Paul Tourniaire, Marius Ilie, Julien Mazières, Anna Vigier, François Ghiringhelli, Nicolas Piton, Jean-Christophe Sabourin, Frédéric Bibeau, Paul Hofman, Nicholas Ayache, Hervé Delingette
{"title":"WhARIO:对接受免疫疗法的患者进行基于全滑动图像的生存分析。","authors":"Paul Tourniaire, Marius Ilie, Julien Mazières, Anna Vigier, François Ghiringhelli, Nicolas Piton, Jean-Christophe Sabourin, Frédéric Bibeau, Paul Hofman, Nicholas Ayache, Hervé Delingette","doi":"10.1117/1.JMI.11.3.037502","DOIUrl":null,"url":null,"abstract":"<p><strong>Purpose: </strong>Immune checkpoint inhibitors (ICIs) are now one of the standards of care for patients with lung cancer and have greatly improved both progression-free and overall survival, although <math><mrow><mo><</mo><mn>20</mn><mo>%</mo></mrow></math> of the patients respond to the treatment, and some face acute adverse events. Although a few predictive biomarkers have integrated the clinical workflow, they require additional modalities on top of whole-slide images and lack efficiency or robustness. In this work, we propose a biomarker of immunotherapy outcome derived solely from the analysis of histology slides.</p><p><strong>Approach: </strong>We develop a three-step framework, combining contrastive learning and nonparametric clustering to distinguish tissue patterns within the slides, before exploiting the adjacencies of previously defined regions to derive features and train a proportional hazards model for survival analysis. We test our approach on an in-house dataset of 193 patients from 5 medical centers and compare it with the gold standard tumor proportion score (TPS) biomarker.</p><p><strong>Results: </strong>On a fivefold cross-validation (CV) of the entire dataset, the whole-slide image-based survival analysis for patients treated with immunotherapy (WhARIO) features are able to separate a low- and a high-risk group of patients with a hazard ratio (HR) of 2.29 (<math><mrow><msub><mi>CI</mi><mn>95</mn></msub><mo>=</mo><mn>1.48</mn></mrow></math> to 3.56), whereas the TPS 1% reference threshold only reaches a HR of 1.81 (<math><mrow><msub><mi>CI</mi><mn>95</mn></msub><mo>=</mo><mn>1.21</mn></mrow></math> to 2.69). Combining the two yields a higher HR of 2.60 (<math><mrow><msub><mi>CI</mi><mn>95</mn></msub><mo>=</mo><mn>1.72</mn></mrow></math> to 3.94). Additional experiments on the same dataset, where one out of five centers is excluded from the CV and used as a test set, confirm these trends.</p><p><strong>Conclusions: </strong>Our uniquely designed WhARIO features are an efficient predictor of survival for lung cancer patients who received ICI treatment. We achieve similar performance to the current gold standard biomarker, without the need to access other imaging modalities, and show that both can be used together to reach even better results.</p>","PeriodicalId":47707,"journal":{"name":"Journal of Medical Imaging","volume":"11 3","pages":"037502"},"PeriodicalIF":1.9000,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11088447/pdf/","citationCount":"0","resultStr":"{\"title\":\"WhARIO: whole-slide-image-based survival analysis for patients treated with immunotherapy.\",\"authors\":\"Paul Tourniaire, Marius Ilie, Julien Mazières, Anna Vigier, François Ghiringhelli, Nicolas Piton, Jean-Christophe Sabourin, Frédéric Bibeau, Paul Hofman, Nicholas Ayache, Hervé Delingette\",\"doi\":\"10.1117/1.JMI.11.3.037502\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Purpose: </strong>Immune checkpoint inhibitors (ICIs) are now one of the standards of care for patients with lung cancer and have greatly improved both progression-free and overall survival, although <math><mrow><mo><</mo><mn>20</mn><mo>%</mo></mrow></math> of the patients respond to the treatment, and some face acute adverse events. Although a few predictive biomarkers have integrated the clinical workflow, they require additional modalities on top of whole-slide images and lack efficiency or robustness. In this work, we propose a biomarker of immunotherapy outcome derived solely from the analysis of histology slides.</p><p><strong>Approach: </strong>We develop a three-step framework, combining contrastive learning and nonparametric clustering to distinguish tissue patterns within the slides, before exploiting the adjacencies of previously defined regions to derive features and train a proportional hazards model for survival analysis. We test our approach on an in-house dataset of 193 patients from 5 medical centers and compare it with the gold standard tumor proportion score (TPS) biomarker.</p><p><strong>Results: </strong>On a fivefold cross-validation (CV) of the entire dataset, the whole-slide image-based survival analysis for patients treated with immunotherapy (WhARIO) features are able to separate a low- and a high-risk group of patients with a hazard ratio (HR) of 2.29 (<math><mrow><msub><mi>CI</mi><mn>95</mn></msub><mo>=</mo><mn>1.48</mn></mrow></math> to 3.56), whereas the TPS 1% reference threshold only reaches a HR of 1.81 (<math><mrow><msub><mi>CI</mi><mn>95</mn></msub><mo>=</mo><mn>1.21</mn></mrow></math> to 2.69). Combining the two yields a higher HR of 2.60 (<math><mrow><msub><mi>CI</mi><mn>95</mn></msub><mo>=</mo><mn>1.72</mn></mrow></math> to 3.94). Additional experiments on the same dataset, where one out of five centers is excluded from the CV and used as a test set, confirm these trends.</p><p><strong>Conclusions: </strong>Our uniquely designed WhARIO features are an efficient predictor of survival for lung cancer patients who received ICI treatment. 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WhARIO: whole-slide-image-based survival analysis for patients treated with immunotherapy.
Purpose: Immune checkpoint inhibitors (ICIs) are now one of the standards of care for patients with lung cancer and have greatly improved both progression-free and overall survival, although of the patients respond to the treatment, and some face acute adverse events. Although a few predictive biomarkers have integrated the clinical workflow, they require additional modalities on top of whole-slide images and lack efficiency or robustness. In this work, we propose a biomarker of immunotherapy outcome derived solely from the analysis of histology slides.
Approach: We develop a three-step framework, combining contrastive learning and nonparametric clustering to distinguish tissue patterns within the slides, before exploiting the adjacencies of previously defined regions to derive features and train a proportional hazards model for survival analysis. We test our approach on an in-house dataset of 193 patients from 5 medical centers and compare it with the gold standard tumor proportion score (TPS) biomarker.
Results: On a fivefold cross-validation (CV) of the entire dataset, the whole-slide image-based survival analysis for patients treated with immunotherapy (WhARIO) features are able to separate a low- and a high-risk group of patients with a hazard ratio (HR) of 2.29 ( to 3.56), whereas the TPS 1% reference threshold only reaches a HR of 1.81 ( to 2.69). Combining the two yields a higher HR of 2.60 ( to 3.94). Additional experiments on the same dataset, where one out of five centers is excluded from the CV and used as a test set, confirm these trends.
Conclusions: Our uniquely designed WhARIO features are an efficient predictor of survival for lung cancer patients who received ICI treatment. We achieve similar performance to the current gold standard biomarker, without the need to access other imaging modalities, and show that both can be used together to reach even better results.
期刊介绍:
JMI covers fundamental and translational research, as well as applications, focused on medical imaging, which continue to yield physical and biomedical advancements in the early detection, diagnostics, and therapy of disease as well as in the understanding of normal. The scope of JMI includes: Imaging physics, Tomographic reconstruction algorithms (such as those in CT and MRI), Image processing and deep learning, Computer-aided diagnosis and quantitative image analysis, Visualization and modeling, Picture archiving and communications systems (PACS), Image perception and observer performance, Technology assessment, Ultrasonic imaging, Image-guided procedures, Digital pathology, Biomedical applications of biomedical imaging. JMI allows for the peer-reviewed communication and archiving of scientific developments, translational and clinical applications, reviews, and recommendations for the field.