A nomogram to predict lymph node metastasis in patients with early gastric cancer

18F-FDG PET/CT-based radiomics nomogram for the preoperative prediction of lymph node metastasis in gastric cancer

Objective

Lymph node metastasis (LNM) is not only one of the important factors affecting the prognosis of gastric cancer but also an important basis for treatment decisions. The purpose of this study was to investigate the value of the radiomics nomogram based on preoperative ¹⁸F-deoxyglucose (FDG) PET/CT primary lesions and clinical risk factors for predicting LNM in gastric cancer (GC).

Methods

We retrospectively analyzed radiomics features of preoperative ¹⁸F-FDG PET/CT images in 224 gastric cancer patients from two centers. The prediction model was developed in the training cohort (n = 134) and validated in the internal (n = 59) and external validation cohorts (n = 31). The least absolute shrinkage and selection operator (LASSO) regression was used to select features and build radiomics signatures. The radiomics feature score (Rad-score) was calculated and established a radiomics signature. Multivariate logistic regression analysis was used to screen independent risk factors for LNM. The minimum Akaike’s information criterion (AIC) was used to select the optimal model parameters to construct a radiomics nomogram. The performance of the nomogram was assessed with calibration, discrimination, and clinical usefulness.

Results

There was no significant difference between the internal verification and external verification of the clinical data of patients (all p > 0.05). The areas under the curve (AUCs) (95% CI) for predicting LNM based on the ¹⁸F-FDG PET/CT radiomics signature in the training cohort, internal validation cohort, and external validation cohort were 0.792 (95% CI: 0.712–0.870), 0.803 (95% CI: 0.681–0.924), and 0.762 (95% CI: 0.579–0.945), respectively. Multivariate logistic regression showed that carbohydrate antigen (CA) 19-9 [OR (95% CI): 10.180 (1.267–81.831)], PET/CT diagnosis of LNM [OR (95% CI): 6.370 (2.256–17.984)], PET/CT Rad-score [OR (95% CI): 16.536 (5.506–49.660)] were independent influencing factors of LNM (all p < 0.05), and a radiomics nomogram was established based on those factors. The AUCs (95% CI) for predicting LNM were 0.861 (95% CI: 0.799–0.924), 0.889 (95% CI: 0.800–0.976), and 0.897 (95% CI: 0.683–0.948) in the training cohort, the internal validation cohort, and the external validation cohort, respectively. Decision curve analysis (DCA) indicated that the ¹⁸F-FDG PET/CT-based radiomics nomogram has good clinical utility.

Conclusions

Radiomics nomogram based on the primary tumor of ¹⁸F-FDG PET/CT could facilitate the preoperative individualized prediction of LNM, which is helpful for risk stratification in GC patients.

Establishment and Validation for Predicting the Lymph Node Metastasis in Early Gastric Adenocarcinoma

Lymph node metastasis (LNM) is considered to be one of the important factors in determining the optimal treatment for early gastric cancer (EGC). This study aimed to develop and validate a nomogram to predict LNM in patients with EGC. A total of 842 cases from the Surveillance, Epidemiology, and End Results (SEER) database were divided into training and testing sets with a ratio of 6 : 4 for model development. Clinical data (494 patients) from the hospital were used for external validation. Univariate and multivariate logistic regression analyses were used to identify the predictors using the training set. Logistic regression, LASSO regression, ridge regression, and elastic-net regression methods were used to construct the model. The performance of the model was quantified by calculating the area under the receiver operating characteristic curve (AUC) with 95% confidence intervals (CIs). Results showed that T stage, tumor size, and tumor grade were independent predictors of LNM in EGC patients. The AUC of the logistic regression model was 0.766 (95% CI, 0.709-0.823), which was slightly higher than that of the other models. However, the AUC of the logistic regression model in external validation was 0.625 (95% CI, 0.537-0.678). A nomogram was drawn to predict LNM in EGC patients based on the logistic regression model. Further validation based on gender, age, and grade indicated that the logistic regression predictive model had good adaptability to the population with grade III tumors, with an AUC of 0.803 (95% CI, 0.606-0.999). Our nomogram showed a good predictive ability and may provide a tool for clinicians to predict LNM in EGC patients.

Development and validation of a nomogram to predict overall survival of T1 esophageal squamous cell carcinoma patients with lymph node metastasis

PURPOSE

To develop a nomogram for predicting the prognosis of T1 esophageal squamous cell carcinoma (ESCC) patients with positive lymph node.

METHODS

T1 ESCC patients with lymph node metastasis diagnosed between 2010 and 2015 were selected from the Surveillance, Epidemiology, and Final Results (SEER) database. The entire cohort was randomly divided in the ratio of 7:3 into a training group (n=457) and validation group (n=192), respectively. Prognostic factors were identified by univariate and multivariate Cox regression models. Harrell's concordance index (C-index), receiver operating characteristic (ROC) curve, and calibration curve were used to evaluate the discrimination and calibration of the nomogram. The accuracy and clinical net benefit of the nomogram compared with the 7^th AJCC staging system were evaluated using net reclassification improvement (NRI), integrated discrimination improvement (IDI), and decision curve analysis (DCA).

RESULTS

The nomogram consisted of eight factors: insurance, T stage, summary stage, primary site, radiation code, chemotherapy, surgery, and radiation sequence with surgery. In the training and validation cohorts, the AUCs exceeded 0.700, and the C-index scores were 0.749 and 0.751, respectively, indicating that the nomogram had good discrimination. The consistency between the survival probability predicted by the nomogram and the actual observed probability was indicated by the calibration curve in the training and validation cohorts. For NRI>0 and IDI>0, the predictive power of the nomogram was more accurate than that of the 7^th AJCC staging system. Furthermore, the DCA curve indicated that the nomogram achieved better clinical utility than the traditional system.

CONCLUSIONS

Unlike the 7^th AJCC staging system, the developed and validated nomogram can help clinical staff to more accurately, personally and comprehensively predict the 1-year and 3-year OS probability of T1 ESCC patients with lymph node metastasis.

A population-based study: how to identify high-risk T1 gastric cancer patients?

In T1 gastric cancer (GC), lymph nodes metastasis (LNM) is considered as a significant prognostic predictor and closely associated with following therapeutic approaches as well as distant metastasis (DM). This study aimed to not only seek risk factors of LNM and DM but also unpack the prognosis in T1 GC patients. We performed a retrospective study enrolling 5547 patients in T1 GC from the Surveillance, Epidemiology, and End Results (SEER) database. Univariate and multivariate logistic regression models were produced to recognize independent risk factors of LNM and DM. Cox regression analyses were performed to identify important prognostic factors of overall survival (OS). Cancer-specific cumulative incidence was plotted by cumulative incidence function. Three nomograms of LNM, DM and OS were established and validated by receiver operating characteristic (ROC) and calibration curves to evaluate discrimination and accuracy. Decision curve analysis (DCA), clinical impact curves (CIC) and subgroups based on risk scores were constructed to measure nomograms clinical utility. The area under the curve (AUC) of LNM nomogram and DM nomogram were 0.735 and 0.896, respectively. OS nomogram was constructed and the corresponding C-index was 0.797. In conclusion, our user-friendly nomograms, which aimed to predict LNM, DM and OS in T1 gastric cancer patients, have shown high efficiency of discrimination and accuracy. These useful and visual tools may have advantageous clinical utility to identify high-risk T1 gastric patients and help clinicians to draw up an individual therapeutic strategy.

Establishment and verification of a nomogram for predicting the risk of lymph node metastasis in early gastric cancer

BACKGROUND

endoscopic submucosal dissection (ESD) has been widely recognized by patients and doctors due to its advantages in early gastric cancer (EGC). The accurate prediction of the risk of lymph node metastasis (LNM) in EGC is important to select suitable treatments with this procedure for patients. Unfortunately, the accuracy of endoscopic ultrasound and computed tomography in the diagnosis of EGC lymph node status is extremely limited. The purpose of the present study was to establish an LNM nomogram risk model of early gastric cancer patients based on clinical data, to guide treatment for clinicians.

METHODS

a retrospective examination of the records of EGC patients undergoing radical gastrectomy from August 2012 to August 2019 in the Gastrointestinal Center of Subei People's Hospital was performed. The clinicopathological data were classified into a training set and validation set according to the time. Univariate and multivariate analyses were performed to identify risk factors related to LNM. A risk model for predicting the occurrence of LNM in EGC was established and validated.

RESULTS

of the 503 EGC patients, 78 (15.5 %) had lymph node metastasis. Logistic stepwise regression analysis showed that the predictive factors included sex, tumor location, tumor diameter, differentiation, ulcer and lymphatic vascular invasion. The discrimination of the LNM prediction model was satisfactory with an AUC of 0.8033 (internal validation) and 0.7353 (external validation). The correction effect of the calibration was satisfactory and the DCA decision curve analysis showed a strong clinical practicability.

CONCLUSION

the nomogram risk prediction model of LNM has been established for EGC patients to assist in formulating personalized treatment plans.

Risk of Lymph Node Metastasis and Feasibility of Endoscopic Treatment in Ulcerative Early Gastric Cancer

BACKGROUND

When the risk of lymph node metastasis (LNM) is considered minimal in patients with early gastric cancer (EGC), endoscopic submucosal dissection (ESD) is an effective alternative to radical resection. This study aims to estimate the feasibility of ESD for EGC with ulceration.

PATIENTS AND METHODS

We retrospectively reviewed data from 691 patients who underwent gastrectomy for EGC with ulceration. Subsequently, a stratification system for lesions was created based on the expanded ESD criteria, and the associations between the subgroups and the rate of LNM were analyzed.

RESULTS

LNM was confirmed in 16.5% (114/691) of patients. Univariate analysis demonstrated that age, sex, tumor size, macroscopic features, depth of invasion, tumor differentiation, Lauren type, lymphovascular invasion (LVI), and perineural invasion were associated with LNM. Multivariate analysis showed that LVI [odds ratio (OR) = 16.761, P < 0.001], SM1 invasion (OR = 2.159, P = 0.028), and SM2 invasion (OR = 3.230, P < 0.001) were independent risk factors for LNM. LNM occurred in undifferentiated mucosal tumors, with ulceration being 1.7% (2/116) when the lesion was smaller than 20 mm. Further stratification revealed that among lesions < 30 mm in size, undifferentiated tumors with SM1 invasion had a higher rate of LNM and a lower disease-free survival rate than differentiated tumors with SM1 invasion and tumors limited to the mucosal layer.

CONCLUSIONS

Depth of invasion and LVI were strongly associated with LNM in ulcerative EGC. Endoscopic resection may be applicable for undifferentiated mucosal ulcerative EGC < 30 mm in size, and additional investigation is needed to evaluate its safety.

Integrating tumor and nodal radiomics to predict lymph node metastasis in gastric cancer

BACKGROUND

To develop and validate a radiomics method via integrating tumor and lymph node radiomics for the preoperative prediction of lymph node (LN) status in gastric cancer (GC).

MATERIALS AND METHODS

We retrospectively collected 170 contrast-enhanced abdominal CT images from GC patients. Five times repeated random hold-out experiment was employed. Tumor and nodal radiomics features were extracted from each individual tumor and LN respectively, and then multi-step feature selection was performed. The optimal tumor and nodal features were selected using Pearson correlation analysis and sequential forward floating selection (SFFS) algorithm. After feature fusion, the SFFS algorithm was used to develop radiomics signatures. The performance of the radiomics signatures developed based on logistic regression classifier was further analyzed and compared using the area under the receiver operating characteristic curve (AUC).

RESULTS

The AUC values, reported as mean ± standard deviation, were 0.9319 ± 0.0129 and 0.8546 ± 0.0261 for the training and validation cohorts respectively. The radiomic signatures could predict LN status, especially in T2-stage, diffuse-type and moderately/well differentiated GC. After integrating clinicopathologic information, the radiomic-clinicopathologic model (training cohort, 0.9432 ± 0.0129; validation cohort, 0.8764 ± 0.0322) showed a better discrimination capability than other radiomics models and clinicopathologic model. The radiomic-clinicopathologic model also showed superior performance to the gastroenterologist' decision in all experiments, and outperformed the radiologist in some experiments.

CONCLUSION

Our proposed method presented good predictive performance and great potential for predicting LNM in GC. As a noninvasive preoperative prediction tool, it can be helpful for guiding the prognosis and treatment decision-making in GC patients.

Nomogram using F-18 fluorodeoxyglucose positron emission tomography/computed tomography for preoperative prediction of lymph node metastasis in gastric cancer

BACKGROUND

Lymph node (LN) metastasis is an important prognostic factor in patients with gastric cancer (GC). However, the evaluation of LN metastasis status in the preoperative setting is not accurate. Therefore, precise preoperative prediction of LN metastasis status is crucial for optimal treatment in patients with GC.

AIM

To develop a preoperative nomogram for LN metastasis using F-18 fluorodeoxyglucose (F-18 FDG) positron emission tomography/computed tomography (PET/CT) and preoperative laboratory test findings in GC.

METHODS

In this study, the data of 566 GC patients who underwent preoperative F-18 FDG PET/CT and subsequent surgical resection were analyzed. The LN metastasis prediction model was developed in the training cohort and validated in the internal validation cohort. Routine preoperative laboratory tests, including albumin and carbohydrate antigen (CA) 19-9 were performed in all patients. Univariate and multivariable logistic regression was performed to validate the preoperative predictive indicators for LN metastasis.

RESULTS

Of the 566 patients, 232 (41%) had confirmed histopathologic LN metastasis. Univariate logistic regression revealed that the tumor location, blood hemoglobin, serum albumin levels, neutrophil to lymphocyte ratio, platelet to lymphocyte ratio, CA 19-9, maximum standardized uptake value (SUVmax) of the primary tumor (T_SUVmax), and SUVmax of LN (N_SUVmax) were significantly associated with LN metastasis. In multivariate analysis, T_SUVmax (OR = 1.08; 95%CI: 1.02–1.15; P = 0.011) and N_SUVmax (OR = 1.49; 95%CI: 1.19–1.97; P = 0.002) were found to be significant predictive factors for LN metastasis. The LN metastasis prediction model using T_SUVmax, N_SUVmax, serum albumin, and CA 19-9 yielded an area under the curve (AUC) of 0.733 (95%CI: 0.683–0.784, P = 0.025) in the training cohort and AUC of 0.756 (95%CI: 0.678–0.833, P < 0.001) in the test cohort.

CONCLUSION

T_SUVmax and N_SUVmax measured by preoperative F-18 FDG PET/CT are independent predictive factors for LN metastasis in GC.

Indication for endoscopic treatment based on the risk of lymph node metastasis in patients with undifferentiated early gastric cancer

BACKGROUND

Despite the risk of lymph node metastasis (LNM), the indications of endoscopic submucosal dissection (ESD) has expanded to undifferentiated type (UD-type) early gastric cancer (EGC). There is debate as to whether the endoscopic resection can be used. This study was conducted to evaluate risk factors for LNM in undifferentiated early gastric cancer, implications for the indication of the ESD so as to providing evidence for proper clinical management for UD-type EGC.

METHOD

We retrospectively analyzed 203 patients with UD-type EGC who underwent gastrectomy for primary gastric adenocarcinoma between 2012 and 2017. We evaluated the relationship between the clinicopathological factors and the presence of LNM using univariable and multivariable logistic regression analyses.

RESULTS

A total of 203 UD-type EGC patients were enrolled, and LNM was positive in 40 cases (19.7%). Multivariable logistic regression analysis identified three independent risk factors for LNM, the tumor size (≥2.0 cm, P < 0.001), depth of invasion (P < 0.001), and lymphatic vessel involvement (LVI, P < 0.001). LNM was observed in 5.9% patients without the three predictive factors in UD-type EGC, whereas 7.7% and 37.7% of patients with one and two risk factors had LNM, respectively. In contrast, the LNM rate was up to be 66.7% in patients with three factors. Of 41 patients satisfying the expanded indication of ESD, 3 patients (7.3%) showed LNM. LNM was not found in any of 12 patients with small intramucosal lesions (<1.0 cm) without LVI.

CONCLUSIONS

LNM-related risk factors were tumor larger than 2.0 cm, submucosal invasion, and the presence of LVI in UD-type EGC. ESD alone may be sufficient treatment for the intramucosal UD-type EGC that is smaller than 1.0 cm in size. When endoscopically resected specimens show unexpectedly larger tumor size, unexpected submucosal and LVI than that determined at pre-ESD endoscopic diagnosis, an additional gastrectomy with lymphadenectomy should be considered.

Development and validation of a nomogram for preoperative prediction of lymph node metastasis in early gastric cancer

Background

The status of lymph nodes in early gastric cancer is critical to make further clinical treatment decision, but the prediction of lymph node metastasis remains difficult before operation. This study aimed to develop a nomogram that contained preoperative factors to predict lymph node metastasis in early gastric cancer patients.

Methods

This study analyzed the clinicopathologic features of 823 early gastric cancer patients who underwent gastrectomy retrospectively, among which 596 patients were recruited in the training cohort and 227 patients in the independent validation cohort. Significant risk factors in univariate analysis were further identified to be independent variables in multivariable logistic regression analysis, which were then incorporated in and presented with a nomogram. And internal and external validation curves were plotted to evaluate the discrimination of the nomogram.

Results

Totally, six independent predictors, including the tumor size, macroscopic features, histology differentiation, P53, carbohydrate antigen 19-9, and computed tomography-reported lymph node status, were enrolled in the nomogram. Both the internal validation in the training cohort and the external validation in the validation cohort showed the nomogram had good discriminations, with a C-index of 0.82 (95%CI, 0.78 to 0.86) and 0.77 (95%CI, 0.60 to 0.94) respectively.

Conclusions

Our study developed a new nomogram which contained the most common and significant preoperative risk factors for lymph node metastasis in patients with early gastric cancer. The nomogram can identify early gastric cancer patients with the high probability of lymph node metastasis and help clinicians make more appropriate decisions in clinical practice.

Predicting lymph node metastasis in early gastric cancer patients: development and validation of a model

Aim: To develop and validate a model to predict possibility of lymph node metastasis (LNM) in early gastric cancer. Materials & methods: An LNM prediction model was developed by logistic regression based on the demographics or characteristics of the tumor (N = 746) and then internally and externally validated (N = 126). Results: Four variables, lymphovascular invasion, differentiated types, diameter of tumor and T stage were screened into the model. The area under the receiver-operating characteristic curve of the model was 0.861 (95% CI: 0.851–0.864) in internal validation and 0.911 (95% CI: 0.848–0.974) in the validation set. Conclusion: The model shows excellent discrimination and calibration performance, and is potential to be a useful clinical model to predict the risk of LNM in early gastric cancer.

Sex-specific differences in risk factors of lymph node metastasis in patients with early gastric cancer

Accurate prediction of lymph node status is of crucial importance in the appropriate treatment planning for patients with early gastric cancer (EGC). Some studies have examined factors predicting lymph node metastasis (LNM) in EGC; however, these studies did not consider sex-specific differences. This study aimed to investigate sex-specific differences in predictive risk factors of LNM in EGC based on surgical specimens. Patients who underwent surgical treatment for EGC between January 2003 and February 2016 were retrospectively evaluated. Patients who underwent previous gastric surgery or treatment for gastric neoplasms were excluded. Finally, 1076 patients treated for EGC were included in the analysis. We analyzed risk factors of LNM by dividing patients into male and female groups. Of 1076 patients (mean age 59.6 years), 69% were men. The overall LNM rate was 9.4%. The LNM rate was lower in men (7.8%) than in women (12.9%). Multivariate analysis showed that elevated type (odds ratio [OR], 2.084; 95% confidence interval [CI]: 1.053–4.125; P = 0.035), submucosal invasion (OR, 2.162; 95% CI: 1.018–4.595; P = 0.045), undifferentiated type (OR, 2.044; 95% CI: 1.107–3.772; P = 0.022), and lymphovascular invasion (LVI) (OR, 7.210; 95% CI: 3.835–13.554; P<0.001) were independent predictive risk factors of LNM in EGC in men. However, only submucosal invasion (OR, 8.772; 95% CI: 2.823–27.259; P<0.001) and LVI (OR, 8.877; 95% CI: 3.861–20.410; P<0.001) were independent predictive risk factors of LNM in EGC in women. Submucosal invasion and LVI were risk factors of LNM in both men and women. However, elevated and undifferentiated types were risk factors in men but not in women. Clinicians should consider these sex-specific differences with regard to individualized management.

A nomogram for predicting the risk of sepsis in patients with acute cholangitis

Objective

Sepsis is a serious complication of acute cholangitis. We aimed to establish a nomogram for predicting the probability of sepsis in patients with acute cholangitis.

Methods

Subjects were patients with acute cholangitis in the Medical Information Mart for Intensive Care database. Extraneous variables were excluded based on stepwise regression. The nomogram was established using logistic regression.

Results

The predictive model comprised five variables: age (odds ratio [OR]: 1.03, 95% confidence interval [CI]: 1.01–1.04), ventilator-support time (OR: 1.004, 95% CI: 1.001–1.008), diabetes (OR: 10.74, 95% CI: 2.80–70.57), coagulopathy (OR: 2.92, 95% CI: 1.83–4.73) and systolic blood pressure (OR: 0.62, 95% CI: 0.41–0.93). The areas under the receiver operating characteristic curve of the nomogram for the training and validation sets were 0.700 and 0.647, respectively. The Hosmer–Lemeshow goodness-of-fit test revealed high concordance between the predicted and observed probabilities for both the training and validation sets. The calibration plot also demonstrated good agreement between the predicted and observed outcomes for both the training and validation sets.

Conclusions

We developed and validated a risk-prediction model for sepsis in patients with acute cholangitis. Our results will be helpful for preventing sepsis in patients with acute cholangitis.

Oncologic Safety of Endoscopic Resection Based on Lymph Node Metastasis in Ulcerative Early Gastric Cancer

Background: There is little evidence regarding appropriate therapeutic modalities for ulcerative-type early gastric cancer (EGC) because the risks and implications of lymph node metastasis are unclear. The indication for endoscopic submucosal dissection (ESD) was investigated for ulcerative-type EGC. Methods: We retrospectively analyzed 192 patients with differentiated ulcerative-type EGC who underwent radical gastrectomy with D2 lymph node dissection. Lymph node metastasis (LNM) risk factors were evaluated using multivariate logistic regression. Results: The LNM rate was 15.1% overall, 0% for mucosa-confined lesions, and 28.2% for submucosa-infiltrating lesions. On multivariate analysis, only lymphovascular invasion (P < .001) was significantly associated with LNM. Among patients with minute submucosal invasion and no lymphovascular invasion, LNM was only observed for tumor sizes ≥2.1 cm. Conclusions: Because LNM risks are negligible, curative ESD could be considered in patients with ulcerative EGC that is confined to the mucosa and histologically differentiated, irrespective of tumor size. In addition, ESD can be attempted for ulcerative EGC with minute submucosal invasion and tumor size <2.1 cm.

Association of the Collagen Signature in the Tumor Microenvironment With Lymph Node Metastasis in Early Gastric Cancer

Importance

Lymph node status is the primary determinant in treatment decision making in early gastric cancer (EGC). Current evaluation methods are not adequate for estimating lymph node metastasis (LNM) in EGC.

Objective

To develop and validate a prediction model based on a fully quantitative collagen signature in the tumor microenvironment to estimate the individual risk of LNM in EGC.

Design, Setting, and Participants

This retrospective study was conducted from August 1, 2016, to May 10, 2018, at 2 medical centers in China (Nanfang Hospital and Fujian Provincial Hospital). Participants included a primary cohort (n = 232) of consecutive patients with histologically confirmed gastric cancer who underwent radical gastrectomy and received a T1 gastric cancer diagnosis from January 1, 2008, to December 31, 2012. Patients with neoadjuvant radiotherapy, chemotherapy, or chemoradiotherapy were excluded. An additional consecutive cohort (n = 143) who received the same diagnosis from January 1, 2011, to December 31, 2013, was enrolled to provide validation. Baseline clinicopathologic data of each patient were collected. Collagen features were extracted in specimens using multiphoton imaging, and the collagen signature was constructed. An LNM prediction model based on the collagen signature was developed and was internally and externally validated.

Main Outcomes and Measures

The area under the receiver operating characteristic curve (AUROC) of the prediction model and decision curve were analyzed for estimating LNM.

Results

In total, 375 patients were included. The primary cohort comprised 232 consecutive patients, in whom the LNM rate was 16.4% (n = 38; 25 men [65.8%] with a mean [SD] age of 57.82 [10.17] years). The validation cohort consisted of 143 consecutive patients, in whom the LNM rate was 20.9% (n = 30; 20 men [66.7%] with a mean [SD] age of 54.10 [13.19] years). The collagen signature was statistically significantly associated with LNM (odds ratio, 5.470; 95% CI, 3.315-9.026; P < .001). Multivariate analysis revealed that the depth of tumor invasion, tumor differentiation, and the collagen signature were independent predictors of LNM. These 3 predictors were incorporated into the new prediction model, and a nomogram was established. The model showed good discrimination in the primary cohort (AUROC, 0.955; 95% CI, 0.919-0.991) and validation cohort (AUROC, 0.938; 95% CI, 0.897-0.981). An optimal cutoff value was selected in the primary cohort, which had a sensitivity of 86.8%, a specificity of 93.3%, an accuracy of 92.2%, a positive predictive value of 71.7%, and a negative predictive value of 97.3%. The validation cohort had a sensitivity of 90.0%, a specificity of 90.3%, an accuracy of 90.2%, a positive predictive value of 71.1%, and a negative predictive value of 97.1%. Among the 375 patients, a sensitivity of 87.3%, a specificity of 92.1%, an accuracy of 91.2%, a positive predictive value of 72.1%, and a negative predictive value of 96.9% were found.

Conclusions and Relevance

This study's findings suggest that the collagen signature in the tumor microenvironment is an independent indicator of LNM in EGC, and the prediction model based on this collagen signature may be useful in treatment decision making for patients with EGC.

Nomogram for predicting level V lymph node metastases in papillary thyroid carcinoma with clinically lateral lymph node metastases: A large retrospective cohort study of 1037 patients from FDUSCC

Objective: Extensive lateral neck dissection (LND), especially routine level V dissection, may potentially increase morbidity and have no significant benefit to survival in patients with papillary thyroid carcinoma (PTC). Our study aimed to determine the pattern and risk factors for level V lymph node metastasis (LNM) and to develop an assessment model based on a nomogram for predicting whether level V dissection is necessary.

Methods: A total of 1037 PTC patients with clinically suspected LNM (N1b) who underwent total thyroidectomy (TT) with central LND and unilateral LND from 2011 to 2015 were retrospectively retrieved. Univariate and multivariate analyses were performed to examine risk factors associated with level V metastasis. A nomogram for predicting level V metastasis was established and internally validated.

Results: The overall rate of level V metastasis was 21.3% (221/1037). Unilateral central LNM (CLNM), level II/III/IV metastasis, extra nodal extension (ENE) and lymph node (LN) size ≥2 cm were found to be independent predictive factors for level V metastasis (P<0.05). In the nomogram, ENE was confirmed as the largest contributor to scores, followed by LN size, unilateral CLNM and level IV/III/II metastasis. The discrimination of the prediction model was 0.738 (95% confidence interval (CI): 0.703-0.773, P<0.001).

Conclusions: The rate of level V metastasis in N1b PTC patients was apparently lower than that of other levels. With the help of our nomogram, a modified lateral neck dissection encompassing levels II-V and a strictly postoperative evaluation may be indicated when the patient has a high score.

A Risk-Scoring Model for Predicting Lymph Node Metastasis in Early Gastric Cancer Patients: a Retrospective Study and External Validation

BACKGROUND

The possibility of lymph node metastasis (LNM) is critical to the assessment of the indication for endoscopic submucosal dissection. Thus, the aim of this study is to identify the risk factors for LNM and construct a risk-scoring model for patients with early gastric cancer to guide treatment.

METHODS

A retrospective examination of reports and studies carried out January 2000 and December 2014 was conducted. A risk-scoring model for predicting LNM was developed based on the data thus collected. In addition, the model is subject to verification and validation by three institutions.

RESULTS

Of the 1029 patients, 228 patients (22%) had LNM. Multivariate analysis showed that female, depressed type, undifferentiated type, submucosa, tumor size, and lymphovascular invasion were significantly associated with LNM. An 11-point risk-scoring model was used to predict LNM risk. An area under the receiver operating characteristic (AUROC) of the risk-scoring model was plotted using the development set and the AUROC of the model [0.76 (95% CI 0.73-0.80)] to predict LNM risk. After internal and external validation, the AUROC curve for predicting LNM was 0.77 (95% CI 0.68-0.86), 0.82 (95% CI 0.72-0.91), and 0.82 (95% CI 0.70-0.94), respectively.

CONCLUSIONS

A risk-scoring model for predicting LNM was developed and validated. It could help with personalized care for patients with EGC.

Nomogram for predicting central lymph node metastasis in papillary thyroid microcarcinoma: A retrospective cohort study of 8668 patients

INTRODUCTION

The status of central lymph node metastasis (CLNM) is crucial to determining the surgical strategies for papillary thyroid micro carcinoma (PTMC). The objective of the study was to establish a nomogram to predict the possibility of CLNM in PTMC patients.

METHODS

A total of 8668 PTMC patients who underwent total thyroidectomy or lobectomy with central lymph node dissection (CLND) from 2006 to 2016 were retrospectively retrieved. Univariate and multivariate analysis were performed to examine risk factors associated with CLNM. A nomogram for predicting CLNM was established and internally validated.

RESULTS

Six variables significantly associated with CLNM were included in our model, these are age <55 years (odd ratio [OR] = 1.779, 95% confidence interval [CI],1.569-2.017; P < 0.001), male sex (OR = 1.718, 95%CI,1.543-1.913; P < 0.001), tumor size 0.5-1.0 cm (OR = 1.985,95%CI,1.761-2.238; P < 0.001), multifocal lesions (OR = 1.263, 95%CI,1.118-1.426; P < 0.001), extra thyroidal extension (ETE) (OR = 1.348, 95%CI,1.092-1.664; P = 0.005) and lateral lymph node metastasis (LLNM) (OR = 5.135, 95%CI, 4.236-6.225; P < 0.001). The discrimination of the prediction model was 0.711 (95%CI, 0.700-0.722; P < 0.001).

CONCLUSIONS

Based on the quantified risk stratification offered by our nomogram, clinicians might have a thorough discussion with PTMC patients during the both pre- and postoperative period. Prophylactic CLND and strict postoperative evaluation may be indicated when the patients have a high nomogram score.

Construction and external validation of a nomogram that predicts lymph node metastasis in early gastric cancer patients using preoperative parameters

Objective

To create a nomogram to predict the incidence of lymph node metastasis (LNM) in early gastric cancer (EGC) patients and to externally validate the nomogram.

Methods

To construct the nomogram, we retrospectively analyzed a primary cohort of 272 EGC patients. Univariate analysis and a binary logistic regression were performed. A nomogram predicting the incidence of LNM in EGC patients was created. The discrimination ability of the nomogram was measured using the concordance index (c-index), and the nomogram was also calibrated. Then, another prospective cohort of 81 patients was analyzed to validate the nomogram.

Results

In the primary cohort, LNM was pathologically confirmed in 37 (13.6%) patients. In multivariate analysis, the presence of an ulcer, the maximum lesion diameter observed via gastroscopy, the thickness of the lesion observed via endoscopic ultrasonography, and the presence of enlarged lymph nodes on computed tomography (CT) were independent risk factors for LNM. A nomogram was then created based on the regression model with the c-index of 0.905, and the calibration curve of the nomogram fell approximately on the ideal 45-degree line. The cut-off score of the nomogram was 110, and the sensitivity, specificity, positive predictive and negative predictive values of the nomogram in the primary cohort were 81.1%, 86.0%, 47.6% and 96.7%, respectively, and in the prospective validation cohort were 75.0%, 91.0%, 60.0% and 95.5%, respectively. The calibration curve of the external validation cohort was almost on the 45-degree line.

Conclusions

We developed an effective nomogram predicting the incidence of LNM for EGC patients.

Comparing single oral contrast-enhanced ultrasonography and double contrast-enhanced ultrasonography in the preoperative Borrmann classification of advanced gastric cancer

Objectives

To evaluate the accuracy of double contrast-enhanced ultrasonography (DCEUS) in preoperative Borrmann classification of advanced gastric cancer (AGC).

Materials and Methods

A total of 162 patients histologically confirmed AGC were enrolled into this study. Single oral contrast-enhanced ultrasonography (SOCEUS) were performed in 80 patients and DCEUS (intravenous microbubbles combined with oral contrast-enhanced ultrasound) were performed in 82 patients preoperatively. The findings of the histopathologic examination of resected specimens after surgery were considered as gold standard. The accuracy of SOCEUS was compared with the accuracy of DCEUS in determining Borrmann classification. Interobserver agreement between two sonographyers of SOCEUS and DCEUS had also been assessed.

Results

The accuracy of SOCEUS and DCEUS in Borrmann classification of advanced gastric cancer were 78.75% and 91.46% respectively. There was a significant difference between two methods (χ² = 5.186, P < 0.05). The interobserver agreement of two methods was both excellent for assessing the Borrmann classification with a Kappa value of 0.777 by SOCEUS and 0.844 by DCEUS.

Conclusions

DCEUS is a valuable method for Borrmann classification with its high accuracy preoperatively. It should be used widely.

Risk factors of lymph node metastasis in 1620 early gastric carcinoma radical resections in Jiangsu Province in China: A multicenter clinicopathological study

OBJECTIVE

To investigate risk factors of lymph node metastasis (LNM) in early gastric carcinoma (EGC) in four tertiary medical centers in Jiangsu Province, China.

METHODS

Among 10 097 consecutive combined gastric cancer radical resections, 1903 EGC were identified and reviewed, 283 excluded and 1620 included in the study. All pathological and some endoscopic reports were reviewed for patients' characteristics, tumor location, gross features, and the number of lymph nodes retrieved and involved. Two pathologists independently investigated the pathological features of tumor type, differentiation, invasion depth, lymphovascular invasion (LVI), and perineural invasion. The data were statistically analyzed to identify risk factors for LNM.

RESULTS

The average number of lymph nodes retrieved was 17.5 per patient. LNM was diagnosed in 15.5%. By univariate analysis, significant risk factors for LNM included age ≥ 41 years, female sex, size over 1 cm, submucosal invasion, poor differentiation, poorly cohesive carcinoma, micropapillary adenocarcinoma, adenocarcinoma mixed with signet-ring cell carcinoma, LVI, perineural invasion, and distal gastric location. By multivariate analysis, independent risk factors for LNM were size ≥ 3 cm (odds ratio [OR] 1.9), poor differentiation (OR 2.5), adenocarcinoma mixed with signet-ring cell carcinoma (OR 1.7), LVI (OR 5.8) and submucosal invasion (OR 2.9). In contrast, size < 3 cm and ulcer were not significant risk factors. Early cardiac carcinoma (OR 0.4) had significantly lower risk.

CONCLUSIONS

Independent risk factors for LNM in EGC in Chinese patients included tumor size ≥ 3 cm, poor differentiation, submucosal invasion, adenocarcinoma mixed with signet-ring cell carcinoma and LVI. Early cardiac carcinoma had a significantly lower risk for LNM.

The diagnostic value of white blood cell, neutrophil, neutrophil-to-lymphocyte ratio, and lymphocyte-to-monocyte ratio in patients with primary angle closure glaucoma

Objective

Inflammatory mechanisms may have a role in the pathogenesis of primary angle closure glaucoma (PACG). The objective of this study was to investigate the diagnostic value of white blood cell (WBC), neutrophil, neutrophil to lymphocyte ratio (NLR), and lymphocyte to monocyte ratio (LMR) in patients with PACG and its association with glaucoma severity.

Method

The study was retrospectively assessed in 771 consecutive PACG patients and 770 control subjects, laboratory parameters and clinical parameters were obtained from a medical data platform. Patients were divided into three groups with different severity based on perimetry, i.e. mild (mean deviation (MD) ≤6.00 dB), moderate (12 dB≥ MD>6 dB) and severe (MD>12 dB). We developed a nomogram to specifically identify individual patient’s risk.

Results

The mean levels of neutrophil, NLR and WBC was higher in PACG than control group, and lowest in the mild PACG group, followed by moderate PACG and severe PACG (p<0.05). The AUROC value of NLR and LMR was found to be 0.719, 0.699, respectively. Multiple linear regressions showed that there was a significant correlation between WBC and MD (B=0.151, p<0.001), neutrophil and MD (B=0.143, p=0.003), NLR and MD (B=0.144, p=0.001), LMR and MD (B=-0.100, p=0.034). Logistic regression analyses revealed that WBC (OR=1.208, 95%CI=1.179-1.238), neutrophil (OR=1.598, 95%CI=1.541-1.656), NLR (OR=2.313, 95%CI=2.200-2.431), and LMR (OR=0.682, 95%CI=0.666-0.699) were associated with PACG.

Conclusion

Our study suggested that WBC, neutrophil, NLR, and LMR was related with PACG, and NLR and LMR may be useful as biomarkers.