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BACKGROUND: Conventional MRI staging can be challenging in the preoperative assessment of rectal cancer. Deep learning methods based on MRI have shown promise in cancer diagnosis and prognostication. However, the value of deep learning in rectal cancer T-staging is unclear. PURPOSE: To develop a deep learning model based on preoperative multiparametric MRI for evaluation of rectal cancer and to investigate its potential to improve T-staging accuracy. STUDY TYPE: Retrospective. POPULATION: After cross-validation, 260 patients (123 with T-stage T1-2 and 134 with T-stage T3-4) with histopathologically confirmed rectal cancer were randomly divided to the training (N = 208) and test sets (N = 52). FIELD STRENGTH/SEQUENCE: 3.0 T/Dynamic contrast enhanced (DCE), T2-weighted imaging (T2W), and diffusion-weighted imaging (DWI). ASSESSMENT: The deep learning (DL) model of multiparametric (DCE, T2W, and DWI) convolutional neural network were constructed for evaluating preoperative diagnosis. The pathological findings served as the reference standard for T-stage. For comparison, the single parameter DL-model, a logistic regression model composed of clinical features and subjective assessment of radiologists were used. STATISTICAL TESTS: The receiver operating characteristic curve (ROC) was used to evaluate the models, the Fleiss' kappa for the intercorrelation coefficients, and DeLong test for compare the diagnostic performance of ROCs. P-values less than 0.05 were considered statistically significant. RESULTS: The Area Under Curve (AUC) of the multiparametric DL-model was 0.854, which was significantly higher than the radiologist's assessment (AUC = 0.678), clinical model (AUC = 0.747), and the single parameter DL-models including T2W-model (AUC = 0.735), DWI-model (AUC = 0.759), and DCE-model (AUC = 0.789). DATA CONCLUSION: In the evaluation of rectal cancer patients, the proposed multiparametric DL-model outperformed the radiologist's assessment, the clinical model as well as the single parameter models. The multiparametric DL-model has the potential to assist clinicians by providing more reliable and precise preoperative T staging diagnosis. EVIDENCE LEVEL: 3 TECHNICAL EFFICACY: Stage 2.
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Aprendizaje Profundo , Imágenes de Resonancia Magnética Multiparamétrica , Neoplasias del Recto , Humanos , Imagen por Resonancia Magnética/métodos , Imágenes de Resonancia Magnética Multiparamétrica/métodos , Estudios RetrospectivosRESUMEN
BACKGROUND: Accurate determination of human epidermal growth factor receptor 2 (HER2) is important for choosing optimal HER2 targeting treatment strategies. HER2-low is currently considered HER2-negative, but patients may be eligible to receive new anti-HER2 drug conjugates. PURPOSE: To use breast MRI BI-RADS features for classifying three HER2 levels, first to distinguish HER2-zero from HER2-low/positive (Task-1), and then to distinguish HER2-low from HER2-positive (Task-2). STUDY TYPE: Retrospective. POPULATION: 621 invasive ductal cancer, 245 HER2-zero, 191 HER2-low, and 185 HER2-positive. For Task-1, 488 cases for training and 133 for testing. For Task-2, 294 cases for training and 82 for testing. FIELD STRENGTH/SEQUENCE: 3.0 T; 3D T1-weighted DCE, short time inversion recovery T2, and single-shot EPI DWI. ASSESSMENT: Pathological information and BI-RADS features were compared. Random Forest was used to select MRI features, and then four machine learning (ML) algorithms: decision tree (DT), support vector machine (SVM), k-nearest neighbors (k-NN), and artificial neural nets (ANN), were applied to build models. STATISTICAL TESTS: Chi-square test, one-way analysis of variance, and Kruskal-Wallis test were performed. The P values <0.05 were considered statistically significant. For ML models, the generated probability was used to construct the ROC curves. RESULTS: Peritumoral edema, the presence of multiple lesions and non-mass enhancement (NME) showed significant differences. For distinguishing HER2-zero from non-zero (low + positive), multiple lesions, edema, margin, and tumor size were selected, and the k-NN model achieved the highest AUC of 0.86 in the training set and 0.79 in the testing set. For differentiating HER2-low from HER2-positive, multiple lesions, edema, and margin were selected, and the DT model achieved the highest AUC of 0.79 in the training set and 0.69 in the testing set. DATA CONCLUSION: BI-RADS features read by radiologists from preoperative MRI can be analyzed using more sophisticated feature selection and ML algorithms to build models for the classification of HER2 status and identify HER2-low. TECHNICAL EFFICACY: Stage 2.
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OBJECTIVES: To compare the diagnostic performance of three readers using BI-RADS and Kaiser score (KS) based on mass and non-mass enhancement (NME) lesions. METHODS: A total of 630 lesions, 393 malignant and 237 benign, 458 mass and 172 NME, were analyzed. Three radiologists with 3 years, 6 years, and 13 years of experience made diagnoses. 596 cases had diffusion-weighted imaging, and the apparent diffusion coefficient (ADC) was measured. For lesions with ADC > 1.4 × 10-3 mm2/s, the KS was reduced by 4 as the modified KS +, and the benefit was assessed. RESULTS: When using BI-RADS, AUC was 0.878, 0.915, and 0.941 for mass, and 0.771, 0.838, 0.902 for NME for Reader-1, 2, and 3, respectively, better for mass than for NME. The diagnostic accuracy of KS was improved compared to BI-RADS for less experienced readers. For Reader-1, AUC was increased from 0.878 to 0.916 for mass (p = 0.005) and from 0.771 to 0.822 for NME (p = 0.124). Based on the cut-off value of BI-RADS ≥ 4B and KS ≥ 5 as malignant, the sensitivity of KS by Readers-1 and -2 was significantly higher for both Mass and NME. When ADC was considered to change to modified KS +, the AUC and the accuracy for all three readers were improved, showing higher specificity with slightly degraded sensitivity. CONCLUSION: The benefit of KS compared to BI-RADS was most noticeable for the less experienced readers in improving sensitivity. Compared to KS, KS + can improve specificity for all three readers. For NME, the KS and KS + criteria need to be further improved. CLINICAL RELEVANCE STATEMENT: KS provides an intuitive method for diagnosing lesions on breast MRI. BI-RADS and KS face greater difficulties in evaluating NME compared to mass lesions. Adding ADC to the KS can improve specificity with slightly degraded sensitivity. KEY POINTS: KS provides an intuitive method for interpreting breast lesions on MRI, most helpful for novice readers. KS, compared to BI-RADS, improved sensitivity in both mass and NME groups for less experienced readers. NME lesions were considered during the development of the KS flowchart, but may need to be better defined.
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OBJECTIVES: To evaluate the diagnostic performance of Kaiser score (KS) adjusted with the apparent diffusion coefficient (ADC) (KS+) and machine learning (ML) modeling. METHODS: A dataset of 402 malignant and 257 benign lesions was identified. Two radiologists assigned the KS. If a lesion with KS > 4 had ADC > 1.4 × 10-3 mm2/s, the KS was reduced by 4 to become KS+. In order to consider the full spectrum of ADC as a continuous variable, the KS and ADC values were used to train diagnostic models using 5 ML algorithms. The performance was evaluated using the ROC analysis, compared by the DeLong test. The sensitivity, specificity, and accuracy achieved using the threshold of KS > 4, KS+ > 4, and ADC ≤ 1.4 × 10-3 mm2/s were obtained and compared by the McNemar test. RESULTS: The ROC curves of KS, KS+, and all ML models had comparable AUC in the range of 0.883-0.921, significantly higher than that of ADC (0.837, p < 0.0001). The KS had sensitivity = 97.3% and specificity = 59.1%; and the KS+ had sensitivity = 95.5% with significantly improved specificity to 68.5% (p < 0.0001). However, when setting at the same sensitivity of 97.3%, KS+ could not improve specificity. In ML analysis, the logistic regression model had the best performance. At sensitivity = 97.3% and specificity = 65.3%, i.e., compared to KS, 16 false-positives may be avoided without affecting true cancer diagnosis (p = 0.0015). CONCLUSION: Using dichotomized ADC to modify KS to KS+ can improve specificity, but at the price of lowered sensitivity. Machine learning algorithms may be applied to consider the ADC as a continuous variable to build more accurate diagnostic models. KEY POINTS: ⢠When using ADC to modify the Kaiser score to KS+, the diagnostic specificity according to the results of two independent readers was improved by 9.4-9.7%, at the price of slightly degraded sensitivity by 1.5-1.8%, and overall had improved accuracy by 2.6-2.9%. ⢠When the KS and the continuous ADC values were combined to train models by machine learning algorithms, the diagnostic specificity achieved by the logistic regression model could be significantly improved from 59.1 to 65.3% (p = 0.0015), while maintaining at the high sensitivity of KS = 97.3%, and thus, the results demonstrated the potential of ML modeling to further evaluate the contribution of ADC. ⢠When setting the sensitivity at the same levels, the modified KS+ and the original KS have comparable specificity; therefore, KS+ with consideration of ADC may not offer much practical help, and the original KS without ADC remains as an excellent robust diagnostic method.
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Neoplasias de la Mama , Imagen de Difusión por Resonancia Magnética , Neoplasias de la Mama/diagnóstico por imagen , Diagnóstico Diferencial , Imagen de Difusión por Resonancia Magnética/métodos , Femenino , Humanos , Aprendizaje Automático , Imagen por Resonancia Magnética/métodos , Curva ROC , Estudios Retrospectivos , Sensibilidad y EspecificidadRESUMEN
BACKGROUND: The novel distal radial artery (dRA) approach is a popular arterial access route for interventional cardiology and neurointerventions. We explored the dRA as an alternative site to the classic forearm radial artery (RA) for perioperative blood pressure monitoring. We hypothesized that dRA catheterization is noninferior to RA for the first attempt success rate. METHODS: This was a single-center, prospective, randomized controlled, noninferiority study. Adult patients who underwent elective surgery at the Jinling Hospital from May 2021 to August 2021 were enrolled. The primary endpoint was to test the noninferiority of the first attempt success rate between the groups. Secondary endpoints included anatomical characteristics, catheterization time, arterial posterior wall puncture rate, postoperative compression time, dampened arterial pressure waveforms, and complications. RESULTS: Totally, 161 patients who received either dRA (n = 81) or RA (n = 80) catheterization were analyzed. The first attempt success rates were 87.7 and 91.3% in the dRA and RA groups, respectively, with a mean difference of - 3.6% (95% CI, - 13.1 to 5.9%). The dRA diameter and cross-sectional area were significantly smaller than those of the RA (P < 0.001). The subcutaneous depth of dRA was significantly greater than that of the RA (P < 0.001). The dRA had a longer catheterization time (P = 0.008) but a shorter postoperative compression time (P < 0.001). The arterial posterior wall puncture rate of dRA was significantly higher than that of the RA (P = 0.006). The dRA had fewer dampened arterial waveforms than RA (P = 0.030) perioperatively. CONCLUSIONS: The dRA is a rational alternative approach to RA for perioperative arterial pressure monitoring and provides a noninferior first attempt success rate. TRIAL REGISTRATION: This study is registered in the Chinese Clinical Trials Registry (registration number: ChiCTR2100043714 , registration date: 27/02/2021).
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Cateterismo Periférico , Arteria Radial , Adulto , Presión Sanguínea , Cateterismo , Antebrazo , Humanos , Estudios Prospectivos , Arteria Radial/diagnóstico por imagen , Ultrasonografía IntervencionalRESUMEN
OBJECTIVES: To apply deep learning algorithms using a conventional convolutional neural network (CNN) and a recurrent CNN to differentiate three breast cancer molecular subtypes on MRI. METHODS: A total of 244 patients were analyzed, 99 in training dataset scanned at 1.5 T and 83 in testing-1 and 62 in testing-2 scanned at 3 T. Patients were classified into 3 subtypes based on hormonal receptor (HR) and HER2 receptor: (HR+/HER2-), HER2+, and triple negative (TN). Only images acquired in the DCE sequence were used in the analysis. The smallest bounding box covering tumor ROI was used as the input for deep learning to develop the model in the training dataset, by using a conventional CNN and the convolutional long short-term memory (CLSTM). Then, transfer learning was applied to re-tune the model using testing-1(2) and evaluated in testing-2(1). RESULTS: In the training dataset, the mean accuracy evaluated using tenfold cross-validation was higher by using CLSTM (0.91) than by using CNN (0.79). When the developed model was applied to the independent testing datasets, the accuracy was 0.4-0.5. With transfer learning by re-tuning parameters in testing-1, the mean accuracy reached 0.91 by CNN and 0.83 by CLSTM, and improved accuracy in testing-2 from 0.47 to 0.78 by CNN and from 0.39 to 0.74 by CLSTM. Overall, transfer learning could improve the classification accuracy by greater than 30%. CONCLUSIONS: The recurrent network using CLSTM could track changes in signal intensity during DCE acquisition, and achieved a higher accuracy compared with conventional CNN during training. For datasets acquired using different settings, transfer learning can be applied to re-tune the model and improve accuracy. KEY POINTS: ⢠Deep learning can be applied to differentiate breast cancer molecular subtypes. ⢠The recurrent neural network using CLSTM could track the change of signal intensity in DCE images, and achieved a higher accuracy compared with conventional CNN during training. ⢠For datasets acquired using different scanners with different imaging protocols, transfer learning provided an efficient method to re-tune the classification model and improve accuracy.
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Neoplasias de la Mama , Algoritmos , Neoplasias de la Mama/diagnóstico por imagen , Humanos , Aprendizaje Automático , Imagen por Resonancia Magnética , Redes Neurales de la ComputaciónRESUMEN
BACKGROUND: Computer-aided methods have been widely applied to diagnose lesions detected on breast MRI, but fully-automatic diagnosis using deep learning is rarely reported. PURPOSE: To evaluate the diagnostic accuracy of mass lesions using region of interest (ROI)-based, radiomics and deep-learning methods, by taking peritumor tissues into consideration. STUDY TYPE: Retrospective. POPULATION: In all, 133 patients with histologically confirmed 91 malignant and 62 benign mass lesions for training (74 patients with 48 malignant and 26 benign lesions for testing). FIELD STRENGTH/SEQUENCE: 3T, using the volume imaging for breast assessment (VIBRANT) dynamic contrast-enhanced (DCE) sequence. ASSESSMENT: 3D tumor segmentation was done automatically by using fuzzy-C-means algorithm with connected-component labeling. A total of 99 texture and histogram parameters were calculated for each case, and 15 were selected using random forest to build a radiomics model. Deep learning was implemented using ResNet50, evaluated with 10-fold crossvalidation. The tumor alone, smallest bounding box, and 1.2, 1.5, 2.0 times enlarged boxes were used as inputs. STATISTICAL TESTS: The malignancy probability was calculated using each model, and the threshold of 0.5 was used to make a diagnosis. RESULTS: In the training dataset, the diagnostic accuracy was 76% using three ROI-based parameters, 84% using the radiomics model, and 86% using ROI + radiomics model. In deep learning using the per-slice basis, the area under the receiver operating characteristic (ROC) was comparable for tumor alone, smallest and 1.2 times box (AUC = 0.97-0.99), which were significantly higher than 1.5 and 2.0 times box (AUC = 0.86 and 0.71, respectively). For per-lesion diagnosis, the highest accuracy of 91% was achieved when using the smallest bounding box, and that decreased to 84% for tumor alone and 1.2 times box, and further to 73% for 1.5 times box and 69% for 2.0 times box. In the independent testing dataset, the per-lesion diagnostic accuracy was also the highest when using the smallest bounding box, 89%. DATA CONCLUSION: Deep learning using ResNet50 achieved a high diagnostic accuracy. Using the smallest bounding box containing proximal peritumor tissue as input had higher accuracy compared to using tumor alone or larger boxes. LEVEL OF EVIDENCE: 3 Technical Efficacy: Stage 2.
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Neoplasias de la Mama , Aprendizaje Profundo , Mama/diagnóstico por imagen , Neoplasias de la Mama/diagnóstico por imagen , Medios de Contraste , Humanos , Imagen por Resonancia Magnética , Estudios RetrospectivosRESUMEN
BACKGROUND: Conventional diffusion-weighted imaging (DWI) with high b-values may improve lesion conspicuity, but with a low signal intensity and thus a low signal-to-noise ratio (SNR). The voxelwise computed DWI (vcDWI) may generate high-quality images with a strong lesion signal and low background. PURPOSE: To evaluate the feasibility and diagnostic performance of vcDWI. STUDY TYPE: Retrospective. POPULATION: In all, 67 patients with 72 lesions, 33 malignant and 39 benign. FIELD STRENGTH/SEQUENCE: 3T, including T2 /T1 , DWI with two b-values, and dynamic contrast-enhanced MRI (DCE-MRI). ASSESSMENT: Computed DWI (cDWI) with high b-values of 1500, 2000, 2500 s/mm2 (cDWI1500 , cDWI2000 , cDWI2500 ) and vcDWI were generated from measured DWI (mDWI). The mDWI, cDWIs and vcDWI were evaluated by three readers independently to determine lesion conspicuity, background signal suppression, overall image quality using 1-5 rating scales, as well as to give BI-RADS scores. The mean apparent diffusion coefficient (ADC) value for each lesion was measured. STATISTICAL TESTS: Agreement among the three readers was evaluated by the intraclass correlation coefficient. Receiver operating characteristic (ROC) analysis was performed to compare the diagnostic performance based on reading of mDWI, cDWIs, vcDWI, and the measured ADC values. RESULTS: vcDWI provided the best lesion conspicuity compared with mDWI and cDWIs (P < 0.005). For overall image quality, vcDWI was significantly better than cDWI (P < 0.005), but not significantly better compared with mDWI for two readers (P = 0.037 and P = 0.013) and significantly worse for the third reader (P < 0.005). Background signal suppression was the best on cDWI2500 , and better on vcDWI than on mDWI, cDWI1500 , and cDWI2000 . The AUC value for differential diagnosis was 0.868 for mDWI, 0.862 for cDWI1500 , 0.781 for cDWI2000 , 0.704 for cDWI2500 , 0.946 for vcDWI, 0.704 for ADC value, and 0.961 for DCE-MRI. DATA CONCLUSION: vcDWI was implemented without increasing scanning time, and it provided excellent lesion conspicuity for detection of breast lesions and assisted in differentiating malignant from benign breast lesions. LEVEL OF EVIDENCE: 4 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018.
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Neoplasias de la Mama/diagnóstico por imagen , Mama/patología , Imagen de Difusión por Resonancia Magnética , Adolescente , Adulto , Anciano , Algoritmos , Área Bajo la Curva , Biopsia , Medios de Contraste , Estudios de Factibilidad , Femenino , Humanos , Procesamiento de Imagen Asistido por Computador , Persona de Mediana Edad , Variaciones Dependientes del Observador , Curva ROC , Estudios Retrospectivos , Procesamiento de Señales Asistido por Computador , Relación Señal-Ruido , Adulto JovenRESUMEN
BACKGROUND: Dreaming is often reported by patients who undergo propofol-based sedation, but there have not been any studies to date focused on the incidence of dreaming and factors associated therewith following the administration of ciprofol anesthesia in patients undergoing painless gastroscopy. The present study was thus developed with the goal of assessing the incidence of dreaming. METHODS: In total, this study enrolled 200 patients undergoing painless gastroscopy. During the procedure, patients' electroencephalographic Bispectral Index (BIS), blood pressure (BP), heart rate (HR), blood oxygen saturation (SpO
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Sueños , Gastroscopía , Humanos , Femenino , Masculino , Persona de Mediana Edad , Incidencia , Sueños/efectos de los fármacos , Adulto , Anciano , AnestesiaRESUMEN
OBJECTIVES: To explore the performance differences of multiple annotations in radiomics analysis and provide a reference for tumour annotation in large-scale medical image analysis. METHODS: A total of 342 patients from two centres who underwent radical resection for rectal cancer were retrospectively studied and divided into training, internal validation, and external validation cohorts. Three predictive tasks of tumour T-stage (pT), lymph node metastasis (pLNM), and disease-free survival (pDFS) were performed. Twelve radiomics models were constructed using Lasso-Logistic or Lasso-Cox to evaluate and four annotation methods, 2D detailed annotation along tumour boundaries (2D), 3D detailed annotation along tumour boundaries (3D), 2D bounding box (2DBB), and 3D bounding box (3DBB) on T2-weighted images, were compared. Radiomics models were used to establish combined models incorporating clinical risk factors. The DeLong test was performed to compare the performance of models using the receiver operating characteristic curves. RESULTS: For radiomics models, the area under the curve values ranged from 0.627 (0.518-0.728) to 0.811 (0.705-0.917) in the internal validation cohort and from 0.619 (0.469-0.754) to 0.824 (0.689-0.918) in the external validation cohort. Most radiomics models based on four annotations did not differ significantly, except between the 3D and 3DBB models for pLNM (p = 0.0188) in the internal validation cohort. For combined models, only the 2D model significantly differed from the 2DBB (p = 0.0372) and 3D models (p = 0.0380) for pDFS. CONCLUSION: Radiomics and combined models constructed with 2D and bounding box annotations showed comparable performances to those with 3D and detailed annotations along tumour boundaries in rectal cancer characterisation and prognosis prediction. CRITICAL RELEVANCE STATEMENT: For quantitative analysis of radiological images, the selection of 2D maximum tumour area or bounding box annotation is as representative and easy to operate as 3D whole tumour or detailed annotations along tumour boundaries. KEY POINTS: There is currently a lack of discussion on whether different annotation efforts in radiomics are predictively representative. No significant differences were observed in radiomics and combined models regardless of the annotations (2D, 3D, detailed, or bounding box). Prioritise selecting the more time and effort-saving 2D maximum area bounding box annotation.
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OBJECTIVES: To analyse the characteristics of basilar artery (BA) fenestrations and their coexistence with aneurysms and other anomalies in a massive cases by computed tomographic angiography (CTA). METHODS: A total of 5,657 sequential cerebral CTA images performed from January 2006 to February 2012 were reviewed. CTA images were obtained from the raw datasets by using volume rendering and maximal intensity projection reconstruction. RESULTS: One hundred and thirty-two (2.33 %) BA fenestrations were detected with CTA, and most common at the proximal segment (n = 124). BA fenestration-associated aneurysms were found in 34 cases and 7 located at the posterior circulation, and the frequency of posterior circulation aneurysms was significantly different in patients with and without BA fenestrations (P = 0.025). Other associated anomalies included arteriovenous malformation (n = 7) and moyamoya disease (n = 6). BA fenestrations were classified into Type I (74 cases), Type II (15 cases), Type III (41 cases) and Type IV (2 cases). A significant difference was observed between Types II + III associated with convex-lens-like and slit-like fenestrations (P = 0.008). CONCLUSIONS: BA fenestrations were found in 2.33 % with CTA. They were significantly more often associated with posterior circulation aneurysms than those without BA fenestration. The anterior inferior cerebral artery (AICA) tends to originate more often from convex-lens-like fenestration than slit-like. KEY POINTS: ⢠Basilar artery fenestrations were found in 2.33 % of patients undergoing CT angiography. ⢠Fenestrations were seen more often in the lower third with slit-like configurations. ⢠No obvious relationship exists between basilar artery fenestration and aneurysm formation. ⢠Basilar artery fenestrations perhaps predispose a patient to posterior circulation aneurysm formation. ⢠The AICA tends to originate more often from convex-lens-like than slit-like fenestrations.
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Arteria Basilar/anomalías , Arteria Basilar/diagnóstico por imagen , Malformaciones Vasculares del Sistema Nervioso Central/diagnóstico por imagen , Malformaciones Vasculares del Sistema Nervioso Central/epidemiología , Angiografía Cerebral/estadística & datos numéricos , Tomografía Computarizada por Rayos X/estadística & datos numéricos , Adolescente , Niño , China/epidemiología , Femenino , Humanos , Masculino , Prevalencia , Reproducibilidad de los Resultados , Factores de Riesgo , Sensibilidad y Especificidad , Adulto JovenRESUMEN
ABSTRACT: Background : Systemic inflammation acts as a contributor to neurologic deficits after cardiac arrest (CA) and cardiopulmonary resuscitation (CPR). Extracellular cold-inducible RNA-binding, protein (CIRP) has been demonstrated to be responsible in part for the inflammation through binding to toll-like receptor 4 (TLR4) after cerebral ischemia. The short peptide C23 derived from CIRP has a high affinity for TLR4, we hypothesize that C23 reduces systemic inflammation after CA/CPR by blocking the binding of CIRP to TLR4. Methods : Adult male SD rats in experimental groups were subjected to 5 min of CA followed by resuscitation. C23 peptide (8 mg/kg) or normal saline was injected intraperitoneally at the beginning of the return of spontaneous circulation (ROSC). Results : The expressions of CIRP, TNF-α, IL-6, and IL-1ß in serum and brain tissues were significantly increased at 24 h after ROSC ( P < 0.05). C23 treatment could markedly decrease the expressions of TNF-α, IL-6, and IL-1ß in serum ( P < 0.05). Besides, it can decrease the expressions of TLR4, TNF-α, IL-6, and IL-1ß in the cortex and hippocampus and inhibit the colocalization of CIRP and TLR4 ( P < 0.05). In addition, C23 treatment can reduce the apoptosis of hippocampus neurons ( P < 0.05). Finally, the rats in the C23 group have improved survival rate and neurological prognosis ( P < 0.05). Conclusions: These findings suggest that C23 can reduce systemic inflammation and it has the potential to be developed into a possible therapy for post-CA syndrome.
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Reanimación Cardiopulmonar , Paro Cardíaco , Animales , Ratas , Masculino , Factor de Necrosis Tumoral alfa/metabolismo , Interleucina-6/metabolismo , Receptor Toll-Like 4 , Ratas Sprague-Dawley , Reanimación Cardiopulmonar/métodos , Péptidos/farmacología , Paro Cardíaco/metabolismo , Inflamación/metabolismoRESUMEN
OBJECTIVE: To test agreement and interchangeability between distal (dRA) and forearm radial arterial (RA) pressures (AP) during general anesthesia (GA) for prone spinal surgery. METHODS: This prospective observational study involved 40 patients scheduled for GA spinal surgery. The right dRA and left forearm RA were cannulated in all patients to continuously measure invasive blood pressures (IBP). We compared the agreement and trending ability of systolic AP (SAP), diastolic AP (DAP), and mean AP (MAP) at each site 15 minutes after tracheal intubation, start of surgery, 30 and 60 minutes after the start of surgery, and after skin suturing. RESULTS: Paired BP values (n = 184) (37 cases) were analyzed. The bias (standard deviation), limits of agreement, and percentage error were: SAP: 0.19 (3.03), -5.75 to 6.12, and 5.04%; DAP: -0.06 (1.75), -3.50 to 3.38, and 5.10%; and MAP: 0.08 (1.52), -2.90 to 3.05, and 3.54%, respectively. The linear regression coefficients of determination were 0.981, 0.982, and 0.988 for SAPs, DAPs, and MAPs, respectively; four-quadrant plot concordance rates were 95.11%, 92.03%, and 92.66%, respectively. CONCLUSION: All arterial BPs showed good agreement and trending capabilities for both the dRA and RA. The dRA may be substituted for the RA in IBP monitoring.
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Presión Arterial , Antebrazo , Humanos , Antebrazo/cirugía , Estudios Prospectivos , Extremidad Superior , ArteriasRESUMEN
Breast cancer is the most common cancer in women. Ultrasound is a widely used screening tool for its portability and easy operation, and DCE-MRI can highlight the lesions more clearly and reveal the characteristics of tumors. They are both noninvasive and nonradiative for assessment of breast cancer. Doctors make diagnoses and further instructions through the sizes, shapes and textures of the breast masses showed on medical images, so automatic tumor segmentation via deep neural networks can to some extent assist doctors. Compared to some challenges which the popular deep neural networks have faced, such as large amounts of parameters, lack of interpretability, overfitting problem, etc., we propose a segmentation network named Att-U-Node which uses attention modules to guide a neural ODE-based framework, trying to alleviate the problems mentioned above. Specifically, the network uses ODE blocks to make up an encoder-decoder structure, feature modeling by neural ODE is completed at each level. Besides, we propose to use an attention module to calculate the coefficient and generate a much refined attention feature for skip connection. Three public available breast ultrasound image datasets (i.e. BUSI, BUS and OASBUD) and a private breast DCE-MRI dataset are used to assess the efficiency of the proposed model, besides, we upgrade the model to 3D for tumor segmentation with the data selected from Public QIN Breast DCE-MRI. The experiments show that the proposed model achieves competitive results compared with the related methods while mitigates the common problems of deep neural networks.
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Neoplasias de la Mama , Neoplasias Mamarias Animales , Femenino , Humanos , Animales , Neoplasias de la Mama/diagnóstico por imagen , Mama , Redes Neurales de la Computación , Procesamiento de Imagen Asistido por ComputadorRESUMEN
OBJECTIVES: To evaluate the influence of menstrual cycle timing on quantitative background parenchymal enhancement and to assess an optimal timing of breast MRI in premenopausal women. METHODS: A total of 197 premenopausal women were enrolled, 120 of which were in the malignant group and 77 in the benign group. Two radiologists depicted the regions of interest (ROI) of the three consecutive biggest slices of glandular tissue in the unaffected side and calculated the ratio (=[SIpost - SIpre]/SIpre) in ROI from the precontrast and early phase to assess BPE quantitatively. Association of BPE with menstrual cycle timing was compared in three categories. The relationships between BPE and age /body mass index (BMI) were also explored. RESULTS: We found that the BPE ratio presented lower in patients with the follicular phase (day1-14) compared to the luteal phase (day15-30) in the benign group (P = .036). Also, the BPE ratio presented significantly lower in the proliferative phase (day5-14) than the menstrual phase (day1-4) and the secretory phase(day15-30) in the benign group (P = .006). While the BPE ratio was not significantly different among the respective weeks (1-4) of the menstrual cycle in the benign group (P > .05). In the malignant group, the BPE ratio did not significantly differ between/among any menstrual cycle phase or week (all P > .05). CONCLUSION: It seems more suitable for Asian women whose lesions need to follow up or are suspected of malignant to undergo breast MRI within the 1st to 14th day of the menstrual cycle, especially on the 5th to 14th day.
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Neoplasias de la Mama , Medios de Contraste , Femenino , Humanos , Aumento de la Imagen , Neoplasias de la Mama/diagnóstico por imagen , Ciclo Menstrual , Imagen por Resonancia Magnética , Estudios RetrospectivosRESUMEN
RATIONALE AND OBJECTIVES: Diagnosis of breast cancer on MRI requires, first, the identification of suspicious lesions; second, the characterization to give a diagnostic impression. We implemented Mask Reginal-Convolutional Neural Network (R-CNN) to detect abnormal lesions, followed by ResNet50 to estimate the malignancy probability. MATERIALS AND METHODS: Two datasets were used. The first set had 176 cases, 103 cancer, and 73 benign. The second set had 84 cases, 53 cancer, and 31 benign. For detection, the pre-contrast image and the subtraction images of left and right breasts were used as inputs, so the symmetry could be considered. The detected suspicious area was characterized by ResNet50, using three DCE parametric maps as inputs. The results obtained using slice-based analyses were combined to give a lesion-based diagnosis. RESULTS: In the first dataset, 101 of 103 cancers were detected by Mask R-CNN as suspicious, and 99 of 101 were correctly classified by ResNet50 as cancer, with a sensitivity of 99/103 = 96%. 48 of 73 benign lesions and 131 normal areas were identified as suspicious. Following classification by ResNet50, only 16 benign and 16 normal areas remained as malignant. The second dataset was used for independent testing. The sensitivity was 43/53 = 81%. Of the total of 121 identified non-cancerous lesions, only 6 of 31 benign lesions and 22 normal tissues were classified as malignant. CONCLUSION: ResNet50 could eliminate approximately 80% of false positives detected by Mask R-CNN. Combining Mask R-CNN and ResNet50 has the potential to develop a fully-automatic computer-aided diagnostic system for breast cancer on MRI.
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Neoplasias de la Mama , Aprendizaje Profundo , Humanos , Femenino , Neoplasias de la Mama/diagnóstico por imagen , Redes Neurales de la Computación , Imagen por Resonancia Magnética/métodosRESUMEN
A total of 457 patients, including 241 HR+/HER2- patients, 134 HER2+ patients, and 82 TN patients, were studied. The percentage of TILs in the stroma adjacent to the tumor cells was assessed using a 10% cutoff. The low TIL percentages were 82% in the HR+ patients, 63% in the HER2+ patients, and 56% in the TN patients (p < 0.001). MRI features such as morphology as mass or non-mass enhancement (NME), shape, margin, internal enhancement, presence of peritumoral edema, and the DCE kinetic pattern were assessed. Tumor sizes were smaller in the HR+/HER2- group (p < 0.001); HER2+ was more likely to present as NME (p = 0.031); homogeneous enhancement was mostly seen in HR+ (p < 0.001); and the peritumoral edema was present in 45% HR+, 71% HER2+, and 80% TN (p < 0.001). In each subtype, the MR features between the high- vs. low-TIL groups were compared. In HR+/HER2-, peritumoral edema was more likely to be present in those with high TILs (70%) than in those with low TILs (40%, p < 0.001). In TN, those with high TILs were more likely to present a regular shape (33%) than those with low TILs (13%, p = 0.029) and more likely to present the circumscribed margin (19%) than those with low TILs (2%, p = 0.009).
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The dopamine D4 receptor gene (DRD4) has been consistently reported to be associated with attention-deficit/hyperactivity disorder (ADHD). Recent studies have linked DRD4 to functional connectivity among specific brain regions. The current study aimed to compare the effects of the DRD4 genotype on functional integrity in drug-naïve ADHD children and healthy children. Resting-state functional MRI images were acquired from 49 children with ADHD and 37 healthy controls (HCs). We investigated the effects of the 2-repeat allele of DRD4 on brain network connectivity in both groups using a parameter called the degree of centrality (DC), which indexes local functional relationships across the entire brain connectome. A voxel-wise two-way ANCOVA was performed to examine the diagnosis-by-genotype interactions on DC maps. Significant diagnosis-by-genotype interactions with DC were found in the temporal lobe, including the left inferior temporal gyrus (ITG) and bilateral middle temporal gyrus (MTG) (GRF corrected at voxel level p < 0.001 and cluster level p < 0.05, two-tailed). With the further subdivision of the DC network according to anatomical distance, additional brain regions with significant interactions were found in the long-range DC network, including the left superior parietal gyrus (SPG) and right middle frontal gyrus (MFG). The post-hoc pairwise analysis found that altered network centrality related to DRD4 differed according to diagnostic status (p < 0.05). This genetic imaging study suggests that the DRD4 genotype regulates the functional integration of brain networks in children with ADHD and HCs differently. This may have important implications for our understanding of the role of DRD4 in altering functional connectivity in ADHD subjects.
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Trastorno por Déficit de Atención con Hiperactividad , Receptores de Dopamina D4 , Trastorno por Déficit de Atención con Hiperactividad/diagnóstico por imagen , Trastorno por Déficit de Atención con Hiperactividad/genética , Encéfalo/diagnóstico por imagen , Estudios de Casos y Controles , Niño , Genotipo , Humanos , Imagen por Resonancia Magnética , Receptores de Dopamina D4/genéticaRESUMEN
Objective: To develop a multi-modality radiomics nomogram based on DCE-MRI, B-mode ultrasound (BMUS) and strain elastography (SE) images for classifying benign and malignant breast lesions. Material and Methods: In this retrospective study, 345 breast lesions from 305 patients who underwent DCE-MRI, BMUS and SE examinations were randomly divided into training (n = 241) and testing (n = 104) datasets. Radiomics features were extracted from manually contoured images. The inter-class correlation coefficient (ICC), Mann-Whitney U test and the least absolute shrinkage and selection operator (LASSO) regression were applied for feature selection and radiomics signature building. Multivariable logistic regression was used to develop a radiomics nomogram incorporating radiomics signature and clinical factors. The performance of the radiomics nomogram was evaluated by its discrimination, calibration, and clinical usefulness and was compared with BI-RADS classification evaluated by a senior breast radiologist. Results: The All-Combination radiomics signature derived from the combination of DCE-MRI, BMUS and SE images showed better diagnostic performance than signatures derived from single modality alone, with area under the curves (AUCs) of 0.953 and 0.941 in training and testing datasets, respectively. The multi-modality radiomics nomogram incorporating the All-Combination radiomics signature and age showed excellent discrimination with the highest AUCs of 0.964 and 0.951 in two datasets, respectively, which outperformed all single modality radiomics signatures and BI-RADS classification. Furthermore, the specificity of radiomics nomogram was significantly higher than BI-RADS classification (both p < 0.04) with the same sensitivity in both datasets. Conclusion: The proposed multi-modality radiomics nomogram based on DCE-MRI and ultrasound images has the potential to serve as a non-invasive tool for classifying benign and malignant breast lesions and reduce unnecessary biopsy.
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Previous studies of brain structural abnormalities in attention-deficit/hyperactivity disorder (ADHD) samples scarcely excluded comorbidity or analyzed them in subtypes. This study aimed to identify neuroanatomical alterations related to diagnosis and subtype of ADHD participants without comorbidity. In our cross-sectional analysis, we used T1-weighted structural MRI images of individuals from the ADHD-200 database. After strict exclusion, 121 age-matched children with uncomorbid ADHD (54 with ADHD-inattentive [iADHD] and 67 with ADHD-combined [cADHD]) and 265 typically developing control subjects (TDC) were included in current investigation. The established method of voxel-based morphometry (VBM8) was used to assess global brain volume and regional grey matter volume (GM). Our results showed that the ADHD patients had more regional GM in the bilateral thalamus relative to the controls. Post hoc analysis revealed that regional GM increase only linked to the iADHD subtype in the right thalamus and precentral gyrus. Besides, the right thalamus volume was positively related to inattentive severity in the iADHD. There were no group differences in global volume. Our results provide preliminary evidence that cerebral structural alterations are tied to uncomorbid ADHD subjects and predominantly attribute to iADHD subtype. Furthermore, the volume of the right thalamus may be relevant to inattentive symptoms in iADHD possibly related to a lack of inhibition of irrelevant sensory input.