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Tumor Cellularity Assessment of Breast Histopathological Slides via Instance Segmentation and Pathomic Features Explainability

The segmentation and classification of cell nuclei are pivotal steps in the pipelines for the analysis of bioimages. Deep learning (DL) approaches are leading the digital pathology field in the context of nuclei detection and classification. Nevertheless, the features that are exploited by DL models...

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Autores principales: Altini, Nicola, Puro, Emilia, Taccogna, Maria Giovanna, Marino, Francescomaria, De Summa, Simona, Saponaro, Concetta, Mattioli, Eliseo, Zito, Francesco Alfredo, Bevilacqua, Vitoantonio
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10135772/
https://www.ncbi.nlm.nih.gov/pubmed/37106583
http://dx.doi.org/10.3390/bioengineering10040396
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author Altini, Nicola
Puro, Emilia
Taccogna, Maria Giovanna
Marino, Francescomaria
De Summa, Simona
Saponaro, Concetta
Mattioli, Eliseo
Zito, Francesco Alfredo
Bevilacqua, Vitoantonio
author_facet Altini, Nicola
Puro, Emilia
Taccogna, Maria Giovanna
Marino, Francescomaria
De Summa, Simona
Saponaro, Concetta
Mattioli, Eliseo
Zito, Francesco Alfredo
Bevilacqua, Vitoantonio
author_sort Altini, Nicola
collection PubMed
description The segmentation and classification of cell nuclei are pivotal steps in the pipelines for the analysis of bioimages. Deep learning (DL) approaches are leading the digital pathology field in the context of nuclei detection and classification. Nevertheless, the features that are exploited by DL models to make their predictions are difficult to interpret, hindering the deployment of such methods in clinical practice. On the other hand, pathomic features can be linked to an easier description of the characteristics exploited by the classifiers for making the final predictions. Thus, in this work, we developed an explainable computer-aided diagnosis (CAD) system that can be used to support pathologists in the evaluation of tumor cellularity in breast histopathological slides. In particular, we compared an end-to-end DL approach that exploits the Mask R-CNN instance segmentation architecture with a two steps pipeline, where the features are extracted while considering the morphological and textural characteristics of the cell nuclei. Classifiers that are based on support vector machines and artificial neural networks are trained on top of these features in order to discriminate between tumor and non-tumor nuclei. Afterwards, the SHAP (Shapley additive explanations) explainable artificial intelligence technique was employed to perform a feature importance analysis, which led to an understanding of the features processed by the machine learning models for making their decisions. An expert pathologist validated the employed feature set, corroborating the clinical usability of the model. Even though the models resulting from the two-stage pipeline are slightly less accurate than those of the end-to-end approach, the interpretability of their features is clearer and may help build trust for pathologists to adopt artificial intelligence-based CAD systems in their clinical workflow. To further show the validity of the proposed approach, it has been tested on an external validation dataset, which was collected from IRCCS Istituto Tumori “Giovanni Paolo II” and made publicly available to ease research concerning the quantification of tumor cellularity.
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spelling pubmed-101357722023-04-28 Tumor Cellularity Assessment of Breast Histopathological Slides via Instance Segmentation and Pathomic Features Explainability Altini, Nicola Puro, Emilia Taccogna, Maria Giovanna Marino, Francescomaria De Summa, Simona Saponaro, Concetta Mattioli, Eliseo Zito, Francesco Alfredo Bevilacqua, Vitoantonio Bioengineering (Basel) Article The segmentation and classification of cell nuclei are pivotal steps in the pipelines for the analysis of bioimages. Deep learning (DL) approaches are leading the digital pathology field in the context of nuclei detection and classification. Nevertheless, the features that are exploited by DL models to make their predictions are difficult to interpret, hindering the deployment of such methods in clinical practice. On the other hand, pathomic features can be linked to an easier description of the characteristics exploited by the classifiers for making the final predictions. Thus, in this work, we developed an explainable computer-aided diagnosis (CAD) system that can be used to support pathologists in the evaluation of tumor cellularity in breast histopathological slides. In particular, we compared an end-to-end DL approach that exploits the Mask R-CNN instance segmentation architecture with a two steps pipeline, where the features are extracted while considering the morphological and textural characteristics of the cell nuclei. Classifiers that are based on support vector machines and artificial neural networks are trained on top of these features in order to discriminate between tumor and non-tumor nuclei. Afterwards, the SHAP (Shapley additive explanations) explainable artificial intelligence technique was employed to perform a feature importance analysis, which led to an understanding of the features processed by the machine learning models for making their decisions. An expert pathologist validated the employed feature set, corroborating the clinical usability of the model. Even though the models resulting from the two-stage pipeline are slightly less accurate than those of the end-to-end approach, the interpretability of their features is clearer and may help build trust for pathologists to adopt artificial intelligence-based CAD systems in their clinical workflow. To further show the validity of the proposed approach, it has been tested on an external validation dataset, which was collected from IRCCS Istituto Tumori “Giovanni Paolo II” and made publicly available to ease research concerning the quantification of tumor cellularity. MDPI 2023-03-23 /pmc/articles/PMC10135772/ /pubmed/37106583 http://dx.doi.org/10.3390/bioengineering10040396 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Altini, Nicola
Puro, Emilia
Taccogna, Maria Giovanna
Marino, Francescomaria
De Summa, Simona
Saponaro, Concetta
Mattioli, Eliseo
Zito, Francesco Alfredo
Bevilacqua, Vitoantonio
Tumor Cellularity Assessment of Breast Histopathological Slides via Instance Segmentation and Pathomic Features Explainability
title Tumor Cellularity Assessment of Breast Histopathological Slides via Instance Segmentation and Pathomic Features Explainability
title_full Tumor Cellularity Assessment of Breast Histopathological Slides via Instance Segmentation and Pathomic Features Explainability
title_fullStr Tumor Cellularity Assessment of Breast Histopathological Slides via Instance Segmentation and Pathomic Features Explainability
title_full_unstemmed Tumor Cellularity Assessment of Breast Histopathological Slides via Instance Segmentation and Pathomic Features Explainability
title_short Tumor Cellularity Assessment of Breast Histopathological Slides via Instance Segmentation and Pathomic Features Explainability
title_sort tumor cellularity assessment of breast histopathological slides via instance segmentation and pathomic features explainability
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10135772/
https://www.ncbi.nlm.nih.gov/pubmed/37106583
http://dx.doi.org/10.3390/bioengineering10040396
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