Positron emission tomography (PET) uses radioactive tracers and enables the functional imaging of several metabolic processes, blood flow measurements, regional chemical composition, and/or chemical absorption. Depending on the targeted processes within the living organism, different tracers are used for various medical conditions, such as cancer, particular brain pathologies, cardiac events, and bone lesions, where the most commonly used tracers are radiolabeled with 18F (e.g., [¹⁸F]-FDG and NA [¹⁸F]). Oxygen-15 isotope is mostly involved in blood flow measurements, whereas a wide array of ¹¹C-based compounds have also been developed for neuronal disorders according to the affected neuroreceptors, prostate cancer, and lung carcinomas. In contrast, the single-photon emission computed tomography (SPECT) technique uses gamma-emitting radioisotopes and can be used to diagnose strokes, seizures, bone illnesses, and infections by gauging the blood flow and radio distribution within tissues and organs. The radioisotopes typically used in SPECT imaging are iodine-123, technetium-99m, xenon-133, thallium-201, and indium-111. This systematic review article aims to clarify and disseminate the available scientific literature focused on PET/SPECT radiotracers and to provide an overview of the conducted research within the past decade, with an additional focus on the novel radiopharmaceuticals developed for medical imaging.

Background: Surface-enhanced Raman spectroscopy (SERS) analysis of urine is a promising liquid biopsy technique for cancer detection. However, its clinical translation is hindered by two major challenges that impact classification efficacy. First, the SERS signal of urine is confounded by fluctuations induced by physiological differences in urine composition such as pH and dilution. Second, the molecular origin of the SERS signal of urine is incompletely understood, limiting the interpretability of machine learning classifiers in terms of specific biochemical markers. Methods: In this pilot study, we analyzed urine samples from breast cancer patients (n = 18) and control subjects (n = 10) at three pH levels (5, 7, and 9). Additionally, we analyzed simulated urine mixtures consisting of uric acid, hypoxanthine, xanthine, and creatinine in physiological concentrations to explain the variation in the SERS spectra at different pH values. Results: Urine at pH 9 yielded the most detailed spectral features. The SERS spectral pattern under alkaline pH reflected greater contributions from hypoxanthine, uric acid, and creatinine, while xanthine contributions diminished due to competitive interactions at the SERS substrate surface. Normalizing SERS signals to the creatinine band at 1420 cm⁻¹ effectively mitigated the confounding effects of urine dilution. Conclusions: Optimizing the pH to 9 and normalizing to creatinine significantly enhances the interpretability and accuracy of SERS-based urine analysis for cancer detection. These findings offer important theoretical and practical advancements for the development of SERS-based liquid biopsy tools for cancer detection.

Background: Surface-enhanced Raman spectroscopy (SERS) analysis of urine is a promising liquid biopsy technique for cancer detection. However, its clinical translation is hindered by two major challenges that impact classification efficacy. First, the SERS signal of urine is confounded by fluctuations induced by physiological differences in urine composition such as pH and dilution. Second, the molecular origin of the SERS signal of urine is incompletely understood, limiting the interpretability of machine learning classifiers in terms of specific biochemical markers. Methods: In this pilot study, we analyzed urine samples from breast cancer patients (n=18) and control subjects (n=10) at three pH levels (5, 7, and 9). Additionally, we analyzed simulated urine mixtures consisting of uric acid, hypoxanthine, xanthine, and creatinine in physiological concentrations to explain the variation in the SERS spectra at different pH values. Results: Urine at pH 9 yielded the most detailed spectral features. The SERS spectral pattern under alkaline pH reflected greater contributions from hypoxanthine, uric acid, and creatinine, while xanthine contributions diminished due to competitive interactions at the SERS substrate surface. Normalizing SERS signals to the creatinine band at 1420 cm⁻¹ effectively mitigated the confounding effects of urine dilution. These adjustments improved the classification accuracy of a random forest model. Conclusions: Optimizing the pH to 9 and normalizing to creatinine significantly enhance the interpretability and accuracy of SERS-based urine analysis for cancer detection. These findings offer important theoretical and practical advancements in the development of SERS-based liquid biopsy tools for cancer detection.