Valerio D'Agostino, Federico Ponti, Claudia Martella, Marco Miceli, Andrea Sambri, Massimiliano De Paolis, Davide Maria Donati, Giuseppe Bianchi, Alessandra Longhi, Amandine Crombé, Paolo Spinnato
{"title":"软组织肉瘤基线磁共振成像的尺寸评估：最长直径、直径之和与积以及体积--哪种测量方法最能预测患者的预后？","authors":"Valerio D'Agostino, Federico Ponti, Claudia Martella, Marco Miceli, Andrea Sambri, Massimiliano De Paolis, Davide Maria Donati, Giuseppe Bianchi, Alessandra Longhi, Amandine Crombé, Paolo Spinnato","doi":"10.1007/s11547-024-01895-8","DOIUrl":null,"url":null,"abstract":"<p><strong>Purpose: </strong>The longest diameter (LD) is a strong prognostic factor for patients with soft-tissue sarcoma (STS). Other dimensional assessments, such as the sum of diameters (SoD), product of diameters (PoD), and volume (3D-COG - proposed by the Children Oncology Group), can be rapidly performed; however, their prognostic values have never been compared to LD. Our goal was to investigate their performance in improving patients' prognostication for STS of the lower limbs.</p><p><strong>Methods: </strong>All consecutive adults managed with curative intent at our sarcoma reference center for a newly diagnosed STS of the lower limbs between 2000 and 2017, with pre-treatment MRI, were included in this retrospective study. Multivariable Cox regression models were trained to predict metastasis-free survival (MFS) in a Training cohort of 66.7% patients based on LD, PoD, SoD, or 3D-COG (and systematically including age, histologic grade, histotype, radiotherapy, chemotherapy, and surgical margins as covariables). The models were then compared on a validation cohort of 33.3% patients using concordance indices (c-index). The same approach was applied for overall survival (OS) and local relapse-free survival (LFS). Measurement reproducibility among three readers was evaluated with an intraclass correlation coefficient (ICC).</p><p><strong>Results: </strong>382 patients were included in the survival modeling (72/253 [28.5%] metastatic relapses in Training and 36/129 [27.9%] metastatic relapses in Validation). Higher dimensions were associated with lower MFS (multivariable hazard ratio [HR] = 2.44 and P = 0.0018 for LD; HR = 1.88 and P = 0.0009 for PoD, HR = 1.52 and P = 0.0041 for SoD; and HR = 1.08 and P = 0.0195 for 3D-COG). Higher c-indices were obtained with PoD model in Training (c-index = 0.772) and Validation (c-index = 0.688), but they were not significantly higher than those obtained with LD model. None of the measurements was associated with LFS or OS. All measurements demonstrated excellent ICC (> 0.95).</p><p><strong>Conclusion: </strong>Regarding its simplicity and good performance, LD appeared as the best metric to incorporate in prognostic models and nomograms for MFS.</p>","PeriodicalId":20817,"journal":{"name":"Radiologia Medica","volume":" ","pages":""},"PeriodicalIF":9.7000,"publicationDate":"2024-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Dimensional assessment on baseline MRI of soft-tissue sarcomas: longest diameter, sum and product of diameters, and volume-which is the best measurement method to predict patients' outcomes?\",\"authors\":\"Valerio D'Agostino, Federico Ponti, Claudia Martella, Marco Miceli, Andrea Sambri, Massimiliano De Paolis, Davide Maria Donati, Giuseppe Bianchi, Alessandra Longhi, Amandine Crombé, Paolo Spinnato\",\"doi\":\"10.1007/s11547-024-01895-8\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Purpose: </strong>The longest diameter (LD) is a strong prognostic factor for patients with soft-tissue sarcoma (STS). Other dimensional assessments, such as the sum of diameters (SoD), product of diameters (PoD), and volume (3D-COG - proposed by the Children Oncology Group), can be rapidly performed; however, their prognostic values have never been compared to LD. Our goal was to investigate their performance in improving patients' prognostication for STS of the lower limbs.</p><p><strong>Methods: </strong>All consecutive adults managed with curative intent at our sarcoma reference center for a newly diagnosed STS of the lower limbs between 2000 and 2017, with pre-treatment MRI, were included in this retrospective study. Multivariable Cox regression models were trained to predict metastasis-free survival (MFS) in a Training cohort of 66.7% patients based on LD, PoD, SoD, or 3D-COG (and systematically including age, histologic grade, histotype, radiotherapy, chemotherapy, and surgical margins as covariables). The models were then compared on a validation cohort of 33.3% patients using concordance indices (c-index). The same approach was applied for overall survival (OS) and local relapse-free survival (LFS). Measurement reproducibility among three readers was evaluated with an intraclass correlation coefficient (ICC).</p><p><strong>Results: </strong>382 patients were included in the survival modeling (72/253 [28.5%] metastatic relapses in Training and 36/129 [27.9%] metastatic relapses in Validation). Higher dimensions were associated with lower MFS (multivariable hazard ratio [HR] = 2.44 and P = 0.0018 for LD; HR = 1.88 and P = 0.0009 for PoD, HR = 1.52 and P = 0.0041 for SoD; and HR = 1.08 and P = 0.0195 for 3D-COG). Higher c-indices were obtained with PoD model in Training (c-index = 0.772) and Validation (c-index = 0.688), but they were not significantly higher than those obtained with LD model. None of the measurements was associated with LFS or OS. All measurements demonstrated excellent ICC (> 0.95).</p><p><strong>Conclusion: </strong>Regarding its simplicity and good performance, LD appeared as the best metric to incorporate in prognostic models and nomograms for MFS.</p>\",\"PeriodicalId\":20817,\"journal\":{\"name\":\"Radiologia Medica\",\"volume\":\" \",\"pages\":\"\"},\"PeriodicalIF\":9.7000,\"publicationDate\":\"2024-10-18\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Radiologia Medica\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1007/s11547-024-01895-8\",\"RegionNum\":1,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Radiologia Medica","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1007/s11547-024-01895-8","RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING","Score":null,"Total":0}
Dimensional assessment on baseline MRI of soft-tissue sarcomas: longest diameter, sum and product of diameters, and volume-which is the best measurement method to predict patients' outcomes?
Purpose: The longest diameter (LD) is a strong prognostic factor for patients with soft-tissue sarcoma (STS). Other dimensional assessments, such as the sum of diameters (SoD), product of diameters (PoD), and volume (3D-COG - proposed by the Children Oncology Group), can be rapidly performed; however, their prognostic values have never been compared to LD. Our goal was to investigate their performance in improving patients' prognostication for STS of the lower limbs.
Methods: All consecutive adults managed with curative intent at our sarcoma reference center for a newly diagnosed STS of the lower limbs between 2000 and 2017, with pre-treatment MRI, were included in this retrospective study. Multivariable Cox regression models were trained to predict metastasis-free survival (MFS) in a Training cohort of 66.7% patients based on LD, PoD, SoD, or 3D-COG (and systematically including age, histologic grade, histotype, radiotherapy, chemotherapy, and surgical margins as covariables). The models were then compared on a validation cohort of 33.3% patients using concordance indices (c-index). The same approach was applied for overall survival (OS) and local relapse-free survival (LFS). Measurement reproducibility among three readers was evaluated with an intraclass correlation coefficient (ICC).
Results: 382 patients were included in the survival modeling (72/253 [28.5%] metastatic relapses in Training and 36/129 [27.9%] metastatic relapses in Validation). Higher dimensions were associated with lower MFS (multivariable hazard ratio [HR] = 2.44 and P = 0.0018 for LD; HR = 1.88 and P = 0.0009 for PoD, HR = 1.52 and P = 0.0041 for SoD; and HR = 1.08 and P = 0.0195 for 3D-COG). Higher c-indices were obtained with PoD model in Training (c-index = 0.772) and Validation (c-index = 0.688), but they were not significantly higher than those obtained with LD model. None of the measurements was associated with LFS or OS. All measurements demonstrated excellent ICC (> 0.95).
Conclusion: Regarding its simplicity and good performance, LD appeared as the best metric to incorporate in prognostic models and nomograms for MFS.
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.
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