cells. Mechanistic studies showed that WDR4 can promote the nuclear localization of DEAD-box helicase 20 (DDX20) and act as an adaptor to bind DDX20 and Early growth response 1 (Egr1), thereby inhibiting Egr1-promoted transcriptional expression of arrestin beta 2 (ARRB2) and ultimately contributing to the progression of bladder cancer. Immunohistochemical analysis confirmed that WDR4 expression is also an independent predictor of LN metastasis in bladder cancer. Our results reveal a novel mechanism of LN metastasis and progression in bladder cancer and identify WDR4 as a potential therapeutic target for metastatic bladder cancer.

Pretreatment carcinoembryonic antigen combined with cancer antigen-125 for predicting lymph node metastasis in endometrial carcinoma: a retrospective cohort study.

To investigate whether the cost-effective, pretreatment tumor markers carcinoembryonic antigen (CEA) and carbohydrate antigen-125 (CA-125) can be used to predict lymph node metastasis (LNM) in endometrioid-type endometrial cancer (EC) and to develop a predictive model. This was a single-center retrospective study of patients with endometrioid-type EC who underwent complete staging surgery between January 2015 and June 2022. We identified the optimal cut-off values of CEA and CA-125 for predicting LNM using receiver operating characteristic (ROC) curves. Stepwise multivariate logistic regression analysis was used to identify independent predictors. A nomogram for predicting LNM was constructed and validated by bootstrap resampling. The optimal cut-off values of CEA and CA-125 were 1.4ng/mL (area under the ROC curve (AUC) 0.62) and 40 U/mL (AUC 0.75), respectively. Multivariate analysis showed that CEA (odds ratio (OR) 1.94; 95% confidence interval (CI) 1.01-3.74) and CA-125 (OR 8.75; 95% CI 4.42-17.31) were independent predictors of LNM. Our nomogram showed adequate discrimination with a concordance index of 0.78. Calibration curves for the probability of LNM showed optimal agreement between the predicted and actual probabilities. The risk of LNM for markers below the cut-offs was 3.6%. The negative predictive value and negative likelihood ratio were 96.6% and 0.26, respectively, with moderate ability to rule out the possibility of LNM. We report a cost-effective method of using pretreatment CEA and CA-125 levels to identify patients with endometrioid-type EC who are at a low risk for LNM, which may guide decision-making regarding aborting lymphadenectomy.

A preoperative nomogram model for the prediction of lymph node metastasis in buccal mucosa cancer.

We sought to construct a nomogram model predicting lymph node metastasis (LNM) in patients with squamous cell carcinoma of the buccal mucosa based on preoperative clinical characteristics. Patients who underwent radical resection of a primary tumor in the buccal mucosa with neck dissection were enrolled. Clinical characteristics independently associated with LNM in multivariate analyses were adopted to build the model. Patients at low risk of LNM were defined by a predicted probability of LNM of less than 5%. Patients who underwent surgery in an earlier period (January 2015-November 2019) were defined as the model development cohort (n = 325), and those who underwent surgery later (November 2019-March 2021) were defined as the validation cohort (n = 140). Age, tumor differentiation, tumor thickness, and clinical N stage assessed by computed tomography/magnetic resonance imaging (cN) were independent predictors of LNM. The nomogram model based on these four predictors showed good discrimination accuracy in both the model development and validation cohorts, with areas under the receiver-operating characteristic curve (AUC) of 0.814 and 0.828, respectively. LNM prediction by the nomogram model was superior to cN in AUC comparisons (0.815 vs. 0.753) and decision curve analysis of the whole cohort. Seventy-one patients were defined as having a low risk of LNM, among whom the actual metastasis rate was only 1.4%. A robust nomogram model for preoperative LNM prediction is built.

Application of machine learning algorithm in prediction of lymph node metastasis in patients with intermediate and high-risk prostate cancer.

This study aims to establish the best prediction model of lymph node metastasis (LNM) in patients with intermediate- and high-risk prostate cancer (PCa) through machine learning (ML), and provide the guideline of accurate clinical diagnosis and precise treatment for clinicals. A total of 24,470 patients with intermediate- and high-risk PCa were included in this study. Multivariate logistic regression model was used to screen the independent risk factors of LNM. At the same time, six algorithms, namely random forest (RF), naive Bayesian classifier (NBC), xgboost (XGB), gradient boosting machine (GBM), logistic regression (LR) and decision tree (DT) are used to establish risk prediction models. Based on the best prediction performance of ML algorithm, a prediction model is established, and the performance of the model is evaluated from three aspects: area under curve (AUC), sensitivity and specificity. In multivariate logistic regression analysis, T stage, PSA, Gleason score and bone metastasis were independent predictors of LNM in patients with intermediate- and high-risk PCa. By comprehensively comparing the prediction model performance of training set and test set, GBM model has the best prediction performance (F1 score = 0.838, AUROC = 0.804). Finally, we developed a preliminary calculator model that can quickly and accurately calculate the regional LNM in patients with intermediate- and high-risk PCa. T stage, PSA, Gleason and bone metastasis were independent risk factors for predicting LNM in patients with intermediate- and high-risk PCa. The prediction model established in this study performs well; however, the GBM model is the best one.

A novel potential inflammation-nutrition biomarker for predicting lymph node metastasis in clinically node-negative colon cancer.

The purpose of this study is to investigate the predictive significance of (platelet × albumin)/lymphocyte ratio (PALR) for lymph node metastasis (LNM) in patients with clinically node-negative colon cancer (cN0 CC). Data from 800 patients with primary CC who underwent radical surgery between March 2016 and June 2021 were reviewed. The non-linear relationship between PALR and the risk of LNM was explored using a restricted cubic spline (RCS) function while a receiver operating characteristic (ROC) curve was developed to determine the predictive value of PALR. Patients were categorized into high- and low-PALR cohorts according to the optimum cut-off values derived from Youden's index. Univariate and multivariate logistic regression analyses were used to identify the independent indicators of LNM. Sensitivity analysis was performed to repeat the main analyses with the quartile of PALR. A total of eligible 269 patients with primary cN0 CC were retrospectively selected. The value of the area under the ROC curve for PALR for predicting LNM was 0.607. RCS visualized the uptrend linear relationship between PALR and the risk of LNM (p-value for non-linearity > 0.05). PALR (odds ratio = 2.118, 95% confidence interval, 1.182-3.786, p = 0.011) was identified as an independent predictor of LNM in patients with cN0 CC. A nomogram incorporating PALR and other independent predictors was constructed with an internally validated concordance index of 0.637. The results of calibration plots and decision curve analysis supported a good performance ability and the sensitivity analysis further confirmed the robustness of our findings. PALR has promising clinical applications for predicting LNM in patients with cN0 CC.

Fibrinogen-to-Lymphocyte Ratio Was an Independent Predictor of Lymph Node Metastasis in Patients with Clinically Node-Negative Advanced-Stage Gastric Cancer.

Various hematological indicators have been reported to predict lymph node metastasis (LNM) in gastric cancer (GC) patients, but the relationship between FLR and LNM has not been studied. Therefore, the aim of this study was to evaluate the role of preoperative fibrinogen-to-lymphocyte ratio (FLR) in predicting LNM in patients with clinically node-negative (cN0) advanced gastric cancer (AGC). We retrospectively reviewed 571 eligible patients with primary AGC adenocarcinoma who underwent radical gastrectomy (discoverycohort). Patients were divided into high and low FLR groups according to the optimal cutoff value determined by Youden index. FLR is an independent predictor of LNM determined by logistic regression and validated in the validation cohort of 207 patients. Receiver operating characteristic (ROC) curve analysis was used to evaluate the predictive value of FLR for LNM. The nonlinear relationship between FLR and LNM risk was assessed using restricted cubic spline. Sensitivity analyses were performed according to FLR quartiles to further assess the robustness of the results. The nomogram was built based on FLR and clinicopathological characteristics, and was evaluated by calibration curves, ROC curve analysis and decision curve analysis. In the discoverycohort, the area under the curve (AUC) value for FLR to predict LNM was 0.592. There is a linear relationship between the FLR value and the risk of LNM, and the risk of LNM increased with FLR value. High FLR level is an independent risk factor for LNM, and the results of sensitivity analysis robust this finding. The nomogram for individual risk assessment performed well. Furthermore, we verified the FLR was an independent predictor of LNM in the validation cohort. FLR was an independent predictor of LNM in patients with cN0 AGC.

Risk Factors for Lymph Node Metastasis of Rectal Neuroendocrine Tumor and Its Prognostic Impact: A Single-Center Retrospective Analysis of 195 Cases with Radical Resection.

The incidence of rectal neuroendocrine tumors (NETs) has been steadily increasing. The risk factors for and prognostic impact of lymph node (LN) metastasis were analyzed in 195 patients with stage I-III rectal NET who underwent radical surgery. This retrospective, single-center study analyzed risk factors for LN metastasis focusing on previously identified factors and a novel risk factor: multiple rectal NETs. The association between LN metastasis and the prognosis was also analyzed. Pathologically, the LN metastasis rate (also the rate of stage III disease) was 39%, which was higher than the clinical LN metastasis rate of 14%. Tumor size > 10 mm, presence of central depression, tumor grade G2, depth of invasion, LN swelling on preoperative imaging (cN1), venous invasion and multiple NETs were identified as risk factors for LN metastasis. As the tumor size and risk factors increased, the rate of LN metastasis increased. Among these 7 factors, venous invasion, cN1, and multiple NETs were identified as independent predictors of LN metastasis. LN metastasis of rectal NETs was associated with significantly poor disease-free and disease-specific survival. As risk factors increase, the potential for rectal NETs to metastasize to the LNs increases and LN metastasis is associated with a poor prognosis. This is the first study to report multiple NETs as a risk factor for LN metastasis. A future study examining the survival benefit of radical surgery accompanying LN dissection compared with local resection is warranted.

Elective Neck Dissection Improves Regional Control in cN0 Minor Salivary Gland Carcinoma in the Oral Cavity

Consensus regarding whether elective neck dissection (END) provides better outcomes than observation in clinically node negative minor salivary gland (MSG) carcinoma is lacking. Therefore, this study aimed to compare the impact of END with that of observation on regional control (RC) and overall survival (OS) and to detect the predictors for lymph-node metastasis in oral MSG carcinoma. A single-institution, retrospective cohort study was designed; it included patients with clinically node negative oral MSG carcinoma treated at a tertiary teaching hospital between January 2002 and January 2022. The primary predictor variable was END and primary outcome variables were RC and OS. The secondary outcome variable was lymph-node metastasis. Other covariates included demographic and pathologic features, TNM stage, and adjuvant treatment. The Kaplan-Meier method and Cox proportional hazards model were used to determine the effect of END on RC and OS. The chi-squared test and logistic regression models were used to identify independent predictors for lymph-node metastasis. A total of 268 patients (107 men and 161 women) with a mean age of 46.4±15.5years were included. The 5-year RC rate was statistically different between the observation and END groups (75%; 95% confidence interval [CI], 67%-83; 95% CI, 81%-93%, respectively; P=.014). Cox regression analysis confirmed that END (hazard ratio [HR] 2.395; 95% CI: 1.433-8.275; P=.034) was independently associated with a decreased risk of regional recurrence. The 5-year OS rates for the observation and END groups were 66% (95% CI, 56-76%) and 76% (95% CI, 66-86%), respectively, and the difference was not statistical (P=.057). Occult metastasis occurred in 24.6% of patients. Primary tumor location on the tongue/floor of the mouth (odds ratio [OR], 4.287; 95% CI, 1.773-9.125; P=.011), T3/4 stage (OR, 3.286; 95% CI, 1.228-8.253; P=.021), and high-grade disease (OR, 6.674; 95% CI, 2.199-14.326; P<.001) were independently associated with an increased risk of occult metastasis. RC was better with END than with observation, but OS was comparable with the two approaches. Primary tumor location on tongue/floor of the mouth, T3/4 stage, and high-grade disease were associated with an increased risk of lymph-node metastasis.

Worst Pattern of Invasion as an Independent Predictor of Lymph node Metastasis and Prognosis in oral Cavity Squamous cell carcinoma - A Retrospective Cohort Study.

Although Worst pattern of invasion (WPOI) is one of the histopathological (HP) markers that has been utilized in risk stratification of oral squamous cell carcinoma (OSCC) patients, its potential as an independent predictive factor for lymph node metastasis (LNM) and prognosis is least analyzed. Aim of the study is to analyze the relationship of various HP parameters to WPOI, their propensity for lymph node metastasis and prognostic value. This retrospective study included 140 patients diagnosed with resectable OSCC who underwent definitive surgery. Multiparametric HP risk assessment was done on the postoperative specimen and patients were categorized as low-risk WPOI (Type 1-3), and high-risk group (type 4 and 5). After categorization, 36.1% patients had low-risk WPOI and 63.9% had high-risk WPOI. Significant association was noted between WPOI and patient's age (p = 0.001), nodal stage (p = 0.001), lymphovascular invasion (LVI) (p = 0.006) and neural invasion (p = 0.001). 87% patients with nodal metastasis had high risk WPOI. LVI (p = 0.014) and WPOI (p < 0.001) had significant predictive role in LNM. High-risk WPOI and bone involvement were found to be predictive factors for overall survival, and only high risk WPOI had strong correlation with disease free survival having significant poor prognosis. Analyzing WPOI is essential in reporting HP specimens in OSCC. High-risk WPOI can act as an independent predictor for LNM, early recurrence and poor prognosis. Incorporation of WPOI into TNM staging is recommended to improve clinician's ability to prognosticate and individualize treatment strategies.