Cancer spheroids are in vitro 3D models that became crucial in nanomaterials science thanks to the possibility of performing high throughput screening of nanoparticles and combined nanoparticle-drug therapies on in vitro models. However, most of the current spheroid analysis methods involve manual steps. This is a time-consuming process and is extremely liable to the variability of individual operators. For this reason, rapid, user-friendly, ready-to-use, high-throughput image analysis software is necessary. In this work, we report the INSIDIA 2.0 macro, which offers researchers high-throughput and high content quantitative analysis of in vitro 3D cancer cell spheroids and allows advanced parametrization of the expanding and invading cancer cellular mass. INSIDIA has been implemented to provide in-depth morphologic analysis and has been used for the analysis of the effect of graphene quantum dots photothermal therapy on glioblastoma (U87) and pancreatic cancer (PANC-1) spheroids. Thanks to INSIDIA 2.0 analysis, two types of effects have been observed: In U87 spheroids, death is accompanied by a decrease in area of the entire spheroid, with a decrease in entropy due to the generation of a high uniform density spheroid core. On the other hand, PANC-1 spheroids’ death caused by nanoparticle photothermal disruption is accompanied with an overall increase in area and entropy due to the progressive loss of integrity and increase in variability of spheroid texture. We have summarized these effects in a quantitative parameter of spheroid disruption demonstrating that INSIDIA 2.0 multiparametric analysis can be used to quantify cell death in a non-invasive, fast, and high-throughput fashion.

Background: Quantum Neural Networks (QNNs) combine quantum computing and artificial intelligence to provide powerful solutions for high-dimensional data analysis. In magnetic resonance imaging (MRI), they address the challenges of advanced imaging sequences and data complexity, enabling faster optimization, enhanced feature extraction, and real-time clinical applications. Methods: A literature review using Scopus, PubMed, IEEE Xplore, ACM Digital Library and arXiv identified 84 studies on QNNs in MRI. After filtering for peer-reviewed original research, 20 studies were analyzed. Key parameters such as datasets, architectures, hardware, tasks, and performance metrics were summarized to highlight trends and gaps. Results: The analysis identified datasets supporting tasks like tumor classification, segmentation, and disease prediction. Architectures included hybrid models (e.g., ResNet34 with quantum circuits) and novel approaches (e.g., Quantum Chebyshev Polynomials). Hardware ranged from high-performance GPUs to quantum-specific devices. Performance varied, with accuracy up to 99.5% in some configurations but lower results for complex or limited datasets. Conclusions: The findings provide the first glimpse into the potential of QNNs in MRI, demonstrating accuracy and specificity in diagnostic tasks and biomarker detection. However, challenges such as dataset variability, limited quantum hardware access, and reliance on simulators remain. Future research should focus on scalable quantum hardware, standardized datasets, and optimized architectures to support clinical applications and precision medicine.

Extracellular vesicles (EVs) are increasingly recognized as promising non-invasive biomarkers for cancer and other diseases, but their clinical translation remains limited by the lack of comprehensive characterization strategies. Spectroscopic approaches such as Fourier-transform infrared (FTIR) spectroscopy can provide a global biochemical fingerprint of intact EVs, but their interpretation requires advanced analytical tools. In this study, we applied an autoencoder-based framework to attenuated total reflection FTIR (ATR-FTIR) spectra of blood-derived components, including plasma, red blood cells (RBCs), RBC-ghosts, and EVs, comprising 278 samples collected from 135 patients, to obtain latent features capable of capturing biologically meaningful variability. The autoencoder compressed spectra into 12 latent features while preserving spectral information with low reconstruction error. Unsupervised UMAP projection of the latent features separated the blood components into different clusters, supporting their biological relevance. The model was then applied to EV spectra from patients with hepatocellular carcinoma (HCC) and cirrhotic controls. Four features significantly differed between the two groups, and an elastic-net regularized logistic model evaluated with a leave-one-out cross-validation framework retained a single latent feature, achieving an out-of-fold ROC AUC of 0.785 (95% CI 0.602–0.967), with performance broadly comparable to that typically reported for AFP, the most commonly used biomarker for HCC. This study provides the first proof-of-concept that an autoencoder can be applied to FTIR spectra of EVs, extracting biologically relevant latent features with potential application in cancer detection.

Purpose: Brachytherapy (BT, interventional radiotherapy) is a well-established radiotherapy technique capable of delivering high doses to tumors while sparing organs at risk (OARs). Currently, the clinically accepted dose calculation algorithm used is TG-43. In the TG-186 report, new...

Interventional radiotherapy (brachytherapy) has become the new therapeutic standard in the management of early stages nasal vestibule tumors; in fact it allows for high local control rates and low toxicity profiles. However, since more and more patients will receive interventional radiotherapy (brachytherapy) as primary treatment, it is desirable to implement novel strategies to reduce the dose to organs at risk with the future aim to result in further lowering long-term side effects. Materials and methods: We were able to identify two different strategies to reduce dose to the treatment volume, including the implantation technique (the implant can be interstitial, endocavitary or mixed and the catheters may be placed either using the Paris system rules or the anatomical approach) and the dose distribution within the implant (the most commonly used parameter to consider is the dose non-uniformity ratio). We subsequently propose two novel strategies to reduce dose to organs at risk, including the use of metal shields for fixed organs as in the case of the eyes and the use of a mouth swab to push away mobile organs, such in the case of the mandible. We used two different algorithms to verify the values namely the TG-43 and the TG-186. Results: We provided an accurate literature review regarding strategies to reduce toxicity to the treatment volume, underlining the pros and cons of all implantation techniques and about the use dose non-uniformity ratio. Regarding the innovative strategies to reduce the dose to organs at risk, we investigated the use of eye shielding and the use of swabs to push away the mandible by performing an innovative calculation using two different algorithms in a series of three consecutive patients. Our results show that the dose reduction, both in the case of the mandible and in the case of eye shielding, was statistically significant. Conclusion: Proper knowledge of the best implantation technique and dose non-uniformity ratio as highlighted by existing literature is mandatory in order to reduce toxicity within the treatment volume. With regard to the dose reduction to the organs at risk we have demonstrated that the use of eye shielding and mouth swab could play a pivotal role in clinical practice; in fact, they are effective at lowering the doses to the surrounding organs and do not require any change to the current clinical workflow.

Introduction: Interventional radiotherapy (brachytherapy, IRT, BT) has evolved with technological advancements, improving dose precision while minimizing exposure to healthy tissues. The integration of 3D-printing technology in IRT has enabled the development of patient-specific devices, optimizing treatment personalization and dosimetric accuracy. Methods: A systematic literature search was conducted in PubMed, Scopus, and Google Scholar to identify studies published between 2020 and 2024 on 3D-printing applications in IRT. The selection process resulted in 74 peer-reviewed articles categorized by radioactive source, brachytherapy technique, endpoint of the 3D-printed product, and study type. Results: The analysis highlights the growing implementation of 3D-printed devices in brachytherapy, particularly in gynecological, prostate, and skin cancers. Most studies focus on technique, including intracavitary, interstitial, and contact applications, with custom applicators and templates emerging as predominant endpoints. The majority of studies involved in vivo clinical applications, followed by in silico computational modeling and in vitro experiments. Conclusions: The upward trend in scientific publications underscores the growing attention on 3D printing for enhancing personalized brachytherapy. The increasing use of 3D-printed templates and applicators highlights their role in optimizing dose delivery and expanding personalized treatment strategies. The current research trend is shifting toward real-world data and in vivo studies to assess clinical applications, ensuring these innovations translate effectively into routine practice. The integration of 3D printing represents a major advancement in radiation oncology, with the potential to enhance treatment efficacy and patient outcomes. Future research should focus on standardizing manufacturing processes and expanding clinical validation to facilitate broader adoption.