整合基于核磁共振成像的放射组学和临床病理特征,对早期宫颈腺癌患者进行术前预后评估:与深度学习方法的比较

IF 3.5 2区 医学 Q2 ONCOLOGY Cancer Imaging Pub Date : 2024-08-01 DOI:10.1186/s40644-024-00747-y
Haifeng Qiu, Min Wang, Shiwei Wang, Xiao Li, Dian Wang, Yiwei Qin, Yongqing Xu, Xiaoru Yin, Marcus Hacker, Shaoli Han, Xiang Li
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Deep learning models were also trained with T2-weighted MRI images and clinicopathological features through Convolutional Neural Network (CNN). Kaplan-Meier curve was analyzed using significant features. In addition, information from another group of 56 AC patients was used for the independent validation. A total of 107 radiomics features and 6 clinicopathological features (age, FIGO stage, differentiation, invasion depth, lymphovascular space invasion (LVSI), and lymph node metastasis (LNM) were included in the analysis. When predicting the 3-year, 4-year, and 5-year DFS, the model trained solely on radiomics features achieved AUC values of 0.659 (95%CI: 0.620–0.716), 0.791 (95%CI: 0.603–0.922), and 0.853 (95%CI: 0.745–0.912), respectively. However, the combined model, incorporating both radiomics and clinicopathological features, outperformed the radiomics model with AUC values of 0.934 (95%CI: 0.885–0.981), 0.937 (95%CI: 0.867–0.995), and 0.916 (95%CI: 0.857–0.970), respectively. 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引用次数: 0

摘要

基于磁共振成像(MRI)的放射组学方法和深度学习方法在宫颈腺癌(AC)中的作用尚未得到探讨。在此,我们旨在开发基于磁共振成像放射组学和临床特征的颈腺癌患者预后预测模型。我们收集并分析了一百九十七名宫颈癌患者的临床和病理信息。从每位患者的 T2 加权磁共振成像中提取了 107 个放射组学特征。使用斯皮尔曼相关和随机森林(RF)算法进行特征选择,并使用支持向量机(SVM)技术建立预测模型。此外,还通过卷积神经网络(CNN)利用 T2 加权 MRI 图像和临床病理特征训练了深度学习模型。利用重要特征对 Kaplan-Meier 曲线进行了分析。此外,另一组 56 例 AC 患者的信息也被用于独立验证。共有 107 个放射组学特征和 6 个临床病理学特征(年龄、FIGO 分期、分化、侵袭深度、淋巴管间隙侵袭(LVSI)和淋巴结转移(LNM))被纳入分析。在预测 3 年、4 年和 5 年 DFS 时,仅根据放射组学特征训练的模型的 AUC 值分别为 0.659(95%CI:0.620-0.716)、0.791(95%CI:0.603-0.922)和 0.853(95%CI:0.745-0.912)。然而,包含放射组学和临床病理学特征的组合模型的 AUC 值分别为 0.934(95%CI:0.885-0.981)、0.937(95%CI:0.867-0.995)和 0.916(95%CI:0.857-0.970),优于放射组学模型。在深度学习模型中,基于 MRI 的模型在 3 年 DFS、4 年 DFS 和 5 年 DFS 预测中的 AUC 分别为 0.857、0.777 和 0.828。而联合深度学习模型的性能有所提高,AUC 分别为 0.903、0.862 和 0.998。0.862 和 0.969。在独立测试集中,组合模型对 3 年 DFS、4 年 DFS 和 5 年 DFS 预测的 AUC 分别为 0.873、0.858 和 0.914。我们证明了在宫颈腺癌中整合基于 MRI 的放射组学和临床病理特征的预后价值。当放射组学和深度学习模型与临床数据相结合时,两者的预测性能都有所提高,这强调了多模态方法在患者管理中的重要性。
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Integrating MRI-based radiomics and clinicopathological features for preoperative prognostication of early-stage cervical adenocarcinoma patients: in comparison to deep learning approach
The roles of magnetic resonance imaging (MRI) -based radiomics approach and deep learning approach in cervical adenocarcinoma (AC) have not been explored. Herein, we aim to develop prognosis-predictive models based on MRI-radiomics and clinical features for AC patients. Clinical and pathological information from one hundred and ninety-seven patients with cervical AC was collected and analyzed. For each patient, 107 radiomics features were extracted from T2-weighted MRI images. Feature selection was performed using Spearman correlation and random forest (RF) algorithms, and predictive models were built using support vector machine (SVM) technique. Deep learning models were also trained with T2-weighted MRI images and clinicopathological features through Convolutional Neural Network (CNN). Kaplan-Meier curve was analyzed using significant features. In addition, information from another group of 56 AC patients was used for the independent validation. A total of 107 radiomics features and 6 clinicopathological features (age, FIGO stage, differentiation, invasion depth, lymphovascular space invasion (LVSI), and lymph node metastasis (LNM) were included in the analysis. When predicting the 3-year, 4-year, and 5-year DFS, the model trained solely on radiomics features achieved AUC values of 0.659 (95%CI: 0.620–0.716), 0.791 (95%CI: 0.603–0.922), and 0.853 (95%CI: 0.745–0.912), respectively. However, the combined model, incorporating both radiomics and clinicopathological features, outperformed the radiomics model with AUC values of 0.934 (95%CI: 0.885–0.981), 0.937 (95%CI: 0.867–0.995), and 0.916 (95%CI: 0.857–0.970), respectively. For deep learning models, the MRI-based models achieved an AUC of 0.857, 0.777 and 0.828 for 3-year DFS, 4-year DFS and 5-year DFS prediction, respectively. And the combined deep learning models got a improved performance, the AUCs were 0.903. 0.862 and 0.969. In the independent test set, the combined model achieved an AUC of 0.873, 0.858 and 0.914 for 3-year DFS, 4-year DFS and 5-year DFS prediction, respectively. We demonstrated the prognostic value of integrating MRI-based radiomics and clinicopathological features in cervical adenocarcinoma. Both radiomics and deep learning models showed improved predictive performance when combined with clinical data, emphasizing the importance of a multimodal approach in patient management.
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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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