Florit Anita, Noortman Wyanne A, Bizzarri Nicolò, Pasciuto Tina, Feudo Vanessa, Annunziata Salvatore, de Geus-Oei Lioe-Fee, Pfaehler Elisabeth, Boellaard Ronald, Gambacorta Maria Antonietta, Zannoni Gian Franco, Ferrandina Gabriella, Sala Evis, Scambia Giovanni, Rufini Vittoria, van Velden Floris H P, Collarino Angela
Nuclear Medicine Unit, Department of Diagnostic Imaging and Oncological Radiotherapy, Fondazione Policlinico Universitario A. Gemelli IRCCS, Largo A. Gemelli, 8, 00168, Rome, Italy.
Biomedical Photonic Imaging Group, University of Twente, Enschede, The Netherlands.
Eur J Nucl Med Mol Imaging. 2025 Jun 18. doi: 10.1007/s00259-025-07405-w.
PURPOSE: This study investigated whether radiomic features extracted from [F]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. [F]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: [F]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.
目的：本研究调查了从新辅助治疗前及治疗两周后采集的[F]FDG-PET扫描中提取的放射组学特征及其变化，是否能为接受新辅助放化疗（CRT）后行根治性手术的局部晚期宫颈癌（LACC）患者提供预后参数。 方法：我们回顾性纳入了2010年至2016年转诊至我院的LACC患者。在新辅助CRT前（基线）和治疗开始两周后（早期）进行[F]FDG-PET/CT检查。在基线期和早期PET图像上，通过半自动勾画原发肿瘤来提取放射组学特征。将放射组学变化值计算为基线期和早期特征之间的相对差异。我们进行了5折交叉验证，并根据复发和癌症特异性死亡进行分层，整合了放射组学特征的降维和各折内具有重要性的变量搜索。在进行监督特征选择后，构建了每个时间点具有最佳表现特征的放射组学模型、临床模型以及联合临床放射组学模型。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：共纳入95例患者。中位随访时间为76.0个月（95%CI：59.5 - 82.1），31.6%的患者出现复发/进展，20.0%的患者死于疾病。没有一个模型能够预测DFS（C指数≤0.72）。OS模型性能的结果稍好一些，基于早期特征的放射组学模型和联合模型的平均C指数均为0.75，基于变化值特征的放射组学模型和联合模型分别为0.79和0.78，临床模型为0.76。 结论：对于接受新辅助CRT后行根治性手术的LACC患者，[F]FDG-PET早期和变化值放射组学特征无法预测DFS。尽管与临床模型相比，放射组学模型和联合模型在预测OS方面表现略有改善，但这些参数的附加值及其在临床实践中的应用似乎有限。
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