Mucoepidermoid carcinoma of salivary glands: A French Network of Rare Head and Neck Tumors (REFCOR) prospective study of 292 cases

Dahan, Laurie Saloner; Giorgi, Roch; Gabory, L. Le Taillandier De; Costes-Martineau, Valérie; Uro‐Coste, Emmanuelle; Mouawad, F.; Bastit, Vianney; Moya‐Plana, Antoine; Even, Caroline; Albert, S.; Andry, Guy; Babin, E.; Bach, C.; Badet, J. M.; Badoual, Cécile; Baglin, A.C.; Banal, A.; Barry, B.; Baudin, Elisabeth; Baujat, B.; Bensadoun, R.J.; Bertolus, Chloé; Bessede, J.; Blanchard, Dominique; Borel, Christian; Bozorg-Grayeli, Alexis; Breheret, R.; Breton, P.; Brugel, L.; Calais, G.; Casiraghi, Odele; Cassagnau, E.; Castillo, L.; Céruse, Philippe; Chabolle, F.; Chevalier, Dominique; Chobaut, J. C.; Choussy, O.; Cosmidis, A.; Coste, Agnès; Costes, Valérie; Crampette, L.; Darrouzet, V.; Demez, Pierre; Dessi, P.; Devauchelle, Bruno; Dolivet, Gilles; Dubrulle, F.; Duflo, S.; Dufour, Xavier; Faivre, Sébastien; Fakhry, Nicolas; Ferron, Christophe; Floret, F.; Gabory, L. de; Garrel, R.; Geoffrois, Lionnel; Gilain, L.; Giovanni, Antoine; Girod, Angélique; Guerrier, B.; Hans, Stéphane; Herman, Philippe; Hofman, P.; Housset, M.; Jankowski, R.; Jégoux, Franck; Juliéron, M.; Kaminsky, Marie Christine; Kolb, F.; Guily, Jean Lacau St; Laccoureye, L.; Lallemant, Benjamin; Lang, Paul; Lartigau, É.; Lavieille, Jean‐Pierre; Lefèvre, Marine; Leroy, Xavier; Malard, O.; Massip, F.; Mauvais, O.; Mérol, J.-C.; Michel, J.; Mom, T.; Morinière, S.; Monès, Erwan de; Moulin, G.; Noël, G.; Poissonnet, G.; Prades, J.‐M.; Radulesco, Thomas; Raucourt, D. De; Reyt, É.; Righini, C.A.; Robin, Y.; Rolland, Frédéric; Ruhin, B.; Sarroul, N.; Schultz, P.; Serrano, Érica Vieira; Sterkers, O.; Strunski, V.; Sudaka, A.; Tassart, M.; Testelin, Sylvie; Thariat, Juliette; Timochenko, A.; Toussaint, B.; Coste, E. Uro; Valette, G.; Abbeele, T. Van Den; Varoquaux, A.; Veillon, F.; Vergez, S.; Wassef, Michel

doi:10.1016/j.ejso.2020.11.123

Cited by 21 publications

(12 citation statements)

References 36 publications

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“…Complete surgery is the mainstay of treatment, regardless of the tumor type and location 2,21,30,33,35,36,67–69 (Level III; Grade A), and represents a major prognostic factor 9,70–74 . Excision biopsy or tumor enucleation should be avoided due to the risk of tumor seeding (Level III; Grade E).…”

Section: Treatment Recommendationsmentioning

confidence: 99%

Salivary gland carcinoma in children and adolescents: The EXPeRT/PARTNER diagnosis and treatment recommendations

Surun

Schneider

Ferrari

et al. 2021

Pediatric Blood & Cancer

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Salivary gland carcinomas (SGCs) are rare during childhood and adolescence. Consequently, no standardized recommendations for the diagnosis and therapeutic management of pediatric SGC are available, and pediatric oncologists and surgeons generally follow adult guidelines. Complete surgical resection with adequate margins constitutes the cornerstone of treatment. However, the indications and modalities of adjuvant therapy remain controversial and may be challenging in view of the potential long‐term toxicities in the pediatric population. This paper presents the consensus recommendations for the diagnosis and treatment of children and adolescents with SGCs, established by the European Cooperative Study Group for Pediatric Rare Tumors (EXPeRT) within the EU‐funded PARTNER project (Paediatric Rare Tumours Network ‐ European Registry).

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Section: Treatment Recommendationsmentioning

confidence: 99%

Salivary gland carcinoma in children and adolescents: The EXPeRT/PARTNER diagnosis and treatment recommendations

Surun

Schneider

Ferrari

et al. 2021

Pediatric Blood & Cancer

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“…Based on the 2017 WHO classification, SGCs consist of 24 different histologic types (2). Due to their different biological behaviors and prognoses, SGCs are divided into three grades: low, intermediate, and high (3). Usually, high-grade SGC is the least frequent but has the worst prognosis.…”

Section: Introductionmentioning

confidence: 99%

Incidence and Prognostic Significance of PD-L1 Expression in High-Grade Salivary Gland Carcinoma

Fang

Yao

et al. 2021

Front. Oncol.

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ObjectivePD-L1 is one of the predictors of immunotherapy efficacy. Our goal was to analyze its expression and prognostic significance in high-grade salivary gland carcinoma (SGC).MethodsPD-L1 expression was evaluated using paraffin-embedded specimens from patients with surgically treated high-grade SGC, and it was scored by the tumor proportion score (TPS), combined positive score (CPS), and immune cell (IC) score. Associations between clinicopathological variables, disease-free survival (DFS), overall survival (OS) and PD-L1 expression were assessed.ResultsTPS≥1% occurred in 47 patients with an incidence of 43.1%, and it was significantly related to an advanced tumor stage. In patients with TPS<1%, TPS ranging from 1% to 20%, and TPS≥20%, the 5-year DFS rates were 36%, 26%, and 13%, respectively, and the difference was significant. In patients with TPS<1%, TPS ranging from 1% to 20%, and TPS≥20%, the 5-year OS rates were 49%, 24%, and 13%, respectively, and the difference was significant. CPS≥1 occurred in 87 patients with an incidence of 79.8%. IC scores of 0, 1, 2, and 3 were noted in 24 (22.0%), 37 (33.9%), 31 (28.4%), and 17 (15.6%) patients, respectively. Both CPS and IC scores had no impact on DFS or OS.ConclusionsThe expression of PD-L1 in tumor cells of high-grade SGCs was not uncommon, and it was significantly associated with tumor stage. PD-L1 expression in tumor cells rather than in immune cells indicated a poor prognosis.

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“…With a median follow-up of 26 months, the 5-year DFS was 69% (12). In the multivariate analysis, diabetes, advanced clinical stage and high histological grade were found to have a significant negative impact on DFS rate (12). As stated by the Authors, these results should be interpreted with caution mainly given the difference in the treatment plan according to histological grading and clinical stage.…”

Section: Discussionmentioning

confidence: 83%

“…It included exclusively mucoepidermoid carcinoma and a total of 292 cases were finally analyzed. With a median follow-up of 26 months, the 5-year DFS was 69% (12). In the multivariate analysis, diabetes, advanced clinical stage and high histological grade were found to have a significant negative impact on DFS rate (12).…”

Section: Discussionmentioning

confidence: 94%

“…Therefore, despite there was some evidence to support a prognostic effect of pN and pT (11), resembling data from our series, it was difficult to adequately compare results. When focusing on DFS, the French Network of Rare Head and Neck Tumors (REFCOR) recently published the largest European prospective study on salivary glands (12). It included exclusively mucoepidermoid carcinoma and a total of 292 cases were finally analyzed.…”

Section: Discussionmentioning

confidence: 99%

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Prediction of Recurrence by Machine Learning in Salivary Gland Cancer Patients After Adjuvant (Chemo)Radiotherapy

Felice

Valentini

Vincentiis

et al. 2021

In Vivo

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Background/Aim: To investigate survival outcomes and recurrence patterns using machine learning in patients with salivary gland malignant tumor (SGMT) undergoing adjuvant chemoradiotherapy (CRT). Patients and Methods: Consecutive SGMT patients were identified, and a data set included nine predictor variables and a dependent variable [disease-free survival (DFS) event] was standardized. The open-source R software was used. Survival outcomes were estimated by the Kaplan-Meier method. The random forest approach was used to select the important explanatory variables. A classification tree that optimally partitioned SGMT patients with different DFS rates was built. Results: In total, 54 SGMT patients were included in the final analysis. Five-year DFS was 62.1%. The top two important variables identified were pathologic node (pN) and pathologic tumor (pT). Based on these explanatory variables, patients were partitioned in three groups, including pN0, pT1-2 pN+ and pT3-4 pN+ with 26%, 38% and 75% probability of recurrence, respectively. Accordingly, 5-year DFS rates were 73.7%, 57.1% and 34.3%, respectively. Conclusion: The proposed decision tree algorithm is an appropriate tool to partition SGMT patients. It can guide decision-making and future research in the SGMT field.Salivary gland malignant tumors (SGMTs) accounts for less than 5% of all head and neck cancers (1). Despite improvement in SGMT diagnosis and management in recent years, the risk of loco-regional recurrence and development of distant metastasis in patients treated with curative intent remains relatively high (up to 35%) (2). It is important to understand what drives SGMT recurrence onset. Different predictors have been found to be relevant, including demographic factors (age and gender) health factors (general condition and co-morbidities) and tumor factors (stage, site, margin status, skin/bone invasion, facial nerve dysfunction, lymph-vascular invasion, histology and grade) (2-4). But no definitive factors have been established to predict recurrence outcomes. Recently the application of artificial intelligence has progressed in the medicine field. Machine learning, including decision tree algorithms, is now considered a valid predictive technique, but its use is sparse in SGMTs (5-6). Based on our clinical data, we applied machine learning approaches to analyze survival outcomes and predict recurrence rate in high risk SGMT patients. The hope is to be useful in future clinical trials design.

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Mucoepidermoid carcinoma of salivary glands: A French Network of Rare Head and Neck Tumors (REFCOR) prospective study of 292 cases

Cited by 21 publications

References 36 publications

Salivary gland carcinoma in children and adolescents: The EXPeRT/PARTNER diagnosis and treatment recommendations

Salivary gland carcinoma in children and adolescents: The EXPeRT/PARTNER diagnosis and treatment recommendations

Incidence and Prognostic Significance of PD-L1 Expression in High-Grade Salivary Gland Carcinoma

Prediction of Recurrence by Machine Learning in Salivary Gland Cancer Patients After Adjuvant (Chemo)Radiotherapy

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