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Lung cancer (LC) is currently one of the main causes of cancer-related deaths worldwide. Low-dose computed tomography (LDCT) of the chest has been proven effective in secondary prevention (i.e., early detection) of LC by several trials. In this work, we investigated the potential impact of radiomics on indeterminate prevalent pulmonary nodule (PN) characterization and risk stratification in subjects undergoing LDCT-based LC screening. As a proof-of-concept for radiomic analyses, the first aim of our study was to assess whether indeterminate PNs could be automatically classified by an LDCT radiomic classifier as solid or sub-solid (first-level classification), and in particular for sub-solid lesions, as non-solid versus part-solid (second-level classification). The second aim of the study was to assess whether an LCDT radiomic classifier could automatically predict PN risk of malignancy, and thus optimize LDCT recall timing in screening programs. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC), accuracy, positive predictive value, negative predictive value, sensitivity, and specificity. The experimental results showed that an LDCT radiomic machine learning classifier can achieve excellent performance for characterization of screen-detected PNs (mean AUC of 0.89 ± 0.02 and 0.80 ± 0.18 on the blinded test dataset for the first-level and second-level classifiers, respectively), providing quantitative information to support clinical management. Our study showed that a radiomic classifier could be used to optimize LDCT recall for indeterminate PNs. According to the performance of such a classifier on the blinded test dataset, within the first 6 months, 46% of the malignant PNs and 38% of the benign ones were identified, improving early detection of LC by doubling the current detection rate of malignant nodules from 23% to 46% at a low cost of false positives. In conclusion, we showed the high potential of LDCT-based radiomics for improving the characterization and optimizing screening recall intervals of indeterminate PNs.

In breast cancer (BC) care, radiotherapy is considered an efficient treatment, prescribed both for controlling localized tumors or as a therapeutic option in case of inoperable, incompletely resected or recurrent tumors. However, approximately 90% of BC-related deaths are due to the metastatic tumor progression. Then, it is strongly desirable to improve tumor radiosensitivity using molecules with synergistic action. The main aim of this study is to develop curcumin-loaded solid nanoparticles (Cur-SLN) in order to increase curcumin bioavailability and to evaluate their radiosensitizing ability in comparison to free curcumin (free-Cur), by using an in vitro approach on BC cell lines. In addition, transcriptomic and metabolomic profiles, induced by Cur-SLN treatments, highlighted networks involved in this radiosensitization ability. The non tumorigenic MCF10A and the tumorigenic MCF7 and MDA-MB-231 BC cell lines were used. Curcumin-loaded solid nanoparticles were prepared using ethanolic precipitation and the loading capacity was evaluated by UV spectrophotometer analysis. Cell survival after treatments was evaluated by clonogenic assay. Dose–response curves were generated testing three concentrations of free-Cur and Cur-SLN in combination with increasing doses of IR (2–9 Gy). IC50 value and Dose Modifying Factor (DMF) was measured to quantify the sensitivity to curcumin and to combined treatments. A multi-“omic” approach was used to explain the Cur-SLN radiosensitizer effect by microarray and metobolomic analysis. We have shown the efficacy of the Cur-SLN formulation as radiosensitizer on three BC cell lines. The DMFs values, calculated at the isoeffect of SF = 50%, showed that the Luminal A MCF7 resulted sensitive to the combined treatments using increasing concentration of vehicled curcumin Cur-SLN (DMF: 1,78 with 10 µM Cur-SLN.) Instead, triple negative MDA-MB-231 cells were more sensitive to free-Cur, although these cells also receive a radiosensitization effect by combination with Cur-SLN (DMF: 1.38 with 10 µM Cur-SLN). The Cur-SLN radiosensitizing function, evaluated by transcriptomic and metabolomic approach, revealed anti-oxidant and anti-tumor effects. Curcumin loaded- SLN can be suggested in future preclinical and clinical studies to test its concomitant use during radiotherapy treatments with the double implications of being a radiosensitizing molecule against cancer cells, with a protective role against IR side effects.

Natural phenomena and mechanisms have always intrigued humans, inspiring the design of effective solutions for real-world problems. Indeed, fascinating processes occur in nature, giving rise to an ever-increasing scientific interest. In everyday life, the amount of heterogeneous biomedical data is increasing more and more thanks to the advances in image acquisition modalities and high-throughput technologies. The automated analysis of these large-scale datasets creates new compelling challenges for data-driven and model-based computational methods. The application of intelligent algorithms, which mimic natural phenomena, is emerging as an effective paradigm for tackling complex problems, by considering the unique challenges and opportunities pertaining to biomedical images. Therefore, the principal contribution of computer science research in life sciences concerns the proper combination of diverse and heterogeneous datasets-i.e., medical imaging modalities (considering also radiomics approaches), Electronic Health Record engines, multi-omics studies, and real-time monitoring-to provide a comprehensive clinical knowledge. In this paper, the state-of-the-art of nature-inspired medical image analysis methods is surveyed, aiming at establishing a common platform for beneficial exchanges among computer scientists and clinicians. In particular, this review focuses on the main natureinspired computational techniques applied to medical image analysis tasks, namely: physical processes, bio-inspired mathematical models, Evolutionary Computation, Swarm Intelligence, and neural computation. These frameworks, tightly coupled with Clinical Decision Support Systems, can be suitably applied to every phase of the clinical workflow. We show that the proper combination of quantitative imaging and healthcare informatics enables an in-depth understanding of molecular processes that can guide towards personalised patient care.

Prostate canceris the most common cancer among US men. However, prostateimaging is still challenging despite the advances in multi-parametric magnetic resonance imaging (MRI), which provides both morphologic and functional information pertaining to the pathological regions. Alongwith whole prostate gland segmentation, distinguishingbetween the centralgland (CG) and peripheralzone (PZ) can guidetoward differential diagnosis, sincethe frequency andseverity of tumors differin these regions; however, theirboundary is often weakand fuzzy. This work presentsa preliminary study on deep learning to automatically delineate the CG and PZ, aiming at evaluating the generalization ability of convolutional neural networks (CNNs) on two multi-centric MRI prostate datasets. Especially, we compared three CNN-based architectures: SegNet, U-Net, and pix2pix. In such a context, the segmentation performances achieved with/without pre-training were compared in 4-fold cross-validation. In general, U-Net outperforms the other methods, especially when training and testing are performed on multiple datasets.

The human epidermal growth factor receptor 2 (HER2) is normally associated with a highly aggressive and infiltrating phenotype in breast cancer lesions with propensity to spread into metastases. In clinic, the detection of HER2 in primary tumors and in their metastases is currently based on invasive methods. Recently, nuclear molecular imaging techniques, including positron emission tomography and single photon emission computed tomography (SPECT), allowed the detection of HER2 lesions in vivo. We have developed a 99mTc-radiolabeled nanosilica system, functionalized with a trastuzumab half-chain, able to act as drug carrier and SPECT radiotracer for the identification of HER2-positive breast cancer cells. To this aim, nanoparticles functionalized or not with trastuzumab half-chain, were radiolabeled using the 99mTc-tricarbonyl approach and evaluated in HER2 positive and negative breast cancer models. Cell uptake experiments, combined with flow cytometry and fluorescence imaging, suggested that active targeting provides higher efficiency and selectivity in tumor detection compared to passive diffusion, indicating that our radiolabeling strategy did not affect the nanoconjugate binding efficiency. Ex vivo biodistribution of 99mTc-nanosilica in a SK-BR-3 (HER2+) tumor xenograft at 4 h postinjection was higher in targeted compared to nontargeted nanosilica, confirming the in vitro data. In addition, viability and toxicity tests provided evidence on nanoparticle safety in cell cultures. Our results encourage further assessment of silica 99mTc-nanoconjugates to validate a safe and versatile nanoreporter system for both diagnosis and treatment of aggressive breast cancer.

Prostate cancer is the most common malignant tumors in men but prostate Magnetic Resonance Imaging (MRI) analysis remains challenging. Besides whole prostate gland segmentation, the capability to differentiate between the blurry boundary of the Central Gland (CG) and Peripheral Zone (PZ) can lead to differential diagnosis, since tumor's frequency and severity differ in these regions. To tackle the prostate zonal segmentation task, we propose a novel Convolutional Neural Network (CNN), called USE-Net, which incorporates Squeeze-and-Excitation (SE) blocks into U-Net. Especially, the SE blocks are added after every Encoder (Enc USE-Net) or Encoder-Decoder block (Enc-Dec USE-Net). This study evaluates the generalization ability of CNN-based architectures on three T2-weighted MRI datasets, each one consisting of a different number of patients and heterogeneous image characteristics, collected by different institutions. The following mixed scheme is used for training/testing: (i) training on either each individual dataset or multiple prostate MRI datasets and (ii) testing on all three datasets with all possible training/testing combinations. USE-Net is compared against three state-of-the-art CNN-based architectures (i.e., U-Net, pix2pix, and Mixed-Scale Dense Network), along with a semi-automatic continuous max-flow model. The results show that training on the union of the datasets generally outperforms training on each dataset separately, allowing for both intra-/cross-dataset generalization. Enc USE-Net shows good overall generalization under any training condition, while Enc-Dec USE-Net remarkably outperforms the other methods when trained on all datasets. These findings reveal that the SE blocks' adaptive feature recalibration provides excellent cross-dataset generalization when testing is performed on samples of the datasets used during training., 44 pages, 6 figures, Accepted to Neurocomputing, Co-first authors: Leonardo Rundo and Changhee Han

Cyclooxygenase-2 (COX-2) is involved in the inflammatory response, and its recurrent overexpression in cancers as well as in neurodegenerative disorders has made it an important target for therapy. For this reason, noninvasive imaging of COX-2 expression may represent an important diagnostic tool. In this work, a COX-2 inhibitor analogue, VA426 [1-(4-fluorophenyl)-3-(2-methoxyethyl)-2-methyl-5-(4-(methylsulfonil)phenyl)-1H-pyrrole], was synthesized and radiolabelled with the 11C radioisotope. The ex vivo biodistribution profile of 11C-VA426 was evaluated in the brain and periphery of healthy rats and mice and in brain and periphery of inflammation models, based on the administration of LPS. 11C-VA426 synthesis with the tBuOK base showed optimal radiochemical yield (15 ± 2%) based on triflate activity, molar activity (range 37–148 GBq/μmol), and radiochemical purity (>95%). Ex vivo biodistribution studies showed a fast uptake of radioactivity but a rapid washout, except in regions expressing COX-2 (lungs, liver, and kidney) both in rats and in mice, with maximum values at 30 and 10 minutes p.i., respectively. LPS administration did not show significant effect on radioactivity accumulation. Celecoxib competition experiments performed in rats and mice treated with LPS produced a general target unrelated reduction of radioactivity concentration in all peripheral tissues and brain areas examined. Finally, in agreement with the negative results obtained from biodistribution experiments, radiometabolites analysis revealed that 11C-VA426 is highly unstable in vivo. This study indicates that the compound 11C-VA426 is not currently suitable to be used as radiopharmaceutical for PET imaging. This family of compounds needs further implementation in order to improve in vivo stability.

The preclinical β-Cube PET scanner features a design with monolithic crystal detectors and DOI estimation. Images are reconstructed with a 3D list mode algorithm that should preserve the full spatial resolution of the system. This design is supposed to provide isotropic spatial resolution in the whole field of view. To test this claim we measured the effective reconstructed spatial resolution using a micro Derenzo phantom (Diameter 40 mm), using 3 isotopes with different positron ranges: 18F, 11C, 68Ga. The phantom was first acquired to measure the transverse resolution positioned in center of the field of view. Then it was shifted radially to the most extreme position allowed and scanned twice: once with the smallest rod in the outermost position, then rotated by 180°. For the 18F acquisition the phantom was also acquired with its axis orthogonal to that of the scanner, to measure the axial resolution. Resolution was quantified using the modulation transfer function. The maximum spatial resolution was observed with 18F, while with 68Ga only the 4.7 mm rods were clearly distinguishable. Modulation transfer function analysis confirms that the spatial resolution is identical along all 3 axes. The resolution loss with the radial distance from the center is minimal.