Deep Learning Combined with Radiologist's Intervention Achieves Accurate Segmentation of Hepatocellular Carcinoma in Dual-Phase Magnetic Resonance Images.

Purpose: Segmentation of hepatocellular carcinoma (HCC) is crucial; however, manual segmentation is subjective and time-consuming. Accurate and automatic lesion contouring for HCC is desirable in clinical practice. In response to this need, our study introduced a segmentation approach for HCC combining deep convolutional neural networks (DCNNs) and radiologist intervention in magnetic resonance imaging (MRI). We sought to design a segmentation method with a deep learning method that automatically segments using manual location information for moderately experienced radiologists. In addition, we verified the viability of this method to assist radiologists in accurate and fast lesion segmentation. Method: In our study, we developed a semiautomatic approach for segmenting HCC using DCNN in conjunction with radiologist intervention in dual-phase gadolinium-ethoxybenzyl-diethylenetriamine penta-acetic acid- (Gd-EOB-DTPA-) enhanced MRI. We developed a DCNN and deep fusion network (DFN) trained on full-size images, namely, DCNN-F and DFN-F. Furthermore, DFN was applied to the image blocks containing tumor lesions that were roughly contoured by a radiologist with 10 years of experience in abdominal MRI, and this method was named DFN-R. Another radiologist with five years of experience (moderate experience) performed tumor lesion contouring for comparison with our proposed methods. The ground truth image was contoured by an experienced radiologist and reviewed by an independent experienced radiologist. Results: The mean DSC of DCNN-F, DFN-F, and DFN-R was 0.69 ± 0.20 (median, 0.72), 0.74 ± 0.21 (median, 0.77), and 0.83 ± 0.13 (median, 0.88), respectively. The mean DSC of the segmentation by the radiologist with moderate experience was 0.79 ± 0.11 (median, 0.83), which was lower than the performance of DFN-R. Conclusions: Deep learning using dual-phase MRI shows great potential for HCC lesion segmentation. The radiologist-aided semiautomated method (DFN-R) achieved improved performance compared to manual contouring by the radiologist with moderate experience, although the difference was not statistically significant.

Clinical outcome is distinct between radiological stricture and endoscopic stricture in ileal Crohn's disease.

OBJECTIVES: Differences in clinical adverse outcomes (CAO) based on different intestinal stricturing definitions in Crohn's disease (CD) are poorly documented. This study aims to compare CAO between radiological strictures (RS) and endoscopic strictures (ES) in ileal CD and explore the significance of upstream dilatation in RS. METHODS: This retrospective double-center study included 199 patients (derivation cohort, n = 157; validation cohort, n = 42) with bowel strictures who simultaneously underwent endoscopic and radiologic examinations. RS was defined as a luminal narrowing with wall thickening relative to the normal gut on cross-sectional imaging (group 1 (G1)), which further divided into G1a (without upstream dilatation) and G1b (with upstream dilatation). ES was defined as an endoscopic non-passable stricture (group 2 (G2)). Strictures met the definitions of RS (with or without upstream dilatation) and ES were categorized as group 3 (G3). CAO referred to stricture-related surgery or penetrating disease. RESULTS: In the derivation cohort, G1b (93.3%) had the highest CAO occurrence rate, followed by G3 (32.6%), G1a (3.2%), and G2 (0%) (p < 0.0001); the same order was found in the validation cohort. The CAO-free survival time was significantly different among the four groups (p < 0.0001). Upstream dilatation (hazard ratio, 1.126) was a risk factor for predicting CAO in RS. Furthermore, when upstream dilatation was added to diagnose RS, 17.6% of high-risk strictures were neglected. CONCLUSIONS: CAO differs significantly between RS and ES, and clinicians should pay more attention to strictures in G1b and G3. Upstream dilatation has an important impact on the clinical outcome of RS but may not be an essential factor for RS diagnosis. CLINICAL RELEVANCE STATEMENT: This study explored the definition of intestinal stricture with the greatest significance for the clinical diagnosis and prognosis of patients with CD, and consequently provided effective auxiliary information for clinicians to formulate strategies for the treatment of CD intestinal strictures. KEY POINTS: • The retrospective double-center study showed that clinical adverse outcome is different between radiological strictures and endoscopic strictures in CD. • Upstream dilatation has an important impact on the clinical outcome of radiological strictures but may not be an essential factor for diagnosis of radiological strictures. • Radiological stricture with upstream dilatation and simultaneous radiological and endoscopic stricture were at increased risk for clinical adverse outcomes; thus, closer monitoring should be considered.

Preoperative computed tomography enterography-based radiomics signature: A potential predictor of postoperative anastomotic recurrence in patients with Crohn's disease.

BACKGROUND: More than half of patients with Crohn's disease (CD) require at least one surgery for symptom management; however, approximately half of the patients may experience postoperative anastomotic recurrence (PAR). OBJECTIVES: This study aims to develop and validate a preoperative computed tomography enterography (CTE)-based radiomics signature to predict early PAR in CD. DESIGN: A total of 186 patients with CD (training cohort, n = 134; test cohort, n = 52) who underwent preoperative CTE and surgery between January 2014 and June 2020 were included in this retrospective multi-centre study. METHODS: 106 radiomic features were initially extracted from intestinal lesions and peri-intestinal mesenteric fat, respectively; significant radiomic features were selected from them and then used to develop intestinal or mesenteric radiomics signatures, using the least absolute shrinkage and selection operator and a Cox regression model. A radiomics-based nomogram incorporating these signatures with clinical-radiological factors was created for comparison with a model based on clinical-radiological features alone. RESULTS: 68 of 134 patients in training cohort and 16 of 52 patients in test cohort suffered from PAR. The intestinal radiomic signature (hazard ratio [HR]: 2.17; 95% confidence interval [CI]: 1.32-3.58; P = 0.002) and mesenteric radiomic signature (HR: 2.19; 95% CI: 1.14-4.19; P = 0.018) were independent risk factors for PAR in the training cohort as per a multivariate analysis. The radiomics-based nomogram (C-index: 0.710; 95% CI: 0.672-0.748) yielded superior predictive performance than the clinical-radiological model (C-index, 0.607; 95% CI: 0.582-0.632) in the test cohort. Decision curve analysis demonstrated that the radiomics-based nomogram outperformed the clinical-radiological model in terms of clinical usefulness. CONCLUSIONS: Preoperative mesenteric and intestinal CTE radiomics signatures are potential non-invasive predictors of PAR in postoperative patients with CD.

Feasibility of Imaging-Based Quantitative Hepatic Function Assessment: Gd-EOB-DTPA-Enhanced Liver MR Imaging Combined with T1 Mapping.

OBJECTIVE: to investigate the feasibility of gadoxetic acid (Gd-EOB-DTPA) enhanced MRI combined with T1 mapping in quantitative hepatic function assessment. METHODS: this study retrospectively enrolled 94 patients with Gd-EOB-DTPA enhanced MRI combined with T1 mapping, divided into group A (grade A, n=73), group B (grade B, n=14) and group C (grade C, n=7) based on Child-Pugh classification. Liver T1 relaxation times on plain scan (T1P) and hepatocellular phase (T1E) were measured. Decrease in T1 (T1D) and the percentage of decrease in T1 (T1D%) were calculated as follows: T1D=T1P-T1E, T1D%= T1D/T1P×100%. The relationship between T1P, T1E, T1D, T1D% and liver function classification was analyzed. RESULTS: T1P, T1D, and T1D% in group A were significantly higher than those of group B and C. T1E in group A was lower than those of group B and C. T1D% was significantly different between group B and C. There was no significant difference in T1P, T1E, T1D between groups B and C. T1E was positively correlated with liver function levels, T1P and T1D had a negative correlation with liver function levels. T1P, T1E, T1D, T1D% were significantly different between cirrhotic and non-cirrhotic groups. T1D% of less than 70% suggests liver dysfunction. CONCLUSION: Gd-EOB-DTPA enhanced liver MRI combined with T1 mapping is feasible for quantitative assessment of hepatic function.

Comparative Efficacy of Xianling Gubao Capsules in Improving Bone Mineral Density in Postmenopausal Osteoporosis: A Network Meta-Analysis.

OBJECTIVE: The clinical efficacy of Xianling Gubao capsule (XLGB) and its combination therapy in the treatment of postmenopausal osteoporosis (PMOP) was systematically evaluated by frequency-based network meta-analysis. METHODS: We searched the China National Knowledge Infrastructure (CNKI), Wanfang, SinoMed, PubMed, Embase and Cochrane Library databases to identify clinical trials of XLGB for the treatment of PMOP from the establishment of each database to November 22, 2021. The quality of the included studies was evaluated by using the risk of bias assessment tool version 2.0 (Rob 2.0) recommended by Cochrane. Stata 14.0 was applied for statistical analysis of the data, and the surface under the cumulative ranking curve (SUCRA) was used to rank the intervention measures of each outcome index. RESULTS: This study included 22 clinical trials (including 19 RCTs and 3 non-RCTs) involving 12 drug therapies. According to the results of the network meta-analysis and SUCRA, the best three interventions for improving lumbar bone mineral density (BMD) are XLGB+BP+calcium (83.7%), XLGB+BP (68.5.7%) and XLGB+VD (67.1%). XLGB+calcium was the best combination regimen for improving femoral neck BMD and increasing bone Gla protein (BGP) and alkaline phosphatase (ALP) contents in serum. The SUCRA values of XLGB+calcium for improving the three outcome indicators were 68.0%, 59.5% and 82.1%, respectively. CONCLUSIONS: The results of this network meta-analysis show that combined application of XLGB can effectively improve BMD and serum BGP and ALP compared to calcium alone, VD or BP. In the future, multicenter, large-sample and double-blind clinical RCTs should be carried out to supplement and verify the results of this study.

Quantification of brown adipose tissue in vivo using synthetic magnetic resonance imaging: an experimental study with mice model.

BACKGROUND: The white adipose tissue (WAT) and brown adipose tissue (BAT) are associated with the development of several obesity-associated disorders. The use of imaging techniques to differentiate BAT from WAT and quantify BAT volume remains challenging, due to limitations such as spatial resolution and magnetic field inhomogeneity. This study aimed to investigate the feasibility for differentiating BAT from WAT, and quantify the BAT volume in vivo using synthetic magnetic resonance imaging (MRI). METHODS: A total of 16 C57BL/6 mice were scanned using synthetic MRI. Quantitative longitudinal relaxation time (T1) and transverse relaxation time (T2) maps were obtained from the original synthetic MRI data using the synthetic MRI software offline. The T1 and T2 values of interscapular BAT (IBAT) and dorsal subcutaneous WAT were measured. The IBAT volume was calculated using synthetic MRI-derived T2-weighted images (T2WIs) based on its morphological characteristics and quantitative tissue values. The body weight of mice was measured, and the IBAT specimens were excised and weighted. The correlation between IBAT volume and the weight of IBAT gross specimen and between IBAT volume and mouse body weight was analyzed. RESULTS: The T1 values of BAT (330.3±19.57 ms) were higher than those of WAT (304.42±4.14 ms) (P<0.001), whereas the T2 values of BAT (66.06±5.06 ms) were lower than those of WAT (88.23±7.68 ms) (P<0.001). The area under the curve (AUC) values of the T1 and T2 for differentiating BAT from WAT was 0.942 and 0.995, respectively. The AUC of the T2 values was higher than that of T1 (P=0.04) using the DeLong test. The optimal cut-off value for T2 was 76 ms for differentiating BAT from WAT (100% sensitivity, 93.7% specificity). A moderate correlation was observed between IBAT volume and the weight of the IBAT gross specimen (r=0.662, P=0.014), and between IBAT volume and mouse body weight (r=0.653, P=0.016). CONCLUSIONS: The quantitative parameters derived using synthetic MRI may be used to detect and differentiate BAT from WAT in vivo. Synthetic MRI may help quantify BAT volume in vivo.

Deep Segmentation Feature-Based Radiomics Improves Recurrence Prediction of Hepatocellular Carcinoma.

Objective and Impact Statement. This study developed and validated a deep semantic segmentation feature-based radiomics (DSFR) model based on preoperative contrast-enhanced computed tomography (CECT) combined with clinical information to predict early recurrence (ER) of single hepatocellular carcinoma (HCC) after curative resection. ER prediction is of great significance to the therapeutic decision-making and surveillance strategy of HCC. Introduction. ER prediction is important for HCC. However, it cannot currently be adequately determined. Methods. Totally, 208 patients with single HCC after curative resection were retrospectively recruited into a model-development cohort (n=180) and an independent validation cohort (n=28). DSFR models based on different CT phases were developed. The optimal DSFR model was incorporated with clinical information to establish a DSFR-C model. An integrated nomogram based on the Cox regression was established. The DSFR signature was used to stratify high- and low-risk ER groups. Results. A portal phase-based DSFR model was selected as the optimal model (area under receiver operating characteristic curve (AUC): development cohort, 0.740; validation cohort, 0.717). The DSFR-C model achieved AUCs of 0.782 and 0.744 in the development and validation cohorts, respectively. In the development and validation cohorts, the integrated nomogram achieved C-index of 0.748 and 0.741 and time-dependent AUCs of 0.823 and 0.822, respectively, for recurrence-free survival (RFS) prediction. The RFS difference between the risk groups was statistically significant (P<0.0001 and P=0.045 in the development and validation cohorts, respectively). Conclusion. CECT-based DSFR can predict ER in single HCC after curative resection, and its combination with clinical information further improved the performance for ER prediction.

Prediction of Early Treatment Response to Initial Conventional Transarterial Chemoembolization Therapy for Hepatocellular Carcinoma by Machine-Learning Model Based on Computed Tomography.

PURPOSE: The treatment response to initial conventional transarterial chemoembolization (cTACE) is essential for the prognosis of patients with hepatocellular carcinoma (HCC). This study explored and verified the feasibility of machine-learning models based on clinical data and contrast-enhanced computed tomography (CT) image findings to predict early responses of HCC patients after initial cTACE treatment. PATIENTS AND METHODS: Overall, 110 consecutive unresectable HCC patients who were treated with cTACE for the first time were retrospectively enrolled. Clinical data and imaging features based on contrast-enhanced CT were collected for the selection of characteristics. Treatment responses were evaluated based on the modified Response Evaluation Criteria in Solid Tumors (mRECIST) by postoperative CT examination within 2 months after the procedure. Python (version 3.70) was used to develop machine learning models. Least absolute shrinkage and selection operator (LASSO) algorithm was applied to select features with the impact on predicting treatment response after the first TACE procedure. Six machine learning algorithms were used to build predictive models, including XGBoost, decision tree, support vector machine, random forest, k-nearest neighbor, and fully convolutional networks, and their performances were compared using receiver operator characteristic (ROC) curves to determine the best performing model. RESULTS: Following TACE, 31 patients (28.2%) were described as responsive to TACE, while 72 patients (71.8%) were nonresponsive to TACE. Portal vein tumor thrombosis type, albumin level, and distribution of tumors within the liver were selected for predictive model building. Among the models, the RF model showed the best performance, with area under the curve (AUC), accuracy, sensitivity, and specificity of 0.802, 0.784, 0.904, and 0.480, respectively. CONCLUSION: Machine learning models can provide an accurate prediction of the early response of initial TACE treatment for HCC, which can help in individualizing clinical decision-making and modification of further treatment strategies for patients with unresectable HCC.

A narrative review of multiple endocrine neoplasia syndromes: genetics, clinical features, imaging findings, and diagnosis.

OBJECTIVE: We aimed to provide ideas for clinicians, especially radiologists, for the diagnosis of multiple endocrine neoplasia (MEN) syndromes. BACKGROUND: MEN syndromes include MEN1, MEN2, and MEN4 and usually involve 2 or more endocrine tumors. The MEN syndromes are a group of euchromatic dominant genetic diseases, and the main genes involved include MEN1 (MEN1), RET (MEN2), and CDKN1B (MEN4). METHODS: In this article, involving 8 cases (4 cases of MEN1, 2 cases of MEN2A, 1 case of MEN2B, 1 case of MEN4) from our center, we introduced the disease spectrum, clinical manifestations (especially imaging findings), and related genes involved in each type of MEN syndromes. We also discussed the differential diagnosis between MEN and sporadic tumors and emphasized that MEN should be screened and the relevant required examinations. CONCLUSIONS: Considering that MEN syndromes involve multiple endocrine gland tumors and nonendocrine organ diseases, it is very important to identify potential patients early and perform multiple examinations on them, including biochemical and multitype, and multisite imaging examinations according to the disease spectrum of each type. Considering that this is a group of genetic diseases, both interviewing patients about their family history and genetic testing are also very important. Only in this way can a comprehensive and accurate diagnosis be made, enabling patients to receive appropriate treatment and improve their prognosis.

Predicting the recurrence risk of pancreatic neuroendocrine neoplasms after radical resection using deep learning radiomics with preoperative computed tomography images.

BACKGROUND: To establish and validate a prediction model for pancreatic neuroendocrine neoplasms (pNENs) recurrence after radical surgery with preoperative computed tomography (CT) images. METHODS: We retrospectively collected data from 74 patients with pathologically confirmed pNENs (internal group: 56 patients, Hospital I; external validation group: 18 patients, Hospital II). Using the internal group, models were trained with CT findings evaluated by radiologists, radiomics, and deep learning radiomics (DLR) to predict 5-year pNEN recurrence. Radiomics and DLR models were established for arterial (A), venous (V), and arterial and venous (A&V) contrast phases. The model with the optimal performance was further combined with clinical information, and all patients were divided into high- and low-risk groups to analyze survival with the Kaplan-Meier method. RESULTS: In the internal group, the areas under the curves (AUCs) of DLR-A, DLR-V, and DLR-A&V models were 0.80, 0.58, and 0.72, respectively. The corresponding radiomics AUCs were 0.74, 0.68, and 0.70. The AUC of the CT findings model was 0.53. The DLR-A model represented the optimum; added clinical information improved the AUC from 0.80 to 0.83. In the validation group, the AUCs of DLR-A, DLR-V, and DLR-A&V models were 0.77, 0.48, and 0.64, respectively, and those of radiomics-A, radiomics-V, and radiomics-A&V models were 0.56, 0.52, and 0.56, respectively. The AUC of the CT findings model was 0.52. In the validation group, the comparison between the DLR-A and the random models showed a trend of significant difference (P=0.058). Recurrence-free survival differed significantly between high- and low-risk groups (P=0.003). CONCLUSIONS: Using DLR, we successfully established a preoperative recurrence prediction model for pNEN patients after radical surgery. This allows a risk evaluation of pNEN recurrence, optimizing clinical decision-making.

Accurate and Feasible Deep Learning Based Semi-Automatic Segmentation in CT for Radiomics Analysis in Pancreatic Neuroendocrine Neoplasms.

Current clinical practice or radiomics studies of pancreatic neuroendocrine neoplasms (pNENs) require manual delineation of the lesions in computed tomography (CT) images, which is time-consuming and subjective. We used a semi-automatic deep learning (DL) method for segmentation of pNENs and verified its feasibility in radiomics analysis. This retrospective study included two datasets: Dataset 1, contrast-enhanced CT images (CECT) of 80 and 18 patients respectively collected from two centers; and Dataset 2, CECT of 56 and 16 patients respectively from two centers. A DL-based semi-automatic segmentation model was developed and validated with Dataset 1 and Dataset 2, and the segmentation results were used for radiomics analysis from which the performance was compared against that based on manual segmentation. The mean Dice similarity coefficient of the trained segmentation model was 81.8% and 74.8% for external validation with Dataset 1 and Dataset 2 respectively. Four classifiers frequently used in radiomics studies were trained and tested with leave-one-out cross-validation strategy. For pathological grading prediction with Dataset 1, the area under the receiver operating characteristic curve (AUC) with semi-automatic segmentation was up to 0.76 and 0.87 respectively for internal and external validation. For recurrence study with Dataset 2, the AUC with semi-automatic segmentation was up to 0.78. All these AUCs were not statistically significant from the corresponding results based on manual segmentation. Our study showed that DL-based semi-automatic segmentation is accurate and feasible for the radiomics analysis in pNENs.

Hepatic mosaic enhancement pattern correlates with increased inflammatory activity and adverse therapeutic outcomes in patients with Crohn's disease.

PURPOSE: This study aimed to evaluate the role of hepatic mosaic enhancement pattern (HMEP) on computed tomography images in the disease activity and therapeutic outcome of Crohn's Disease (CD). METHODS: Twenty-five CD patients with HMEP comprised the HMEP group, and 25 CD patients without HMEP, who had a similar onset age, sex, and disease course with those in the HMEP group, comprised the non-HMEP group. No underlying liver/biliary disease was observed in any of the patients. Clinical characteristics, laboratory test results, Lémann index, and CD endoscopic index of severity (CDEIS) were compared between the groups using the Student t-, Mann-Whitney U, Chi square, or Fisher's exact tests. Patients received top-down, step-up, or traditional treatment during the follow-up period. After the 1-year follow-up, therapeutic outcomes (active inflammation [CDEIS > 3.5 if the endoscopic data were available, or C-reactive protein level > 5 mg/L if the endoscopic data were unavailable] or remission) were evaluated. RESULTS: The occurrence rate of fistulas/abscesses was higher in the HMEP group (84%, 21/25) than in the non-HMEP group (48%, 12/25) with no statistical significance (P = 0.056). The HMEP group showed a higher C-reactive protein level (P = 0.001), erythrocyte sedimentation rate (P = 0.013), and blood platelet count (P = 0.005). There was no significant difference in therapeutic strategies between the groups (P = 0.509). The HMEP group showed a significantly lower remission ratio after anti-inflammatory treatment than the non-HMEP group (P = 0.045). CONCLUSIONS: HMEP was correlated with increased inflammatory activity and adverse therapeutic outcomes in CD. This finding provided insights regarding novel markers of CD diagnosis and treatment.

Degree of Creeping Fat Assessed by Computed Tomography Enterography is Associated with Intestinal Fibrotic Stricture in Patients with Crohn's Disease: A Potentially Novel Mesenteric Creeping Fat Index.

BACKGROUND AND AIMS: Emerging evidence points to a link between creeping fat and the pathogenesis of Crohn's disease [CD]. Non-invasive assessment of the severity of creeping fat on cross-sectional imaging modality has seldom been investigated. This study aimed to develop and characterize a novel mesenteric creeping fat index [MCFI] based on computed tomography [CT] in CD patients. METHODS: MCFI was developed based on vascular findings on CT in a retrospective cohort [n = 91] and validated in a prospective cohort [n = 30]. The severity of creeping fat was graded based on the extent to which mesenteric fat extended around the intestinal circumference using the vessels in the fat as a marker. The accuracy of MCFI was assessed by comparing it with the degree of creeping fat observed in surgical specimens. The relationship between MCFI and fibrostenosis was characterized by determining if these correlated. The accuracy of MCFI was compared with other radiographic indices [i.e. visceral to subcutaneous fat area ratio and fibrofatty proliferation score]. RESULTS: In the retrospective cohort, MCFI had moderate accuracy in differentiating moderate-severe from mild fibrostenosis (area under the receiver operating characteristic [ROC] curve [AUC] = 0.799; p = 0.000). ROC analysis in the retrospective cohort identified a threshold MCFI of > 3 which accurately differentiated fibrostenosis severity in the prospective cohort [AUC = 0.756; p = 0.018]. An excellent correlation was shown between MCFI and the extent of fat wrapping in specimens in the prospective cohort [r = 0.840, p = 0.000]. Neither visceral to subcutaneous fat area ratio nor fibrofatty proliferation score correlated well with the degree of intestinal fibrosis. CONCLUSIONS: MCFI can accurately characterize the extent of mesenteric fat wrapping in surgical specimens. It may become another non-invasive measure of CD fibrostenosis.

Regional liver function analysis with gadoxetic acid-enhanced MRI and virtual hepatectomy: prediction of postoperative short-term outcomes for HCC.

OBJECTIVES: To explore the role of quantitative regional liver function assessed by preoperative gadoxetic acid-enhanced MRI with computer-aided virtual hepatectomy to predict short-term outcomes after major hepatectomy for HCC. METHODS: We retrospectively reviewed the records of 133 consecutive patients with HCC who underwent preoperative gadoxetic acid-enhanced MRI and indocyanine green (ICG) test. Forty-five patients received open major hepatectomy. Liver function reserve and the future liver remnant were evaluated by computer-aided virtual hepatectomy. Global liver functional parameters included the T1 relaxation time reduction rate (T1ratio) and functional liver volume (FV), whereas regional parameters included the rT1pos, rT1ratio, remnant FV (rFV), and remnant FV ratio (rFVratio) of the remnant liver. The functional parameters of the MRI and ICG were used to predict the short-term outcomes (liver failure and major complications) after major hepatectomy. RESULTS: The T1ratio and FV were correlated with the ICG test (rho = - 0.304 and - 0.449, p < 0.05). FV < 682.8 ml indicated preoperative ICG-R15 ≥ 14% with 0.765 value of the area under the curve (AUC). No patient who underwent major resection with good liver functional reserve (ICG < 14%) and enough future remnant volume (> 30% standard LV) developed liver failure. Low rT1ratio (< 66.5%) and high rT1pos (> 217.5 ms) may predict major complications (AUC = 0.831 and 0.756, respectively; p < 0.05). The rT1ratio was an independent risk factor for postoperative major complications (odds ratio [OR] = 0.845, 95% CI, 0.736-0.966; p < 0.05). CONCLUSION: Preoperative gadoxetic acid-enhanced MRI with computer-aided virtual hepatectomy may facilitate optimal assessment of regional liver functional reserve to predict short-term outcomes after major hepatectomy for HCC. KEY POINTS: • Preoperative gadoxetic acid-enhanced MRI with virtual hepatectomy and volumetric analysis can provide precise liver volume and regional functional assessment. • Quantitative regional liver function assessed by gadoxetic acid-enhanced MRI can predict the short-term outcomes after major hepatectomy in patients with HCC. • The regional liver function assessed by gadoxetic acid-enhanced MRI is an independent risk factor for postoperative major complications.

Repression of FBXW7 by HES5 contributes to inactivation of the TGF-ß signaling pathway and alleviation of endometriosis.

Endometriosis (EMS) is a gynecologic disorder associated with infertility and characterized by the endometrial-type mucosa outside the uterine cavity. Currently available treatment modalities are limited to undesirable effects. Thus, in the present study, we sought to study the pathogenesis mechanism of EMS. For this purpose, the ectopic and eutopic endometrial tissues were resected from 86 patients with EMS and 54 infertile patients without EMS, respectively. The regulatory mechanism among HES family bHLH transcription factor 5 (HES5), transforming growth factor-beta (TGF-ß)-induced factor 1 (TGIF1), F-box, and WD repeat domain containing 7 (FBXW7) was studied by performing co-immunoprecipitation, dual-luciferase reporter gene assay, and chromatin immunoprecipitation, respectively. A mouse model of EMS was established to verify the aforementioned regulatory mechanism in vivo. Upregulation of HES5 and TGIF1, as well as downregulation of FBXW7, was observed in EMS endometrial tissues and human endometrial stromal cells (hESCs), respectively. The overexpression of HES5 was found to suppress the FBXW7 transcription and TGIF1 degradation, resulting in the inactivation of the TGF-ß signaling pathway, as well as inhibition of hESC proliferation and invasion, thereby enhancing apoptosis. Results from a mouse model of EMS showed that the presence of HES5 contributed to the alleviation of EMS. Collectively, we attempted to provide a mechanistic insight into the unrecognized roles of the HES5/FBXW7 in EMS progression.

A novel identification system combining diffusion kurtosis imaging with conventional magnetic resonance imaging to assess intestinal strictures in patients with Crohn's disease.

PURPOSE: To determine the utility of diffusion kurtosis imaging (DKI) for assessing bowel fibrosis and to establish a new magnetic resonance imaging (MRI)-based classification based on DKI and conventional MRI parameters for characterizing intestinal strictures in Crohn's disease (CD) using the histological evaluation of resected intestine samples as the reference standard. METHODS: Thirty-one patients with CD undergoing preoperative conventional MRI and diffusion-weighted imaging (DWI) (b values = 0-2000 s/mm2) were consecutively enrolled. We classified the mural T2-weighted signal intensity and arterial-phase enhancement patterns on conventional MRI. We also measured DWI-derived apparent diffusion coefficients (ADCs) and DKI-derived apparent diffusion for non-Gaussian distribution (Dapp) and apparent diffusional kurtosis (Kapp). A new MRI-based classification was established to characterize intestinal strictures in CD. Its performance was validated in nine additional patients with CD. RESULTS: Histological inflammation grades were significantly correlated to T2-weighted signal intensity (r = 0.477; P < 0.001) and ADC (r = - 0.226; P = 0.044). Histological fibrosis grades were moderately correlated to Kapp (r = 0.604, P < 0.001); they were also correlated to Dapp (r = - 0.491; P < 0.001) and ADC (r = - 0.270; P = 0.015). T2-weighted signal intensity could differentiate between no-to-mild and moderate-to-severe bowel inflammation (sensitivity, 0.970; specificity, 0.479). Kapp could differentiate between no-to-mild and moderate-to-severe bowel fibrosis (sensitivity, 0.959; specificity, 0.781). The agreement between the new MRI-based classification and the histological classification was moderate in the test (κ = 0.507; P < 0.001) and validation (κ = 0.530; P < 0.001) sets. CONCLUSIONS: DKI can be used to assess bowel fibrosis. The new MRI-based classification can help to distinguish between fibrotic and inflammatory intestinal strictures in patients with CD.

Diameter of Superior Rectal Vein - CT Predictor of KRAS Mutation in Rectal Carcinoma.

BACKGROUND: The purpose of this study was to investigate the feasibility of CT parameters to predict the presence of KRAS mutations in rectal cancer patients. The relationship between the presence of a KRAS mutation and pathological findings was evaluated simultaneously. METHODS: Eighty-nine patients (29 females, 60 males, age 27-90, mean 59.7±12 years) with pathologically proven rectal cancer were enrolled. A KRAS mutation test was completed following surgery. Parameters evaluated on CT included the tumor location, the diameter of the superior rectal vein (SRV) and inferior mesenteric vein (IMV), the presence of calcification, ulceration, lymph node enlargement (LNE), distant metastasis, tumor shape (intraluminal polypoid mass, infiltrative mass, or bulky), circumferential extent (C0-C1/4, C1/4-C1/2, C1/2-C3/4, or C3/4-C1), enhanced pattern (homogeneous or heterogeneous), CT ratio, and the length of the tumor (LOT). Pathological findings included lymphovascular emboli, signet ring cell, peripheral fat interval infiltration, focal ulcer, lymph node metastasis, tumor pathological type, and differentiation extent. The correlations between KRAS status and CT parameters, and KRAS status and pathological findings were investigated. The accuracy of CT characteristics for predicting KRAS mutation was evaluated. RESULTS: A KRAS mutation was detected in 42 cases. On CT image, the diameter of the SRV was significantly increased in the KRAS mutation group compared to in the KRAS wild-type group (4.6±0.9 mm vs 4.2±0.9 mm, p=0.02), and LNE was more likely to occur in the KRAS mutation group (73.3% vs 26.7%, p=0.03). There was no significant difference between the KRAS mutation group and the KRAS wild-type group on the other CT parameters (location, IMV, calcification, ulcer, distant metastasis, tumor shape, enhanced pattern, circumferential extent, CT ratio, and LOT). In the pathological findings, a KRAS mutation was more likely to occur in the middle differentiation group (p=0.03). No significant difference was found between the KRAS mutation group and the KRAS wild-type group in the presence of lymphovascular emboli, signet ring cell, peripheral fat interval infiltration, focal ulcer, lymph node metastasis, and tumor pathological type. With the best cut-off value of 4.07 mm, the AUC of the SRV to predict a KRAS mutation was 0.63 with a sensitivity of 76.2% and a specificity of 48.9%. CONCLUSION: It was feasible to use the diameter of the SRV to predict a KRAS mutation in rectal cancer patients, and LNE also can be regarded as an important clue on preoperative CT images.

A Case of a Huge Inferior Vena Cava Leiomyosarcoma: Precise Preoperative Evaluation with Gadobutrol-Enhanced MRI.

BACKGROUND: Leiomyosarcoma of the inferior vena cava (IVC) is a rare malignant tumour with poor prognosis. Surgical resection is the first line of treatment to achieve the best possible outcome. However, precise preoperative evaluation is essential to guide therapeutic decisions. Here, the preoperative evaluation potential of gadobutrol-enhanced magnetic resonance imaging (MRI) was assessed in the management of a 42-year-old patient with a large IVC mass. METHODS: The patient first underwent enhanced computed tomography (CT), but the relationship between the left renal vein and the mass in the dilated IVC was ambiguous, and it remained unclear whether the right hepatic vein was invaded by the lesion. To make a precise assessment of the tumour, the patient subsequently underwent high-resolution MRI angiography examination combined with high-concentration contrast medium gadobutrol. RESULTS: MRI demonstrated the integrity of the right hepatic vein and the left renal vein. Following a multidisciplinary consultation, a complicated surgery including complete resection of the mass, artificial vessel replacement of IVC, total hepatectomy, and bilateral nephrectomy with liver and kidney auto-transplantation was performed successfully. The surgical plan formulated after reviewing the MRI preoperatively was adhered to the course of the surgery. Postoperative CT re-examination showed that the blood flow of the artificial blood vessel and the right hepatic vein was unobstructed. Histopathological examination confirmed the tumour to be a leiomyosarcoma. CONCLUSION: Preoperative imaging diagnosis and assessment have important implications for the surgical planning of IVC leiomyosarcoma. High-resolution MRI angiography examination with high concentration contrast medium gadobutrol may be of particular benefit in IVC tumours.

Systematic Analysis of Autophagy-Related Signature Uncovers Prognostic Predictor for Acute Myeloid Leukemia.

Autophagy, a highly conserved cellular protein degradation process, has been involved in acute myeloid leukemia (AML). The present study aims to establish a novel, autophagy-related prognostic signature for prediction of AML prognosis. Differentially expressed autophagy-related genes in AML and healthy samples were screened using GSE1159. Univariate Cox regression analysis was applied to determine survival-associated autophagy-related genes in The Cancer Genome Atlas (TCGA) AML cohort. Lasso regression was performed to develop multiple-gene prognostic signatures. A novel six-gene signature (including CASP3, CHAF1B, KLHL24, OPTN, VEGFA, and VPS37C) DC was established for AML prognosis prediction. The Kaplan-Meier survival analysis revealed that patients in the high-risk score group had poorer overall survival (OS). The receiver operating characteristic (ROC) curve validated its good performance in survival prediction in TCGA AML cohort, and the area under the curve value was 0.817. Moreover, our signature could independently predict OS. A nomogram was constructed, including the six-gene signature and other clinical parameters, and predictive efficiency was confirmed using the ROC curve and calibration curve. Furthermore, gene set enrichment analyses identified several tumor-associated pathways that may contribute to explain the potential molecular mechanisms of our signature. Overall, we developed a new autophagy-associated gene signature and nomogram to predict OS of AML patients, which may help in clinical decision-making for AML treatment.

Comprehensive Characterization of the Prognosis Value of Alternative Splicing Events in Acute Myeloid Leukemia.

Increasing evidence have demonstrated that dysregulated alternative splicing (AS) events promoted tumor development and was correlated with worse prognosis in the context of certain malignancies. Nevertheless, a comprehensive examination of the prognosis role of AS events in acute myeloid leukemia (AML) has not yet been illuminated. In this study, univariate and multivariate Cox regression analysis were used to identify survival-related AS events and independent prognostic predictors. The interaction between splicing factors (SFs) and AS events was visualized by Cytoscape. A total of 3013 survival-associated AS events in 1977 genes were screened in 151 AML patients. Interestingly, the majority (2031 events) were revealed to be protective factors. Furthermore, the prediction models were constructed for each type of AS and all of them displayed good performance in predicting prognosis, considering their area under curve values of the receiver operating characteristic were all above 0.7. Notably, the splicing regulatory network displayed the underlying interaction networks between SFs and AS events. Taken together, our study demonstrated the survival-related AS events in AML and uncovered the possible association between SFs and prognostic AS events, which provide new prognostic biomarkers and aid to develop novel targets for AML therapy.