Liu Lili, Wan Haoming, Liu Li, Wang Jie, Tang Yibo, Cui Shaoguo, Li Yongmei
Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, No. 1 Youyi Road, Yuzhong District, Chongqing 400016, China.
Department of Radiology, Chongqing General Hospital, Chongqing 401120, China.
Diagnostics (Basel). 2023 Feb 16;13(4):748. doi: 10.3390/diagnostics13040748.
This study aims to use a deep learning method to develop a signature extract from preoperative magnetic resonance imaging (MRI) and to evaluate its ability as a non-invasive recurrence risk prognostic marker in patients with advanced high-grade serous ovarian cancer (HGSOC). Our study comprises a total of 185 patients with pathologically confirmed HGSOC. A total of 185 patients were randomly assigned in a 5:3:2 ratio to a training cohort (n = 92), validation cohort 1 (n = 56), and validation cohort 2 (n = 37). We built a new deep learning network from 3839 preoperative MRI images (T2-weighted images and diffusion-weighted images) to extract HGSOC prognostic indicators. Following that, a fusion model including clinical and deep learning features is developed to predict patients' individual recurrence risk and 3-year recurrence likelihood. In the two validation cohorts, the consistency index of the fusion model was higher than both the deep learning model and the clinical feature model (0.752, 0.813 vs. 0.625, 0.600 vs. 0.505, 0.501). Among the three models, the fusion model had a higher AUC than either the deep learning model or the clinical model in validation cohorts 1 or 2 (AUC = was 0.986, 0.961 vs. 0.706, 0.676/0.506, 0.506). Using the DeLong method, the difference between them was statistically significant ( < 0.05). The Kaplan-Meier analysis distinguished two patient groups with high and low recurrence risk ( = 0.0008 and 0.0035, respectively). Deep learning may be a low-cost, non-invasive method for predicting risk for advanced HGSOC recurrence. Deep learning based on multi-sequence MRI serves as a prognostic biomarker for advanced HGSOC, which provides a preoperative model for predicting recurrence in HGSOC. Additionally, using the fusion model as a new prognostic analysis means that can use MRI data can be used without the need to follow-up the prognostic biomarker.
本研究旨在使用深度学习方法从术前磁共振成像（MRI）中提取特征，并评估其作为晚期高级别浆液性卵巢癌（HGSOC）患者非侵入性复发风险预后标志物的能力。我们的研究共纳入185例经病理证实的HGSOC患者。185例患者按5:3:2的比例随机分配至训练队列（n = 92）、验证队列1（n = 56）和验证队列2（n = 37）。我们从3839张术前MRI图像（T2加权图像和扩散加权图像）构建了一个新的深度学习网络，以提取HGSOC预后指标。在此之后，开发了一个包括临床和深度学习特征的融合模型，以预测患者的个体复发风险和3年复发可能性。在两个验证队列中，融合模型的一致性指数高于深度学习模型和临床特征模型（分别为0.752、0.813与0.625、0.600与0.505、0.501）。在这三个模型中，融合模型在验证队列1或2中的AUC高于深度学习模型或临床模型（AUC分别为0.986、0.961与0.706、0.676/0.506、0.506）。使用DeLong方法，它们之间的差异具有统计学意义（<0.05）。Kaplan-Meier分析区分出了复发风险高和低的两组患者（分别为= 0.0008和0.0035）。深度学习可能是一种低成本、非侵入性的预测晚期HGSOC复发风险的方法。基于多序列MRI的深度学习可作为晚期HGSOC的预后生物标志物，为HGSOC复发预测提供术前模型。此外，使用融合模型作为一种新的预后分析方法意味着无需对预后生物标志物进行随访即可使用MRI数据。
