Quantitative nuclear phenotype signatures predict nodal disease in oral squamous cell carcinoma.

<h4>Background</h4>Early-stage oral squamous cell carcinoma (OSCC) patients have a one-in-four risk of regional metastasis (LN+), which is also the most significant prognostic factor for survival. As there are no validated biomarkers for predicting LN+ in early-stage OSCC, elective neck...

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Autores principales:	Kelly Yi Ping Liu, Sarah Yuqi Zhu, Alan Harrison, Zhao Yang Chen, Martial Guillaud, Catherine F Poh
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/f1acb1bb69d64b41813b4b138b971940
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spelling	oai:doaj.org-article:f1acb1bb69d64b41813b4b138b9719402021-12-02T20:04:19ZQuantitative nuclear phenotype signatures predict nodal disease in oral squamous cell carcinoma.1932-620310.1371/journal.pone.0259529https://doaj.org/article/f1acb1bb69d64b41813b4b138b9719402021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0259529https://doaj.org/toc/1932-6203<h4>Background</h4>Early-stage oral squamous cell carcinoma (OSCC) patients have a one-in-four risk of regional metastasis (LN+), which is also the most significant prognostic factor for survival. As there are no validated biomarkers for predicting LN+ in early-stage OSCC, elective neck dissection often leads to over-treatment and under-treatment. We present a machine-learning-based model using the quantitative nuclear phenotype of cancer cells from the primary tumor to predict the risk of nodal disease.<h4>Methods and findings</h4>Tumor specimens were obtained from 35 patients diagnosed with primary OSCC and received surgery with curative intent. Of the 35 patients, 29 had well (G1) or moderately (G2) differentiated tumors, and six had poorly differentiated tumors. From each, two consecutive sections were stained for hematoxylin & eosin and Feulgen-thionin staining. The slides were scanned, and images were processed to curate nuclear morphometric features for each nucleus, measuring nuclear morphology, DNA amount, and chromatin texture/organization. The nuclei (n = 384,041) from 15 G1 and 14 G2 tumors were randomly split into 80% training and 20% test set to build the predictive model by using Random Forest (RF) analysis which give each tumor cell a score, NRS. The area under ROC curve (AUC) was 99.6% and 90.7% for the training and test sets, respectively. At the cutoff score of 0.5 as the median NRS of each region of interest (n = 481), the AUC was 95.1%. We then developed a patient-level model based on the percentage of cells with an NRS ≥ 0.5. The prediction performance showed AUC of 97.7% among the 80% (n = 23 patient) training set and with the cutoff of 61% positive cells achieved 100% sensitivity and 91.7% specificity. When applying the 61% cutoff to the 20% test set patients, the model achieved 100% accuracy.<h4>Conclusions</h4>Our findings may have a clinical impact with an easy, accurate, and objective biomarker from routine pathology tissue, providing an unprecedented opportunity to improve neck management decisions in early-stage OSCC patients.Kelly Yi Ping LiuSarah Yuqi ZhuAlan HarrisonZhao Yang ChenMartial GuillaudCatherine F PohPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 11, p e0259529 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Kelly Yi Ping Liu Sarah Yuqi Zhu Alan Harrison Zhao Yang Chen Martial Guillaud Catherine F Poh Quantitative nuclear phenotype signatures predict nodal disease in oral squamous cell carcinoma.
description	<h4>Background</h4>Early-stage oral squamous cell carcinoma (OSCC) patients have a one-in-four risk of regional metastasis (LN+), which is also the most significant prognostic factor for survival. As there are no validated biomarkers for predicting LN+ in early-stage OSCC, elective neck dissection often leads to over-treatment and under-treatment. We present a machine-learning-based model using the quantitative nuclear phenotype of cancer cells from the primary tumor to predict the risk of nodal disease.<h4>Methods and findings</h4>Tumor specimens were obtained from 35 patients diagnosed with primary OSCC and received surgery with curative intent. Of the 35 patients, 29 had well (G1) or moderately (G2) differentiated tumors, and six had poorly differentiated tumors. From each, two consecutive sections were stained for hematoxylin & eosin and Feulgen-thionin staining. The slides were scanned, and images were processed to curate nuclear morphometric features for each nucleus, measuring nuclear morphology, DNA amount, and chromatin texture/organization. The nuclei (n = 384,041) from 15 G1 and 14 G2 tumors were randomly split into 80% training and 20% test set to build the predictive model by using Random Forest (RF) analysis which give each tumor cell a score, NRS. The area under ROC curve (AUC) was 99.6% and 90.7% for the training and test sets, respectively. At the cutoff score of 0.5 as the median NRS of each region of interest (n = 481), the AUC was 95.1%. We then developed a patient-level model based on the percentage of cells with an NRS ≥ 0.5. The prediction performance showed AUC of 97.7% among the 80% (n = 23 patient) training set and with the cutoff of 61% positive cells achieved 100% sensitivity and 91.7% specificity. When applying the 61% cutoff to the 20% test set patients, the model achieved 100% accuracy.<h4>Conclusions</h4>Our findings may have a clinical impact with an easy, accurate, and objective biomarker from routine pathology tissue, providing an unprecedented opportunity to improve neck management decisions in early-stage OSCC patients.
format	article
author	Kelly Yi Ping Liu Sarah Yuqi Zhu Alan Harrison Zhao Yang Chen Martial Guillaud Catherine F Poh
author_facet	Kelly Yi Ping Liu Sarah Yuqi Zhu Alan Harrison Zhao Yang Chen Martial Guillaud Catherine F Poh
author_sort	Kelly Yi Ping Liu
title	Quantitative nuclear phenotype signatures predict nodal disease in oral squamous cell carcinoma.
title_short	Quantitative nuclear phenotype signatures predict nodal disease in oral squamous cell carcinoma.
title_full	Quantitative nuclear phenotype signatures predict nodal disease in oral squamous cell carcinoma.
title_fullStr	Quantitative nuclear phenotype signatures predict nodal disease in oral squamous cell carcinoma.
title_full_unstemmed	Quantitative nuclear phenotype signatures predict nodal disease in oral squamous cell carcinoma.
title_sort	quantitative nuclear phenotype signatures predict nodal disease in oral squamous cell carcinoma.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/f1acb1bb69d64b41813b4b138b971940
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_version_	1718375637811462144

Quantitative nuclear phenotype signatures predict nodal disease in oral squamous cell carcinoma.

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