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":"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","authors":"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","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":null,"pages":null},"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":"{\"title\":\"Optimising inter-patient image registration for image-based data mining in breast radiotherapy\",\"authors\":\"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\",\"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\":null,\"pages\":null},\"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}","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}
Optimising inter-patient image registration for image-based data mining in breast radiotherapy
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.