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Repeatability of two semi-automatic artificial intelligence approaches for tumor segmentation in PET

BACKGROUND: Positron emission tomography (PET) is routinely used for cancer staging and treatment follow-up. Metabolic active tumor volume (MATV) as well as total MATV (TMATV—including primary tumor, lymph nodes and metastasis) and/or total lesion glycolysis derived from PET images have been identif...

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Autores principales: Pfaehler, Elisabeth, Mesotten, Liesbet, Kramer, Gem, Thomeer, Michiel, Vanhove, Karolien, de Jong, Johan, Adriaensens, Peter, Hoekstra, Otto S., Boellaard, Ronald
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Berlin Heidelberg 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7788118/
https://www.ncbi.nlm.nih.gov/pubmed/33409747
http://dx.doi.org/10.1186/s13550-020-00744-9
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author Pfaehler, Elisabeth
Mesotten, Liesbet
Kramer, Gem
Thomeer, Michiel
Vanhove, Karolien
de Jong, Johan
Adriaensens, Peter
Hoekstra, Otto S.
Boellaard, Ronald
author_facet Pfaehler, Elisabeth
Mesotten, Liesbet
Kramer, Gem
Thomeer, Michiel
Vanhove, Karolien
de Jong, Johan
Adriaensens, Peter
Hoekstra, Otto S.
Boellaard, Ronald
author_sort Pfaehler, Elisabeth
collection PubMed
description BACKGROUND: Positron emission tomography (PET) is routinely used for cancer staging and treatment follow-up. Metabolic active tumor volume (MATV) as well as total MATV (TMATV—including primary tumor, lymph nodes and metastasis) and/or total lesion glycolysis derived from PET images have been identified as prognostic factor or for the evaluation of treatment efficacy in cancer patients. To this end, a segmentation approach with high precision and repeatability is important. However, the implementation of a repeatable and accurate segmentation algorithm remains an ongoing challenge. METHODS: In this study, we compare two semi-automatic artificial intelligence (AI)-based segmentation methods with conventional semi-automatic segmentation approaches in terms of repeatability. One segmentation approach is based on a textural feature (TF) segmentation approach designed for accurate and repeatable segmentation of primary tumors and metastasis. Moreover, a convolutional neural network (CNN) is trained. The algorithms are trained, validated and tested using a lung cancer PET dataset. The segmentation accuracy of both segmentation approaches is compared using the Jaccard coefficient (JC). Additionally, the approaches are externally tested on a fully independent test–retest dataset. The repeatability of the methods is compared with those of two majority vote (MV2, MV3) approaches, 41%SUV(MAX), and a SUV > 4 segmentation (SUV4). Repeatability is assessed with test–retest coefficients (TRT%) and intraclass correlation coefficient (ICC). An ICC > 0.9 was regarded as representing excellent repeatability. RESULTS: The accuracy of the segmentations with the reference segmentation was good (JC median TF: 0.7, CNN: 0.73). Both segmentation approaches outperformed most other conventional segmentation methods in terms of test–retest coefficient (TRT% mean: TF: 13.0%, CNN: 13.9%, MV2: 14.1%, MV3: 28.1%, 41%SUV(MAX): 28.1%, SUV4: 18.1%) and ICC (TF: 0.98, MV2: 0.97, CNN: 0.99, MV3: 0.73, SUV4: 0.81, and 41%SUV(MAX): 0.68). CONCLUSION: The semi-automatic AI-based segmentation approaches used in this study provided better repeatability than conventional segmentation approaches. Moreover, both algorithms lead to accurate segmentations for both primary tumors as well as metastasis and are therefore good candidates for PET tumor segmentation.
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spelling pubmed-77881182021-01-14 Repeatability of two semi-automatic artificial intelligence approaches for tumor segmentation in PET Pfaehler, Elisabeth Mesotten, Liesbet Kramer, Gem Thomeer, Michiel Vanhove, Karolien de Jong, Johan Adriaensens, Peter Hoekstra, Otto S. Boellaard, Ronald EJNMMI Res Original Research BACKGROUND: Positron emission tomography (PET) is routinely used for cancer staging and treatment follow-up. Metabolic active tumor volume (MATV) as well as total MATV (TMATV—including primary tumor, lymph nodes and metastasis) and/or total lesion glycolysis derived from PET images have been identified as prognostic factor or for the evaluation of treatment efficacy in cancer patients. To this end, a segmentation approach with high precision and repeatability is important. However, the implementation of a repeatable and accurate segmentation algorithm remains an ongoing challenge. METHODS: In this study, we compare two semi-automatic artificial intelligence (AI)-based segmentation methods with conventional semi-automatic segmentation approaches in terms of repeatability. One segmentation approach is based on a textural feature (TF) segmentation approach designed for accurate and repeatable segmentation of primary tumors and metastasis. Moreover, a convolutional neural network (CNN) is trained. The algorithms are trained, validated and tested using a lung cancer PET dataset. The segmentation accuracy of both segmentation approaches is compared using the Jaccard coefficient (JC). Additionally, the approaches are externally tested on a fully independent test–retest dataset. The repeatability of the methods is compared with those of two majority vote (MV2, MV3) approaches, 41%SUV(MAX), and a SUV > 4 segmentation (SUV4). Repeatability is assessed with test–retest coefficients (TRT%) and intraclass correlation coefficient (ICC). An ICC > 0.9 was regarded as representing excellent repeatability. RESULTS: The accuracy of the segmentations with the reference segmentation was good (JC median TF: 0.7, CNN: 0.73). Both segmentation approaches outperformed most other conventional segmentation methods in terms of test–retest coefficient (TRT% mean: TF: 13.0%, CNN: 13.9%, MV2: 14.1%, MV3: 28.1%, 41%SUV(MAX): 28.1%, SUV4: 18.1%) and ICC (TF: 0.98, MV2: 0.97, CNN: 0.99, MV3: 0.73, SUV4: 0.81, and 41%SUV(MAX): 0.68). CONCLUSION: The semi-automatic AI-based segmentation approaches used in this study provided better repeatability than conventional segmentation approaches. Moreover, both algorithms lead to accurate segmentations for both primary tumors as well as metastasis and are therefore good candidates for PET tumor segmentation. Springer Berlin Heidelberg 2021-01-06 /pmc/articles/PMC7788118/ /pubmed/33409747 http://dx.doi.org/10.1186/s13550-020-00744-9 Text en © The Author(s) 2021 Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
spellingShingle Original Research
Pfaehler, Elisabeth
Mesotten, Liesbet
Kramer, Gem
Thomeer, Michiel
Vanhove, Karolien
de Jong, Johan
Adriaensens, Peter
Hoekstra, Otto S.
Boellaard, Ronald
Repeatability of two semi-automatic artificial intelligence approaches for tumor segmentation in PET
title Repeatability of two semi-automatic artificial intelligence approaches for tumor segmentation in PET
title_full Repeatability of two semi-automatic artificial intelligence approaches for tumor segmentation in PET
title_fullStr Repeatability of two semi-automatic artificial intelligence approaches for tumor segmentation in PET
title_full_unstemmed Repeatability of two semi-automatic artificial intelligence approaches for tumor segmentation in PET
title_short Repeatability of two semi-automatic artificial intelligence approaches for tumor segmentation in PET
title_sort repeatability of two semi-automatic artificial intelligence approaches for tumor segmentation in pet
topic Original Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7788118/
https://www.ncbi.nlm.nih.gov/pubmed/33409747
http://dx.doi.org/10.1186/s13550-020-00744-9
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