Graduate Institute of Biomedical Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan; Graduate Institute of Applied Science and Technology, National Taiwan University of Science and Technology, Taipei, Taiwan.
Department of Gynecology and Obstetrics, Tri-Service General Hospital, Taipei, Taiwan; Graduate Institute of Medical Sciences, National Defense Medical Center, Taipei, Taiwan.
Comput Med Imaging Graph. 2022 Jul;99:102093. doi: 10.1016/j.compmedimag.2022.102093. Epub 2022 Jun 16.
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 decease. Surgical debulking of tumors following chemotherapy is the conventional treatment for advanced carcinoma, but patients with such treatment remain at great risk for recurrence and developing drug resistance, and only about 30 % of the women affected will be cured. Bevacizumab is a humanized monoclonal antibody, which blocks VEGF signaling in cancer, inhibits angiogenesis and causes tumor shrinkage, and has been recently approved by FDA as a monotherapy for advanced ovarian cancer in combination with chemotherapy. Considering the cost, potential toxicity, and finding that only a portion of patients will benefit from these drugs, the identification of new predictive method for the treatment of ovarian cancer remains an urgent unmet medical need. In this study, we develop weakly supervised deep learning approaches to accurately predict therapeutic effect for bevacizumab of ovarian cancer patients from histopathological hematoxylin and eosin stained whole slide images, without any pathologist-provided locally annotated regions. To the authors' best knowledge, this is the first model demonstrated to be effective for prediction of the therapeutic effect of patients with epithelial ovarian cancer to bevacizumab. Quantitative evaluation of a whole section dataset shows that the proposed method achieves high accuracy, 0.882 ± 0.06; precision, 0.921 ± 0.04, recall, 0.912 ± 0.03; F-measure, 0.917 ± 0.07 using 5-fold cross validation and outperforms two state-of-the art deep learning approaches Coudray et al. (2018), Campanella et al. (2019). For an independent TMA testing set, the three proposed methods obtain promising results with high recall (sensitivity) 0.946, 0.893 and 0.964, respectively. The results suggest that the proposed method could be useful for guiding treatment by assisting in filtering out patients without positive therapeutic response to suffer from further treatments while keeping patients with positive response in the treatment process. Furthermore, according to the statistical analysis of the Cox Proportional Hazards Model, patients who were predicted to be invalid by the proposed model had a very high risk of cancer recurrence (hazard ratio = 13.727) than patients predicted to be effective with statistical signifcance (p < 0.05).
尽管在过去的二十年中，卵巢癌的手术和化疗取得了进展，但超过 70%的晚期患者仍会出现复发和死亡。化疗后对肿瘤进行手术减瘤是晚期癌的常规治疗方法，但接受这种治疗的患者仍面临复发和产生耐药性的巨大风险，只有约 30%的受影响女性能够治愈。贝伐单抗是一种人源化单克隆抗体，可阻断癌症中的 VEGF 信号，抑制血管生成并导致肿瘤缩小，最近已被 FDA 批准与化疗联合用于治疗晚期卵巢癌的单药治疗。考虑到成本、潜在毒性以及发现只有一部分患者将从这些药物中受益，因此，寻找新的预测卵巢癌治疗效果的方法仍然是一种迫切未满足的医疗需求。在这项研究中，我们开发了弱监督深度学习方法，从组织病理学苏木精和伊红染色的全幻灯片图像中准确预测卵巢癌患者接受贝伐单抗治疗的效果，而无需病理学家提供任何局部注释区域。据作者所知，这是第一个被证明对预测上皮性卵巢癌患者对贝伐单抗治疗效果有效的模型。对整个数据集的定量评估表明，所提出的方法在使用 5 折交叉验证时具有很高的准确性，为 0.882±0.06；精度为 0.921±0.04，召回率为 0.912±0.03；F1 分数为 0.917±0.07，优于两种最先进的深度学习方法 Coudray 等人（2018 年）、Campanella 等人（2019 年）。对于独立的 TMA 测试集，所提出的三种方法均获得了有希望的结果，具有较高的召回率（灵敏度），分别为 0.946、0.893 和 0.964。结果表明，该方法可用于通过协助筛选对治疗无积极反应的患者，避免因进一步治疗而遭受痛苦，同时使对治疗有积极反应的患者继续接受治疗，从而有助于指导治疗。此外，根据 Cox 比例风险模型的统计分析，被预测为无效的患者发生癌症复发的风险很高（风险比=13.727），与预测为有效的患者有统计学意义（p<0.05）。
