Publications by authors named "Giulia Buizza"

Background: Chest radiographs (CXRs) are still of crucial importance in primary diagnostics, but their interpretation poses difficulties at times.

Research Question: Can a convolutional neural network-based artificial intelligence (AI) system that interprets CXRs add value in an emergency unit setting?

Study Design And Methods: A total of 563 CXRs acquired in the emergency unit of a major university hospital were retrospectively assessed twice by three board-certified radiologists, three radiology residents, and three emergency unit-experienced nonradiology residents (NRRs). They used a two-step reading process: (1) without AI support; and (2) with AI support providing additional images with AI overlays.

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Purpose: To develop and validate an artificial intelligence algorithm for the positioning assessment of tracheal tubes (TTs) and central venous catheters (CVCs) in supine chest radiographs (SCXRs) by using an algorithm approach allowing for adjustable definitions of intended device positioning.

Materials And Methods: Positioning quality of CVCs and TTs is evaluated by spatially correlating the respective tip positions with anatomical structures. For CVC analysis, a configurable region of interest is defined to approximate the expected region of well-positioned CVC tips from segmentations of anatomical landmarks.

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Purpose: To define an optimal set of b-values for accurate derivation of diffusion MRI parameters in the brain with segmented Intravoxel Incoherent Motion (IVIM) model.

Methods: Simulations of diffusion signals were performed to define an optimal set of b-values targeting different perfusion regimes, by relying on an optimization procedure which minimizes the total relative error on estimated IVIM parameters computed with a segmented fitting procedure. Then, the optimal b-values set was acquired in vivo on healthy subjects and skull base chordoma patients to compare the optimized protocol with a clinical one.

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Background: Quantitative imaging such as Diffusion-Weighted MRI (DW-MRI) can be exploited to non-invasively derive patient-specific tumor microstructure information for tumor characterization and local recurrence risk prediction in radiotherapy.

Purpose: To characterize tumor microstructure according to proliferative capacity and predict local recurrence through microstructural markers derived from pre-treatment conventional DW-MRI, in skull-base chordoma (SBC) patients treated with proton (PT) and carbon ion (CIRT) radiotherapy.

Methods: Forty-eight patients affected by SBC, who underwent conventional DW-MRI before treatment and were enrolled for CIRT (n = 25) or PT (n = 23), were retrospectively selected.

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Purpose: To investigate the static magnetic field generated by a proton pencil beam as a candidate for range verification by means of Monte Carlo simulations, thereby improving upon existing analytical calculations. We focus on the impact of statistical current fluctuations and secondary protons and electrons.

Methods: We considered a pulsed beam (10 s pulse duration) during the duty cycle with a peak beam current of 0.

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Objective: Carbon ion radiation therapy (CIRT) is an emerging radiation technique with advantageous physical and radiobiologic properties compared to conventional radiotherapy (RT) providing better response in case of radioresistant and hypoxic tumors. Our aim is to critically review if functional imaging techniques could play a role in predicting outcome of CIRT-treated tumors, as already proven for conventional RT.

Methods: 14 studies, concerning Magnetic resonance imaging (MRI) and Positron Emission Tomography (PET), were selected after a comprehensive search on multiple electronic databases from January 2000 to March 2020.

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Purpose: To evaluate changes in diffusion and perfusion-related properties of white matter (WM) induced by proton therapy, which is capable of a greater dose sparing to organs at risk with respect to conventional X-ray radiotherapy, and to eventually expose early manifestations of delayed neuro-toxicities.

Methods: Apparent diffusion coefficient (ADC) and IVIM parameters (D, D* and f) were estimated from diffusion-weighted MRI (DWI) in 46 patients affected by meningioma and treated with proton therapy. The impact on changes in diffusion and perfusion-related WM properties of dose and time, as well as the influence of demographic and pre-treatment clinical information, were investigated through linear mixed-effects models.

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Skull-base chordoma (SBC) can be treated with carbon ion radiotherapy (CIRT) to improve local control (LC). The study aimed to explore the role of multi-parametric radiomic, dosiomic and clinical features as prognostic factors for LC in SBC patients undergoing CIRT. Before CIRT, 57 patients underwent MR and CT imaging, from which tumour contours and dose maps were obtained.

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Purpose: To assess early microstructural changes of meningiomas treated with proton therapy through quantitative analysis of intravoxel incoherent motion (IVIM) and diffusion-weighted imaging (DWI) parameters.

Methods: Seventeen subjects with meningiomas that were eligible for proton therapy treatment were retrospectively enrolled. Each subject underwent a magnetic resonance imaging (MRI) including DWI sequences and IVIM assessments at baseline, immediately before the 1st (t0), 10th (t10), 20th (t20), and 30th (t30) treatment fraction and at follow-up.

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Purpose: Proton therapy could benefit from noninvasively gaining tumor microstructure information, at both planning and monitoring stages. The anatomical location of brain tumors, such as meningiomas, often hinders the recovery of such information from histopathology, and conventional noninvasive imaging biomarkers, like the apparent diffusion coefficient (ADC) from diffusion-weighted MRI (DW-MRI), are nonspecific. The aim of this study was to retrieve discriminative microstructural markers from conventional ADC for meningiomas treated with proton therapy.

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Purpose: Meningiomas are mainly benign tumors, though a considerable proportion shows aggressive behaviors histologically consistent with atypia/anaplasia. Histopathological grading is usually assessed through invasive procedures, which is not always feasible due to the inaccessibility of the lesion or to treatment contraindications. Therefore, we propose a multi-parametric MRI assessment as a predictor of meningioma histopathological grading.

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The integration of magnetic resonance imaging (MRI) for guidance in external beam radiotherapy has faced significant research and development efforts in recent years. The current availability of linear accelerators with an embedded MRI unit, providing volumetric imaging at excellent soft tissue contrast, is expected to provide novel possibilities in the implementation of image-guided adaptive radiotherapy (IGART) protocols. This study reviews open medical physics issues in MR-guided radiotherapy (MRgRT) implementation, with a focus on current approaches and on the potential for innovation in IGART.

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Article Synopsis
  • The study aimed to see if baseline ADC (apparent diffusion coefficient) from DWI (diffusion-weighted imaging) could predict how patients with sacral chordoma responded to carbon ion radiotherapy (CIRT) when surgery wasn’t an option.
  • Fifty-nine patients underwent CIRT, with MRIs taken before treatment and regularly for over a year, analyzing lesion volume and ADC characteristics.
  • The results indicated that patients with disease progression had higher median ADC values at baseline compared to those achieving a partial response or stable disease, suggesting that baseline ADC could help identify patients less likely to benefit from treatment.
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Multiparametric MRI is a remarkable imaging method for the assessment of patho-physiological processes. In particular, brain tumor characterization has taken advantage of the development of advanced techniques such as Diffusion- (DWI) and Perfusion- (PWI) Weighted Imaging, but a thorough analysis of meningiomas is still lacking despite the variety of computational methods proposed. We compute perfusion and diffusion parametric maps relying on a well-defined methodological workflow, investigating possible correlations between pure and diffusion-based perfusion parameters in a cohort of 26 patients before proton therapy.

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Purpose: To derive personalized tumour control probability (TCP) models, using diffusion-weighted (DW-) MRI for defining initial tumour cellular density in skull-base chordoma patients undergoing carbon-ion radiotherapy (CIRT).

Materials And Methods: 67 patients affected by skull-base chordoma were enrolled for a standardized CIRT treatment (70.4 Gy (RBE) prescription dose).

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Purpose: To explore prognostic and predictive values of a novel quantitative feature set describing intra-tumor heterogeneity in patients with lung cancer treated with concurrent and sequential chemoradiotherapy.

Methods: Longitudinal PET-CT images of 30 patients with non-small cell lung cancer were analysed. To describe tumor cell heterogeneity, the tumors were partitioned into one to ten concentric regions depending on their sizes, and, for each region, the change in average intensity between the two scans was calculated for PET and CT images separately to form the proposed feature set.

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Purpose: A new set of quantitative features that capture intensity changes in PET/CT images over time and space is proposed for assessing the tumor response early during chemoradiotherapy. The hypothesis whether the new features, combined with machine learning, improve outcome prediction is tested.

Methods: The proposed method is based on dividing the tumor volume into successive zones depending on the distance to the tumor border.

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