Temporal Validation of an FDG-PET-Radiomic Model for Distant-Relapse-Free-Survival After Radio-Chemotherapy for Pancreatic Adenocarcinoma.

IF 4.5 2区 医学 Q1 ONCOLOGY
Cancers Pub Date : 2025-03-20 DOI:10.3390/cancers17061036
Monica Maria Vincenzi, Martina Mori, Paolo Passoni, Roberta Tummineri, Najla Slim, Martina Midulla, Gabriele Palazzo, Alfonso Belardo, Emiliano Spezi, Maria Picchio, Michele Reni, Arturo Chiti, Antonella Del Vecchio, Claudio Fiorino, Nadia Gisella Di Muzio
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引用次数: 0

Abstract

Background/Objectives: Pancreatic cancer is a very aggressive disease with a poor prognosis, even when diagnosed at an early stage. This study aimed to validate and refine a radiomic-based [18F]FDG-PET model to predict distant relapse-free survival (DRFS) in patients with unresectable locally advanced pancreatic cancer (LAPC). Methods: A Cox regression model incorporating two radiomic features (RFs) and cancer stage (III vs. IV) was temporally validated using a larger cohort (215 patients treated between 2005-2022). Patients received concurrent chemoradiotherapy with capecitabine and hypo-fractionated Intensity Modulated Radiotherapy (IMRT). Data were split into training (145 patients, 2005-2017) and validation (70 patients, 2017-2022) groups. Seventy-eight RFs were extracted, harmonized, and analyzed using machine learning to develop refined models. Results: The model incorporating Statistical-Percentile10, Morphological-ComShift, and stage demonstrated moderate predictive accuracy (training: C-index = 0.632; validation: C-index = 0.590). When simplified to include only Statistical-Percentile10, performance improved slightly in the validation group (C-index = 0.601). Adding GLSZM3D-grayLevelVariance to Statistical-Percentile10, while excluding Morphological-ComShift, further enhanced accuracy (training: C-index = 0.654; validation: C-index = 0.623). Despite these refinements, all versions showed similar moderate ability to stratify patients into risk classes. Conclusions: [18F]FDG-PET radiomic features are robust predictors of DRFS after chemoradiotherapy in LAPC. Despite moderate performance, these models hold promise for patient risk stratification. Further validation with external cohorts is ongoing.

背景/目标:胰腺癌是一种侵袭性很强的疾病,即使在早期确诊,预后也很差。本研究旨在验证和完善基于放射组学的[18F]FDG-PET模型,以预测无法切除的局部晚期胰腺癌(LAPC)患者的无远处复发生存期(DRFS)。方法:利用更大的队列(2005-2022年间接受治疗的215名患者)对包含两种放射学特征(RF)和癌症分期(III期与IV期)的Cox回归模型进行了时间验证。患者同时接受卡培他滨化疗和低分次调强放疗(IMRT)。数据分为训练组(145 名患者,2005-2017 年)和验证组(70 名患者,2017-2022 年)。提取、统一和分析了78个RFs,并使用机器学习建立了完善的模型。结果:包含统计百分位数10(Statistical-Percentile10)、形态-ComShift和分期的模型显示出中等预测准确性(训练:C-指数=0.632;验证:C-指数=0.590)。当简化到只包括统计珀森迪尔10时,验证组的性能略有提高(C-index = 0.601)。将 GLSZM3D-grayLevelVariance 加入 Statistical-Percentile10,同时排除 Morphological-ComShift,进一步提高了准确性(训练:C-index = 0.654;验证:C-index = 0.623)。尽管进行了这些改进,但所有版本在对患者进行风险分层方面都显示出类似的中等能力。结论[18F]FDG-PET放射学特征是预测LAPC化疗后DRFS的可靠指标。尽管表现一般,但这些模型有望对患者进行风险分层。目前正在通过外部队列进行进一步验证。
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来源期刊
Cancers
Cancers Medicine-Oncology
CiteScore
8.00
自引率
9.60%
发文量
5371
审稿时长
18.07 days
期刊介绍: Cancers (ISSN 2072-6694) is an international, peer-reviewed open access journal on oncology. It publishes reviews, regular research papers and short communications. Our aim is to encourage scientists to publish their experimental and theoretical results in as much detail as possible. There is no restriction on the length of the papers. The full experimental details must be provided so that the results can be reproduced.
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