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    99mTc Radionuclide-labeled and hydrogel-coated BaTiO3 nanocomposites
    (Elsevier, 2024) Ekici, Sema; Ozdemir, Semra; Puren, Busra Aydur
    The current developments such as improving of radionuclides and drug carriers in nuclear medicine has been utilized for cancer diagnosis, prevention and treatment, in other words in theranostics. In radionuclide-based imaging, radionuclides can be conjugated to the nanoparticle and directed to the targeted tissue. Inorganic nanoparticles are capable of imaging and treatment of tumors and have demonstrated unique physicochemical and biological properties. In present study, m-BaTiO3 nanocomposites were prepared by coating biocompatible and nanosized BaTiO3 particles (tra-BaTiO3) with poly (3-acrylamidopropyl) trimethyl-ammonium chloride (pAPTMACl) ionic hydrogel. The prepared m-BaTiO3 nanocomposites were then modified with (TcO4-)-Tc-99m selected as radionuclide and 8-hydroxy-7-iodo-5-quinoline sulfonic acid (SQ) which is a drug used in the treatment of cancer, to form m-BaTiO3-(TcO4-)-Tc-99m and m-BaTiO3-SQ radiopharmaceutical nanocomposites, respectively. The characterizations of prepared nanoparticles were carried out with ATR-linked FT-IR and Raman spectroscopy, DLS, zeta potential, TEM, SEM, SEM-Element Mapping analyzes. Radiolabeling studies of m-BaTiO3 nano- composites were monitored with a radionuclide dose calibrator. The suspension stability of tra-BaTiO3 nano-particles was increased after the conjugation of (TcO4-)-Tc-99m and SQ molecules. Particle size values ranking found as; m-BaTiO3-(TcO4-)-Tc-99m < m-BaTiO3-SQ < m-BaTiO3 < tra-BaTiO3. Zeta potential values were measured as -9.80 +/- 1.12 mV,-7.54 +/- 4.46 mV, -21.30 +/- 0.32 mV, -25.60 +/- 0.35 mV for tra-BaTiO3 , m-BaTiO3 , m-BaTiO3-(TcO4-)-Tc-99m, m-BaTiO3-SQ, respectively. The amount of (99m) TcO4- attached to the tra-BaTiO3 nanoparticles increased threefold, after coating with the hydrogel. It was observed that 76 % of SQ drug molecules loaded onto m-BaTiO3-SQ nanocomposites was released within 10 h. Herein, we highlighted the design of BaTiO3 nano-particles with biocompatible and flexible pAPTMACl hydrogel, (99m) Tc radionuclide, and SQ cancer drug for using of radiopharmaceuticals in cancer imaging and radiotherapy in order to be a pioneer study for our researches in the future.
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    Diagnostic Performance of Machine Learning Models Based on 18F-FDG PET/CT Radiomic Features in the Classification of Solitary Pulmonary Nodules
    (Galenos Publ House, 2022) Salihoglu, Yavuz Sami; Erdemir, Rabiye Uslu; Puren, Busra Aydur; Ozdemir, Semra; Uyulan, Caglar; Erguzel, Turker Tekin; Tekin, Huseyin Ozan
    Objectives: This study aimed to evaluate the ability of (18)fluorine-fluorodeoxyglucose (F-18-FDG) positron emission tomography/computed tomography (PET/CT) radiomic features combined with machine learning methods to distinguish between benign and malignant solitary pulmonary nodules (SPN). Methods: Data of 48 patients with SPN detected on F-18-FDG PET/CT scan were evaluated retrospectively. The texture feature extraction from PET/CT images was performed using an open-source application (LIFEx). Deep learning and classical machine learning algorithms were used to build the models. Final diagnosis was confirmed by pathology and follow-up was accepted as the reference. The performances of the models were assessed by the following metrics: Sensitivity, specificity, accuracy, and area under the receiver operator characteristic curve (AUC). Results: The predictive models provided reasonable performance for the differential diagnosis of SPNs (AUCs similar to 0.81). The accuracy and AUC of the radiomic models were similar to the visual interpretation. However, when compared to the conventional evaluation, the sensitivity of the deep learning model (88% vs. 83%) and specificity of the classic learning model were higher (86% vs. 79%). Conclusion: Machine learning based on F-18-FDG PET/CT texture features can contribute to the conventional evaluation to distinguish between benign and malignant lung nodules.