Evaluation of the modified Pittsburgh classification for predicting the disease‐free survival outcome of squamous cell carcinoma of the external auditory canal. Issue 12 (13th August 2020)

• Record Type: Journal Article
• Authors: Nabuurs, Cindy H.; Kievit, Wietske; Labbé, Nilou; Leemans, C. René; Smit, Conrad F. G. M.; van den Brekel, Michiel W. M.; Pauw, Robert J.; van der Laan, Bernard F. A. M.; Jansen, Jeroen C.; Lacko, Martin; Braunius, Weibel W.; Morita, Shinya; Wierzbicka, Małgorzata; Matoba, Takuma; Hanai, Nobuhiro; Takes, Robert P.; Kunst, Henricus P. M.
• Is Part Of: Head & neck. Volume 42:Issue 12(2020)
• Page Start: 3609
• Page End: 3622
• Publication Date: 2020-08-13
• Subjects: disease‐free survival -- neoplasm staging -- prognosis -- squamous cell carcinoma -- temporal bone; Head -- Diseases -- Periodicals; Neck -- Diseases -- Periodicals; Head -- Periodicals; Neck -- Periodicals; Face -- Periodicals; 617.51059
• DOI: 10.1002/hed.26424
• Languages: English
• ISSNs: 1043-3074

Abstract

Abstract: Background: Squamous cell carcinoma (SCC) of the external auditory canal (EAC) is a rare disease, which is commonly classified with the modified Pittsburgh classification. Our aim was to evaluate the predictive performance of this classification in relation to disease‐free survival (DFS). Methods: We examined retrospective data from a nationwide Dutch cohort study including patients with primary EAC SCC. These data were combined with individual patient data from the literature. Using the combined data, the predictive performances were calculated using the c‐index. Results: A total of 381 patients were included, 294 for clinical and 281 for the pathological classification analyses. The c‐indices of the clinical and the pathological modified Pittsburgh classification predicting DFS were 0.725 (0.668‐0.782) and 0.729 (0.672‐0.786), respectively. Conclusion: The predictive performance of the modified Pittsburgh classification system as such appears to be acceptable to predict the DFS of EAC SCC. Other factors need to be added to a future model to improve the predicted performance.