Oncotarget

Research Papers:

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, David Hebbelstrup Jensen, Giedrius Lelkaitis, Katalin Kiss, Birgitte Charabi, Lena Specht and Christian von Buchwald _

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Oncotarget. 2017; 8:18227-18237. https://doi.org/10.18632/oncotarget.15314

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Abstract

Nicklas Juel Pedersen1, David Hebbelstrup Jensen1, Giedrius Lelkaitis2, Katalin Kiss2, Birgitte Charabi1, Lena Specht3, Christian von Buchwald1

1Department of Otorhinolaryngology, Head and Neck Surgery and Audiology, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark

2Department of Pathology, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark

3Department 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.


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