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.

Characteristics of lymph node stations/basins metastasis and construction and validation of a preoperative combination prediction model that accurately excludes lymph node metastasis in early gastric cancer

Objective:To explore the candidate indications for function-preserving curative gastrectomy and sentinel lymph node navigation surgery in early gastric cancer(EGC).Methods:The clinicopathological data of 561 patients with EGC who underwent radical gastrectomy for gastric cancer at Peking University Cancer Hospital from November 2010 to November 2020 with postoperative pathological stage pT1 and complete examination data,were collected.Pearson’s Chi-square test was used and binary logistic regression was employed for univariate and multivariate analyses.Combined analysis of multiple risk and protective factors for lymph node metastasis(LNM)of EGC was performed.A negative predictive value(NPV)combination model was built and validated.Results:LNM occurred in 85 of 561 patients with EGC,and the LNM rate was 15.15%.NPV for LNM reached 100%based on three characteristics,including ulcer-free,moderately well differentiation and patient<65years old or tumor located at the proximal 1/3 of the stomach.Regarding lymphatic basin metastasis,multivariate analysis showed that the metastatic proportion of the left gastric artery lymphatic basin was significantly higher in male patients compared with female patients(65.96%vs.38.89%,P<0.05).The proportion of right gastroepiploic artery lymphatic basin metastasis in patients with a maximum tumor diameter>2 cm was significantly greater than that noted in patients with a maximum tumor diameter≤2 cm(60.78%vs.28.13%,P<0.05).Conclusions:Characteristics of lymph node stations/basins metastasis will facilitate precise lymph node resection.The NPV for LNM reaches 100%based on the following two conditions:young and middle-aged EGC patients,well-differentiated tumors,and without ulcers;or well-differentiated tumors,without ulcers,and tumors located in the proximal stomach.These findings can be used as the recommended indications for functionpreserving curative gastrectomy and sentinel lymph node navigation surgery.

Prediction of lymph node metastasis and sentinel node navigation surgery for patients with early-stage gastric cancer

Accurate prediction of lymph node(LN) status is crucially important for appropriate treatment planning in patients with early gastric cancer(EGC). However,consensus on patient and tumor characteristics associated with LN metastasis are yet to be reached. Through systematic search,we identified several independent variables associated with LN metastasis in EGC,which should be included in future research to assess which of these variables remain as significant predictors of LN metastasis. On the other hand,even if we use these promising parameters,we should realize the limitation and the difficulty of predicting LN metastasis accurately. The sentinel LN(SLN) is defined as first possible site to receive cancer cells along the route of lymphatic drainage from the primary tumor. The absence of metastasis in SLN is believed to correlate with the absence of metastasis in downstream LNs. In this review,we have attempted to focus on several independent parameters which have close relationship between tumor and LN metastasis in EGC. In addition,we evaluated the history of sentinel node navigation surgery and the usefulness for EGC.

Prognostic values of the integrated model incorporating the volume of metastatic regional cervical lymph node and pretreatment serum Epstein-Barr virus DNA copy number in predicting distant metastasis in patients with N1 nasopharyngeal carcinoma

Background: According to the 7 th edition of the American Joint Committee on Cancer(AJCC) staging system, over50% of patients with nasopharyngeal carcinoma(NPC) have N1 disease at initial diagnosis. However, patients with N1 NPC are relatively under-researched, and the metastasis risk of this group is not well-stratified. This study aimed to evaluate the prognostic values of gross tumor volume of metastatic regional lymph node(GTVnd) and pretreatment serum copy number of Epstein-Barr virus(EBV) DNA in predicting distant metastasis of patients with N1 NPC, and to develop an integrated prognostic model that incorporates GTVnd and EBV DNA copy number for this group of patients.Methods: The medical records of 787 newly diagnosed patients with nonmetastatic, histologically proven N1 NPC who were treated at Sun Yat-sen University Cancer Center between November 2009 and February 2012 were analyzed. Computed tomography-derived GTVnd was measured using the summation-of-area technique. Blood samples were collected before treatment to quantify plasma EBV DNA. The receiver operating characteristic(ROC) curve analysis was used to evaluate the cut-off point for GTVnd, and the area under the ROC curve was used to assess the predicted validity of GTVnd. The survival rates were assessed by Kaplan-Meier analysis, and the survival curves were compared using a log-rank test. Multivariate analysis was conducted using the Cox proportional hazard regression model.Results: The 5-year distant metastasis-free survival(DMFS) rates for patients with GTVnd > 18.9 vs.≤ 18.9 mL were82.2% vs. 93.2%(P < 0.001), and for patients with EBV DNA copy number > 4000 vs. < 4000 copies/mL were 83.5% vs.93.9%(P < 0.001). After adjusting for GTVnd, EBV DNA copy number, and T category in the Cox regression model, both GTVnd > 18.9 mL and EBV DNA copy number > 4000 copies/mL were significantly associated with poor prognosis(both P < 0.05). According to combination of GTVnd and EBV DNA copy number, all patients were divided into low-,moderate-, and high-risk groups, with the 5-year DMFS rates of 96.1,87.4, and 73.8%, respectively(P < 0.001). Multivariate analysis confirmed the prognostic value of this model for distant metastatic risk stratification(hazard ratio [HR],4.17; 95% confidence interval [CI] 2.34-7.59; P < 0.001).Conclusions: GTVnd and serum EBV DNA copy number are independent prognostic factors for predicting distant metastasis in NPC patients with N1 disease. The prognostic model incorporating GTVnd and EBV DNA copy number may improve metastatic risk stratification for this group of patients.

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.

Preoperative prediction of lymph node metastasis using deep learning-based features

Lymph node involvement increases the risk of breast cancer recurrence.An accurate non-invasive assessment of nodal involvement is valuable in cancer staging,surgical risk,and cost savings.Radiomics has been proposed to pre-operatively predict sentinel lymph node(SLN)status;however,radiomic models are known to be sensitive to acquisition parameters.The purpose of this study was to develop a prediction model for preoperative prediction of SLN metastasis using deep learning-based(DLB)features and compare its predictive performance to state-of-the-art radiomics.Specifically,this study aimed to compare the generalizability of radiomics vs DLB features in an independent test set with dissimilar resolution.Dynamic contrast-enhancement images from 198 patients(67 positive SLNs)were used in this study.Of these subjects,163 had an in-plane resolution of 0.7×0.7 mm^(2),which were randomly divided into a training set(approximately 67%)and a validation set(approximately 33%).The remaining 35 subjects with a different in-plane resolution(0.78×0.78 mm^(2))were treated as independent testing set for generalizability.Two methods were employed:(1)conventional radiomics(CR),and(2)DLB features which replaced hand-curated features with pre-trained VGG-16 features.The threshold determined using the training set was applied to the independent validation and testing dataset.Same feature reduction,feature selection,model creation procedures were used for both approaches.In the validation set(same resolution as training),the DLB model outperformed the CR model(accuracy 83%vs 80%).Furthermore,in the independent testing set of the dissimilar resolution,the DLB model performed markedly better than the CR model(accuracy 77%vs 71%).The predictive performance of the DLB model outperformed the CR model for this task.More interestingly,these improvements were seen particularly in the independent testing set of dissimilar resolution.This could indicate that DLB features can ultimately result in a more generalizable model.

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.

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.

Analysis of factors related to non-sentinel lymph node metastasis in 296 sentinel lymph node-positive Chinese breast cancer patients

Objective: Axillary lymph node dissection(ALND) may be unnecessary in 20%–60% of breast cancer patients with sentinel lymph node(NSLN) metastasis. The aim of the present study was to review the medical records of Chinese patients with early-stage breast cancer and positive NSLN metastasis to identify clinicopathological characteristics as risk factors for non-NSLN metastasis.Methods: The medical records of 2008 early-stage breast cancer patients who received intraoperative sentinel lymph node biopsy(SLNB) between 2006 and 2016 were retrospectively reviewed. These patients were clinically and radiologically lymph nodenegative and had no prior history of receiving neoadjuvant chemotherapy or endocrinotherapy. The clinicopathological characteristics of patients with positive NSLN metastasis who underwent ALND were investigated.Results: In the present study, 296 patients with positive NSLN metastases underwent ALND. Positive non-NSLN metastases were confirmed in 95 patients(32.1%). On univariate analysis, ≥ 3 positive NSLN metastases(P <0.01), NSLN macrometastases(P =0.023), and lymphovascular invasion(P = 0.04) were associated with non-NSLN metastasis(P <0.05). In multivariate analysis, the number of positive SLNs was the most significant predictor of non-SLN metastasis. For patients with 0, 1, 2, or 3 associated risk factors, the non-SLN metastatic rates were 11.5%, 22.5%, 35.2%, and 73.1%, respectively.Conclusions: The number of positive NSLNs, NSLN macrometastases, and lymphovascular invasion were correlated with nonSLN metastasis. The number of positive SLNs was an independent predictor for non-NSLN metastasis. When 2 or 3 risk factors were present in one patient, the probability of non-NSLN was higher than that in the American College of Surgeons Oncology Group Z0011 trial(27.3%); thus, avoiding ALND should be considered carefully.

Machine learning-based gray-level co-occurrence matrix signature for predicting lymph node metastasis in undifferentiated-type early gastric cancer

BACKGROUND The most important consideration in determining treatment strategies for undifferentiated early gastric cancer(UEGC)is the risk of lymph node metastasis(LNM).Therefore,identifying a potential biomarker that predicts LNM is quite useful in determining treatment.AIM To develop a machine learning(ML)-based integral procedure to construct the LNM gray-level co-occurrence matrix(GLCM)prediction model.METHODS We retrospectively selected 526 cases of UEGC confirmed through pathological examination after radical gastrectomy without endoscopic treatment in four tertiary hospitals between January 2015 to December 2021.We extracted GLCM-based features from grayscale images and applied ML to the classification of candidate predictive variables.The robustness and clinical utility of each model were evaluated based on the following factors:Receiver operating characteristic curve(ROC),decision curve analysis,and clinical impact curve.RESULTS GLCM-based feature extraction significantly correlated with LNM.The top 7 GLCM-based factors included inertia value 0°(IV_0),inertia value 45°(IV_45),inverse gap 0°(IG_0),inverse gap 45°(IG_45),inverse gap full angle(IG_all),Haralick 30°(Haralick_30),Haralick full angle(Haralick_all),and Entropy.The areas under the ROC curve(AUCs)of the random forest classifier(RFC)model,support vector machine,eXtreme gradient boosting,artificial neural network,and decision tree ranged from 0.805[95%confidence interval(CI):0.258-1.352]to 0.925(95%CI:0.378-1.472)in the training set and from 0.794(95%CI:0.237-1.351)to 0.912(95%CI:0.355-1.469)in the testing set,respectively.The RFC(training set:AUC:0.925,95%CI:0.378-1.472;testing set:AUC:0.912,95%CI:0.355-1.469)model that incorporates Entropy,Haralick_all,Haralick_30,IG_all,IG_45,IG_0,and IV_45 had the highest predictive accuracy.CONCLUSION The evaluation results indicate that the method of selecting radiological and textural features becomes more effective in the LNM discrimination against UEGC patients.Additionally,the MLbased prediction model developed using the RFC can be used to derive treatment options and identify LNM,which can hence improve clinical outcomes.

A nomogram for predicting lymph node metastasis in superficial esophageal squamous cell carcinoma

Superficial esophageal squamous cell carcinoma(SESCC)is defined as carcinoma with mucosal or submucosal invasion,regardless of regional lymph node metastasis(LNM).The lymph node status is not only a key factor to determine the training strategy,but also the most important prognostic factor in esophageal cancer.In this study,we establish a clinical nomogram for predicting LNM in patients with SESCC.A predictive model was established based on the training cohort composed of 711 patients who underwent esophagectomy for SESCC from December 2009 to June 2018.A prospective cohort of 203 patients from June 2018 to January 2019 was used for validation.Favorable calibration and well-fitted decision curve analysis were conducted and good discrimination was observed(concordance index[C-index],0.860;95%confidence interval[CI],0.825-0.894)through internal validation.The external validation cohort presented good discrimination(C-index,0.916;95%CI,0.860-0.971).This model may facilitate the prediction of LNM in patients with SESCCs.

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.

Factors contributing to lymph node occult metastasis in supraglottic laryngeal carcinoma cT2-T4 N0M0 and metastasis predictive equation

Objective： To investigate factors that contribute to lymph node metastasis（LNM） from clinical cT2-T4 N0M0（cN0） supraglottic laryngeal carcinoma（SLC）, and to predict the risk of occult metastasis before surgery.Methods： A total of 121 patients who received surgery were retrospectively analyzed. Relevant factors regarding cervical LNM were analyzed. Multivariate analyses were conducted to predict the region where the metastasis occurred and prognosis. Results： The overall metastatic rate of c N0 SLC was 28.1%. Metastatic rates were 15.4%, 32.5% and 35.7% for T2, T3 and T4, respectively. Metastatic rates for SLC levels II, III and IV were 19.6%, 17.2% and 3.6%, respectively. A regression equation was formulated to predict the probability of metastasis in cN0 SLC as follows： Pn=e（–3.874＋0.749T3＋1.154T4＋1.935P1＋1.750P2）/[1＋e（–3.874＋0.749T3＋1.154T4＋1.935P1＋1.750P2）]. Approximately 0.2% of patients experienced LNM with no recurrence of laryngeal cancer. Comparison of the intergroup survival curves between patients with and without LNM indicated a statistically significant difference（P=0.029）.Conclusions： Cervical lymph node metastatic rates tended to increase in tandem with T stage in patients with LNM in cN0 SLC, and neck dissection is advised for these patients. Moreover, cervical LNM in cN0 SLC showed a sequential pattern and may be predicted.

Combining lymph node ratio to develop prognostic models for postoperative gastric neuroendocrine neoplasm patients

BACKGROUND Lymph node ratio(LNR)was demonstrated to play a crucial role in the prognosis of many tumors.However,research concerning the prognostic value of LNR in postoperative gastric neuroendocrine neoplasm(NEN)patients was limited.AIM To explore the prognostic value of LNR in postoperative gastric NEN patients and to combine LNR to develop prognostic models.METHODS A total of 286 patients from the Surveillance,Epidemiology,and End Results database were divided into the training set and validation set at a ratio of 8:2.92 patients from the First Affiliated Hospital of Soochow University in China were designated as a test set.Cox regression analysis was used to explore the relationship between LNR and disease-specific survival(DSS)of gastric NEN patients.Random survival forest(RSF)algorithm and Cox proportional hazards(CoxPH)analysis were applied to develop models to predict DSS respectively,and compared with the 8th edition American Joint Committee on Cancer(AJCC)tumornode-metastasis(TNM)staging.RESULTS Multivariate analyses indicated that LNR was an independent prognostic factor for postoperative gastric NEN patients and a higher LNR was accompanied by a higher risk of death.The RSF model exhibited the best performance in predicting DSS,with the C-index in the test set being 0.769[95%confidence interval(CI):0.691-0.846]outperforming the CoxPH model(0.744,95%CI:0.665-0.822)and the 8th edition AJCC TNM staging(0.723,95%CI:0.613-0.833).The calibration curves and decision curve analysis(DCA)demonstrated the RSF model had good calibration and clinical benefits.Furthermore,the RSF model could perform risk stratification and individual prognosis prediction effectively.CONCLUSION A higher LNR indicated a lower DSS in postoperative gastric NEN patients.The RSF model outperformed the CoxPH model and the 8th edition AJCC TNM staging in the test set,showing potential in clinical practice.

Factors related to lymph node metastasis and surgical strategy used to treat early gastric carcinoma

AIM:The prognosis of early gastric carcinoma (EGC) is generally excellent after surgery. The presence or absence of lymph node metastasis in EGC is an important prognostic factor. The survival and recurrence rates of node-negative EGC are much better than those of node-positive EGC. This study examined the factors related to lymph node metastasis in EGC to determine the appropriate treatment for EGC.METHODS: We investigated 748 patients with EGC who underwent surgery between January 1985 and December 1999 at the Division of Gastroenterologic Surgery, Department of Surgery, Chonnam National University Hospital. Several clinicopathologic factors were investigated to analyze their relationship to lymph node metastasis: age, sex, tumor location, tumor size, gross type, histologic type, depth of invasion, extent of lymph node dissection, type of operation,and DNA ploidy.RESULTS:Lymph node metastases were found in 75 patients (10.0%). Univariate analysis showed that male sex, tumor size larger than 2.0cm, submucosal invasion of tumor, histologic differentiation, and DNA ploidy pattern were risk factors for regional lymph node metastasis in EGC patients. However, a multivariate analysis showed that three risk factors were associated with lymph node metastasis:large tumor size, undifferentiated histologic type and submucosal invasion. No statistical relationship was found for age, sex, tumor location, gross type, or DNA ploidy in multivariate analysis. The 5-year survival rate was 94.2% for those without lymph node metastasis and 87.3% for those with lymph node metastasis, and the difference was significant (P<0.05).CONCLUSION: In patients with EGC, the survival rate of patients with positive lymph nodes is significantly worse than that of patients with no lymph node metastasis. Therefore,a standard D2 lymphadenectomy should be performed in patients at high risk of lymph node metastasis: large tumor size, undifferentiated histologic type and submucosal invasion.

Lymph node metastasis in early gastric cancer with submucosal invasion:Feasibility of minimally invasive surgery

AIM:To explore the feasibility of pertorming minimally invasive surgery(MIS)on subsets of submucosal gastric cancers that are unlikely to have regional lymph node metastasis. METHODS:A total of 105 patients underwent radical gastrectomy with lymph node dissection for submucosal gastric cancer at our hospital from January 1995 to December 1995.Besides investigating many clinicopathological features such as tumor size,gross appearance,and differentiation, we measured the depth of invasion into submucosa minutely and analyzed the clinicopathologic features of these patients regarding lymph node metastasis. RESULTS:The rate of lymph node metastasis in cases where the depth of invasion was<500 μm,500-2 000 μm,or >2 000 μm was 9%(2/23),19%(7136),and 33%(15/46), respectively(P<0.05).In univariate analysis,no significant correlation was found between lymph node metastasis and clinicopathological characteristics such as age,sex,tumor location,gross appearance,tumor differentiation,Lauren's classification,and lymphatic invasion.In multivariate analysis, tumor size(>4 cm vs≤2 cm,odds ratio=4.80, P=0.04)and depth of invasion(>2 000 μm vs ≤500 μm, odds ratio=6.81,P=0.02)were significantly correlated with lymph node metastasis.Combining the depth and size in cases where the depth of invasion was less than 500 μm, we found that lymph node metastasis occurred where the tumor size was greater than 4 cm.In cases where the tumor size was less than 2 cm,lymph node metastasis was found only where the depth of tumor invasion was more than 2 000 μm. CONCLUSION:MIS can be applied to submucosal gastric cancer that is less than 2 cm in size and 500 μm in depth.

Which Lymph Nodes Should Be Exactly Removed during Breast Cancer Surgery to Prevent Metastasis?

The aim of the study was to identify the relationship between molecular subtypes of breast cancer (BC) and the morphological characteristics of axillary lymph nodes (ALN) and metastatic risk in BC patients to clarify danger degree and justification of removal before metastases appear. Material and methods. Tumor molecular subtypes of 116 female BC patients aged 24 - 75 (53.9 ± 0.8) were determined by tumor tissue immunohistological examination (obtained by tru-cut biopsy), and the BC was classified as Luminal A, Luminal B/HER2−, Luminal B/HER2+, TNBC, and HER2+ subtypes. To interpret the results for the BC receptor status, immunohistochemical analysis was performed and interpreted according to the Allred scale. Lymph node size, shape, structure and conglomerates availability were recorded according to ultrasonography (USG) examination evaluated on “LOGIQ C5-Premium” (2012). Blood CA-15-3 levels were analyzed using a COBAS-e 411 automated analyzer. Statistical analysis of the obtained results was carried out using the SPSS-26 software package, and based on the t-Student-Bonferroni and H-Kruskal-Wallis criteria. The sensitivity and specificity of the indicators studied were determined using ROC statistical analysis. Results showed a significant association of some subtypes, as well as receptor expression, with tumor metastasis to ALN. Conclusion: 1) The HER2+ subtype is the most aggressive in terms of ALN metastasis. Although TNBC is the most aggressive subtype in general, it is characterized by fewer metastases to the ALN than the HER2+ subtype. 2) Metastatic ALNs can be distinguished based on their cortical structure before tumor tissue biopsy, which is economically profitable. These LNs can be removed without biopsy.

Predictors to assess non-sentinel lymph node status in breast cancer patients with only one sentinel lymph node metastasis

Background The purpose of this study was to investigate the feasibility of avoiding axillary lymph node dissection （ALND） for patients with only one sentinel lymph node （SLN） metastasis. The characteristics and predictive factors for non-sentinel lymph node （NSLN） metastasis of patients with single positive SLN were also analyzed. Methods Patients with no and only one SLN metastasis （OIn and 1In group, n ≥2） were selected from 1228 cases of invasive breast carcinoma, who underwent axillary dissection in Shandong Cancer Hospital between November 1999 and December 2011, to compare the characteristics of NSLN metastasis between them. For the 1In group, the factors that influenced the NSLN metastasis were analyzed by univariate and multivariate analysis. Results Differences of the NSLN metastasis between the OIn and the 1In groups were significant （P 〈0.001）. There was no significant difference between the axillary lymph node metastasis on level III in 1In group and OIn group （P=0.570）. When the total SLN number was 〉4 and with one positive case, the NSLN metastasis was not significantly different from that in the OIn group （P=-0.118）. In the 1In group, clinical tumor size （P = 0.012）, over-expression of Her-2 （P=0.003）, tumor grade （P=0.018） and the total number of SLN （P=0.047） significantly correlated with non-SLN metastasis. Clinical tumor size （P=0.015） and the expression of Her-2 （P=0.01） were independent predictive factors for non-SLN metastasis by the Logistic regression model. Conclusion Under certain conditions, breast cancer patients with single SLN metastasis could avoid ALND.

Development and validation of a radiomics nomogram for preoperative prediction of lymph node metastasis in colorectal cancer

With the support by the National Natural Science Foundation of China,the research team jointly led by Porf.Liu Zaiyi(刘再毅)at Guangdong General Hospital and Prof.Tian Jie(田捷)at the Key Laboratory of Molecular Imaging,Chinese Academy of Sciences,developed a CT-based radiomics prediction model to preoperatively predict the lymph node metastasis in colorectal cancer(CRC),which was published

Risk of lymph node metastases in patients with T1b oesophageal adenocarcinoma: A retrospective single centre experience

AIM To assess clinical outcomes for submucosal (T1b) oesophageal adenocarcinoma (OAC) patients managed with either surgery or endoscopic eradication therapy.METHODS Patients found to have T1b OAC following endoscopic resection between January 2008 to February 2016 at University College London Hospital were retrospectively analysed. Patients were split into low-risk and high-risk groups according to established histopathological criteria and were then further categorised according to whether they underwent surgical resection or conservative management. Study outcomes include the presence of lymphnode metastases, disease-specific mortality and overall survival. RESULTS A total of 60 patients were included; 22 patients were surgically managed (1 low-risk and 21 high-risk patients) whilst 38 patients were treated conservatively (12 low-risk and 26 high-risk). Overall, lymph node metastases (LNM) were detected in 10 patients (17%); six of these patients had undergone conservative management and LNM were detected at a median of 4 mo after endoscopic mucosal resection (EMR). All LNM occurred in patients with highrisk lesions and this represented 21% of the total high-risk lesions. Importantly, there was no statistically significant difference in tumor-related deaths between those treated surgically or conservatively (P = 0.636) and disease-specific survival time was also comparable between the two treatment strategies (P = 0.376).CONCLUSION T1b tumours without histopathological high-risk markers of LNM can be treated endoscopically with good outcomes. In selected patients, endoscopic therapy may be appropriate for high-risk lesions.