Complementary role of the Memorial Sloan Kettering Cancer Center nomogram to the American Joint Committee on Cancer system for the prediction of relapse of major salivary gland carcinoma after surgery

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 Random Forest Model for Post-Treatment Survival Prediction in Patients with Non-Squamous Cell Carcinoma of the Head and Neck

BACKGROUND

Compared to squamous cell carcinoma, head and neck non-squamous cell carcinoma (HNnSCC) is rarer. Integrated survival prediction tools are lacking.

METHODS

4458 patients of HNnSCC were collected from the SEER database. The endpoints were overall survivals (OSs) and disease-specific survivals (DSSs) of 3 and 5 years. Cases were stratified-randomly divided into the train & validation (70%) and test cohorts (30%). Tenfold cross validation was used in establishment of the model. The performance was evaluated with the test cohort by the receiver operating characteristic, calibration, and decision curves.

RESULTS

The prognostic factors found with multivariate analyses were used to establish the prediction model. The area under the curve (AUC) is 0.866 (95%CI: 0.844-0.888) for 3-year OS, 0.862 (95%CI: 0.842-0.882) for 5-year OS, 0.902 (95%CI: 0.888-0.916) for 3-year DSS, and 0.903 (95%CI: 0.881-0.925) for 5-year DSS. The net benefit of this model is greater than that of the traditional prediction methods. Among predictors, pathology, involved cervical nodes level, and tumor size are found contributing the most variance to the prediction. The model was then deployed online for easy use.

CONCLUSIONS

The present study incorporated the clinical, pathological, and therapeutic features comprehensively and established a clinically effective survival prediction model for post-treatment HNnSCC patients.

A nomogram involving immune-inflammation index for predicting distant metastasis-free survival of major salivary gland carcinoma following postoperative radiotherapy

BACKGROUND

Postoperative radiotherapy (PORT) is beneficial in the improvement of local-regional control and overall survival (OS) for major salivary gland carcinomas (SGCs), and distant metastasis remained the main failure pattern. This study was designed to develop a nomogram model involving immune-inflammation index to predict distant metastasis-free survival (DMFS) of major SGCs.

PATIENTS AND METHODS

A total of 418 patients with major SGCs following PORT were randomly divided into a training (n = 334) and validation set (n = 84). The pre-radiotherapy neutrophil-to-lymphocyte ratio (NLR), and platelet-to-lymphocyte ratio (PLR) were calculated and transformed as continuous variables for every patient. Associations between DMFS and variables were performed by univariate and multivariable analysis using Log-rank and Cox regression methods. A nomogram was constructed based on the prognostic factors identified by the Cox hazards model. The decision curve analysis (DCA) was conducted with the training and validation set.

RESULTS

The estimated 3-, 5-, and 10-year DMFS were 79.4%, 71.8%, and 59.1%, respectively. The multivariate analysis revealed that age (p = 0.033), advanced T stage (p = 0.003), positive N stage (p < 0.001), high-risk pathology (p = 0.011), and high PLR (p = 0.001) were significantly associated with worse DMFS. The nomogram showed good calibration and discrimination in the training (AUC = 80.9) and validation set (AUC = 87.9). Furthermore, the DCA demonstrated favorable applicability, and a significant difference (p < 0.001) was observed for the DMFS between the subgroups based on the nomogram points.

CONCLUSION

The nomogram incorporating clinicopathological features and PLR presented accurate individual prediction for DMFS of the patients with major SGCs following PORT. Further external validation of the model is warranted for clinical utility.

Regression Derived Staging Model to Predict Overall and Disease Specific Survival in Patients With Major Salivary Gland Carcinomas With Independent External Validation

The current American Joint Cancer Committee (AJCC) staging system for salivary gland tumors does not include histology and grade in its classification despite their proven prognostic importance. We planned to analyze if a modified staging system integrating these two factors into the staging improves prognostic performance and then validate it externally.

Incorporation of tumor grade and histology in current AJCC staging results in better hazard discrimination.

Development of a prognostic score for recommended transarterial chemoembolization candidates with spontaneous rupture of hepatocellular carcinoma

BACKGROUND

Although transarterial chemoembolization (TACE) has been widely used for treating the spontaneous rupture of hepatocellular carcinoma (HCC), no existing model exists for predicting survival. The aim of this study was thus to develop and validate a nomogram for estimating the prognosis in patients with ruptured HCC upon undergoing TACE treatment.

METHODS

This study included 55 patients with spontaneously ruptured HCC who underwent TACE treatment between January 2015 and April 2019. The diagnosis of spontaneous HCC rupture was based on the disruption of the peritumoral liver capsule with surrounding fluid in the perihepatic region. The prognostic nomogram was constructed using the independent predictors assessed by the multivariate Cox proportional hazards model.

RESULTS

The median overall survival (OS) was 6.4 months, with 6-month and 1-year survival rates of 52.7% and 41.8%, respectively. In the univariate analysis, the size of the largest tumor, total bilirubin (TBIL) levels, and aspartate aminotransferase (AST) levels were associated with the OS of patients. Multivariate analysis suggested that TBIL levels (HR =0.358, P=0.036) and diameter of the largest tumor (HR =1.012, P=0.044) were independent prognostic factors for predicting the OS. Based on these variables, we developed and validated a nomogram for the risk stratification of HCC rupture after TACE treatment for individual patients. According to the nomogram risk assessment, we were able to evaluate the approximate 1- and 2-year survival rates based on patients' tumor diameter and TBIL level after TACE treatment of ruptured HCC. The concordance index for the OS prediction was 0.748 (95% CI: 0.691-0.805). This newly developed nomogram represents an intuitive tool for predicting the OS of patients with ruptured HCC.

CONCLUSIONS

This study indicated that TBIL levels and diameter of the largest tumor were independent prognostic factors for predicting the OS of ruptured HCC. This study may help maximize favorable TACE treatment outcomes.

Validated Prognostic Nomograms for Patients With Parotid Carcinoma Predicting 2- and 5-Year Tumor Recurrence-Free Interval Probability

Introduction: Salivary gland malignancies are rare tumors with a heterogenous histological and clinical appearance. Previously, we identified multiple prognostic factors in patients with parotid cancer and developed prognostic indices which have repeatedly been validated internationally, demonstrating their general applicability and lasting relevance. Recently, nomograms gained popularity as a prognostic tool. Thus, in this research we aimed to construct nomograms based on our previous validated prognostic models.

Material and Methods: Nomograms were constructed using the previously reported dataset of 168 patients with parotid cancer which was used to develop pre- and postoperative prognostic scores, PS1 and PS2, respectively. Concordance indices for PS1 and PS2 were previously estimated at 0.74 and 0.71, respectively, and are in line with other, widely accepted oncological nomograms.

Results: Pre- and postoperative nomograms predicting 2- and 5-year tumor recurrence-free survival probability are presented. All previously multivariately identified and validated prognostic factors, are incorporated (T size, N classification, pain, age at diagnosis, skin invasion, facial nerve dysfunction, perineural growth, and positive surgical margins). Examples of clinical application and interpretation are given.

Conclusions: The presented prognostic nomograms for predicting 2- and 5-year tumor recurrence-free probability in patients with parotid cancer are powerful, user-friendly, visual tools and are based on internationally validated prognostic indices. They allow for a reliable prognostic assessment and result in a more individualized estimate of the risk for recurrence than the prognostic grouping based on PS1 and PS2. This facilitates assigning trial-patients to risk groups, and may assist in therapeutic decision making and determining appropriate follow-up intervals in clinical practice.

Develop and validation a nomogram to predict the recurrent probability in patients with major salivary gland cancer

Objectives: Prediction of recurrent risk in patients with major salivary gland carcinoma (MSGC) after surgical treatment is an important but difficult task because of a broad spectrum of tumor histological subtypes and diverse clinical behaviors. This study aimed to develop and validate a nomogram to predict the recurrent probability in patients with MSGC.

Methods: A total of 231 consecutive patients with MSGC received curative-intend surgery between 2002 and 2014 from one medical center were selected as the training set. Clinicopathologic variables with the most significant values in the multivariate Cox regression were selected to build into a nomogram to estimate the recurrence probability. An independent validation set of 139 patients treated at the same period from 3 other hospitals were selected for external validation and calibration.

Results: The nomogram was developed on six significant predictive factors, including the smoking history, tumor grade, perineural invasion, lymphatic invasion, pathologic T- and N-classification, of tumor recurrence retained in the multivariate Cox model. The nomogram had a highly predictive performance, with a bootstrapped corrected concordance index of 0.82 for the training set and 0.78 for the validation set. The nomogram showed good calibration in predict 2-year and 5-year recurrence probability both in the training and validation set.

Conclusions: We developed and externally validated an accurate nomogram for prediction the tumor recurrence probability of patients with MSGC after surgical treatment. This nomogram may be used to assist clinician and patient in elaborating the recurrent risk and making decision for appropriate adjuvant treatment.