1. ▫$[^{18}\mathrm F]$▫FDG PET immunotherapy radiomics signature (iRADIOMICS) predicts response of non-small-cell lung cancer patients treated with pembrolizumabDamijan Valentinuzzi, Martina Vrankar, Nina Boc, Valentina Ahac, Žiga Zupančič, Mojca Unk, Katja Škalič, Ivana Žagar, Andrej Studen, Urban Simončič, Jens C. Eickhoff, Robert Jeraj, 2020, original scientific article Keywords: medical physics, medical imaging, oncology Published in DiRROS: 15.07.2024; Views: 144; Downloads: 42
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2. Prepoznavanje ogroženosti za nastanek raka dojk na mamografskih slikahŽan Klaneček, Andrej Studen, Katja Jarm, Mateja Krajc, Miloš Vrhovec, Robert Jeraj, 2024, published professional conference contribution Abstract: Za prehod s populacijskega na personalizirano presejanje za raka dojk je v prvi vrsti potrebno natančno prepoznavanje ogroženosti za razvoj raka dojk. Standardni modeli, ki temeljijo na klasičnih značilkah, niso najbolj zanesljivi. Z razvojem umetne inteligence, predvsem na področju globokega učenja, se je izkazalo, da modeli, ki so naučeni na mamografskih slikah, dosegajo signifikantno boljše rezultate pri napovedovanju ogroženosti. Trenutno je najboljši model za napovedovanje ogroženosti MIRAI, ki je bil uspešno validiran na različnih populacijah. A vendar so rezultati še daleč od popolnih in možnosti za izboljšave je ogromno, predvsem na področju razširitve uporabnosti modela za različne proizvajalce mamografskih aparatov, vključevanja longitudinalnih sprememb in uporabe segmentiranih slik dojke. Keywords: obvladovanje raka, presejalni programi, rak dojk, mamografija Published in DiRROS: 06.06.2024; Views: 274; Downloads: 114
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3. Fizikalni modeli imunoterapije in radioterapijeDamijan Valentinuzzi, Martina Vrankar, Katja Uršič Valentinuzzi, Mojca Unk, Olga Gordeeva, Alen Hadžić, Gregor Serša, Maja Čemažar, Robert Jeraj, 2023, published scientific conference contribution Abstract: V prispevku bomo predstavili rezultate fizikalnih modelov imunoterapije, ki smo jih razvili v programski skupini Medicinska fizika na Fakulteti za matematiko in fiziko Univerze v Ljubljani. Glavni cilj raziskav je bila prepoznava bioloških značilnosti tumorjev, od katerih je odvisen odziv na zdravljenje s protitelesi receptorja programirane celične smrti l (angl. anti-programmed-death-1 (anti-PD-1)). Posebno pozornost smo namenili meritvam modelskih parametrov ter preverbi rezultatov modeliranja, kar je eden izmed predpogojev za uspešno translacijo modeliranja v rutinsko predklinično in klinično prakso. Predstavili bomo tudi načrte za prihodnost, tj. modeliranje kombinacije anti-PD-1 in radioterapije. Keywords: imunoterapija, radioterapija, medicinska fizika, tumorji Published in DiRROS: 16.06.2023; Views: 585; Downloads: 156
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4. Quantitative imaging biomarkers of immune-related adverse events in immune-checkpoint blockade-treated metastatic melanoma patients : a pilot studyNežka Hribernik, Daniel T. Huff, Andrej Studen, Katarina Zevnik, Žan Klaneček, Hamid Emamekhoo, Katja Škalič, Robert Jeraj, Martina Reberšek, 2022, original scientific article Abstract: Purpose: To develop quantitative molecular imaging biomarkers of immune-related adverse event (irAE) development in malignant melanoma (MM) patients receiving immune-checkpoint inhibitors (ICI) imaged with 18F-FDG PET/CT. Methods: 18F-FDG PET/CT images of 58 MM patients treated with anti-PD-1 or anti-CTLA-4 ICI were retrospectively analyzed for indication of irAE. Three target organs, most commonly affected by irAE, were considered: bowel, lung, and thyroid. Patient charts were reviewed to identify which patients experienced irAE, irAE grade, and time to irAE diagnosis. Target organs were segmented using a convolutional neural network (CNN), and novel quantitative imaging biomarkers - SUV percentiles (SUVX%) of 18F-FDG uptake within the target organs - were correlated with the clinical irAE status. Area under the receiver-operating characteristic curve (AUROC) was used to quantify irAE detection performance. Patients who did not experience irAE were used to establish normal ranges for target organ 18F-FDG uptake. Results: A total of 31% (18/58) patients experienced irAE in the three target organs: bowel (n=6), lung (n=5), and thyroid (n=9). Optimal percentiles for identifying irAE were bowel (SUV95%, AUROC=0.79), lung (SUV95%, AUROC=0.98), and thyroid (SUV75%, AUROC=0.88). Optimal cut-offs for irAE detection were bowel (SUV95%>2.7 g/mL), lung (SUV95%>1.7 g/mL), and thyroid (SUV75%>2.1 g/mL). Normal ranges (95% confidence interval) for the SUV percentiles in patients without irAE were bowel [1.74, 2.86 g/mL], lung [0.73, 1.46 g/mL], and thyroid [0.86, 1.99 g/mL]. Conclusions: Increased 18F-FDG uptake within irAE-affected organs provides predictive information about the development of irAE in MM patients receiving ICI and represents a potential quantitative imaging biomarker for irAE. Some irAE can be detected on 18F-FDG PET/CT well before clinical symptoms appear. Keywords: melanoma, malignant melanoma, immune-checkpoint inhibitors, molecular imaging biomarkers Published in DiRROS: 07.09.2022; Views: 731; Downloads: 240
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