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Recent advances in Quantum Machine Learning (QML) have provided benefits to several computational processes, drastically reducing the time complexity. Another approach of combining quantum information theory with machine learning—without involving quantum computers—is known as Quantum-inspired Machine Learning (QiML), which exploits the expressive power of the quantum language to increase the accuracy of the process (rather than reducing the time complexity). In this work, we propose a large-scale experiment based on the application of a binary classifier inspired by quantum information theory to the biomedical imaging context in clonogenic assay evaluation to identify the most discriminative feature, allowing us to enhance cell colony segmentation. This innovative approach offers a two-fold result: (1) among the extracted and analyzed image features, homogeneity is shown to be a relevant feature in detecting challenging cell colonies; and (2) the proposed quantum-inspired classifier is a novel and outstanding methodology, compared to conventional machine learning classifiers, for the evaluation of clonogenic assays.

Glioblastoma (GBM) is one of the most lethal types of tumor due to its high recurrence level in spite of aggressive treatment regimens involving surgery, radiotherapy and chemotherapy. Hypoxia is a feature of GBM, involved in radioresistance, and is known to be at the origin of treatment failure. The aim of this work was to assess the therapeutic potential of a new targeted c-SRC inhibitor molecule, named Si306, in combination with X-rays on the human glioblastoma cell lines, comparing normoxia and hypoxia conditions. For this purpose, the dose modifying factor and oxygen enhancement ratio were calculated to evaluate the Si306 radiosensitizing effect. DNA damage and the repair capability were also studied from the kinetic of &gamma, H2AX immunodetection. Furthermore, motility processes being supposed to be triggered by hypoxia and irradiation, the role of c-SRC inhibition was also analyzed to evaluate the migration blockage by wound healing assay. Our results showed that inhibition of the c-SRC protein enhances the radiotherapy efficacy both in normoxic and hypoxic conditions. These data open new opportunities for GBM treatment combining radiotherapy with molecularly targeted drugs to overcome radioresistance.

The purpose of this paper is to characterize the skin deterministic damage due to the effect of proton beam irradiation in mice occurred during a long-term observational experiment. This study was initially defined to evaluate the insurgence of myelopathy irradiating spinal cords with the distal part of a Spread-out Bragg peak (SOBP). To the best of our knowledge, no study has been conducted highlighting high grades of skin injury at the dose used in this paper. Nevertheless these effects occurred. In this regard, the experimental evidence of significant insurgence of skin injury induced by protons using a SOBP configuration will be shown. Skin damages were classified into six scores (from 0 to 5) according to the severity of the injuries and correlated to ED50 (i.e. the radiation dose at which 50% of animals show a specific score) at 40 days post-irradiation (d.p.i.). The effects of radiation on the overall animal wellbeing have been also monitored and the severity of radiation-induced skin injuries was observed and quantified up to 40 d.p.i.

Despite of the development of advanced segmentation techniques, achieving accurate and reproducible gross tumor volume (GTV) segmentation results is still an important challenge in neuro-radiosurgery. Nowadays, magnetic resonance imaging (MRI) is the most prominent modality in radiation therapy for soft-tissue anatomical districts. Gamma Knife stereotactic neuro-radiosurgery is a minimally invasive technology for dealing with inaccessible or insufficiently treated tumors with traditional surgery or radiotherapy. During a treatment planning phase, the GTV is generally contoured by experienced neurosurgeons and radiation oncologists using fully manual segmentation procedures on MR images. Unfortunately, this operative methodology is definitely time-expensive and operator-dependent. Delineation result repeatability, in terms of both intra- and inter-operator reliability, can be achieved only by using computer-assisted approaches. In this paper a novel semi-automatic seeded image segmentation method, based on a cellular automata model, for MRI brain cancer detection and delineation is proposed. This approach, called GTVcut, employs an adaptive seed selection strategy and helps to segment the GTV, by identifying the target volume to be treated using the Gamma Knife device. The accuracy of GTVcut was evaluated on a dataset composed of 32 brain cancers, using both spatial overlap-based and distance-based metrics. The achieved experimental results are very reproducible, showing the effectiveness and the clinical feasibility of the proposed approach.

Prostate imaging analysis is difficult in diagnosis, therapy, and staging of prostate cancer. In clinical practice, Magnetic Resonance Imaging (MRI) is increasingly used thanks to its morphologic and functional capabilities. However, manual detection and delineation of prostate gland on multispectral MRI data is currently a time-expensive and operator-dependent procedure. Efficient computer-assisted segmentation approaches are not yet able to address these issues, but rather have the potential to do so. In this paper, a novel automatic prostate MR image segmentation method based on the Fuzzy C-Means (FCM) clustering algorithm, which enables multispectral T1-weighted (T1w) and T2-weighted (T2w) MRI anatomical data processing, is proposed. This approach, using an unsupervised Machine Learning technique, helps to segment the prostate gland effectively. A total of 21 patients with suspicion of prostate cancer were enrolled in this study. Volume-based metrics, spatial overlap-based metrics and spatial distance-based metrics were used to quantitatively evaluate the accuracy of the obtained segmentation results with respect to the gold-standard boundaries delineated manually by an expert radiologist. The proposed multispectral segmentation method was compared with the same processing pipeline applied on either T2w or T1w MR images alone. The multispectral approach considerably outperforms the monoparametric ones, achieving an average Dice Similarity Coefficient 90.77 ± 1.75, with respect to 81.90 ± 6.49 and 82.55 ± 4.93 by processing T2w and T1w imaging alone, respectively. Combining T2w and T1w MR image structural information significantly enhances prostate gland segmentation by exploiting the uniform gray appearance of the prostate on T1w MRI.

Magnetic Resonance Imaging has gained popularity in breast cancer diagnosis since the introduction of contrast media, and recent developments in MRI have demonstrated a new potential use in diagnosis. Indeed, the application of in vivo spectroscopy to mammary tissue has revealed that the spectral appearance of choline could be a marker of malignancy, but early diagnosis and unambiguous breast cancer characterization could benefit by a standardized protocol for the simultaneous use of Dynamic Contrast Enhancement MRI and Magnetic Resonance Spectroscopy and combined interpretation of associated markers. A total of 29 female patients took part in the study. The combined protocol was performed on a General Electric Signa HDtx 1.5 Tesla, and the DCE data analysis was performed through an evaluation of the ROI signal intensity over time. The MRS data analysis evaluated choline concentration and the signal to noise ratio of the choline peak. Sensitivity, specificity and accuracy were assessed by the Receiver Operating Characteristic methodology for Dynamic Contrast Enhancement, Magnetic Resonance Spectroscopy and their linear combination. We performed a linear combination of Dynamic Contrast Enhancement MRI and 1H-Magnetic Resonance Spectroscopy by distribution free approach to obtain a high level diagnostic index. Combining the results of the two diagnostic tests has resulted in a new, very effective, diagnostic index able to discriminate between patients with and without malignant disease.