Construction of a pathological risk model of occult lymph node metastases for prognostication by semi-automated image analysis of tumor budding in early-stage oral squamous cell carcinoma

// Nicklas Juel Pedersen 1 , David Hebbelstrup Jensen 1 , Giedrius Lelkaitis 2 , Katalin Kiss 2 , Birgitte Charabi 1 , Lena Specht 3 , Christian von Buchwald 1 1 Department of Otorhinolaryngology, Head and Neck Surgery and Audiology, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark 2 Department of Pathology, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark 3 Department of Oncology, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark Correspondence to: Christian von Buchwald, email: christian.von.buchwald@regionh.dk Keywords: oral squamous cell carcinoma, digital pathology, tumor budding, REMARK guidelines Received: September 30, 2016 Accepted: November 30, 2016 Published: February 14, 2017 ABSTRACT It is challenging to identify at diagnosis those patients with early oral squamous cell carcinoma (OSCC), who have a poor prognosis and those that have a high risk of harboring occult lymph node metastases. The aim of this study was to develop a standardized and objective digital scoring method to evaluate the predictive value of tumor budding. We developed a semi-automated image-analysis algorithm, Digital Tumor Bud Count (DTBC), to evaluate tumor budding. The algorithm was tested in 222 consecutive patients with early-stage OSCC and major endpoints were overall (OS) and progression free survival (PFS). We subsequently constructed and cross-validated a binary logistic regression model and evaluated its clinical utility by decision curve analysis. A high DTBC was an independent predictor of both poor OS and PFS in a multivariate Cox regression model. The logistic regression model was able to identify patients with occult lymph node metastases with an area under the curve (AUC) of 0.83 (95% CI: 0.78–0.89, P <0.001) and a 10-fold cross-validated AUC of 0.79. Compared to other known histopathological risk factors, the DTBC had a higher diagnostic accuracy. The proposed, novel risk model could be used as a guide to identify patients who would benefit from an up-front neck dissection.