Desmoid fibromatoses (DFs) are locally aggressive tumors composed of monoclonal fibroblasts within an abundant extracellular matrix. Systemic doxorubicin treatment is effective, but toxic. We investigated arterial doxorubicin eluting embolization (DEE), an approach characterized by high drug concentrations in the tumor alongside limited systemic drug exposure. The primary and secondary endpoints were radiological response using MRI and RECIST 1.1, respectively. The study included 24 patients (median age, 24; interquartile range, 16–34 years). Data were collected prospectively for 9 patients and retrospectively for 15 patients. The most frequent tumor locations were chest/abdomen wall and neck/shoulder/axilla (29% each). Of 24 patients, 7 (24%) were treatment naïve, and 17 (71%) had received one or two prior treatments. Patients underwent a median of two treatments (range, 1–4), with a median of 49 mg (range, 8–75) doxorubicin/treatment. Efficacy outcomes were available for 23 patients. With a median follow-up of 8 months (interquartile range, 3–13), median tumor volumes decreased by 59% (interquartile range, 40–71%) and T2 signal intensity decreased by 36% (interquartile range, 19–55%). Of 23 patients, 9 (39%), 12 (52%), and 2 (9%) had a partial response, stable disease, and progressive disease, respectively. DEE was safe and well tolerated, with one reported grade 3–4 adverse event (cord injury). In conclusion, DEE was safe and achieved rapid clinical/volumetric responses in DFs.

The objective of the study was to compare the outcome measures of patients with endometrial adenocarcinoma diagnosed by endometrial biopsy, uterine curettage, or hysteroscopy. Medical records of 392 women diagnosed with apparent early-stage endometrial adenocarcinoma were reviewed. Data concerning the mode of diagnosis, histologic type and grade, surgical stage, peritoneal washings and lymph nodes status, and patient's outcome were retrieved. During the study period, 99 (25.3%) cases were diagnosed by endometrial biopsy, 193 (49.2%) by uterine curettage, and 100 (25.5%) by hysteroscopy. There were 347 (88.5%) cases of endometrioid adenocarcinoma and 45 (11.5%) of poor histologic types, including serous papillary, clear cell, and small cell cancer. Three hundred and sixteen (80.6%) patients had stage I disease, 8 (2.0%) stage II, and 68 (17.4%) stage III. Peritoneal cytology was positive in only one case. Recurrent disease occurred in 6.9% patients, of which 50% had local recurrence and 50% had distant. Recurrent disease was found in 15.2% patients diagnosed by endometrial biopsy, in 4.7% where uterine curettage was used, and in 5% when hysteroscopy was applied. No statistically significant difference in the survival rate between the different diagnostic methods applied was found, although a higher recurrence rate was noted following endometrial biopsy. After a median follow-up time of 25 months for patients undergoing hysteroscopy, there was no difference in recurrence rates and/or overall survival compared to other diagnostic procedures implying that hysteroscopy can be safely used in the diagnosis of endometrial cancer.

In the prostate, water diffusion is faster when moving parallel to duct and gland walls than when moving perpendicular to them, but these data are not currently utilized in multiparametric magnetic resonance imaging (mpMRI) for prostate cancer (PCa) detection. Diffusion tensor imaging (DTI) can quantify the directional diffusion of water in tissue and is applied in brain and breast imaging. Our aim was to determine whether DTI may improve PCa detection. We scanned patients undergoing mpMRI for suspected PCa with a DTI sequence. We calculated diffusion metrics from DTI and diffusion weighted imaging (DWI) for suspected lesions and normal-appearing prostate tissue, using specialized software for DTI analysis, and compared predictive values for PCa in targeted biopsies, performed when clinically indicated. DTI scans were performed on 78 patients, 42 underwent biopsy and 16 were diagnosed with PCa. The median age was 62 (IQR 54.4–68.4), and PSA 4.8 (IQR 1.3–10.7) ng/mL. DTI metrics distinguished PCa lesions from normal tissue. The prime diffusion coefficient (λ₁) was lower in both peripheral-zone (p < 0.0001) and central-gland (p < 0.0001) cancers, compared to normal tissue. DTI had higher negative and positive predictive values than mpMRI to predict PCa (positive predictive value (PPV) 77.8% (58.6–97.0%), negative predictive value (NPV) 91.7% (80.6–100%) vs. PPV 46.7% (28.8–64.5%), NPV 83.3% (62.3–100%)). We conclude from this pilot study that DTI combined with T2-weighted imaging may have the potential to improve PCa detection without requiring contrast injection.

Background: A unique scanning protocol combining coronary computed tomography angiography (CTA) with routine abdominal CTA is being used at the Rabin Medical Center as a method of screening high-risk candidates for living kidney donation. We aim to evaluate the potential impact of coronary CTA on the decision regarding eligibility for kidney donation and its correlation with abdominal aortic calcification (AAC). Methods: CCTA and abdominal CTA results of potential living kidney donors evaluated for donation between September 2020 and November 2021 were retrieved. A retrospective analysis of the abdominal CTA was used to calculate the AAC. Patients’ demographic, clinical, and imaging data were collected from the electronic files, as well as the final decision regarding eligibility for donation. Results: A total of 62 potential kidney donors were evaluated for donation using the combined scan. The mean age was 53.8 years, with male predominance (59.7%). Significant coronary artery stenosis (≥70% luminal stenosis) was present in two patients (3.2%), whereas five patients (8%) had moderate stenosis (50–69%). Thirteen patients (21%) were disqualified from donation due to moderate-to-significant coronary artery disease or abdominal atherosclerosis. The correlation between the coronary artery calcium score and the AAC was found to be positive, with a Pearson correlation coefficient of 0.88 (p < 0.001). Conclusions: The use of coronary CTA in the evaluation of potential kidney donors may has a potential impact on the decision regarding eligibility for donation. A high correlation between the coronary artery calcium score and the AAC was found.

Background: Computed tomography (CT)-derived body composition parameters, including skeletal muscle and fat indices, are prognosticators in oncology. Most studies focus on baseline body-composition parameters; however, changes during treatment may provide better prognostic value. Standardized methods for measuring/reporting these parameters remain limited. Methods: This retrospective study included patients who were treated with immunotherapy for non-small cell lung cancer (NSCLC), renal cell carcinoma (RCC), or melanoma between 2017 and 2024 and had technically adequate baseline and follow-up CT scans. Body composition was analyzed using a novel, fully automated software (CompoCT) for L3 slice selection and segmentation. Body composition indices (e.g., skeletal muscle index [SMI]) were calculated by dividing the cross-sectional area by the patient’s height squared. Results: The cohort included 376 patients (mean [SD] age 66.4 [11.4] years, 67.3% male, 72.6% NSCLC, 14.6% RCC, and 12.8% melanoma). During a median follow-up of 21 months, 220 (58.5%) died. Baseline body composition parameters were not associated with mortality, except for a weak protective effect of higher SMI (HR = 0.98, p = 0.043). In contrast, longitudinal decreases were strongly associated with increased mortality. Relative decreases in SMI (HR, 1.17; 95% CI, 1.07–1.27) or subcutaneous fat index (SFI) (HR, 1.11; 95% CI, 1.07–1.15) significantly increased mortality risk. Multivariate models showed similar concordance (0.65) and identified older age, NSCLC tumor type, and relative decreases in SMI and SFI (per 5% units) as independent predictors of mortality. Conclusions: Longitudinal decreases in skeletal muscle and subcutaneous fat were independent predictors of mortality in immunotherapy-treated patients. Automated CT-based body composition analysis may support treatment decisions during immunotherapy.

Background: Recent advancements in Artificial Intelligence (AI) have the potential to address the challenges of mammographic screening programs by enhancing the performance of Computer-Aided Detection (CAD) systems, improving detection accuracy, and reducing false positive rates and recall rates. These systems were mostly investigated by control trials using cancer-enriched datasets and multiple readers. Objectives: This study aims to evaluate the real-world impact of AI integration on the performance of a breast cancer screening program. Methods: In January 2021, our mammography unit integrated an AI system (iCAD version 2.0) into its mammographic screening protocol. This study evaluates audit data of 31,176 mammograms interpreted between 2017 and 2021, comparing 24,373 mammograms prior to AI implementation and 6803 after the integration. Logistic regression analysis was used to assess the statistical significance of changes in key screening metrics, with a significance level of p < 0.05. Results: This study assesses the impact of artificial intelligence (AI) on mammographic screening. The cancer detection rate increased significantly from 6.2 per 1000 in 2019 to 9.3 per 1000 in 2021, with cancers detected on mammograms rising to 98%. Stage 1 cancer detection reached 100%, and the false negative rate dropped to 0%. Additionally, ductal carcinoma in situ (DCIS) detection decreased from 36.4% in 2019 to 20% in 2021. These findings highlight AI’s effectiveness in improving cancer detection accuracy and efficiency. Conclusions: The integration of AI into mammographic screening demonstrated promising results in improving cancer detection rates and reducing false negative rates. These findings highlight AI’s potential to enhance screening efficacy.