A comparison of 2D and 3D magnetic resonance imaging-based intratumoral and peritumoral radiomics models for the prognostic prediction of endometrial cancer: a pilot study.

IF 3.5 2区 医学 Q2 ONCOLOGY Cancer Imaging Pub Date : 2024-07-31 DOI:10.1186/s40644-024-00743-2
Ruixin Yan, Siyuan Qin, Jiajia Xu, Weili Zhao, Peijin Xin, Xiaoying Xing, Ning Lang
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Abstract

Background: Accurate prognostic assessment is vital for the personalized treatment of endometrial cancer (EC). Although radiomics models have demonstrated prognostic potential in EC, the impact of region of interest (ROI) delineation strategies and the clinical significance of peritumoral features remain uncertain. Our study thereby aimed to explore the predictive performance of varying radiomics models for the prediction of LVSI, DMI, and disease stage in EC.

Methods: Patients with 174 histopathology-confirmed EC were retrospectively reviewed. ROIs were manually delineated using the 2D and 3D approach on T2-weighted MRI images. Six radiomics models involving intratumoral (2Dintra and 3Dintra), peritumoral (2Dperi and 3Dperi), and combined models (2Dintra + peri and 3Dintra + peri) were developed. Models were constructed using the logistic regression method with five-fold cross-validation. Area under the receiver operating characteristic curve (AUC) was assessed, and was compared using the Delong's test.

Results: No significant differences in AUC were observed between the 2Dintra and 3Dintra models, or the 2Dperi and 3Dperi models in all prediction tasks (P > 0.05). Significant difference was observed between the 3Dintra and 3Dperi models for LVSI (0.738 vs. 0.805) and DMI prediction (0.719 vs. 0.804). The 3Dintra + peri models demonstrated significantly better predictive performance in all 3 prediction tasks compared to the 3Dintra model in both the training and validation cohorts (P < 0.05).

Conclusions: Comparable predictive performance was observed between the 2D and 3D models. Combined models significantly improved predictive performance, especially with 3D delineation, suggesting that intra- and peritumoral features can provide complementary information for comprehensive prognostication of EC.

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基于二维和三维磁共振成像的瘤内和瘤周放射组学模型用于子宫内膜癌预后预测的比较:一项试点研究。
背景:准确的预后评估对于子宫内膜癌(EC)的个性化治疗至关重要。尽管放射组学模型已经证明了子宫内膜癌的预后潜力,但感兴趣区(ROI)划分策略的影响以及瘤周特征的临床意义仍不确定。因此,我们的研究旨在探索不同放射组学模型在预测EC的LVSI、DMI和疾病分期方面的预测性能:方法:对174例组织病理学确诊的EC患者进行回顾性研究。在T2加权核磁共振成像上使用二维和三维方法手动划分ROI。建立了六个放射组学模型,包括瘤内(2Dintra 和 3Dintra )、瘤周(2Dperi 和 3Dperi )和组合模型(2Dintra + 瘤周和 3Dintra + 瘤周)。模型的建立采用了逻辑回归法,并进行了五次交叉验证。评估接收者操作特征曲线下面积(AUC),并使用德隆检验进行比较:结果:在所有预测任务中,2Dintra 和 3Dintra 模型、2Dperi 和 3Dperi 模型的 AUC 均无明显差异(P > 0.05)。在 LVSI(0.738 vs. 0.805)和 DMI 预测(0.719 vs. 0.804)方面,3Dintra 和 3Dperi 模型之间存在显著差异。与 3Dintra 模型相比,3Dintra + peri 模型在所有 3 项预测任务中的预测性能在训练组和验证组中都明显优于 3Dintra 模型(P 结论:3Dintra + peri 模型在所有 3 项预测任务中的预测性能在训练组和验证组中都明显优于 3Dintra 模型):二维和三维模型的预测性能相当。组合模型明显提高了预测性能,尤其是在三维划分时,这表明瘤内和瘤周特征可为心血管疾病的综合预后提供互补信息。
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来源期刊
Cancer Imaging
Cancer Imaging ONCOLOGY-RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING
CiteScore
7.00
自引率
0.00%
发文量
66
审稿时长
>12 weeks
期刊介绍: Cancer Imaging is an open access, peer-reviewed journal publishing original articles, reviews and editorials written by expert international radiologists working in oncology. The journal encompasses CT, MR, PET, ultrasound, radionuclide and multimodal imaging in all kinds of malignant tumours, plus new developments, techniques and innovations. Topics of interest include: Breast Imaging Chest Complications of treatment Ear, Nose & Throat Gastrointestinal Hepatobiliary & Pancreatic Imaging biomarkers Interventional Lymphoma Measurement of tumour response Molecular functional imaging Musculoskeletal Neuro oncology Nuclear Medicine Paediatric.
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