A dynamic prognostic nomogram to predict the benefit from surgical treatment modality for patients with laryngeal squamous cell carcinoma

LncRNA NEAT1 promotes proliferation, migration, and invasion of laryngeal squamous cell carcinoma cells through miR-411-3p/FZD3-mediated Wnt signaling pathway

The lncRNA NEAT1 has been shown to promote the progression of several cancers, containing laryngeal squamous cell carcinoma (LSCC). However, the precise mechanism by which it promotes LSCC progression remains unclear. In this study, we verified the high expression of lncRNA NEAT1 in LSCC tissues and cells using RT-qPCR. Analysis of clinical data exhibited that high expression of lncRNA NEAT1 was associated with a history of smoking, worse T stage, lymph node metastasis, and later TNM stage in patients with LSCC. The promotion effect of lncRNA NEAT1 on LSCC cell proliferation, migration, invasion, and tumor growth in vivo was verified by CCK-8, plate clone formation, Transwell, and nude mouse tumorigenicity assays. Bioinformatics prediction and double luciferase reporter gene assay verified the binding of miR-411-3p to lncRNA NEAT1 and FZD3 mRNA, and inhibition of miR-411-3p reversed the inhibitory effect of lncRNA NEAT1 on FZD3 expression in LSCC cells. We also verified that lncRNA NEAT1-mediated FZD3 activation in the Wnt pathway affects LSCC development. In conclusion, we demonstrate that lncRNA NEAT1 promotes the progression of LSCC, and propose that the lncRNA NEAT1/miR-411-3p/FZD3 axis may be an effective target for LSCC therapy.

Development and validation of nomogram models for predicting postoperative prognosis of early-stage laryngeal squamous cell carcinoma

BACKGROUND

We aimed to investigate the postoperative prognosis in patients with early-stage laryngeal squamous cell carcinoma (LSCC) in association with the preoperative blood markers and clinicopathological characteristics and to develop nomograms for individual risk prediction.

METHODS

The clinical data of 353 patients with confirmed early-stage LSCC between 2009 and 2018 were retrospectively retrieved from the First Affiliated Hospital with Nanjing Medical University. All patients were randomly divided into the training and testing groups in a 7:3 ratio. Univariate and multivariate analyses were performed, followed by the construction of nomograms to predict recurrence-free survival (RFS) and overall survival (OS). Finally, the nomograms were verified internally, and the predictive capability of the nomograms was evaluated and compared with that of tumour T staging.

RESULTS

Univariate and multivariate analyses identified platelet counts (PLT), fibrinogen (FIB), and platelet to lymphocyte ratio (PLR) were independent factors for RFS, and FIB, systemic immune-inflammation index (SII), and haemoglobin (HGB) were independent prognostic factors for OS. The nomograms showed higher predictive C-indexes than T staging. Furthermore, decision curve analysis (DCA) revealed that the net benefit of the nomograms' calculation model was superior to that of T staging.

CONCLUSIONS

We established and validated nomograms to predict postoperative 1-, 3- and 5-year RFS and OS in patients with early-stage LSCC based on significant blood markers and clinicopathological characteristics. These models might help clinicians make personalized treatment decisions.

A novel postoperative nomogram and risk classification system for individualized estimation of survival among patients with parotid gland carcinoma after surgery

BACKGROUND

Parotid gland carcinoma (PGC) is a rare but aggressive head and neck cancer, and the prognostic model associated with survival after surgical resection has not yet been established. This study aimed to construct a novel postoperative nomogram and risk classification system for the individualized prediction of overall survival (OS) among patients with resected PGC.

METHODS

Patients with PGC who underwent surgery between 2004 and 2015 from the Surveillance, Epidemiology, and End Results (SEER) database were randomized into training and validation cohorts (7:3). A nomogram developed using independent prognostic factors based on the results of the multivariate Cox regression analysis. Harrell's concordance index (C-index), time-dependent area under the curve (AUC), and calibration plots were used to validate the performance of the nomogram. Moreover, decision curve analysis (DCA) was performed to compare the clinical use of the nomogram with that of traditional TNM staging.

RESULTS

In this study, 5077 patients who underwent surgery for PGC were included. Age, sex, marital status, tumor grade, histology, TNM stage, surgery type, radiotherapy, and chemotherapy were independent prognostic factors. Based on these independent factors, a postoperative nomogram was developed. The C-index of the proposed nomogram was 0.807 (95% confidence interval 0.797-0.817). Meanwhile, the time-dependent AUC (> 0.8) indicated that the nomogram had a satisfactory discriminative ability. The calibration curves showed good concordance between the predicted and actual probabilities of OS, and DCA curves indicated that the nomogram had a better clinical application value than the traditional TNM staging. Moreover, a risk classification system was built that could perfectly classify patients with PGC into three risk groups.

CONCLUSIONS

This study constructed a novel postoperative nomogram and corresponding risk classification system to predict the OS of patients with PGC after surgery. These tools can be used to stratify patients with high or low risk of mortality and provide high-risk patients with more directed therapies and closer follow-up.

A deep learning-based model predicts survival for patients with laryngeal squamous cell carcinoma: a large population-based study

OBJECTIVES

To assess the performance of DeepSurv, a deep learning-based model in the survival prediction of laryngeal squamous cell carcinoma (LSCC) using the Surveillance, Epidemiology, and End Results (SEER) database.

METHODS

In this large population-based study, we developed and validated a deep learning survival neural network using pathologically diagnosed patients with LSCC from the SEER database between January 2010 and December 2018. Totally 13 variables were included in this network, including patients baseline characteristics, stage, grade, site, tumor extension and treatment details. Based on the total risk score derived from this algorithm, a three-knot restricted cubic spline was plotted to exhibit the difference of survival benefits from two treatment modalities.

RESULTS

Totally 6316 patients with LSCC were included in the study, of which 4237 cases diagnosed between 2010 and 2015 were selected as the development cohort, and the rest (2079 cases diagnosed from 2016 to 2018) were the validation cohort. A state-of-the-art deep learning-based model based on 23 features (i.e., 13 variables) was generated, which showed more superior performance in the prediction of overall survival (OS) than the tumor, node, and metastasis (TNM) staging system (C-index for DeepSurv vs TNM staging = 0.71; 95% CI 0.69-0.74 vs 0.61; 95% CI 0.60-0.63). Interestingly, a significantly nonlinear association between total risk score and treatment effectiveness was observed. When the total risk score ranges 0.1-1.5, surgical treatment brought more survival benefits than nonsurgical one for LSCC patients, especially in 70.5% of patients staged III-IV.

CONCLUSIONS

The deep learning-based model shows more potential benefits in survival estimation for patients with LSCC, which may potentially serve as an auxiliary approach to provide reliable treatment recommendations.

A novel risk classification system based on the eighth edition of TNM frameworks for esophageal adenocarcinoma patients: A deep learning approach

Objective

To develop and validate a deep learning predictive model with better performance in survival estimation of esophageal adenocarcinoma (EAC).

Method

Cases diagnosed between January 2010 and December 2018 were extracted from the Surveillance, Epidemiology, and End Results (SEER) database. A deep learning survival neural network was developed and validated based on 17 variables, including demographic information, clinicopathological characteristics, and treatment details. Based on the total risk score derived from this algorithm, a novel risk classification system was constructed and compared with the 8th edition of the tumor, node, and metastasis (TNM) staging system.

Results

Of 7,764 EAC patients eligible for the study, 6,818 (87.8%) were men and the median (interquartile range, IQR) age was 65 (58-72) years. The deep learning model generated significantly superior predictions to the 8th edition staging system on the test data set (C-index: 0.773 [95% CI, 0.757-0.789] vs. 0.683 [95% CI, 0.667-0.699]; P < 0.001). Calibration curves revealed that the deep learning model was well calibrated for 1- and 3-year OS, most points almost directly distributing on the 45° line. Decision curve analyses (DCAs) showed that the novel risk classification system exhibited a more significant positive net benefit than the TNM staging system. A user-friendly and precise web-based calculator with a portably executable file was implemented to visualize the deep learning predictive model.

Conclusion

A deep learning predictive model was developed and validated, which possesses more excellent calibration and discrimination abilities in survival prediction of EAC. The novel risk classification system based on the deep learning algorithm may serve as a useful tool in clinical decision making given its easy-to-use and better clinical applicability.