Radiologia Medica最新文献

Environmental exposures-such as airborne pollutants, metals, and urban stressors-contribute to cancer development and progression, yet their downstream biological effects remain difficult to characterize in vivo. Quantitative medical imaging may help fill this gap. Radiomics, in particular, offers access to tissue-level patterns shaped by chronic injury and microenvironmental remodeling. In this review, we discuss the rationale for linking geospatial exposure assessment with CT- and MRI-derived imaging biomarkers and outline how radiologic features may reflect processes associated with long-term environmental stress, including oxidative damage, inflammation, and metabolic or immune dysregulation. We also summarize epidemiologic evidence across major cancer types to contextualize where imaging-exposure integration is most plausible. A methodological workflow is presented, covering exposure assignment, imaging standardization, feature extraction, and strategies for harmonizing and modeling high-dimensional exposomic and radiomic data. Considerations related to confounding, data governance, and equity are also addressed, as these factors are integral to responsible implementation. Viewed in this light, imaging can be interpreted as an intermediate phenotype of the exposome-capturing aspects of tumor and peritumoral biology influenced by external stressors. This perspective may expand the role of radiology in precision oncology and generate new hypotheses about how environmental conditions shape cancer biology.

Purpose: Combined hepatocellular-cholangiocarcinoma (cHCC-CC) is a rare primary liver cancer with heterogeneous radiologic and pathologic characteristics. This scoping review evaluates MRI characteristics of cHCC-CC, its classification using the Liver Imaging Reporting and Data System (LI-RADS), and its association with biomarkers and patient prognosis.

Methods: A comprehensive search of medical research databases, grey literature, and references of included studies was performed from inception to September 2024 to identify articles evaluating cHCC-CC using MRI following PRISMA-ScR methodology. We extracted individual MRI imaging characteristics and LI-RADS categorization data to achieve a quantitative summary of the existing literature. A subgroup analysis was conducted for studies that evaluated biomarker and prognostic data.

Results: Forty studies including 1767 cHCC-CC cases were evaluated. Most common MRI characteristics included T2 hyperintensity (96%), diffusion restriction (93%), hepatobiliary phase hypoenhancement (91%), arterial enhancement (86%), and non-peripheral washout (83%). Overall, 44-78% of cHCC-CCs demonstrated major LI-RADS features of HCC, 7-31% showed ancillary features that favor HCC, and 10-46% exhibited LR-M characteristics that are classically associated with intrahepatic cholangiocarcinoma (ICC). The majority of cHCC-CCs were accurately characterized as LR-M (57%), but a considerable proportion were categorized as LR-4 (10%) and LR-5 (27%), with the latter demonstrating HCC dominant features. cHCC-CC categorized as LR-M was associated with worse prognosis than those categorized as LR-4 or LR-5. Discordant alpha fetoprotein (AFP) and carbohydrate antigen 19-9 (CA 19-9) values raise suspicion for the diagnosis of cHCC-CC. Due to the rarity of cHCC-CC, there is considerable heterogeneity of the available literature and geographic bias.

Conclusion: Greater than half of cHCC-CCs can be accurately characterized as LR-M using LI-RADS criteria. However, a large minority are characterized as LR-4 or LR-5, reflecting dominant HCC features. Misclassification of cHCC-CCs as LR-5 can have management implications including inappropriate transplant eligibility. LR-M categorization is associated with worse outcomes, suggesting that LI-RADS categorization has prognostic value. Future integration of imaging features and biomarkers can be used to better evaluate for cHCC-CC.

Purpose: To evaluate the value of whole-lesion histogram analysis derived from mono-exponential, bi-exponential, and stretched-exponential DWI models in differentiating benign from malignant breast lesions and exploring molecular subtypes.

Material and methods: This retrospective study included 112 patients with 90 malignant lesions (17 Luminal A, 39 Luminal B, 18 HER2-positive, 10 triple-negative, and 6 undetermined) and 22 benign lesions, all examined with 1.5 T MRI. Histogram parameters-apparent diffusion coefficient (ADC), true diffusion (Dt), pseudo-diffusion (Dp), perfusion fraction (f), distributed diffusion coefficient (DDC), and heterogeneity index (alpha)-were analyzed using the Mann-Whitney U test, Kruskal-Wallis test, logistic regression, ROC analysis, the DeLong test, and the chi-square test.

Results: Histogram parameters from all models showed significant differences between benign and malignant lesions, with high diagnostic performance (AUC range: 0.898-0.938). However, combining the models did not significantly improve the AUC (p > 0.05). In molecular subtype analyses, DDC_75% differed significantly between Luminal A and triple-negative subtypes (p = 0.035); Dt_50%, Dt_75%, and DDC_75% distinguished Luminal B from triple-negative subtypes (p = 0.016, 0.021, and 0.041, respectively); and ADC_kurtosis and DDC_kurtosis showed significant differences between HER2-positive and triple-negative subtypes (p = 0.021 and 0.029, respectively). ROC analysis indicated variable diagnostic efficacy among parameters across molecular subtypes, and model combinations did not enhance AUC values.

Conclusion: Whole-lesion histogram analysis based on multi-model DWI shows potential for characterizing breast lesions. These exploratory findings, derived from an imbalanced single-center cohort, require further validation in larger prospective studies before clinical application.

Purpose: To evaluate MRI changes in T2-weighted imaging (T2WI) signal intensity (T2SI) as a potential imaging marker for assessing response to radiotherapy (RT) in pediatric low-grade glioma (pLGG).

Materials and methods: This retrospective study analyzed imaging data of 56 pLGG patients (mean age, 12.4 ± 3.5 years; 33/56 [58.9%] male) treated with photon-based or proton-based RT within the SIOP-LGG 2004 study and registry. Tumor signal characteristics on T2WI were qualitatively and quantitatively assessed at baseline and up to 24 months post-RT. Tumor volumes were calculated, and correlations between ∆T2SI and volumetric changes were examined. Statistical tests included inferential tests, correlation analysis, and linear regression.

Results: At baseline, 87.5% tumors were rated as hyperintense, while none was rated hypointense. The mean ratio between T2SI of the tumors compared to the cerebral cortex was 1.70. A significant decrease in T2SI was observed over time with the strongest decrease at 24 months post-RT (- 18.7%; p = 0.002). ∆T2SI correlated significantly with tumor volume reduction (r = 0.46, p < 0.001) and response assessment (ρ = 0.51, p < 0.001). There was no significant influence of age, sex, tumor location, histology, or RT type on ∆T2SI. Cases of pseudoprogression cases exhibited stable T2SI despite transient increases in contrast enhancement or tumor volume.

Conclusion: A reduction in T2SI was consistently associated with tumor volume reduction, suggesting that a decrease in T2SI may serve as an additional imaging marker of a positive response to RT in pLGG patients.

Background: Artificial Intelligence (AI) in radiology has shown promise in detecting fractures on initial X-rays. However, the role of follow-up examinations in enhancing AI performance remains unexplored. This study evaluates the impact of including follow-up X-rays on the performance of neural networks in detecting pediatric wrist fractures.

Methods: Using the publicly available GRAZPEDWRI-DX dataset of 20,327 pediatric wrist X-rays, we created four training datasets: initial X-rays alone and combinations with follow-up X-rays (with and without casts). Two neural networks, EfficientNet (image classification) and YOLOv8 (object detection), were trained and evaluated using precision, recall, F1 score, and AP metrics. The dataset was divided into training, validation, and test sets, with 500 initial X-rays separated and reserved for testing.

Results: EfficientNet models showed no statistically significant improvements in classification performance with the inclusion of follow-up X-rays. In contrast, YOLOv8 demonstrated improved object detection metrics, particularly AP50 (p = 0.003) and F1 score (p = 0.009), when follow-up X-rays were included. The improvement was most evident when both cast and non-cast follow-ups were incorporated.

Conclusion: Adding follow-up X-rays did not enhance classification performance but improved fracture localization in object detection tasks. These findings suggest that including follow-up data shows no relevant improvement in the detection rate of fractures but can enhance AI applications for pediatric wrist fracture detection, particularly for object detection models.

Objective: To explore the prognostic value of body compositions and radiomics in patients with resectable colon cancer, and to develop and validate a clinical-radiomics model for predicting the postoperative overall survival of patients with resectable colon cancer.

Methods: This study included 296 patients (43 months of median follow-up) with resectable colon cancer. Non-contrast CT images were used to quantify the body composition at the level of the third lumbar vertebra. Radiomics features were extracted from portal venous-phase CT scans. The recursive feature elimination and the least absolute shrinkage and selection operator regression were used for feature selection and construction of radiomic signatures. Univariate and multivariate Cox regression analysis were used to identify body composition. Combined with radiomics features, clinical-radiomics prediction model was constructed and plotted by nomogram, with performance metrics including the area under the receiver operating characteristic curve, calibration curves, decision curve analysis, and integrated discrimination improvement index.

Result: Low skeletal muscle density (HR = 0.398, 95%CI = 0.168-0.939, P = 0.035) and low visceral fat area (HR = 0.238, 95%CI = 0.108-0.524, P < 0.001) were significantly associated with poor OS. The integrated clinical-radiomics model achieved C-index of 0.802 and 0.786 in the training and test cohorts, with superior 3-year OS AUC values of 0.804 and 0.828. Furthermore, clinical-radiomics model has a significant improvement in performance compared with radiomics model (IDI: 23.2%, P < 0.001) and clinical model (IDI:5.2%, P = 0.008).

Conclusion: Nomogram combining body composition and tumor radiomics features can help predict the long-term prognosis of patients with resectable colon cancer and may serve as an effective tool to promote individualized treatment.

Aim: The AI case malignancy score (AI-CMS) represents the AI algorithm's confidence (from 0 to 100%) that a mammography exam is malignant. This work aims to retrospectively evaluate, through simulation on real-world data, a strategy that integrates AI-CMS into a standard screening scenario to reduce the radiologists' workload.

Methods: A total of 89176 consecutive screening exams from the 2023-2024 Reggio Emilia Breast Screening Program (REBSP) were retrospectively considered, which included 479 biopsy-proven cancers (interval cancers were only partially available, therefore false negatives beyond those detected in the real screening workflow could not be assessed). In the proposed strategy, computer-aided detection (CAD) acts as a reader (CR), recalling women with an AI-CMS greater than a predefined threshold (ranging from 5 to 25%). If the first radiologist (HR1) disagrees with CR, the case goes to a second radiologist (HR2) and, in case of human disagreement, to a third radiologist (HR3). For each threshold, final recall rate (RR), cancer detection rate (CDR), number of detected cancers (DC), predictive positive value (PPV) of recalls, false positive rate (FPR), human reading workload, and economic impact were estimated.

Results: At AI-CMS thresholds of 5%, 8%, 10%, 15%, 20%, and 25%, human workload decrease ranged from 13.4% to 36.1%. The final RR decreased between 4.3% and 4.0%, slightly lower than the current 4.4% with human double reading. The PPV ranged from 12.6% to 13.3%, higher than the current PPV of 12.2%. The FPR ranged from 3.8% to 3.5%, down from the current 3.9%. With thresholds up to 5%, no true positive cases were missed, maintaining the CDR of 5.4‰ of those detected by current double reading. Considering CAD payback periods of either 6 or 8 years, financial savings from our strategy ranged from approximately 17800 to over 590,000€.

Conclusion: Integrating AI-CMS support into a standard screening scenario could substantially reduce the screen-reading workload and slightly reduce unnecessary ascertainments without affecting the cancer detection rate. This approach, although limited by its retrospective simulation design and the partial availability of interval cancer data, has also proven to be economically sustainable.

The parapharyngeal space is a complex anatomical site in the head and neck which may harbour clinically occult pathology given its deep-seated location. The vast majority of parapharyngeal space lesions are of intermediate or hyperintense signal on T2W sequences. This review focuses on T2 hypointense parapharyngeal space lesions which are rare and may constitute a diagnostic dilemma. We present the differential diagnosis of these lesions, highlighting the pertinent radiological findings and identifying a histological correlation for the low T2 signal. A brief discussion of the physics principles accounting for these imaging features is also included. We propose a diagnostic algorithm to facilitate diagnosis and avoid unnecessary biopsy, whenever possible.