Optimising inter-patient image registration for image-based data mining in breast radiotherapy

IF 3.4 Q2 ONCOLOGY
Tanwiwat Jaikuna , Fiona Wilson , David Azria , Jenny Chang-Claude , Maria Carmen De Santis , Sara Gutiérrez-Enríquez , Marcel van Herk , Peter Hoskin , Lea Kotzki , Maarten Lambrecht , Zoe Lingard , Petra Seibold , Alejandro Seoane , Elena Sperk , R Paul Symonds , Christopher J. Talbot , Tiziana Rancati , Tim Rattay , Victoria Reyes , Barry S. Rosenstein , Eliana Vasquez Osorio
{"title":"Optimising inter-patient image registration for image-based data mining in breast radiotherapy","authors":"Tanwiwat Jaikuna ,&nbsp;Fiona Wilson ,&nbsp;David Azria ,&nbsp;Jenny Chang-Claude ,&nbsp;Maria Carmen De Santis ,&nbsp;Sara Gutiérrez-Enríquez ,&nbsp;Marcel van Herk ,&nbsp;Peter Hoskin ,&nbsp;Lea Kotzki ,&nbsp;Maarten Lambrecht ,&nbsp;Zoe Lingard ,&nbsp;Petra Seibold ,&nbsp;Alejandro Seoane ,&nbsp;Elena Sperk ,&nbsp;R Paul Symonds ,&nbsp;Christopher J. Talbot ,&nbsp;Tiziana Rancati ,&nbsp;Tim Rattay ,&nbsp;Victoria Reyes ,&nbsp;Barry S. Rosenstein ,&nbsp;Eliana Vasquez Osorio","doi":"10.1016/j.phro.2024.100635","DOIUrl":null,"url":null,"abstract":"<div><h3>Background and purpose</h3><p>Image-based data mining (IBDM) requires spatial normalisation to reference anatomy, which is challenging in breast radiotherapy due to variations in the treatment position, breast shape and volume. We aim to optimise spatial normalisation for breast IBDM.</p></div><div><h3>Materials and methods</h3><p>Data from 996 patients treated with radiotherapy for early-stage breast cancer, recruited in the REQUITE study, were included. Patients were treated supine (n = 811), with either bilateral or ipsilateral arm(s) raised (551/260, respectively) or in prone position (n = 185). Four deformable image registration (DIR) configurations for extrathoracic spatial normalisation were tested. We selected the best-performing DIR configuration and further investigated two pathways: <em>i</em>) registering prone/supine cohorts independently and <em>ii</em>) registering all patients to a supine reference. The impact of arm positioning in the supine cohort was quantified. DIR accuracy was estimated using Normalised Cross Correlation (NCC), Dice Similarity Coefficient (DSC), mean Distance to Agreement (MDA), 95 % Hausdorff Distance (95 %HD), and inter-patient landmark registration uncertainty (ILRU).</p></div><div><h3>Results</h3><p>DIR using B-spline and normalised mutual information (NMI) performed the best across all evaluation metrics. Supine-supine registrations yielded highest accuracy (0.98 ± 0.01, 0.91 ± 0.04, 0.23 ± 0.19 cm, 1.17 ± 1.18 cm, 0.51 ± 0.26 cm for NCC, DSC, MDA, 95 %HD, and ILRU), followed by prone-prone and supine-prone registrations. Arm positioning had no significant impact on registration performance. For the best DIR strategy, uncertainty of 0.44 and 0.81 cm in the breast and shoulder regions was found.</p></div><div><h3>Conclusions</h3><p>B-spline algorithm using NMI and registered supine and prone cohorts independently provides the most optimal spatial normalisation strategy for breast IBDM.</p></div>","PeriodicalId":36850,"journal":{"name":"Physics and Imaging in Radiation Oncology","volume":"32 ","pages":"Article 100635"},"PeriodicalIF":3.4000,"publicationDate":"2024-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2405631624001052/pdfft?md5=a49b0d252ac1037ec02849d1e69a131d&pid=1-s2.0-S2405631624001052-main.pdf","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Physics and Imaging in Radiation Oncology","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2405631624001052","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ONCOLOGY","Score":null,"Total":0}
引用次数: 0

Abstract

Background and purpose

Image-based data mining (IBDM) requires spatial normalisation to reference anatomy, which is challenging in breast radiotherapy due to variations in the treatment position, breast shape and volume. We aim to optimise spatial normalisation for breast IBDM.

Materials and methods

Data from 996 patients treated with radiotherapy for early-stage breast cancer, recruited in the REQUITE study, were included. Patients were treated supine (n = 811), with either bilateral or ipsilateral arm(s) raised (551/260, respectively) or in prone position (n = 185). Four deformable image registration (DIR) configurations for extrathoracic spatial normalisation were tested. We selected the best-performing DIR configuration and further investigated two pathways: i) registering prone/supine cohorts independently and ii) registering all patients to a supine reference. The impact of arm positioning in the supine cohort was quantified. DIR accuracy was estimated using Normalised Cross Correlation (NCC), Dice Similarity Coefficient (DSC), mean Distance to Agreement (MDA), 95 % Hausdorff Distance (95 %HD), and inter-patient landmark registration uncertainty (ILRU).

Results

DIR using B-spline and normalised mutual information (NMI) performed the best across all evaluation metrics. Supine-supine registrations yielded highest accuracy (0.98 ± 0.01, 0.91 ± 0.04, 0.23 ± 0.19 cm, 1.17 ± 1.18 cm, 0.51 ± 0.26 cm for NCC, DSC, MDA, 95 %HD, and ILRU), followed by prone-prone and supine-prone registrations. Arm positioning had no significant impact on registration performance. For the best DIR strategy, uncertainty of 0.44 and 0.81 cm in the breast and shoulder regions was found.

Conclusions

B-spline algorithm using NMI and registered supine and prone cohorts independently provides the most optimal spatial normalisation strategy for breast IBDM.

为乳腺放射治疗中基于图像的数据挖掘优化患者间图像配准
背景和目的基于图像的数据挖掘(IBDM)需要参照解剖学进行空间归一化,由于治疗位置、乳房形状和体积的变化,这在乳腺放疗中具有挑战性。我们的目标是优化乳腺 IBDM 的空间归一化。材料与方法纳入了在 REQUITE 研究中招募的 996 名早期乳腺癌放疗患者的数据。患者采用仰卧位(811 人)、双侧或同侧手臂抬高(分别为 551/260 人)或俯卧位(185 人)进行治疗。我们测试了四种用于胸廓外空间归一化的可变形图像配准(DIR)配置。我们选择了表现最好的 DIR 配置,并进一步研究了两种途径:i)独立配准俯卧/仰卧队列;ii)将所有患者配准到仰卧参照物。对仰卧队列中手臂定位的影响进行了量化。使用归一化交叉相关性 (NCC)、骰子相似系数 (DSC)、平均一致距离 (MDA)、95 % Hausdorff 距离 (95 %HD) 和患者间地标注册不确定性 (ILRU) 对 DIR 的准确性进行了评估。仰卧位登记的准确率最高(0.98 ± 0.01、0.91 ± 0.04、0.23 ± 0.19 厘米、1.17 ± 1.18 厘米、0.51 ± 0.26 厘米,NCC、DSC、MDA、95 %HD 和 ILRU),其次是俯卧位和仰卧位登记。手臂定位对配准性能没有明显影响。对于最佳的 DIR 策略,乳房和肩部区域的不确定性分别为 0.44 厘米和 0.81 厘米。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
Physics and Imaging in Radiation Oncology
Physics and Imaging in Radiation Oncology Physics and Astronomy-Radiation
CiteScore
5.30
自引率
18.90%
发文量
93
审稿时长
6 weeks
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信