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Computer aided detection in prostate cancer diagnostics: A promising alternative to biopsy? A retrospective study from 104 lesions with histological ground truth
BACKGROUND: Prostate cancer (PCa) diagnosis by means of multiparametric magnetic resonance imaging (mpMRI) is a current challenge for the development of computer-aided detection (CAD) tools. An innovative CAD-software (Watson Elementary(™)) was proposed to achieve high sensitivity and specificity, a...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5638330/ https://www.ncbi.nlm.nih.gov/pubmed/29023572 http://dx.doi.org/10.1371/journal.pone.0185995 |
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author | Thon, Anika Teichgräber, Ulf Tennstedt-Schenk, Cornelia Hadjidemetriou, Stathis Winzler, Sven Malich, Ansgar Papageorgiou, Ismini |
author_facet | Thon, Anika Teichgräber, Ulf Tennstedt-Schenk, Cornelia Hadjidemetriou, Stathis Winzler, Sven Malich, Ansgar Papageorgiou, Ismini |
author_sort | Thon, Anika |
collection | PubMed |
description | BACKGROUND: Prostate cancer (PCa) diagnosis by means of multiparametric magnetic resonance imaging (mpMRI) is a current challenge for the development of computer-aided detection (CAD) tools. An innovative CAD-software (Watson Elementary(™)) was proposed to achieve high sensitivity and specificity, as well as to allege a correlate to Gleason grade. AIM/OBJECTIVE: To assess the performance of Watson Elementary(™) in automated PCa diagnosis in our hospital´s database of MRI-guided prostate biopsies. METHODS: The evaluation was retrospective for 104 lesions (47 PCa, 57 benign) from 79, 64.61±6.64 year old patients using 3T T2-weighted imaging, Apparent Diffusion Coefficient (ADC) maps and dynamic contrast enhancement series. Watson Elementary(™) utilizes signal intensity, diffusion properties and kinetic profile to compute a proportional Gleason grade predictor, termed Malignancy Attention Index (MAI). The analysis focused on (i) the CAD sensitivity and specificity to classify suspect lesions and (ii) the MAI correlation with the histopathological ground truth. RESULTS: The software revealed a sensitivity of 46.80% for PCa classification. The specificity for PCa was found to be 75.43% with a positive predictive value of 61.11%, a negative predictive value of 63.23% and a false discovery rate of 38.89%. CAD classified PCa and benign lesions with equal probability (P 0.06, χ(2) test). Accordingly, receiver operating characteristic analysis suggests a poor predictive value for MAI with an area under curve of 0.65 (P 0.02), which is not superior to the performance of board certified observers. Moreover, MAI revealed no significant correlation with Gleason grade (P 0.60, Pearson´s correlation). CONCLUSION: The tested CAD software for mpMRI analysis was a weak PCa biomarker in this dataset. Targeted prostate biopsy and histology remains the gold standard for prostate cancer diagnosis. |
format | Online Article Text |
id | pubmed-5638330 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-56383302017-10-20 Computer aided detection in prostate cancer diagnostics: A promising alternative to biopsy? A retrospective study from 104 lesions with histological ground truth Thon, Anika Teichgräber, Ulf Tennstedt-Schenk, Cornelia Hadjidemetriou, Stathis Winzler, Sven Malich, Ansgar Papageorgiou, Ismini PLoS One Research Article BACKGROUND: Prostate cancer (PCa) diagnosis by means of multiparametric magnetic resonance imaging (mpMRI) is a current challenge for the development of computer-aided detection (CAD) tools. An innovative CAD-software (Watson Elementary(™)) was proposed to achieve high sensitivity and specificity, as well as to allege a correlate to Gleason grade. AIM/OBJECTIVE: To assess the performance of Watson Elementary(™) in automated PCa diagnosis in our hospital´s database of MRI-guided prostate biopsies. METHODS: The evaluation was retrospective for 104 lesions (47 PCa, 57 benign) from 79, 64.61±6.64 year old patients using 3T T2-weighted imaging, Apparent Diffusion Coefficient (ADC) maps and dynamic contrast enhancement series. Watson Elementary(™) utilizes signal intensity, diffusion properties and kinetic profile to compute a proportional Gleason grade predictor, termed Malignancy Attention Index (MAI). The analysis focused on (i) the CAD sensitivity and specificity to classify suspect lesions and (ii) the MAI correlation with the histopathological ground truth. RESULTS: The software revealed a sensitivity of 46.80% for PCa classification. The specificity for PCa was found to be 75.43% with a positive predictive value of 61.11%, a negative predictive value of 63.23% and a false discovery rate of 38.89%. CAD classified PCa and benign lesions with equal probability (P 0.06, χ(2) test). Accordingly, receiver operating characteristic analysis suggests a poor predictive value for MAI with an area under curve of 0.65 (P 0.02), which is not superior to the performance of board certified observers. Moreover, MAI revealed no significant correlation with Gleason grade (P 0.60, Pearson´s correlation). CONCLUSION: The tested CAD software for mpMRI analysis was a weak PCa biomarker in this dataset. Targeted prostate biopsy and histology remains the gold standard for prostate cancer diagnosis. Public Library of Science 2017-10-12 /pmc/articles/PMC5638330/ /pubmed/29023572 http://dx.doi.org/10.1371/journal.pone.0185995 Text en © 2017 Thon et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Thon, Anika Teichgräber, Ulf Tennstedt-Schenk, Cornelia Hadjidemetriou, Stathis Winzler, Sven Malich, Ansgar Papageorgiou, Ismini Computer aided detection in prostate cancer diagnostics: A promising alternative to biopsy? A retrospective study from 104 lesions with histological ground truth |
title | Computer aided detection in prostate cancer diagnostics: A promising alternative to biopsy? A retrospective study from 104 lesions with histological ground truth |
title_full | Computer aided detection in prostate cancer diagnostics: A promising alternative to biopsy? A retrospective study from 104 lesions with histological ground truth |
title_fullStr | Computer aided detection in prostate cancer diagnostics: A promising alternative to biopsy? A retrospective study from 104 lesions with histological ground truth |
title_full_unstemmed | Computer aided detection in prostate cancer diagnostics: A promising alternative to biopsy? A retrospective study from 104 lesions with histological ground truth |
title_short | Computer aided detection in prostate cancer diagnostics: A promising alternative to biopsy? A retrospective study from 104 lesions with histological ground truth |
title_sort | computer aided detection in prostate cancer diagnostics: a promising alternative to biopsy? a retrospective study from 104 lesions with histological ground truth |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5638330/ https://www.ncbi.nlm.nih.gov/pubmed/29023572 http://dx.doi.org/10.1371/journal.pone.0185995 |
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