An exploratory assessment of early and delta PET radiomic features for outcome prediction in locally advanced cervical cancer.

IF 8.6 1区 医学 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING
Anita Florit,Wyanne A Noortman,Nicolò Bizzarri,Tina Pasciuto,Vanessa Feudo,Salvatore Annunziata,Lioe-Fee de Geus-Oei,Elisabeth Pfaehler,Ronald Boellaard,Maria Antonietta Gambacorta,Gian Franco Zannoni,Gabriella Ferrandina,Evis Sala,Giovanni Scambia,Vittoria Rufini,Floris H P van Velden,Angela Collarino
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引用次数: 0

Abstract

PURPOSE This study investigated whether radiomic features extracted from [18F]FDG-PET scans acquired before and two weeks after neoadjuvant treatment, and their variation, provided prognostic parameters in locally advanced cervical cancer (LACC) patients treated with neoadjuvant chemo-radiotherapy (CRT) followed by radical surgery. METHODS We retrospectively included LACC patients referred to our Institution from 2010 to 2016. [18F]FDG-PET/CT was performed before neoadjuvant CRT (baseline) and two weeks after the start of treatment (early). Radiomic features were extracted after semi-automatic delineation of the primary tumour, on baseline and early PET images. Delta radiomics were calculated as the relative differences between baseline and early features. We performed 5-fold cross-validation stratified for recurrence and cancer-specific death, integrating dimensionality reduction of the radiomic features and variable hunting with importance within the folds. After supervised feature selection, radiomic models with the best-performing features for each timepoint, as well as clinical models and combined clinico-radiomic models, were built. Model performances are presented as C-indices, for prediction of recurrence/progression (disease-free survival, DFS) and cancer-specific death (overall survival, OS). RESULTS 95 patients were included. With a median follow-up of 76.0 months (95% CI: 59.5-82.1), 31.6% of patients had recurrence/progression and 20.0% died of disease. None of the models could predict DFS (C-indices ≤ 0.72). Model performances for OS yielded slightly better results, with mean C-indices of 0.75 for both the radiomic and combined model based on early features, 0.79 and 0.78 for the radiomic and combined model derived from delta features, and 0.76 for the clinical models. CONCLUSION [18F]FDG-PET early and delta radiomic features could not predict DFS in patients with LACC treated with neoadjuvant CRT followed by radical surgery. Although slightly improved performances for the radiomic and combined models were observed in the prediction of OS compared to the clinical model, the added value of these parameters and their inclusion in the clinical practice seems to be limited.
局部晚期宫颈癌早期和三角洲PET放射学特征预测预后的探索性评估。
目的:本研究探讨在新辅助治疗前和两周后获得的[18F]FDG-PET扫描的放射学特征及其变化,是否为局部晚期宫颈癌(LACC)患者提供了新辅助化疗放疗(CRT)后根治性手术的预后参数。方法回顾性纳入2010年至2016年在我院就诊的LACC患者。[18F]在新辅助CRT前(基线)和治疗开始后两周(早期)进行FDG-PET/CT检查。在基线和早期PET图像上对原发肿瘤进行半自动描绘后提取放射学特征。δ放射组学计算基线和早期特征之间的相对差异。我们对复发和癌症特异性死亡进行了5次交叉验证分层,整合了放射学特征的降维和变量搜索在折叠中的重要性。经过监督特征选择,构建每个时间点表现最佳特征的放射组学模型,以及临床模型和临床-放射组学联合模型。模型性能以c指数表示,用于预测复发/进展(无病生存期,DFS)和癌症特异性死亡(总生存期,OS)。结果纳入95例患者。中位随访76.0个月(95% CI: 59.5-82.1), 31.6%的患者出现复发/进展,20.0%死于疾病。所有模型均不能预测DFS (c指数≤0.72)。OS的模型性能略好,基于早期特征的放射组学和联合模型的平均c指数为0.75,基于delta特征的放射组学和联合模型的平均c指数为0.79和0.78,临床模型的平均c指数为0.76。结论[18F]FDG-PET早期和三角放射学特征不能预测新辅助CRT后根治性手术的LACC患者的DFS。虽然与临床模型相比,放射组学和联合模型在预测OS方面的表现略有改善,但这些参数的附加价值及其在临床实践中的应用似乎有限。
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来源期刊
CiteScore
15.60
自引率
9.90%
发文量
392
审稿时长
3 months
期刊介绍: The European Journal of Nuclear Medicine and Molecular Imaging serves as a platform for the exchange of clinical and scientific information within nuclear medicine and related professions. It welcomes international submissions from professionals involved in the functional, metabolic, and molecular investigation of diseases. The journal's coverage spans physics, dosimetry, radiation biology, radiochemistry, and pharmacy, providing high-quality peer review by experts in the field. Known for highly cited and downloaded articles, it ensures global visibility for research work and is part of the EJNMMI journal family.
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