ABSTRACT
BACKGROUND: The identification of histopathology in metastatic non-seminomatous testicular germ cell tumors (TGCT) before post-chemotherapy retroperitoneal lymph node dissection (PC-RPLND) holds significant potential to reduce treatment-related morbidity in young patients, addressing an important survivorship concern. AIM: To explore this possibility, we conducted a study investigating the role of computed tomography (CT) radiomics models that integrate clinical predictors, enabling personalized prediction of histopathology in metastatic non-seminomatous TGCT patients prior to PC-RPLND. In this retrospective study, we included a cohort of 122 patients. METHODS: Using dedicated radiomics software, we segmented the targets and extracted quantitative features from the CT images. Subsequently, we employed feature selection techniques and developed radiomics-based machine learning models to predict histological subtypes. To ensure the robustness of our procedure, we implemented a 5-fold cross-validation approach. When evaluating the models' performance, we measured metrics such as the area under the receiver operating characteristic curve (AUC), sensitivity, specificity, precision, and F-score. RESULT: Our radiomics model based on the Support Vector Machine achieved an optimal average AUC of 0.945. CONCLUSIONS: The presented CT-based radiomics model can potentially serve as a non-invasive tool to predict histopathological outcomes, differentiating among fibrosis/necrosis, teratoma, and viable tumor in metastatic non-seminomatous TGCT before PC-RPLND. It has the potential to be considered a promising tool to mitigate the risk of over- or under-treatment in young patients, although multi-center validation is critical to confirm the clinical utility of the proposed radiomics workflow.
ABSTRACT
Post-chemotherapy retroperitoneal lymph node dissection (PC-RPLND) in non-seminomatous germ-cell tumor (NSTGCTs) is a complex procedure. We evaluated whether 3D computed tomography (CT) rendering and their radiomic analysis help predict resectability by junior surgeons. The ambispective analysis was performed between 2016-2021. A prospective group (A) of 30 patients undergoing CT was segmented using the 3D Slicer software while a retrospective group (B) of 30 patients was evaluated with conventional CT (without 3D reconstruction). CatFisher's exact test showed a p-value of 0.13 for group A and 1.0 for Group B. The difference between the proportion test showed a p-value of 0.009149 (IC 0.1-0.63). The proportion of the correct classification showed a p-value of 0.645 (IC 0.55-0.87) for A, and 0.275 (IC 0.11-0.43) for Group B. Furthermore, 13 shape features were extracted: elongation, flatness, volume, sphericity, and surface area, among others. Performing a logistic regression with the entire dataset, n = 60, the results were: Accuracy: 0.7 and Precision: 0.65. Using n = 30 randomly chosen, the best result obtained was Accuracy: 0.73 and Precision: 0.83, with a p-value: 0.025 for Fisher's exact test. In conclusion, the results showed a significant difference in the prediction of resectability with conventional CT versus 3D reconstruction by junior surgeons versus experienced surgeons. Radiomic features used to elaborate an artificial intelligence model improve the prediction of resectability. The proposed model could be of great support in a university hospital, allowing it to plan the surgery and to anticipate complications.
ABSTRACT
In the present work, we propose the development of a novel carrier that does not need organic solvents for its preparation and with the potential for the intravenous delivery of lipophilic and hydrophilic drugs. Named lipomics, this is a mixed colloid of micelles incorporated within a liposome. This system was designed through ternary diagrams and characterized by physicochemical techniques to determine the particle size, zeta potential, shape, morphology, and stability properties. The lipomics were subjected to electron microscopy (SEM, TEM, and STEM) to evaluate their physical size and morphology. Finally, pharmacokinetic studies were performed by radiolabeling the lipomics with Technetium-99m chelated with BMEDA to evaluate the in vivo biodistribution through techniques of molecular imaging (microSPECT/CT) in rats. Radiolabeling efficiency was used to compare the encapsulation efficiency of the hydrophilic and lipophilic molecules in lipomics and liposomes. According to the results, lipomics are potentially carriers of lipophilic and hydrophilic drugs.
ABSTRACT
With the aim improving drug delivery, liposomes have been employed as carriers for chemotherapeutics achieving promising results; their co-encapsulation with magnetic nanoparticles is evaluated in this work. The objective of this study was to examine the physicochemical characteristics, the pharmacokinetic behaviour, and the efficacy of pegylated liposomes loaded with cisplatin and magnetic nanoparticles (magnetite) (Cis-MLs). Cis-MLs were prepared by a modified reverse-phase evaporation method. To characterize their physicochemical properties, an evaluation was made of particle size, ζ-potential, phospholipid and cholesterol concentration, phase transition temperature (Tm), the encapsulation efficiency of cisplatin and magnetite, and drug release profiles. Additionally, pharmacokinetic studies were conducted on normal Wistar rats, while apoptosis and the cytotoxic effect were assessed with HeLa cells. We present a method for simultaneously encapsulating cisplatin at the core and also embedding magnetite nanoparticles on the membrane of liposomes with a mean vesicular size of 104.4 ± 11.5 nm and a ζ-potential of -40.5 ± 0.8 mV, affording a stable formulation with a safe pharmacokinetic profile. These liposomes elicited a significant effect on cell viability and triggered apoptosis in HeLa cells.