Radiologia Medica最新文献

Purpose: To evaluate the correlation between ectopic adipose tissue and iron overload severity in patients with hemochromatosis.

Material and methods: A retrospective cohort of 52 patients who underwent liver iron concentration quantification from January 2015 to October 2023 using a 3.0T MRI scanner. R2* relaxation times and proton density fat fraction (PDFF) were assessed for the entire liver volume and a specific region of interest (ROI) placed in the right lobe. Total body fat (TF), subcutaneous fat (SCF), intermuscular fat (IMF), and visceral fat (VSF) percentages were calculated from a single axial slice at the level of the third lumbar vertebra. Additionally, ratios of IMF-to-VSF, IMF-to-SCF, and SCF-to-VSF were calculated. Standard iron laboratory parameters were collected at least one month prior to MRI. Pearson correlation coefficient was used for correlation analysis.

Results: The mean age of participants was 53.9 ± 19.6 years. IMF positively correlated with R2* values in the ROI (p = 0.005, r_s = 0.382) and entire liver (p = 0.016, r_s = 0.332). Conversely, VSF negatively correlated with R2* values from the ROI (p = < 0.001, r_s = - 0.488) and entire liver (p = < 0.001, r_s = - 0.459). Positive correlations were also found between IMF-to-VSF and R2* of the ROI (p = 0.003, r_s = 0.400) and whole liver (p = 0.008, r_s = 0.364). Ferritin levels positively correlated with R2* values calculated from ROI (p = 0.002, r_s = 0.417) and whole liver volume (p = 0.004, r_s = 0.397). A positive correlation was noted between PDFF of the entire liver and TF (p = 0.024, rs = 0.313).

Conclusion: The percentage of Intermuscular and visceral adipose tissues correlates with the severity of liver iron overload in hemochromatosis patients.

Background: Early and accurate identification of the metastatic tumor types of brain metastasis (BM) is essential for appropriate treatment and management.

Methods: A total of 450 patients were enrolled from two centers as a primary cohort who carry 764 BMs originated from non-small cell lung cancer (NSCLC, patient = 173, lesion = 187), small cell lung cancer (SCLC, patient = 84, lesion = 196), breast cancer (BC, patient = 119, lesion = 200), and gastrointestinal cancer (GIC, patient = 74, lesion = 181). A third center enrolled 28 patients who carry 67 BMs (NSCLC = 24, SCLC = 22, BC = 10, and GIC = 11) to form an external test cohort. All patients received contrast-enhanced T1-weighted (T1CE) and T2-weighted (T2W) MRI scans at 3.0 T before treatment. Radiomics features were calculated from BM and brain-to-tumor interface (BTI) region in the MRI image and screened using least absolute shrinkage and selection operator (LASSO) to construct the radiomics signature (RS). Volume of peritumor edema (VPE) was calculated and combined with RS to create a joint model. Performance of the models was assessed by receiver operating characteristic (ROC).

Results: The BTI-based RS showed better performance compared to BM-based RS. The combined models integrating BTI features and VPE can improve identification performance in AUCs in the training (LC/NLC vs. SCLC/NSCLC vs. BC/GIC, 0.803 vs. 0.949 vs. 0.918), internal validation (LC/NLC vs. SCLC/NSCLC vs. BC/GIC, 0.717 vs. 0.854 vs. 0.840), and external test (LC/NLC vs. SCLC/NSCLC vs. BC/GIC, 0.744 vs. 0.839 vs. 0.800) cohorts.

Conclusion: This study indicated that BTI-based radiomics features and VPE are associated with the metastatic tumor types of BM.

Verifying patients' position and internal anatomical changes are important steps in the radiotherapy of rectal cancer. Cone Beam Computed Tomography (CBCT) is an advanced imaging method that allows for the quantification of these modifications, ensuring the delivery of radiation dose to the tumor volume, while protecting surrounding organs at risk. The aim of this review is to discuss and analyze the benefits offered by this method of imaging on board the linear accelerator. In view of this, a systematic search of the scientific literature in the Medline/PubMed database was performed for publications over the last decade, with 20 articles found to be relevant for this study. To highlight the benefits of this imaging technique in rectal cancer, the frequency of CBCT use, identification of tumor volume and organs at risk on CBCT images, quantification of the movement of these organs and tumor volume, analysis of positioning errors as well as evaluation of dosimetric parameters were analyzed.

Objective: This study aimed to investigate the integration of dual-energy CT (DECT) into CT-guided bone biopsy procedures, comparing it with conventional CT techniques. The focus was on technical aspects, accuracy and radiation dose exposure.

Materials and methods: A total of 51 bone biopsies were conducted, with 36 using conventional CT and 15 utilizing DECT. Patient data, lesion characteristics and biopsy techniques were analyzed. Statistical analyses, including Fisher's exact test and independent samples t-test, were performed to compare accuracy and radiation doses between the two methods.

Results: DECT-guided biopsies demonstrated a significantly higher accuracy (93.33%) compared to conventional CT biopsies (86.11%). The radiation dose exposure for DECT was comparable to conventional CT. DECT's ability to differentiate tissues, especially in bone marrow edema detection, led to higher precision.

Conclusion: Integrating DECT into CT-guided bone biopsies enhances tissue differentiation and accuracy without significantly increasing radiation exposure. This advancement holds promise for improving musculoskeletal interventional radiology, leading to more precise diagnoses, informed treatment decisions and improved patient outcomes.

Purpose: Oral mucositis (OM) is one of the most prevalent and crippling treatment-related toxicities experienced by nasopharyngeal carcinoma (NPC) patients receiving radiotherapy (RT), posing a tremendous adverse impact on quality of life. This multi-center study aimed to develop and externally validate a multi-omic prediction model for severe OM.

Methods: Four hundred and sixty-four histologically confirmed NPC patients were retrospectively recruited from two public hospitals in Hong Kong. Model development was conducted on one institution (n = 363), and the other was reserved for external validation (n = 101). Severe OM was defined as the occurrence of CTCAE grade 3 or higher OM during RT. Two predictive models were constructed: 1) conventional clinical and DVH features and 2) a multi-omic approach including clinical, radiomic and dosiomic features.

Results: The multi-omic model, consisting of chemotherapy status and radiomic and dosiomic features, outperformed the conventional model in internal and external validation, achieving AUC scores of 0.67 [95% CI: (0.61, 0.73)] and 0.65 [95% CI: (0.53, 0.77)], respectively, compared to the conventional model with 0.63 [95% CI: (0.56, 0.69)] and 0.56 [95% CI: (0.44, 0.67)], respectively. In multivariate analysis, only the multi-omic model signature was significantly correlated with severe OM in external validation (p = 0.017), demonstrating the independent predictive value of the multi-omic approach.

Conclusion: A multi-omic model with combined clinical, radiomic and dosiomic features achieved superior pre-treatment prediction of severe OM. Further exploration is warranted to facilitate improved clinical decision-making and enable more effective and personalized care for the prevention and management of OM in NPC patients.

Purpose: To develop a combined approach using shear wave elastography (SWE) and conventional ultrasound (US) to determine the extent of positive axillary lymph nodes (LNs) following neoadjuvant therapy (NAT) in breast cancer patients with nodal involvement.

Methods: This prospective, multicenter study was registered on the Chinese Clinical Trial Registry (ChiCTR2400085035). From October 2018 to February 2024, a total of 303 breast cancer patients with biopsy-proven positive LN were enrolled. The conventional US features of axillary LNs and SWE characteristics of breast lesions after NAT were analyzed. The diagnostic performances of axilla US, breast SWE, and their combination in detecting residual metastasis in axillary level III after NAT were assessed.

Results: Pathologically positive LN(s) in axilla level III were detected in 13.75% of cases following NAT. The kappa value for the axilla level with positive LN confirmed by surgical pathology and detected by US is 0.39 (p < 0.001). The AUC of conventional axilla US to determine the status of axilla level III LNs after NAT was 0.67, with a sensitivity of 51.52%, a specificity of 74.36%. The breast SWE displayed moderate performance for detecting residual metastasis in axilla level III following NAT, with an AUC of 0.79, sensitivity of 84.85%, and specificity of 74.36%. Compared to axilla US and breast SWE alone, the combination of axilla US with breast SWE achieved a stronger discriminatory ability (AUC, 0.86 vs 0.67 vs 0.79, p < 0.05, Delong's test) and precise calibration (X² = 13.90, p = 0.085, HL test), with an improved sensitivity of 93.94% and a comparable specificity of 75.64%%.

Conclusions: SWE outperformed conventional US in identifying the axilla levels with nodal metastasis following NAT in patients with initially diagnosed positive axilla. Furthermore, combining breast SWE with axilla US showed good diagnostic performance for detecting residual metastasis in axilla level III after NAT.

Objective: The purpose of this study is to demonstrate the consistency and reproducibility of quantitative SPECT/CT by evaluating the maximum SUV (SUV_max) in normal bone, to provide the reference value of metastatic lesions, and to evaluate the clinical implication of SUV_max changes of osseous metastasis during treatment.

Material and methods: This prospective imaging sub-study was performed as part of a phase 2 clinical trial of patients with metastatic castration-resistant prostate cancer (mCRPC) randomized to the combination of pembrolizumab plus radium-223 or to radium-223 alone (NCT03093428). The maximum standardized uptake value (SUV_max) and mean Hounsfield Unit (HU_mean) of normal bone as well as metastases were measured using a 1.5 cm region of interest (ROI) on CT and xSPECT Quant reconstruction on the baseline study (S₀) and restaging scans. The most tracer-avid metastatic lesion in each patient on S₀ was selected as a target lesion, and changes of SUV_max and HU_mean of the target lesion were compared on the first restaging scan (S₁). Correlations between the percentage changes of SUV_max of the target lesion with alkaline phosphatase (ALP) and prostate-specific antigen (PSA) were assessed.

Results: Twenty-one patients were enrolled on the imaging sub-study of which 15 had paired baseline S₀ and S₁ data. On S₀, the median SUV_max and HU_mean of normal bone was 5.85 g/mL (0.42-14.98) and 133.03 (range, 28.47-461.91), respectively. The median SUV_max and HU_mean of metastasis were 42.2 g/mL (range, 17.96-143.36) and 549.58 (177.87-1107.64), respectively. There was significant reduction in SUV_max (- 40.1%, range - 86.2 to + 23.5%), p < 0.001) and increase in HU_mean (+ 8.3%, range - 11.3 to + 61.7%, p = 0.0479, Wilcoxon signed-rank test) of target lesions between S₀ and S₁. Spearman correlation between the percentage changes of SUV_max of a target lesion and both serum PSA (r = 0.33, p = 0.226) and ALP (r = 0.45, p = 0.094) were not statistically significant.

Conclusion: Quantitative SPECT/CT provides consistent and objective imaging parameters, which can help monitor tumor burden. The median SUVmax of metastasis at baseline was roughly 7.2-fold higher than normal bone. Quantitative SPECT/CT may help visualize the early osteoblastic treatment response in prostate cancer patients treated with radium-223 alone or combined with pembrolizumab.

Thermal ablation is widely accepted as an effective and safe method for treating benign thyroid nodules. Many studies reporting short-term results have consistently demonstrated the efficacy and safety of thermal ablation. However, as the clinical application of thermal ablation grows and follow-up periods extend, long-term clinical outcomes of thermal ablation have revealed several issues, including regrowth and diagnosis of malignancy in ablated lesions. In this systematic review, we analyze the long-term clinical outcomes of thyroid thermal ablation, focusing on regrowth, delayed surgery, and the potential for malignancy after thermal ablation and propose solutions to address these unresolved issues and enhance the management of benign thyroid nodules through thermal ablation.

Background: Accurate prognostication of overall survival (OS) for non-small cell lung cancer (NSCLC) patients receiving definitive radiotherapy (RT) is crucial for developing personalized treatment strategies. This study aims to construct an interpretable prognostic model that combines radiomic features extracted from normal lung and from primary tumor with clinical parameters. Our model aimed to clarify the complex, nonlinear interactions between these variables and enhance prognostic accuracy.

Methods: We included 661 stage III NSCLC patients from three multi-national datasets: a training set (N = 349), test-set-1 (N = 229), and test-set-2 (N = 83), all undergoing definitive RT. A total of 104 distinct radiomic features were separately extracted from the regions of interest in the lung and the tumor. We developed four predictive models using eXtreme gradient boosting and selected the top 10 features based on the Shapley additive explanations (SHAP) values. These models were the tumor radiomic model (Model-T), lung radiomic model (Model-L), a combined radiomic model (Model-LT), and an integrated model incorporating clinical parameters (Model-LTC). Model performance was evaluated through Harrell's concordance index, Kaplan-Meier survival curves, time-dependent area under the receiver operating characteristic curve (AUC), calibration curves, and decision curve analysis. Interpretability was assessed using the SHAP framework.

Results: Model-LTC exhibited superior performance, with notable predictive accuracy (C-index: training set, 0.87; test-set-2, 0.76) and time-dependent AUC above 0.75. Complex nonlinear relationships and interactions were evident among the model's variables.

Conclusion: The integration of radiomic and clinical factors within an interpretable framework significantly improved OS prediction. The SHAP analysis provided insightful interpretability, enhancing the model's clinical applicability and potential for aiding personalized treatment decisions.