Ovarian cancer, predominantly epithelial ovarian cancer (EOC), is a global health concern due to its high mortality rate. Despite the progress made during the last two decades in the surgery and chemotherapy of ovarian cancer, more than 70% of advanced patients are with recurrent cancer and disease. Bevacizumab is a humanized monoclonal antibody, which blocks signaling in cancer, inhibits angiogenesis and causes tumor shrinkage, and has been recently approved by the FDA as a monotherapy for advanced ovarian cancer in combination with chemotherapy. Unfortunately, Bevacizumab may also induce harmful adverse effects, such as hypertension, bleeding, arterial thromboembolism, poor wound healing and gastrointestinal perforation. Given the expensive cost and unwanted toxicities, there is an urgent need for predictive methods to identify who could benefit from bevacizumab. Of the 18 (approved) requests from 5 countries, 6 teams using 284 whole section WSIs for training to develop fully automated systems submitted their predictions on a test set of 180 tissue core images, with the corresponding ground truth labels kept private. This paper summarizes the 5 qualified methods successfully submitted to the international challenge of automated prediction of treatment effectiveness in ovarian cancer using the histopathologic images (ATEC23) held at the 26th International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI) in 2023 and evaluates the methods in comparison with 5 state of the art deep learning approaches. This study further assesses the effectiveness of the presented prediction models as indicators for patient selection utilizing both Cox proportional hazards analysis and Kaplan–Meier survival analysis. A robust and cost-effective deep learning pipeline for digital histopathology tasks has become a necessity within the context of the medical community. This challenge highlights the limitations of current MIL methods, particularly within the context of prognosis-based classification tasks, and the importance of DCNNs like inception that has nonlinear convolutional modules at various resolutions to facilitate processing the data in multiple resolutions, which is a key feature required for pathology related prediction tasks. This further suggests the use of feature reuse at various scales to improve models for future research directions. In particular, this paper releases the labels of the testing set and provides applications for future research directions in precision oncology to predict ovarian cancer treatment effectiveness and facilitate patient selection via histopathological images.

中文翻译：

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ATEC23 挑战：使用组织病理学图像自动预测卵巢癌的治疗效果


卵巢癌，主要是上皮性卵巢癌（EOC），因其高死亡率而成为全球健康问题。尽管过去二十年在卵巢癌的手术和化疗方面取得了进展，但超过 70% 的晚期患者患有复发性癌症和疾病。贝伐珠单抗是一种人源化单克隆抗体，可阻断癌症信号传导、抑制血管生成并导致肿瘤缩小，最近已被 FDA 批准作为与化疗联合治疗晚期卵巢癌的单一疗法。不幸的是，贝伐单抗也可能引起有害的副作用，例如高血压、出血、动脉血栓栓塞、伤口愈合不良和胃肠道穿孔。考虑到昂贵的成本和不必要的毒性，迫切需要预测方法来确定谁可以从贝伐珠单抗中受益。在来自 5 个国家的 18 项（已批准的）请求中，6 个团队使用 284 个全截面 WSI 进行培训以开发全自动系统，提交了对 180 个组织核心图像的测试集的预测，相应的地面真实标签保密。本文总结了在 2023 年第 26 届国际医学图像计算和计算机辅助干预会议 (MICCAI) 上成功提交的利用组织病理学图像自动预测卵巢癌治疗效果国际挑战 (ATEC23) 的 5 种合格方法，以及与 5 种最先进的深度学习方法进行比较来评估这些方法。本研究利用 Cox 比例风险分析和 Kaplan-Meier 生存分析进一步评估了所提出的预测模型作为患者选择指标的有效性。 用于数字组织病理学任务的强大且具有成本效益的深度学习管道已成为医学界的必需品。这一挑战凸显了当前 MIL 方法的局限性，特别是在基于预后的分类任务的背景下，以及像 inception 这样的 DCNN 的重要性，它具有各种分辨率的非线性卷积模块，以便于以多种分辨率处理数据，这是一个关键特征病理相关预测任务所需。这进一步表明在不同尺度上使用特征重用来改进未来研究方向的模型。特别是，本文发布了测试集的标签，并为精准肿瘤学的未来研究方向提供了应用，以预测卵巢癌的治疗效果，并通过组织病理学图像方便患者选择。