Clinicopathologic characteristics at breast cancer diagnosis and at mBC diagnosis.

Background/Objectives: Surveillance of women at increased risk for breast cancer requires high-sensitivity imaging. This study compared contrast-enhanced mammography (CEM) with low-energy CEM (LE-CEM) and breast MRI in this population. Methods: This retrospective analysis included 461 women enrolled in a high-risk imaging protocol (March 2019-October 2022). Lifetime breast cancer risk was estimated using the Tyrer-Cuzick model. All participants underwent CEM and breast MRI ≥ 72 h apart. LE-CEM images were used as a surrogate for digital mammography. Four readers independently interpreted LE-CEM and CEM; a separate group of four readers interpreted MRI. Diagnostic performance was assessed using sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) within a multireader, multicase framework. Noninferiority testing was performed with Δ = 0.05. Mean glandular dose (MGD) for CEM was recorded. Results: CEM showed higher sensitivity than LE-CEM (83.0% vs. 68.9%, p < 0.001) with similar specificity (90.8% vs. 92.5%, p = 0.176). Mean AUC increased from 0.856 for LE-CEM to 0.936 for CEM (p < 0.001). CEM and MRI showed comparable sensitivity (82.9% vs. 86.1%, p = 0.547), specificity (90.8% vs. 86.5%, p = 0.318), and mean AUC (0.936 vs. 0.933; p = 0.839), confirming noninferiority of CEM. MGD per view ranged from 1.56 to 3.10 mGy. Conclusions: Contrast-enhanced mammography provides diagnostic accuracy equivalent to breast MRI and superior to LE-CEM, with acceptable radiation dose, supporting its use for surveillance in women at increased breast cancer risk.

OS after brain metastasis diagnosis according to brain screening practice.

Cumulative incidence curves for diagnosis of brain metastases (A), diagnosis of symptomatic brain metastases (B), and diagnosis of asymptomatic brain metastases (C) according to brain screening practice.

Purpose: According to recent EANO-ESMO guidelines, proactive brain imaging can be considered in asymptomatic patients with HER2+ metastatic breast cancer (mBC) because of high risk of developing brain metastases. However, optimal imaging modality and timing remain unclear. We retrospectively assessed the impact of contrast-enhanced CT screening on symptomatic brain metastases in patients with HER2+ mBC.Experimental Design: Consecutive patients newly diagnosed with HER2+ mBC treated with trastuzumab-pertuzumab plus taxane (2014-2024) were retrospectively identified. Brain screening was defined as at least one contrast-enhanced brain CT scan per year without neurologic symptoms during the first 2 years after diagnosis.Results: Among 148 identified patients, 73 underwent brain screening and 75 did not. The median number of annual brain CT scans during the first 2 years was 2.0 (IQR, 1.2-2.5) and 0.0 (IQR, 0-0.5) in the screening and nonscreening groups, respectively. Thirty (20.3%) patients developed brain metastases during the first 2 years. The cumulative brain metastasis incidence was significantly higher in patients undergoing screening (30.6% vs. 12.3%, Gray's P = 0.004), but symptomatic brain metastases were significantly lower in patients undergoing screening (0% vs. 9.5%, Gray's P = 0.012). Patients undergoing screening had better preserved performance status at brain metastasis diagnosis (P = 0.002) and a numerical trend toward fewer brain metastases (P = 0.057). Treatment patterns after brain metastasis diagnosis were similar, although whole-brain radiotherapy was used less often in the screening group (14.3% vs. 44.4%, P = 0.073).Conclusions: Brain screening with CT scans was associated with fewer symptomatic brain metastases and better performance status at brain metastasis diagnosis, supporting proactive imaging in HER2+ mBC. Prospective studies are warranted to define optimal timing and imaging modalities.

Conventional age-based breast cancer screening ignores substantial inter-individual risk variation, contributing to overdiagnosis, false positives, and missed opportunities for earlier detection in high-risk women. Mammography-based artificial intelligence (AI) may enable risk-stratified screening and more efficient workflows. To systematically review evidence on mammography-based AI for personalized breast cancer screening, covering risk prediction, detection/triage, decision support, and associated ethical, economic, and equity implications. We searched MEDLINE/PubMed, Embase, Scopus, Web of Science, and the Cochrane Library (January 2015–November 2025) for studies evaluating AI-enabled personalization in breast cancer screening. Two reviewers independently screened 612 records, assessed 77 full texts, and included 30 studies; data were synthesized narratively. Image-based deep-learning risk models consistently outperformed traditional clinical risk tools and enriched future cancers within small high-risk strata, including cancers presenting as interval cancers in recent validations. Prospective trials and real-world implementations indicate that AI-supported reading can maintain or modestly improve cancer detection while reducing radiologist workload by roughly 40–50

To enhance the quality of organized mammographic screening in Italy, in accordance with national legislation, a multidisciplinary panel of experts applied the Grading of Recommendations, Assessment, Development and Evaluation (GRADE)-ADOLOPMENT approach to adopt or adapt the European Commission Initiative on Breast Cancer (ECIBC) guidelines concerning the use of digital breast tomosynthesis (DBT). As prerequisite conditions for DBT adoption, the panel defines a full extension to women in the 45-74 age range, sufficient technical and professional resources, and an adequate monitoring system. The panel recommends the use of either DBT or digital mammography (DM) for asymptomatic women participating in organized screening programmes. However, it suggests prioritizing DBT in women with high mammographic breast density(classified as BI-RADS class c or d) when density has been previously assessed with DM. In the case of limited resources, priority in the implementation should be given to women with extremely dense breasts (BI-RADS class d). The use of DBT as an additional screening tool alongside DM is not recommended. These guidelines aim to provide a tailored approach for screening women with high mammographic breast density, improving detection while optimizing resource allocation in the context of organized screening. However, these recommendations also apply to the setting of spontaneous screening.

BACKGROUND:Tobacco cessation support should be offered to all participants with active tobacco use within lung cancer (LC) screening programs. Among the pharmacologic options, cytisine is effective and safe, although data in individuals aged 65 years and older remain limited. METHODS:From September 2022, all participants with active tobacco use enrolled in the Rete Italiana Screening Polmonare (RISP) LC screening trial were offered a smoking cessation program, with optional cytisine treatment (1.5 mg on an escalating and deescalating schedule). The primary end point was self-reported continuous smoking abstinence during the 2-week period after completion of the smoking cessation program, assessed at predefined counseling visits or through telephone follow-up when needed. Secondary end points included cigarette consumption reduction, the feasibility of a cessation program within the Italian health care system, and cytisine safety. RESULTS:Overall, 10,191 participants were enrolled; 7805 had a current smoking history, of whom 3548 (45.5%) pursued a tobacco cessation program and 1375 (38.7%) accepted cytisine treatment. The smoking cessation rate was 50.1% among cytisine users versus 8.1% for those receiving counseling alone (n = 2173) (p <0.05), adjusted OR: 14.53 (95% confidence interval [CI]: 11.67-18.08). The cytisine group reported a greater reduction in cigarette consumption (23.4% versus 10.0%, p <0.0001). Cytisine uptake was associated with greater smoking exposure (pack-years, baseline exhaled carbon monoxide) and motivation for smoking cessation (all p <0.05). The most frequent adverse events (AEs) were nausea (36.2%), insomnia (19.0%), and abdominal discomfort (18.1%). Efficacy and toxicity were similar in volunteers aged 65 years and older (n = 428, 31.1%); women experienced more AEs, particularly abdominal discomfort (p <0.05). Dropouts occurred in 15.0% of cytisine users; 28.0% of these experienced at least one AE. CONCLUSIONS:This is the largest analysis of cytisine use within an LC screening program, providing supportive evidence of its potential effectiveness and safety profile, including in adults aged 65 years and older.

Clinical characteristics at brain metastasis diagnosis and first treatments received (locoregional and systemic) after brain metastasis diagnosis.

The widespread implementation of population-based mammographic screening has markedly increased the detection of ductal carcinoma in situ (DCIS), without a proportional reduction in breast cancer-specific mortality. This divergence has intensified concerns regarding overdiagnosis and overtreatment and has prompted increasing interest in treatment de-escalation and active surveillance strategies. Breast imaging remains indispensable for DCIS detection, extent assessment, and longitudinal monitoring. However, although imaging features correlate with histopathologic risk factors at the population level, their ability to predict individual biological progression is inherently probabilistic and limited. Overinterpretation of imaging phenotypes as surrogates of invasive destiny risks inappropriate reassurance or unjustified therapeutic escalation, particularly in the context of high-sensitivity modalities that may overestimate disease extent or trigger additional interventions without proven outcome benefits. This review examines the modality-specific roles of mammography, ultrasound, breast magnetic resonance imaging (MRI), contrast-enhanced mammography (CEM), and emerging artificial intelligence (AI) approaches within contemporary DCIS management, with particular attention to their implementation in active surveillance trials such as LORIS, COMET, LORD, and LORETTA. Across modalities, imaging primarily reflects lesion morphology, spatial distribution, and vascular behaviour, and functions most reliably as a risk-filtering and safety-gating instrument aimed at excluding radiologically unsafe scenarios, including occult invasion, underestimated disease extent, or imaging evolution incompatible with continued observation. By delineating both the capabilities and the epistemological limits of imaging, this review proposes a structured clinical decision framework in which imaging supports—but does not independently determine—risk-adapted management. Disciplined integration of imaging into multidisciplinary decision-making is essential to enable safe de-escalation, prevent false reassurance, and align DCIS care with patient-centred and value-based principles.

Currently, percutaneous sampling via core needle or vacuum-assisted biopsy is the primary choice to guide the management of patients with clinical or screen-detected breast lesions. Preoperative biopsies allow physicians to get pathological diagnoses as well as key prognostic and predictive data about the nature of the investigated process. Namely, adequate biopsy sampling is crucial for assigning lesions to one diagnostic category (B1-B5). Similarly, evaluating morphological (histotype, vascular invasion, necrosis, etc.) and immunohistochemical/molecular features (ER, PR, Ki-67, and HER2) is the key to address the most effective therapies, especially in the neoadjuvant setting. The multidisciplinary team should always discuss the results of percutaneous biopsies, whose global integration with clinical and radiological findings will drive the adoption of specific treatment options, particularly for uncertain (B3) and suspicious/malignant (B4-B5) lesions. In the present work, we report a comprehensive overview of breast percutaneous biopsy techniques, diagnostic categories, and multidisciplinary management based on widely acknowledged evidence of good clinical practice.

Background and objectiveSelecting the optimal treatment for locally advanced non-small cell lung cancer (LA-NSCLC) is complex and typically requires multidisciplinary tumor board (MTB) evaluation. This study investigated whether machine learning (ML) models trained on MTB decisions could support treatment selection by integrating clinicopathological characteristics with radiomic features from both the primary tumor and mediastinal lymph nodes (LN).Materials and methodsWe retrospectively analyzed patients with LA-NSCLC whose treatments had been decided by an expert MTB. Patients were categorized into three pathways: (A) upfront surgery, (B) neoadjuvant systemic treatment followed by surgery, (C) concurrent chemoradiotherapy. Baseline CT scans were segmented to extract radiomic features from primary tumors and mediastinal LNs. Two ML models were developed based on clinicopathological and radiomic data, using MTB decisions as ground truth: (1) A vs. Rest and (2) B vs. C. Performance was assessed in independent training and test cohorts using the area under the receiver operating characteristic curve (AUC) and accuracy.ResultsIn the training cohort, the A vs. Rest achieved an AUC of 0.847 and accuracy of 0.795 with 13 features, while the B vs. C model reached an AUC of 0.740 and accuracy of 0.700 with 9 features. In the test cohort, results remained robust, with an AUC of 0.808 (accuracy 0.700) for A vs. Rest and an AUC of 0.754 (accuracy 0.740) for B vs. C.ConclusionsML models combining clinicopathological and radiomic features can reproduce MTB treatment recommendations for LA-NSCLC with good accuracy. This approach may provide decision in settings with limited MTB expertise and promote more consistent treatment allocation.

BACKGROUND:Phantoms play a critical role in mammography quality control (QC) by providing standardized conditions for evaluating image quality (IQ) metrics. However, inter-phantom variability may affect the reliability of these metrics, especially for inter-system comparisons. The aim of this study was to quantify the intra- and inter-phantom variability of IQ metrics using a set of theoretically identical phantoms. METHODS:Twenty-four TORMAS phantoms were imaged ten times each using a mammography unit under standardized high-dose conditions. Images were analyzed using automated software to extract 64 IQ metrics, including contrast-to-noise ratio (CNR) as well as modulation transfer function (MTF)-related and other metrics. Outliers were identified and excluded. Variability was assessed by calculating intra- and inter-phantom variances and coefficients of variation (COVs). The relative contributions of intra- and inter-phantom variability to total variability were also determined. RESULTS:Two defective phantoms were excluded. Analysis of 64 IQ metrics across 22 phantoms showed higher inter-phantom variability compared to intra-phantom variability. Mean intra- and inter-phantom COVs were 6.9% and 15.1% for the 34 CNR metrics, 4.8% and 5.4% for the 5 MTF-related metrics, 0.14% and 0.75% for the 10 contrast metrics, 4.9% and 14.8% for the 15 noise metrics, respectively. Inter-phantom variability contributed 84.2% to total variability, highlighting its dominance. CONCLUSION:Inter-phantom variability significantly affects IQ metrics, emphasizing the importance of using the same phantom for inter-system comparisons to avoid confounding results. Conversely, phantoms are well-suited for assessing system reproducibility over time, focus on inter-system variability while consistently using a single phantom. RELEVANCE STATEMENT:This study highlights the significant impact of inter-phantom variability on image quality assessment, emphasizing the importance of using the same phantom for benchmarking imaging systems. These findings are crucial for optimizing quality control protocols and ensuring reliable, reproducible evaluations. KEY POINTS:Inter-phantom variability exceeded intra-phantom variability across all image quality metrics of digital mammography. Subtle details showed higher total variability compared to more distinct features. Modulation transfer function metrics exhibited comparable intra- and inter-phantom variability, highlighting positioning sensitivity. Inter-phantom variability contributes 84% to total variability, impacting imaging system comparisons. Using the same phantom ensures reliability in imaging system performance evaluations.

To present the prevalence screening results of the RIsk-Based Breast Screening (RIBBS) study (ClinicalTrials.gov NCT05675085), a quasi-experimental population-based study evaluating a personalized screening model for women aged 45–49. This model uses digital breast tomosynthesis (DBT) and stratifies participants by risk and breast density, incorporating tailored screening intervals with or without supplemental imaging (ultrasound, US, and breast MRI), with the goal of reducing advanced breast cancer (BC) incidence compared to annual digital mammography (DM). An interventional cohort of 10,269 women aged 45 was enrolled (January 2020–December 2021. Participants underwent DBT and completed a BC risk questionnaire. Volumetric breast density and lifetime risk were used to assign five subgroups to tailored screening regimens: low-risk low-density (LR–LD), low-risk high-density (LR–HD), intermediate-risk low-density (IR–LD), intermediate-risk high-density (IR–HD), and high-risk (HR). Screening performance was compared with an observational control cohort of 43,838 women undergoing annual DM. Compared to LR–LD, intermediate-risk groups showed a 4.9- (IR–LD) and 4.6-fold (IR–HD) higher prevalence of BC, driven by a 7.1- and 7.1-fold higher prevalence of pT1c tumors. The interventional cohort had lower recall rate (rate ratio, 0.5), higher surgery rate (1.9) and increased prevalence of DCIS (2.9), pT1c (2.3) and grade 3 tumors (2.4), compared to controls. The prevalence screening demonstrated the feasibility of using DBT and —in high-density subgroups— supplemental US. The stratification criteria effectively identified subpopulations with different BC prevalence. Increasing the detection rate of pT1c tumors is not sufficient but necessary to achieve a reduction in advanced BC incidence.

Background/Objectives: To simplify the decision-making process in radiomics by employing RadiomiX, an algorithm designed to automatically identify the best model combination and validate them across multiple environments was developed, thus enhancing the reliability of results. Methods: RadiomiX systematically tests classifier and feature selection method combinations known to be suitable for radiomic datasets to determine the best-performing configuration across multiple train–test splits and K-fold cross-validation. The framework was validated on four public retrospective radiomics datasets including lung nodules, metastatic breast cancer, and hepatic encephalopathy using CT, PET/CT, and MRI modalities. Model performance was assessed using the area under the receiver-operating-characteristic curve (AUC) and accuracy metrics. Results: RadiomiX achieved superior performance across four datasets: LLN (AUC = 0.850 and accuracy = 0.785), SLN (AUC = 0.845 and accuracy = 0.754), MBC (AUC = 0.889 and accuracy = 0.833), and CHE (AUC = 0.837 and accuracy = 0.730), significantly outperforming original published models (p < 0.001 for LLN/SLN and p = 0.023 for MBC accuracy). When original published models were re-evaluated using ten-fold cross-validation, their performance decreased substantially: LLN (AUC = 0.783 and accuracy = 0.731), SLN (AUC = 0.748 and accuracy = 0.714), MBC (AUC = 0.764 and accuracy = 0.711), and CHE (AUC = 0.755 and accuracy = 0.677), further highlighting RadiomiX’s methodological advantages. Conclusions: Systematically testing model combinations using RadiomiX has led to significant improvements in performance. This emphasizes the potential of automated ML as a step towards better-performing and more reliable radiomic models.

Abstract Background Automatic exposure control (AEC) plays a crucial role in mammography by determining the exposure conditions needed to achieve specific image quality based on the absorption characteristics of compressed breasts. This study aimed to characterize the behavior of AEC for digital mammography (DM), digital breast tomosynthesis (DBT), and low-energy (LE) and high-energy (HE) acquisitions used in contrast-enhanced mammography (CEM) for three mammography systems from two manufacturers. Methods Using phantoms simulating various breast thicknesses, 363 studies were acquired using all available AEC modes 165 DM, 132 DBT, and 66 LE-CEM and HE-CEM. AEC behaviors were compared across systems and modalities to assess the impact of different technical components and manufacturers’ strategies on the resulting mean glandular doses (MGDs) and image quality metrics such as contrast-to-noise ratio (CNR). Results For all systems and modalities, AEC increased MGD for increasing phantom thicknesses and decreased CNR. The median MGD values (interquartile ranges) were 1.135 mGy (0.772–1.668) for DM, 1.257 mGy (0.971–1.863) for DBT, 1.280 mGy (0.937–1.878) for LE-CEM, and 0.630 mGy (0.397–0.713) for HE-CEM. Medians CNRs were 14.2 (7.8–20.2) for DM, 4.91 (2.58–7.20) for a single projection in DBT, 11.9 (8.0–18.2) for LE-CEM, and 5.2 (3.6–9.2) for HE-CEM. AECs showed high repeatability, with variations lower than 5% for all modes in DM, DBT, and CEM. Conclusions The study revealed substantial differences in AEC behavior between systems, modalities, and AEC modes, influenced by technical components and manufacturers’ strategies, with potential implications in radiation dose and image quality in clinical settings. Relevance statement The study emphasized the central role of automatic exposure control in DM, DBT, and CEM acquisitions and the great variability in dose and image quality among manufacturers and between modalities. Caution is needed when generalizing conclusions about differences across mammography modalities. Key points • AEC plays a crucial role in DM, DBT, and CEM. • AEC determines the “optimal” exposure conditions needed to achieve specific image quality. • The study revealed substantial differences in AEC behavior, influenced by differences in technical components and strategies. Graphical Abstract

PURPOSE:We present a comprehensive investigation into the organizational, social, and ethical impact of implementing digital breast tomosynthesis (DBT) as a primary test for breast cancer screening in Italy. The analyses aimed to assess the feasibility of DBT specifically for all women aged 45-74, women aged 45-49 only, or those with dense breasts only. METHODS:Questions were framed according to the European Network of Health Technology Assessment (EuNetHTA) Screening Core Model to produce evidence for the resources, equity, acceptability, and feasibility domains of the Grading of Recommendations Assessment, Development and Evaluation (GRADE) decision framework. The study integrated evidence from the literature, the MAITA DBT trials, and Italian pilot programs. Structured interviews, surveys, and systematic reviews were conducted to gather data on organizational impact, acceptability among women, reading and acquisition times, and the technical requirements of DBT in screening. RESULTS:Implementing DBT could significantly affect the screening program, primarily due to increased reading times and the need for additional human resources (radiologists and radiographers). Participation rates in DBT screening were similar, if not better, to those observed with standard digital mammography, indicating good acceptability among women. The study also highlighted the necessity for specific training for radiographers. The interviewed key persons unanimously considered feasible tailored screening strategies based on breast density or age, but they require effective communication with the target population. CONCLUSIONS:An increase in radiologists' and radiographers' workload limits the feasibility of DBT screening. Tailored screening strategies may maximize the benefits of DBT while mitigating potential challenges.

Abstract Background Dual-energy subtraction (DES) imaging is critical in contrast-enhanced mammography (CEM), as the recombination of low-energy (LE) and high-energy (HE) images produces contrast enhancement while reducing anatomical noise. The study's purpose was to compare the performance of the DES algorithm among three different CEM systems using a commercial phantom. Methods A CIRS Model 022 phantom, designed for CEM, was acquired using all available automatic exposure modes (AECs) with three CEM systems from three different manufacturers (CEM1, CEM2, and CEM3). Three studies were acquired for each system/AEC mode to measure both radiation dose and image quality metrics, including estimation of measurement error. The mean glandular dose (MGD) calculated over the three acquisitions was used as the dosimetry index, while contrast-to-noise ratio (CNR) was obtained from LE and HE images and DES images and used as an image quality metric. Results On average, the CNR of LE images of CEM1 was 2.3 times higher than that of CEM2 and 2.7 times higher than that of CEM3. For HE images, the CNR of CEM1 was 2.7 and 3.5 times higher than that of CEM2 and CEM3, respectively. The CNR remained predominantly higher for CEM1 even when measured from DES images, followed by CEM2 and then CEM3. CEM1 delivered the lowest MGD (2.34 ± 0.03 mGy), followed by CEM3 (2.53 ± 0.02 mGy) in default AEC mode, and CEM2 (3.50 ± 0.05 mGy). The doses of CEM2 and CEM3 increased by 49.6% and 8.0% compared with CEM1, respectively. Conclusion One system outperformed others in DES algorithms, providing higher CNR at lower doses. Relevance statement This phantom study highlighted the variability in performance among the DES algorithms used by different CEM systems, showing that these differences can be translated in terms of variations in contrast enhancement and radiation dose. Key Points DES images, obtained by recombining LE and HE images, have a major role in CEM. Differences in radiation dose among CEM systems were between 8.0% and 49.6%. One DES algorithm achieved superior technical performance, providing higher CNR values at a lower radiation dose. Graphical Abstract

Radiomics, analysing quantitative features from medical imaging, has rapidly become an emerging field in translational oncology. Radiomics has been investigated in several neoplastic malignancies as it might allow for a non-invasive tumour characterization and for the identification of predictive and prognostic biomarkers. Over the last few years, evidence has been accumulating regarding potential clinical applications of machine learning in many crucial moments of cancer patients’ history. However, the incorporation of radiomics in clinical decision-making process is still limited by low data reproducibility and study variability. Moreover, the need for prospective validations and standardizations is emerging.In this narrative review, we summarize current evidence regarding radiomic applications in high-incidence cancers (breast and lung) for screening, diagnosis, staging, treatment choice, response, and clinical outcome evaluation. We also discuss pro and cons of the radiomic approach, suggesting possible solutions to critical issues which might invalidate radiomics studies and propose future perspectives.