Purpose: This multicentric retrospective study aimed to evaluate the diagnostic outcomes, adherence to guideline-based recommendations, and sustainability implications of 280 initial chest CT scans performed without contrast. The scans were conducted at the Radiology Unit of the Foundation IRCCS Ca’ Granda Ospedale Maggiore Policlinico of Milan and the Radiology Department of Treviglio-Caravaggio Hospital-ASST Bergamo Ovest. The study focused on optimizing patient selection and radiological recommendations to align with evidence-based guidelines. Materials and methods: This retrospective study included 280 patients (mean age 61.68 years; 51.07% women, 48.93% men) who underwent their first chest CT scan without contrast. Diagnostic outcomes were analyzed across different clinical queries. Incidental findings and radiologists’ recommendations were assessed for alignment with Fleischner Society and ACR guidelines. Deviations were categorized as unnecessary imaging suggestions or missed indications. Radiation exposure was quantified using the dose-length product (DLP). Results: Diagnostic outcomes were positive in 54.64% of cases. Incidental findings occurred in 34.64% of cases, with guideline adherence at 81.43%. Deviations included unnecessary imaging in 14.29% and missed follow-up indications in 4.28% of cases. The median DLP was 237.5 mGy·cm (IQR 171.8–320.1). Conclusion: This study highlights significant opportunities to refine patient selection and radiological recommendations through adherence to Fleischner Society and ACR guidelines. By integrating evidence-based practices into routine workflows, the findings advocate for reduced unnecessary imaging, enhanced diagnostic pathways, and sustainable healthcare practices in chest CT 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.

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.