Improving diagnostic performance of rib fractures for the night shift in radiology department using a computer-aided diagnosis system based on deep learning: A clinical retrospective study.

OBJECTIVE: To investigate the application value of a computer-aided diagnosis (CAD) system based on deep learning (DL) of rib fractures for night shifts in radiology department. METHODS: Chest computed tomography (CT) images and structured reports were retrospectively selected from the picture archiving and communication system (PACS) for 2,332 blunt chest trauma patients. In all CT imaging examinations, two on-duty radiologists (radiologists I and II) completed reports using three different reading patterns namely, P1 = independent reading during the day shift; P2 = independent reading during the night shift; and P3 = reading with the aid of a CAD system as the concurrent reader during the night shift. The locations and types of rib fractures were documented for each reading. In this study, the reference standard for rib fractures was established by an expert group. Sensitivity and false positives per scan (FPS) were counted and compared among P1, P2, and P3. RESULTS: The reference standard verified 6,443 rib fractures in the 2,332 patients. The sensitivity of both radiologists decreased significantly in P2 compared to that in P1 (both p <  0.017). The sensitivities of both radiologists showed no statistical difference between P3 and P1 (both p >  0.017). Radiologist I's FPS increased significantly in P2 compared to P1 (p <  0.017). The FPS of radiologist I showed no statistically significant difference between P3 and P1 (p >  0.017). The FPS of Radiologist II showed no statistical difference among all three reading patterns (p >  0.05). CONCLUSIONS: DL-based CAD systems can be integrated into the workflow of radiology departments during the night shift to improve the diagnostic performance of CT rib fractures.

Quantitative influence of adaptive statistical iterative reconstruction on CT image histology of patients with primary colorectal cancer.

OBJECTIVE: To investigate the quantitative influence of ASIR (adaptive statistical iterative reconstruction) on CT(computed tomography) image histology of patients with primary colorectal cancer. METHODS: Sixty three patients with primary colorectal cancer were prospectively selected in the Jingzhou Central Hospital from January 2017 to December 2018; all patients were planned for contrast-enhanced CT examination and 20% ASIR incremental reconstruction. For reasons of interest, single- and multi-slice scans and radio-histological analysis were performed: ASIR effects were calculated by multilevel linear regression method. RESULTS: The total of 56 CT data sets were collected and analyzed. Incremental ASIR levels showed significant statistical changes in most radiohistological features (P<0.05). Single event and multilevel analysis of first-order statistical characteristics showed relatively small changes (median standardization effect B = 0.08). The change level of second-order statistical characteristics in single-level analysis (median B = 0.36) were significantly higher than multilevel analysis (median B = 0.13). The fractal characteristics showed significant statistical changes only in single-level analysis (median B = 0.49). CONCLUSIONS: The incremental level of ASIR can significantly affect the quantification of CT radiohistology of primary colorectal cancer. The second-order statistical and fractal characteristics obtained by single-level analysis can be more variable than those obtained by multi-level analysis.

Computed tomography-measured body composition can predict long-term outcomes for stage I-III colorectal cancer patients.

Background: There remains a pressing need to identify biomarkers capable of reliably predicting prognostic outcomes for colorectal cancer (CRC) patients. As several body composition parameters have recently been reported to exhibit varying levels of prognostic significance in particular cancers, the present study was devised to assess the ability of body composition to predict long-term outcomes for CRC patients with different stages of disease. Methods: In total, this retrospective analysis enrolled 327 stage I-III CRC patients whose medical records were accessed for baseline demographic and clinical data. Primary outcomes for these patients included disease-free and overall survival (DFS and OS). The prognostic performance of different musculature, visceral, and subcutaneous fat measurements from preoperative computed tomography (CT) scans was assessed. Results: Over the course of follow-up, 93 of the enrolled patients experienced recurrent disease and 39 died. Through multivariate Cox regression analyses, the visceral/subcutaneous fat area (V/S) ratio was found to be independently associated with patient DFS (HR=1.93, 95% CI: 1.24-3.01, P=0.004), and the skeletal muscle index (SMI) as an independent predictor for OS (HR=0.43, 95% CI: 0.21-0.89, P=0.023). Through subgroup analyses, higher V/S ratios were found to be correlated with reduced DFS among patients with stage T3/4 (P=0.011), lymph node metastasis-positive (P=0.002), and TNM stage III (P=0.002) disease, whereas a higher SMI was associated with better OS in all T stages (P=0.034, P=0.015), lymph node metastasis-positive cases (P=0.020), and in patients with TNM stage III disease (P=0.020). Conclusion: Both the V/S ratio and SMI offer potential utility as clinical biomarkers associated with long-term CRC patient prognosis. A higher V/S ratio and a lower SMI are closely related to poorer outcomes in patients with more advanced disease.

A nomogram to predict severe COVID-19 patients with increased pulmonary lesions in early days.

Objectives: This study aimed to predict severe coronavirus disease 2019 (COVID-19) progression in patients with increased pneumonia lesions in the early days. A simplified nomogram was developed utilizing artificial intelligence (AI)-based quantified computed tomography (CT). Methods: From 17 December 2019 to 20 February 2020, a total of 246 patients were confirmed COVID-19 infected in Jingzhou Central Hospital, Hubei Province, China. Of these patients, 93 were mildly ill and had follow-up examinations in 7 days, and 61 of them had enlarged lesions on CT scans. We collected the neutrophil-to-lymphocyte ratio (NLR) and three quantitative CT features from two examinations within 7 days. The three quantitative CT features of pneumonia lesions, including ground-glass opacity volume (GV), semi-consolidation volume (SV), and consolidation volume (CV), were automatically calculated using AI. Additionally, the variation volumes of the lesions were also computed. Finally, a nomogram was developed using a multivariable logistic regression model. To simplify the model, we classified all the lesion volumes based on quartiles and curve fitting results. Results: Among the 93 patients, 61 patients showed enlarged lesions on CT within 7 days, of whom 19 (31.1%) developed any severe illness. The multivariable logistic regression model included age, NLR on the second time, an increase in lesion volume, and changes in SV and CV in 7 days. The personalized prediction nomogram demonstrated strong discrimination in the sample, with an area under curve (AUC) and the receiver operating characteristic curve (ROC) of 0.961 and a 95% confidence interval (CI) of 0.917-1.000. Decision curve analysis illustrated that a nomogram based on quantitative AI was clinically useful. Conclusion: The integration of CT quantitative changes, NLR, and age in this model exhibits promising performance in predicting the progression to severe illness in COVID-19 patients with early-stage pneumonia lesions. This comprehensive approach holds the potential to assist clinical decision-making.

Traumatic Brain Magnetic Resonance Imaging Feature Extraction Based on Variable Model Algorithm in Stroke Examination.

The purpose of this study was to explore the diagnostic value of different sequence scanning of nonparametric variable model-based cranial magnetic resonance imaging (MRI) for ischemic stroke. A histogram analysis-based nonparametric variable model was proposed first, which was compared with the parametric deformation (PD) model and geometric deformation (GD) model. Then, 116 patients with acute ischemic stroke were selected as the research subjects. Routine MRI (T2WI, T1WI, FLAIR, DWI, SWI, and 3D TOF MRA) and MR SCALE-PWI were performed. The results showed that the nonparametric variable model algorithm was relatively complete in the actual segmentation results of MRI images, and the display clarity of lesions was better than PD and GD algorithms. The diagnostic sensitivity, specificity, and overall performance of the variable model algorithm were significantly higher than those of the other two algorithms (P < 0.05). According to ROC curve analysis, the AUC areas of DWI, SWI, 3D TOF MRA, and MR SCALE-PWI for the diagnosis of ischemic penumbra were 0.793, 0.825, 0.871, and 0.933, respectively. In summary, the segmentation results of MRI images by the nonparametric variable model based on histogram analysis were relatively complete, and the clarity of lesions was better than that of the traditional model. MRI images can effectively identify the occurrence of ischemic stroke. Moreover, MR SCALE-PWI had a good early identification effect on ischemic penumbra, which can reduce unnecessary treatment for patients.

A Deep Learning Radiomics Model to Identify Poor Outcome in COVID-19 Patients With Underlying Health Conditions: A Multicenter Study.

OBJECTIVE: Coronavirus disease 2019 (COVID-19) has caused considerable morbidity and mortality, especially in patients with underlying health conditions. A precise prognostic tool to identify poor outcomes among such cases is desperately needed. METHODS: Total 400 COVID-19 patients with underlying health conditions were retrospectively recruited from 4 centers, including 54 dead cases (labeled as poor outcomes) and 346 patients discharged or hospitalized for at least 7 days since initial CT scan. Patients were allocated to a training set (n = 271), a test set (n = 68), and an external test set (n = 61). We proposed an initial CT-derived hybrid model by combining a 3D-ResNet10 based deep learning model and a quantitative 3D radiomics model to predict the probability of COVID-19 patients reaching poor outcome. The model performance was assessed by area under the receiver operating characteristic curve (AUC), survival analysis, and subgroup analysis. RESULTS: The hybrid model achieved AUCs of 0.876 (95% confidence interval: 0.752-0.999) and 0.864 (0.766-0.962) in test and external test sets, outperforming other models. The survival analysis verified the hybrid model as a significant risk factor for mortality (hazard ratio, 2.049 [1.462-2.871], P < 0.001) that could well stratify patients into high-risk and low-risk of reaching poor outcomes (P < 0.001). CONCLUSION: The hybrid model that combined deep learning and radiomics could accurately identify poor outcomes in COVID-19 patients with underlying health conditions from initial CT scans. The great risk stratification ability could help alert risk of death and allow for timely surveillance plans.

Knockdown of Rhotekin 2 expression suppresses proliferation and invasion and induces apoptosis in hepatocellular carcinoma cells.

Hepatocellular carcinoma (HCC), which is one of the most common types of cancer worldwide, has been ranked as the third leading cause of cancer­associated mortality worldwide. Rhotekin 2 (RTKN2), a Rho­guanosine triphosphatase (GTPase) effector, has been reported to be anti­apoptotic. However, the molecular mechanism underlying the biological function of RTKN2 in HCC is poorly defined. The current study reported that RTKN2 was overexpressed in 83% of HCC specimens compared with adjacent noncancerous tissues (n=30). Depletion of RTKN2 in HCC cells, HepG2 and BEL­7404 by RNA interference led to marked inhibition of cell proliferation and cell cycle progression. Notably, RTKN2 silencing significantly reduced the levels of cell cycle­associated proteins, proliferating cell nuclear antigen and cyclin­dependent kinase 1. Additionally, it was identified that downregulation of RTKN2 in HCC cells notably induced cell apoptosis, while significantly repressing cell invasion. These data suggest that RTKN2 may act as an oncogene and inhibition of RTKN2 may be part of a novel therapeutic strategy for targeted HCC therapy.