Machine Learning Based Prediction of Squamous Cell Carcinoma in Ex Vivo Confocal Laser Scanning Microscopy

Image classification with convolutional neural networks (CNN) offers an unprecedented opportunity to medical imaging. Regulatory agencies in the USA and Europe have already cleared numerous deep learning/machine learning based medical devices and algorithms. While the field of radiology is on the fo...

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Autores principales:	Cristel Ruini, Sophia Schlingmann, Žan Jonke, Pinar Avci, Víctor Padrón-Laso, Florian Neumeier, Istvan Koveshazi, Ikenna U. Ikeliani, Kathrin Patzer, Elena Kunrad, Benjamin Kendziora, Elke Sattler, Lars E. French, Daniela Hartmann
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	squamous cell carcinoma ex vivo confocal laser scanning microscopy reflectance confocal microscopy fluorescence confocal microscopy digital pathology digital staining Neoplasms. Tumors. Oncology. Including cancer and carcinogens RC254-282
Acceso en línea:	https://doaj.org/article/f55b4b793c9c407f97c8de4c2c434cba
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spelling	oai:doaj.org-article:f55b4b793c9c407f97c8de4c2c434cba2021-11-11T15:33:51ZMachine Learning Based Prediction of Squamous Cell Carcinoma in Ex Vivo Confocal Laser Scanning Microscopy10.3390/cancers132155222072-6694https://doaj.org/article/f55b4b793c9c407f97c8de4c2c434cba2021-11-01T00:00:00Zhttps://www.mdpi.com/2072-6694/13/21/5522https://doaj.org/toc/2072-6694Image classification with convolutional neural networks (CNN) offers an unprecedented opportunity to medical imaging. Regulatory agencies in the USA and Europe have already cleared numerous deep learning/machine learning based medical devices and algorithms. While the field of radiology is on the forefront of artificial intelligence (AI) revolution, conventional pathology, which commonly relies on examination of tissue samples on a glass slide, is falling behind in leveraging this technology. On the other hand, ex vivo confocal laser scanning microscopy (ex vivo CLSM), owing to its digital workflow features, has a high potential to benefit from integrating AI tools into the assessment and decision-making process. Aim of this work was to explore a preliminary application of CNN in digitally stained ex vivo CLSM images of cutaneous squamous cell carcinoma (cSCC) for automated detection of tumor tissue. Thirty-four freshly excised tissue samples were prospectively collected and examined immediately after resection. After the histologically confirmed ex vivo CLSM diagnosis, the tumor tissue was annotated for segmentation by experts, in order to train the MobileNet CNN. The model was then trained and evaluated using cross validation. The overall sensitivity and specificity of the deep neural network for detecting cSCC and tumor free areas on ex vivo CLSM slides compared to expert evaluation were 0.76 and 0.91, respectively. The area under the ROC curve was equal to 0.90 and the area under the precision-recall curve was 0.85. The results demonstrate a high potential of deep learning models to detect cSCC regions on digitally stained ex vivo CLSM slides and to distinguish them from tumor-free skin.Cristel RuiniSophia SchlingmannŽan JonkePinar AvciVíctor Padrón-LasoFlorian NeumeierIstvan KoveshaziIkenna U. IkelianiKathrin PatzerElena KunradBenjamin KendzioraElke SattlerLars E. FrenchDaniela HartmannMDPI AGarticlesquamous cell carcinomaex vivo confocal laser scanning microscopyreflectance confocal microscopyfluorescence confocal microscopydigital pathologydigital stainingNeoplasms. Tumors. Oncology. Including cancer and carcinogensRC254-282ENCancers, Vol 13, Iss 5522, p 5522 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	squamous cell carcinoma ex vivo confocal laser scanning microscopy reflectance confocal microscopy fluorescence confocal microscopy digital pathology digital staining Neoplasms. Tumors. Oncology. Including cancer and carcinogens RC254-282
spellingShingle	squamous cell carcinoma ex vivo confocal laser scanning microscopy reflectance confocal microscopy fluorescence confocal microscopy digital pathology digital staining Neoplasms. Tumors. Oncology. Including cancer and carcinogens RC254-282 Cristel Ruini Sophia Schlingmann Žan Jonke Pinar Avci Víctor Padrón-Laso Florian Neumeier Istvan Koveshazi Ikenna U. Ikeliani Kathrin Patzer Elena Kunrad Benjamin Kendziora Elke Sattler Lars E. French Daniela Hartmann Machine Learning Based Prediction of Squamous Cell Carcinoma in Ex Vivo Confocal Laser Scanning Microscopy
description	Image classification with convolutional neural networks (CNN) offers an unprecedented opportunity to medical imaging. Regulatory agencies in the USA and Europe have already cleared numerous deep learning/machine learning based medical devices and algorithms. While the field of radiology is on the forefront of artificial intelligence (AI) revolution, conventional pathology, which commonly relies on examination of tissue samples on a glass slide, is falling behind in leveraging this technology. On the other hand, ex vivo confocal laser scanning microscopy (ex vivo CLSM), owing to its digital workflow features, has a high potential to benefit from integrating AI tools into the assessment and decision-making process. Aim of this work was to explore a preliminary application of CNN in digitally stained ex vivo CLSM images of cutaneous squamous cell carcinoma (cSCC) for automated detection of tumor tissue. Thirty-four freshly excised tissue samples were prospectively collected and examined immediately after resection. After the histologically confirmed ex vivo CLSM diagnosis, the tumor tissue was annotated for segmentation by experts, in order to train the MobileNet CNN. The model was then trained and evaluated using cross validation. The overall sensitivity and specificity of the deep neural network for detecting cSCC and tumor free areas on ex vivo CLSM slides compared to expert evaluation were 0.76 and 0.91, respectively. The area under the ROC curve was equal to 0.90 and the area under the precision-recall curve was 0.85. The results demonstrate a high potential of deep learning models to detect cSCC regions on digitally stained ex vivo CLSM slides and to distinguish them from tumor-free skin.
format	article
author	Cristel Ruini Sophia Schlingmann Žan Jonke Pinar Avci Víctor Padrón-Laso Florian Neumeier Istvan Koveshazi Ikenna U. Ikeliani Kathrin Patzer Elena Kunrad Benjamin Kendziora Elke Sattler Lars E. French Daniela Hartmann
author_facet	Cristel Ruini Sophia Schlingmann Žan Jonke Pinar Avci Víctor Padrón-Laso Florian Neumeier Istvan Koveshazi Ikenna U. Ikeliani Kathrin Patzer Elena Kunrad Benjamin Kendziora Elke Sattler Lars E. French Daniela Hartmann
author_sort	Cristel Ruini
title	Machine Learning Based Prediction of Squamous Cell Carcinoma in Ex Vivo Confocal Laser Scanning Microscopy
title_short	Machine Learning Based Prediction of Squamous Cell Carcinoma in Ex Vivo Confocal Laser Scanning Microscopy
title_full	Machine Learning Based Prediction of Squamous Cell Carcinoma in Ex Vivo Confocal Laser Scanning Microscopy
title_fullStr	Machine Learning Based Prediction of Squamous Cell Carcinoma in Ex Vivo Confocal Laser Scanning Microscopy
title_full_unstemmed	Machine Learning Based Prediction of Squamous Cell Carcinoma in Ex Vivo Confocal Laser Scanning Microscopy
title_sort	machine learning based prediction of squamous cell carcinoma in ex vivo confocal laser scanning microscopy
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/f55b4b793c9c407f97c8de4c2c434cba
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Machine Learning Based Prediction of Squamous Cell Carcinoma in Ex Vivo Confocal Laser Scanning Microscopy

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