Editorial for nomograms-based prediction of overall and cancer-specific survivals for patients diagnosed with major salivary gland carcinoma

Salivary gland cancer is a relatively rare group of cancers (1 case per 100,000) with a heterogenous histology comprised of up to 22 subtypes (1). The prognosis depends on certain factors including the histological subtype, grade, and regional metastasis. Literature quotes survival rates ranging from 19.6% to 84.7% (2,3). This wide range underlines the need for a better understanding of prognostic factors in these cancers. As a clinician, due to the rarity of these cancers, formulating treatment algorithms and counselling patients is often challenging. Thus, availability of reliable predictive algorithms is helpful for evidence based clinical decision making. Multiple studies that have looked at variables influencing the survival in individual subtypes of salivary gland cancers. Studies have shown that histological grade is a more relevant predictor of prognosis and response to treatment in salivary gland cancers compared to histological subtype (4,5). A predictive nomogram developed by Ali et al. in 2013, using a cohort of 301 patients, described vascular and perineural invasion, nodal status, grade, and age as the most predictive variables for recurrence in salivary gland cancer (6). Compared to traditional methods including the American Joint Committee on Cancer (AJCC) TNM (tumor node metastasis) staging system, nomograms may do a better job of predicting risk in patients (7). Staging using the TNM system is effective in predicting outcome for a patient population, however, it is less effective when applied to an individual patient.

Guo et al. in the current study have used data of major salivary gland cancer (MGSC) patients from the Surveillance, Epidemiology, and End Results (SEER) database and developed a predictive nomogram model for overall survival (OS) and cancer-specific survival (CSS) (8). The study included 11,362 patients with 7,953 and 3,409 patients in the training and validation groups, respectively. The results of Cox regression model during the multivariate analysis showed that age, race, gender, AJCC stage, T and N stage, tumor size, type of treatment modality used (namely surgery), and histological type significantly correlated with OS (P<0.001). Previous studies have shown several of these factors to adversely affect survival (6). For OS, the area under the curve (AUC) of the training dataset using the nomogram was 83.5 (82.4–84.7), and the AUC of the AJCC staging was 71.6 (70.2–73.0). This was similar to the results for CSS with the nomogram yielding AUC of 83.9 (82.6–85.2) versus the AUC of 73.9 (72.4–75.3) using the AJCC staging for the training dataset. The nomogram also had a higher concordance (C-index) for OS 0.796 (0.788–0.804); 0.787 (0.774–0.799) compared to AJCC stage (0.751, 0.739–0.762; 0.730, 0.712–0.748) in the training group and validation group, respectively. C-index of the nomogram was also superior in predicting CSS when compared to the AJCC staging with 0.806 (0.797–0.815) in training group and 0.798 (0.78–0.81) in validation group and 0.781 (0.769–0.793) and 0.769 (0.750–0.788) in training set and validation set, respectively for AJCC staging. Further, the Brier score of the nomogram was a better predictor of OS and CSS in both training and validation sets. Overall, these results suggest the superiority of the nomogram to the AJCC staging in predicting both 3- and 5-year survival in MSGC. Further, the calibration curves deduced by the current nomogram were congruent with the reference line making them highly accurate when compared to the other systems.

Certain salivary gland cancers like adenoid cystic carcinoma and salivary duct carcinoma have predilection for perineural invasion (PNI). Studies have shown that PNI is associated with aggressive cancers (9). PNI along with variables like lymphovascular emboli, tobacco, and alcohol use have prognostic importance in salivary gland cancer. However, the SEER database does not capture these variables and thus are not included in developing the nomogram in this study. The role of targeted therapy in MSGC is evolving (10,11). Although surgery with or without adjuvant radiotherapy is the mainstay of treatment of MSGC, availability of other treatment options adds to the repertoire of the treating physician especially for patients who are unable to tolerate traditional treatment measures. Again, this information is not captured by the SEER database which is an inherent limitation of using this type of population-based database.

An accurate predictive algorithm is paramount to assess the survival among cancer patients and a well-constructed nomogram plays an important role in this regard. The nomogram presented in the current study has a C-index of close to 0.8 for both training and validation sets; this was higher than the prediction with the AJCC classification. A nomogram developed by Lu et al. used six predictive factors that showed significance on the Multivariate Cox regression model (12). These included tumor grade, lymphatic invasion, PNI, smoking habit, pathologic T and N classification. These factors, although reported in literature as being high risk in predicting tumor recurrence, have not been consistent across the board (13-15). These factors are similar to the ones used in the development of the nomogram by Memorial Sloan Kettering Cancer Center (MSKCC) (6). The MSCKCC nomogram did well with predicting recurrence rates in low-risk groups but generally overestimated the 5-year recurrence-free probability in high-risk groups (16). Further, the MSKCC nomogram did not have significant advantage over the AJCC staging in stages III and IVa. Consistent with the previous nomograms, the results presented in the current study have similar C-index scores in both the validation and training datasets (6-8,17). Further, external validation in future studies will help in gauging the utility of this nomogram in a wider population. The current study included histological subtype as one of the variables which was not included in the externally validated MSKCC nomogram.

Overall, this is an important study to formulate a reliable predictive algorithm for survival in MSGC. The superiority of the current and previously published nomograms over more universally accepted systems like the AJCC system underlines the importance of considering several other risk factors in stratifying and prognosticating patients with major salivary gland cancers.

Acknowledgments

Funding: None.

Footnote

Provenance and Peer Review: This article was commissioned by the editorial office, Annals of Translational Medicine. The article did not undergo external peer review.

Conflicts of Interest: Both authors have completed the ICMJE uniform disclosure form (available at https://dx.doi.org/10.21037/atm-2021-10). The authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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