Gu Bingxin, Meng Mingyuan, Bi Lei, Kim Jinman, Feng David Dagan, Song Shaoli
Department of Nuclear Medicine, Fudan University Shanghai Cancer Center, Shanghai, China.
Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China.
Front Oncol. 2022 Jul 29;12:899351. doi: 10.3389/fonc.2022.899351. eCollection 2022.
Deep learning-based radiomics (DLR) has achieved great success in medical image analysis and has been considered a replacement for conventional radiomics that relies on handcrafted features. In this study, we aimed to explore the capability of DLR for the prediction of 5-year progression-free survival (PFS) in advanced nasopharyngeal carcinoma (NPC) using pretreatment PET/CT images.
A total of 257 patients (170/87 patients in internal/external cohorts) with advanced NPC (TNM stage III or IVa) were enrolled. We developed an end-to-end multi-modality DLR model, in which a 3D convolutional neural network was optimized to extract deep features from pretreatment PET/CT images and predict the probability of 5-year PFS. The TNM stage, as a high-level clinical feature, could be integrated into our DLR model to further improve the prognostic performance. For a comparison between conventional radiomics and DLR, 1,456 handcrafted features were extracted, and optimal conventional radiomics methods were selected from 54 cross-combinations of six feature selection methods and nine classification methods. In addition, risk group stratification was performed with clinical signature, conventional radiomics signature, and DLR signature.
Our multi-modality DLR model using both PET and CT achieved higher prognostic performance (area under the receiver operating characteristic curve (AUC) = 0.842 ± 0.034 and 0.823 ± 0.012 for the internal and external cohorts) than the optimal conventional radiomics method (AUC = 0.796 ± 0.033 and 0.782 ± 0.012). Furthermore, the multi-modality DLR model outperformed single-modality DLR models using only PET (AUC = 0.818 ± 0.029 and 0.796 ± 0.009) or only CT (AUC = 0.657 ± 0.055 and 0.645 ± 0.021). For risk group stratification, the conventional radiomics signature and DLR signature enabled significant difference between the high- and low-risk patient groups in both the internal and external cohorts ( < 0.001), while the clinical signature failed in the external cohort ( = 0.177).
Our study identified potential prognostic tools for survival prediction in advanced NPC, which suggests that DLR could provide complementary values to the current TNM staging.
基于深度学习的放射组学（DLR）在医学图像分析中取得了巨大成功，并被认为可替代依赖手工特征的传统放射组学。在本研究中，我们旨在探讨DLR利用治疗前PET/CT图像预测晚期鼻咽癌（NPC）5年无进展生存期（PFS）的能力。
共纳入257例晚期NPC（TNM分期为III期或IVa期）患者（内部/外部队列分别为170/87例）。我们开发了一种端到端多模态DLR模型，其中优化了一个3D卷积神经网络，以从治疗前PET/CT图像中提取深度特征并预测5年PFS的概率。TNM分期作为一种高级临床特征，可整合到我们的DLR模型中以进一步提高预后性能。为了比较传统放射组学和DLR，提取了1456个手工特征，并从六种特征选择方法和九种分类方法的54种交叉组合中选择了最佳传统放射组学方法。此外，使用临床特征、传统放射组学特征和DLR特征进行风险组分层。
我们使用PET和CT的多模态DLR模型具有更高的预后性能（内部/外部队列的受试者操作特征曲线下面积（AUC）分别为0.842±0.034和0.823±0.012），优于最佳传统放射组学方法（AUC分别为0.796±0.033和0.782±0.012）。此外，多模态DLR模型优于仅使用PET（AUC分别为0.818±0.029和0.796±0.009）或仅使用CT（AUC分别为0.657±至0.055和0.645±0.021）的单模态DLR模型。对于风险组分层，传统放射组学特征和DLR特征在内部和外部队列的高风险和低风险患者组之间均产生了显著差异（<0.001），而临床特征在外部队列中未显示差异（=0.177）。
我们的研究确定了晚期NPC生存预测的潜在预后工具，这表明DLR可为当前TNM分期提供补充价值。
