PDF(Pubmed)
Abstract：
Dogs with B-cell lymphoma typically respond well to first-line CHOP-based chemotherapy, but there is no standard of care for relapsed patients. To help veterinary oncologists select effective drugs for dogs with lymphoid malignancies such as B-cell lymphoma, we have developed multimodal machine learning models that integrate data from multiple tumor profiling modalities and predict the likelihood of a positive clinical response for 10 commonly used chemotherapy drugs. Here we report on clinical outcomes that occurred after oncologists received a prediction report generated by our models. Remarkably, we found that dogs that received drugs predicted to be effective by the models experienced better clinical outcomes by every metric we analyzed (overall response rate, complete response rate, duration of complete response, patient survival times) relative to other dogs in the study and relative to historical controls.
摘要：
患有B细胞淋巴瘤的狗通常对基于CHOP的一线化疗反应良好，但是对于复发患者没有标准的治疗方法。为了帮助兽医肿瘤学家为患有B细胞淋巴瘤等淋巴恶性肿瘤的狗选择有效的药物，我们开发了多模式机器学习模型,该模型整合了来自多种肿瘤分析模式的数据,并预测了10种常用化疗药物临床疗效阳性的可能性.在这里，我们报告了肿瘤学家收到我们的模型生成的预测报告后发生的临床结果。值得注意的是,我们发现接受模型预测有效药物的狗，通过我们分析的每个指标（总体反应率，完全反应率，完整响应的持续时间，患者生存时间）相对于研究中的其他狗和相对于历史对照。
