Multimodal integration of radiology and pathology signatures for distinguishing between aldosterone-producing adenomas and nonfunctional adrenal adenomas.

OBJECTIVE: To develop and validate a nomogram combining radiomics and pathology features to distinguish between aldosterone-producing adenomas (APAs) and nonfunctional adrenal adenomas (NF-AAs). METHODS: Consecutive patients diagnosed with adrenal adenomas via computed tomography (CT) or pathologic analysis between January 2011 and November 2022 were eligible for inclusion in this retrospective study. CT images and hematoxylin & eosin-stained slides were used for annotation and feature extraction. The selected radiomics and pathology features were used to develop a risk model using various machine learning models, and the area under the receiver operating characteristic curve (AUC) was determined to evaluate diagnostic performance. The predicted results from radiomics and pathology features were combined and visualized using a nomogram. RESULTS: A total of 211 patients (APAs, n = 59; NF-AAs, n = 152) were included in this study, with patients randomly divided into either the training set or the testing set at a ratio of 8:2. The ExtraTrees model yielded a sensitivity of 0.818, a specificity of 0.733, and an accuracy of 0.756 (AUC = 0.817; 95% confidence interval [CI]: 0.675-0.958) in the radiomics testing set and a sensitivity of 0.999, a specificity of 0.842, and an accuracy of 0.867 (AUC = 0.905, 95% CI: 0.792-1.000) in the pathology testing set. A nomogram combining radiomics and pathology features demonstrated a strong performance (AUC = 0.912; 95% CI: 0.807-1.000). CONCLUSION: A nomogram combining radiomics and pathology features demonstrated strong predictive accuracy and discrimination capability. This model may help clinicians to distinguish between APAs and NF-AAs.

Distinguishing between aldosterone-producing adenomas and non-functional adrenocortical adenomas using the YOLOv5 network.

BACKGROUND: You Only Look Once version 5 (YOLOv5), a one-stage deep-learning (DL) algorithm for object detection and classification, offers high speed and accuracy for identifying targets. PURPOSE: To investigate the feasibility of using the YOLOv5 algorithm to non-invasively distinguish between aldosterone-producing adenomas (APAs) and non-functional adrenocortical adenomas (NF-ACAs) on computed tomography (CT) images. MATERIAL AND METHODS: A total of 235 patients who were diagnosed with ACAs between January 2011 and July 2022 were included in this study. Of the 215 patients, 81 (37.7%) had APAs and 134 (62.3%) had NF-ACAs' they were randomly divided into either the training set or the validation set at a ratio of 9:1. Another 20 patients, including 8 (40.0%) with APA and 12 (60.0%) with NF-ACA, were collected for the testing set. Five submodels (YOLOv5n, YOLOv5s, YOLOv5m, YOLOv5l, and YOLOv5x) of YOLOv5 were trained and evaluated on the datasets. RESULTS: In the testing set, the mAP_0.5 value for YOLOv5x (0.988) was higher than the values for YOLOv5n (0.969), YOLOv5s (0.965), YOLOv5m (0.974), and YOLOv5l (0.983). The mAP_0.5:0.95 value for YOLOv5x (0.711) was also higher than the values for YOLOv5n (0.587), YOLOv5s (0.674), YOLOv5m (0.671), and YOLOv5l (0.698) in the testing set. The inference speed of YOLOv5n was 2.4 ms in the testing set, which was the fastest among the five submodels. CONCLUSION: The YOLOv5 algorithm can accurately and efficiently distinguish between APAs and NF-ACAs on CT images, especially YOLOv5x has the best identification performance.

Deep learning algorithm applied to plain CT images to identify superior mesenteric artery abnormalities.

OBJECTIVES: Atypical presentations, lack of biomarkers, and low sensitivity of plain CT can delay the diagnosis of superior mesenteric artery (SMA) abnormalities, resulting in poor clinical outcomes. Our study aims to develop a deep learning (DL) model for detecting SMA abnormalities in plain CT and evaluate its performance in comparison with a clinical model and radiologist assessment. MATERIALS AND METHODS: A total of 1048 patients comprised the internal (474 patients with SMA abnormalities, 474 controls) and external testing (50 patients with SMA abnormalities, 50 controls) cohorts. The internal cohort was divided into the training cohort (n = 776), validation cohort (n = 86), and internal testing cohort (n = 86). A total of 5 You Only Look Once version 8 (YOLOv8)-based DL submodels were developed, and the performance of the optimal submodel was compared with that of a clinical model and of experienced radiologists. RESULTS: Of the submodels, YOLOv8x had the best performance. The area under the curve (AUC) of the YOLOv8x submodel was higher than that of the clinical model (internal test set: 0.990 vs 0.878, P =.002; external test set: 0.967 vs 0.912, P =.140) and that of all radiologists (P <.001). The YOLOv8x submodel, when compared with radiologist assessment, demonstrated higher sensitivity (internal test set: 100.0 % vs 70.7 %, P =.002; external test set: 96.0 % vs 68.8 %, P <.001) and specificity (internal test set: 90.7 % vs 66.0 %, P =.025; external test set: = 88.0 % vs 66.0 %, P <.001). CONCLUSION: Using plain CT images, YOLOv8x was able to efficiently identify cases of SMA abnormalities. This could potentially improve early diagnosis accuracy and thus improve clinical outcomes.

The pathogenesis of superior mesenteric artery dissection: An in-depth study based on fluid-structure interaction and histology analysis.

OBJECTIVE: To explore the role of hemodynamic factors in the occurrence of superior mesenteric artery (SMA) dissection (SMAD) using a fluid-structure interaction (FSI) simulation method, and to identify histopathologic changes occurring in the wall of the SMA. METHODS: A total of 122 consecutive patients diagnosed with SMAD and 122 controls were included in this study. Hemodynamic factors were calculated using a FSI simulation method. Additionally, SMA specimens obtained from 12 cadavers were stained for histological quantitative analysis. RESULTS: The mean aortomesenteric angle (59.7° ± 21.4° vs 48.2° ± 16.8°; p < .001) and SMA maximum curvature (0.084 ± 0.078 mm-1 vs 0.032 ± 0.023 mm-1; p < .001) were higher in SMAD patients than the controls. Larger aortomesenteric angles and SMA curvatures were associated with higher and more concentrated wall shear stress at anterior wall of the SMA curve segment, co-located with the dissection origins. The mean thickness of media (325.18 ± 44.87 µm vs 556.92 ± 138.32 µm; p = .003) was thinner in the anterior wall of the SMA curve than in the posterior wall. The area fractions of elastin (17.96% ± 3.36% vs 27.06% ± 4.18%; p = .002) and collagen (45.43% ± 6.89% vs 55.57% ± 7.57%; p = .036) were lower in anterior wall of the SMA curve than in posterior wall. CONCLUSION: Increased aortomesenteric angle and SMA curvature are risk factors for SMAD. Both of these factors can cause local hemodynamic abnormalities, which can lead to histopathologic changes in anterior wall of SMA.

The efficacy of a paeoniflorin-sodium alginate-gelatin skin scaffold for the treatment of diabetic wound: An in vivo study in a rat model.

OBJECTIVE: To investigate the efficacy of a paeoniflorin-sodium alginate (SA)-gelatin skin scaffold for treating diabetic wound in a rat model. METHODS: Bioinks were prepared using various percentages of paeoniflorin in the total weight of a solution containing SA and gelatin. Skin scaffolds containing 0%, 1%, 3%, 5%, and 10% paeoniflorin were printed using 3D bioprinting technology, and scaffold microstructure was observed with scanning electron microscopy. Skin scaffolds were then used in rats with diabetic wounds. H&E staining, Masson staining, and immunohistochemical staining for IL-1ß and CD31 were performed on days 7 and 14. RESULTS: All skin scaffolds had a mesh-like structure with uniform pore distribution. Wounds healed well in each group, with the 1% and 3% groups demonstrating the most complete healing. H&E staining showed that skin accessory organs had appeared in each group. On day 7, collagen deposition in the 3% group was higher than in the other groups (P＜0.05), and IL-1ß infiltration was lower in the 10% group than in the 3% group (P = 0.002). On day 14, IL-1ß infiltration was not significantly different between the 10% and 3% groups (P = 0.078). The CD31 level was higher in the 3% group than in the other groups on days 7 and 14 (P＜0.05). CONCLUSION: A 3% paeoniflorin-SA-gelatin skin scaffold promoted the healing of diabetic wounds in rats. This scaffold promoted collagen deposition and microvascular regeneration and demonstrated anti-inflammatory properties, suggesting that this scaffold type could be used to treat diabetic wounds.