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.

Discrimination of benign and malignant breast lesions on dynamic contrast-enhanced magnetic resonance imaging using deep learning.

PURPOSE: To evaluate the capability of deep transfer learning (DTL) and fine-tuning methods in differentiating malignant from benign lesions in breast dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). METHODS: The diagnostic efficiencies of the VGG19, ResNet50, and DenseNet201 models were tested under the same dataset. The model with the highest performance was selected and modified utilizing three fine-tuning strategies (S1-3). Fifty additional lesions were selected to form the validation set to verify the generalization abilities of these models. The accuracy (Ac) of the different models in the training and test sets, as well as the precision (Pr), recall rate (Rc), F1 score (), and area under the receiver operating characteristic curve (AUC), were primary performance indicators. Finally, the kappa test was used to compare the degree of agreement between the DTL models and pathological diagnosis in differentiating malignant from benign breast lesions. RESULTS: The Pr, Rc, f1, and AUC of VGG19 (86.0%, 0.81, 0.81, and 0.81, respectively) were higher than those of DenseNet201 (70.0%, 0.61, 0.63, and 0.61, respectively) and ResNet50 (61.0%, 0.59, 0.59, and 0.59). After fine-tuning, the Pr, Rc, f1, and AUC of S1 (87.0%, 0.86, 0.86, and 0.86, respectively) were higher than those of VGG19. Notably, the degree of agreement between S1 and pathological diagnosis in differentiating malignant from benign breast lesions was 0.720 (κ = 0.720), which was higher than that of DenseNet201 (κ = 0.440), VGG19 (κ = 0.640), and ResNet50 (κ = 0.280). CONCLUSION: The VGG19 model is an effective method for identifying benign and malignant breast lesions on DCE-MRI, and its performance can be further improved via fine-tuning. Overall, our findings insinuate that this technique holds potential clinical application value.

Development and Validation of a Radiomics Nomogram for Liver Metastases Originating from Gastric and Colorectal Cancer.

The origin of metastatic liver tumours (arising from gastric or colorectal sources) is closely linked to treatment choices and survival prospects. However, in some instances, the primary lesion remains elusive even after an exhaustive diagnostic investigation. Consequently, we have devised and validated a radiomics nomogram for ascertaining the primary origin of liver metastases stemming from gastric cancer (GCLMs) and colorectal cancer (CCLMs). This retrospective study encompassed patients diagnosed with either GCLMs or CCLMs, comprising a total of 277 GCLM cases and 278 CCLM cases. Radiomic characteristics were derived from venous phase computed tomography (CT) scans, and a radiomics signature (RS) was computed. Multivariable regression analysis demonstrated that gender (OR = 3.457; 95% CI: 2.102-5.684; p < 0.001), haemoglobin levels (OR = 0.976; 95% CI: 0.967-0.986; p < 0.001), carcinoembryonic antigen (CEA) levels (OR = 0.500; 95% CI: 0.307-0.814; p = 0.005), and RS (OR = 2.147; 95% CI: 1.127-4.091; p = 0.020) exhibited independent associations with GCLMs as compared to CCLMs. The nomogram, combining RS with clinical variables, demonstrated strong discriminatory power in both the training (AUC = 0.71) and validation (AUC = 0.78) cohorts. The calibration curve, decision curve analysis, and clinical impact curves revealed the clinical utility of this nomogram and substantiated its enhanced diagnostic performance.

A nomogram based on CT texture features to predict the response of patients with advanced pancreatic cancer treated with chemotherapy.

OBJECTIVE: This study aimed to evaluate the predictive value of computed tomography (CT) texture features in the treatment response of patients with advanced pancreatic cancer (APC) receiving palliative chemotherapy. METHODS: This study enrolled 84 patients with APC treated with first-line chemotherapy and conducted texture analysis on primary pancreatic tumors. 59 patients and 25 were randomly assigned to the training and validation cohorts at a ratio of 7:3. The treatment response to chemotherapy was evaluated according to the Response Evaluation Criteria in Solid Tumors (RECIST1.1). The patients were divided into progressive and non-progressive groups. The least absolute shrinkage selection operator (LASSO) was applied for feature selection in the training cohort and a radiomics signature (RS) was calculated. A nomogram was developed based on a multivariate logistic regression model incorporating the RS and carbohydrate antigen 19-9 (CA19-9), and was internally validated using the C-index and calibration plot. We performed the decision curve analysis (DCA) and clinical impact curve analysis to reflect the clinical utility of the nomogram. The nomogram was further externally confirmed in the validation cohort. RESULTS: The multivariate logistic regression analysis indicated that the RS and CA19-9 were independent predictors (P < 0.05), and a trend was found for chemotherapy between progressive and non-progressive groups. The nomogram incorporating RS, CA19-9 and chemotherapy showed favorable discriminative ability in the training (C-index = 0.802) and validation (C-index = 0.920) cohorts. The nomogram demonstrated favorable clinical utility. CONCLUSION: The RS of significant texture features was significantly associated with the early treatment effect of patients with APC treated with chemotherapy. Based on the RS, CA19-9 and chemotherapy, the nomogram provided a promising way to predict chemotherapeutic effects for APC patients.

Reducing the number of unnecessary biopsies for mammographic BI-RADS 4 lesions through a deep transfer learning method.

BACKGROUND: In clinical practice, reducing unnecessary biopsies for mammographic BI-RADS 4 lesions is crucial. The objective of this study was to explore the potential value of deep transfer learning (DTL) based on the different fine-tuning strategies for Inception V3 to reduce the number of unnecessary biopsies that residents need to perform for mammographic BI-RADS 4 lesions. METHODS: A total of 1980 patients with breast lesions were included, including 1473 benign lesions (185 women with bilateral breast lesions), and 692 malignant lesions collected and confirmed by clinical pathology or biopsy. The breast mammography images were randomly divided into three subsets, a training set, testing set, and validation set 1, at a ratio of 8:1:1. We constructed a DTL model for the classification of breast lesions based on Inception V3 and attempted to improve its performance with 11 fine-tuning strategies. The mammography images from 362 patients with pathologically confirmed BI-RADS 4 breast lesions were employed as validation set 2. Two images from each lesion were tested, and trials were categorized as correct if the judgement (≥ 1 image) was correct. We used precision (Pr), recall rate (Rc), F1 score (F1), and the area under the receiver operating characteristic curve (AUROC) as the performance metrics of the DTL model with validation set 2. RESULTS: The S5 model achieved the best fit for the data. The Pr, Rc, F1 and AUROC of S5 were 0.90, 0.90, 0.90, and 0.86, respectively, for Category 4. The proportions of lesions downgraded by S5 were 90.73%, 84.76%, and 80.19% for categories 4 A, 4B, and 4 C, respectively. The overall proportion of BI-RADS 4 lesions downgraded by S5 was 85.91%. There was no significant difference between the classification results of the S5 model and pathological diagnosis (P = 0.110). CONCLUSION: The S5 model we proposed here can be used as an effective approach for reducing the number of unnecessary biopsies that residents need to conduct for mammographic BI-RADS 4 lesions and may have other important clinical uses.

An allosteric modulator of the adenosine A1 receptor potentiates the antilipolytic effect in rat adipose tissue.

The adenosine A1 receptor plays important roles in tuning free fatty acid (FFA) levels and represents an attractive target for metabolic disorders. Though remarkable progress has been achieved in the exploitation of effective (orthosteric) A1 receptor agonists in modulating aberrant FFA levels, the effect of A1 receptor allosteric modulation on lipid homeostasis is less investigated. Herein we sought to explore the effect of an allosteric modulator on the action of an A1 receptor orthosteric agonist in regulating the lipolytic process in vitro and in vivo. We examined the binding kinetics of a selective A1 receptor agonist 2-chloro-N6-cyclopentyladenosine (CCPA) in the absence or presence of an allosteric modulator (2-amino-4,5-dimethyl-3-thienyl)-[3-(trifluoromethyl)-phenyl]methanone (PD81,723) on rat adipocyte membranes. We also examined the allosteric effects of PD81,723 on mediating the CCPA-induced inhibition of cAMP accumulation, HSL (hormone-sensitive lipase) phosphorylation and FFA production in in vitro and in vivo models. Our results demonstrated that PD81,723 slowed down the dissociation of CCPA from the A1 receptor, which, consequently, potentiated the antilipolytic action of CCPA through downregulating the cAMP/HSL pathway. Our study exemplified the application of A1 receptor allosteric modulators as an alternative for metabolic disease treatments.

Classification of Breast Lesions on DCE-MRI Data Using a Fine-Tuned MobileNet.

It is crucial to diagnose breast cancer early and accurately to optimize treatment. Presently, most deep learning models used for breast cancer detection cannot be used on mobile phones or low-power devices. This study intended to evaluate the capabilities of MobileNetV1 and MobileNetV2 and their fine-tuned models to differentiate malignant lesions from benign lesions in breast dynamic contrast-enhanced magnetic resonance images (DCE-MRI).

Gabapentin alleviates myocardial ischemia-reperfusion injury by increasing the protein expression of GABAARδ.

Gabapentin is a commonly used analgesic in the clinic to reduce opioid consumption. It is well known that gabapentin can reduce cerebral ischemia-reperfusion injury (IRI). However, it remains unclear whether gabapentin can reduce myocardial IRI. Before the performance of myocardial ischemia and reperfusion (I/R), rats received gabapentin without or with an intravenous injection of PI3K inhibitor (LY294002), or an intraspinal injection of lentivirus-mediated GABAARδ-shRNA. The myocardial IRI were evaluated by calculating the infarction area, arrhythmia score and myocardial apoptosis. The activity of PI3K/Akt and the expression of GABAARδ were quantified by western blotting. The effect of gabapentin on myocardial I/R was further demonstrated in vitro by establishing oxygen-glucose deprivation and reoxygenation in cardiomyocytes. After I/R in vivo, there were significant increases in infarction area, arrhythmia and Bax protein expression in the myocardium, as well as a decrease of GABAARδ in the spinal cord. Meanwhile, I/R also decreased the protein expression of PI3K/Akt and Bcl-2. Gabapentin pretreatment successfully attenuated IRI including reducing the myocardial infarction area and apoptosis. This effect was abolished by both the systemic inhibition of PI3K/Akt and the intraspinal suppression of GABAARδ. However, gabapentin pretreatment failed to prevent cellular injury induced by OGD/R in cardiomyocytes. Therefore, the myocardial protective effect of gabapentin may be attributed to activating PI3K/Akt in the myocardium and upregulating GABAARδ in the spinal cord. Gabapentin achieved a potent protective effect on the myocardium during the course of routine clinical treatment.

Detection and classification of breast lesions with You Only Look Once version 5.

Aim: To explore the ability of You Only Look Once version 5 (YOLOv5) to detect and classify breast lesions on dynamic contrast-enhanced MRI. Methods: Four YOLOv5 submodels were examined. A total of 2124 and 2226 images of benign and malignant lesions were obtained, respectively. Precision, recall rate and mean average precision were used to evaluate model performance. Results: The precision (0.916) and mean average precision _0.5 (0.894) of YOLOv5s were higher than those of YOLOv5m (0.832, 0.794), YOLOv5l (0.843, 0.803) and YOLOv5x (0.854, 0.821). In the validation set, YOLOv5s required 1.1 ms to detect lesions per image. Conclusion: YOLOv5s was the fastest and had the highest precision among the four YOLOv5 submodels for the detection and classification of breast lesions on dynamic contrast-enhanced MRI. It has a greater clinical application value.

You Only Look Once version 5 (YOLOv5) is the latest YOLO series, which may be a useful tool for detecting and classifying breast lesions on dynamic contrast-enhanced MRI (DCE-MRI) and help clinicians make a rapid, accurate diagnosis and provide treatment. Data were retrospectively collected from a single-center study. The performances of the four submodels (YOLOv5s, YOLOv5m, YOLOv5l and YOLOv5x) were compared. The diagnostic performances of YOLOv5s were comparable with some convolutional neural network models for breast lesion identification in breast ultrasonography and mammography. This study may provide novel insights into the detection and classification of breast lesions on DCE-MRI. Thus, a sufficiently large series of data and high-quality DCE-MRIs are warranted. Owing to its applications in artificial intelligence-assisted imaging diagnosis, this method has promising prospects.

Differentiation of breast lesions on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) using deep transfer learning based on DenseNet201.

In order to achieve better performance, artificial intelligence is used in breast cancer diagnosis. In this study, we evaluated the efficacy of different fine-tuning strategies of deep transfer learning (DTL) based on the DenseNet201 model to differentiate malignant from benign lesions on breast dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). We collected 4260 images of benign lesions and 4140 images of malignant lesions of the breast pertaining to pathologically confirmed cases. The benign and malignant groups was randomly divided into a training set and a testing set at a ratio of 9:1. A DTL model based on the DenseNet201 model was established, and the effectiveness of 4 fine-tuning strategies (S0: strategy 0, S1: strategy; S2: strategy; and S3: strategy) was compared. Additionally, DCE-MRI images of 48 breast lesions were selected to verify the robustness of the model. Ten images were obtained for each lesion. The classification was considered correct if more than 5 images were correctly classified. The metrics for model performance evaluation included accuracy (Ac) in the training and testing sets, precision (Pr), recall rate (Rc), f1 score (f1), and area under the receiver operating characteristic curve (AUROC) in the validation set. The Ac of the 4 fine-tuning strategies reached 100.00% in the training set. The S2 strategy exhibited good convergence in the testing set. The Ac of S2 was 98.01% in the testing set, which was higher than those of S0 (93.10%), S1 (90.45%), and S3 (93.90%). The average classification Pr, Rc, f1, and AUROC of S2 in the validation set were (89.00%, 80.00%, 0.81, and 0.79, respectively) higher than those of S0 (76.00%, 67.00%, 0.69, and 0.65, respectively), S1 (60.00%, 60.00%, 0.60, 0.66, and respectively), and S3 (77.00%, 73.00%, 0.74, 0.72, respectively). The degree of coincidence between S2 and the histopathological method for differentiating between benign and malignant breast lesions was high (κ = 0.749). The S2 strategy can improve the robustness of the DenseNet201 model in relatively small breast DCE-MRI datasets, and this is a reliable method to increase the Ac of discriminating benign from malignant breast lesions on DCE-MRI.

Intravoxel Incoherent Motion and Dynamic Contrast-Enhanced Magnetic Resonance Imaging to Early Detect Tissue Injury and Microcirculation Alteration in Hepatic Injury Induced by Intestinal Ischemia-Reperfusion in a Rat Model.

BACKGROUND: Intravoxel incoherent motion (IVIM) can provide quantitative information about water diffusion and perfusion that can be used to evaluate hepatic injury, but it has not been studied in hepatic injury induced by intestinal ischemia-reperfusion (IIR). Dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) can provide perfusion data, but it is unclear whether it can provide useful information for assessing hepatic injury induced by IIR. PURPOSE: To examine whether IVIM and DCE-MRI can detect early IIR-induced hepatic changes, and to evaluate the relationship between IVIM and DCE-derived parameters and biochemical indicators and histological scores. STUDY TYPE: Prospective pre-clinical study. POPULATION: Forty-two male Sprague-Dawley rats. FIELD STRENGTH/SEQUENCE: IVIM-diffusion-weighted imaging (DWI) using diffusion-weighted echo-planar imaging sequence and DCE-MRI using fast spoiled gradient recalled-based sequence at 3.0 T. ASSESSMENT: All rats were randomly divided into the control group (Sham), the simple ischemia group, the ischemia-reperfusion (IR) group (IR1h, IR2h, IR3h, and IR4h) in a model of secondary hepatic injury caused by IIR, and IIR was induced by clamping the superior mesenteric artery for 60 minutes and then removing the vascular clamp. Advanced Workstation (AW) 4.6 was used to calculate the imaging parameters (apparent diffusion coefficient [ADC], true diffusion coefficient [D], perfusion-related diffusion [D* ] and volume fraction [f]) of IVIM. OmniKinetics (OK) software was used to calculate the DCE imaging parameters (Ktrans , Kep , and Ve ). Alanine aminotransferase (ALT) and aspartate aminotransferase (AST) were analyzed with an automatic biochemical analyzer. Superoxide dismutase (SOD) activity was assessed using the nitro-blue tetrazolium method. Malondialdehyde (MDA) was determined by thiobarbituric acid colorimetry. Histopathology was performed with hematoxylin and eosin staining. STATISTICAL TESTS: One-way analysis of variance (ANOVA) and Bonferroni post-hoc tests were used to analyze the imaging parameters and biochemical indicators among the different groups. Pearson correlation analysis was applied to determine the correlation between imaging parameters and biochemical indicators or histological score. RESULTS: ALT and MDA reached peak levels at IR4h, while SOD reached the minimum level at IR4h (all P < 0.05). ADC, D, D* , and f gradually decreased as reperfusion continued, and Ktrans and Ve gradually increased (all P < 0.05). The degrees of change for f and Ve were greater than those of other imaging parameters at IR1h (all P < 0.05). All groups showed good correlation between imaging parameters and SOD and MDA (r[ADC] = 0.615, -0.666, r[D] = 0.493, -0.612, r[D* ] = 0.607, -0.647, r[f] = 0.637, -0.682, r[Ktrans ] = -0.522, 0.500, r[Ve ] = -0.590, 0.665, respectively; all P < 0.05). However, the IR groups showed poor or no correlation between the imaging parameters and SOD and MDA (P [Ktrans and MDA] = 0.050, P [D and SOD] = 0.125, P [the remaining imaging parameters] < 0.05). All groups showed a positive correlation between histological score and Ktrans and Ve (r = 0.775, 0.874, all P < 0.05), and a negative correlation between histological score and ADC, D, f, and D* (r = -0.739, -0.821, -0.868, -0.841, respectively; all P < 0.05). For the IR groups, there was a positive correlation between histological score and Ktrans and Ve (r = 0.747, 0.802, all P < 0.05), and a negative correlation between histological score and ADC, D, f, and D* (r = -0.567, -0.712, -0.715, -0.779, respectively; all P < 0.05). DATA CONCLUSION: The combined application of IVIM and DCE-MRI has the potential to be used as an imaging tool for monitoring IIR-induced hepatic histopathology. LEVEL OF EVIDENCE: 1 TECHNICAL EFFICACY STAGE: 2.

Mechanisms by which fibroblast growth factor 20 improves motor performance in a mouse model of Parkinson's disease.

Genome-wide studies have reported that Parkinson's disease is associated with abnormal expression of various growth factors. In this study, male C57BL/6 mice aged 10 weeks were used to establish Parkinson's disease models using an intraperitoneal injection of 60 mg/kg 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine. 28 days later, 10 or 100 ng fibroblast growth factor 20 was injected intracerebroventricularly. The electrophysiological changes in the mouse hippocampus were recorded using a full-cell patch clamp. Expression of Kv4.2 in the substantia nigra was analyzed using a western blot assay. Serum malondialdehyde levels were analyzed by enzyme-linked immunosorbent assay. The motor coordination of mice was evaluated using the rotarod test. The results showed that fibroblast growth factor 20 decreased A-type potassium current in neurons of the substantia nigra, increased long-term potentiation amplitude in the hippocampus, and downregulated Kv4.2 expression. A high dose of fibroblast growth factor 20 reduced serum malondialdehyde levels and enhanced the motor coordination of mice. These findings confirm that fibroblast growth factor 20 has a therapeutic effect on the toxicity induced by 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine, and its mechanism of action is associated with the inhibition of A-type K+ currents and Kv4.2 expression. All animal procedures were approved by the Animal Care and Use Committee of Qilu Hospital of Shandong University, China in 2017 (approval No. KYLL-2017-0012).

Long non-coding RNA ENST00462717 suppresses the proliferation, survival, and migration by inhibiting MDM2/MAPK pathway in glioma.

Gliomas are the most common and aggressive primary malignant tumor in the central nervous system, and requires new biomarkers and therapeutic methods. Long noncoding RNAs (lncRNAs) are important factors in numerous human diseases, including cancer. But studies on lncRNAs and gliomas are limited. In this study, we investigated the expression patterns of lncRNAs in 3 pairs of glioma samples and adjacent non-tumor tissues via microarray and selected the most down-regulated lnc00462717 to further verify its roles in glioma. We observed that decreased lnc00462717 expression was associated with the malignant status in glioma. In vitro experiment demonstrated that lnc00462717 overexpression suppressed glioma cell proliferation, survival and migration while knockdown of lnc00462717 had an opposite result. Moreover, we identified MDM2 as a direct target of lnc00462717 and lnc00462717 played a role by partially regulating the MDM2/MAPK pathway. In conclusion, lnc00462717 may function in suppressing glioma cell proliferation, survival, migration and may potentially serve as a novel biomarker and therapeutic target for glioma.