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The potential of real-world data to inform clinical trial design and supplement control arms has gained much interest in recent years. The most common approach relies on reproducing control arm outcomes by matching real-world patient cohorts to clinical trial baseline populations. However, recent studies pointed out that there is a lack of replicability, generalisability, and consensus. In this article, we propose a novel approach that aims to explore and examine these discrepancies by concomitantly investigating the impact of selection criteria and operations on the measurements of outcomes from the patient data. We tested the approach on a dataset consisting of small-cell lung cancer patients receiving platinum-based chemotherapy regimens from a real-world data cohort (n = 223) and six clinical trial control arms (n = 1224). The results showed that the discrepancy between real-world and clinical trial data potentially depends on differences in both patient populations and operational conditions (e.g., frequency of assessments, and censoring), for which further investigation is required. Discovering and accounting for confounders, including hidden effects of differences in operations related to the treatment process and clinical trial study protocol, would potentially allow for improved translation between clinical trials and real-world data. Continued development of the method presented here to systematically explore and account for these differences could pave the way for transferring learning across clinical studies and developing mutual translation between the real-world and clinical trials to inform clinical study design., QC 20240829

Process mining is a relatively new method that connects data science andprocess modelling. In the past years a series of applications with health careproduction data have been presented in process discovery, conformance check andsystem enhancement. In this paper we apply process mining on clinical oncologicaldata with the purpose of studying survival outcomes and chemotherapy treatmentdecision in a real-world cohort of small cell lung cancer patients treated atKarolinska University Hospital (Stockholm, Sweden). The results highlighted thepotential role of process mining in oncology to study prognosis and survivaloutcomes with longitudinal models directly extracted from clinical data derivedfrom healthcare., QC 20230628

Background: Improved molecular diagnosis is needed in prostate cancer (PC). Fine needle aspiration (FNA) is a minimally invasive biopsy technique, less traumatic compared to core needle biopsy, and could be useful for diagnosis of PC. Molecular biomarkers (BMs) in FNA-samples can be assessed for prediction, eg of immunotherapy efficacy before treatment as well as at treatment decision time points during disease progression. Methods: In the present pilot study, the expression levels of 151 BM proteins were analysed by proximity extension assay in FNA-samples from 16 patients, including benign prostate lesions (n = 3) and cancers (n = 13). An ensemble data analysis strategy was applied using several machine learning models. Results: Twelve potentially predictive BM proteins correlating with International Society of Urological Pathology grade groups were identified, among them vimentin, tissue factor pathway inhibitor 2, and integrin beta-5. The validity of the results was supported by network analysis that showed functional associations between most of the identified putative BMs. We also showed that multiple immune checkpoint targets can be assessed (eg PD-L1, CD137, and Galectin-9), which may support the selection of immunotherapy in advanced PC. Results are promising but need further validation in a larger cohort. Conclusions: Our pilot study represents a “proof of concept” and shows that multiplex profiling of potential diagnostic and predictive BM proteins is feasible on tumour material obtained by FNA sampling of prostate cancer. Moreover, our results demonstrate that an ensemble data analysis strategy may facilitate the identification of BM signatures in pilot studies when the patient cohort is limited.

Purpose: Stereotactic body radiation therapy for tumors near the central airways implies high-grade toxic effects, as concluded from the HILUS trial. However, the small sample size and relatively few events limited the statistical power of the study. We therefore pooled data from the prospective HILUS trial with retrospective data from patients in the Nordic countries treated outside the prospective study to evaluate toxicity and risk factors for high-grade toxic effects. Methods and Materials: All patients were treated with 56 Gy in 8 fractions. Tumors within 2 cm of the trachea, the mainstem bronchi, the intermediate bronchus, or the lobar bronchi were included. The primary endpoint was toxicity, and the secondary endpoints were local control and overall survival. Clinical and dosimetric risk factors were analyzed for treatment-related fatal toxicity in univariable and multivariable Cox regression analyses. Results: Of 230 patients evaluated, grade 5 toxicity developed in 30 patients (13%), of whom 20 patients had fatal bronchopulmonary bleeding. The multivariable analysis revealed tumor compression of the tracheobronchial tree and maximum dose to the mainstem or intermediate bronchus as significant risk factors for grade 5 bleeding and grade 5 toxicity. The 3-year local control and overall survival rates were 84% (95% CI, 80%-90%) and 40% (95% CI, 34%-47%), respectively. Conclusions: Tumor compression of the tracheobronchial tree and high maximum dose to the mainstem or intermediate bronchus increase the risk of fatal toxicity after stereotactic body radiation therapy in 8 fractions for central lung tumors. Similar dose constraints should be applied to the intermediate bronchus as to the mainstem bronchi.

Background and purpose: We aimed to assess if radiation dose escalation to either the whole primary tumour, or to an 18F-FDG-PET defined subvolume within the primary tumour known to be at high risk of local relapse, could improve local control in patients with locally advanced non-small-cell lung cancer. Materials and methods: Patients with inoperable, stage II-III NSCLC were randomised (1:1) to receive dose-escalated radiotherapy to the whole primary tumour or a PET-defined subvolume, in 24 fractions. The primary endpoint was freedom from local failure (FFLF), assessed by central review of CT-imaging. A phase II ‘pick-the-winner’ design (alpha = 0.05; beta = 0.80) was applied to detect a 15 % increase in FFLF at 1-year. ClinicalTrials.gov:NCT01024829. Results: 150 patients were enrolled. 54 patients were randomised to the whole tumour group and 53 to the PET-subvolume group. The trial was closed early due to slow accrual. Median dose/fraction to the boosted volume was 3.30 Gy in the whole tumour group, and 3.50 Gy in the PET-subvolume group. The 1-year FFLF rate was 97 % (95 %CI 91–100) in whole tumour group, and 91 % (95 %CI 82–100) in the PET-subvolume group. Acute grade ≥ 3 adverse events occurred in 23 (43 %) and 20 (38 %) patients, and late grade ≥ 3 in 12 (22 %) and 17 (32 %), respectively. Grade 5 events occurred in 19 (18 %) patients in total, of which before disease progression in 4 (7 %) in the whole tumour group, and 5 (9 %) in the PET-subvolume group. Conclusion: Both strategies met the primary objective to improve local control with 1-year rates. However, both strategies led to unexpected high rates of grade 5 toxicity. Dose differentiation, improved patient selection and better sparing of central structures are proposed to improve dose-escalation strategies.