Background: Radiomics, an evolving paradigm in medical imaging, involves the quantitative analysis of tumor features and demonstrates promise in predicting treatment responses and outcomes. This study aims to investigate the predictive capacity of radiomics for genetic alterations in non-small cell lung cancer (NSCLC). Methods: This exploratory, observational study integrated radiomic perspectives using computed tomography (CT) and genomic perspectives through next-generation sequencing (NGS) applied to liquid biopsies. Associations between radiomic features and genetic mutations were established using the Area Under the Receiver Operating Characteristic curve (AUC-ROC). Machine learning techniques, including Support Vector Machine (SVM) classification, aim to predict genetic mutations based on radiomic features. The prognostic impact of selected gene variants was assessed using Kaplan–Meier curves and Log-rank tests. Results: Sixty-six patients underwent screening, with fifty-seven being comprehensively characterized radiomically and genomically. Predominantly males (68.4%), adenocarcinoma was the prevalent histological type (73.7%). Disease staging is distributed across I/II (38.6%), III (31.6%), and IV (29.8%). Significant correlations were identified with mutations of ROS1 p.Thr145Pro (shape_Sphericity), ROS1 p.Arg167Gln (glszm_ZoneEntropy, firstorder_TotalEnergy), ROS1 p.Asp2213Asn (glszm_GrayLevelVariance, firstorder_RootMeanSquared), and ALK p.Asp1529Glu (glcm_Imc1). Patients with the ROS1 p.Thr145Pro variant demonstrated markedly shorter median survival compared to the wild-type group (9.7 months vs. not reached, p = 0.0143; HR: 5.35; 95% CI: 1.39–20.48). Conclusions: The exploration of the intersection between radiomics and cancer genetics in NSCLC is not only feasible but also holds the potential to improve genetic predictions and enhance prognostic accuracy.

Introduction. Coronavirus SARS-CoV-2, the causative agent of COVID-19, primarily causes a respiratory tract infection that is not limited to respiratory distress syndrome, but it is also implicated in other body systems. Systemic complications were reported due to an exaggerated inflammatory response, which involves severe alveolar damage in the lungs and exacerbates the hypercoagulation that leads to venous thrombosis, ischemic attack, vascular dysfunction and infarction of visceral abdominal organs. Some complications are related to anticoagulant drugs that are administrated to stabilize hypercoagulability, but increase the risk of bleeding, hematoma and hemorrhage. The aim of this study is to report the diagnostic role of CT in the early diagnosis and management of patients with severe COVID-19 complications through the most interesting cases in our experience. Material and Methods. The retrospective analysis of patients studied for COVID-19 in our institution and hospitals, which are part of the university training network, was performed. Cases. Pneumomediastinum, cortical kidney necrosis, splenic infarction, cerebral ischemic stroke, thrombosis of the lower limb and hematomas are the most major complications that are reviewed in this study. Conclusions. Since the onset of the COVID-19 pandemic, the CT imaging modality with its high sensitivity and specificity remains the preferred imaging choice to diagnose early the different complications associated with COVID-19, such as thrombosis, ischemic stroke, infarction and pneumomediastinum, and their management, which significantly improved the outcomes.

Purpose: To assess the qualitative relationship between liquid biopsy and conventional tissue biopsy. As a secondary target, we evaluated the relationship between the liquid biopsy results and the T stage, N stage, M stage, and compared to grading. Methods: The Local Ethics Committee of the “Università degli Studi della Campania Luigi Vanvitelli”, with the internal resolution number 24997/2020 of 12.11.2020, approved this spontaneous prospective study. According to the approved protocol, patients with lung cancer who underwent Fine-Needle Aspiration Cytology (FNAC), CT-guided biopsy, and liquid biopsy were enrolled. A Yates chi-square test was employed to analyze differences in percentage values of categorical variables. A p-value < 0.05 was considered statistically significant. Data analysis was performed using the Matlab Statistic Toolbox (The MathWorks, Inc., Natick, MA, USA). Results: When a genetic mutation is present on the pathological examination, this was also detected on the liquid biopsy. ROS1 and PDL1 mutations were found in 2/29 patients, while EGFR Exon 21 was identified in a single patient. At liquid biopsy, 26 mutations were identified in the analyzed samples. The mutations with the highest prevalence rate in the study populations were: ALK (Ile1461Val), found in 28/29 patients (96.6%), EML4 (Lys398Arg), identified in 16/29 (55.2%) patients, ALK (Asp1529Glu), found in 14/29 (48.3%) patients, EGFR (Arg521Lys), found in 12/29 (41.4%) patients, ROS (Lys2228Gln), identified in 11/29 (37.9%) patients, ROS (Arg167Gln) and ROS (Ser2229Cys), identified in 10/29 (34.5%) patients, ALK (Lys1491Arg) and PIK3CA (Ile391Met), identified in 8/29 (27.6%) patients, ROS (Thr145Pro), identified in 6/29 (20.7%) patients, and ROS (Ser1109Leu), identified in 4/29 (13.8%) patients. No statistically significant differences can be observed in the mutation rate between the adenocarcinoma population and the squamous carcinoma population (p > 0.05, Yates chi-square test). Conclusions: We showed that, when a genetic mutation was detected in pathological examination, this was always detected by liquid biopsy, demonstrating a very high concordance rate of genomic testing between tissues and their corresponding mutations obtained by liquid biopsy, without cases of false-negative results. In addition, in our study, liquid biopsy highlighted 26 mutations, with the prevalence of ALK mutation in 96.6% of patients, supporting the idea that this approach could be an effective tool in cases with insufficient tumor tissue specimens or in cases where tissue specimens are not obtainable.

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.

This study aimed to determine if diffusion-weighted imaging (DWI) can differentiate between benign and malignant lymph nodes in patients with head and neck cancer. A total of 55 patients with confirmed head and neck cancer and enlarged neck nodes were enrolled and evaluated by two radiologists using a workstation. Lymph nodes were analyzed using 3D regions of interest (ROIs) placed on T2-weighted images and compared to the corresponding DWI images. This study found that DWI and ADC values can be used to assess metastatic lymph nodes in the neck and that the sensitivity, specificity, and AUC of a narrower ROI for recognizing metastases were greater compared to the ADC value of the whole node. The study also found that the size of the ROI affects ADC values. The results suggest that DWI can accurately predict the status of cervical lymph nodes in patients with head and neck cancer and that it may be useful in diagnosing, determining the stage, developing a treatment plan, and monitoring these patients.

Introduction: Small hyperechoic renal masses are frequently detected incidentally on conventional ultrasound and are often presumed to represent benign lesions, particularly angiomyolipomas. However, malignant renal tumors, including renal cell carcinoma, may also appear hyperechoic when small, creating a diagnostic challenge at first-line imaging. This study aimed to evaluate the feasibility and exploratory diagnostic performance of B-mode ultrasound radiomics for differentiating benign and malignant small hyperechoic renal masses. Methods: This retrospective single-center study included adult patients with incidentally detected small hyperechoic renal masses measuring ≤3 cm and examined between July 2022 and April 2025. All lesions underwent standardized B-mode ultrasound assessment and multidisciplinary review. Final diagnosis was established by histopathology when available or by longitudinal ultrasound follow-up stability for lesions considered benign. Lesions were manually segmented on representative B-mode DICOM images, and original radiomic features were extracted using PyRadiomics version 3.0 according to standardized definitions compatible with the Image Biomarker Standardisation Initiative framework. A total of 114 original radiomic features were extracted from each lesion. The primary comparison was benign versus malignant lesions. Diagnostic performance was assessed using feature-level receiver operating characteristic analysis. Results: Forty-two lesions were included in the final radiomic cohort, including 26 malignant renal cell carcinomas and 16 benign angiomyolipomas. Malignant lesions included papillary renal cell carcinoma, chromophobe renal cell carcinoma, and clear-cell renal cell carcinoma. All malignant lesions were histologically confirmed. Among benign lesions, 14 angiomyolipomas were classified based on longitudinal ultrasound stability, whereas 2 were confirmed by ultrasound-guided percutaneous biopsy after mild dimensional increase during imaging surveillance. Among the extracted radiomic features, firstorder_Variance and firstorder_MeanAbsoluteDeviation showed the highest exploratory discriminatory performance, each achieving an area under the receiver operating characteristic curve of 0.837. Both features are first-order measures of gray-level dispersion within the segmented lesion. Higher values were observed in malignant lesions, suggesting greater intralesional grayscale heterogeneity compared with benign angiomyolipomas. Conclusions: B-mode ultrasound radiomics is feasible for the quantitative assessment of small hyperechoic renal masses and may provide complementary information for differentiating benign angiomyolipomas from malignant renal cell carcinomas. firstorder_Variance emerged as a representative candidate imaging biomarker of grayscale dispersion, with firstorder_MeanAbsoluteDeviation showing concordant performance as a related dispersion measure. These findings should be considered preliminary and hypothesis-generating and require validation in larger multicenter cohorts before clinical implementation.