Sentinel Lymph Node Metastasis on Clinically Negative Patients: Preliminary Results of a Machine Learning Model Based on Histopathological Features

The reported incidence of node metastasis at sentinel lymph node biopsy is generally low, so that the majority of women underwent unnecessary invasive axilla surgery. Although the sentinel lymph node biopsy is time consuming and expensive, it is still the intra-operative exam with the highest perfor...

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Autores principales:	Annarita Fanizzi, Vito Lorusso, Albino Biafora, Samantha Bove, Maria Colomba Comes, Cristian Cristofaro, Maria Digennaro, Vittorio Didonna, Daniele La Forgia, Annalisa Nardone, Domenico Pomarico, Pasquale Tamborra, Alfredo Zito, Angelo Virgilio Paradiso, Raffaella Massafra
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	sentinel lymph node biopsy breast cancer machine learning histopathological features clinically negative lymph node Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
Acceso en línea:	https://doaj.org/article/084b05829b084401907db37b3eb07724
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spelling	oai:doaj.org-article:084b05829b084401907db37b3eb077242021-11-11T15:23:50ZSentinel Lymph Node Metastasis on Clinically Negative Patients: Preliminary Results of a Machine Learning Model Based on Histopathological Features10.3390/app1121103722076-3417https://doaj.org/article/084b05829b084401907db37b3eb077242021-11-01T00:00:00Zhttps://www.mdpi.com/2076-3417/11/21/10372https://doaj.org/toc/2076-3417The reported incidence of node metastasis at sentinel lymph node biopsy is generally low, so that the majority of women underwent unnecessary invasive axilla surgery. Although the sentinel lymph node biopsy is time consuming and expensive, it is still the intra-operative exam with the highest performance, but sometimes surgery is achieved without a clear diagnosis and also with possible serious complications. In this work, we developed a machine learning model to predict the sentinel lymph nodes positivity in clinically negative patients. Breast cancer clinical and immunohistochemical features of 907 patients characterized by a clinically negative lymph node status were collected. We trained different machine learning algorithms on the retrospective collected data and selected an optimal subset of features through a sequential forward procedure. We found comparable performances for different classification algorithms: on a hold-out training set, the logistics regression classifier with seven features, i.e., tumor diameter, age, histologic type, grading, multiplicity, in situ component and Her2-neu status reached an AUC value of 71.5% and showed a better trade-off between sensitivity and specificity (69.4 and 66.9%, respectively) compared to other two classifiers. On the hold-out test set, the performance dropped by five percentage points in terms of accuracy. Overall, the histological characteristics alone did not allow us to develop a support tool suitable for actual clinical application, but it showed the maximum informative power contained in the same for the resolution of the clinical problem. The proposed study represents a starting point for future development of predictive models to obtain the probability for lymph node metastases by using histopathological features combined with other features of a different nature.Annarita FanizziVito LorussoAlbino BiaforaSamantha BoveMaria Colomba ComesCristian CristofaroMaria DigennaroVittorio DidonnaDaniele La ForgiaAnnalisa NardoneDomenico PomaricoPasquale TamborraAlfredo ZitoAngelo Virgilio ParadisoRaffaella MassafraMDPI AGarticlesentinel lymph node biopsybreast cancermachine learninghistopathological featuresclinically negative lymph nodeTechnologyTEngineering (General). Civil engineering (General)TA1-2040Biology (General)QH301-705.5PhysicsQC1-999ChemistryQD1-999ENApplied Sciences, Vol 11, Iss 10372, p 10372 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	sentinel lymph node biopsy breast cancer machine learning histopathological features clinically negative lymph node Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
spellingShingle	sentinel lymph node biopsy breast cancer machine learning histopathological features clinically negative lymph node Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999 Annarita Fanizzi Vito Lorusso Albino Biafora Samantha Bove Maria Colomba Comes Cristian Cristofaro Maria Digennaro Vittorio Didonna Daniele La Forgia Annalisa Nardone Domenico Pomarico Pasquale Tamborra Alfredo Zito Angelo Virgilio Paradiso Raffaella Massafra Sentinel Lymph Node Metastasis on Clinically Negative Patients: Preliminary Results of a Machine Learning Model Based on Histopathological Features
description	The reported incidence of node metastasis at sentinel lymph node biopsy is generally low, so that the majority of women underwent unnecessary invasive axilla surgery. Although the sentinel lymph node biopsy is time consuming and expensive, it is still the intra-operative exam with the highest performance, but sometimes surgery is achieved without a clear diagnosis and also with possible serious complications. In this work, we developed a machine learning model to predict the sentinel lymph nodes positivity in clinically negative patients. Breast cancer clinical and immunohistochemical features of 907 patients characterized by a clinically negative lymph node status were collected. We trained different machine learning algorithms on the retrospective collected data and selected an optimal subset of features through a sequential forward procedure. We found comparable performances for different classification algorithms: on a hold-out training set, the logistics regression classifier with seven features, i.e., tumor diameter, age, histologic type, grading, multiplicity, in situ component and Her2-neu status reached an AUC value of 71.5% and showed a better trade-off between sensitivity and specificity (69.4 and 66.9%, respectively) compared to other two classifiers. On the hold-out test set, the performance dropped by five percentage points in terms of accuracy. Overall, the histological characteristics alone did not allow us to develop a support tool suitable for actual clinical application, but it showed the maximum informative power contained in the same for the resolution of the clinical problem. The proposed study represents a starting point for future development of predictive models to obtain the probability for lymph node metastases by using histopathological features combined with other features of a different nature.
format	article
author	Annarita Fanizzi Vito Lorusso Albino Biafora Samantha Bove Maria Colomba Comes Cristian Cristofaro Maria Digennaro Vittorio Didonna Daniele La Forgia Annalisa Nardone Domenico Pomarico Pasquale Tamborra Alfredo Zito Angelo Virgilio Paradiso Raffaella Massafra
author_facet	Annarita Fanizzi Vito Lorusso Albino Biafora Samantha Bove Maria Colomba Comes Cristian Cristofaro Maria Digennaro Vittorio Didonna Daniele La Forgia Annalisa Nardone Domenico Pomarico Pasquale Tamborra Alfredo Zito Angelo Virgilio Paradiso Raffaella Massafra
author_sort	Annarita Fanizzi
title	Sentinel Lymph Node Metastasis on Clinically Negative Patients: Preliminary Results of a Machine Learning Model Based on Histopathological Features
title_short	Sentinel Lymph Node Metastasis on Clinically Negative Patients: Preliminary Results of a Machine Learning Model Based on Histopathological Features
title_full	Sentinel Lymph Node Metastasis on Clinically Negative Patients: Preliminary Results of a Machine Learning Model Based on Histopathological Features
title_fullStr	Sentinel Lymph Node Metastasis on Clinically Negative Patients: Preliminary Results of a Machine Learning Model Based on Histopathological Features
title_full_unstemmed	Sentinel Lymph Node Metastasis on Clinically Negative Patients: Preliminary Results of a Machine Learning Model Based on Histopathological Features
title_sort	sentinel lymph node metastasis on clinically negative patients: preliminary results of a machine learning model based on histopathological features
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/084b05829b084401907db37b3eb07724
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Sentinel Lymph Node Metastasis on Clinically Negative Patients: Preliminary Results of a Machine Learning Model Based on Histopathological Features

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