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The most common mesenchymal tumors are gastrointestinal stromal tumors (GISTs), which have malignant potential and can occur anywhere along the gastrointestinal system. Imaging methods are important and indispensable of GISTs in diagnosis, risk staging, therapy, and follow-up. The recommended imaging method for staging and follow-up is computed tomography (CT) according to current guidelines. Artificial intelligence (AI) applies and elaborates theses, procedures, modes, and utilization systems for simulating, enlarging, and stretching the intellectual capacity of humans. Recently, researchers have done a few studies to explore AI applications in GIST imaging. This article reviews the present AI studies in GISTs imaging, including preoperative diagnosis, risk stratification and prediction of prognosis, gene mutation, and targeted therapy response.

The biological behavior of carcinoma of the esophagogastric junction (CEGJ) is different from that of gastric or esophageal cancer. Differentiating squamous cell carcinoma of the esophagogastric junction (SCCEG) from adenocarcinoma of the esophagogastric junction (AEG) can indicate Siewert stage and whether the surgical route for patients with CEGJ is transthoracic or transabdominal, as well as aid in determining the extent of lymph node dissection. With the development of neoadjuvant therapy, preoperative determination of pathological type can help in the selection of neoadjuvant radiotherapy and chemotherapy regimens.To establish and evaluate computed tomography (CT)-based multiscale and multiphase radiomics models to distinguish SCCEG and AEG preoperatively.We retrospectively analyzed the preoperative contrasted-enhanced CT imaging data of single-center patients with pathologically confirmed SCCEG (The 2D-venous model (5 features, AUC: 0.849) performed better than 2D-arterial (5 features, AUC: 0.808). The 2D-arterial-venous combined model could further enhance the performance (AUC: 0.869). The 3D-venous model (7 features, AUC: 0.877) performed better than 3D-arterial (10 features, AUC: 0.876). And the 3D-arterial-venous combined model (AUC: 0.904) outperformed other single-phase-based models. The venous model showed a positive improvement compared with the arterial model (NRI0, IDI0), and the 3D-venous and combined models showed a significant positive improvement compared with the 2D-venous and combined models (The combined arterial-venous CT radiomics model based on 3D segmentation can improve the performance in differentiating EGJ squamous cell carcinoma from adenocarcinoma.

BACKGROUND Hepatocellular carcinoma (HCC) ranks second in terms of cancer mortality worldwide. Molecular magnetic resonance imaging (MRI) targeting HCC biomarkers such as alpha-fetoprotein (AFP) or glypican-3 (GPC3) offers new strategies to enhance specificity and help early diagnosis of HCC. However, the existing iron oxide nanoparticle-based MR molecular probes singly target AFP or GPC3, which may hinder their efficiency to detect heterogeneous micro malignant HCC tumors < 1 cm (MHCC). We hypothesized that the strategy of double antibody-conjugated iron oxide nanoparticles which simultaneously target AFP and GPC3 antigens may potentially be used to overcome the tumor heterogeneity and enhance the detection rate for MRI-based MHCC diagnosis. AIM To synthesize an AFP/GPC3 double antibody-labeled iron oxide MRI molecular probe and to assess its impact on MRI specificity and sensitivity at the cellular level. METHODS A double antigen-targeted MRI probe for MHCC anti-AFP-USPIO-anti-GPC3 (UAG) was developed by simultaneously conjugating AFP andGPC3 antibodies to a 5 nm ultra-small superparamagnetic iron oxide nanoparticle (USPIO). At the same time, the singly labeled probes of anti-AFP-USPIO (UA) and anti-GPC3-USPIO (UG) and non-targeted USPIO (U) were also prepared for comparison. The physical characterization including morphology (transmission electron microscopy), hydrodynamic size, and zeta potential (dynamic light scattering) was conducted for each of the probes. The antigen targeting and MRI ability for these four kinds of USPIO probes were studied in the GPC3-expressing murine hepatoma cell line Hepa1-6/GPC3. First, AFP and GPC3 antigen expression in Hepa1-6/GPC3 cells was confirmed by flow cytometry and immunocytochemistry. Then, the cellular uptake of USPIO probes was investigated by Prussian blue staining assay and in vitro MRI (T2-weighted and T2-map) with a 3.0 Tesla clinical MR scanner. RESULTS Our data showed that the double antibody-conjugated probe UAG had the best specificity in targeting Hepa1-6/GPC3 cells expressing AFP and GPC3 antigens compared with single antibody-conjugated and unconjugated USPIO probes. The iron Prussian blue staining and quantitative T2-map MRI analysis showed that, compared with UA, UG, and U, the uptake of double antigen-targeted UAG probe demonstrated a 23.3% (vs UA), 15.4% (vs UG), and 57.3% (vs U) increased Prussian stained cell percentage and a 14.93% (vs UA), 9.38% (vs UG), and 15.3% (vs U) reduction of T2 relaxation time, respectively. Such bi-specific probe might have the potential to overcome tumor heterogeneity. Meanwhile, the coupling of two antibodies did not influence the magnetic performance of USPIO, and the relatively small hydrodynamic size (59.60 ± 1.87 nm) of double antibody-conjugated USPIO probe makes it a viable candidate for use in MHCC MRI in vivo, as they are slowly phagocytosed by macrophages. CONCLUSION The bi-specific probe presents enhanced targeting efficiency and MRI sensitivity to HCC cells than singly- or non-targeted USPIO, paving the way for in vivo translation to further evaluate its clinical potential.

Hepatocellular carcinoma (HCC) is the second leading cause of cancer-related mortality. HCC-targeted magnetic resonance imaging (MRI) is an effective noninvasive diagnostic method that involves targeting clinically-related HCC biomarkers, such as alpha-fetoprotein (AFP) or glypican-3 (GPC3), with iron oxide nanoparticles. However,To validate the effectiveness of a bi-specific probeThe single- and double-antibody-conjugated 5-nm USPIO probes, including anti-AFP-USPIO (UA), anti-GPC3-USPIO (UG), and anti-AFP-USPIO-anti-GPC3 (UAG), were synthesized. T1- and T2-weighted MRI were performed on day 10 after establishment of the orthotopic HCC mouse model. Following intravenous injection of U, UA, UG, and UAG probes, T1- and T2-weighted images were obtained at 12, 12, and 32 h post-injection. At the end of scanning, mice were euthanized, and a histologic analysis was performed on tumor samples.T1- and T2-weighted MRI showed that absolute tumor-to-background ratios in UAG-treated HCC mice peaked at 24 h post-injection, with the T1- and T2-weighted signals increasing by 46.7% and decreasing by 11.1%, respectively, relative to pre-injection levels. Additionally, T1-weighted contrast in the UAG-treated group at 24 h post-injection was enhanced 1.52-, 2.64-, and 4.38-fold compared to those observed for single-targeted anti-GPC3-USPIO, anti-AFP-USPIO, and non-targeted USPIO probes, respectively. Comparison of U-, UA-, UG-, and UAG-treated tumor sections revealed that UAG-treated mice exhibited increased stained regions compared to those observed in UG- or UA-treated mice.The bi-specific T1-positive contrast-enhanced MRI probe (UAG) for HCC demonstrated increased specificity and sensitivity to diagnose early-stage HCC irrespective of tumor size and/or heterogeneity.

Jian Zhao,1,2,* Wei Zhang,1,2,* Jun Zhang,1 Yun-Tian Chen,1 Wen-Jie Ma,3 Si-Yun Liu,4 Fu-Yu Li,3,* Bin Song1,* 1Department of Radiology, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, People’s Republic of China; 2Department of Radiology, Armed Police Force Hospital of Sichuan, Leshan 614000, Sichuan, People’s Republic of China; 3Department of Biliary Surgery, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, People’s Republic of China; 4GE Healthcare (China), Beijing 100176, People’s Republic of China*These authors contributed equally to this workCorrespondence: Bin Song Email songlab_radiology@163.comPurpose: In current clinical practice, early recurrence (ER) is not commonly discussed in perihilar cholangiocarcinoma (pCCA), and its risk factors for this disease have not been well clarified. We carried out this study to analyze the risk factors contributing to ER and explored the prognostic factors after curative resection for pCCA.Patients and Methods: A total of 335 consecutive pCCA patients were retrospectively analyzed. Risk factors contributing to ER were evaluated using univariate and multivariate logistic regression analyses. Prognostic factors of the ER group were determined by univariate and multivariate Cox regression models. The overall survival (OS) rate was calculated using the Kaplan–Meier method. The Log rank test was used for OS comparison.Results: Of the 335 cases, 258 patients (77.0%) developed tumor recurrence, 136 patients (40.6%) developed ER, and 122 patients (36.4%) developed late recurrence (LR) postoperatively. The median OS of the ER and LR groups was 15 months and 36 months, respectively (P< 0.001). The multivariate analysis revealed that poor pathological differentiation (P=0.006; moderate vs well, odds ratio [OR]=2.162, 95% confidence interval [CI] 0.753– 6.208, P=0.152; poor vs well, OR=4.839, 95% CI 1.544– 15.170, P=0.007), perineural invasion (OR=4.797, 95% CI 1.586– 14.510, P=0.005), and high levels of preoperative carbohydrate antigen 19– 9 (CA19-9) (OR=2.205, 95% CI 1.208– 4.026, P=0.010) were independent risk factors of developing ER after resection. Adjuvant chemotherapy (HR=0.383, 95% CI 0.154– 0.953, P=0.039) remained as the independent protective factor of OS in patients with ER.Conclusion: It is recommended that patients with poorly differentiated tumors, presence of perineural invasion, and high levels of preoperative CA19-9 receive closer follow-up and adjuvant chemotherapy following surgery.Keywords: perihilar cholangiocarcinoma, adjuvant chemotherapy, overall survival, curative-intent resection, early recurrence

BackgroundSurvival status prediction for perihilar cholangiocarcinoma (pCCA) patients is essential for postoperative clinical decision making. This study aimed to develop and validate prediction models for overall survival (OS) evaluation in pCCA patients preoperatively.Materials and MethodsA total of 184 patients who had curative resection for pCCA between January 2010 and December 2018 were enrolled. 110 patients were randomly selected for model development while other 74 patients for model testing. Imaging-derived radiomics signatures were developed. Independent preoperative clinical predictors were involved independently or in combination with radiomics signatures to construct different preoperative models through multivariable Cox proportional hazards method. The nomograms were constructed to predict OS, and the performance of which was evaluated by the discrimination ability, time-dependent receiver operating characteristic curve (ROC), calibration curve and decision curve.ResultsThe clinical model (Modelclinic) was constructed based on three independent variables including preoperative CEA, cN stage and invasion of hepatic artery in images. The model with best performance (Modelclinic&AP&PVP) was build based on three independent variables, SignatureAP and SignaturePVP. In training and testing cohorts, the concordance indexes (C-indexes) of Modelclinic were 0.846 (95% CI, 0.735-0.957) and 0.755 (95% CI, 0.540-969), and Modelclinic&AP&PVP manifested favorable performance with C-indexes of 0.962 (95% CI, 0.905-1) and 0.814 (95% CI, 0.569-1), and both of them outperformed TNM staging system (C-indexes, 0.616, 95% CI, 0.522-0.711 and 0.599, 95% CI, 0.490-0.708). Good agreement was observed in the calibration curves, and favorable clinical utility was validated using the decision curve analysis both for Modelclinic and Modelclinic&AP&PVP.ConclusionsTwo preoperative nomograms were constructed to predict 1-, 3- and 5-years survival following surgery for individual pCCA patients. Such methods are easy to be performed which have clinical application potential for decision-making and patients stratification in randomized, controlled trials.

Background Both the 7th and 8th editions of the American Joint Committee on Cancer (AJCC) staging system for perihilar cholangiocarcinoma (pCCA) had the same definition for T2a and T2b. But the value of this classification as prognostic factor remains unclear. Methods 178 patients with stage T2a or T2b who underwent curative intent resection for pCCA between Jan 2010 and Dec 2018 were enrolled. Relationships between survival and clinicopathological factors including patient demographics and tumor characteristics were evaluated using univariate and multivariate Cox regression analysis. The overall survival (OS) were calculated by Kaplan-Meier method. Results There was no significant difference in OS between T2a and T2b groups, and the median OS duration were 37 and 31 months (P = 0.354). Both the 7th and 8th edition of the AJCC TNM staging demonstrated a poor prognostic predictive performance. High level of preoperative AST (≥85.0 IU/L) and CA19–9 (≥1000 U/mL), vascular resection and lower pathological differentiation of the tumor were the independent predictors for poor survival after resection. Conclusion The newly released 8th edition of AJCC staging system demonstrated a poor ability to discriminate the prognosis of patients with stage T2a and T2b pCCA after resection.

Additional file 1 Table S1. Univariate regression analyses of the prognostic factors.

Background The association between G protein β-polypeptide 3 gene (GNB3) c.825C > T polymorphism (rs5443) and the risk of overweight/obesity has been investigated in many published studies, but the results were conflicting and inconclusive. A meta-analysis was performed to make a more accurate assessment of the relationship. Methods The PubMed, ProQuest Health & Medical Complete, Web of Science, Chinese Biomedical Medical databases (CBM), Chinese National Knowledge Infrastructure (CNKI), and Wan Fang databases were searched to identify eligible literatures. Pooled odds ratios (ORs) with the corresponding 95% confidence intervals (CIs) were used to assess the strength of association between GNB3 c.825C > T polymorphism and overweight/obesity. Results Eleven articles including 15 case–control studies with a total of 10,396 subjects (3171 cases of overweight/obesity and 7225 controls) were enrolled in the meta-analysis. The GNB3 c.825C > T was significantly associated with overweight/obesity under a recessive model (OR = 1.22, 95% CI: 1.04–1.44, P = 0.015). Moreover, the GNB3 825T allele was obviously associated with overweight alone in all inheritable models (P T polymorphism and overweight/obesity risk in males under an allelic model (P = 0.008), a homozygous model (P = 0.014), a recessive model (P = 0.005), and a dominant model (P = 0.049). And the results also showed that GNB3 c.825C > T polymorphism was significantly associated with overweight/obesity in subgroups of mean age less than 30 years, consistent with HWE, and high-quality studies (P = 0.027, P = 0.043, P = 0.040, respectively) under a recessive model, but not in other subgroups. Meta-regression also revealed that P value of HWE, publication year, and the quality scores of studies were the sources of heterogeneity in a recessive model and an allelic model. “Leave one out” sensitivity analyses indicated that the association was more significant after excluding some studies. The funnel plot and Egger's linear regression test and Begg's test revealed no apparent publication bias. Conclusion This meta-analysis suggests that the presence of TT homozygote might be one of the genetic factors susceptible to overweight/obesity and that males or aged under 30 years increase the genetic susceptibility.

To investigate whether Liver Imaging Reporting and Data System (LI-RADS) imaging features at preoperative gadoxetic acid-enhanced MRI can predict microvascular invasion (MVI) and histologic grade of hepatocellular carcinoma (HCC) and to evaluate their associations with recurrence after curative resection of single HCC.From July 2015 to September 2018, 111 consecutive patients with pathologically confirmed HCC who underwent gadoxetic acid-enhanced MRI within 1 month before surgery were included in this retrospective study. Significant MRI findings and clinical parameters for predicting MVI, high-grade HCCs and postoperative recurrence were identified by logistic regression model and Cox proportional hazards model.Twenty-six of 111 (23.4 %) patients had MVI and 36 of 111 (32.4 %) patients had high-grade HCCs, whereas 44 of 95 (46.3 %) patients experienced recurrence. Tumor size5 cm (OR = 9.852; p 0.001) and absence of nodule-in-nodule architecture (OR = 8.302; p = 0.001) were independent predictors of MVI. Enhancing capsule (OR = 4.396; p = 0.004) and corona enhancement (OR = 3.765; p = 0.021) were independent predictors of high-grade HCCs. Blood products in mass (HR = 2.275; p = 0.009), corona enhancement (HR = 4.332; p 0.001), and serum AFP level400 ng/mL (HR = 2.071; p = 0.023) were independent predictors of recurrence.LI-RADS imaging features can be used as potential biomarkers for predicting aggressive pathologic features and recurrence of HCC. The identification of prognostic LI-RADS imaging features may facilitate the selection of surgical candidates and optimize the management of HCC patients.

Iron nanoparticles have an increasingly more and more important role in MR molecular imaging due to their novel magnetic and surface chemical properties. They provide new possibilities for noninvasive diagnosis and treatment monitoring, especially for tissues that are rich in macrophages. The smaller size and prolongation of the plasma half-life change the in vivo fate of ultrasmall superparamagnetic iron oxide (USPIO) nanoparticles captured by liver in reticuloendothelial system (RES) or mononuclear phagocytic system (MPS). However, there is still a lack of MR imaging studies on the liver assessing USPIO nanoparticles in vitro phantom and in vivo rat liver imaging characteristics of USPIO nanoparticles (T1 relaxation rates compared with commercial gadolinium chelates. In addition, USPIO nanoparticles (in vivo demonstrated both positive (T1) and negative (T2) liver contrast enhancement in healthy rats’ liver. Furthermore, USPIO nanoparticles showed relatively good in vitro biocompatibility and fast clearance (within 45.17 minutes after intravenous injection) in the normal liver. Taken together, these data might inspire a new personalized and precise diagnostic tool and stimulate new applications for specific targeted molecular probes.

Objective To investigate the preoperative predictive value of non-invasive imaging biomarkers for programmed cell death protein 1/programmed cell death protein ligand 1 (PD-1/PD-L1) expression and outcome in intrahepatic cholangiocarcinoma (ICC) using machine learning.Methods PD-1/PD-L1 expression in 98 ICC patients was assessed by immunohistochemistry, and their prognostic effects were analysed using Cox regression and Kaplan-Meier analysis. Radiomic features were extracted from MRI in the arterial and portal vein phases, and three sets of Radiomics score (Radscore) with good performance were derived respectively as biomarkers for predicting PD-1, PD-L1 expression and overall survival (OS). PD-1 and PD-L1 expression models were developed using the Radscore (arterial phase), clinico-radiological factors and clinical factors, individually and in combination. The imaging-based OS predictive model was constructed by combining independent predictors among clinico-radiological, clinical factors and OS Radscore. Pathology-based OS model using pathological and clinical factors was also constructed and compared with imaging-based OS model.Results The highest area under the curves of the models predicting PD-1 and PD-L1 expression was 0.897 and 0.890, respectively. PD-1+ and PD-L1+ cases had worse outcomes than negative cases. The 5-year survival rates of PD-1+ and PD-1− cases were 12.5% and 48.3%, respectively (p

In the past years, the routing problems of wireless sensor network (WSN) have attracted great interest, and many routing protocols for WSN have being proposed. However, these routing protocols pay little attention to the transmission reliability. Therefore, this paper proposes a new multipath ring routing algorithm for WSNs. The proposed algorithm utilizes ring level to separate sensor nodes into several sections, which can improve the reliability of the data transmission. Using the multipath ring routing data delivery improves as an average of 27.58% to leach routing and 113.06% to directing routing.