René M Winter, Simon Boeke, Sara Leibfarth, Jonas Habrich, Kerstin Clasen, Konstantin Nikolaou, Daniel Zips, Daniela Thorwarth
{"title":"头颈癌放疗预后的临床前磁共振成像生物标志物的临床验证。","authors":"René M Winter, Simon Boeke, Sara Leibfarth, Jonas Habrich, Kerstin Clasen, Konstantin Nikolaou, Daniel Zips, Daniela Thorwarth","doi":"10.1016/j.radonc.2024.110702","DOIUrl":null,"url":null,"abstract":"<p><strong>Purpose: </strong>To retrain a model based on a previously identified prognostic imaging biomarker using apparent diffusion coefficient (ADC) values from diffusion-weighted magnetic resonance imaging (DW-MRI) in a preclinical setting and validate the model using clinical DW-MRI data of patients with locally advanced head-and-neck cancer (HNC) acquired before radiochemotherapy.</p><p><strong>Material and methods: </strong>A total of 31 HNC patients underwent T2-weighted and DW-MRI using 3 T MRI before radiochemotherapy (35 x 2 Gy). Gross tumor volumes (GTV) were delineated based on T2-weighted and b500 images. A preclinical model previously revealed that the size of high-risk subvolumes (HRS) defined by a band of ADC-values was correlated to radiation resistance. To validate this model, different bands of ADC-values were tested using two-sided thresholds on the low-ADC histogram flank to determine HRSs inside the GTV and correlated to treatment outcome after three years. The best model was used to fit a logistic regression model. Stratification potential regarding outcome was internally validated using bootstrap, receiver-operator-characteristic (ROC)-analysis, Kaplan-Meier- and Cox-method, and compared to GTV, ADC<sub>mean</sub> and clinical factors.</p><p><strong>Results: </strong>The best model was defined by 800<ADC<1100·10<sup>-6</sup>mm<sup>2</sup>/s and correlated significantly to treatment outcome (p = 0.003). Optimal HRS cut-off value was found to be 5.8 cm<sup>3</sup> according to ROC-analysis. This HRS demonstrated highly significant stratification potential (p < 0.001, bootstrap AUC ≥ 0.84) similar to GTV size (p < 0.001, AUC ≥ 0.79), in contrast to ADC<sub>mean</sub> (p = 0.361, AUC = 0.53).</p><p><strong>Conclusions: </strong>A preclinical prognostic model defined by an ADC-based HRS was successfully retrained and validated in HNC patients treated with radiochemotherapy. After thorough external validation, such functional HRS based on a band of ADC values may in the future allow interventional response-adaptive MRI-guided radiotherapy in online and offline approaches.</p>","PeriodicalId":21041,"journal":{"name":"Radiotherapy and Oncology","volume":" ","pages":"110702"},"PeriodicalIF":4.9000,"publicationDate":"2024-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Clinical validation of a prognostic preclinical magnetic resonance imaging biomarker for radiotherapy outcome in head-and-neck cancer.\",\"authors\":\"René M Winter, Simon Boeke, Sara Leibfarth, Jonas Habrich, Kerstin Clasen, Konstantin Nikolaou, Daniel Zips, Daniela Thorwarth\",\"doi\":\"10.1016/j.radonc.2024.110702\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Purpose: </strong>To retrain a model based on a previously identified prognostic imaging biomarker using apparent diffusion coefficient (ADC) values from diffusion-weighted magnetic resonance imaging (DW-MRI) in a preclinical setting and validate the model using clinical DW-MRI data of patients with locally advanced head-and-neck cancer (HNC) acquired before radiochemotherapy.</p><p><strong>Material and methods: </strong>A total of 31 HNC patients underwent T2-weighted and DW-MRI using 3 T MRI before radiochemotherapy (35 x 2 Gy). Gross tumor volumes (GTV) were delineated based on T2-weighted and b500 images. A preclinical model previously revealed that the size of high-risk subvolumes (HRS) defined by a band of ADC-values was correlated to radiation resistance. To validate this model, different bands of ADC-values were tested using two-sided thresholds on the low-ADC histogram flank to determine HRSs inside the GTV and correlated to treatment outcome after three years. The best model was used to fit a logistic regression model. Stratification potential regarding outcome was internally validated using bootstrap, receiver-operator-characteristic (ROC)-analysis, Kaplan-Meier- and Cox-method, and compared to GTV, ADC<sub>mean</sub> and clinical factors.</p><p><strong>Results: </strong>The best model was defined by 800<ADC<1100·10<sup>-6</sup>mm<sup>2</sup>/s and correlated significantly to treatment outcome (p = 0.003). Optimal HRS cut-off value was found to be 5.8 cm<sup>3</sup> according to ROC-analysis. This HRS demonstrated highly significant stratification potential (p < 0.001, bootstrap AUC ≥ 0.84) similar to GTV size (p < 0.001, AUC ≥ 0.79), in contrast to ADC<sub>mean</sub> (p = 0.361, AUC = 0.53).</p><p><strong>Conclusions: </strong>A preclinical prognostic model defined by an ADC-based HRS was successfully retrained and validated in HNC patients treated with radiochemotherapy. After thorough external validation, such functional HRS based on a band of ADC values may in the future allow interventional response-adaptive MRI-guided radiotherapy in online and offline approaches.</p>\",\"PeriodicalId\":21041,\"journal\":{\"name\":\"Radiotherapy and Oncology\",\"volume\":\" \",\"pages\":\"110702\"},\"PeriodicalIF\":4.9000,\"publicationDate\":\"2024-12-27\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Radiotherapy and Oncology\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1016/j.radonc.2024.110702\",\"RegionNum\":1,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ONCOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Radiotherapy and Oncology","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1016/j.radonc.2024.110702","RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ONCOLOGY","Score":null,"Total":0}
Clinical validation of a prognostic preclinical magnetic resonance imaging biomarker for radiotherapy outcome in head-and-neck cancer.
Purpose: To retrain a model based on a previously identified prognostic imaging biomarker using apparent diffusion coefficient (ADC) values from diffusion-weighted magnetic resonance imaging (DW-MRI) in a preclinical setting and validate the model using clinical DW-MRI data of patients with locally advanced head-and-neck cancer (HNC) acquired before radiochemotherapy.
Material and methods: A total of 31 HNC patients underwent T2-weighted and DW-MRI using 3 T MRI before radiochemotherapy (35 x 2 Gy). Gross tumor volumes (GTV) were delineated based on T2-weighted and b500 images. A preclinical model previously revealed that the size of high-risk subvolumes (HRS) defined by a band of ADC-values was correlated to radiation resistance. To validate this model, different bands of ADC-values were tested using two-sided thresholds on the low-ADC histogram flank to determine HRSs inside the GTV and correlated to treatment outcome after three years. The best model was used to fit a logistic regression model. Stratification potential regarding outcome was internally validated using bootstrap, receiver-operator-characteristic (ROC)-analysis, Kaplan-Meier- and Cox-method, and compared to GTV, ADCmean and clinical factors.
Results: The best model was defined by 800-6mm2/s and correlated significantly to treatment outcome (p = 0.003). Optimal HRS cut-off value was found to be 5.8 cm3 according to ROC-analysis. This HRS demonstrated highly significant stratification potential (p < 0.001, bootstrap AUC ≥ 0.84) similar to GTV size (p < 0.001, AUC ≥ 0.79), in contrast to ADCmean (p = 0.361, AUC = 0.53).
Conclusions: A preclinical prognostic model defined by an ADC-based HRS was successfully retrained and validated in HNC patients treated with radiochemotherapy. After thorough external validation, such functional HRS based on a band of ADC values may in the future allow interventional response-adaptive MRI-guided radiotherapy in online and offline approaches.
期刊介绍：
Radiotherapy and Oncology publishes papers describing original research as well as review articles. It covers areas of interest relating to radiation oncology. This includes: clinical radiotherapy, combined modality treatment, translational studies, epidemiological outcomes, imaging, dosimetry, and radiation therapy planning, experimental work in radiobiology, chemobiology, hyperthermia and tumour biology, as well as data science in radiation oncology and physics aspects relevant to oncology.Papers on more general aspects of interest to the radiation oncologist including chemotherapy, surgery and immunology are also published.
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