Diagnostic and Interventional Imaging最新文献

Purpose: The purpose of this study was to assess the capabilities of MRI-based Node Reporting and Data System (Node-RADS) in diagnosing regional lymph node metastasis (RLNM) and to estimate its prognostic significance in patients with renal cell carcinomas (RCCs).

Materials and methods: Patients with RCC who underwent nephrectomy and regional lymph node dissection between January 2010 and August 2023 were retrospectively included. Two senior radiologists scored lymph nodes in consensus using MRI-based Node-RADS. The performance of MRI-based Node-RADS for the diagnosis of RLNM was estimated using area under receiver operating characteristic (AUC) curves and compared against size criteria. Three additional readers scored all lesions to assess interobserver agreement. Progression-free survival and overall survival were estimated and compared between patients with low (1-3) and high (4-5) scores.

Results: Overall, 216 patients with RCC were enrolled, including 58 with RLNM. There were 157 men and 59 women with a median age of 54 years (range: 8-83 years). Node-RADS showed larger AUC (0.93 [95 % confidence interval (CI): 0.87-0.97]) and higher specificity (96.8 % [95 % CI: 92.8-99.0]) compared to size criteria (0.88 [95 % CI: 0.83-0.94] and 87.3 % [95 % CI: 81.1-92.1], respectively) for the diagnosis of RLNM (P = 0.039 and P < 0.001, respectively). Substantial interobserver agreement in Node-RADS scoring was obtained between the three readers (weighted κ, 0.75 [95 % CI: 0.69-0.80]). During a median follow-up of 56 months, patients with high Node-RADS score experienced poorer progression-free survival (P < 0.001) and overall survival (P < 0.001) than those with low Node-RADS score. At multivariable Cox regression analysis, Node-RADS was an independent variable associated with RCC prognosis after adjustment for confounders.

Conclusions: The MRI-based Node-RADS demonstrates notable performance in detecting RLNM and showed potential prognostic significance for RCCs.

Purpose: The purpose of this study was to evaluate an artificial intelligence (AI) software that automatically detects and quantifies breast arterial calcifications (BAC).

Materials and methods: Women who underwent both mammography and thoracic computed tomography (CT) from 2009 to 2018 were retrospectively included in this single-center study. Deep learning-based software was used to automatically detect and quantify BAC with a BAC AI score ranging from 0 to 10-points. Results were compared using Spearman correlation test with a previously described BAC manual score based on radiologists' visual quantification of BAC on the mammogram. Coronary artery calcification (CAC) score was manually scored using a 12-point scale on CT. The diagnostic performance of the marked BAC AI score (defined as BAC AI score ≥ 5) for the detection of marked CAC (CAC score ≥ 4) was analyzed in terms of sensitivity, specificity, accuracy and area under the receiver operating characteristic curve (AUC).

Results: A total of 502 women with a median age of 62 years (age range: 42-96 years) were included. The BAC AI score showed a very strong correlation with the BAC manual score (r = 0.83). Marked BAC AI score had 32.7 % sensitivity (37/113; 95 % confidence interval [CI]: 24.2-42.2), 96.1 % specificity (374/389; 95 % CI: 93.7-97.8), 71.2 % positive predictive value (37/52; 95 % CI: 56.9-82.9), 83.1 % negative predictive value (374/450; 95 % CI: 79.3-86.5), and 81.9 % accuracy (411/502; 95 % CI: 78.2-85.1) for the diagnosis of marked CAC. The AUC of the marked BAC AI score for the diagnosis of marked CAC was 0.64 (95 % CI: 0.60-0.69).

Conclusion: The automated BAC AI score shows a very strong correlation with manual BAC scoring in this external validation cohort. The automated BAC AI score may be a useful tool to promote the integration of BAC into mammography reports and to improve awareness of a woman's cardiovascular risk status.

Purpose: The purpose of this study was to evaluate the diagnostic performance of automated deep learning in the detection of coronary artery disease (CAD) on photon-counting coronary CT angiography (PC-CCTA).

Materials and methods: Consecutive patients with suspected CAD who underwent PC-CCTA between January 2022 and December 2023 were included in this retrospective, single-center study. Non-ultra-high resolution (UHR) PC-CCTA images were analyzed by artificial intelligence using two deep learning models (CorEx, Spimed-AI), and compared to human expert reader assessment using UHR PC-CCTA images. Diagnostic performance for global CAD assessment (at least one significant stenosis ≥ 50 %) was estimated at patient and vessel levels.

Results: A total of 140 patients (96 men, 44 women) with a median age of 60 years (first quartile, 51; third quartile, 68) were evaluated. Significant CAD on UHR PC-CCTA was present in 36/140 patients (25.7 %). The sensitivity, specificity, accuracy, positive predictive value), and negative predictive value of deep learning-based CAD were 97.2 %, 81.7 %, 85.7 %, 64.8 %, and 98.9 %, respectively, at the patient level and 96.6 %, 86.7 %, 88.1 %, 53.8 %, and 99.4 %, respectively, at the vessel level. The area under the receiver operating characteristic curve was 0.90 (95 % CI: 0.83-0.94) at the patient level and 0.92 (95 % CI: 0.89-0.94) at the vessel level.

Conclusion: Automated deep learning shows remarkable performance for the diagnosis of significant CAD on non-UHR PC-CCTA images. AI pre-reading may be of supportive value to the human reader in daily clinical practice to target and validate coronary artery stenosis using UHR PC-CCTA.

Purpose: The purpose of this study was to compare levels of pain and anxiety during percutaneous ultrasound-guided liver biopsy between patients receiving standard of care and those receiving standard of care plus the support of Ericksonian hypnosis.

Materials and methods: This prospective, single-center, single-blind, randomized controlled superiority trial included 70 participants. Participants were randomly assigned to either the standard of care group and received oral anxiolytic medications with reassuring conversational support, or to the experimental group, and received Ericksonian hypnosis (i.e., conversational hypnosis) in addition to standard of care. The primary outcome was the level of pain experienced during the biopsy, measured on a 10-point visual analog scale (0 indicating no pain to 10 indicating excruciating pain). Secondary outcomes included anxiety level during the biopsy, pain level within one hour of the biopsy measured using the same 10-point visual analog scale, amount of analgesic medication taken in the 24 h following the biopsy, and patient willingness to undergo another ultrasound-guided percutaneous liver biopsy in the future.

Results: Thirty-six participants were included in the standard of care group, and 34 were included in the experimental group. The mean score of pain experienced during the biopsy was lower in the experimental group (2.4 ± 1.9 [standard deviation (SD)]) compared to the standard of care group (4.4 ± 2.6 [SD]) (P = 0.001). The level of anxiety experienced during the biopsy was lower in the hypnosis group (2.1 ± 1.8 [SD]) compared to the standard of care group (4.8 ± 2.4 [SD]) (P < 0.001). No significant differences in other secondary outcomes were observed between the two groups.

Conclusion: The addition of Ericksonian hypnosis to standard of care reduces the pain experienced by patients during percutaneous ultrasound-guided percutaneous liver biopsy by comparison with standard of care alone.

Purpose: The purpose of this study was to compare lung image quality obtained with ultra-high resolution (UHR) spectral photon-counting CT (SPCCT) with that of dual-layer CT (DLCT), at standard and low dose levels using an image quality phantom and an anthropomorphic lung phantom.

Methods: An image quality phantom was scanned using a clinical SPCCT prototype and an 8 cm collimation DLCT from the same manufacturer at 10 mGy. Additional acquisitions at 6 mGy were performed with SPCCT only. Images were reconstructed with dedicated high-frequency reconstruction kernels, slice thickness between 0.58 and 0.67 mm, and matrix between 512² and 1024² mm, using a hybrid iterative algorithm at level 6. Noise power spectrum (NPS), task-based transfer function (TTF) for iodine and air inserts, and detectability index (d') were assessed for ground-glass and solid nodules of 2 mm to simulate highly detailed lung lesions. Subjective analysis of an anthropomorphic lung phantom was performed by two radiologists using a five-point quality score.

Results: At 10 mGy, noise magnitude was reduced by 29.1 % with SPCCT images compared to DLCT images for all parameters (27.1 ± 11.0 [standard deviation (SD)] HU vs. 38.2 ± 1.0 [SD] HU, respectively). At 6 mGy with SPCCT images, noise magnitude was reduced by 8.9 % compared to DLCT images at 10 mGy (34.8 ± 14.1 [SD] HU vs. 38.2 ± 1.0 [SD] HU, respectively). At 10 mGy and 6 mGy, average NPS spatial frequency (f_av) was greater for SPCCT images (0.75 ± 0.17 [SD] mm^-1) compared to DLCT images at 10 mGy (0.55 ± 0.04 [SD] mm^-1) while remaining constant from 10 to 6 mGy. At 10 mGy, TTF at 50 % (f₅₀) was greater for SPCCT images (0.92 ± 0.08 [SD] mm^-1) compared to DLCT images (0.67 ± 0.06 [SD] mm^-1) for both inserts. At 6 mGy, f₅₀ decreased by 1.1 % for SPCCT images, while remaining greater compared to DLCT images at 10 mGy (0.91 ± 0.06 [SD] mm^-1 vs. 0.67 ± 0.06 [SD] mm^-1, respectively). At both dose levels, d' were greater for SPCCT images compared to DLCT for all clinical tasks. Subjective analysis performed by two radiologists revealed a greater median image quality for SPCCT (5; Q1, 4; Q3, 5) compared to DLCT images (3; Q1, 3; Q3, 3).

Conclusion: UHR SPCCT outperforms DLCT in terms of image quality for lung imaging. In addition, UHR SPCCT contributes to a 40 % reduction in radiation dose compared to DLCT.