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Radial Data Mining to Identify Density–Dose Interactions That Predict Distant Failure Following SABR
PURPOSE: Lower dose outside the planned treatment area in lung stereotactic radiotherapy has been linked to increased risk of distant metastasis (DM) possibly due to underdosage of microscopic disease (MDE). Independently, tumour density on pretreatment computed tomography (CT) has been linked to ri...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8959483/ https://www.ncbi.nlm.nih.gov/pubmed/35356210 http://dx.doi.org/10.3389/fonc.2022.838155 |
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author | Davey, Angela van Herk, Marcel Faivre-Finn, Corinne McWilliam, Alan |
author_facet | Davey, Angela van Herk, Marcel Faivre-Finn, Corinne McWilliam, Alan |
author_sort | Davey, Angela |
collection | PubMed |
description | PURPOSE: Lower dose outside the planned treatment area in lung stereotactic radiotherapy has been linked to increased risk of distant metastasis (DM) possibly due to underdosage of microscopic disease (MDE). Independently, tumour density on pretreatment computed tomography (CT) has been linked to risk of MDE. No studies have investigated the interaction between imaging biomarkers and incidental dose. The interaction would showcase whether the impact of dose on outcome is dependent on imaging and, hence, if imaging could inform which patients require dose escalation outside the gross tumour volume (GTV). We propose an image-based data mining methodology to investigate density–dose interactions radially from the GTV to predict DM with no a priori assumption on location. METHODS: Dose and density were quantified in 1-mm annuli around the GTV for 199 patients with early-stage lung cancer treated with 60 Gy in 5 fractions. Each annulus was summarised by three density and three dose parameters. For parameter combinations, Cox regressions were performed including a dose–density interaction in independent annuli. Heatmaps were created that described improvement in DM prediction due to the interaction. Regions of significant improvement were identified and studied in overall outcome models. RESULTS: Dose–density interactions were identified that significantly improved prediction for over 50% of bootstrap resamples. Dose and density parameters were not significant when the interaction was omitted. Tumour density variance and high peritumour density were associated with DM for patients with more cold spots (less than 30-Gy EQD2) and non-uniform dose about 3 cm outside of the GTV. Associations identified were independent of the mean GTV dose. CONCLUSIONS: Patients with high tumour variance and peritumour density have increased risk of DM if there is a low and non-uniform dose outside the GTV. The dose regions are independent of tumour dose, suggesting that incidental dose may play an important role in controlling occult disease. Understanding such interactions is key to identifying patients who will benefit from dose-escalation. The methodology presented allowed spatial dose–density interactions to be studied at the exploratory stage for the first time. This could accelerate the clinical implementation of imaging biomarkers by demonstrating the impact of incidental dose for tumours of varying characteristics in routine data. |
format | Online Article Text |
id | pubmed-8959483 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-89594832022-03-29 Radial Data Mining to Identify Density–Dose Interactions That Predict Distant Failure Following SABR Davey, Angela van Herk, Marcel Faivre-Finn, Corinne McWilliam, Alan Front Oncol Oncology PURPOSE: Lower dose outside the planned treatment area in lung stereotactic radiotherapy has been linked to increased risk of distant metastasis (DM) possibly due to underdosage of microscopic disease (MDE). Independently, tumour density on pretreatment computed tomography (CT) has been linked to risk of MDE. No studies have investigated the interaction between imaging biomarkers and incidental dose. The interaction would showcase whether the impact of dose on outcome is dependent on imaging and, hence, if imaging could inform which patients require dose escalation outside the gross tumour volume (GTV). We propose an image-based data mining methodology to investigate density–dose interactions radially from the GTV to predict DM with no a priori assumption on location. METHODS: Dose and density were quantified in 1-mm annuli around the GTV for 199 patients with early-stage lung cancer treated with 60 Gy in 5 fractions. Each annulus was summarised by three density and three dose parameters. For parameter combinations, Cox regressions were performed including a dose–density interaction in independent annuli. Heatmaps were created that described improvement in DM prediction due to the interaction. Regions of significant improvement were identified and studied in overall outcome models. RESULTS: Dose–density interactions were identified that significantly improved prediction for over 50% of bootstrap resamples. Dose and density parameters were not significant when the interaction was omitted. Tumour density variance and high peritumour density were associated with DM for patients with more cold spots (less than 30-Gy EQD2) and non-uniform dose about 3 cm outside of the GTV. Associations identified were independent of the mean GTV dose. CONCLUSIONS: Patients with high tumour variance and peritumour density have increased risk of DM if there is a low and non-uniform dose outside the GTV. The dose regions are independent of tumour dose, suggesting that incidental dose may play an important role in controlling occult disease. Understanding such interactions is key to identifying patients who will benefit from dose-escalation. The methodology presented allowed spatial dose–density interactions to be studied at the exploratory stage for the first time. This could accelerate the clinical implementation of imaging biomarkers by demonstrating the impact of incidental dose for tumours of varying characteristics in routine data. Frontiers Media S.A. 2022-03-09 /pmc/articles/PMC8959483/ /pubmed/35356210 http://dx.doi.org/10.3389/fonc.2022.838155 Text en Copyright © 2022 Davey, van Herk, Faivre-Finn and McWilliam https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Oncology Davey, Angela van Herk, Marcel Faivre-Finn, Corinne McWilliam, Alan Radial Data Mining to Identify Density–Dose Interactions That Predict Distant Failure Following SABR |
title | Radial Data Mining to Identify Density–Dose Interactions That Predict Distant Failure Following SABR |
title_full | Radial Data Mining to Identify Density–Dose Interactions That Predict Distant Failure Following SABR |
title_fullStr | Radial Data Mining to Identify Density–Dose Interactions That Predict Distant Failure Following SABR |
title_full_unstemmed | Radial Data Mining to Identify Density–Dose Interactions That Predict Distant Failure Following SABR |
title_short | Radial Data Mining to Identify Density–Dose Interactions That Predict Distant Failure Following SABR |
title_sort | radial data mining to identify density–dose interactions that predict distant failure following sabr |
topic | Oncology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8959483/ https://www.ncbi.nlm.nih.gov/pubmed/35356210 http://dx.doi.org/10.3389/fonc.2022.838155 |
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