Asper Nora, Witschel Hans Friedrich, von Stockar Louise, Laurenzi Emanuele, Kolberg Hans Christian, Vetter Marcus, Roth Sven, Kullak-Ublick Gerd, Trojan Andreas
Center for Dental Medicine and Faculty of Medicine, University of Zurich, 8032 Zurich, Switzerland.
School of Business, FHNW, University of Applied Sciences and Arts Northwestern Switzerland, 4600 Olten, Switzerland.
Curr Oncol. 2025 Jun 6;32(6):334. doi: 10.3390/curroncol32060334.
In patients undergoing systemic treatment for cancer, symptom tracking via electronic patient-reported outcomes (ePROs) has been used to optimize communication and monitoring, and facilitate the early detection of adverse effects and to compare the side effects of similar drugs. We aimed to examine whether the patterns in electronic patient-reported outcomes, without any additional clinician data input, are predictive of the underlying cancer type and reflect tumor- and treatment-associated symptom clusters (SCs). The data were derived from a total of 226 patients who self-reported on the presence and severity (according to the Common Terminology Criteria for Adverse Events (CTCAEs)) of more than 90 available symptoms via the medidux app (versions 2.0 and 3.2, developed by mobile Health AG based in Zurich, Switzerland). Among these, 172 had breast cancer as the primary tumor, 19 had lung, 16 had gut, 12 had blood-lymph, and 7 had prostate cancer. For this secondary analysis, a subgroup of 25 patients with breast cancer were randomly selected to reduce the risk of overfitting. The symptoms were aggregated by counting the days on which a particular symptom was reported, resulting in a symptom vector for each patient. A logistic regression model was trained to predict the type of the respective tumor from the symptom vectors, and the symptoms with coefficients above (0.1) were graphically displayed. The machine learning model was not able to recognize any of the patients with prostate and blood-lymph cancer, likely as these cancer types were barely represented in the dataset. The Area Under the Curve (AUC) values for the three remaining cancer types were breast cancer: 0.74 (95% CI [0.624, 0.848]); gut cancer: 0.78 (95% CI [0.659, 0.893]); and lung cancer: 0.63 (95% CI [0.495, 0.771]). Despite the small datasets, for the breast and gut cancers, the respective models demonstrated a fair predictive performance (AUC > 0.7). The generalization of the findings are limited especially due to the heterogeneity of the dataset. This line of research could be especially interesting to monitor individual treatment trajectories. Deviations in the electronic patient-reported symptoms from the treatment-associated symptom patterns could dynamically indicate treatment non-adherence or lower treatment efficacy, without clinician input or additional costs. Similar analyses on larger patient cohorts are needed to validate these preliminary findings and to identify specific and robust treatment profiles.
在接受癌症全身治疗的患者中，通过电子患者报告结局（ePROs）进行症状跟踪已被用于优化沟通和监测，促进不良反应的早期发现，并比较相似药物的副作用。我们旨在研究在没有任何额外临床医生数据输入的情况下，电子患者报告结局的模式是否能预测潜在的癌症类型，并反映肿瘤和治疗相关的症状群（SCs）。数据来自总共226名患者，他们通过medidux应用程序（版本2.0和3.2，由瑞士苏黎世的移动健康股份公司开发）自我报告了90多种可用症状的存在情况和严重程度（根据不良事件通用术语标准（CTCAEs））。其中，172例以乳腺癌为原发性肿瘤，19例为肺癌，16例为肠癌，12例为血液淋巴癌，7例为前列腺癌。对于这项二次分析，随机选择了25例乳腺癌患者的亚组以降低过度拟合的风险。通过计算报告特定症状的天数来汇总症状，从而为每个患者生成一个症状向量。训练了一个逻辑回归模型，以从症状向量预测各自肿瘤的类型，并以图形方式显示系数大于（0.1）的症状。机器学习模型无法识别任何前列腺癌和血液淋巴癌患者，可能是因为这些癌症类型在数据集中几乎没有代表性。其余三种癌症类型的曲线下面积（AUC）值分别为：乳腺癌：0.74（95%置信区间[0.624, 0.848]）；肠癌：0.78（95%置信区间[0.659, 0.893]）；肺癌：0.63（95%置信区间[0.495, 0.771]）。尽管数据集较小，但对于乳腺癌和肠癌，各自的模型表现出了较好的预测性能（AUC > 0.7）。由于数据集的异质性，研究结果的普遍性尤其有限。这条研究路线对于监测个体治疗轨迹可能特别有趣。电子患者报告的症状与治疗相关症状模式的偏差可以动态表明治疗依从性差或治疗效果较低，而无需临床医生输入或额外费用。需要对更大的患者队列进行类似分析，以验证这些初步发现并确定具体且可靠的治疗概况。
