Radiologia Medica最新文献

Purpose: To evaluate a deep learning-based pipeline using a Dense-UNet architecture for the assessment of acute intracranial hemorrhage (ICH) on non-contrast computed tomography (NCCT) head scans after traumatic brain injury (TBI).

Materials and methods: This retrospective study was conducted using a prototype algorithm that evaluated 502 NCCT head scans with ICH in context of TBI. Four board-certified radiologists evaluated in consensus the CT scans to establish the standard of reference for hemorrhage presence and type of ICH. Consequently, all CT scans were independently analyzed by the algorithm and a board-certified radiologist to assess the presence and type of ICH. Additionally, the time to diagnosis was measured for both methods.

Results: A total of 405/502 patients presented ICH classified in the following types: intraparenchymal (n = 172); intraventricular (n = 26); subarachnoid (n = 163); subdural (n = 178); and epidural (n = 15). The algorithm showed high diagnostic accuracy (91.24%) for the assessment of ICH with a sensitivity of 90.37% and specificity of 94.85%. To distinguish the different ICH types, the algorithm had a sensitivity of 93.47% and a specificity of 99.79%, with an accuracy of 98.54%. To detect midline shift, the algorithm had a sensitivity of 100%. In terms of processing time, the algorithm was significantly faster compared to the radiologist's time to first diagnosis (15.37 ± 1.85 vs 277 ± 14 s, p < 0.001).

Conclusion: A novel deep learning algorithm can provide high diagnostic accuracy for the identification and classification of ICH from unenhanced CT scans, combined with short processing times. This has the potential to assist and improve radiologists' ICH assessment in NCCT scans, especially in emergency scenarios, when time efficiency is needed.

Purpose: To evaluate if background parenchymal enhancement (BPE) on contrast-enhanced mammography (CEM), graded according to the 2022 CEM-dedicated Breast Imaging Reporting and Data System (BI-RADS) lexicon, is associated with breast density, menopausal status, and age.

Methods: This bicentric retrospective analysis included CEM examinations performed for the work-up of suspicious mammographic findings. Three readers independently and blindly evaluated BPE on recombined CEM images and breast density on low-energy CEM images. Inter-reader reliability was estimated using Fleiss κ. Multivariable binary logistic regression was performed, dichotomising breast density and BPE as low (a/b BI-RADS categories, minimal/mild BPE) and high (c/d BI-RADS categories, moderate/marked BPE).

Results: A total of 200 women (median age 56.8 years, interquartile range 50.5-65.6, 140/200 in menopause) were included. Breast density was classified as a in 27/200 patients (13.5%), as b in 110/200 (55.0%), as c in 52/200 (26.0%), and as d in 11/200 (5.5%), with moderate inter-reader reliability (κ = 0.536; 95% confidence interval [CI] 0.482-0.590). BPE was minimal in 95/200 patients (47.5%), mild in 64/200 (32.0%), moderate in 25/200 (12.5%), marked in 16/200 (8.0%), with substantial inter-reader reliability (κ = 0.634; 95% CI 0.581-0.686). At multivariable logistic regression, premenopausal status and breast density were significant positive predictors of high BPE, with adjusted odds ratios of 6.120 (95% CI 1.847-20.281, p = 0.003) and 2.416 (95% CI 1.095-5.332, p = 0.029) respectively.

Conclusion: BPE on CEM is associated with well-established breast cancer risk factors, being higher in women with higher breast density and premenopausal status.

Purpose: To systematically review the use of artificial intelligence (AI) in musculoskeletal (MSK) ultrasound (US) with an emphasis on AI algorithm categories and validation strategies.

Material and methods: An electronic literature search was conducted for articles published up to January 2024. Inclusion criteria were the use of AI in MSK US, involvement of humans, English language, and ethics committee approval.

Results: Out of 269 identified papers, 16 studies published between 2020 and 2023 were included. The research was aimed at predicting diagnosis and/or segmentation in a total of 11 (69%) out of 16 studies. A total of 11 (69%) studies used deep learning (DL)-based algorithms, three (19%) studies employed conventional machine learning (ML)-based algorithms, and two (12%) studies employed both conventional ML- and DL-based algorithms. Six (38%) studies used cross-validation techniques with K-fold cross-validation being the most frequently employed (n = 4, 25%). Clinical validation with separate internal test datasets was reported in nine (56%) papers. No external clinical validation was reported.

Conclusion: AI is a topic of increasing interest in MSK US research. In future studies, attention should be paid to the use of validation strategies, particularly regarding independent clinical validation performed on external datasets.

Purpose: To assess the ability of tumor apparent diffusion coefficient (ADC) values obtained from multiparametric magnetic resonance imaging (mpMRI) to predict the risk of 5-year biochemical recurrence (BCR) after radical prostatectomy (RP).

Materials and methods: This retrospective analysis included 1207 peripheral and 232 non-peripheral zone prostate cancer (PCa) patients who underwent mpMRI before RP (2012-2015), with the outcome of interest being 5-year BCR. ADC was evaluated as a continuous variable and as categories: low (< 850 µm²/s), intermediate (850-1100 µm²/s), and high (> 1100 µm²/s). Kaplan-Meier curves with log-rank testing of BCR-free survival, multivariable Cox proportional hazard regression models were formed to estimate the risk of BCR.

Results: Among the 1439 males with median age 63 (± 7) years, the median follow-up was 59 months, and 306 (25%) patients experienced BCR. Peripheral zone PCa patients with BCR had lower tumor ADC values than those without BCR (874 versus 1025 µm²/s, p < 0.001). Five-year BCR-free survival rates were 52.3%, 74.4%, and 87% for patients in the low, intermediate, and high ADC value categories, respectively (p < 0.0001). Lower ADC was associated with BCR, both as continuously coded variable (HR: 5.35; p < 0.001) and as ADC categories (intermediate versus high ADC-HR: 1.56, p = 0.017; low vs. high ADC-HR; 2.36, p < 0.001). In the non-peripheral zone PCa patients, no association between ADC and BCR was observed.

Conclusion: Tumor ADC values and categories were found to be predictive of the 5-year BCR risk after RP in patients with peripheral zone PCa and may serve as a prognostic biomarker.

Objectives: To evaluate the ability of 50-keV virtual monoenergetic images (VMI) to depict abdominal arteries in abdominal CT angiography (CTA) compared with 70-keV VMI with photon-counting detector CT (PCD-CT).

Methods: Fifty consecutive patients who underwent multiphase abdominal scans between March and April 2023 were included. Signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were quantitatively assessed for the abdominal aorta (AA), celiac artery (CeA), superior mesenteric artery (SMA), renal artery (RA), and right hepatic artery (RHA) at both 50- and 70-keV VMI. In addition, 3D images from CTA were analyzed to measure arterial lengths and evaluate the visualization of distal branches.

Results: Significantly higher SNR and CNR were observed at 50-keV compared to 70-keV VMI for all arteries: AA (36.54 and 48.28 vs. 25.70 and 28.46), CeA (22.39 and 48.38 vs. 19.09 and 29.15), SMA (23.34 and 49.34 vs. 19.67 and 29.71), RA (22.88 and 48.84 vs. 20.15 and 29.41), and RHA (14.38 and 44.41 vs. 13.45 and 27.18), all p < 0.05. Arterial lengths were also significantly longer at 50-keV: RHA (192.6 vs. 180.3 mm), SMA (230.9 vs. 216.5 mm), and RA (95.9 vs. 92.0 mm), all p < 0.001.

Conclusion: In abdominal CTA with PCD-CT, 50-keV VMI demonstrated superior quantitative image quality compared to 70-keV VMI. In addition, 50-keV VMI 3D CTA allowed better visualization of abdominal artery branches, highlighting its potential clinical advantage for improved imaging and detailed assessment of abdominal arteries.

Purpose: To test the inter-reader agreement in classifying pulmonary hypertension (PH) on chest contrast-enhanced computed tomography (CECT) between a consensus of two cardio-pulmonary-devoted radiologists (CRc) and inexperienced readers (radiology residents, RRs) when using a CECT-based quick hands-on tool built upon PH imaging literature, i.e., the "Rapid Access and Practical Information Digest on Computed Tomography for PH-RAPID-CT-PH".

Material and methods: The observational study retrospectively included 60 PH patients who underwent CECT between 2015 and 2022. Four RRs independently reviewed all CECTs and classified each case into one of the five PH groups per the 2022 ESC/ERS guidelines. While RR3 and RR4 (RAPID-CT-PH group) used RAPID-CT-PH, RR1 and RR2 (control group) did not. RAPID-CT-PH and control groups' reports were compared with CRc using unweighted Cohen's Kappa (k) statistics. RRs' report completeness and reporting time were also compared using the Wilcoxon-Mann-Whitney test.

Results: The inter-reader agreement in classifying PH between the RAPID-CT-PH group and CRc was substantial (k = 0.75 for RR3 and k = 0.65 for RR4); while, it was only moderate for the control group (k = 0.57 for RR1 and k = 0.49 for RR2). Using RAPID-CT-PH resulted in significantly higher report completeness (all p < 0.0001) and significantly lower reporting time (p < 0.0001) compared to the control group.

Conclusion: RRs using RAPID-CT-PH showed a substantial agreement with CRc on CECT-based PH classification. RAPID-CT-PH improved report completeness and reduced reporting time. A quick hands-on tool for classifying PH on chest CECT may help inexperienced radiologists effectively contribute to the PH multidisciplinary team.

Purpose: To investigate whether MRI-guided transrectal laser ablation is safe and effective for the treatment of lower urinary tract symptoms caused by BPH.

Materials and methods: This single-center retrospective cohort study evaluated men who underwent MRI-guided transrectal laser ablation for BPH between February 2017 and July 2021. Age, prostate-specific antigen, prostate volume, prior surgical BPH treatments if any, International Prostate Symptom Score (IPSS) and Sexual Health Inventory of Men (SHIM) were collected. The primary outcome measures assessed were change in IPSS and SHIM 6, 12 and 24 months after laser ablation and adverse events.

Results: Fifty-two patients were included, having completed at least one follow-up survey. The mean patient age was 62.9 ± 5.7 years, and mean prostate volume was 80.2 ± 39.2 cc. Eighteen patients (34.6%) had received a prior BPH treatment. The IPSS scores dropped an average of 16.7 ± 7.0 (p < 0.001), 16.9 ± 7.5 (p < 0.001) and 17.1 ± 7.2 (p < 0.001) points from baseline at 6, 12 and 24 months, respectively. There was no statistically significant difference in IPSS score drop between patients who had received a prior BPH procedure and those who had not (p = 0.628). The SHIM scores showed a statistically insignificant increase at all time points. Nineteen patients (36.5%) reported a complication. There were 12 grade II complications (23%) and seven grade I complications (13.5%). There were no grade III or higher complications.

Conclusion: Transrectal MRI-guided focal laser ablation is safe and effective for the treatment of lower urinary tract symptoms caused by BPH, with a significant improvement in symptom severity after 2 years.

Purpose: This study quantifies the impact on budget and cost per health benefit of implementing digital breast tomosynthesis (DBT) in place of digital mammography (DM) for breast cancer screening among asymptomatic women in Italy.

Methods: A budget impact analysis and a cost consequence analysis were conducted using parameters from the MAITA project and literature. The study considered four scenarios for DBT implementation, i.e., DBT for all women, DBT for women aged 45-49 years, DBT based on breast density (BI-RADS C + D or D only), and compared these to the current DM screening. Healthcare provider's perspective was adopted, including screening, diagnosis, and cancer treatment costs.

Results: Introducing DBT for all women would increase overall screening costs by 20%. Targeting DBT to women aged 45-49 years or with dense breasts would result in smaller cost increases (3.2% for age-based and 1.4-10.7% for density-based scenarios). The cost per avoided interval cancer was significantly higher when DBT was applied to all women compared to targeted approaches. The cost per gained early-detected cancer slightly increases in targeted approaches, while the assumptions on the clinical significance and overdiagnosis of cancers detected by DBT and not by DM have a strong impact.

Conclusions: Implementing DBT as a primary breast cancer test in screening programs in Italy would lead to a substantial increase in costs. Tailoring DBT use to women aged 45-49 or with dense breasts could enhance the feasibility and sustainability of the intervention. Further research is needed to clarify the impact of DBT on overdiagnosis and the long-term outcomes.