Arnaud Beddok, Kira Grogg, Christophe Nioche, Laura Rozenblum, Fanny Orlhac, Valentin Calugaru, Gilles Crehange, Helen A Shih, Thibault Marin, Irène Buvat, Georges El Fakhri
{"title":"预测头颈癌再照射后肿瘤复发部位:已发表的[18F]-FDG PET放射学特征的回顾性外部验证","authors":"Arnaud Beddok, Kira Grogg, Christophe Nioche, Laura Rozenblum, Fanny Orlhac, Valentin Calugaru, Gilles Crehange, Helen A Shih, Thibault Marin, Irène Buvat, Georges El Fakhri","doi":"10.1007/s11547-025-02072-1","DOIUrl":null,"url":null,"abstract":"<p><strong>Purpose: </strong>This study evaluates the efficacy of a previously published [18F]-FDG PET radiomic signature in predicting locoregional failure locations post-reirradiation in head and neck cancer (HNC) patients, using an independent cohort from a different institution.</p><p><strong>Materials and methods: </strong>Among the 66 patients reirradiated for recurrent HNC at Massachusetts General Hospital between 2012 and 2022, 31 underwent pre-reirradiation PET, constituting the external cohort for this analysis. These patients were characterized using the same radiomic features as the original model (Intensity_histogram_min, Kurtosis, Correlation, and Contrast), projected as a supplementary individual onto the published first principal component, and assigned to one of two groups using the published cutoff. The cutoff was then optimized for the external cohort to determine the loss of performance due to technical or population shifts.</p><p><strong>Results: </strong>Among the 31 patients, 22 experienced a second locoregional failure, distributed between 12 \"in-field\" and 10 \"outside\" recurrences. With the original cutoff, the model achieved a BA of 70% and a positive predictive value (PPV) of 86% for detecting \"in-field\" recurrences. After recalibrating the cutoff, the model achieved a BA of 78% and a PPV of 89%, close to the 84.5% BA obtained in the original article.</p><p><strong>Conclusion: </strong>The study validates the ability of the previously established PET radiomic signature to predict \"in-field\" relapses following reRT with a high PPV. These results support the potential of PET radiomics in identifying patients who may benefit from \"in-field\" dose escalation in reRT schemes. The model is freely available through the user-friendly LIFEx software.</p>","PeriodicalId":20817,"journal":{"name":"Radiologia Medica","volume":" ","pages":""},"PeriodicalIF":4.8000,"publicationDate":"2025-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Predicting tumor recurrence site after reirradiation in head and neck cancer: a retrospective external validation of a published [18F]-FDG PET radiomic signature.\",\"authors\":\"Arnaud Beddok, Kira Grogg, Christophe Nioche, Laura Rozenblum, Fanny Orlhac, Valentin Calugaru, Gilles Crehange, Helen A Shih, Thibault Marin, Irène Buvat, Georges El Fakhri\",\"doi\":\"10.1007/s11547-025-02072-1\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Purpose: </strong>This study evaluates the efficacy of a previously published [18F]-FDG PET radiomic signature in predicting locoregional failure locations post-reirradiation in head and neck cancer (HNC) patients, using an independent cohort from a different institution.</p><p><strong>Materials and methods: </strong>Among the 66 patients reirradiated for recurrent HNC at Massachusetts General Hospital between 2012 and 2022, 31 underwent pre-reirradiation PET, constituting the external cohort for this analysis. These patients were characterized using the same radiomic features as the original model (Intensity_histogram_min, Kurtosis, Correlation, and Contrast), projected as a supplementary individual onto the published first principal component, and assigned to one of two groups using the published cutoff. The cutoff was then optimized for the external cohort to determine the loss of performance due to technical or population shifts.</p><p><strong>Results: </strong>Among the 31 patients, 22 experienced a second locoregional failure, distributed between 12 \\\"in-field\\\" and 10 \\\"outside\\\" recurrences. With the original cutoff, the model achieved a BA of 70% and a positive predictive value (PPV) of 86% for detecting \\\"in-field\\\" recurrences. After recalibrating the cutoff, the model achieved a BA of 78% and a PPV of 89%, close to the 84.5% BA obtained in the original article.</p><p><strong>Conclusion: </strong>The study validates the ability of the previously established PET radiomic signature to predict \\\"in-field\\\" relapses following reRT with a high PPV. These results support the potential of PET radiomics in identifying patients who may benefit from \\\"in-field\\\" dose escalation in reRT schemes. 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Predicting tumor recurrence site after reirradiation in head and neck cancer: a retrospective external validation of a published [18F]-FDG PET radiomic signature.
Purpose: This study evaluates the efficacy of a previously published [18F]-FDG PET radiomic signature in predicting locoregional failure locations post-reirradiation in head and neck cancer (HNC) patients, using an independent cohort from a different institution.
Materials and methods: Among the 66 patients reirradiated for recurrent HNC at Massachusetts General Hospital between 2012 and 2022, 31 underwent pre-reirradiation PET, constituting the external cohort for this analysis. These patients were characterized using the same radiomic features as the original model (Intensity_histogram_min, Kurtosis, Correlation, and Contrast), projected as a supplementary individual onto the published first principal component, and assigned to one of two groups using the published cutoff. The cutoff was then optimized for the external cohort to determine the loss of performance due to technical or population shifts.
Results: Among the 31 patients, 22 experienced a second locoregional failure, distributed between 12 "in-field" and 10 "outside" recurrences. With the original cutoff, the model achieved a BA of 70% and a positive predictive value (PPV) of 86% for detecting "in-field" recurrences. After recalibrating the cutoff, the model achieved a BA of 78% and a PPV of 89%, close to the 84.5% BA obtained in the original article.
Conclusion: The study validates the ability of the previously established PET radiomic signature to predict "in-field" relapses following reRT with a high PPV. These results support the potential of PET radiomics in identifying patients who may benefit from "in-field" dose escalation in reRT schemes. The model is freely available through the user-friendly LIFEx software.
期刊介绍:
Felice Perussia founded La radiologia medica in 1914. It is a peer-reviewed journal and serves as the official journal of the Italian Society of Medical and Interventional Radiology (SIRM). The primary purpose of the journal is to disseminate information related to Radiology, especially advancements in diagnostic imaging and related disciplines. La radiologia medica welcomes original research on both fundamental and clinical aspects of modern radiology, with a particular focus on diagnostic and interventional imaging techniques. It also covers topics such as radiotherapy, nuclear medicine, radiobiology, health physics, and artificial intelligence in the context of clinical implications. The journal includes various types of contributions such as original articles, review articles, editorials, short reports, and letters to the editor. With an esteemed Editorial Board and a selection of insightful reports, the journal is an indispensable resource for radiologists and professionals in related fields. Ultimately, La radiologia medica aims to serve as a platform for international collaboration and knowledge sharing within the radiological community.