An interactive Bayesian model for prediction of lymph node ratio and survival in pancreatic cancer patients

The Prognostic Significance of Lymph Node Ratio for Esophageal Cancer: A Meta-Analysis

INTRODUCTION

This meta-analysis aimed to investigate the prognostic significance of positive lymph node ratio (LNR) in patients with esophageal cancer.

MATERIALS AND METHODS

The meta-analysis following Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. We conducted a systematic search of relevant literature published until April 2022 in PubMed, EMBASE, and the Cochrane Library. The primary and secondary outcomes were overall survival (OS) and disease-free survival (DFS), with corresponding hazard ratios (HR) and 95% confidence intervals (CI). The included studies were subgrouped based on age, study area, adjuvant therapy, sensitivity analysis, and assessment of publication bias. We analyzed and discussed the results.

RESULTS

We included 21 studies with 29 cohorts and 11,849 patients. The Newcastle-Ottawa Scale scores of the included studies were no less than six, indicating high research quality. The combined results of HR and 95% CI showed that patients with esophageal cancer with a lower LNR had better OS (HR, 2.58; 95% CI, 2.15-3.11; P < 0.001) and DFS (HR, 3.07; 95% CI, 1.85-5.10; P < 0.001). The subgroup analysis suggested that geographic region, age, and adjuvant therapy affected OS. When any cohort was excluded, no significant changes were observed in the pooled HR of the OS group, indicating reliable and robust results. Egger's and Begg's tests showed no potential publication bias in the studies that used OS as an outcome measurement index, indicating reliable results. Sensitivity analyses and assessments of publication bias (<10) were not performed because of an insufficient number of DFS studies.

CONCLUSION

Patients with a lower positive LNR had a higher survival rate, suggesting that positive LNR may be a promising predictor of EC prognosis in esophageal cancer. After radical resection of esophageal cancer, the ratio of the number of dissected lymph nodes to the number of positive lymph nodes in patients with esophageal cancer should be considered to accurately evaluate the prognosis.

A population-based nomogram to individualize treatment modality for pancreatic cancer patients underlying surgery

As the most aggressive tumor, TNM staging does not accurately identify patients with pancreatic cancer who are sensitive to therapy. This study aimed to identify associated risk factors and develop a nomogram to predict survival in pancreatic cancer surgery patients and to select the most appropriate comprehensive treatment regimen. First, the survival difference between radiotherapy and no radiotherapy was calculated based on propensity score matching (PSM). Cox regression was conducted to select the predictors of overall survival (OS). The model was constructed using seven variables: histologic type, grade, T stage, N stage, stage, chemotherapy and radiotherapy. All patients were classified into high- or low-risk groups based on the nomogram. The nomogram model for OS was established and showed good calibration and acceptable discrimination (C-index 0.721). Receiver operating characteristic curve (ROC) and DCA curves showed that nomograms had better predictive performance than TNM stage. Patients were divided into low-risk and high-risk groups according to nomogram scores. Radiotherapy is recommended for high-risk patients but not for low-risk patients. We have established a well-performing nomogram to effectively predict the prognosis of pancreatic cancer patients underlying surgery. The web version of the nomogram https://rockeric.shinyapps.io/DynNomapp/ may contribute to treatment optimization in clinical practice.

Application of artificial intelligence to pancreatic adenocarcinoma

BACKGROUND AND OBJECTIVES

Pancreatic cancer (PC) is one of the deadliest cancers worldwide although substantial advancement has been made in its comprehensive treatment. The development of artificial intelligence (AI) technology has allowed its clinical applications to expand remarkably in recent years. Diverse methods and algorithms are employed by AI to extrapolate new data from clinical records to aid in the treatment of PC. In this review, we will summarize AI's use in several aspects of PC diagnosis and therapy, as well as its limits and potential future research avenues.

METHODS

We examine the most recent research on the use of AI in PC. The articles are categorized and examined according to the medical task of their algorithm. Two search engines, PubMed and Google Scholar, were used to screen the articles.

RESULTS

Overall, 66 papers published in 2001 and after were selected. Of the four medical tasks (risk assessment, diagnosis, treatment, and prognosis prediction), diagnosis was the most frequently researched, and retrospective single-center studies were the most prevalent. We found that the different medical tasks and algorithms included in the reviewed studies caused the performance of their models to vary greatly. Deep learning algorithms, on the other hand, produced excellent results in all of the subdivisions studied.

CONCLUSIONS

AI is a promising tool for helping PC patients and may contribute to improved patient outcomes. The integration of humans and AI in clinical medicine is still in its infancy and requires the in-depth cooperation of multidisciplinary personnel.

Prognosticating Outcome in Pancreatic Head Cancer With the use of a Machine Learning Algorithm

Background: The purpose of this project is to identify prognostic features in resectable pancreatic head adenocarcinoma and use these features to develop a machine learning algorithm that prognosticates survival for patients pursuing pancreaticoduodenectomy. Methods: A retrospective cohort study of 93 patients who underwent a pancreaticoduodenectomy was performed. The patients were analyzed in 2 groups: Group 1 (n = 38) comprised of patients who survived < 2 years, and Group 2 (n = 55) comprised of patients who survived > 2 years. After comparing the two groups, 9 categorical features and 2 continuous features (11 total) were selected to be statistically significant (p < .05) in predicting outcome after surgery. These 11 features were used to train a machine learning algorithm that prognosticates survival. Results: The algorithm obtained 75% accuracy, 41.9% sensitivity, and 97.5% specificity in predicting whether survival is less than 2 years after surgery. Conclusion: A supervised machine learning algorithm that prognosticates survival can be a useful tool to personalize treatment plans for patients with pancreatic cancer.

SOURCE-PANC: A Prediction Model for Patients With Metastatic Pancreatic Ductal Adenocarcinoma Based on Nationwide Population-Based Data

BACKGROUND

A prediction model for overall survival (OS) in metastatic pancreatic ductal adenocarcinoma (PDAC) including patient and treatment characteristics is currently not available, but it could be valuable for supporting clinicians in patient communication about expectations and prognosis. We aimed to develop a prediction model for OS in metastatic PDAC, called SOURCE-PANC, based on nationwide population-based data.

MATERIALS AND METHODS

Data on patients diagnosed with synchronous metastatic PDAC in 2015 through 2018 were retrieved from the Netherlands Cancer Registry. A multivariate Cox regression model was created to predict OS for various treatment strategies. Available patient, tumor, and treatment characteristics were used to compose the model. Treatment strategies were categorized as systemic treatment (subdivided into FOLFIRINOX, gemcitabine/nab-paclitaxel, and gemcitabine monotherapy), biliary drainage, and best supportive care only. Validation was performed according to a temporal internal-external cross-validation scheme. The predictive quality was assessed with the C-index and calibration.

RESULTS

Data for 4,739 patients were included in the model. Sixteen predictors were included: age, sex, performance status, laboratory values (albumin, bilirubin, CA19-9, lactate dehydrogenase), clinical tumor and nodal stage, tumor sublocation, presence of distant lymph node metastases, liver or peritoneal metastases, number of metastatic sites, and treatment strategy. The model demonstrated a C-index of 0.72 in the internal-external cross-validation and showed good calibration, with the intercept and slope 95% confidence intervals including the ideal values of 0 and 1, respectively.

CONCLUSIONS

A population-based prediction model for OS was developed for patients with metastatic PDAC and showed good performance. The predictors that were included in the model comprised both baseline patient and tumor characteristics and type of treatment. SOURCE-PANC will be incorporated in an electronic decision support tool to support shared decision-making in clinical practice.

Defining a minimum number of examined lymph nodes improves the prognostic value of lymphadenectomy in pancreas ductal adenocarcinoma

BACKGROUND

Lymph node (LN) metastasis is associated with decreased survival following resection for pancreatic ductal adenocarcinoma (PDAC). In N0 disease, increasing total evaluated LN (ELN) correlates with improved outcomes suggesting patients may be understaged when LNs are undersampled. We aim to assess the optimal number of examined lymph nodes (ELN) following pancreatectomy.

METHODS

Data from 1837 patients undergoing surgery were prospectively collected. The binomial probability law was utilized to analyze the minimum number of examined LNs (minELN) and accurately characterize each histopathologic stage. LN ratio (LNR) was compared to American Joint Committee on Cancer (AJCC) guidelines.

RESULTS

As ELN total increased, the likelihood of finding node positive disease increased. An evaluation based upon the binomial probability law suggested an optimal minELN of 12 for accurate AJCC N staging. As the number of ELNs increased, the discriminatory capacity of alternative strategies to characterize LN disease exceeded that offered by AJCC N stage.

CONCLUSION

This is the first study dedicated to optimizing histopathologic staging in PDAC using models of minELN informed by the binomial probability law. This study highlights two separate cutoffs for ELNs depending upon prognostic goal and validates that 12 LNs are adequate to determine AJCC N stage for the majority of patients.

Score for the Overall Survival Probability of Patients With Pancreatic Adenocarcinoma of the Body and Tail After Surgery: A Novel Nomogram-Based Risk Assessment

Pancreatic adenocarcinoma of the body and tail often has a dismal prognosis and lacks a specific prognostic stage. The aim of this study was to construct a nomogram for predicting survival of patients with pancreatic adenocarcinoma of the body and tail after surgery. Data of patients were selected from the Surveillance, Epidemiology, and End Results (SEER) database and from medical records of Sun Yat-sen University Cancer Center (SYSUCC). In a multivariate analysis for overall survival (OS), the following six variables were identified as independent predictors and incorporated into the nomogram: age, tumor differentiation, tumor size, lymph node ratio (LNR), and chemotherapy. A nomogram was built based on independent risk predictors. The concordance index (C-index) for nomogram, Tumor-Node-Metastasis (TNM) 7th and 8th stage system were 0.775 [95% confidence interval (CI), 0.731–0.819], 0.617 (95%CI, 0.575–0.659), and 0.632 (95%CI, 0.588–0.676), respectively. The calibrated nomogram predicted survival rates which closely corresponded to the actual survival rates. Furthermore, the values of the area under receiver operating characteristic (ROC) curves (AUC) of the nomograms were higher than those of the TNM 7th or 8th stage system in predicting 1-, 2-, and 3-year survival of patients in training and external validation cohorts. The well-calibrated nomogram could be used to predict prognosis for patients with pancreatic adenocarcinoma of the body and tail after surgery.

Resectable pancreatic ductal adenocarcinoma: association between preoperative CT texture features and metastatic nodal involvement

Background

To explore the relationship between the lymph node status and preoperative computed tomography images texture features in pancreatic cancer.

Methods

A total of 155 operable pancreatic cancer patients (104 men, 51 women; mean age 63.8 ± 9.6 years), who had undergone contrast-enhanced computed tomography in the arterial and portal venous phases, were enrolled in this retrospective study. There were 73 patients with lymph node metastases and 82 patients without nodal involvement. Four different data sets, with thin (1.25 mm) and thick (5 mm) slices (at arterial phase and portal venous phase) were analysed. Texture analysis was performed by using MaZda software. A combination of feature selection algorithms was used to determine 30 texture features with the optimal discriminative performance for differentiation between lymph node positive and negative groups. The prediction performance of the selected feature was evaluated by receiver operating characteristic (ROC) curve analysis.

Results

There were 10 texture features with significant differences between two groups and significance in ROC analysis were identified. They were WavEnLH_s-2(wavelet energy with rows and columns are filtered with low pass and high pass frequency bands with scale factors 2) from wavelet-based features, 135dr_LngREmph (long run emphasis in 135 direction) and 135dr_Fraction (fraction of image in runs in 135 direction) from run length matrix-based features, and seven variables of sum average from coocurrence matrix-based features (SumAverg). The ideal cutoff value for predicting lymph node metastases was 270 for WavEnLH_s-2 (positive likelihood ratio 2.08). In addition, 135dr_LngREmph and 135dr_Fraction were correlated with the ratio of metastatic to examined lymph nodes.

Conclusions

Preoperative computed tomography high order texture features provide a useful imaging signature for the prediction of nodal involvement in pancreatic cancer.

Bayesian Inference of Lymph Node Ratio Estimation and Survival Prognosis for Breast Cancer Patients

OBJECTIVE

We evaluated the prognostic value of lymph node ratio (LNR) for the survival of breast cancer patients using Bayesian inference.

METHODS

Data on 5,279 women with infiltrating duct and lobular carcinoma breast cancer, diagnosed from 2006-2010, was obtained from the NCI SEER Cancer Registry. A prognostic modeling framework was proposed using Bayesian inference to estimate the impact of LNR in breast cancer survival. Based on the proposed model, we then developed a web application for estimating LNR and predicting overall survival.

RESULTS

The final survival model with LNR outperformed the other models considered (C-statistic 0.71). Compared to directly measured LNR, estimated LNR slightly increased the accuracy of the prognostic model. Model diagnostics and predictive performance confirmed the effectiveness of Bayesian modeling and the prognostic value of the LNR in predicting breast cancer survival.

CONCLUSION

The estimated LNR was found to have a significant predictive value for the overall survival of breast cancer patients.

SIGNIFICANCE

We used Bayesian inference to estimate LNR which was then used to predict overall survival. The models were developed from a large population-based cancer registry. We also built a user-friendly web application for individual patient survival prognosis. The diagnostic value of the LNR and the effectiveness of the proposed model were evaluated by comparisons with existing prediction models.

A prognostic Bayesian network that makes personalized predictions of poor prognostic outcome post resection of pancreatic ductal adenocarcinoma

BACKGROUND

The narrative surrounding the management of potentially resectable pancreatic cancer is complex. Surgical resection is the only potentially curative treatment. However resection rates are low, the risk of operative morbidity and mortality are high, and survival outcomes remain poor. The aim of this study was to create a prognostic Bayesian network that pre-operatively makes personalized predictions of post-resection survival time of 12months or less and also performs post-operative prognostic updating.

METHODS

A Bayesian network was created by synthesizing data from PubMed post-resection survival analysis studies through a two-stage weighting process. Input variables included: inflammatory markers, tumour factors, tumour markers, patient factors and, if applicable, response to neoadjuvant treatment for pre-operative predictions. Prognostic updating was performed by inclusion of post-operative input variables including: pathology results and adjuvant therapy.

RESULTS

77 studies (n = 31,214) were used to create the Bayesian network, which was validated against a prospectively maintained tertiary referral centre database (n = 387). For pre-operative predictions an Area Under the Curve (AUC) of 0.7 (P value: 0.001; 95% CI 0.589-0.801) was achieved accepting up to 4 missing data-points in the dataset. For prognostic updating an AUC 0.8 (P value: 0.000; 95% CI:0.710-0.870) was achieved when validated against a dataset with up to 6 missing pre-operative, and 0 missing post-operative data-points. This dropped to AUC: 0.7 (P value: 0.000; 95% CI:0.667-0.818) when the post-operative validation dataset had up to 2 missing data-points.

CONCLUSION

This Bayesian network is currently unique in the way it utilizes PubMed and patient level data to translate the existing empirical evidence surrounding potentially resectable pancreatic cancer to make personalized prognostic predictions. We believe such a tool is vital in facilitating better shared decision-making in clinical practice and could be further developed to offer a vehicle for delivering personalized precision medicine in the future.

A systematic review of methodological quality of model development studies predicting prognostic outcome for resectable pancreatic cancer

OBJECTIVES

To assess the methodological quality of prognostic model development studies pertaining to post resection prognosis of pancreatic ductal adenocarcinoma (PDAC).

DESIGN/SETTING

A narrative systematic review of international peer reviewed journals DATA SOURCE: Searches were conducted of: MEDLINE, Embase, PubMed, Cochrane database and Google Scholar for predictive modelling studies applied to the outcome of prognosis for patients with PDAC post resection. Predictive modelling studies in this context included prediction model development studies with and without external validation and external validation studies with model updating. Data was extracted following the Checklist for critical Appraisal and data extraction for systematic Reviews of prediction Modelling Studies (CHARMS) checklist.

PRIMARY AND SECONDARY OUTCOME MEASURES

Primary outcomes were all components of the CHARMS checklist. Secondary outcomes included frequency of variables included across predictive models.

RESULTS

263 studies underwent full text review. 15 studies met the inclusion criteria. 3 studies underwent external validation. Multivariable Cox proportional hazard regression was the most commonly employed modelling method (n=13). 10 studies were based on single centre databases. Five used prospective databases, seven used retrospective databases and three used cancer data registry. The mean number of candidate predictors was 19.47 (range 7 to 50). The most commonly included variables were tumour grade (n=9), age (n=8), tumour stage (n=7) and tumour size (n=5). Mean sample size was 1367 (range 50 to 6400). 5 studies reached statistical power. None of the studies reported blinding of outcome measurement for predictor values. The most common form of presentation was nomograms (n=5) and prognostic scores (n=5) followed by prognostic calculators (n=3) and prognostic index (n=2).

CONCLUSIONS

Areas for improvement in future predictive model development have been highlighted relating to: general aspects of model development and reporting, applicability of models and sources of bias.

TRIAL REGISTRATION NUMBER

CRD42018105942.

Overall survival in patients over 40 years old with surgically resected pancreatic carcinoma: a SEER-based nomogram analysis

BACKGROUND

The aim of this study was to identify the determinants of overall survival (OS) within patients over 40 years old with surgically resected pancreatic carcinoma (PC), and to develop a nomogram with the intention of OS predicting.

METHODS

A total of 6341 patients of 40 years of age or later with surgically resected PC between 2010 and 2015 were enrolled from the Surveillance, Epidemiology, and End Results (SEER) program and randomly assigned into training set (4242 cases) and validation set (2099 cases). A nomogram was constructed for predicting 1-, 2- and 3-years OS based on univairate and multivariate Cox regression. The C-index and calibration plot were adopted to assess the nomogram performance.

RESULTS

Our analysis showed that age, location of carcinoma in pancreas, tumor grade, TNM stage, size of carcinoma together with lymph node ratio (LNR) were considered to be independent overall survival predictors. A nomogram based on these six factors was developed with C-index being 0.680 (95%CI: 0.667-0.693). All calibration curves of OS fitted well. The OS curves stratified by nomogram-predicted probability score (≥20, 10-19 and < 10) demonstrated statistically significant difference not only within training set but also in validation set.

CONCLUSIONS

The present nomogram for OS predicting can serve as the efficacious survival-predicting model and assist in accurate decision-making for patients over 40 years old with surgically resected PC.

Personalized Pancreatic Cancer Management: A Systematic Review of How Machine Learning Is Supporting Decision-making

This review critically analyzes how machine learning is being used to support clinical decision-making in the management of potentially resectable pancreatic cancer. Following PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-analyses) guidelines, electronic searches of MEDLINE, Embase, PubMed, and Cochrane Database were undertaken. Studies were assessed using the checklist for critical appraisal and data extraction for systematic reviews of prediction modeling studies (CHARMS) checklist. In total 89,959 citations were retrieved. Six studies met the inclusion criteria. Three studies were Markov decision-analysis models comparing neoadjuvant therapy versus upfront surgery. Three studies predicted survival time using Bayesian modeling (n = 1) and artificial neural network (n = 1), and one study explored machine learning algorithms including Bayesian network, decision trees, k-nearest neighbor, and artificial neural networks. The main methodological issues identified were limited data sources, which limits generalizability and potentiates bias; lack of external validation; and the need for transparency in methods of internal validation, consecutive sampling, and selection of candidate predictors. The future direction of research relies on expanding our view of the multidisciplinary team to include professionals from computing and data science with algorithms developed in conjunction with clinicians and viewed as aids, not replacement, to traditional clinical decision-making.

Systematic review of clinical prediction models for survival after surgery for resectable pancreatic cancer

BACKGROUND

As more therapeutic options for pancreatic cancer are becoming available, there is a need to improve outcome prediction to support shared decision-making. A systematic evaluation of prediction models in resectable pancreatic cancer is lacking.

METHODS

This systematic review followed the CHARMS and PRISMA guidelines. PubMed, Embase and Cochrane Library databases were searched up to 11 October 2017. Studies reporting development or validation of models predicting survival in resectable pancreatic cancer were included. Models without performance measures, reviews, abstracts or more than 10 per cent of patients not undergoing resection in postoperative models were excluded. Studies were appraised critically.

RESULTS

After screening 4403 studies, 22 (44 319 patients) were included. There were 19 model development/update studies and three validation studies, altogether concerning 21 individual models. Two studies were deemed at low risk of bias. Eight models were developed for the preoperative setting and 13 for the postoperative setting. Most frequently included parameters were differentiation grade (11 of 21 models), nodal status (8 of 21) and serum albumin (7 of 21). Treatment-related variables were included in three models. The C-statistic/area under the curve values ranged from 0·57 to 0·90. Based on study design, validation methods and the availability of web-based calculators, two models were identified as the most promising.

CONCLUSION

Although a large number of prediction models for resectable pancreatic cancer have been reported, most are at high risk of bias and have not been validated externally. This overview of prognostic factors provided practical recommendations that could help in designing easily applicable prediction models to support shared decision-making.

Overall survival and cancer-specific survival in patients with surgically resected pancreatic head adenocarcinoma: A competing risk nomogram analysis

Background: The objective of this study was to estimate probabilities of overall survival (OS) and cancer-specific survival (CSS) in patients with pancreatic head adenocarcinoma after surgery. In addition, we attempted to build nomograms to predict prognosis of these patients.

Methods: Patients diagnosed with surgically resected pancreatic head adenocarcinoma between 2004 and 2014 were selected for the study from the Surveillance, Epidemiology, and End Results (SEER) database. Nomograms were established for estimating 1-, 2- and 3-year OS and CSS based on Cox regression model and Fine and Grey's model. The performance of the nomogram was measured by concordance index (C-index) and the area under receiver operating characteristic (ROC) curve (AUC).

Results: A total of 2374 patients were retrospectively collected from the SEER database. The discrimination of nomogram for OS prediction was superior to that of the Tumor-Node-Metastasis (TNM) 7th or 8th edition stage systems (C-index = 0.640, 95% CI, 0.618 - 0.662 vs 0.573, 95% CI, 0.554 - 0.593, P < 0.001; 0.640, 95% CI, 0.618 - 0.662 vs 0.596, 95% CI, 0.586 - 0.607, P < 0.001, respectively). The comparisons of values of AUC showed that the established nomograms displayed better discrimination power than TNM 7th or 8th stage systems for predicting both OS and CSS.

Conclusions: The nomograms which could predict 1-, 2- and 3-year OS and CSS were established in this study. Our nomograms showed a relatively good performance and could be served as an effective tool for prognostic evaluation of patients with pancreatic head adenocarcinoma after surgery.

Comparison of prognostic prediction between nomogram based on lymph node ratio and AJCC 8th staging system for patients with resected pancreatic head carcinoma: a SEER analysis

Background

The prognosis of pancreatic carcinoma (PC) remains poor and the American Joint Committee on Cancer (AJCC) 8th staging system for survival prediction in PC patients after curative resection is still limited. Thus, the aim of this study is to refine a valuable prognostic model and novel staging system for PC with curative resection.

Methods

The data of 3,458 patients used in this study were retrieved from the Surveillance, Epidemiology, and End Results database registry of National Cancer Institute. The prognostic value of lymph node ratio (LNR) was analyzed in the primary cohort and prognostic nomogram based on the LNR was established to create a novel staging system. Then, analyses were conducted to evaluate the application of the formulated nomogram staging system and the AJCC 8th staging system. The predictive performance of model was further validated in the internal validation cohort.

Results

Significant positive correlations were found between LNR and all factors except for surgical procedures. The results of univariate and multivariate analyses showed that LNR was identified as an independent prognostic indicator for overall survival (OS) in both primary and validation cohorts (all P < 0.001). A prognostic nomogram based on the LNR was formulated to obtain superior discriminatory abilities. Compared with the AJCC 8th staging system, the formulated nomogram staging system showed higher hazard ratios of stage II, III, and IV disease (reference to stage I disease) that were 1.637, 2.300, and 3.521, respectively, by univariate analyses in the primary cohort and the distinction between stage I, II, and III disease at the beginning or end of the survival curves was more apparent. All these results were further verified in the validation cohort.

Conclusion

LNR can be considered as a useful independent prognostic indicator for PC patients after curative resection regardless of the surgical procedures. Compared with the AJCC 8th staging system, the formulated nomogram showed superior predictive accuracy for OS and its novel staging system revealed better risk stratification.

Role of mathematical medicine in gastrointestinal carcinoma: Current status and perspectives

Mathematical medicine has already played an important role in clinical and basic research as a major interdisciplinary branch of medicine. Mathematical medicine has an important role not only in imaging diagnosis, image storage and transmission in gastrointestinal (GI) cancer, but also in tumor precision therapy. Specifically, in the field of minimally invasive treatment such as precise ablation, 3-dimension modeling, navigation, and surgical simulation significantly improve the therapeutic safety and efficiency in GI cancer. In addition, in the era of big data, data analysis and individualized therapy using mathematical medicine will become a trend in the future, offering an effective method for diagnosing and treating GI cancer and promoting clinical and scientific research.

Prognostic significance of metastatic lymph node ratio in squamous cell carcinoma of the cervix

Purpose

Metastatic lymph node ratio (MLNR) was reported to be an important prognostic factor in several tumors. However, depth of primary tumor invasion is also important in cervical cancer prognostic analysis. In this study, the objective was to determine if MLNR can be used to define a high-risk category of patients with squamous cell carcinoma of the cervix (SCC). And we combined MLNR and depth of invasion to investigate whether prognosis of SCC can be predicted better.

Patients and methods

We performed a retrospective review of patients with SCC who underwent radical hysterectomy and pelvic lymphadenectomy at QiLu Hospital of Shandong University from January 2007 to December 2009. Prognostic factors for disease-free survival (DFS) and overall survival (OS) were identified by univariate and multivariate analyses.

Results

One hundred and ninety-eight patients met the inclusion criteria and were included in the analysis. By cut-point survival analysis, MLNR cutoff was designed as 0.2. On multivariate analysis, an MLNR >0.2 was associated with a worse OS (hazard ratio [HR] =2.560, 95% CI 1.275–5.143, P=0.008) and DFS (HR =2.404, 95% CI 1.202–4.809, P=0.013). Depth of invasion cutoff was designed as invasion >1/2 cervix wall and was associated with a worse OS (HR =1.806, 95% CI 1.063–3.070, P=0.029) and DFS (HR =1.900, 95% CI 1.101–3.279, P=0.021). In addition, subgroup analysis revealed significant difference in OS and DFS rates between different MLNR categories within the same depth of invasion category (P<0.05), however, not between different depth of invasion categories within the same MLNR category (P>0.05).

Conclusion

MLNR may be used as the independent prognostic parameter in patients with SCC. Combined MLNR and depth of invasion can predict both OS and DFS better in SCC than one factor. Besides, MLNR appears to be a better prognostic value than depth of invasion for SCC.

Identification of an N staging system that predicts oncologic outcome in resected left-sided pancreatic cancer

In this study, we investigated which N staging system was the most accurate at predicting survival in pancreatic cancer patients.Lymph node (LN) metastasis is known to be one of the important prognostic factors in resected pancreatic cancer. There are several LN evaluation systems to predict oncologic impact.From January 1992 to December 2014, 77 medical records of patients who underwent radical pancreatectomy for left-sided pancreatic cancer were reviewed retrospectively. Clinicopathologic variables including pN stage, total number of retrieved LNs (N-RLN), lymph node ratio (LNR), and absolute number of LN metastases (N-LNmet) were evaluated. Disease-free survival (DFS) and disease-specific survival (DSS) were analyzed according to these 4 LN staging systems.In univariate analysis, pN stage (pN0 vs pN1: 17.5 months vs 7.9 months, P = 0.001), LNR (<0.08 vs ≥0.08: 17.5 months vs 4.4 months, P < 0.001), and N-LNmet (#N = 0 vs #N = 1 vs #N≥2: 17.5 months vs 11.0 months vs 6.4 months, P = 0.002) had a significant effect on DFS, whereas the pN stage (pN0 vs pN1: 35.3 months vs 16.7 months, P = 0.001), LNR (<0.08 vs ≥0.08: 37.1 months vs 15.0 months, P < 0.001), and N-LNmet (#N = 0 vs #N = 1 vs #N≥2: 35.3 months vs 18.4 months vs 16.4 months, P = 0.001) had a significant effect on DSS. In multivariate analysis, N-LNmet (#N≥2) was identified as an independent prognostic factor of oncologic outcome (DFS and DSS: Exp (β) = 2.83, P = 0.001, and Exp (β) = 3.17, P = 0.001, respectively).Absolute number of lymph node metastases predicted oncologic outcome in resected left-sided pancreatic cancer patients.

The lymphatic system and pancreatic cancer

This review summarizes current knowledge of the biology, pathology and clinical understanding of lymphatic invasion and metastasis in pancreatic cancer. We discuss the clinical and biological consequences of lymphatic invasion and metastasis, including paraneoplastic effects on immune responses and consider the possible benefit of therapies to treat tumors that are localized to lymphatics. A review of current techniques and methods to study interactions between tumors and lymphatics is presented.

Lymph node ratio is an independent prognostic factor for patients after resection of pancreatic cancer

Background

The prognostic value of lymph node ratio (LNR) in pancreatic cancer remains controversial. In the current retrospective study, we assessed the value of LNR on predicting the survival of postoperative patients with pancreatic cancer.

Methods

Medical records of patients who underwent pancreatic resection for pancreatic cancer in the department of general surgery, Qilu Hospital, Shandong University were reviewed retrospectively. Demographic, clinicopathological, tumor-specific data, and histopathological reports were collected. Univariate and multivariate survival analyses were performed.

Results

A total of 83 patients with pancreatic cancer were collected. The mean number of examined LN was 8.2 ± 6.1 (0 to 26). Differential degree (low) (P = 0.019, hazard ratio (HR) = 2.276, 95% confidence interval (CI): 1.171 to 4.424) and LNR >0.2 (P = 0.018, HR = 2.685, 95% CI: 1.253 to 5.756) were independent adverse prognostic factors according to the multivariate survival analysis.

Conclusions

Our study indicated that LNR >0.2 was an independent adverse prognostic factor for pancreatic cancer, which may provide important information for prognostic assessment.

Prognostic significance of lymph node ratio in esophageal cancer

N staging predicting esophageal cancer patient prognosis has been studied. Lymph node ratio, which is considered to show metastatic lymph node status more accurately, is found to have prognostic significance in several tumors. We investigated whether lymph node ratio (LNR) was associated with the prognosis of esophageal cancer in this study. Esophageal cancer patients who underwent esophagectomy at Qilu Hospital of Shandong University from January 2007 to December 2008 were studied. A total of 209 cases were evaluated in this study. The median disease-free survival (DFS) of this cohort was 35.2 months, and 5-year DFS rate was 32.1%. The median overall survival (OS) was 46.4 months, and 5-year OS rate was 40.0%. Kaplan-Meier survival analysis revealed that patients with LNR higher than 0.2 had significantly poorer DFS (p < 0.001) and OS (p < 0.001) than those with LNR less than 0.2. In a multivariate analysis, LNR was found to be an independent prognostic factor for DFS (p = 0.008, HR 1.863, 95% CI 1.180-2.942) and OS (p = 0.025, HR 1.708, 95% CI 1.068-2.731). N stage (p = 0.028, HR 1.626, 95% CI 1.055-2.506) was also found to be an independent prognostic factors for OS. Subgroups analysis revealed significant difference in OS and DFS rates between different LNR categories within the same N stages (p < 0.05) but not between different N stages within the same LNR category (p > 0.05). LNR was recognized as an independent factor in both OS and DFS in esophageal cancer. Besides, LNR showed a better prognostic value than N stage for esophageal cancer.