Although cancer patients survive years after oncologic therapy, they are plagued with long-lasting or permanent residual symptoms, whose severity, rate of development, and resolution after treatment vary largely between survivors. The analysis and interpretation of symptoms is complicated by their partial co-occurrence, variability across populations and across time, and, in the case of cancers that use radiotherapy, by further symptom dependency on the tumor location and prescribed treatment. We describe THALIS, an environment for visual analysis and knowledge discovery from cancer therapy symptom data, developed in close collaboration with oncology experts. Our approach leverages unsupervised machine learning methodology over cohorts of patients, and, in conjunction with custom visual encodings and interactions, provides context for new patients based on patients with similar diagnostic features and symptom evolution. We evaluate this approach on data collected from a cohort of head and neck cancer patients. Feedback from our clinician collaborators indicates that THALIS supports knowledge discovery beyond the limits of machines or humans alone, and that it serves as a valuable tool in both the clinic and symptom research.
翻译:虽然癌症患者在接受肿瘤治疗多年后仍存活下来,但他们仍受到长期或永久的残余症状的困扰,其严重程度、发育速度和治疗后解析率在幸存者之间差别很大。对症状的分析和解释由于部分共发、不同人口和不同时间的变异,以及就使用放射疗法的癌症而言,由于进一步依赖肿瘤的位置和处方治疗而变得复杂。我们描述THALIS,这是一个通过癌症治疗症状数据进行视觉分析和知识发现的环境,是与肿瘤专家密切合作开发的。我们的方法对病人群采用不受监督的机器学习方法,结合定制的视觉编码和互动,为具有类似诊断特征和症状演变的病人提供了环境。我们评估了从一群头部和颈部癌症患者收集到的数据的方法。我们的临床合作者的反馈表明,TALIS支持超出机器或人类的界限以外的知识发现,并且它作为临床和症状研究的宝贵工具。