Fifty-five cases of hepatic alveolar echinococcosis combined with lymph node metastasis:A retrospective study

BACKGROUND Lymph node metastasis is a specific type of metastasis in hepatic alveolar echinococcosis(AE).Currently,there is a scarcity of describing the clinical characteristics and lymph node metastasis rules of patients with hepatic AE combined with lymph node metastasis and its mechanism and management are still controversial.Radical hepatectomy combined with regional lymph node dissection is a better treatment.AIM To analyse the clinical features of hepatic AE combined with lymph node metastasis to explore its treatment and efficacy.METHODS A total of 623 patients with hepatic AE admitted to the First Affiliated Hospital of Xinjiang Medical University from 1 January 2012 to 1 January 2022 were retrospectively analysed.Fifty-five patients with combined lymph node metastasis were analysed for their clinical data,diagnosis and treatment methods,follow-up efficacy,and characteristics of lymph node metastasis.Finally,we comparatively analysed the lymph node metastasis rates at different sites.Categorical variables are expressed as frequencies and percentages,and the analysis of difference was performed using theχ2 test.The Bonferroni method was used for pairwise comparisons when statistical differences existed between multiple categorical variables.RESULTS A lymph node metastasis rate of 8.8%(55/623)was reported in patients with hepatic AE,with a female predilection(69.1%)and a statistically significant sex difference(χ2=8.018,P=0.005).Of the 55 patients with lymph node metastasis,72.7%had a parasite lesion,neighbouring organ invasion,and metastasis stage of P3N1M0 and above,of which 67.3%,78.2%,and 34.5%of hepatic AE lesions invaded the bile ducts,blood vessels,and distant metastases,respectively.Detection rates of lymph node metastasis of 16.4%,21.7%,and 34.2%were reported for a preoperative abdominal ultrasound,magnetic resonance imaging,and computed tomography examinations.All patients were intraoperatively suspected with enlarged lymph nodes and underwent radical hepatectomy combined with regional lymph node dissection.After surgery,a routine pathological examination was conducted on the resected lymph nodes.A total of 106 positive lymph nodes were detected in six groups at various sites,including 51 single-group metastasis cases and four multi-group metastasis cases.When the metastasis rates at different sites were statistically analysed,we observed that the metastasis rate in the para-hepatoduodenal ligament lymph nodes was significantly higher than that of the other sites(χ^(2)=128.089,P=0.000<0.05).No statistical difference was observed in the metastasis rate between the five other groups.Clavien-Dindo grade IIIa complication occurred in 14 cases,which improved after administering symptomatic treatment.Additionally,lymph node dissection-related complications were not observed.Recurrence after 2 years was observed in one patient.CONCLUSION Lymph node metastasis is a rare form of metastasis in hepatic AE,which is more frequent in women.Parahepatoduodenal ligament lymph nodes are commonly observed.Radical hepatectomy combined with regional lymph node dissection is a safe,effective,and feasible treatment for liver AE combined with lymph node metastasis.

Histological Risk Factors for Lymph Node Metastasis in pT1 Colorectal Cancer:Does Submucosal Invasion Depth Really Matter?

Objective After endoscopic resection of colorectal cancer with submucosal invasion(pT1 CRC),additional surgical treatment is recommended if deep submucosal invasion(DSI)is present.This study aimed to further elucidate the risk factors for lymph node metastasis(LNM)in patients with pT1 CRC,especially the effect of DSI on LNM.Methods Patients with pT1 CRC who underwent lymph node dissection were selected.The Chi-square test and multivariate logistic regression were used to analyze the relationship between clinicopathological characteristics and LNM.The submucosal invasion depth(SID)was measured via 4 methods and analyzed with 3 cut-off values.Results Twenty-eight of the 239 patients presented with LNM(11.7%),and the independent risk factors for LNM included high histological grade(P=0.003),lymphovascular invasion(LVI)(P=0.004),intermediate to high budding(Bd 2/3)(P=0.008),and cancer gland rupture(CGR)(P=0.008).Moreover,the SID,width of submucosal invasion(WSI),and area of submucosal invasion(ASI)were not significantly different.When one,two,three or more risk factors were identified,the LNM rates were 1.1%(1/95),12.5%(7/56),and 48.8%(20/41),respectively.Conclusion Indicators such as the SID,WSI,and ASI are not risk factors for LNM and are subjective in their measurement,which renders them relatively inconvenient to apply in clinical practice.In contrast,histological grade,LVI,tumor budding and CGR are relatively straightforward to identify and have been demonstrated to be statistically significant.It would be prudent to focus on these histological factors rather than subjective measurements.

Deep learning-based automatic pipeline system for predicting lateral cervical lymph node metastasis in patients with papillary thyroid carcinoma using computed tomography:A multi-center study

Objective:The assessment of lateral lymph node metastasis(LLNM)in patients with papillary thyroid carcinoma(PTC)holds great significance.This study aims to develop and evaluate a deep learning-based automatic pipeline system(DLAPS)for diagnosing LLNM in PTC using computed tomography(CT).Methods:A total of 1,266 lateral lymph nodes(LLNs)from 519 PTC patients who underwent CT examinations from January 2019 to November 2022 were included and divided into training and validation set,internal test set,pooled external test set,and prospective test set.The DLAPS consists of an auto-segmentation network based on RefineNet model and a classification network based on ensemble model(ResNet,Xception,and DenseNet).The performance of the DLAPS was compared with that of manually segmented DL models,the clinical model,and Node Reporting and Data System(Node-RADS).The improvement of radiologists’diagnostic performance under the DLAPS-assisted strategy was explored.In addition,bulk RNA-sequencing was conducted based on 12 LLNs to reveal the underlying biological basis of the DLAPS.Results:The DLAPS yielded good performance with area under the receiver operating characteristic curve(AUC)of 0.872,0.910,and 0.822 in the internal,pooled external,and prospective test sets,respectively.The DLAPS significantly outperformed clinical models(AUC 0.731,P<0.001)and Node-RADS(AUC 0.602,P<0.001)in the internal test set.Moreover,the performance of the DLAPS was comparable to that of the manually segmented deep learning(DL)model with AUCs ranging 0.814−0.901 in three test sets.Furthermore,the DLAPSassisted strategy improved the performance of radiologists and enhanced inter-observer consistency.In clinical situations,the rate of unnecessary LLN dissection decreased from 33.33%to 7.32%.Furthermore,the DLAPS was associated with the cell-cell conjunction in the microenvironment.Conclusions:Using CT images from PTC patients,the DLAPS could effectively segment and classify LLNs non-invasively,and this system had a good generalization ability and clinical applicability.

Risk factors for lymph node metastasis in superficial esophageal squamous cell carcinoma

In this editorial,we comment on the article by Wang et al published in the recent issue of the World Journal of Gastroenterology in 2023.We focused on identifying risk factors for lymph node metastasis(LNM)in superficial esophageal squamous cell carcinoma(SESCC)patients and how to construct a simple and reliable clinical prediction model to assess the risk of LNM in SESCC patients,thereby helping to guide the selection of an appropriate treatment plan.The current standard treatment for SESCC is radical esophagectomy with lymph node dissection.However,esophagectomy is associated with considerable morbidity and mortality.Endoscopic resection(ER)offers a safer and less invasive alternative to surgical resection and can enable the patient's quality of life to be maintained while providing a satisfactory outcome.However,since ER is a localized treatment that does not allow for lymph node dissection,the risk of LNM in SESCC limits the effectiveness of ER.Understanding LNM status can aid in determining whether patients with SESCC can be cured by ER without the need for additional esophagectomy.Previous studies have shown that tumor size,macroscopic type of tumor,degree of differentiation,depth of tumor invasion,and lymphovascular invasion are factors associated with LNM in patients with SESCC.In addition,tumor budding is commonly associated with LNM,recurrence,and distant metastasis,but this topic has been less covered in previous studies.By comprehensively evaluating the above risk factors for LNM,useful evidence can be obtained for doctors to select appropriate treatments for SESCC patients.

Magnetic resonance imaging-based lymph node radiomics for predicting the metastasis of evaluable lymph nodes in rectal cancer

BACKGROUND Lymph node(LN)staging in rectal cancer(RC)affects treatment decisions and patient prognosis.For radiologists,the traditional preoperative assessment of LN metastasis(LNM)using magnetic resonance imaging(MRI)poses a challenge.AIM To explore the value of a nomogram model that combines Conventional MRI and radiomics features from the LNs of RC in assessing the preoperative metastasis of evaluable LNs.METHODS In this retrospective study,270 LNs(158 nonmetastatic,112 metastatic)were randomly split into training(n=189)and validation sets(n=81).LNs were classified based on pathology-MRI matching.Conventional MRI features[size,shape,margin,T2-weighted imaging(T2WI)appearance,and CE-T1-weighted imaging(T1WI)enhancement]were evaluated.Three radiomics models used 3D features from T1WI and T2WI images.Additionally,a nomogram model combining conventional MRI and radiomics features was developed.The model used univariate analysis and multivariable logistic regression.Evaluation employed the receiver operating characteristic curve,with DeLong test for comparing diagnostic performance.Nomogram performance was assessed using calibration and decision curve analysis.RESULTS The nomogram model outperformed conventional MRI and single radiomics models in evaluating LNM.In the training set,the nomogram model achieved an area under the curve(AUC)of 0.92,which was significantly higher than the AUCs of 0.82(P<0.001)and 0.89(P<0.001)of the conventional MRI and radiomics models,respectively.In the validation set,the nomogram model achieved an AUC of 0.91,significantly surpassing 0.80(P<0.001)and 0.86(P<0.001),respectively.CONCLUSION The nomogram model showed the best performance in predicting metastasis of evaluable LNs.

Analysis of lymph node metastasis and survival prognosis in early gastric cancer patients: A retrospective study

BACKGROUND Early gastric cancer(EGC)is a common malignant tumor of the digestive system,and its lymph node metastasis and survival prognosis have been concerning.By retrospectively analyzing the clinical data of EGC patients,we can better under-stand the status of lymph node metastasis and its impact on survival and prog-nosis.AIM To evaluate the prognosis of EGC patients and the factors that affect lymph node METHODS The clinicopathological data of 1011 patients with EGC admitted to our hospital between January 2015 and December 2023 were collected in a retrospective cohort study.There were 561 males and 450 females.The mean age was 58±11 years.The patient underwent radical gastrectomy.The status of lymph node metastasis in each group was determined according to the pathological examination results of surgical specimens.The outcomes were as follows:(1)Lymph node metastasis in EGC patients;(2)Analysis of influencing factors of lymph node metastasis in EGC;and(3)Analysis of prognostic factors in patients with EGC.Normally distributed measurement data are expressed as mean±SD,and a t test was used for comparisons between groups.The data are expressed as absolute numbers or percentages,and the chi-square test was used for comparisons between groups.Rank data were compared using a nonparametric rank sum test.A log-rank test and a logistic regression model were used for univariate analysis.A logistic stepwise regression model and a Cox stepwise regression model were used for multivariate analysis.The Kaplan-Meier method was used to calculate the survival rate and construct survival curves.A log-rank test was used for survival analysis.RESULTS Analysis of influencing factors of lymph node metastasis in EGC.The results of the multifactor analysis showed that tumor length and diameter,tumor site,tumor invasion depth,vascular thrombus,and tumor differentiation degree were independent influencing factors for lymph node metastasis in patients with EGC(odds ratios=1.80,1.49,2.65,5.76,and 0.60;95%CI:1.29–2.50,1.11–2.00,1.81–3.88,3.87-8.59,and 0.48-0.76,respectively;P<0.05).Analysis of prognostic factors in patients with EGC.All 1011 patients with EGC were followed up for 43(0–13)months.The 3-year overall survival rate was 97.32%.Multivariate analysis revealed that age>60 years and lymph node metastasis were independent risk factors for prognosis in patients with EGC(hazard ratio=9.50,2.20;95%CI:3.31-27.29,1.00-4.87;P<0.05).Further analysis revealed that the 3-year overall survival rates of gastric cancer patients aged>60 years and≤60 years were 99.37%and 94.66%,respectively,and the difference was statistically significant(P<0.05).The 3-year overall survival rates of patients with and without lymph node metastasis were 95.42%and 97.92%,respectively,and the difference was statistically significant(P<0.05).CONCLUSION The lymph node metastasis rate of EGC patients was 23.64%.Tumor length,tumor site,tumor infiltration depth,vascular cancer thrombin,and tumor differentiation degree were found to be independent factors affecting lymph node metastasis in EGC patients.Age>60 years and lymph node metastasis are independent risk factors for EGC prognosis.

Predictive value of machine learning models for lymph node metastasis in gastric cancer: A two-center study

BACKGROUND Gastric cancer is one of the most common malignant tumors in the digestive system,ranking sixth in incidence and fourth in mortality worldwide.Since 42.5%of metastatic lymph nodes in gastric cancer belong to nodule type and peripheral type,the application of imaging diagnosis is restricted.AIM To establish models for predicting the risk of lymph node metastasis in gastric cancer patients using machine learning(ML)algorithms and to evaluate their pre-dictive performance in clinical practice.METHODS Data of a total of 369 patients who underwent radical gastrectomy at the Depart-ment of General Surgery of Affiliated Hospital of Xuzhou Medical University(Xuzhou,China)from March 2016 to November 2019 were collected and retro-spectively analyzed as the training group.In addition,data of 123 patients who underwent radical gastrectomy at the Department of General Surgery of Jining First People’s Hospital(Jining,China)were collected and analyzed as the verifi-cation group.Seven ML models,including decision tree,random forest,support vector machine(SVM),gradient boosting machine,naive Bayes,neural network,and logistic regression,were developed to evaluate the occurrence of lymph node metastasis in patients with gastric cancer.The ML models were established fo-llowing ten cross-validation iterations using the training dataset,and subsequently,each model was assessed using the test dataset.The models’performance was evaluated by comparing the area under the receiver operating characteristic curve of each model.RESULTS Among the seven ML models,except for SVM,the other ones exhibited higher accuracy and reliability,and the influences of various risk factors on the models are intuitive.CONCLUSION The ML models developed exhibit strong predictive capabilities for lymph node metastasis in gastric cancer,which can aid in personalized clinical diagnosis and treatment.

GLI1 and PTTG1 expression in colorectal carcinoma patients undergoing radical surgery and their correlation with lymph node metastasis

BACKGROUND Few studies have investigated the expression of GLI1 and PTTG1 in patients undergoing radical surgery for colorectal carcinoma(CRC)and their association with lymph node metastasis(LNM).Therefore,more relevant studies and analyses need to be conducted.AIM To explore GLI1 and PTTG1 expression in patients undergoing radical surgery for CRC and their correlation with LNM.METHODS This study selected 103 patients with CRC admitted to our hospital between April 2020 and April 2023.Sample specimens of CRC and adjacent tissues were collected to determine the positive rates and expression levels of GLI1 and PTTG1.The correlation of the two genes with patients’clinicopathological data(e.g.,LNM)was explored,and differences in GLI1 and PTTG1 expression between patients with LNM and those without were analyzed.Receiver operating characteristic(ROC)curves were plotted to evaluate the predictive potential of the two genes for LNM in patients with CRC.RESULTS Significantly higher positive rates and expression levels of GLI1 and PTTG1 wereobserved in CRC tissue samples compared with adjacent tissues.GLI1 and PTTG1 were strongly linked to LNM in patients undergoing radical surgery for CRC,with higher GLI1 and PTTG1 levels found in patients with LNM than in those without.The areas under the ROC curve of GLI1 and PTTG1 in assessing LNM in patients with CRC were 0.824 and 0.811,respectively.CONCLUSION GLI1 and PTTG1 expression was upregulated in patients undergoing radical surgery for CRC and are significantly related to LNM in these patients.Moreover,high GLI1 and PTTG1 expression can indicate LNM in patients with CRC undergoing radical surgery.The expression of both genes has certain diagnostic and therapeutic significance.

Assessment of programmed death-ligand 1 expression in primary tumors and paired lymph node metastases of gastric adenocarcinoma

BACKGROUND Anti-programmed death-1/programmed death-ligand 1(PD-1/PD-L1)immuno-therapy has demonstrated promising results on gastric cancer(GC).However,PD-L1 can express differently between metastatic sites and primary tumors(PT).AIM To compare PD-L1 status in PT and matched lymph node metastases(LNM)of GC patients and to determine the correlation between the PD-L1 status and clinicopathological characteristics.METHODS We retrospectively reviewed 284 GC patients who underwent D2-gastrectomy.PD-L1 was evaluated by immunohistochemistry(clone SP142)using the com-bined positive score.All PD-L1+PT staged as pN+were also tested for PD-L1 expression in their LNM.PD-L1(-)GC with pN+served as the comparison group.RESULTS Among 284 GC patients included,45 had PD-L1+PT and 24 of them had pN+.For comparison,44 PD-L1(-)cases with pN+were included(sample loss of 4 cases).Of the PD-L1+PT,54.2%(13/24 cases)were also PD-L1+in the LNM.Regarding PD-L1(-)PT,9.1%(4/44)had PD-L1+in the LNM.The agreement between PT and LNM had a kappa value of 0.483.Larger tumor size and moderate/severe peritumoral inflammatory response were associated with PD-L1 positivity in both sites.There was no statistical difference in overall survival for PT and LNM according to the PD-L1 status(P=0.166 and P=0.837,respectively).CONCLUSION Intra-patient heterogeneity in PD-L1 expression was observed between the PT and matched LNM.This disagreement in PD-L1 status may emphasize the importance of considering different tumor sites for analyses to select patients for immunotherapy.

Prediction of lymph node metastasis in early gastric signet-ring cell carcinoma:A real-world retrospective cohort study

BACKGROUND Signet-ring cell carcinoma(SRCC)was previously thought to have a worse prognosis than other differentiated gastric cancer(GC),however,recent studies have shown that the prognosis of SRCC is related to pathological type.We hypothesize that patients with SRCC and with different SRCC pathological components have different probability of lymph node metastasis(LNM).AIM To establish models to predict LNM in early GC(EGC),including early gastric SRCC.METHODS Clinical data from EGC patients who had undergone gastrectomy at the First Affiliated Hospital of Nanjing Medical University from January 2012 to March 2022 were reviewed.The patients were divided into three groups based on type:Pure SRCC,mixed SRCC,and non-signet ring cell carcinoma(NSRC).The risk factors were identified through statistical tests using SPSS 23.0,R,and EmpowerStats software.RESULTS A total of 1922 subjects with EGC were enrolled in this study,and included 249 SRCC patients and 1673 NSRC patients,while 278 of the patients(14.46%)presented with LNM.Multivariable analysis showed that gender,tumor size,depth of invasion,lymphovascular invasion,ulceration,and histological subtype were independent risk factors for LNM in EGC.Establishment and analysis using prediction models of EGC showed that the artificial neural network model was better than the logistic regression model in terms of sensitivity and accuracy(98.0%vs 58.1%,P=0.034;88.4%vs 86.8%,P<0.001,respectively).Among the 249 SRCC patients,LNM was more common in mixed(35.06%)rather than in pure SRCC(8.42%,P<0.001).The area under the ROC curve of the logistic regression model for LNM in SRCC was 0.760(95%CI:0.682-0.843),while the area under the operating characteristic curve of the internal validation set was 0.734(95%CI:0.643-0.826).The subgroups analysis of pure types showed that LNM was more common in patients with a tumor size>2 cm(OR=5.422,P=0.038).CONCLUSION A validated prediction model was developed to recognize the risk of LNM in EGC and early gastric SRCC,which can aid in pre-surgical decision making of the best method of treatment for patients.

Nomogram established using risk factors of early gastric cancer for predicting the lymph node metastasis

BACKGROUND For the prognosis of patients with early gastric cancer(EGC),lymph node metastasis(LNM)plays a crucial role.A thorough and precise evaluation of the patient for LNM is now required.AIM To determine the factors influencing LNM and to construct a prediction model of LNM for EGC patients.METHODS Clinical information and pathology data of 2217 EGC patients downloaded from the Surveillance,Epidemiology,and End Results database were collected and analyzed.Based on a 7:3 ratio,1550 people were categorized into training sets and 667 people were assigned to testing sets,randomly.Based on the factors influencing LNM determined by the training sets,the nomogram was drawn and verified.RESULTS Based on multivariate analysis,age at diagnosis,histology type,grade,T-stage,and size were risk factors of LNM for EGC.Besides,nomogram was drawn to predict the risk of LNM for EGC patients.Among the categorical variables,the effect of grade(well,moderate,and poor)was the most significant prognosis factor.For training sets and testing sets,respectively,area under the receiver-operating characteristic curve of nomograms were 0.751[95%confidence interval(CI):0.721-0.782]and 0.786(95%CI:0.742-0.830).In addition,the calibration curves showed that the prediction model of LNM had good consistency.CONCLUSION Age at diagnosis,histology type,grade,T-stage,and tumor size were independent variables for LNM in EGC.Based on the above risk factors,prediction model may offer some guiding implications for the choice of subsequent therapeutic approaches for EGC.

Risk factors and a predictive nomogram for lymph node metastasis in superficial esophageal squamous cell carcinoma

BACKGROUND Superficial esophageal squamous cell carcinoma(ESCC)is defined as cancer infiltrating the mucosa and submucosa,regardless of regional lymph node metastasis(LNM).Endoscopic resection of superficial ESCC is suitable for lesions that have no or low risk of LNM.Patients with a high risk of LNM always need further treatment after endoscopic resection.Therefore,accurately assessing the risk of LNM is critical for additional treatment options.AIM To analyze risk factors for LNM and develop a nomogram to predict LNM risk in superficial ESCC patients.METHODS Clinical and pathological data of superficial ESCC patients undergoing esophagectomy from January 1,2009 to January 31,2016 were collected.Logistic regression analysis was used to predict LNM risk factors,and a nomogram was developed based on risk factors derived from multivariate logistic regression analysis.The receiver operating characteristic(ROC)curve was used to obtain the accuracy of the nomogram model.RESULTSA total of 4660 patients with esophageal cancer underwent esophagectomy.Of these,474 superficial ESCC patientswere enrolled in the final analysis,with 322 patients in the training set and 142 patients in the validation set.Theprevalence of LNM was 3.29%(5/152)for intramucosal cancer and increased to 26.40%(85/322)for submucosalcancer.Multivariate logistic analysis showed that tumor size,invasive depth,tumor differentiation,infiltrativegrowth pattern,tumor budding,and lymphovascular invasion were significantly correlated with LNM.Anomogram using these six variables showed good discrimination with an area under the ROC curve of 0.789(95%CI:0.737-0.841)in the training set and 0.827(95%CI:0.755-0.899)in the validation set.CONCLUSIONWe developed a useful nomogram model to predict LNM risk for superficial ESCC patients which will facilitateadditional decision-making in treating patients who undergo endoscopic resection.

ScRNA-seq reveals the correlation between M2 phenotype of tumorassociated macrophages and lymph node metastasis of breast cancer

The process of lymphatic metastasis was proved to be associated with podoplanin-expressing macrophages in breast cancer(BC).This study aimed to investigate the role of the M2 phenotype of tumor-associated macrophages and mine the key M2 macrophages-related genes for lymph node metastasis in BC.We downloaded the GSE158399 dataset from the Gene Expression Omnibus(GEO)database,which includes transcriptomic profiles of individual cells from primary tumors,negative lymph nodes(NLNs),and positive lymph nodes(PLNs)of breast cancer patients.The cell subsets were identified by clustering analysis after quality control of the scRNA-seq using Seurat.The activation and migration capability of M2 macrophages were evaluated with R package“GSVA”.The key M2 macrophages-related genes were screened from the differential expressed genes(DEGs)and M2 macrophages activation and migration gene sets collected from MSigDB database.Our analysis identified three main cell types in primary tumors,NLNs,and PLNs:basal cells,luminal cells,and immune cell subsets.The further cell type classification of immune cell subsets indicated M2 macrophages accumulation in NLs and PLs.The GSVA enrichment scores for activation and migration capability were increased significantly in M2 macrophages from primary tumors than NLNs and PLNs(pvalue<0.001).Seven M2 macrophages activation-related and 15 M2 macrophages migration-related genes were significantly up-regulated in primary tumors than NLNs and PLNs.The proportion and GSVA enrichment scores for activation and migration of M2 macrophages may be potential markers for lymph node metastasis in breast cancer.Our study demonstrated that twenty-two up-regulated mRNA may be possible therapeutic targets for lymph node metastasis in breast cancer.

Nomogram based on multimodal magnetic resonance combined with B7-H3mRNA for preoperative lymph node prediction in esophagus cancer

Accurate preoperative prediction of lymph node metastasis(LNM)in esophageal cancer(EC)patients is of crucial clinical significance for treatment planning and prognosis.AIM To develop a clinical radiomics nomogram that can predict the preoperative lymph node(LN)status in EC patients.METHODS A total of 32 EC patients confirmed by clinical pathology(who underwent surgical treatment)were included.Real-time fluorescent quantitative reverse transcription-polymerase chain reaction was used to detect the expression of B7-H3 mRNA in EC tissue obtained during preoperative gastroscopy,and its correlation with LNM was analyzed.Radiomics features were extracted from multi-modal magnetic resonance imaging of EC using Pyradiomics in Python.Feature extraction,data dimensionality reduction,and feature selection were performed using XGBoost model and leave-one-out cross-validation.Multivariable logistic regression analysis was used to establish the prediction model,which included radiomics features,LN status from computed tomography(CT)reports,and B7-H3 mRNA expression,represented by a radiomics nomogram.Receiver operating characteristic area under the curve(AUC)and decision curve analysis(DCA)were used to evaluate the predictive performance and clinical application value of the model.RESULTS The relative expression of B7-H3 mRNA in EC patients with LNM was higher than in those without metastasis,and the difference was statistically significant(P<0.05).The AUC value in the receiver operating characteristic(ROC)curve was 0.718(95%CI:0.528-0.907),with a sensitivity of 0.733 and specificity of 0.706,indicating good diagnostic performance.The individualized clinical prediction nomogram included radiomics features,LN status from CT reports,and B7-H3 mRNA expression.The ROC curve demonstrated good diagnostic value,with an AUC value of 0.765(95%CI:0.598-0.931),sensitivity of 0.800,and specificity of 0.706.DCA indicated the practical value of the radiomics nomogram in clinical practice.CONCLUSION This study developed a radiomics nomogram that includes radiomics features,LN status from CT reports,and B7-H3 mRNA expression,enabling convenient preoperative individualized prediction of LNM in EC patients.

Predicting lymph node metastasis in colorectal cancer:An analysis of influencing factors to develop a risk model

BACKGROUND Colorectal cancer(CRC)is a significant global health issue,and lymph node metastasis(LNM)is a crucial prognostic factor.Accurate prediction of LNM is essential for developing individualized treatment strategies for patients with CRC.However,the prediction of LNM is challenging and depends on various factors such as tumor histology,clinicopathological features,and molecular characteristics.The most reliable method to detect LNM is the histopathological examination of surgically resected specimens;however,this method is invasive,time-consuming,and subject to sampling errors and interobserver variability.AIM To analyze influencing factors and develop and validate a risk prediction model for LNM in CRC based on a large patient queue.METHODS This study retrospectively analyzed 300 patients who underwent CRC surgery at two Peking University Shenzhen hospitals between January and December 2021.A deep learning approach was used to extract features potentially associated with LNM from primary tumor histological images while a logistic regression model was employed to predict LNM in CRC using machine-learning-derived features and clinicopathological variables as predictors.RESULTS The prediction model constructed for LNM in CRC was based on a logistic regression framework that incorporated machine learning-extracted features and clinicopathological variables.The model achieved high accuracy(0.86),sensitivity(0.81),specificity(0.87),positive predictive value(0.66),negative predictive value(0.94),area under the curve for the receiver operating characteristic(0.91),and a low Brier score(0.10).The model showed good agreement between the observed and predicted probabilities of LNM across a range of risk thresholds,indicating good calibration and clinical utility.CONCLUSION The present study successfully developed and validated a potent and effective risk-prediction model for LNM in patients with CRC.This model utilizes machine-learning-derived features extracted from primary tumor histology and clinicopathological variables,demonstrating superior performance and clinical applicability compared to existing models.The study provides new insights into the potential of deep learning to extract valuable information from tumor histology,in turn,improving the prediction of LNM in CRC and facilitate risk stratification and decision-making in clinical practice.

Prediction of lymph node metastasis in early esophageal cancer

BACKGROUND Given the poor prognosis of patients with lymph node metastasis,estimating the lymph node status in patients with early esophageal cancer is crucial.Indicators that could be used to predict lymph node metastasis in early esophageal cancer have been reported in many recent studies,but no recent studies have included a review of this subject.AIM To review indicators predicting lymph node metastasis in early esophageal squamous cell carcinoma(ESCC)and early esophageal adenocarcinoma(EAC).METHODS We searched PubMed with“[early esophageal cancer(Title/Abstract)]and[lymph node(Title/Abstract)]”or“[early esophageal carcinoma(Title/Abstract)]and[lymph node(Title/Abstract)]”or“[superficial esophageal cancer(Title/Abstract)]and[lymph node(Title/Abstract)].”A total of 29 studies were eligible for analysis.RESULTS Preoperative imaging(size),serum markers(microRNA-218),postoperative pathology and immunohistochemical analysis(depth of invasion,tumor size,differentiation grade,lymphovascular invasion,neural invasion,expression of PIM-1<30%)were predictive factors for lymph node metastasis in both early ESCC and EAC.Serum markers(thymidine kinase 1≥3.38 pmol/L;cytokeratin 19 fragment antigen 21-1>3.30 ng/mL;stathmin-1)and postoperative pathology and immunohistochemical analysis(overexpression of cortactin,mixed-lineage leukaemia 2,and stanniocalcin-1)were predictive for lymph node metastasis in early ESCC.Transcription of CD69,myeloid differentiation protein 88 and toll-like receptor 4 and low expression of olfactomedin 4 were predictive of lymph node metastasis in early EAC.A total of 6 comprehensive models for early ESCC,including logistic regression model,nomogram,and artificial neural network(ANN),were reviewed.The areas under the receiver operating characteristic curve of these models reached 0.789-0.938,and the ANN performed best.As all these models relied on postoperative pathology,further models focusing on serum markers,imaging and immunohistochemical indicators are still needed.CONCLUSION Various factors were predictive of lymph node metastasis in early esophageal cancer,and present comprehensive models predicting lymph node metastasis in early ESCC mainly relied on postoperative pathology.Further studies focusing on serum markers,imaging and immunohistochemical indicators are still in need.

Malignant proliferative ependymoma of the neck with lymph node metastasis:A case report

BACKGROUND Malignant proliferating trichilemmal tumor(MPTT)is an infrequent malignant neoplasm originating from cutaneous appendages,with only a handful of documented cases.This report delineates a unique instance of MPTT situated in the neck,accompanied by lymph node metastasis.A comprehensive exposition of its clinical trajectory and imaging manifestation is presented,aiming to enhance comprehension and management of this atypical ailment.CASE SUMMARY Patient concerns:A 79-year-old male presented with a longstanding right neck mass persisting for over six decades,exhibiting recent enlargement over the past year.Diagnoses:Enhanced magnetic resonance imaging of the neck unveiled an elliptical mass on the right neck side,characterized by an ill-defined border and a heterogeneous signal pattern.The mass exhibited subdued signal intensity on T1-weighted imaging(T1WI)and a heterogeneous high signal on T2-weighted imaging(T2WI),interspersed with a lengthy T1 and T2 cystic signal motif.Close anatomical association with the submandibular gland joint was noted,and intravenous gadolinium diethylene triamine pentaacetic acid administration facilitated conspicuous enhancement.Substantial enhancement of the solid segment prompted an initial preoperative diagnosis of malignant nerve sheath tumor.However,post-surgery histopathological and immunohistochemical analysis conclusively confirmed the diagnosis as malignant hyperplastic external hair root sheath tumor.Intervention:Complete excision of the tumor was successfully executed.Outcomes:The patient experienced a favorable postoperative recovery.CONCLUSION Malignant proliferative trichilemmal tumor external hair root sheath tumor is a cystic-solid lesion,appearing as low signal on T1WI images or high signal on T2WI with enhancement of the solid component.Suspicions of malignancy are heightened when the tumor border is indistinct,tissue planes are breached,or when linear or patchy high signals are observed in the subcutaneous tissue on T1 liver acquisition with volume acceleration enhanced images along with intermediate signal on T2WI and restricted diffusion on diffusion-weighted imaging images.Strong consideration for malignancy should arise if there are signs of compromised adjacent tissue relationships or direct invasion evident on imaging.We have incorporated the above-mentioned content into the entire manuscript.

Establishment of Risk Prediction Model and Nomogram for Lymph Node Metastasis of Cervical Cancer: Based on SEER Database

Objective: To predict the risk factors of lymph node metastasis in cervical cancer by using large sample clinical data, and to construct and verify the nomogram for predicting lymph node metastasis. Methods: A total of 5940 patients with cervical cancer from 2004 to 2015 in the National Cancer Institute Surveillance Epidemiology and End Results database were retrospectively screened and randomly assigned to training group (n = 4172) and validation group (n = 1768). Multivariate Logistic regression analysis was used, and the optimal model was selected according to AIC or BIC and likelihood ratio test, and a nomogram was drawn. The accuracy and robustness of the prediction model were evaluated in three aspects: discrimination, calibration and clinical net benefit. Results: The prediction model based on race, tumor tissue differentiation degree, tumor histopathological type, distant metastasis of tumor, tumor diameter and other risk factors was successfully established and a nomogram was constructed. The AUCs of training group and validation group were: 0.736 and 0.714, respectively. And the p-values of the Hosmer-Lemeshow test were 0.28 and 0.11, respectively. The calibration curve was in good agreement with the ideal curve. It had high accuracy and applicability after internal verification. Conclusion: A prediction model is constructed based on the risk factors of lymph node metastasis of cervical cancer. The nomogram has a good effective prediction and can provide a theoretical basis for clinicians to assess the disease quickly before surgery.

Vascular Endothelial Growth Factor C Expression in Gastric Carcinoma and Its Relationship with Lymph Node Metastasis

Objective: To study the expression of vascular endothelial growth factor C (VEGF-C) in gastric carcinoma and its relationship with lymph node metastasis. Methods: The expression of VEGF-C mRNA in 5 gastric carcinoma cell lines was detected by reverse transcription-polymerase chain reaction (RT-PCR). Simultaneously, the expression of VEGF-C protein in gastric carcinoma tissues, which were obtained from 63 patients who underwent radical gastrectomy, was detected by immunohistochemistry. Results: Three of the 5 gastric carcinoma cell lines, MKN-45, SGC-7901 and AGS, expressed VEGF-C mRNA. VEGF-C protein was expressed in 52.4% (33/63) of patients. VEGF-C protein expression was more frequently found in tumors with lymph node metastasis than in those without (P<0.01). VEGF-C protein expression was also closely related to lymphatic invasion (P<0.01) and TNM stage (P<0.01). However, there was no significant correlation between VEGF-C expression and the age, gender, tumor size, tumor location, Lauren classification, depth of invasion, and vascular invasion. Conclusion: The expression of VEGF-C is closely related to lymph node metastasis of gastric carcinoma, and lymphangiogenesis might be a new target for treatment of gastric carcinoma.

Role of routine lymph node dissection alongside resection ofintrahepatic cholangiocarcinoma: Systematic review and metaanalysis

BACKGROUND The global incidence of intrahepatic cholangiocarcinoma(ICCA)is soaring.Due to often delayed presentation,only a narrow spectrum of the disease is usually surgically resectable.To more accurately stage the disease,reduce recurrence,and improve overall survival,surgical teams are increasingly performing intraoperative lymph node dissection(LND)as well.This procedure has its associated morbidity,while there is no consensus or formal guidelines on its role in this setting.Hence,there is a need to better delineate the evidence for performing LND alongside surgical resection of the ICCA.AIM To perform a systematic review and meta-analysis on the role of LND in improving prognostication and survival post-resection of ICCA.METHODS We performed a systematic literature search using Pubmed,Medline,Embase,and the Cochrane Library,for all studies involving LND,ICCA,and surgical resection using several keywords,Medical Subject Headings(MeSH)tags,and appropriate synonyms.All clinical studies comparing curative intent resection of ICCA with LND vs resection without LND were included,while single-arm case series,studies with insufficient data,and duplicates were excluded.We included all English-language studies from the different academic databases up till early December 2022.The primary outcome measures were set for overall survival(OS)and disease-free survival(DFS).RESULTS This systematic review and meta-analysis included 15 studies that fulfilled the selection criteria comprising 11413 patients with surgically-resectable ICCA,of whom 6424(56.3%)underwent hepatectomy with LND while the remainder underwent hepatectomy only.In patients who underwent LND,on average,27.7%of the resected lymph nodes were positive for metastatic disease.Overall,the results showed that performing LND did not significantly improve OS or DFS.However,the effect of LND on OS showed a degree of variability by geographical region,in Eastern and Western countries.As LND is increasingly being performed,further time-based analysis was undertaken to identify time-dependent changes in the role of LND.An increasing adoption of LND was not associated with improved OS.Furthermore,no roles were identified for neoadjuvant/adjuvant chemotherapy or increasing lymph node retrieval in improving OS either.CONCLUSION LND might aid in staging,prognosticating,and deciding further management of resected ICCA,but does not improve OS and DFS and is unsuitable for high-risk patients unlikely to benefit from further treatments.