Drury Bevis, Machado Inês P, Gao Zeyu, Buddenkotte Thomas, Mahani Golnar, Funingana Gabriel, Reinius Marika, McCague Cathal, Woitek Ramona, Sahdev Anju, Sala Evis, Brenton James D, Crispin-Ortuzar Mireia
Department of Physics, University of Cambridge, Cambridge, United Kingdom.
Department of Oncology, University of Cambridge, Cambridge, United Kingdom.
Int J Comput Assist Radiol Surg. 2025 Sep 3. doi: 10.1007/s11548-025-03484-0.
: High-grade serous ovarian carcinoma (HGSOC) is characterised by significant spatial and temporal heterogeneity, often presenting at an advanced metastatic stage. One of the most common treatment approaches involves neoadjuvant chemotherapy (NACT), followed by surgery. However, the multi-scale complexity of HGSOC poses a major challenge in evaluating response to NACT.
: Here, we present a multi-task deep learning approach that facilitates simultaneous segmentation of pelvic/ovarian and omental lesions in contrast-enhanced computerised tomography (CE-CT) scans, as well as treatment response assessment in metastatic ovarian cancer. The model combines multi-scale feature representations from two identical U-Net architectures, allowing for an in-depth comparison of CE-CT scans acquired before and after treatment. The network was trained using 198 CE-CT images of 99 ovarian cancer patients for predicting segmentation masks and evaluating treatment response.
: It achieves an AUC of 0.78 (95% CI [0.70-0.91]) in an independent cohort of 98 scans of 49 ovarian cancer patients from a different institution. In addition to the classification performance, the segmentation Dice scores are only slightly lower than the current state-of-the-art for HGSOC segmentation.
: This work is the first to demonstrate the feasibility of a multi-task deep learning approach in assessing chemotherapy-induced tumour changes across the main disease burden of patients with complex multi-site HGSOC, which could be used for treatment response evaluation and disease monitoring.
高级别浆液性卵巢癌（HGSOC）具有显著的空间和时间异质性，常于晚期转移阶段出现。最常见的治疗方法之一是新辅助化疗（NACT），随后进行手术。然而，HGSOC的多尺度复杂性给评估对NACT的反应带来了重大挑战。
在此，我们提出一种多任务深度学习方法，该方法有助于在对比增强计算机断层扫描（CE-CT）扫描中同时分割盆腔/卵巢和网膜病变，以及评估转移性卵巢癌的治疗反应。该模型结合了来自两个相同U-Net架构的多尺度特征表示，从而能够对治疗前后获取的CE-CT扫描进行深入比较。使用99例卵巢癌患者的198张CE-CT图像对网络进行训练，以预测分割掩码并评估治疗反应。
在来自不同机构的49例卵巢癌患者的98次扫描的独立队列中，其AUC为0.78（95%CI[0.70-0.91]）。除分类性能外，分割Dice分数仅略低于目前HGSOC分割的最先进水平。
这项工作首次证明了多任务深度学习方法在评估复杂多部位HGSOC患者主要疾病负担的化疗诱导肿瘤变化方面的可行性，可用于治疗反应评估和疾病监测。
