Quantitative Imaging in Medicine and Surgery最新文献

Background: This was a retrospective, large-sample, case-control study assessing the complications associated with synchronous microwave ablation (MWA) and biopsy for pulmonary sub-solid nodules or ground-glass nodules (GGNs) versus MWA alone. We aimed to verify the safety of synchronous MWA and biopsy for treating GGNs.

Methods: From May 2020 to December 2021, 326 patients with GGNs were enrolled. Among them, 164 patients underwent MWA alone (group A) and 162 patients underwent synchronous MWA and biopsy (group B). We assessed the complications, technical success, and positivity rate of the biopsy.

Results: The major complications were similar between the two groups, and included pneumothorax (group A vs. group B, 19.5% vs. 13.6%; P=0.150), hemothorax (0.6% vs. 1.2%; P=1.000), pleural effusion (1.2% vs. 0.6%; P=1.000), and pulmonary infection (4.9% vs. 6.2%; P=0.609). No massive hemoptysis, bronchopleural fistula, or air embolism developed. Minor complications including intrapulmonary hemorrhage (group A vs. group B, 28.7% vs. 62.3%, P<0.001), mild pneumothorax (20.7% vs. 29.6%, P=0.587), mild ipsilateral pleural effusion (30.5% vs. 27.8%, P=0.590), mild bilateral pleural effusion (16.5% vs. 22.2%, P=0.188), and subcutaneous emphysema (4.3% vs. 5.6%, P=0.498) were observed. The side effects, including pain, cough, post-ablation syndrome, and post-ablation chronic pain syndrome, were similar between the two groups. The positive diagnosis rate of biopsy in group B was 88.3%.

Conclusions: Compared with MWA alone, synchronous MWA and biopsy did not increase the risk of major complications. Although some minor complications developed, synchronous MWA and biopsy is safe for treating pulmonary GGNs.

Background: Lung adenocarcinoma associated with cystic airspace (LACA) was once considered an uncommon manifestation of lung adenocarcinoma (LUAD), and understandings of it are limited; however, it is being observed more frequently in clinical practice. This study sought to assess the prevalence of LACA, and compare the high-resolution computed tomography (HRCT) features of LACA in patients with varying degrees of invasiveness.

Methods: This study retrospectively reviewed the HRCT scans of 1,525 patients with LUAD ≤3 cm in diameter at the Shanghai Chest Hospital between January 2016 and May 2016. Each nodule was examined to detect the presence of cystic airspace. Additionally, we analyzed the qualitative HRCT findings of the cystic airspaces, including the pattern, number, wall component density, distribution, inner surface, mural nodules, septa, and vessels passing through the cystic airspace using the Pearson χ² test or Fisher's exact test as appropriate. We also analyzed the quantitative measurements, such as the cystic airspace diameter, wall thickness, and thin-wall proportion, using a one-way analysis of variance or the Kruskal-Wallis rank-sum test as appropriate.

Results: LACAs were observed on HRCT in 11.5% (176/1,525) of the patients, of whom 7.1% (36/505) had pure ground-glass nodules, 13.5% (112/830) had mixed ground-glass nodules, and 14.7% (28/190) had solid nodules (P=0.001). The surgical procedures for LACAs varied (P=0.012). The incidence of LACAs increased as nodule diameter and invasiveness increased (both P<0.001). Statistically significant differences were observed in the wall component density, distribution, septa, vessels passing through the cystic airspace, cystic airspace diameter, wall thickness, and thin-wall proportion among the preinvasive lesion (PL), minimally invasive adenocarcinoma (MIA), and invasive adenocarcinoma (IAC) groups (P<0.001, P=0.024, P=0.001, P=0.025, P=0.001, P<0.001, and P<0.001, respectively). Wall component density increased as invasiveness increased (P<0.001). Unlike those in the MIAs and IACs, cystic airspaces in PLs typically lacked septa (P=0.001, and P<0.001, respectively). The IACs had larger cystic airspace diameters than the PLs (6.5 vs. 3.7 mm) (P<0.001). The IACs also had thicker wall thickness (11.8 vs. 6.8 mm, 11.8 vs. 8.3 mm) (P<0.001, and P<0.001, respectively) and smaller thin-wall proportions (181.5° vs. 264.8°, 181.5° vs. 223.8°) (P<0.001, and P=0.039, respectively) than the PLs and MIAs.

Conclusions: The prevalence and characteristics of cystic airspaces on HRCT can be used to predict invasiveness in patients with LUADs ≤3 cm in diameter.

Background: Breast cancer is one of the most common malignancies in women worldwide, and early and accurate diagnosis is crucial for improving treatment outcomes. Conventional ultrasound (CUS) is a widely used screening method for breast cancer; however, the subjective nature of interpreting the results can lead to diagnostic errors. The current study sought to estimate the effectiveness of using a GoogLeNet deep-learning convolutional neural network (CNN) model to identify benign and malignant breast masses based on CUS.

Methods: A literature search was conducted of the Embase, PubMed, Web of Science, Wanfang, China National Knowledge Infrastructure (CNKI), and other databases to retrieve studies related to GoogLeNet deep-learning CUS-based models published before July 15, 2023. The diagnostic performance of the GoogLeNet models was evaluated using several metrics, including pooled sensitivity (PSEN), pooled specificity (PSPE), the positive likelihood ratio (PLR), the negative likelihood ratio (NLR), the diagnostic odds ratio (DOR), and the area under the curve (AUC). The quality of the included studies was evaluated using the Quality Assessment of Diagnostic Accuracy Studies Scale (QUADAS). The eligibility of the included literature were independently searched and assessed by two authors.

Results: All of the 12 studies that used pathological findings as the gold standard were included in the meta-analysis. The overall average estimation of sensitivity and specificity was 0.85 [95% confidence interval (CI): 0.80-0.89] and 0.86 (95% CI: 0.78-0.92), respectively. The PLR and NLR were 6.2 (95% CI: 3.9-9.9) and 0.17 (95% CI: 0.12-0.23), respectively. The DOR was 37.06 (95% CI: 20.78-66.10). The AUC was 0.92 (95% CI: 0.89-0.94). No obvious publication bias was detected.

Conclusions: The GoogLeNet deep-learning model, which uses a CNN, achieved good diagnostic results in distinguishing between benign and malignant breast masses in CUS-based images.

Background: Differential diagnosis in radiology relies on the accurate identification of imaging patterns. The use of large language models (LLMs) in radiology holds promise, with many potential applications that may enhance the efficiency of radiologists' workflow. The study aimed to evaluate the efficacy of generative pre-trained transformer (GPT)-4, a LLM, in providing differential diagnoses in neuroradiology, comparing its performance with board-certified neuroradiologists.

Methods: Sixty neuroradiology reports with variable diagnoses were inserted into GPT-4, which was tasked with generating a top-3 differential diagnosis for each case. The results were compared to the true diagnoses and to the differential diagnoses provided by three blinded neuroradiologists. Diagnostic accuracy and agreement between readers were assessed.

Results: Of the 60 patients (mean age 47.8 years, 65% female), GPT-4 correctly included the diagnoses in its differentials in 61.7% (37/60) of cases, while the neuroradiologists' accuracy ranged from 63.3% (38/60) to 73.3% (44/60). Agreement between GPT-4 and the neuroradiologists, and among the neuroradiologists was fair to moderate [Cohen's kappa (kw) 0.34-0.44 and kw 0.39-0.54, respectively].

Conclusions: GPT-4 shows potential as a support tool for differential diagnosis in neuroradiology, though it was outperformed by human experts. Radiologists should remain mindful to the limitations of LLMs, while harboring their potential to enhance educational and clinical work.

Background: Metal structures are a source of artifacts that significantly complicate the interpretation of magnetic resonance imaging (MRI). The use of prostate MRI as a preliminary test in men with a suspicion on prostate cancer leads to an increased use of the test. The aim of this study was to solve a clinically significant problem: to ensure the reduction of artifacts from metal hip implants during prostate MRI. Another goal was to evaluate the impact of artifact reduction methods on quantitative measurements.

Methods: The prostate gland (PG) phantom model was a cylinder filled with an aqueous solution of polyvinylpyrrolidone at the concentrations of 40%, 30%, and 20% [central zone (CZ), peripheral zone (PZ), and "lesion", respectively]. Phantom MRI study was conducted on Philips Ingenia 1.5T and Philips Ingenia 3T scanners.

Results: For 1.5 T, the reduction in the influence of artifacts inside region of interest (ROI) was observed, expressed in a decrease in the average apparent diffusion coefficient (ADC) (CZ, PZ, "lesion") for the manual artifact reduction (MAR) and ZOOM (title of software artifact reduction) techniques compared to the standard method. For 3T this effect was not detected. The same ADC results were obtained for Standard and MAR techniques, and increased ADC values for ZOOM. Despite the fact that the spread of ADC values on 3.0T scanners was minimal, there was a significant deviation of ADC values from the reference ones (up to 30.4%). Therefore, it is necessary to use a correction coefficient in the ADC calculation for the 3.0 T device. In the presented clinical case, high-quality tomograms were obtained without any artifacts, despite the presence of two hip replacement devices in the scanning area.

Conclusions: The accurate prostate MRI in the presence of implants is essential for an accurate diagnosis. This approach allows to reduce artifacts from hip implants, to visualize PG and periprostatic tissue in the best way, and to detect malignant and benign changes.