THALIS: Human-Machine Analysis of Longitudinal Symptoms in Cancer Therapy.
THALIS: Human-Machine Analysis of Longitudinal Symptoms in Cancer Therapy.
复制标题
DOI：
10.1109/tvcg.2021.3114810
复制
发表时间：
2022-01
影响因子：
5.2
通讯作者：
Marai GE
中科院分区：
文献类型：
--
作者：
Floricel C;Nipu N;Biggs M;Wentzel A;Canahuate G;Van Dijk L;Mohamed A;Fuller CD;Marai GE
虽然癌症患者在肿瘤治疗后存活数年，但他们受到长期或永久残留症状的困扰，其严重程度，发展速度和治疗后的解决方案在幸存者之间存在很大差异。症状的分析和解释因其部分共现、跨人群和跨时间的变异性而变得复杂，并且在使用放射疗法的癌症的情况下，还因症状对肿瘤位置和处方治疗的进一步依赖性而变得复杂。我们描述了THALIS，一个从癌症治疗症状数据中进行可视化分析和知识发现的环境，与肿瘤学专家密切合作开发。我们的方法在患者队列中利用无监督机器学习方法，并结合自定义视觉编码和交互，根据具有相似诊断特征和症状演变的患者为新患者提供上下文。我们评估这种方法收集的数据，从一个队列的头颈癌患者。我们的临床医生合作者的反馈表明，THALIS支持超越机器或人类局限的知识发现，并且它在临床和症状研究中都是一个有价值的工具。
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.