Maria Carla Gilardi’s research while affiliated with Università degli Studi di Milano-Bicocca and other places

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.

Parkinson’s disease (PD) is characterized by the loss of dopaminergic neurons in the substantia nigra pars compacta (SNpc) and the appearance of α-synuclein insoluble aggregates known as Lewy bodies. Neurodegeneration is accompanied by neuroinflammation mediated by cytokines and chemokines produced by the activated microglia. Several studies demonstrated that such an inflammatory process is an early event, and contributes to oxidative stress and mitochondrial dysfunctions. α-synuclein fibrillization and aggregation activate microglia and contribute to disease onset and progression. Mutations in different genes exacerbate the inflammatory phenotype in the monogenic compared to sporadic forms of PD. Positron Emission Tomography (PET) and Single Photon Emission Computed Tomography (SPECT) with selected radiopharmaceuticals allow in vivo imaging of molecular modifications in the brain of living subjects. Several publications showed a reduction of dopaminergic terminals and dopamine (DA) content in the basal ganglia, starting from the early stages of the disease. Moreover, non-dopaminergic neuronal pathways are also affected, as shown by in vivo studies with serotonergic and glutamatergic radiotracers. The role played by the immune system during illness progression could be investigated with PET ligands that target the microglia/macrophage Translocator protein (TSPO) receptor. These agents have been used in PD patients and rodent models, although often without attempting correlations with other molecular or functional parameters. For example, neurodegeneration and brain plasticity can be monitored using the metabolic marker 2-Deoxy-2-[18F]fluoroglucose ([18F]-FDG), while oxidative stress can be probed using the copper-labeled diacetyl-bis(N-methyl-thiosemicarbazone) ([Cu]-ATSM) radioligand, whose striatal-specific binding ratio in PD patients seems to correlate with a disease rating scale and motor scores. Also, structural and functional modifications during disease progression may be evaluated by Magnetic Resonance Imaging (MRI), using different parameters as iron content or cerebral volume. In this review article, we propose an overview of in vivo clinical and non-clinical imaging research on neuroinflammation as an emerging marker of early PD. We also discuss how multimodal-imaging approaches could provide more insights into the role of the inflammatory process and related events in PD development.

Prostate cancer is the most common cancer among US men. However, prostate imaging 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. Along with whole prostate gland segmentation, distinguishing between the central gland (CG) and peripheral zone (PZ) can guide toward differential diagnosis, since the frequency and severity of tumors differ in these regions; however, their boundary is often weak and fuzzy. This work presents a 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.

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.

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)-1 H -pyrrole], was synthesized and radiolabelled with the ¹¹ C radioisotope. The ex vivo biodistribution profile of ¹¹ C-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. ¹¹ C-VA426 synthesis with the t BuOK base showed optimal radiochemical yield (15 ± 2%) based on triflate activity, molar activity (range 37–148 GBq/ μ mol), 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 ¹¹ C-VA426 is highly unstable in vivo. This study indicates that the compound ¹¹ C-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.

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.

Breast cancer (BC) is the most common cancer in women, highly heterogeneous at both the clinical and molecular level. Radiation therapy (RT) represents an efficient modality to treat localized tumor in BC care, although the choice of a unique treatment plan for all BC patients, including RT, may not be the best option. Technological advances in RT are evolving with the use of charged particle beams (i.e. protons) which, due to a more localized delivery of the radiation dose, reduce the dose administered to the heart compared with conventional RT. However, few data regarding proton-induced molecular changes are currently available. The aim of this study was to investigate and describe the production of immunological molecules and gene expression profiles induced by proton irradiation. We performed Luminex assay and cDNA microarray analyses to study the biological processes activated following irradiation with proton beams, both in the non-tumorigenic MCF10A cell line and in two tumorigenic BC cell lines, MCF7 and MDA-MB-231. The immunological signatures were dose dependent in MCF10A and MCF7 cell lines, whereas MDA-MB-231 cells show a strong pro-inflammatory profile regardless of the dose delivered. Clonogenic assay revealed different surviving fractions according to the breast cell lines analyzed. We found the involvement of genes related to cell response to proton irradiation and reported specific cell line- and dose-dependent gene signatures, able to drive cell fate after radiation exposure. Our data could represent a useful tool to better understand the molecular mechanisms elicited by proton irradiation and to predict treatment outcome.

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.

Background and objectives: Image segmentation represents one of the most challenging issues in medical image analysis to distinguish among different adjacent tissues in a body part. In this context, appropriate image pre-processing tools can improve the result accuracy achieved by computer-assisted segmentation methods. Taking into consideration images with a bimodal intensity distribution, image binarization can be used to classify the input pictorial data into two classes, given a threshold intensity value. Unfortunately, adaptive thresholding techniques for two-class segmentation work properly only for images characterized by bimodal histograms. We aim at overcoming these limitations and automatically determining a suitable optimal threshold for bimodal Magnetic Resonance (MR) images, by designing an intelligent image analysis framework tailored to effectively assist the physicians during their decision-making tasks. Methods: In this work, we present a novel evolutionary framework for image enhancement, automatic global thresholding, and segmentation, which is here applied to different clinical scenarios involving bimodal MR image analysis: (i) uterine fibroid segmentation in MR guided Focused Ultrasound Surgery, and (ii) brain metastatic cancer segmentation in neuro-radiosurgery therapy. Our framework exploits MedGA as a pre-processing stage. MedGA is an image enhancement method based on Genetic Algorithms that improves the threshold selection, obtained by the efficient Iterative Optimal Threshold Selection algorithm, between the underlying sub-distributions in a nearly bimodal histogram. Results: The results achieved by the proposed evolutionary framework were quantitatively evaluated, showing that the use of MedGA as a pre-processing stage outperforms the conventional image enhancement methods (i.e., histogram equalization, bi-histogram equalization, Gamma transformation, and sigmoid transformation), in terms of both MR image enhancement and segmentation evaluation metrics. Conclusions: Thanks to this framework, MR image segmentation accuracy is considerably increased, allowing for measurement repeatability in clinical workflows. The proposed computational solution could be well-suited for other clinical contexts requiring MR image analysis and segmentation, aiming at providing useful insights for differential diagnosis and prognosis.