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Performance of an automated registration-based method for longitudinal lesion matching and comparison to inter-reader variability

Objective. Patients with metastatic disease are followed throughout treatment with medical imaging, and accurately assessing changes of individual lesions is critical to properly inform clinical decisions. The goal of this work was to assess the performance of an automated lesion-matching algorithm...

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Autores principales: Huff, Daniel T, Santoro-Fernandes, Victor, Chen, Song, Chen, Meijie, Kashuk, Carl, Weisman, Amy J, Jeraj, Robert, Perk, Timothy G
Formato: Online Artículo Texto
Lenguaje:English
Publicado: IOP Publishing 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10461173/
https://www.ncbi.nlm.nih.gov/pubmed/37567220
http://dx.doi.org/10.1088/1361-6560/acef8f
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author Huff, Daniel T
Santoro-Fernandes, Victor
Chen, Song
Chen, Meijie
Kashuk, Carl
Weisman, Amy J
Jeraj, Robert
Perk, Timothy G
author_facet Huff, Daniel T
Santoro-Fernandes, Victor
Chen, Song
Chen, Meijie
Kashuk, Carl
Weisman, Amy J
Jeraj, Robert
Perk, Timothy G
author_sort Huff, Daniel T
collection PubMed
description Objective. Patients with metastatic disease are followed throughout treatment with medical imaging, and accurately assessing changes of individual lesions is critical to properly inform clinical decisions. The goal of this work was to assess the performance of an automated lesion-matching algorithm in comparison to inter-reader variability (IRV) of matching lesions between scans of metastatic cancer patients. Approach. Forty pairs of longitudinal PET/CT and CT scans were collected and organized into four cohorts: lung cancers, head and neck cancers, lymphomas, and advanced cancers. Cases were also divided by cancer burden: low-burden (<10 lesions), intermediate-burden (10–29), and high-burden (30+). Two nuclear medicine physicians conducted independent reviews of each scan-pair and manually matched lesions. Matching differences between readers were assessed to quantify the IRV of lesion matching. The two readers met to form a consensus, which was considered a gold standard and compared against the output of an automated lesion-matching algorithm. IRV and performance of the automated method were quantified using precision, recall, F1-score, and the number of differences. Main results. The performance of the automated method did not differ significantly from IRV for any metric in any cohort (p > 0.05, Wilcoxon paired test). In high-burden cases, the F1-score (median [range]) was 0.89 [0.63, 1.00] between the automated method and reader consensus and 0.93 [0.72, 1.00] between readers. In low-burden cases, F1-scores were 1.00 [0.40, 1.00] and 1.00 [0.40, 1.00], for the automated method and IRV, respectively. Automated matching was significantly more efficient than either reader (p < 0.001). In high-burden cases, median matching time for the readers was 60 and 30 min, respectively, while automated matching took a median of 3.9 min Significance. The automated lesion-matching algorithm was successful in performing lesion matching, meeting the benchmark of IRV. Automated lesion matching can significantly expedite and improve the consistency of longitudinal lesion-matching.
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spelling pubmed-104611732023-08-29 Performance of an automated registration-based method for longitudinal lesion matching and comparison to inter-reader variability Huff, Daniel T Santoro-Fernandes, Victor Chen, Song Chen, Meijie Kashuk, Carl Weisman, Amy J Jeraj, Robert Perk, Timothy G Phys Med Biol Paper Objective. Patients with metastatic disease are followed throughout treatment with medical imaging, and accurately assessing changes of individual lesions is critical to properly inform clinical decisions. The goal of this work was to assess the performance of an automated lesion-matching algorithm in comparison to inter-reader variability (IRV) of matching lesions between scans of metastatic cancer patients. Approach. Forty pairs of longitudinal PET/CT and CT scans were collected and organized into four cohorts: lung cancers, head and neck cancers, lymphomas, and advanced cancers. Cases were also divided by cancer burden: low-burden (<10 lesions), intermediate-burden (10–29), and high-burden (30+). Two nuclear medicine physicians conducted independent reviews of each scan-pair and manually matched lesions. Matching differences between readers were assessed to quantify the IRV of lesion matching. The two readers met to form a consensus, which was considered a gold standard and compared against the output of an automated lesion-matching algorithm. IRV and performance of the automated method were quantified using precision, recall, F1-score, and the number of differences. Main results. The performance of the automated method did not differ significantly from IRV for any metric in any cohort (p > 0.05, Wilcoxon paired test). In high-burden cases, the F1-score (median [range]) was 0.89 [0.63, 1.00] between the automated method and reader consensus and 0.93 [0.72, 1.00] between readers. In low-burden cases, F1-scores were 1.00 [0.40, 1.00] and 1.00 [0.40, 1.00], for the automated method and IRV, respectively. Automated matching was significantly more efficient than either reader (p < 0.001). In high-burden cases, median matching time for the readers was 60 and 30 min, respectively, while automated matching took a median of 3.9 min Significance. The automated lesion-matching algorithm was successful in performing lesion matching, meeting the benchmark of IRV. Automated lesion matching can significantly expedite and improve the consistency of longitudinal lesion-matching. IOP Publishing 2023-09-07 2023-08-28 /pmc/articles/PMC10461173/ /pubmed/37567220 http://dx.doi.org/10.1088/1361-6560/acef8f Text en © 2023 The Author(s). Published on behalf of Institute of Physics and Engineering in Medicine by IOP Publishing Ltd https://creativecommons.org/licenses/by/4.0/Original content from this work may be used under the terms of the Creative Commons Attribution 4.0 licence (https://creativecommons.org/licenses/by/4.0/) . Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.
spellingShingle Paper
Huff, Daniel T
Santoro-Fernandes, Victor
Chen, Song
Chen, Meijie
Kashuk, Carl
Weisman, Amy J
Jeraj, Robert
Perk, Timothy G
Performance of an automated registration-based method for longitudinal lesion matching and comparison to inter-reader variability
title Performance of an automated registration-based method for longitudinal lesion matching and comparison to inter-reader variability
title_full Performance of an automated registration-based method for longitudinal lesion matching and comparison to inter-reader variability
title_fullStr Performance of an automated registration-based method for longitudinal lesion matching and comparison to inter-reader variability
title_full_unstemmed Performance of an automated registration-based method for longitudinal lesion matching and comparison to inter-reader variability
title_short Performance of an automated registration-based method for longitudinal lesion matching and comparison to inter-reader variability
title_sort performance of an automated registration-based method for longitudinal lesion matching and comparison to inter-reader variability
topic Paper
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10461173/
https://www.ncbi.nlm.nih.gov/pubmed/37567220
http://dx.doi.org/10.1088/1361-6560/acef8f
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