Salvatore Cappabianca

Background: Accurate preoperative staging is the cornerstone of therapeutic decision-making in gastric cancer (GC), yet standard modalities often fail to capture the full extent of disease, particularly in diffuse and poorly cohesive histotypes. This review aims to provide a comprehensive update on diagnostic imaging for GC, evaluating the established roles of CT, EUS, and PET/CT alongside the emerging capabilities of Magnetic Resonance Imaging (MRI) and Artificial Intelligence (AI). Methods: A structured narrative review was conducted by searching indexed biomedical databases for studies published between 2015 and 2024. A structured literature search screening process identified 410 relevant studies focusing on T, N, and M staging accuracy, quantitative imaging biomarkers, and radiomics. Results: While Multidetector CT remains the universal first-line modality, its sensitivity declines in infiltrative tumors and low-volume peritoneal carcinomatosis. EUS retains superiority for early (T1-T2) lesions but may offer limited value in advanced stages. Conversely, MRI (leveraging diffusion-weighted imaging (DWI) and multiparametric protocols) indicates superior soft-tissue contrast, potentially outperforming CT in the assessment of serosal invasion, nodal involvement, and occult peritoneal metastases. Furthermore, emerging fibroblast activation protein inhibitor (FAPI) PET tracers show promise in overcoming the limitations of FDG in mucinous and diffuse GC. Finally, radiomics and deep learning models are providing novel quantitative biomarkers for non-invasive risk stratification. Conclusions: Contemporary GC staging requires a tailored, multimodality approach. Evidence supports the increasing integration of MRI and quantitative imaging into clinical workflows to overcome the limitations of conventional techniques and support precision oncology.

Background: Accurate characterization of complex renal cystic lesions is essential for individualized patient management, as enhancement patterns of septa and walls determine Bosniak classification, malignancy risk, and tailored follow-up strategies. While contrast-enhanced ultrasound (CEUS) is widely used to assess enhancement, Superb Microvascular Imaging (SMI) offers a non-contrast alternative that is capable of detecting slow-flow microvascular signals. This study aimed to evaluate the diagnostic concordance, accuracy, and reproducibility of SMI compared with CEUS in the Bosniak 2019 classification, and to explore its role in personalized imaging pathways for patients with contraindications to contrast media. Methods: Eighty patients (92 cystic renal lesions) who underwent both SMI and CEUS between January 2024 and July 2025 were retrospectively analyzed. Lesions were categorized using the Bosniak 2019 criteria. CEUS served as the reference standard. Concordance between modalities was evaluated using Cohen’s κ, and diagnostic accuracy was determined by ROC analysis. Inter- and intra-reader agreement were assessed with κ and intraclass correlation coefficients (ICC), respectively. Histopathologic confirmation was available for resected Bosniak III–IV lesions. Results: SMI showed excellent concordance with CEUS (κ = 0.84, 95% CI 0.76–0.91; overall agreement 83.7%). Concordance was perfect for Bosniak I–II, good for IIF (85%), and moderate for III (68%) and IV (64%) categories. Using CEUS as the reference, SMI achieved a sensitivity of 88.5%, specificity of 90.0%, and AUC of 0.94 for distinguishing low- from high-risk lesions. Inter-reader (κ = 0.83) and intra-reader (ICC = 0.91) agreements were excellent. Among 18 surgically resected Bosniak III–IV lesions, 14 (77.8%) were malignant; SMI correctly identified 12/14 malignant and 3/4 benign cases. Conclusions: SMI shows high diagnostic accuracy and reproducibility in the assessment of complex renal cystic lesions, with strong concordance to CEUS within the Bosniak 2019 system. By providing vascular characterization without contrast administration, SMI supports more personalized renal cyst management, enabling safer imaging for patients at risk from contrast agents and potentially reducing unnecessary interventions. Further multicenter validation is warranted to define its integration into precision-oriented multiparametric renal ultrasound protocols.

Mitral annular disjunction (MAD) is a structural abnormality of the mitral valve increasingly detected with advanced cardiac imaging, particularly cardiac magnetic resonance (CMR). However, the clinical impact of different degrees of disjunction and the lack of standardized measurement criteria remain controversial. This study aimed to describe a series of patients with MAD assessed by CMR and to discuss, in the context of current literature, potential cut-off values that may distinguish physiological from pathological MAD. We retrospectively identified all CMR examinations performed at our institution over a 6-month period in which MAD was visible in at least two cine steady-state free precession (SSFP) projections. For each patient, we recorded MAD extent, presence of mitral valve prolapse/regurgitation, late gadolinium enhancement (LGE) pattern, and main clinical presentation. Nine patients (mean age 57 years; 5 men) were included. Larger MAD distances (>4 mm) were frequently associated with non-ischemic LGE in the basal lateral wall and with valvular abnormalities, whereas smaller disjunctions (≤3 mm) were often observed in patients without significant structural disease. Non-ischemic LGE was present in 6/9 patients, all with MAD > 5 mm. These observations, together with published data, support the hypothesis that small degrees of MAD may represent a frequent anatomical variant, while more extensive disjunction, especially when associated with fibrosis, may indicate a pathological substrate for arrhythmias. Standardized CMR-based criteria and validated MAD cut-off values are needed to improve risk stratification and to incorporate MAD assessment into routine clinical practice.

Background: Radiomics enables the extraction of quantitative imaging biomarkers that can non-invasively capture tumor biology and treatment response. Delta-radiomics, by assessing temporal changes in radiomic features, may improve reproducibility and reveal early therapy-induced alterations. This study investigated whether delta-texture features from contrast-enhanced CT could predict progression-free survival (PFS) and overall survival (OS) in patients with metastatic colorectal cancer (mCRC) liver metastases treated with cetuximab rechallenge plus avelumab within the CAVE trial. Methods: This retrospective substudy included 42 patients enrolled in the multicenter CAVE phase II trial with evaluable liver metastases on baseline and first restaging CT. Liver lesions were manually segmented by two readers, and radiomic features were extracted according to IBSI guidelines. Delta-values were calculated as relative changes between baseline and post-treatment scans. Reproducibility (ICC > 0.70), univariate and multivariable analyses, ROC/AUC, bootstrap resampling, cross-validation, and decision curve analysis were performed to evaluate predictive performance and clinical utility. Results: Among reproducible features, delta-GLCM Homogeneity emerged as the most robust predictor. A decrease in homogeneity independently correlated with longer PFS (HR = 0.32, p = 0.003) and OS (HR = 0.41, p = 0.021). The combined clinical–radiomic model achieved good discrimination (AUC 0.94 training, 0.74 validation) and stable performance on internal validation (bootstrap C-index 0.77). Decision curve analysis indicated greater net clinical benefit compared with clinical variables alone. Conclusions: This exploratory study provides preliminary evidence that delta-GLCM Homogeneity may serve as a reproducible imaging biomarker of response and survival in mCRC patients receiving cetuximab plus avelumab rechallenge. If validated in larger, independent cohorts, delta-radiomics could enable early identification of non-responders and support personalized treatment adaptation in immuno-targeted therapy. Given the small sample size, the potential for overfitting should be considered. Future work should prioritize prospective multicenter validation with a pre-registered, locked model and explore multimodal integration (radiogenomics, circulating biomarkers, and AI-driven fusion of imaging with clinical/omic data) to strengthen translational impact. Beyond imaging advances, these findings align with broader trends in personalized oncology, including response-adaptive strategies, multimodal biomarker integration, and AI-enabled decision support.

Introduction: Ductal carcinoma in situ (DCIS) is a non-invasive precursor of breast cancer, usually detected on mammography as clustered microcalcifications. Many cases, however, lack calcifications and require complementary imaging. This study aimed to describe the multimodal imaging features of DCIS and evaluate the radiology–pathology correlation. Methods: We retrospectively reviewed 75 women (aged 36–52 years) with biopsy-proven DCIS (January 2023–June 2025). All underwent mammography, targeted ultrasound, and dynamic contrast-enhanced 1.5T MRI. Imaging findings were correlated with histopathology, and logistic regression was used to explore predictors of MRI kinetics. Results: Mammography detected microcalcifications in 53.8% of patients, while 46.2% showed no calcifications. Ultrasound frequently revealed non-mass, duct-oriented hypoechoic abnormalities in non-calcified cases. MRI consistently demonstrated non-mass enhancement, with weak or persistent kinetics without washout in 69.2% and washout in 30.8%. A moderate correlation between MRI and histological extent was found (r = 0.62, p < 0.001), with MRI tending to overestimate lesion size. Oral contraceptive use was common (61.5%) but not significantly associated with kinetic pattern or grade. Conclusions: Mammography remains essential for calcified DCIS, whereas MRI enhances detection of non-calcified lesions. Persistent kinetics without washout may represent a typical imaging feature of DCIS. However, moderate radiology–pathology concordance and frequent overestimation highlight the need for careful interpretation. These findings support a multimodal diagnostic approach that can improve detection accuracy and assist in more tailored surgical planning.

The integration of multi-omics technologies is transforming the landscape of cancer management, offering unprecedented insights into tumor biology, early diagnosis, and personalized therapy. This review provides a comprehensive overview of the current state of omics approaches, with a particular focus on the application of genomics, NMR-based metabolomics, and radiomics in non-small cell lung cancer (NSCLC). Genomics currently represents one of the most established omics technologies in oncology, as it enables the identification of genetic alterations that drive tumor initiation, progression, and therapeutic response. Interestingly, genomic analyses have revealed that many tumors harbor mutations in genes encoding metabolic enzymes, thus establishing a tight connection between genomics and tumor metabolism. In parallel, metabolomics profiling—by capturing the metabolic phenotype of tumors—has, in recent years, identified specific biomarkers associated with tumor burden, progression, and prognosis. Such findings have catalyzed growing interest in metabolomics as a complementary approach to better characterize cancer biology and discover novel diagnostic and therapeutic targets. Moreover, radiomics, through the extraction of quantitative features from standard imaging modalities, captures tumor heterogeneity and contributes predictive information on tumor biology, treatment response, and clinical outcomes. As a non-invasive and widely available technique, radiomics has the potential to support longitudinal monitoring and individualized treatment planning. Both metabolomics and radiomics, when integrated with genomic data, could support a more comprehensive understanding of NSCLC and pave the way for the development of non-invasive, predictive models and personalized therapeutic strategies. In addition, we explore the specific contributions of these technologies in enhancing clinical decision-making for lung cancer patients, with particular attention to their potential in early diagnosis, treatment selection, and real-time monitoring.

The aim was to assess the impact of glucagon-like peptide-1 receptor agonists (GLP-1 RA) treatment on the progression of ascending aorta dilatation in patients with type 2 diabetes mellitus (T2DM). A total of 127 T2DM patients with subclinical ascending aortic dilatation (35–45 mm) were prospectively enrolled. Fifty-seven initiated GLP-1 RA therapy (liraglutide, semaglutide, or dulaglutide), while 70 continued on standard care. Ascending aortic diameter was measured by computed tomography angiography (CTA) at baseline and 24 months, alongside circulating markers of vascular remodeling: matrix metalloproteinase-9 (MMP-9), tissue inhibitor of metalloproteinases-1 (TIMP-1), C-reactive protein (CRP), and osteoprotegerin (OPG). Progression of aortic dilatation was significantly lower in the GLP-1 RA group compared with controls (+0.36 ± 0.20 mm vs. +1.05 ± 0.28 mm; p < 0.001). Therapy correlated with decreased MMP-9 and CRP (p < 0.01) and increased TIMP-1 and OPG (p < 0.05). The use of GLP-1 RA was an independent predictor of low progression, even in multivariate models after adjusting for demographic, metabolic, and biomarker data. GLP-1 RA therapy was associated with reduced progression of ascending aortic dilatation in T2DM, supporting a potential vasoprotective role beyond glucose lowering.

Advances in additive manufacturing (AM) have revolutionized various sectors, including aerospace engineering, where the use of lattice structures has enabled the development of lightweight high-performance components with optimized mechanical properties. Building on these engineering principles, this study explores the application of aerospace-derived lattice design strategies to the biomedical field, specifically for the replication of human lung alveolar structures. The objective is to create anatomically accurate 3D-printed lung models suitable for surgical planning. Finite element analyses have been conducted using a CAD model of adult lungs, including the application of lattice structures generated through nTopology software, to evaluate the elasticity and density, critical for simulating lung mechanics. A preliminary prototype has been produced using stereolithography and flexible resin, showing the potential for realistic tactile feedback.

Modern external beam radiotherapy (EBRT) on lung cancer improved dose distribution thanks to advanced dose calculation algorithms, but side effects and relapses can occur in any case onset. Differential diagnosis of relapses and side effects is difficult, and when computed tomography (CT) is uncertain 18-fluorodeoxyglucose-positron emission tomography/computed tomography (18F-FDG-PET/CT) can support the diagnosis, even if it can also be difficult to construe. The aim of this retrospective analysis was to evaluate 18F-FDG PET/CT qualitative patterns and semiquantitative parameters, both automatic and preceded by physicians, in interpreting lung lesions in the radiotherapy (RT) lung irradiation field. In total, 94 patients (pts) submitted to EBRT (3 months before) for stage II lung cancer were included (74 men, 20 women, mean age of 68 years old, range of 49–84 years old). CT scans were performed on pts, which showed lung lesions in the RT field. 18F-FDG-PET/CT scans were analyzed qualitatively as negative or positive, and the presence of the lung area with a high 18F-FDG uptake pattern was distinguished as the following: focal/wide, deep/shade, or homogeneous/inhomogeneous. Furthermore, the following semiquantitative parameters were collected: gSUVmax (global standardized uptake value max), MTV (tumor metabolic volume), metabolic spatial distribution (MSD) = proximal SUVmax/distal SUVmax, and intratumoral difference in spatial distribution (IDSD%) = [distal SUVmax/proximal SUVmax] × 100. 18F-FDG PET/CT was related to the pts’ outcome (biopsy and/or clinical–instrumental follow-up): positive for lung relapse, negative if the lesions were phlogistic. The following diagnostic performance parameters of 18F-FDG PET/CT were calculated: sensitivity (Sens), specificity (Spec), diagnostic accuracy (DA), positive predictive value (PPV), and negative predictive value (NPV). Qualitative variables were compared by Chi-squared test, while for semiquantitative parameters Student’s t-test was applied; p < 0.05 was considered statistically significant. Statistics tests were performed with MedCalc V.22.018 ©2024. In 76/94 (80.8%) pts, 18F-FDG uptake was higher compared to the background; in 18/94 (19.2%) no high 18F-FDG uptake areas were detected. Outcome was positive for lung relapse in 49/94 pts, while negative in 45/94, with disease prevalence of 52.13% (95%CI = 41.57–62.54%). In the 18/94 pts without high 18F-FDG uptake, the outcome was negative for lung relapse. In 49/76 pts with higher 18F-FDG uptake, the outcome confirmed the presence of relapse, while in 27/76 the lesion was phlogistic. Results about the Sens, Spec, DA, PPV, and NPV (95%CI) were, respectively: 100% (92.75–100%), 40% (25.7–55.67%), 71.28% (61.02–80.14%), 64.47% (58.84–69.73%), and 100% (81.47–100%). Chi-square test showed significant statistical difference between the positive and negative outcome for patterns focal/wide (p = 0.02) and deep/shade (p < 0.00001). A total of 35/49 (71.4%) pts with lung relapse had a focal lesion and 15/27 (55.6%) with phlogosis had a wide pattern. A total of 34/49 (69.4%) pts with lung relapse had a deep pattern and 25/27 (92.6%) with lung phlogosis had the shade one. Significant difference was observed in evaluating the three patterns (p = 0.00007), with prevalence of “focal/deep/homogeneous” patterns in lung relapse and “wide/shade/inhomogeneous” in phlogosis. gSUVmax, MTV, MSD, and IDSD% were in the following order: in the 76 pts, 5.63 (1.4–24.7), 42.49 (4.94–193), 3.61 (1–5.54), and 70.7% (18–100%); in the 49/76 true positive pts, 6.93 (1.5–24.7), 35.28 (4.94–85.99), 3.30 (1.05–5.54), and (18–95%); in the 27/76 false positive pts, 3.27 (1.4–19.2), 38.37 (4.94–193), 1.57 (1–2.13), and 78.6% (4.7–100%). The difference was statistically significant only for MSD (t = 2.779; p = 0.0069) and IDSD% (t = 2.769; p = 0.0071). 18F-FDG-PET/CT confirms its high sensitivity and NPV in evaluating lung lesions after RT. To improve physician confidence in interpreting lung 18F-FDG uptake without further support, MSD and IDSD% could be considered. Heterogeneity of lung lesions, especially in radiotreated tissue, can be turned from a drawback to a resource and analyzed for differentiating relapses from EBRT side effects. Considering the calculation of semiquantitative parameters that require “human intelligence”, even if slightly more time-consuming, can improve the nuclear physician’s confidence in interpreting 18F-FDG PET/CT images.