Prognostic value of combining clinical factors, 18F-FDG PET-based intensity, volumetric features, and deep learning predictor in patients with EGFR-mutated lung adenocarcinoma undergoing targeted therapies: a cross-scanner and temporal validation study
IF 2.5 4区 医学Q2 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING
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引用次数: 0
Abstract
Objective
To investigate the prognostic value of 18F-FDG PET-based intensity, volumetric features, and deep learning (DL) across different generations of PET scanners in patients with epidermal growth factor receptor (EGFR)-mutated lung adenocarcinoma receiving tyrosine kinase inhibitor (TKI) treatment.
Methods
We retrospectively analyzed the pre-treatment 18F-FDG PET of 217 patients with advanced-stage lung adenocarcinoma and actionable EGFR mutations who received TKI as first-line treatment. Patients were separated into analog (n = 166) and digital (n = 51) PET cohorts. 18F-FDG PET-derived intensity, volumetric features, ResNet-50 DL of the primary tumor, and clinical variables were used to predict progression-free survival (PFS). Independent prognosticators were used to develop prediction model. Model was developed and validated in the analog and digital PET cohorts, respectively.
Results
In the analog PET cohort, female sex, stage IVB status, exon 19 deletion, SUVmax, metabolic tumor volume, and positive DL prediction independently predicted PFS. The model devised from these six prognosticators significantly predicted PFS in the analog (HR = 1.319, p < 0.001) and digital PET cohorts (HR = 1.284, p = 0.001). Our model provided incremental prognostic value to staging status (c-indices = 0.738 vs. 0.558 and 0.662 vs. 0.598 in the analog and digital PET cohorts, respectively). Our model also demonstrated a significant prognostic value for overall survival (HR = 1.198, p < 0.001, c-index = 0.708 and HR = 1.256, p = 0.021, c-index = 0.664 in the analog and digital PET cohorts, respectively).
Conclusions
Combining 18F-FDG PET-based intensity, volumetric features, and DL with clinical variables may improve the survival stratification in patients with advanced EGFR-mutated lung adenocarcinoma receiving TKI treatment. Implementing the prediction model across different generations of PET scanners may be feasible and facilitate tailored therapeutic strategies for these patients.
在接受靶向治疗的表皮生长因子受体突变肺腺癌患者中结合临床因素、基于 18F-FDG PET 的强度、容积特征和深度学习预测因子的预后价值:一项跨扫描仪和时间验证研究。
目的研究在接受酪氨酸激酶抑制剂(TKI)治疗的表皮生长因子受体(EGFR)突变肺腺癌患者中,不同代PET扫描仪基于18F-FDG PET的强度、容积特征和深度学习(DL)的预后价值:我们回顾性分析了217例接受TKI一线治疗的可作用表皮生长因子受体突变晚期肺腺癌患者的治疗前18F-FDG PET。患者被分为模拟 PET 组(166 人)和数字 PET 组(51 人)。18F-FDG PET衍生强度、体积特征、原发肿瘤的ResNet-50 DL和临床变量被用来预测无进展生存期(PFS)。独立的预后指标被用于建立预测模型。分别在模拟和数字 PET 队列中建立并验证了模型:在模拟 PET 群体中,女性性别、IVB 期状态、19 号外显子缺失、SUVmax、代谢肿瘤体积和 DL 阳性预测可独立预测 PFS。根据这六项预后指标建立的模型可显著预测模拟组的生存期(HR = 1.319,p 结论:18F-FDG PET 可显著预测模拟组的生存期:将基于18F-FDG PET的强度、容积特征和DL与临床变量相结合,可改善接受TKI治疗的晚期EGFR突变肺腺癌患者的生存分层。在不同世代的 PET 扫描仪上应用该预测模型可能是可行的,并有助于为这些患者量身定制治疗策略。
期刊介绍:
Annals of Nuclear Medicine is an official journal of the Japanese Society of Nuclear Medicine. It develops the appropriate application of radioactive substances and stable nuclides in the field of medicine.
The journal promotes the exchange of ideas and information and research in nuclear medicine and includes the medical application of radionuclides and related subjects. It presents original articles, short communications, reviews and letters to the editor.