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

Publikation: Bidrag til tidsskriftTidsskriftartikelForskningfagfællebedømt

Standard

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. / Pedersen, Nicklas Juel; Jensen, David Hebbelstrup; Lelkaitis, Giedrius; Kiss, Katalin; Charabi, Birgitte; Specht, Lena; von Buchwald, Christian.

I: OncoTarget, Bind 8, Nr. 11, 02.2017, s. 18227-18237.

Publikation: Bidrag til tidsskriftTidsskriftartikelForskningfagfællebedømt

Harvard

Pedersen, NJ, Jensen, DH, Lelkaitis, G, Kiss, K, Charabi, B, Specht, L & von Buchwald, C 2017, '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', OncoTarget, bind 8, nr. 11, s. 18227-18237. https://doi.org/10.18632/oncotarget.15314

APA

Pedersen, N. J., Jensen, D. H., Lelkaitis, G., Kiss, K., Charabi, B., Specht, L., & von Buchwald, C. (2017). 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. OncoTarget, 8(11), 18227-18237. https://doi.org/10.18632/oncotarget.15314

Vancouver

Pedersen NJ, Jensen DH, Lelkaitis G, Kiss K, Charabi B, Specht L o.a. 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. OncoTarget. 2017 feb;8(11):18227-18237. https://doi.org/10.18632/oncotarget.15314

Author

Pedersen, Nicklas Juel ; Jensen, David Hebbelstrup ; Lelkaitis, Giedrius ; Kiss, Katalin ; Charabi, Birgitte ; Specht, Lena ; von Buchwald, Christian. / 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. I: OncoTarget. 2017 ; Bind 8, Nr. 11. s. 18227-18237.

Bibtex

@article{cac56fdb8df242f69e306f532488483c,
title = "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",
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 crossvalidated 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.",
keywords = "Digital pathology, Oral squamous cell carcinoma, REMARK guidelines, Tumor budding",
author = "Pedersen, {Nicklas Juel} and Jensen, {David Hebbelstrup} and Giedrius Lelkaitis and Katalin Kiss and Birgitte Charabi and Lena Specht and {von Buchwald}, Christian",
year = "2017",
month = "2",
doi = "10.18632/oncotarget.15314",
language = "English",
volume = "8",
pages = "18227--18237",
journal = "OncoTarget",
issn = "1949-2553",
publisher = "Impact Journals LLC",
number = "11",

}

RIS

TY - JOUR

T1 - 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

AU - Pedersen, Nicklas Juel

AU - Jensen, David Hebbelstrup

AU - Lelkaitis, Giedrius

AU - Kiss, Katalin

AU - Charabi, Birgitte

AU - Specht, Lena

AU - von Buchwald, Christian

PY - 2017/2

Y1 - 2017/2

N2 - 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 crossvalidated 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.

AB - 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 crossvalidated 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.

KW - Digital pathology

KW - Oral squamous cell carcinoma

KW - REMARK guidelines

KW - Tumor budding

U2 - 10.18632/oncotarget.15314

DO - 10.18632/oncotarget.15314

M3 - Journal article

C2 - 28212555

AN - SCOPUS:85015152938

VL - 8

SP - 18227

EP - 18237

JO - OncoTarget

JF - OncoTarget

SN - 1949-2553

IS - 11

ER -

ID: 188872225