Development of Nomograms for Predicting Prognosis of Pancreatic Cancer after Pancreatectomy: A Multicenter Study

Predicting lymph node metastasis using preoperative parameters in patients with T1 ampulla of vater cancer

BACKGROUND

Lymph node (LN) metastasis is an established prognostic factor for patients with surgically resected ampulla of Vater (AoV) cancer. The standard procedure for radical resection, including removal of regional LNs, is pancreaticoduodenectomy (PD); however, local excision has been considered as an alternative option for patients in the early stage cancer with significant comorbidities. In the present study, we elucidated the preoperative factors associated with LN metastasis to determine the appropriate surgical extent for T1 AoV cancer.

METHODS

We included patients who underwent surgery for T1 AoV cancer at Samsung Medical Center and Severance Hospital between 2000 and 2019. Risk factors were analyzed to identify the preoperative parameters associated with LN metastasis or regional LN recurrence during follow-up. Finally, using the identified risk factors, a prediction model was constructed.

RESULTS

Among 342 patients, 311 patients underwent PD, whereas 31 patients underwent transduodenal ampullectomy. Fourty-eight patients had LN metastasis according to pathology report, and two patients presented with regional LN recurrence. Age, carbohydrate antigen 19 - 9 (CA 19 - 9), and tumor differentiation were identified as factors associated with the increased risk of LN metastasis or regional LN recurrence. The area under the curve of the prediction model with these three factors was 0.728.

CONCLUSION

Our newly developed prediction model using age, CA 19 - 9, and tumor differentiation can help select patients who require PD over local excision. Nevertheless, additional in-depth analysis is warranted to select appropriate surgical extent for patients with presumed T1 AoV cancer.

Prognostic significance of preoperative lymphocytes, albumin, and neutrophils (LANR) index in resectable pancreatic ductal adenocarcinoma

PURPOSE

The index composed of preoperative lymphocytes, albumin, and neutrophils (LANR), a new composite score based on inflammatory response and nutritional status, has been reported to be associated with the prognosis of multiple types of cancer, but the role of LANR in the prognosis of resectable pancreatic ductal adenocarcinoma (PDAC) has not yet been elucidated.

PATIENTS AND METHODS

The data of 142 patients with PDAC who underwent radical resection in the Affiliated Hospital of Jiangnan University from January 2015 to December 2018 were retrospectively analyzed. Receiver Operating Characteristic (ROC) curves were generated to determine the optimal cut-off values for these parameters, as well as the sensitivity and specificity of LANR in predicting survival. The Kaplan-Meier method was used to draw the survival curves. Log rank test was used for univariate analysis, and Cox proportional hazards regression model was used for multivariate analysis.  RESULTS: The optimal cut-off value of LANR was 18.145, and a low preoperative LANR was significantly correlated with the location of the tumor (p = 0.047). Multivariate analysis showed that tumor differentiation degree (HR:2.357, 95%CI:1.388-4.003,p = 0.002), lymph node metastasis (HR:1.755, 95%CI: 1.115-2.763, p = 0.015), TNM stage (HR:4.686, 95%CI: 2.958-7.425, p < 0.001), preoperative cancer antigen 19 - 9 levels (HR:1.001, 95%CI: 1.000-1.001, p < 0.001) and preoperative LANR (HR:0.221, 95%CI: 0.111-0.441, p < 0.001) were independent risk factors for a poor prognosis in patients undergoing radical resection of PDAC.

CONCLUSION

This study found that preoperative LANR can be used to assess the prognosis of radical resection in patients with PDAC; those with low preoperative LANR had a worse outcome.

Identifying Effective Biomarkers for Accurate Pancreatic Cancer Prognosis Using Statistical Machine Learning

Pancreatic cancer (PC) has one of the lowest survival rates among all major types of cancer. Consequently, it is one of the leading causes of mortality worldwide. Serum biomarkers historically correlate well with the early prognosis of post-surgical complications of PC. However, attempts to identify an effective biomarker panel for the successful prognosis of PC were almost non-existent in the current literature. The current study investigated the roles of various serum biomarkers including carbohydrate antigen 19-9 (CA19-9), chemokine (C-X-C motif) ligand 8 (CXCL-8), procalcitonin (PCT), and other relevant clinical data for identifying PC progression, classified into sepsis, recurrence, and other post-surgical complications, among PC patients. The most relevant biochemical and clinical markers for PC prognosis were identified using a random-forest-powered feature elimination method. Using this informative biomarker panel, the selected machine-learning (ML) classification models demonstrated highly accurate results for classifying PC patients into three complication groups on independent test data. The superiority of the combined biomarker panel (Max AUC-ROC = 100%) was further established over using CA19-9 features exclusively (Max AUC-ROC = 75%) for the task of classifying PC progression. This novel study demonstrates the effectiveness of the combined biomarker panel in successfully diagnosing PC progression and other relevant complications among Egyptian PC survivors.

Construction and validation of a nomogram based on the log odds of positive lymph nodes to predict cancer-specific survival in patients with small cell lung cancer after surgery

Background

The lymph node ratio (LNR) is useful for predicting survival in patients with small cell lung cancer (SCLC). The present study compared the effectiveness of the N stage, number of positive LNs (NPLNs), LNR, and log odds of positive LNs (LODDS) to predict cancer-specific survival (CSS) in patients with SCLC.

Materials and methods

674 patients were screened using the Surveillance Epidemiology and End Results database. The Kaplan-Meier survival and receiver operating characteristic (ROC) curves were performed to address optimal estimation of the N stage, NPLNs, LNR, and LODDS to predict CSS. The optimal LN status group was incorporated into a nomogram to estimate CSS in SCLC patients. The ROC curve, decision curve analysis, and calibration plots were utilized to test the discriminatory ability and accuracy of this nomogram.

Results

The LODDS model showed the highest accuracy compared to the N stage, NPLNs, and LNR in predicting CSS for SCLC patients. LODDS, age, sex, tumor size, and radiotherapy status were included in the nomogram. The results of calibration plots provided evidences of nice consistency. The ROC and DCA plots suggested a better discriminatory ability and clinical applicability of this nomogram than the 8th TNM and SEER staging systems.

Conclusions

LODDS demonstrated a better predictive power than other LN schemes in SCLC patients after surgery. A novel LODDS-incorporating nomogram was built to predict CSS in SCLC patients after surgery, proving to be more precise than the 8th TNM and SEER staging.

Development and Validation of a Nomogram for Preoperative Prediction of Early Recurrence after Upfront Surgery in Pancreatic Ductal Adenocarcinoma by Integrating Deep Learning and Radiological Variables

Around 80% of pancreatic ductal adenocarcinoma (PDAC) patients experience recurrence after curative resection. We aimed to develop a deep-learning model based on preoperative CT images to predict early recurrence (recurrence within 12 months) in PDAC patients. The retrospective study included 435 patients with PDAC from two independent centers. A modified 3D-ResNet18 network was used for a deep learning model construction. A nomogram was constructed by incorporating deep learning model outputs and independent preoperative radiological predictors. The deep learning model provided the area under the receiver operating curve (AUC) values of 0.836, 0.736, and 0.720 in the development, internal, and external validation datasets for early recurrence prediction, respectively. Multivariate logistic analysis revealed that higher deep learning model outputs (odds ratio [OR]: 1.675; 95% CI: 1.467, 1.950; p < 0.001), cN1/2 stage (OR: 1.964; 95% CI: 1.036, 3.774; p = 0.040), and arterial involvement (OR: 2.207; 95% CI: 1.043, 4.873; p = 0.043) were independent risk factors associated with early recurrence and were used to build an integrated nomogram. The nomogram yielded AUC values of 0.855, 0.752, and 0.741 in the development, internal, and external validation datasets. In conclusion, the proposed nomogram may help predict early recurrence in PDAC patients.

Factors affecting HPV infection in U.S. and Beijing females: A modeling study

Background

Human papillomavirus (HPV) infection is an important carcinogenic infection highly prevalent among many populations. However, independent influencing factors and predictive models for HPV infection in both U.S. and Beijing females are rarely confirmed. In this study, our first objective was to explore the overlapping HPV infection-related factors in U.S. and Beijing females. Secondly, we aimed to develop an R package for identifying the top-performing prediction models and build the predictive models for HPV infection using this R package.

Methods

This cross-sectional study used data from the 2009-2016 NHANES (a national population-based study) and the 2019 data on Beijing female union workers from various industries. Prevalence, potential influencing factors, and predictive models for HPV infection in both cohorts were explored.

Results

There were 2,259 (NHANES cohort, age: 20-59 years) and 1,593 (Beijing female cohort, age: 20-70 years) participants included in analyses. The HPV infection rate of U.S. NHANES and Beijing females were, respectively 45.73 and 8.22%. The number of male sex partners, marital status, and history of HPV infection were the predominant factors that influenced HPV infection in both NHANES and Beijing female cohorts. However, condom application was not an independent influencing factor for HPV infection in both cohorts. R package Modelbest was established. The nomogram developed based on Modelbest package showed better performance than the nomogram which only included significant factors in multivariate regression analysis.

Conclusion

Collectively, despite the widespread availability of HPV vaccines, HPV infection is still prevalent. Compared with condom promotion, avoidance of multiple sexual partners seems to be more effective for preventing HPV infection. Nomograms developed based on Modelbest can provide improved personalized risk assessment for HPV infection. Our R package Modelbest has potential to be a powerful tool for future predictive model studies.

The Development of a Prediction Model Based on Random Survival Forest for the Postoperative Prognosis of Pancreatic Cancer: A SEER-Based Study

Simple Summary

Surgery is the main treatment to cure pancreatic cancer (PC). However, the 5-year survival rate of surgical resection is only 10–20%. The aim of our study was to develop a prediction model with the novel machine learning algorithm random survival forest (RSF) and to offer easy-to-use prediction tools, including risk stratification and individual prognosis. The study would benefit patients and physicians in postoperative management and facilitate personalized medicine.

Abstract

Accurate prediction for the prognosis of patients with pancreatic cancer (PC) is a emerge task nowadays. We aimed to develop survival models for postoperative PC patients, based on a novel algorithm, random survival forest (RSF), traditional Cox regression and neural networks (Deepsurv), using the Surveillance, Epidemiology, and End Results Program (SEER) database. A total of 3988 patients were included in this study. Eight clinicopathological features were selected using least absolute shrinkage and selection operator (LASSO) regression analysis and were utilized to develop the RSF model. The model was evaluated based on three dimensions: discrimination, calibration, and clinical benefit. It found that the RSF model predicted the cancer-specific survival (CSS) of the postoperative PC patients with a c-index of 0.723, which was higher than the models built by Cox regression (0.670) and Deepsurv (0.700). The Brier scores at 1, 3, and 5 years (0.188, 0.177, and 0.131) of the RSF model demonstrated the model’s favorable calibration and the decision curve analysis illustrated the model’s value of clinical implement. Moreover, the roles of the key variables were visualized in the Shapley Additive Explanations plotting. Lastly, the prediction model demonstrates value in risk stratification and individual prognosis. In this study, a high-performance prediction model for PC postoperative prognosis was developed, based on RSF The model presented significant strengths in the risk stratification and individual prognosis prediction.