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Heterogeneity describes the differences among cancer cells within and between tumors. It refers to cancer cells describing variations in morphology, transcriptional profiles, metabolism, and metastatic potential. More recently, the field has included the characterization of the tumor immune microenvironment and the depiction of the dynamics underlying the cellular interactions promoting the tumor ecosystem evolution. Heterogeneity has been found in most tumors representing one of the most challenging behaviors in cancer ecosystems. As one of the critical factors impairing the long-term efficacy of solid tumor therapy, heterogeneity leads to tumor resistance, more aggressive metastasizing, and recurrence. We review the role of the main models and the emerging single-cell and spatial genomic technologies in our understanding of tumor heterogeneity, its contribution to lethal cancer outcomes, and the physiological challenges to consider in designing cancer therapies. We highlight how tumor cells dynamically evolve because of the interactions within the tumor immune microenvironment and how to leverage this to unleash immune recognition through immunotherapy. A multidisciplinary approach grounded in novel bioinformatic and computational tools will allow reaching the integrated, multilayered knowledge of tumor heterogeneity required to implement personalized, more efficient therapies urgently required for cancer patients.

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.

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 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 the frequency and severity of tumors 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, i.e., one of the most effective CNNs in biomedical image segmentation. 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. Therefore, we should consider multi-dataset t

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 r

Understanding the molecular mechanisms of metastatic dissemination, the leading cause of death in cancer patients, is required to develop novel, effective therapies. Extravasation, an essential rate-limiting process in the metastatic cascade, includes three tightly coordinated steps: cancer cell adhesion to the endothelium, trans-endothelial migration, and early invasion into the secondary site. Focal adhesion proteins, including Tln1 and FAK, regulate the cytoskeleton dynamics: dysregulation of these proteins is often associated with metastatic progression and poor prognosis.

Background: There is extensive scientific evidence that radiation therapy (RT) is a crucial treatment, either alone or in combination with other treatment modalities, for many types of cancer, including breast cancer (BC). BC is a heterogeneous disease at both clinical and molecular levels, presenting distinct subtypes linked to the hormone receptor (HR) status and associated with different clinical outcomes. The aim of this study was to assess the molecular changes induced by high doses of ionizing radiation (IR) on immortalized and primary BC cell lines grouped according to Human epidermal growth factor receptor (HER2), estrogen, and progesterone receptors, to study how HR status influences the radiation response. Our genomic approach using in vitro and ex-vivo models (e.g., primary cells) is a necessary first step for a translational study to describe the common driven radio-resistance features associated with HR status. This information will eventually allow clinicians to prescribe more personalized total doses or associated targeted therapies for specific tumor subtypes, thus enhancing cancer radio-sensitivity. Methods: Nontumorigenic (MCF10A) and BC (MCF7 and MDA-MB-231) immortalized cell lines, as well as healthy (HMEC) and BC (BCpc7 and BCpcEMT) primary cultures, were divided into low grade, high grade, and healthy groups according to their HR status. At 24 h post-treatment, the gene expression profiles induced by two doses of IR treatment with 9 and 23 Gy were analyzed by cDNA microarray technology to select and compare the differential gene and pathway expressions among the experimental groups. Results: We present a descriptive report of the substantial alterations in gene expression levels and pathways after IR treatment in both immortalized and primary cell cultures. Overall, the IR-induced gene expression profiles and pathways appear to be cell-line dependent. The data suggest that some specific gene and pathway signatures seem to be linked to HR status

Preclinical studies represent an important step towards a deep understanding of the biological response to ionizing radiations. The effectiveness of proton therapy is higher than photons and, for clinical purposes, a fixed value of 1.1 is used for the relative biological effectiveness (RBE) of protons considered 1.1. Recent in vitro studies have reported that the RBE along the spread-out Bragg peak (SOBP) is not constant and, in particular, the RBE value increases on the distal part of SOBP. The present work has been carried-out in the perspective of a preclinical hadrontherapy facility at LNS-INFN and was focused on the experimental preparation of an in vivo study concerning the RBE variation along the SOBP. The main purpose of this work was to determine, using GEANT4-based Monte Carlo simulations, the best configuration for small animal treatments. The developed GEANT4 application simulates the proton-therapy beam line of LNS-INFN (CATANA facility) and allows to import the DICOM-CT images as targets. The RBE will be evaluated using a deterministic radiation damage like myelopathy as end-point. In fact, the dose at which the 50% of animals will show the myelopathy is supposed to be LET-dependent. In this work, we studied different treatment configurations in order to choose the best two that maximize the LET difference reducing as much as possible the dose released to healthy tissue. The results will be useful to plan hadrontherapy treatments for preclinical in vivo studies and, in particular, for the future in vivo RBE studies.

Background/aim: In breast cancer (BC) care, radiation therapy (RT) is an efficient treatment to control localized tumor. Radiobiological research is needed to understand molecular differences that affect radiosensitivity of different tumor subtypes and the response variability. The aim of this study was to analyze gene expression profiling (GEP) in primary BC cells following irradiation with doses of 9 Gy and 23 Gy delivered by intraoperative electron radiation therapy (IOERT) in order to define gene signatures of response to high doses of ionizing radiation. Materials and Methods: We performed GEP by cDNA microarrays and evaluated cell survival after IOERT treatment in primary BC cell cultures. Real-time quantitative reverse transcription polymerase chain reaction (qRT-PCR) was performed to validate candidate genes. Results: We showed, for the first time, a 4-gene and a 6-gene signature, as new molecular biomarkers, in two primary BC cell cultures after exposure at 9 Gy and 23 Gy respectively, for which we observed a significantly high survival rate. Conclusion: Gene signatures activated by different doses of ionizing radiation may predict response to RT and contribute to defining a personalized biological-driven treatment plan.

objective: Technological advances in radiation therapy are evolving with the use of hadrons, such as protons, indicated for tumors where conventional radiotherapy does not give significant advantages or for tumors located in sensitive regions, which need the maximum of dose-saving of the surrounding healthy tissues. The genomic response to conventional and non-conventional linear energy transfer exposure is a poor investigated topic and became an issue of radiobiological interest. The aim of this work was to analyze and compare molecular responses in term of gene expression profiles, induced by electron and proton irradiation in breast cancer cell lines. Methods: We studied the gene expression profiling differences by cDNA microarray activated in response to electron and proton irradiation with different linear energy transfer values, among three breast cell lines (the tumorigenic MCF7 and MDA-MB-231 and the non-tumorigenic MCF10A), exposed to the same sublethal dose of 9 Gy. results: Gene expression profiling pathway analyses showed the activation of different signaling and molecular networks in a cell line and radiation type-dependent manner. MCF10A and MDA-MB-231 cell lines were found to induce factors and pathways involved in the immunological process control. conclusion: Here, we describe in a detailed way the gene expression profiling and pathways activated after electron and proton irradiation in breast cancer cells. Summarizing, although specific pathways are activated in a radiation type-dependent manner, each cell line activates overall similar molecular networks in response to both these two types of ionizing radiation.

The breast cancer (BC) disease is characterized by a wide heterogeneity at both clinical and molecular level, showing distinct subtypes with different clinical outcomes. Thus, the choice of the therapeutic plan, such as the type of radiotherapy (RT) need to take into account this complexity. Indeed, the proton therapy (PT) shows a medical benefit compared to conventional X-ray RT, as regards the localized delivery of the radiation dose sparing health tissues, but few data regarding proton-induced molecular changes are currently available. The aim of this study was therefore to investigate the production of immunological molecules and gene expression profiles induced by proton irradiation on BC cell lines. Clonogenic survival assay, luminex assay and cDNA microarray gene expression analyses were performed both in the non-tumorigenic MCF10A cell line and in two tumorigenic MCF7 and MDA-MB-231 cell lines, following irradiation with 0.5, 2 and 9 Gy of clinical proton beams. We found that proton irradiation induced gene expression changes useful to define a cell line and dose-dependent gene signatures. The lack of molecular data in the literature can be filled by data here presented that could represent a useful tool to better understand the molecular mechanisms elicited by protons predicting the treatment outcome.

Tipifarnib is a potent and highly selective inhibitor of farnesyltransferase (FTase). FTase catalyzes the posttranslational attachment of farnesyl groups to signaling proteins that are required for localization to cell membranes. Although all RAS isoforms are FTase substrates, only HRAS is exclusively dependent upon farnesylation, raising the possibility that HRAS-mutant tumors might be susceptible to tipifarnib-mediated inhibition of FTase. Here, we report the characterization of tipifarnib activity in a wide panel of HRAS-mutant and wild-type head and neck squamous cell carcinoma (HNSCC) xenograft models. Tipifarnib treatment displaced both mutant and wild-type HRAS from membranes but only inhibited proliferation, survival, and spheroid formation of HRAS-mutant cells. In vivo, tipifarnib treatment induced tumor stasis or regression in all six HRAS-mutant xenografts tested but displayed no activity in six HRAS wild-type patient-derived xenograft (PDX) models. Mechanistically, drug treatment resulted in the reduction of MAPK pathway signaling, inhibition of proliferation, induction of apoptosis, and robust abrogation of neovascularization, apparently via effects on both tumor cells and endothelial cells. Bioinformatics and quantitative image analysis further revealed that FTase inhibition induces progressive squamous cell differentiation in tipifarnib-treated HNSCC PDXs. These preclinical findings support that HRAS represents a druggable oncogene in HNSCC through FTase inhibition by tipifarnib, thereby identifying a precision therapeutic option for HNSCCs harboring HRAS mutations.

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.

Purpose: Ionizing radiation (IR) treatment activates inflammatory processes causing the release of a great amount of molecules able to affect the cell survival. The aim of this study was to analyze the cytokine signature of conditioned medium produced by non-tumorigenic mammary epithelial cell line MCF10A, as well as MCF7 and MDA-MB-231 breast cancer cell lines, after single high doses of IR in order to understand their role in high radiation response. Materials and methods: We performed a cytokine profile of irradiated conditioned media of MCF10A, MCF7 and MDA-MB-231 cell lines treated with 9 or 23 Gy, by Luminex and ELISA analyses. Results: Overall, our results show that both 9 Gy and 23 Gy of IR induce the release within the first 72 h of cytokines and growth factors potentially able to influence the tumor outcome, with a dose-independent and cell-line dependent signature. Moreover, our results show that the cell-senescence phenomenon does not correlate with the amount of ‘senescence-associated secretory phenotype’ (SASP) molecules released in media. Thus, additional mechanisms are probably involved in this process. Conclusions: These data open the possibility to evaluate cytokine profile as useful marker in modulating the personalized radiotherapy in breast cancer care.

An algorithm for delineating complex head and neck cancers in positron emission tomography (PET) images is presented in this article. An enhanced random walk (RW) algorithm with automatic seed detection is proposed and used to make the segmentation process feasible in the event of inhomogeneous lesions with bifurcations. In addition, an adaptive probability threshold and a k-means based clustering technique have been integrated in the proposed enhanced RW algorithm. The new threshold is capable of following the intensity changes between adjacent slices along the whole cancer volume, leading to an operator-independent algorithm. Validation experiments were first conducted on phantom studies: High Dice similarity coefficients, high true positive volume fractions, and low Hausdorff distance confirm the accuracy of the proposed method. Subsequently, forty head and neck lesions were segmented in order to evaluate the clinical feasibility of the proposed approach against the most common segmentation algorithms. Experimental results show that the proposed algorithm is more accurate and robust than the most common algorithms in the literature. Finally, the proposed method also shows real-time performance, addressing the physician’s requirements in a radiotherapy environment.

The goal of radiation therapy (RT) is to deliver the therapeutic dose to target tissues minimizing the risks of normal tissue complication. Nowadays, technological advances in radiation delivery and the introduction of particle therapies have strongly limited the amount of dose distributed to normal tissues and enhanced the tumor killing capacity. Here, we discuss about the state of art in RT treatment modalities, tumor sensitivity and radiation toxicity. A special insight is dedicated to the role of tumor heterogeneity, cancer stem cells (CSCs) and hypoxia. In addition, in this review we provide an overview of the most recently studies evaluating the potential role of "omic" biomarkers for a personalized biological-driven treatment planning, useful to maximize the radiotherapy success without normal tissues complications.

Aim: A small number of studies evaluated the detection rate of lesions from bladder carcinoma (BC) of 18 F-FDG PET/CT in the restaging process. However, the prognostic role of FDG PET/CT still remains unclear. The aim of the present study was to evaluate the accuracy, the effect upon treatment decision, and the prognostic value of FDG PET/CT in patients with suspected recurrent BC. Materials and Methods: Forty-one patients affected by BC underwent FDG PET/CT for restaging purpose. The diagnostic accuracy of visually interpreted FDG PET/CT was assessed compared to histology (n = 8), other diagnostic imaging modalities (contrast-enhanced CT in 38/41 patients and MRI in 15/41) and clinical follow-up (n = 41). Semiquantitative PET values (SUVmax, SUVmean, SUL, MTV, TLG) were calculated using a graph-based method. Progression-free survival (PFS) and overall survival (OS) were assessed by using Kaplan-Meier curves. The risk of progression (hazard ratio, HR) was computed by Cox regression analysis by considering all the available variables. Results: PET was considered positive in 21 of 41 patients. Of these, recurrent BC was confirmed in 20 (95 %). Sensitivity, specificity, positive predictive value, negative predictive value, and accuracy of FDG PET/CT were 87 %, 94 %, 95 %, 85 %, 90 %. AUC was 0.9 (95 %IC 0.8-1). Bayesian positive and negative likelihood ratios were 14.5 and 0.13, respectively. FDG PET/CT findings modified the therapeutic approach in 16 patients (modified therapy in 10 PET-positive patients, watch-and-wait in six PET-negative patients). PFS was significantly longer in patients with negative scan vs. those with pathological findings (85 % vs. 24 %, p < 0.05; HR = 12.4; p = 0.001). Moreover, an unremarkable study was associated with a longer OS (88 % vs. 47 % after 2 years and 87 % vs. 25 % after 3 years, respectively, p < 0.05). Standardized uptake value (SUV)max > 6 and total

Aim of this work is the study of the preliminary steps to perform a particle treatment of cancer cells inoculated in small animals and to realize a preclinical hadrontherapy facility. A well-defined dosimetric protocol was developed to explicate the steps needed in order to perform a precise proton irradiation in small animals and achieve a highly conformal dose into the target. A precise homemade positioning and holding system for small animals was designed and developed at INFN-LNS in Catania (Italy), where an accurate Monte Carlo simulation was developed, using Geant4 code to simulate the treatment in order to choose the best animal position and perform accurately all the necessary dosimetric evaluations. The Geant4 application can also be used to realize dosimetric studies and its peculiarity consists in the possibility to introduce the real target composition in the simulation using the DICOM micro-CT image. This application was fully validated comparing the results with the experimental measurements. The latter ones were performed at the CATANA (Centro di AdroTerapia e Applicazioni Nucleari Avanzate) facility at INFN-LNS by irradiating both PMMA and water solid phantom. Dosimetric measurements were performed using previously calibrated EBT3 Gafchromic films as a detector and the results were compared with the Geant4 simulation ones. In particular, two different types of dosimetric studies were performed: the first one involved irradiation of a phantom made up of water solid slabs where a layer of EBT3 was alternated with two different slabs in a sandwich configuration, in order to validate the dosimetric distribution. The second one involved irradiation of a PMMA phantom made up of a half hemisphere and some PMMA slabs in order to simulate a subcutaneous tumour configuration, normally used in preclinical studies. In order to evaluate the accordance between experimental and simulation results, two different statistical tests were made: Kolmogorov test and gamma

BACKGROUND: Statistical Parametric Mapping (SPM) has been applied for single-subject evaluation of [18F]FDG uptake in Alzheimer Disease (AD). In a single-subject framework, the patient is compared to a dataset of [18F]FDG PET images from healthy subjects (HS) evaluating brain metabolic abnormalities. No studies exist that assess the effects on SPM analysis of HS [18F]FDG PET datasets acquired from different subjects and using different PET scanners including the same or different PET scanners than those used for patients. This work aims to elucidate this issue from a methodological perspective. METHODS: We considered six different [18F]FDG PET datasets, from different HS populations, acquired by different PET scanners. We applied SPM5 procedures for single-subject comparison with each of the six HS datasets in 10 probable AD patients showing the typical [18F] FDG pattern. We also implemented the same comparison in 3 probable AD patients and in 7 patients with a clinical diagnosis of Mild Cognitive Impariment (MCI), showing subtle changes on visual inspection of [18F]FDG distribution. RESULTS: Considering the 10 patients with the typical [18F]FDG pattern, the results were comparable for all the SPM maps. In the 3 probable AD patients with subtle changes in [18F]FDG distribution, no significant AD pattern emerged when a small number (<20) of HS was used, whereas a significant AD pattern appeared when a large (>50) HS image set was used. In the 7 considered MCI patients the use of a large (>50) HS image set allowed to assess significant hypometabolic patterns related to a probable neurodegenerative pathology. CONCLUSIONS: The use of large HS datasets of PET scans (>50) is recommended for single-subject SPM analysis. On condition that appropriate preprocessing steps are provided, large HS datasets can include HS images acquired with different PET systems, not including images from the same scanner of that used for patients

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 b

Nowadays, uterine fibroids can be treated using Magnetic Resonance guided Focused Ultrasound Surgery (MRgFUS), which is a non-invasive therapy exploiting thermal ablation. In order to measure the Non-Perfused Volume (NPV) for treatment response assessment, the ablated fibroid areas (i.e., Region of Treatment, ROT) are manually contoured by a radiologist. The current operator-dependent methodology could affect the subsequent follow-up phases, due to the lack of result repeatability. In addition, this fully manual procedure is time-consuming, considerably increasing execution times. These critical issues can be addressed only by means of accurate and efficient automated Pattern Recognition approaches. In this contribution, we evaluate two computer-assisted segmentation methods, which we have already developed and validated, for uterine fibroid segmentation in MRgFUS treatments. A quantitative comparison on segmentation accuracy, in terms of area-based and distance-based metrics, was performed. The clinical feasibility of these approaches was assessed from physicians’ perspective, by proposing an integrated solution.

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 evalu

Background: A new transcranial focused ultrasound device has been developed that can induce hyperthermia in a large tissue volume. The purpose of this work is to investigate theoretically how glioblastoma multiforme (GBM) can be effectively treated by combining the fast hyperthermia generated by this focused ultrasound device with external beam radiotherapy. Methods/Design: To investigate the effect of tumor growth, we have developed a mathematical description of GBM proliferation and diffusion in the context of reaction-diffusion theory. In addition, we have formulated equations describing the impact of radiotherapy and heat on GBM in the reaction-diffusion equation, including tumor regrowth by stem cells. This formulation has been used to predict the effectiveness of the combination treatment for a realistic focused ultrasound heating scenario. Our results show that patient survival could be significantly improved by this combined treatment modality. Discussion: High priority should be given to experiments to validate the therapeutic benefit predicted by our model.

The aim of the present study was to evaluate the changes in cervical cancer glucose metabolism for different levels of cellular differentiation. The metabolic activity was measured by standardized uptake value (SUV), SUV normalized to lean body mass, metabolic tumor volume and total lesion glycolysis using fluorine-18 fluorodeoxyglucose positron emission tomography (PET). A correlation study of these values could be used to facilitate therapeutic choice and to improve clinical practice and outcome. This study considered 32 patients with diagnosed cervical cancers, at different International Federation of Gynecology and Obstetrics stages. Glucose metabolism was assessed by PET examination, and histological specimens were examined to determine their initial grade of differentiation. A correlation study of these values was evaluated. Histological examination showed that all cases were of squamous cell carcinoma. Regarding the differentiation of the tumor, 19 well- to moderately-differentiated tumors and 13 poorly-differentiated tumors were determined. Negative findings for correlations between metabolic parameters and initial grade of histological differentiation were found, and considering that histological grade has been shown to have no consistent prognostic value in cervical cancer treatment, PET imaging could play a significant role in cervical cancer prognosis.