Risk factors and a predictive nomogram for lymph node metastasis of superficial esophagogastric junction cancer

Development and Validation of a Prognostic Molecular Phenotype and Clinical Characterization in Grade III Diffuse Gliomas Treatment with Radio-Chemotherapy

Background

The relationship between molecular phenotype and prognosis in high-grade gliomas (WHO III and IV, HGG) treated with radiotherapy and chemotherapy is not fully understood and needs further exploration.

Methods

The HGG patients following surgery and treatment with radiotherapy and chemotherapy. Univariate and multivariate Cox analyses were used to assess the independent prognostic factors. The nomogram model was established, and its accuracy was determined via the calibration plots.

Results

A total of 215 and 88 patients had grade III glioma and grade IV glioma, respectively. Grade III oligodendroglioma (OG-G3) patients had the longest mPFS and mOS than other grade III pathology, while grade III astrocytoma (AA-G3) patients were close to IDH-1 wildtype glioblastoma (GBM) and had a poor prognosis. The IDH-1 mutant group had a better mPFS and mOS than the IDH-1 wildtype group in all grade III patients, OG-G3 and AA-G3 patients. Furthermore, 1p/19q co-deletion group had a longer mPFS and mOS than 1p/19q non-deletion group in all grade III patients. IDH-1 mutation and 1p/19q co-deletion patients had the best prognosis than other molecular types. Also, the MGMT methylation and IDH-1 mutation or 1p/19q co-deletion group had a longer mPFS and mOS than the MGMT unmethylation and IDH-1 wildtype or 1p/19q non-codeletion of grade III patients. In addition, the low Ki-67 expression group had a better prognosis than high Ki-67 expression group in grade III patients. Univariate and multivariate COX showed that 1p/19q co-deletion and MGMT methylation were the independent prognostic factors for mPFS and mOS. The calibration curve showed that the established nomogram could well predict the survival based on these covariates.

Conclusion

The AA-G3 with IDH-1 wildtype, MGMT unmethylation or 1p/19q non-codeletion patients was resistant to radiotherapy and chemotherapy, has a poor prognosis and needs a more active treatment.

The development and validation of automated machine learning models for predicting lymph node metastasis in Siewert type II T1 adenocarcinoma of the esophagogastric junction

Background

Lymph node metastasis (LNM) is considered an essential prognosis factor for adenocarcinoma of the esophagogastric junction (AEG), which also affects the treatment strategies of AEG. We aimed to evaluate automated machine learning (AutoML) algorithms for predicting LNM in Siewert type II T1 AEG.

Methods

A total of 878 patients with Siewert type II T1 AEG were selected from the Surveillance, Epidemiology, and End Results (SEER) database to develop the LNM predictive models. The patients from two hospitals in Suzhou were collected as the test set. We applied five machine learning algorithms to develop the LNM prediction models. The performance of predictive models was assessed using various metrics including accuracy, sensitivity, specificity, the area under the curve (AUC), and receiver operating characteristic (ROC) curve.

Results

Patients with LNM exhibited a higher proportion of male individuals, a poor degree of differentiation, and submucosal infiltration, with statistical differences. The deep learning (DL) model demonstrated relatively good accuracy (0.713) and sensitivity (0.868) among the five models. Moreover, the DL model achieved the highest AUC (0.781) and sensitivity (1.000) in the test set.

Conclusion

The DL model showed good predictive performance among five AutoML models, indicating the advantage of AutoML in modeling LNM prediction in patients with Siewert type II T1 AEG.

A Nomogram Based on Nutrition-Related Indicators and Computed Tomography Imaging Features for Predicting Preoperative Lymph Node Metastasis in Curatively Resected Esophagogastric Junction Adenocarcinoma

BACKGROUNDS

Preoperative noninvasive tools to predict pretreatment lymph node metastasis (PLNM) status accurately for esophagogastric junction adenocarcinoma (EJA) are few. Thus, the authors aimed to construct a nomogram for predicting PLNM in curatively resected EJA.

METHODS

This study enrolled 638 EJA patients who received curative surgery resection and divided them randomly (7:3) into training and validation groups. For nomogram construction, 26 candidate parameters involving 21 preoperative clinical laboratory blood nutrition-related indicators, computed tomography (CT)-reported tumor size, CT-reported PLNM, gender, age, and body mass index were screened.

RESULTS

In the training group, Lasso regression included nine nutrition-related blood indicators in the PLNM-prediction nomogram. The PLNM prediction nomogram yielded an area under the receiver operating characteristic (ROC) curve of 0.741 (95 % confidence interval [CI], 0.697-0.781), which was better than that of the CT-reported PLNM (0.635; 95% CI 0.588-0.680; p < 0.0001). Application of the nomogram in the validation cohort still gave good discrimination (0.725 [95% CI 0.658-0.785] vs 0.634 [95% CI 0.563-0.700]; p = 0.0042). Good calibration and a net benefit were observed in both groups.

CONCLUSIONS

This study presented a nomogram incorporating preoperative nutrition-related blood indicators and CT imaging features that might be used as a convenient tool to facilitate the preoperative individualized prediction of PLNM for patients with curatively resected EJA.

D-dimer to lymphocyte ratio can serve as a potential predictive and prognostic value in colorectal cancer patients with liver metastases

BACKGROUND

The intent of this research was to generate and investigate the D-dimer to lymphocyte ratio (DLR) capacity to forecast the risk and prognosis of colorectal cancer liver metastases (CRCLM).

METHODS

From January 2010 to December 2019, 177 clinicopathologically confirmed colorectal cancer (CRC) patients (89 in the control group and 88 in the experimental group) were identified at the Affiliated Cancer Hospital of Guangxi Medical University. Multivariate Cox regression analysis was used to screen independent predictive diagnostic and prognostic factors of liver metastasis in CRC, and receiver operating characteristic (ROC) curves and Kaplan‒Meier (K‒M) curves were established to analyze the diagnostic and predictive prognostic efficacy of the DLR in the development of CRCLM.

RESULTS

Patients with CRCLM had higher DLR levels and D-dimer levels in their blood, with statistically significant differences (p < 0.001). DLR might be employed as a predictor for the development of CRCLM, according to ROC curve research (sensitivity 0.670, specificity 0.775, area under the curve 0.765). D-dimer, lymphocyte count CEA, CA125, and CA199 were not linked to prognosis in patients with CRCLM in Cox regression analysis of dichotomous variables. In contrast, DLR level was a possible risk factor for the prognosis of patients with CRCLM (HR = 2.108, p = 0.047), and age, T stage, and DLR level (DLR < 0.4) were connected with the prognosis of patients with CRCLM (p < 0.05).

CONCLUSION

DLR serves as a risk indicator for the development of CRCLM.

Based on clinical Ki-67 expression and serum infiltrating lymphocytes related nomogram for predicting the diagnosis of glioma-grading

Background

Compelling evidence indicates that elevated peripheral serum lymphocytes are associated with a favorable prognosis in various cancers. However, the association between serum lymphocytes and glioma is contradictory. In this study, a nomogram was established to predict the diagnosis of glioma-grading through Ki-67 expression and serum lymphocytes.

Methods

We performed a retrospective analysis of 239 patients diagnosed with LGG and 178 patients with HGG. Immunohistochemistry was used to determine the Ki-67 expression. Following multivariate logistic regression analysis, a nomogram was established and used to identify the most related factors associated with HGG. The consistency index (C-index), decision curve analysis (DCA), and a calibration curve were used to validate the model.

Results

The number of LGG patients with more IDH1/2 mutations and 1p19q co-deletion was greater than that of HGG patients. The multivariate logistic analysis identified Ki-67 expression, serum lymphocyte count, and serum albumin (ALU) as independent risk factors associated with HGG, and these factors were included in a nomogram in the training cohort. In the validation cohort, the nomogram demonstrated good calibration and high consistency (C-index = 0.794). The Spearman correlation analysis revealed a significant association between HGG and serum lymphocyte count (r = −0.238, P &lt;0.001), ALU (r = −0.232, P &lt;0.001), and Ki-67 expression (r = 0.457, P &lt;0.001). Furthermore, the Ki-67 expression was negatively correlated with the serum lymphocyte count (r = −0.244, P &lt;0.05). LGG patients had lower Ki-67 expression and higher serum lymphocytes compared with HGG patients, and a combination of these two variables was significantly higher in HGG patients.

Conclusion

The constructed nomogram is capable of predicting the diagnosis of glioma-grade. A decrease in the level of serum lymphocyte count and increased Ki-67 expression in HGG patients indicate that their immunological function is diminished and the tumor is more aggressive.

Laparoscopic-assisted vs open transhiatal gastrectomy for Siewert type II adenocarcinoma of the esophagogastric junction: A retrospective cohort study

BACKGROUND

The studies of laparoscopic-assisted transhiatal gastrectomy (LTG) in patients with Siewert type II adenocarcinoma of the esophagogastric junction (AEG) are scarce.

AIM

To compare the surgical efficiency of LTG with the open transhiatal gastrectomy (OTG) for patients with Siewert type II AEG.

METHODS

We retrospectively evaluated a total of 578 patients with Siewert type II AEG who have undergone LTG or OTG at the First Medical Center of the Chinese People’s Liberation Army General Hospital from January 2014 to December 2019. The short-term and long-term outcomes were compared between the LTG (n = 382) and OTG (n = 196) groups.

RESULTS

Compared with the OTG group, the LTG group had a longer operative time but less blood loss, shorter length of abdominal incision and an increased number of harvested lymph nodes (P < 0.05). Patients in the LTG group were able to eat liquid food, ambulate, expel flatus and discharge sooner than the OTG group (P < 0.05). No significant differences were found in postoperative complications and R0 resection. The 3-year overall survival and disease-free survival performed better in the LTG group compared with that in the OTG group (88.2% vs 79.2%, P = 0.011; 79.7% vs 73.0%, P = 0.002, respectively). In the stratified analysis, both overall survival and disease-free survival were better in the LTG group than those in the OTG group for stage II/III patients (P < 0.05) but not for stage I patients.

CONCLUSION

For patients with Siewert type II AEG, LTG is associated with better short-term outcomes and similar oncology safety. In addition, patients with advanced stage AEG may benefit more from LTG in the long-term outcomes.

Clinical Risk Prediction Model for Neoadjuvant Therapy in Resectable Esophageal Adenocarcinoma

GOALS AND BACKGROUND

Clinical staging with endoscopic ultrasound (EUS) and positron emission tomography (PET) is used to identify esophageal adenocarcinoma (EAC) patients with locally advanced disease and therefore, benefit from neoadjuvant therapy. However, EUS is operator dependent and subject to interobserver variability. Therefore, we aimed to identify clinical predictors of locally advanced EAC and build a predictive model that can be used as an adjunct to current staging methods.

STUDY

This was a cross-sectional study of patients with EAC who underwent preoperative staging with EUS and PET scan followed by definitive therapy at our institution from January 2011 to December 2017. Demographic data, symptoms, endoscopic findings, EUS, and PET scan findings were obtained.

RESULTS

Four hundred and twenty-six patients met the study criteria, of which 86 (20.2%) patients had limited stage EAC and 340 (79.8%) had locally advanced disease. The mean age was 65.4±10.3 years of which 356 (83.6%) were men and 393 (92.3%) were White. On multivariable analysis, age (above 75 or below 65 y), dysphagia [odds ratio (OR): 2.84], weight loss (OR: 2.06), protruding tumor (OR: 2.99), and tumor size >2 cm (OR: 3.3) were predictive of locally advanced disease, while gastrointestinal bleeding (OR: 0.36) and presence of visible Barrett's esophagus (OR: 0.4) were more likely to be associated with limited stage. A nomogram for predicting the risk of locally advanced EAC was constructed and internally validated.

CONCLUSIONS

We constructed a nomogram to facilitate an individualized prediction of the risk of locally advanced EAC. This model can aid in decision making for neoadjuvant therapy in EAC.

A Population-Based Study: How to Identify High-Risk T1-2 Esophageal Cancer Patients?

Background

Due to individualized conditions of lymph node metastasis (LNM) and distant metastasis (DM), the following therapeutic strategy and diagnosis of T1-2 esophageal cancer (ESCA) patients are varied. A prediction model for identifying risk factors for LNM, DM, and overall survival (OS) of high-risk T1-2 ESCA patients is of great significance to clinical practice.

Methods

A total of 1,747 T1-2 ESCA patients screened from the surveillance, epidemiology, and end results (SEER) database were retrospectively analyzed for their clinical data. Univariate and multivariate logistic regression models were established to screen out risk factors for LNM and DM of T1-2 ESCA patients, while those of OS were screened out using the Cox regression analysis. The identified risk factors for LNM, DM, and OS were then subjected to the establishment of three nomograms, respectively. The accuracy of the nomograms was evaluated by depicting the calibration curve, and the predictive value and clinical utility were evaluated by depicting the clinical impact curve (CIC) and decision curve analysis (DCA), respectively.

Results

The age, race, tumor grade, tumor size, and T-stage were significant factors for predicting LNM of T1-2 ESCA patients (p < 0.05). The age, T-stage, tumor grade, and tumor size were significant factors for predicting DM of T1-2 ESCA patients (p < 0.05). The age, race, sex, histology, primary tumor site, tumor size, N-stage, M-stage, and surgery were significant factors for predicting OS of T1-2 ESCA patients (p < 0.05). The C-indexes of the three nomograms constructed by these factors were 0.737, 0.764, and 0.740, respectively, suggesting that they were clinically effective.

Conclusions

The newly constructed nomograms can objectively and accurately predict the LNM, DM, and OS of T1-2 ESCA patients, which contribute to the individualized decision making before clinical management.

Endoscopic Versus Surgical Therapy for Early Esophagogastric Junction Adenocarcinoma Based on Lymph Node Metastasis Risk: A Population-Based Analysis

Background

In this study, we aimed to compare the prognosis and lymph node metastasis (LNM) risk in patients with early-stage esophagogastric junction (EGJ) adenocarcinoma after endoscopic treatment (ET) or radical surgery.

Methods

We collected data from eligible patients based on the Surveillance, Epidemiology, and End Results (SEER) database between 2004 and 2016. Logistic regression analysis was used to determine independent predictors of LNM (examination of at least 16 lymph nodes). Cox regression analysis and propensity score-matched (PSM) analysis were subsequently utilized to compare the overall survival (OS) and cancer-specific survival (CSS) of patients treated with ET or radical surgery.

Results

In total, 3708 patients were identified. Among them, 856 patients had greater than or equal to 16 examined lymph nodes (LNs) (LNE≥16). The LNM rates were 18.8% in all patients 8.3% in T1a patients and 24.6% in T1b patients. Independent predictors of LNM were submucosal invasion, tumor size ≥3cm and decreasing differentiation (P<0.05). The LNM rate decreased to approximately 5.3% in T1b tumors with well differentiation and tumor size <3cm. However, the LNM incidence increased to 17.9% or 33.3% in T1a tumors with poor differentiation or with both tumor size≥3cm and poor differentiation. Cox regression analysis demonstrated CSS was not significantly different in early-stage EGJ adenocarcinoma patients undergoing ET and those treated with radical surgery (HR= 1.004, P=0.974), which were robustly validated after PSM analysis. Moreover, subgroup analysis stratified by T1a and T1b showed similar results.

Conclusions

The findings of this study indicated ET as an alternative to radical surgery in early EGJ adenocarcinoma.

Preoperative Predictors of Lymph Node Metastasis in Colon Cancer

Background

Lymph node metastasis (LNM) is a well-established prognostic factor for colon cancer. Preoperative LNM evaluation is relevant for planning colon cancer treatment. The aim of this study was to construct and evaluate a nomogram for predicting LNM in primary colon cancer according to pathological features.

Patients and Methods

Six-hundred patients with clinicopathologically confirmed colon cancer (481 cases in the training set and 119 cases in the validation set) were enrolled in the Affiliated Cancer Hospital of Guangxi Medical University from January 2010 to December 2019. The expression of molecular markers (p53 and β-catenin) was determined by immunohistochemistry. Multivariate logistic regression was used to screen out independent risk factors, and a nomogram was established. The accuracy and discriminability of the nomogram were evaluated by consistency index and calibration curve.

Results

Univariate logistic analysis revealed that LNM in colon cancer is significantly correlated (P <0.05) with tumor size, grading, stage, preoperative carcinoembryonic antigen (CEA) level, and peripheral nerve infiltration (PNI). Multivariate logistic regression analysis confirmed that CEA, grading, and PNI were independent prognostic factors of LNM (P <0.05). The nomogram for predicting LNM risk showed acceptable consistency and calibration capability in the training and validation sets.

Conclusions

Preoperative CEA level, grading, and PNI were independent risk factor for LNM. Based on the present parameters, the constructed prediction model of LNM has potential application value.

A Nomogram for Predicting Risk of Thromboembolism in Gastric Cancer Patients Receiving Chemotherapy

Purpose: The aims of this study were to develop and validate a novel nomogram to predict thromboembolism (TE) in gastric cancer (GC) patients receiving chemotherapy and to test its predictive ability.

Methods: This retrospective study included 544 GC patients who received chemotherapy as the initial treatment at two medical centers. Among the 544 GC patients who received chemotherapy, 275 and 137 patients in the First Affiliated Hospital of Nanchang University from January 2014 to March 2019 were enrolled in the training cohort and the validation cohort, respectively. A total of 132 patients in the Beilun branch of the First Affiliated Hospital of Zhejiang University from January 2015 to August 2019 were enrolled in external validation cohorts. The nomogram was based on parameters determined by univariate and multivariate logistic analyses. The prediction performance of the nomogram was measured by the area under the receiver operating characteristic curve (AUROC), the calibration curve, and decision curve analysis (DCA). The applicability of the nomogram was internally and independently validated.

Results: The predictors included the Eastern Cooperative Oncology Group Performance Status (ECOG), presence of an active cancer (AC), central venous catheter (CVC), and D-dimer levels. These risk factors are shown on the nomogram and verified. The nomogram demonstrated good discrimination and fine calibration with an AUROC of 0.875 (0.832 in internal validation and 0.807 in independent validation). The DCA revealed that the nomogram had a high clinical application value.

Conclusions: We propose the nomogram for predicting TE in patients with GC receiving chemotherapy, which can help in making timely personalized clinical decisions for different risk populations.

A predictive nomogram for lymph node metastasis of incidental gallbladder cancer: a SEER population-based study

BACKGROUND

Existing imaging techniques have a low ability to detect lymph node metastasis (LNM) of gallbladder cancer (GBC). Gallbladder removal by laparoscopic cholecystectomy can provide pathological information regarding the tumor itself for incidental gallbladder cancer (IGBC). The purpose of this study was to identify the risk factors associated with LNM of IGBC and to establish a nomogram to improve the ability to predict the risk of LNM for IGBC.

METHODS

A total of 796 patients diagnosed with stage T1/2 GBC between 2004 and 2015 who underwent surgery and lymph node evaluation were enrolled in this study. We randomly divided the dataset into a training set (70%) and a validation set (30%). A logistic regression model was used to construct the nomogram in the training set and then was verified in the validation set. Nomogram performance was quantified with respect to discrimination and calibration.

RESULTS

The rates of LNM in T1a, T1b and T2 patients were 7, 11.1 and 44.3%, respectively. Tumor diameter, T stage, and tumor differentiation were independent factors affecting LNM. The C-index and AUC of the training set were 0.718 (95% CI, 0.676-0.760) and 0.702 (95% CI, 0.659-0.702), respectively, demonstrating good prediction performance. The calibration curves showed perfect agreement between the nomogram predictions and actual observations. Decision curve analysis showed that the LNM nomogram was clinically useful when the risk was decided at a possibility threshold of 2-63%. The C-index and AUC of the validation set were 0.73 (95% CI: 0.665-0.795) and 0.692 (95% CI: 0.625-0.759), respectively.

CONCLUSION

The nomogram established in this study has good prediction ability. For patients with IGBC requiring re-resection, the model can effectively predict the risk of LNM and make up for the inaccuracy of imaging.