Serena Carriero

Diagnostic imaging is central to contemporary healthcare, but increasing demand has intensified concerns regarding inappropriate and low-value examinations. Imaging appropriateness should be understood as a multidimensional quality framework rather than a simple reduction in examination volume. This narrative review synthesizes evidence on clinical outcomes, radiation protection, healthcare costs, organizational efficiency, equity, incidental findings, and environmental sustainability. A structured PubMed search was complemented by targeted database and institutional searches and selective citation tracking. Low-value imaging may disrupt diagnostic pathways through diagnostic uncertainty, unnecessary follow-up, avoidable radiation exposure, additional costs, waiting-list pressure, and environmental burden. Mitigation strategies include referral guidelines, clinical decision support, radiologist-led vetting, standardized incidental findings management, and audit and feedback. Their effects vary by setting and outcome: improved referral scores do not necessarily reduce utilization or improve patient outcomes. Environmental estimates depend on imaging modalities, electricity supply, and assessment boundaries, while proposed artificial intelligence applications require prospective validation. We translate this evidence into an operational pathway linking examination justification, modality and protocol selection, reporting, follow-up, and service evaluation. Imaging appropriateness therefore requires reducing low-value examinations while preserving diagnostic safety and timely, equitable access to clinically beneficial imaging.

Radiomics has produced tens of thousands of publications yet almost no handcrafted radiomic signatures in routine clinical use, and the reasons are increasingly understood to be problems of reproducibility and clinical translation rather than of algorithms. This critical narrative review argues that the field systematically generates paper-grade evidence—findings sufficient to publish—far faster than decision-grade evidence—findings sufficient to change clinical practice. Drawing on meta-scientific research, we describe seven fragility mechanisms (publication bias, analytical flexibility, underpowering, HARKing [hypothesizing after the results are known], citation distortion, cognitive bias, and misaligned incentives) and show why radiomics is structurally exposed to all of them simultaneously: high-dimensional feature spaces, acquisition-dependent measurement instability, segmentation variability, retrospective single-centre data, small samples, and leakage-prone validation. We then summarise empirical evidence on the radiomics literature, which remains pervaded by suboptimal methodological quality, near-absent negative results, limited external validation, sparse calibration and clinical-utility assessment, low data and code sharing, and a measurable retraction signal. We interpret these patterns as the output of a self-reinforcing system rather than isolated errors, and argue that better algorithms alone cannot resolve them. Finally, we argue that closing this gap requires not better models but evidentiary discipline: the consistent, enforceable application of standards the field already has, and the calibration of published claims to the strength of the underlying evidence.

Radiomics has produced tens of thousands of publications yet almost no tools in routine clinical use, and the reasons are increasingly understood to be problems of reproducibility and clinical translation rather than of algorithms. This critical narrative review argues that the field systematically generates paper-grade evidence—findings sufficient to publish—far faster than decision-grade evidence—findings sufficient to change clinical practice. Drawing on meta-scientific research, we describe seven fragility mechanisms (publication bias, analytical flexibility, underpowering, HARKing [hypothesizing after the results are known], citation distortion, cognitive bias, and misaligned incentives) and show why radiomics is structurally exposed to all of them simultaneously: high-dimensional feature spaces, acquisition-dependent measurement instability, segmentation variability, retrospective single-centre data, small samples, and leakage-prone validation. We then summarise empirical evidence on the radiomics literature, which remains pervaded by suboptimal methodological quality, near-absent negative results, limited external validation, sparse calibration and clinical-utility assessment, low data and code sharing, and a measurable retraction signal. We interpret these patterns as the output of a self-reinforcing system rather than isolated errors, and argue that better algorithms alone cannot resolve them. Finally, we argue that closing this gap requires not better models but evidentiary discipline: the consistent, enforceable application of standards the field already has, and the calibration of published claims to the strength of the underlying evidence.

Objectives: This study evaluates the impact of neoadjuvant chemotherapy (NAT) on breast tissue density and examines the correlation between density changes and pathological response, while also assessing inter-reader agreement among radiologists and artificial intelligence (AI) tools. Methods: The study included 135 women with triple-negative and/or HER2+ invasive ductal breast cancer who underwent NAT. Digital mammography was performed pre- and post-NAT, with evaluations by radiologists of varying experience levels and comparison using an in-house developed AI tool based on AlexNet CNN architecture. Statistical analyses included Wilcoxon signed-rank test, linear regression, McNemar test, Spearman’s rank correlation, and intraclass correlation coefficients (ICC). Results: Post-NAT assessments showed a significant decrease in breast tissue density, with initial heterogeneously dense and extremely dense breasts decreasing from 32% and 15% to 18% and 7%, respectively (P < .003). Significant relationships were found between density reduction and age, menopausal status, and baseline density, and the reduction correlated moderately with pathological response to NAT. Intraclass correlation coefficient values reflected good agreement among experienced radiologists (0.68), fair agreement between expert and resident (0.55), poor agreement between expert and non-radiologist (0.39), and substantial agreement between the expert and AI tool (0.78). Conclusions: Neoadjuvant chemotherapy significantly reduces breast tissue density, correlating with pathological response, and AI tools provide consistent density evaluations. Artificial intelligence tools show promise in improving assessment consistency, with substantial agreement with expert radiologists. The findings highlight the need for standardized protocols and training to optimize breast density evaluations and suggest further refinement and validation of AI tools for clinical use. Advances in knowledge: This study evaluates the impact of NAT on breast tissue density while investigating its correlation with pathological response and inter-reader agreement among radiologists and AI tools. It contributes to the field by demonstrating the potential of AI to improve consistency in density evaluations and emphasizing the importance of standardized protocols and training for optimized clinical assessments.

Purpose As data about the environmental sustainability of core-needle breast biopsy are lacking, this study aims to investigate current practices regarding materials usage, waste generation, and pre-procedural and procedural variability. Methods A panel of five breast imaging experts developed a 56-item online questionnaire, distributed from February 20 to March 31, 2024, to members of APS Senonetwork Italia. The questionnaire explored materials usage for core-needle breast biopsy during all pre-procedural and procedural steps. Responses were analyzed using descriptive statistics. Results A total of 176 responses from 101 centers were received, with 135 respondents actively performing core-needle breast biopsies. The study revealed substantial variability in sterility practices, with only 0.7% (1/135) of respondents meeting all sterility criteria. While many centers used sterile materials, their application was inconsistent, leading to suboptimal sterility and excessive waste. The most frequently used materials included sterile gloves (56.3%), sterile drapes (34.8%), and probe covers (37.5%). However, only 5.9% of respondents used sterile gowns, and 9.0% used sterile caps. Post-procedural infection rates were reported as uncommon, with 86.7% of respondents never or rarely encountering infections. Additionally, after completing the biopsy, 66.7% of respondents discarded both used and unused materials, contributing to biomedical waste. Conclusions This study highlights the potential for materials usage optimization and waste reduction in core-needle breast biopsy across centers affiliated with APS Senonetwork Italia. As infectious complications were most frequently reported as rare or absent, minimization of unnecessary use of sterile and non-sterile materials could contribute to sustainable healthcare practices while maintaining high-quality patient care.

This review is a brief overview of the current status and the potential role of artificial intelligence (AI) in interventional radiology (IR). The literature published in the last decades was reviewed and the technical developments in terms of radiomics, virtual reality, robotics, fusion imaging, cone-beam computed tomography (CBCT) and Imaging Guidance Software were analyzed. The evidence shows that AI significatively improves pre-procedural planning, intra-procedural navigation, and post-procedural assessment. Radiomics extracts features from optical images of personalized treatment strategies. Virtual reality offers innovative tools especially for training and procedural simulation. Robotic systems, combined with AI, could enhance precision and reproducibility of IR procedures while reducing operator exposure to X-ray. Fusion imaging and CBCT, augmented by AI software, improve real-time guidance and procedural outcomes.

Acute pulmonary embolism (APE) represents a significant cause of morbidity and mortality worldwide, requiring rapid and precise diagnosis and effective therapy strategies. Computed Tomography Pulmonary Angiography (CTPA) is currently the gold standard technique for diagnosing PE; however, it presents some disadvantages, including limited sensitivity in detecting sub-segmental emboli and contrast-related risks. Recent advancements in imaging technologies, including Dual-Energy Computed Tomography (DECT) and Photon Counting (PC), offer improved sensitivity and specificity for APE and perfusion abnormalities detection. Digital Dynamic Radiography (DDR) perfusion imaging represents a novel imaging that allows pulmonary perfusion assessment without contrast medium administration, able to detect anomalies at the patient’s bedside, representing a promising advancement, particularly for critically ill or contrast-allergic patients. In parallel, interventional radiology has become integral to APE management, particularly for high-risk and intermediate–high-risk patients, with evolving intravascular treatment techniques such as catheter-directed thrombolysis, mechanical thrombectomy, and thrombus aspiration. This narrative review provides an overview of the latest developments in APE diagnostic imaging and interventional radiology, contextualizing them within current guideline recommendations for endovascular treatment.

Pulmonary embolism (PE) remains a critical condition that demands rapid and accurate diagnosis, for which computed tomographic pulmonary angiography (CTPA) is widely recognized as the diagnostic gold standard. However, recent advancements in imaging technologies—such as dual-energy computed tomography (DECT), photon-counting CT (PCD-CT), and artificial intelligence (AI)—offer promising enhancements to traditional diagnostic methods. This study reviews past, current and emerging technologies, focusing on their potential to optimize diagnostic accuracy, reduce contrast volumes and radiation doses, and streamline clinical workflows. DECT, with its dual-energy imaging capabilities, enhances image clarity even with lower contrast media volumes, thus reducing patient risk. Meanwhile, PCD-CT has shown potential for dose reduction and superior image resolution, particularly in challenging cases. AI-based tools further augment diagnostic speed and precision by assisting radiologists in image analysis, consequently decreasing workloads and expediting clinical decision-making. Collectively, these innovations hold promise for improved clinical management of PE, enabling not only more accurate diagnoses but also safer, more efficient patient care. Further research is necessary to fully integrate these advancements into routine clinical practice, potentially redefining diagnostic workflows for PE and enhancing patient outcomes.

Background: Radiomics is changing clinical practice by providing quantitative information from images to improve diagnosis, prognosis, and treatment planning. This study aims to investigate a radiomics model developed from contrast-enhanced mammography (CEM) images to predict disease-free survival (DFS) and overall survival (OS) in breast cancer (BC) patients. Methods: From January 2013 to December 2015, all consecutive BC patients who underwent CEM before biopsy at a referral center were enrolled. Clinical data included histological results, receptor profiles, and follow-up (DFS and OS). A region of interest (ROI) of the enhancing lesion was selected from recombined CEM images by experienced radiologists, and radiomic features were extracted. A Cox-LASSO model assigned coefficients to the features, generating patient radiomic scores (RSs), which were dichotomized for graphical representation. Model performance was assessed using the C index. Results: The study included 126 BC patients with predominantly “mass”-type lesions (95%) and a median follow-up of 6.88 years (IQR 3.10–8.15). The median age of the patients at the time of examination was 49.2 years (IQR: [42.33–56.98]). Radiomic and clinical–radiomic models showed significant associations between RS, DFS, and OS, with patients with RS below the median showing a better prognosis (p < 0.001). Bootstrap testing confirmed a good model fit for OS prediction, with median C-index values of 0.82 for the clinical model and 0.84 for the clinical–radiomic model. Conclusions: Radiomic analysis of CEM images may predict DFS and OS in BC patients, offering additional prognostic value beyond clinical models alone.

Background/Objectives: the aim of this study was to evaluate the safety and efficacy of percutaneous transhepatic lithotripsy using the SpyGlassDSTM cholangioscopy system for the treatment of difficult stones. Methods: Retrospectively, all patients treated with percutaneous transhepatic lithotripsy using SpyGlassDSTM cholangioscopy system were analyzed. As primary outcome measures, the following data were assessed: the presence of a previous history of the hepatobiliary disease, location of stones, reasons for the choice of the procedure, previous balloon bilioplasty, type of pre-procedural imaging, procedural time, technical success, clinical success, and post-procedural complications (according to CIRSE classification). Clinical success was considered “primary” when achieved with a single treatment, and “secondary” if more than one treatment was required in the duration of follow-up. Results: 10 patients (6 males and 4 females, mean age = 64 years, SD = 22), all with cholangitis due to gallstones, underwent 11 PTL procedures using SpyGlassDSTM. Technical and clinical successes were achieved in all patients (100%). Primary success was observed in 4/10 (40%) patients, while the remaining 6/10 (60%) patients undergoing re-treatment, and all showed secondary success (100%). No periprocedural complications were observed. In 10/11 procedures (90%), no relevant adverse events were recorded within the first thirty days of follow-up. In 1/11 case (9%), mild complications (grade I according to CIRSE classification) were registered in the following days after the procedure (<30 days). Conclusions: in conclusion, the treatment of percutaneous transhepatic lithotripsy using SpyGlassDSTM cholangioscopy of difficult stones has been demonstrated as efficient and safe treatment.