Fatih Kizilaslan

• Kobbeltvedt, Marthe Rosvoll; Lobmaier, Ingvild Victoria Koren; Spreafico, M.; Callegaro, D.; Miceli, R. & Kizilaslan, Fatih [Show all 13 contributors for this article] (2025). A comparison of the risk prediction models PERSARC and Sarculator in patients with localized soft tissue sarcoma of the extremities and trunk wall. ESMO Open. 10(8). doi: 10.1016/j.esmoop.2025.105517. Full text in Research Archive Show summary

  Background Different risk classification criteria are used to select patients with localized soft tissue sarcoma of the extremities and trunk wall for neoadjuvant/adjuvant chemotherapy. The two most frequently used risk classification methods are PERSARC and Sarculator prediction models. The main aim was to evaluate and compare these two methods. Materials and methods The study cohort consisted of 664 patients treated at Oslo University Hospital from 1998 to 2017. Predicted probabilities of distant metastasis (DM) and overall survival (OS) were calculated, and risk classification was carried out according to previously defined thresholds. Hazard ratios were estimated using Cox proportional hazards regression models. Interaction between neoadjuvant/adjuvant chemotherapy and risk groups was included in the models to investigate the effect of chemotherapy according to risk group. Results A high degree of correlation was found between PERSARC and Sarculator in predicted 5-year probability of DM and 5-year OS. A total of 215 of 664 (32%) and 221 of 569 (39%) patients were classified as high-risk according to Sarculator and PERSARC, respectively, with agreement found in 511 of 569 patients (90%). Patients classified as high-risk by only one method had similar disease-free survival and OS as patients who were high-risk using both methods. Based on this, patients classified as high-risk by at least one method were grouped as ‘combined high-risk’ and compared with previously established risk classification criteria. Neoadjuvant/adjuvant chemotherapy was associated with improved OS and disease-free survival in all high-risk groups. Conclusions A high degree of agreement between PERSARC and Sarculator predictions was observed. Patients classified as high-risk by only one method had similar outcomes to those who were high-risk using both. Chemotherapy was associated with improved outcome in the PERSARC, Sarculator, and combined high-risk group. Patients classified as high-risk by one of these methods could be considered for neoadjuvant/adjuvant chemotherapy.

• Kizilaslan, Fatih; Swanson, David Michael & Vitelli, Valeria (2025). A Weibull mixture cure frailty model for high-dimensional covariates. Statistical Methods in Medical Research. ISSN 0962-2802. 34(6), p. 1192–1218. doi: 10.1177/09622802251327687. Full text in Research Archive Show summary

  A novel mixture cure frailty model is introduced for handling censored survival data. Mixture cure models are preferable when the existence of a cured fraction among patients can be assumed. However, such models are heavily underexplored: frailty structures within cure models remain largely undeveloped, and furthermore, most existing methods do not work for high-dimensional datasets, when the number of predictors is significantly larger than the number of observations. In this study, we introduce a novel extension of the Weibull mixture cure model that incorporates a frailty component, employed to model an underlying latent population heterogeneity with respect to the outcome risk. Additionally, high-dimensional covariates are integrated into both the cure rate and survival part of the model, providing a comprehensive approach to employ the model in the context of high-dimensional omics data. We also perform variable selection via an adaptive elastic-net penalization, and propose a novel approach to inference using the expectation–maximization (EM) algorithm. Extensive simulation studies are conducted across various scenarios to demonstrate the performance of the model, and results indicate that our proposed method outperforms competitor models. We apply the novel approach to analyze RNAseq gene expression data from bulk breast cancer patients included in The Cancer Genome Atlas (TCGA) database. A set of prognostic biomarkers is then derived from selected genes, and subsequently validated via both functional enrichment analysis and comparison to the existing biological literature. Finally, a prognostic risk score index based on the identified biomarkers is proposed and validated by exploring the patients’ survival.

• Cüran, Gülce & Kizilaslan, Fatih (2025). Statistical inference on stress-strength reliability for the Marshall-Olkin family of distributions. Soft Computing - A Fusion of Foundations, Methodologies and Applications. ISSN 1432-7643. 30, p. 189–208. doi: 10.1007/s00500-025-10968-3. Full text in Research Archive
• Tangen, Jorun; Nguyen, Thuy Mi Tran; Melichova, Daniela; Klæboe, Lars Gunnar; Forså, Marianne Inngjerdingen & Andresen, Kristoffer [Show all 13 contributors for this article] (2024). Left atrial volume assessed by echocardiography identifies patients with high risk of adverse outcome after acute myocardial infarction. Echo Research & Practice. 11(1). doi: 10.1186/s44156-024-00060-1. Full text in Research Archive Show summary

  Background The left atrial (LA) volume has been demonstrated to be an important predictor of adverse outcome in patients with various cardiac conditions, including acute myocardial infarction (AMI). However, new treatment strategies in patients with AMI have led to better patient outcomes. We hypothesised that increased LA size could still predict mortality in patients with AMI despite improved treatment strategies. Methods We included patients with AMI in a prospective multicenter cohort study and the study patients were enrolled from 2014 to 2022. We recorded echocardiographic and clinical data during their index hospitalisation. Indexed LA volume (LAVi) was assessed in all patients and was used as a continuous variable in the univariate and multivariate Cox regression analysis. The study took place over a period of five years and median follow-up time was 3.8 years (range 3.1 to 5.0 years). The primary study outcomes were all-cause mortality and major adverse cardiac events (MACE). MACE was defined as hospital readmission due to myocardial infarction, cardiac arrest, stroke, heart failure, or onset of new atrial fibrillation. Results We included 487 patients (69 ± 12 years old, 26% female) with AMI. During the follow-up period all-cause mortality was 50 (10.3%) and patients who reached the primary outcomes were 153 (31.4%). The deceased patients had higher LAVi compared to survivors (40.0 ± 12.9 mL/m2 vs. 29.7 ± 11.2 mL/m2, p < 0.001). Factors associated with all-cause mortality and MACE were age, year of enrollment, left ventricular (LV) ejection fraction, LV global longitudinal strain (GLS), LV filling pressure, moderate or severe mitral regurgitation and LAVi. GLS and EF were segregated into two distinct models due to their moderately high correlation (r = 0.57, p < 0.001). LAVi remained as an independent echocardiographic predictor of primary outcomes after adjusting for the covariates above in two separates multivariable Cox regression models (hazard ratio 1.02/1.02 mL/m2 [95% CI 1.01–1.03/1.01–1.03], p = 0.006/0.003). Conclusions Our study demonstrated that LA dilatation is an independent echocardiographic predictor of mortality and MACE in patients with AMI despite improved treatment strategies. This finding highlights the potential of using LAVi as a marker for prognostication in these patients.

• Demiray, Duygu & Kizilaslan, Fatih (2023). Reliability estimation of a consecutive k-out-of-n system for non-identical strength components with applications to wind speed data. Quality Technology & Quantitative Management (QTQM). ISSN 1684-3703. doi: 10.1080/16843703.2023.2173426. Full text in Research Archive

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