Exposure to ionizing radiation (IR) is inevitable in various X-ray imaging examinations, with computed tomography (CT) being a major contributor to increased human radiation exposure. Ionizing radiation may cause structural damage to macromolecules, particularly DNA, mostly through an indirect pathway in diagnostic imaging. The indirect pathway primarily involves the generation of reactive oxygen species (ROS) due to water radiolysis induced by IR, leading to DNA damage, including double-strand breaks (DSB), which are highly cytotoxic. Antioxidants, substances that prevent oxidative damage, are proposed as potential radioprotective agents. This Study Protocol article presents the rationale for selecting vitamin C as a preventive measure against CT-associated IR-induced DNA damage, to be investigated in a randomized placebo-controlled trial, with a full in vivo design, using an oral easy-to-use schedule administration in the outpatient setting, for the single CT examination with the highest total global IR dose burden (contrast-enhanced abdomen and pelvis CT). The study also aims to explore the mediating role of oxidative stress, and it has been written in adherence to the Standard Protocol Items recommendations.

Background/Objectives: The accurate quantification of ground-glass opacities (GGOs) and consolidation volumes has prognostic value in COVID-19 patients. Nevertheless, the accurate manual quantification of the corresponding volumes remains a time-consuming task. Deep learning (DL) has demonstrated good performance in the segmentation of normal lung parenchyma and COVID-19 pneumonia. We introduce a Human-in-the-Loop (HITL) strategy for the segmentation of normal lung parenchyma and COVID-19 pneumonia that is both time efficient and quality effective. Furthermore, we propose a Gaussian Mixture Model (GMM) to classify GGO and consolidation based on a probabilistic characterization and case-sensitive thresholds. Methods: A total of 65 Computed Tomography (CT) scans from 64 patients, acquired between March 2020 and June 2021, were randomly selected. We pretrained a 3D-UNet with an international dataset and implemented a HITL strategy to refine the local dataset with delineations by teams of medical interns, radiology residents, and radiologists. Following each HITL cycle, 3D-UNet was re-trained until the Dice Similarity Coefficients (DSCs) reached the quality criteria set by radiologists (DSC = 0.95/0.8 for the normal lung parenchyma/COVID-19 pneumonia). For the probabilistic characterization, a Gaussian Mixture Model (GMM) was fitted to the Hounsfield Units (HUs) of voxels from the CT scans of patients with COVID-19 pneumonia on the assumption that two distinct populations were superimposed: one for GGO and one for consolidation. Results: Manual delineation of the normal lung parenchyma and COVID-19 pneumonia was performed by seven teams on 65 CT scans from 64 patients (56 ± 16 years old (μ ± σ), 46 males, 62 with reported symptoms). Automated lung/COVID-19 pneumonia segmentation with a DSC > 0.96/0.81 was achieved after three HITL cycles. The HITL strategy improved the DSC by 0.2 and 0.5 for the normal lung parenchyma and COVID-19 pneumonia segmentation, respectively. The distribution of the patient-specific thresholds derived from the GMM yielded a mean of −528.4 ± 99.5 HU (μ ± σ), which is below most of the reported fixed HU thresholds. Conclusions: The HITL strategy allowed for fast and effective annotations, thereby enhancing the quality of segmentation for a local CT dataset. Probabilistic characterization of COVID-19 pneumonia by the GMM enabled patient-specific segmentation of GGO and consolidation. The combination of both approaches is essential to gain confidence in DL approaches in our local environment. The patient-specific probabilistic approach, when combined with the automatic quantification of COVID-19 imaging findings, enhances the understanding of GGO and consolidation during the course of the disease, with the potential to improve the accuracy of clinical predictions.

Computed tomography (CT) is fundamental to modern medicine, yet ionizing radiation (IR) exposure causes DNA damage. Although often underestimated, at current doses, CT may account for ~5% of new cancer diagnoses. Complementary radioprotective approaches beyond dose reduction are needed. We conducted a prospective observational study to characterize IR-induced oxidative stress (OS)-mediated DNA damage in modern CT to explore potential antioxidant-based radioprotective strategies. In volunteers not exposed to IR (A_NONE) and in patients with two-phase abdominal–pelvis CT (B_EXPOSURE), blood samples were collected at T_BASE-min 0 and T_POST-min 60 to measure biomarkers of OS (oxidative damage and antioxidant capacity) and DNA damage. Thirty-five subjects (n = 17 A_NONE/18 B_EXPOSURE) were studied. Body mass index and DNA damage in T_BASE were comparable between groups. In A_NONE, biomarkers of OS and DNA damage did not change between T_BASE and T_POST (p > 0.05 for all). In B_EXPOSURE, DNA damage was significantly increased [15% (−15–60); p < 0.001], which was associated with consistent increased antioxidant enzyme activity [p < 0.05 for all antioxidant enzymes]. In modern CT with relatively low effective dose (ED) levels, a significant increase in DNA damage was observed along with increased antioxidant enzyme activity as defensive response and marker of OS-mediated damage-mediating mechanisms. These findings warrant interventional studies to evaluate antioxidant-based radioprotective strategies.

Prostate cancer (PCa) is the most common malignancy in men worldwide. Multiparametric MRI (mpMRI) improves the detection of clinically significant PCa (csPCa); however, it remains limited by false-positive findings and inter-observer variability. Time-dependent diffusion (TDD) MRI provides microstructural information that may enhance csPCa characterization beyond standard mpMRI. This prospective observational diagnostic accuracy study protocol describes the evaluation of PROS-TD-AI, an in-house developed AI workflow integrating TDD-derived metrics for zone-aware csPCa risk prediction. PROS-TD-AI will be compared with PI-RADS v2.1 in routine clinical imaging using MRI-targeted prostate biopsy as the reference standard.

Prostate cancer (PCa) is the most frequently diagnosed malignancy in men and the eighth leading cause of cancer death worldwide. Multiparametric MRI (mpMRI) has become central to the diagnostic pathway for men at intermediate risk, improving detection of clinically significant PCa (csPCa) while reducing unnecessary biopsies and over-diagnosis. However, mpMRI remains limited by false positives, false negatives, and moderate to substantial interobserver agreement. Time-dependent diffusion (TDD) MRI, a novel sequence that enables tissue microstructure characterization, has shown encouraging preclinical performance in distinguishing clinically significant from insignificant PCa. Combining TDD-derived metrics with machine learning may provide robust, zone-specific risk prediction with less dependence on reader training and improved accuracy compared to current standard-of-care. This study protocol outlines the rationale and describes the prospective evaluation of a home-developed AI-enhanced TDD-MRI software (PROSTDAI) in routine diagnostic care, assessing its added value against PI-RADS v2.1 and validating results against MRI-guided prostate biopsy.