## 4 Discussion  
Digitalization is a global megatrend affecting the basic structures of human interaction. Digital transformation of healthcare aims to deliver the positive impact of technology in many forms, e.g., telemedicine, AI-aided medical devices, and vast data pools to create predictive analytics. Evolving data show that electronic health record-based predictive algorithms may improve clinicians' prognostication and decision-making [31]. A recent study on the use of radiomics and machine learning revealed that the algorithm utilizing individual CT scans of advanced melanoma patients receiving single anti-PD-1 therapy outperformed traditional RECIST 1.1 criteria in predicting treatment response [32]. However, utilization of ePROs in creating ML algorithms is a novel approach. In this study, we investigated ML modeling that combines prospectively collected data on ePROs, demographics, laboratory values, irAEs, and treatment responses. The aim of the study was to investigate whether these data inputs could be used to predict treatment benefit from ICI therapies in metastatic cancers. The results showed that it is possible to predict ORR with a high specificity, even using data from a patient cohort of multiple cancer types. The study highlights the possibilities of using pooled data from various sources for ML models and potentials of these models to improve the clinical value of cancer treatments. Parallel to traditional follow-up of cancer patients, ePROs enable capturing of symptoms in a timely and comprehensive manner, and it integrates the patients' perspective into the cancer care continuum [17; 33]. Previous studies have provided evidence that ePRO follow-up can improve QoL, reduce emergency clinic visits, and, more importantly, improve survival in chemotherapy-treated patients with advanced cancers and in lung cancer [20; 21]. We have previously shown that ePRO follow-up is also feasible for cancer patients receiving ICIs and that ePRO-collected symptom profiles mimic the AE results of ICI registration studies [26; 27]. In addition, our earlier studies have highlighted the possibilities of ePRO data inputted ML models in facilitation of irAE detection, which could improve their treatment [29]. Furthermore, since irAEs often are linked to improved outcomes in patients treated with ICIs [23; 24; 25; 26], we speculated that ePRO-collected symptom data could be used to predict treatment benefit. Compared to healthcare professional-collected symptoms, ePRO might provide additional value to the symptom assessment, especially with low-grade symptoms without external presentation such as itching. As far as we know, the present study is the first ever to combine ePRO-collected symptom data with ML modeling to predict treatment response with ICIs. Even though this has been intensively studied for years, there are no known universal predictive factors for ICI benefit in cancer treatment suitable for clinical practice for multiple cancer types [34]. Our results indicate that multiple data points and sources collected over time can be used to generate an adaptive ML model able to predict treatment outcomes. Due to the complexity of cancer immunology, we speculate that no single universal marker for ICI benefit will be discovered, and more effort should be used in analyzing multidimensional data, combining not only tumor features but also clinical data, such as ePRO symptoms and routine laboratory values. Furthermore, our study suggests that ePRO data could be used as a non-invasive indicator for immune activation and, therefore, surrogate for ICI treatment benefit. There are several limitations when interpreting our results. Our patient cohort is limited in size, which could decrease the generalizability of the results. Our model had high specificity for treatment benefit but only a moderate level of sensitivity, which might relate to the small cohort size or be a general feature of these models.