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Artificial Intelligence System for Predicting Prostate Cancer Lesions from Shear Wave Elastography Measurements

(1) Objective: To design an artificial intelligence system for prostate cancer prediction using the data obtained by shear wave elastography of the prostate, by comparing it with the histopathological exam of the prostate biopsy specimens. (2) Material and methods: We have conducted a prospective st...

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Autores principales: Secasan, Ciprian Cosmin, Onchis, Darian, Bardan, Razvan, Cumpanas, Alin, Novacescu, Dorin, Botoca, Corina, Dema, Alis, Sporea, Ioan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9221963/
https://www.ncbi.nlm.nih.gov/pubmed/35735445
http://dx.doi.org/10.3390/curroncol29060336
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author Secasan, Ciprian Cosmin
Onchis, Darian
Bardan, Razvan
Cumpanas, Alin
Novacescu, Dorin
Botoca, Corina
Dema, Alis
Sporea, Ioan
author_facet Secasan, Ciprian Cosmin
Onchis, Darian
Bardan, Razvan
Cumpanas, Alin
Novacescu, Dorin
Botoca, Corina
Dema, Alis
Sporea, Ioan
author_sort Secasan, Ciprian Cosmin
collection PubMed
description (1) Objective: To design an artificial intelligence system for prostate cancer prediction using the data obtained by shear wave elastography of the prostate, by comparing it with the histopathological exam of the prostate biopsy specimens. (2) Material and methods: We have conducted a prospective study on 356 patients undergoing transrectal ultrasound-guided prostate biopsy, for suspicion of prostate cancer. All patients were examined using bi-dimensional shear wave ultrasonography, which was followed by standard systematic transrectal prostate biopsy. The mean elasticity of each of the twelve systematic biopsy target zones was recorded and compared with the pathological examination results in all patients. The final dataset has included data from 223 patients with confirmed prostate cancer. Three machine learning classification algorithms (logistic regression, a decision tree classifier and a dense neural network) were implemented and their performance in predicting the positive lesions from the elastographic data measurements was assessed. (3) Results: The area under the curve (AUC) results were as follows: for logistic regression—0.88, for decision tree classifier—0.78 and for the dense neural network—0.94. Further use of an upsampling strategy for the training set of the neural network slightly improved its performance. Using an ensemble learning model, which combined the three machine learning models, we have obtained a final accuracy of 98%. (4) Conclusions: Bi-dimensional shear wave elastography could be very useful in predicting prostate cancer lesions, especially when it benefits from the computational power of artificial intelligence and machine learning algorithms.
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spelling pubmed-92219632022-06-24 Artificial Intelligence System for Predicting Prostate Cancer Lesions from Shear Wave Elastography Measurements Secasan, Ciprian Cosmin Onchis, Darian Bardan, Razvan Cumpanas, Alin Novacescu, Dorin Botoca, Corina Dema, Alis Sporea, Ioan Curr Oncol Article (1) Objective: To design an artificial intelligence system for prostate cancer prediction using the data obtained by shear wave elastography of the prostate, by comparing it with the histopathological exam of the prostate biopsy specimens. (2) Material and methods: We have conducted a prospective study on 356 patients undergoing transrectal ultrasound-guided prostate biopsy, for suspicion of prostate cancer. All patients were examined using bi-dimensional shear wave ultrasonography, which was followed by standard systematic transrectal prostate biopsy. The mean elasticity of each of the twelve systematic biopsy target zones was recorded and compared with the pathological examination results in all patients. The final dataset has included data from 223 patients with confirmed prostate cancer. Three machine learning classification algorithms (logistic regression, a decision tree classifier and a dense neural network) were implemented and their performance in predicting the positive lesions from the elastographic data measurements was assessed. (3) Results: The area under the curve (AUC) results were as follows: for logistic regression—0.88, for decision tree classifier—0.78 and for the dense neural network—0.94. Further use of an upsampling strategy for the training set of the neural network slightly improved its performance. Using an ensemble learning model, which combined the three machine learning models, we have obtained a final accuracy of 98%. (4) Conclusions: Bi-dimensional shear wave elastography could be very useful in predicting prostate cancer lesions, especially when it benefits from the computational power of artificial intelligence and machine learning algorithms. MDPI 2022-06-10 /pmc/articles/PMC9221963/ /pubmed/35735445 http://dx.doi.org/10.3390/curroncol29060336 Text en © 2022 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
Secasan, Ciprian Cosmin
Onchis, Darian
Bardan, Razvan
Cumpanas, Alin
Novacescu, Dorin
Botoca, Corina
Dema, Alis
Sporea, Ioan
Artificial Intelligence System for Predicting Prostate Cancer Lesions from Shear Wave Elastography Measurements
title Artificial Intelligence System for Predicting Prostate Cancer Lesions from Shear Wave Elastography Measurements
title_full Artificial Intelligence System for Predicting Prostate Cancer Lesions from Shear Wave Elastography Measurements
title_fullStr Artificial Intelligence System for Predicting Prostate Cancer Lesions from Shear Wave Elastography Measurements
title_full_unstemmed Artificial Intelligence System for Predicting Prostate Cancer Lesions from Shear Wave Elastography Measurements
title_short Artificial Intelligence System for Predicting Prostate Cancer Lesions from Shear Wave Elastography Measurements
title_sort artificial intelligence system for predicting prostate cancer lesions from shear wave elastography measurements
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9221963/
https://www.ncbi.nlm.nih.gov/pubmed/35735445
http://dx.doi.org/10.3390/curroncol29060336
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