论文标题
深度学习得出的组织病理学图像评分增加了第3阶段成功的临床试验概率
Deep Learning Derived Histopathology Image Score for Increasing Phase 3 Clinical Trial Probability of Success
论文作者
论文摘要
第三阶段临床试验中的失败导致肿瘤学中药物开发的昂贵成本。为了大幅度降低这种成本，需要在药物开发过程的早期对肿瘤学治疗的反应者进行识别，并在计划3阶段临床试验之前，患者数据有限。尽管样本量较小，但我们开创了使用深度学习的衍生数字病理学评分，以根据来自1期非小细胞肺癌临床试验的肿瘤活检样品中表达的靶抗原的免疫组织化学图像来识别响应者。基于重复的10倍交叉验证，与基于肿瘤的临床基准相比，平均衍生得分的ROC曲线平均获得了4％的AUC和Precision-Recall曲线的AUC曲线高6％。在一组独立的独立测试集中，我们还证明，与TPS临床基准相比，富集人群中的深度学习得分在数值上至少提高了25％。
Failures in Phase 3 clinical trials contribute to expensive cost of drug development in oncology. To drastically reduce such cost, responders to an oncology treatment need to be identified early on in the drug development process with limited amount of patient data before the planning of Phase 3 clinical trials. Despite the challenge of small sample size, we pioneered the use of deep-learning derived digital pathology scores to identify responders based on the immunohistochemistry images of the target antigen expressed in tumor biopsy samples from a Phase 1 Non-small Cell Lung Cancer clinical trial. Based on repeated 10-fold cross validations, the deep-learning derived score on average achieved 4% higher AUC of ROC curve and 6% higher AUC of Precision-Recall curve comparing to the tumor proportion score (TPS) based clinical benchmark. In a small independent testing set of patients, we also demonstrated that the deep-learning derived score achieved numerically at least 25% higher responder rate in the enriched population than the TPS clinical benchmark.
