Accurate prognostic stratification of invasive ductal carcinoma (IDC) of breast is essential for personalized breast cancer treatment, but single-index DWI fails to capture the complex non-Gaussian diffusion of intratumoral water molecules. This study aims to evaluate the diagnostic efficacy of combining dynamic enhanced magnetic resonance imaging (DCE-MRI) features and quantitative parameters from six diffusion models in predicting prognostic factors of mass-type breast invasive ductal carcinoma (IDC). 100 patients with mass-type IDC who underwent routine breast MRI and multi-b-value DWI examinations in our hospital were enrolled. All patients were confirmed pathologically and had immunohistochemical results for Ki-67 and tumor-infiltrating lymphocytes (TILs). Among them, 15 cases exhibited low Ki-67 expression, while 85 cases showed high Ki-67 expression; 54 cases were categorized in the low TILs group, and 46 cases were classified in the medium-high TILs group. Clinical features, routine MRI features, and parameters from six diffusion models, including continuous-time random walk (CTRW), fractional order calculus (FROC), stretched exponential model (SEM), intravoxel incoherent motion magnetic resonance imaging (IVIM), and diffusion kurtosis imaging (DKI), were recorded. Independent predictive factors were identified through multivariate regression analysis. Receiver operating characteristic (ROC) curves were constructed to evaluate and compare the diagnostic efficacy of both independent and combined parameters. The Delong test was used to compare the diagnostic efficacy of each model. Multivariate logistic regression analysis identified αCTRW as an independent predictor of Ki-67 and TILs levels, with AUCs of 0.732 and 0.649, respectively. Additionally, the combination of time signal curve (TIC) and αCTRW for predicting TILs levels yielded an AUC of 0.700. There was no statistically significant difference between the AUCs of αCTRW and the combined diagnostic model (αCTRW + TIC). CTRW-α is a promising non-invasive imaging biomarker for predicting Ki-67 and TILs levels in mass-type IDC. By capturing intratumoral water molecule diffusion heterogeneity, it supports accurate preoperative prognostic stratification and personalized treatment decision-making, highlighting the value of multi-exponential DWI models in breast cancer imaging. Not applicable.

Contrast-enhanced CT is commonly used in the evaluation of hepatic metastatic lesions. This prospective study aimed to assess the capability of artificial intelligence iterative reconstruction (AIIR) in low-dose CT for detection of hepatic metastases. Thirty-two patients with hepatic metastases were enrolled and underwent dual-phase CT scans in the venous phase. Each patient received a standard-dose (SD) scan (120 kVp, 180 mAs) followed by a reduced-dose (RD) scan (120 kVp, 30 mAs). All data were analyzed. Compared with the SD CT scan, the volume CT dose index (CTDIvol), dose length product (DLP), and effective dose (ED) were significantly lower in the RD CT, with an average radiation reduction of approximately 80%. Radiation dose reduction significantly increased the image noise and degraded the overall image quality. However, higher reconstruction levels resulted in an increasingly prominent "waxy" texture on CT images. There were significant (P < 0.05) differences in liver noise, subcutaneous fat noise, liver parenchymal SNR, portal vein SNR, liver parenchymal contrast-to-noise ratio (CNR), and portal vein CNR among the groups, with the ranking of noise, SNR and CNR: RD AIIR Group 1 < RD AIIR Group 3 < RD AIIR Group 5 < SD Karl group < RD Karl group. In the SD Karl group, 218 metastatic nodules were identified in 32 patients, whereas in the RD Karl group, 176 (80.7%) nodules were detected. However, the RD AIIR 3 group identified 204 (93.6%) nodules. As the metastatic nodules size decreased, the detection rate in the RD Karl significantly declined, with the detection rate 14.29% in the RD Karl for nodules < 5 mm. In RD AIIR 3, the detection rate reached 100% for lesions ≥ 10 mm and 57.14% for lesions < 5 mm, which was significantly higher than in the RD Karl group (P < 0.05). The CNR of intrahepatic nodules in the SD Karl, RD Karl, and RD AIIR 3 was 2.68 (2.17, 3.80), 2.18 (1.64, 2.79), and 6.34 (4.34, 8.36), respectively, with significant differences among them. The signal-to-noise ratio of the lesions in the RD AIIR 3 group was the highest. AIIR-assisted low-dose CT reconstruction yields image quality comparable to standard-dose CT, while preserving diagnostic performance for the detection of small hepatic metastases.

Background: One of the most common primary tumor sources of brain metastases (BMs) is lung cancer. As certain magnetic resonance imaging (MRI) features overlap between the pathological and genetic subtypes of lung cancer BMs, directly determining the primary site based on these features remains a challenge. Thus, identifying the MRI features of different subtypes of lung cancer BMs is crucial in order to facilitate early diagnosis and treatment. This study aimed to characterize the MRI characteristics distinct to the various subtypes of lung cancer BMs in order to inform clinical decision-making. Methods: Data from 1,129 patients diagnosed with lung cancer BMs (a total of 8,312 lesions) from three institutions, including clinicopathological information and MRI features, were retrospectively analyzed. Among these cases of BMs, 369 (2,780 lesions) originated from small-cell lung cancer (SCLC) and 760 (5,532 lesions) from non-small cell lung cancer (NSCLC). Among the patients with NSCLC, there were 689 cases (5,243 lesions) of adenocarcinoma (AD) and 71 cases (289 lesions) of squamous cell carcinoma (SCC). Regarding epidermal growth factor receptor (EGFR) status, there were 188 wild-type cases (1,257 lesions) and 344 mutant-type cases (2,880 lesions). This study was divided into three parts. For Part I (comparison between SCLC and NSCLC), Part II (comparison between AD and SCC), and Part III (comparison between EGFR wild type and mutant type), a stepwise in-depth analysis was performed-from the level of pathological classification to the level of gene mutation status-of the clinical characteristics of patients with lung cancer BMs and of the quantity, size, location, and signal characteristics of BMs lesions based on brain MRI. According to different signal combinations of DWI and CE-T1WI, the BMs lesions were divided into seven patterns, namely Pattern I-VII: Pattern I: DWI-negative + CE-T1WI-positive; Pattern II: DWI-negative + CE-T1WI ring; Pattern III: DWI-positive + CE-T1WI-positive; Pattern IV: DWI ring + CE-T1WI-positive; Pattern V: DWI-positive + CE-T1WI ring; Pattern VI: DWI ring + CE-T1WI ring; Pattern VII: DWI-positive + CE-T1WI-negative. "Positive" indicates homogeneous hyperintensity on DWI or CE-T1WI, while "negative" indicates hypointensity or isointensity on DWI or CE-T1WI, and "ring" refers to ring enhancement. Results: In the Part I analysis, SCLC BMs tended to be multiple (>10 lesions; 0.5-1 cm in size), occur in the frontal/parietal lobes and periventricular regions, and have higher proportions of patterns consisting of diffusion-weighted imaging (DWI)-positive plus contrast-enhanced T1-weighted imaging (CE-T1WI) ring features, DWI ring plus CE-T1WI ring features, and DWI-positive plus C E-T1WI-negative features (all P values <0.05). NSCLC BMs had higher proportions of patterns consisting of DWI-negative plus CE-T1WI-positive features and DWI ring plus CE-T1WI-positive features (P<0.05). In the Part II analysis, as compared to AD BMs, SCC BMs have more peritumoral edema, and occur in the centrum semiovale, with higher proportions of patterns consisting of DWI ring plus CE-T1WI ring features and DWI-positive plus CE-T1WI-negative features (P<0.05). EGFR mutant-type BMs tended to be multiple (>10 lesions; <1 cm in size) and have less hemorrhage compared to wild-type BMs (P>0.05). DWI hyperintensity without CE-T1WI enhancement was more common in SCLC BMs than in NSCLC BMs (20.6% vs. 4.5%; P<0.001) and in SCC BMs than in AD BMs (33.9% vs. 2.8%; P<0.001). Conclusions: MRI and clinical features may provide the ability to noninvasively distinguish between SCLC and NSCLC and between AD and SCC, as well as to partially indicate EGFR status. DWI hyperintensity without CE-T1WI enhancement might serve as a key subtype-specific feature that could aid in clinical decision-making.

BACKGROUND:Hepatic metastatic neuroendocrine neoplasms (HM-NENs) have few treatment biomarkers and low survival rates. We created a clinical-radiomics fusion model based on non-contrast computed tomography (NCCT) to predict Surufatinib efficacy in HM-NENs. We presented it as a nomogram, meeting unmet requirements in precision medicine. METHODS:This retrospective study included 76 HM-NEN patients (131 hepatic metastases) treated with Surufatinib. Regions of interest (ROI) were manually segmented, and the best response to Surufatinib was decided based on Modified Response Evaluation Criteria in Solid Tumor (mRECIST). Radiomics features were extracted from the pretreatment NCCT. The Least Absolute Shrinkage and Selection Operator (LASSO) was used to select radiomics features and calculate a Radiomics score (Radscore). Multivariable logistic regression analysis was utilized to create the clinical-radiomics fusion model, which included clinical characteristics and Radscore and was displayed as a nomogram. The area under the receiver operating characteristic curve (ROC) was used to assess model performance, and internal validation was done using the bootstrap resampling approach. RESULTS:After multivariate logistic regression analysis, the Radscore, the diameter of hepatic metastasis, the number of hepatic metastases, and extrahepatic metastasis were included as predictors in the final model. The area under the curve (AUC) of the clinical-radiomics fusion model to predict the response of Surufatinib of HM-NENs was 0.926 (95% confidence interval [CI]: 0.881-0.971). The AUC verified by bootstrap was 0.926 (95% CI: 0.880-0.966), indicating a good performance of the fusion model. CONCLUSION:The clinical-radiomics fusion model can effectively identify patients with HM-NENs sensitive to Surufatinib therapy. The nomogram provides clinicians with a convenient and dependable tool for decision-making.

To construct and validate a model based on clinical characteristics and magnetic resonance imaging (MRI) radiomics to predict 1-year efficacy of epidermal growth factor receptor tyrosine kinase inhibitors (EGFR-TKIs) in patients with EGFR-mutant non-small cell lung cancer (NSCLC) brain metastases (BMs). This study retrospectively analyzed data from 338 patients with EGFR-mutant NSCLC BMs from three centers, including MRI, clinical and pathological data, and radiological features. Based on the selected significant radiomic features from intratumoral regions extracted from CE-T1WI, while exploring the value of features in 3/5/8 mm peritumoral regions, seven commonly used machine learning algorithms were compared to select the optimal one for model construction, and the best algorithm was selected for model construction. In the model predicting 1-year therapeutic efficacy, clinical, radiomic, and combined models were constructed separately. The model performance was evaluated using receiver operating characteristic curves. The final development cohort comprised 285 patients from Center 1, while the external validation set included 57 patients from Centers 2 and 3. In the model predicting 1-year EGFR-TKIs efficacy, the random forest algorithm, which showed the best application, was used to construct the model. Compared with the radiomic and clinical models, the combined model exhibited superior area under the curve performance in the test set (0.756 vs. 0.644 vs. 0.668). In the external validation set, the combined model achieved an area under the curve of 0.743 (95

BackgroundGastrointestinal neuroendocrine tumor (GI-net) is a rare heterogeneous tumor, and there is a lack of models to predict its prognosis. Our study aims to develop and validate two new nomograms to predict the overall survival (OS) and cancer-specific survival (CSS) of GI-net patients and investigate their application value.MethodsSEER*Stat 8.4.4 software was used to download clinicopathological information of GI-net patients between 2010 and 2015 from the Surveillance, Epidemiology, and End Results (SEER) database. These patients were randomly divided into a training group (n=3007) and an internal-validation group (n=1289) at a 7:3 ratio. Patients from the Fourth Hospital of Hebei Medical University were enrolled in this study to form the external-validation group (n=86). Univariate and multivariate Cox analyses were performed to explore the independent prognostic factors and establish two nomograms. The concordance index (C-index), area under the time-dependent receiver operating characteristic curve (AUC), calibration curve, and decision curve analysis (DCA) were used to evaluate the nomograms. X-tile was used to divide GI-net patients into high-, medium-, and low-risk groups. Kaplan–Meier (KM) curves and log-rank tests were used to compare survival differences among the three groups.ResultsSeven variables (age, site, size, grade, M stage, surgery, and chemotherapy) were selected to establish the nomogram for OS, and 6 variables (age, size, grade, M stage, surgery, and chemotherapy) were selected for CSS. The C indices (0.785, 0.813, and 0.936 in the training, internal-validation, and external-validation groups for OS; 0.888, 0.893, and 0.930 for CSS, respectively) and AUCs (≥0.7) indicated that the nomograms had satisfactory discriminative ability. Calibration curve analysis and DCA revealed that the nomogram had a satisfactory ability to predict OS and CSS. KM curves indicated that each of the two nomograms clearly differentiated the high-, medium-, and low-risk groups. In addition, two online risk calculators were developed to predict the OS and CSS of these patients visually.ConclusionsOur nomograms may play an important role in predicting 3- and 5-year OS and CSS for GI-net patients. Risk stratification systems and online risk calculators can be utilized in clinical practice to help doctors create personalized treatment plans.

Diagnosis of peritoneal invasion, lymph node metastasis, and hepatic metastasis is crucial in the decision-making process of ovarian tumor treatment. This study aimed to test the feasibility of low-dose abdominopelvic CT with an artificial intelligence iterative reconstruction (AIIR) for diagnosing peritoneal invasion, lymph node metastasis, and hepatic metastasis in pre-operative imaging of ovarian tumor. This study prospectively enrolled 88 patients with pathology-confirmed ovarian tumors, where routine-dose CT at portal venous phase (120 kVp/ref. 200 mAs) with hybrid iterative reconstruction (HIR) was followed by a low-dose scan (120 kVp/ref. 40 mAs) with AIIR. The performance of diagnosing peritoneal invasion and lymph node metastasis was assessed using receiver operating characteristic (ROC) analysis with pathological results serving as the reference. The hepatic parenchymal metastases were diagnosed and signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were measured. The perihepatic structures were also scored on the clarity of porta hepatis, gallbladder fossa and intersegmental fissure. The effective dose of low-dose CT was 79.8

Background:Hepatic metastatic neuroendocrine neoplasms (HM-NENs) have few treatment biomarkers and low survival rates. We created a clinical-radiomics fusion model to predict surufatinib efficacy in HM-NENs and presented it as a nomogram, meeting unmet requirements in precision hepatology. Methods:This study included 76 HM-NEN patients (131 hepatic metastases) treated with surufatinib. SlicerRadiomics was used to extract radiomics features from arterial phase computed tomography (APCT). The least absolute shrinkage and selection operator (LASSO) was used to select radiomics features and calculate a radiomics score (Radscore). Multivariable logistic regression analysis was utilized to create the clinical-radiomics fusion model, which included clinical characteristics and Radscore and was displayed as a nomogram. The area under the receiver operating characteristic curve (ROC) was used to assess model performance, and internal validation was done using the bootstrap resampling approach. Results:After multivariate logistic regression analysis, the Radscore, Ki67 antigen (Ki67), number of hepatic metastases, and extrahepatic metastasis were included as predictors in the final model. The area under the curve (AUC) of the clinical-radiomics fusion model to predict the response of surufatinib of HM-NENs was 0.928 (95% CI: 0.885 - 0.971). The AUC verified by bootstrap is 0.928 (95% CI: 0.881-0.965), indicating a good performance of the fusion model. Conclusion:The clinical-radiomics fusion model can effectively identify patients with HM-NENs sensitive to surufatinib therapy. The nomogram provided clinicians with a convenient and dependable tool for decision-making.

BackgroundHepatocellular carcinoma (HCC) often exhibits microvascular invasion (MVI), a feature with unclear mechanisms. Therefore, it is crucial to resolve its related cellular populations and molecular networks using single-cell analysis.MethodsBoth single-cell RNA sequencing (scRNA-seq) and RNA-seq data for HCC were obtained from public databases. ScRNA-seq data were clustered and annotated using Seurat. DAVID, CellChat, and Monocle 2 were used for scRNA-seq functional enrichment analysis, intercellular communication analysis, and cell trajectory analysis, respectively. We further assessed key gene expression in HCC cell lines and examined their effects on cell functions using CCK-8, scratch, and transwell assays.ResultsThe HCC ecosystem comprising myofibroblasts (MFs), hepatocytes, proliferative hepatocytes, endothelial cells, dendritic cells, proliferative NK/T cells, plasma B cells, and macrophages was revealed. MFs showed the greatest difference between MVI-absent and MVI-present patients and were subdivided into five clusters. Key genes for angiogenesis are overexpressed in MF2 cells and enriched in the pathways of angiogenesis, cell migration, cell proliferation, and signal transduction. Pseudotime analysis revealed MF2 cells from MVI-present patients clustered at the terminal state and positively correlated with angiogenesis. CAMK2N1 in the markers of MF2 cells was significantly associated with advanced M stage and poor prognosis. Further cellular assays showed that CAMK2N1 expression was downregulated in HCC cells, and its knockdown increased the proliferation, migration, and invasion levels of cancer cells.ConclusionThis study highlighted the role and potential mechanism of MFs in promoting MVI formation and provides a potential marker for HCC prognosis among MF markers.

Background:Breast cancer (BC) is the most prevalent malignant tumor among women worldwide, significantly impacting women's health and lives. The accurate assessment of axillary lymph node (ALN) status is critical for BC staging, treatment planning, and the evaluation of overall survival outcomes. This study aimed to explore the relationship between preoperative ultrafast dynamic contrast-enhanced magnetic resonance imaging (UF-DCE MRI) parameters and ALN metastasis in patients with mass-type invasive ductal carcinoma (IDC) of the breast, and to construct a nomogram model for predicting ALN metastasis. Methods:Preoperative UF-DCE MRI images and medical records of 96 breast IDC patients (38 with ALN metastasis, 58 without) confirmed by pathology were retrospectively analyzed. Conventional MRI features, UF-DCE MRI parameters, DCE parameters, and clinical features were evaluated. Receiver operating characteristic (ROC) curves and nomograms for univariate parameters and combined diagnostic efficiency were constructed. Results:Time-to-enhancement (TTE), time-to-peak (TTP) after enhancement, and time to center of maximum slope (TTMS) were significantly lower in the ALN metastasis group (10.05±4.91 vs. 15.59±15.04 s, 85.89±33.93 vs. 208.27±102.28 s, 19.05±22.25 vs. 19.81±9.29 s; all P<0.05), whereas tumor size was significantly smaller in the non-ALN metastasis group (2.2±1.1 vs. 3.0±1.5 cm, P<0.001). No other clinical or imaging parameters showed significant differences between groups. TTP had the best diagnostic efficacy for ALN metastasis, with an area under the curve (AUC) of 0.865 [95% confidence interval (CI): 0.794-0.937]. The combined parameter prediction model improved the diagnostic efficacy, with an AUC of 0.919 (95% CI: 0.864-0.974). The nomogram indicated that TTP had the greatest impact on lymph node metastasis, followed by tumor size and apparent diffusion coefficient (ADC). The nomogram indicated that metastasis probability = escore/1 + escore, with score = 0.70 * tumor size - 2.49 * ADC - 0.03 * TTP + 3.78. Conclusions:Multiple UF-DCE MRI parameters can predict ALN metastasis in patients with mass-like breast IDC before operation. The nomogram model combined with clinical and UF-DCE MRI parameters can better assist clinicians in making personalized treatment plans for patients.

Background: Improving immunotherapy efficacy for EGFR-negative lung adenocarcinoma (LUAD) patients remains a critical challenge, and the therapeutic effect of immunotherapy is largely determined by the tumor microenvironment (TME). Tumor-associated macrophages (TAMs) are the top-ranked immune infiltrating cells in the TME, and M2-TAMs exert potent roles in tumor promotion and chemotherapy resistance. An M2-TAM-based prognostic signature was constructed by integrative analysis of single-cell RNA-seq (scRNA-seq) and bulk RNA-seq data to reveal the immune landscape and select drugs in EGFR-negative LUAD. Methods: M2-TAM-based biomarkers were obtained from the intersection of bulk RNA-seq data and scRNA-seq data. After consensus clustering of EGFR-negative LUAD into different clusters based on M2-TAM-based genes, we compared the prognosis, clinical features, estimate scores, immune infiltration, and checkpoint genes among the clusters. Next, we combined univariate Cox and LASSO regression analyses to establish an M2-TAM-based prognostic signature. Results: CCL20, HLA-DMA, HLA-DRB5, KLF4, and TMSB4X were verified as prognostic M2-like TAM-related genes by univariate Cox and LASSO regression analyses. IPS and TMB analyses revealed that the high-risk group responded better to common immunotherapy. Conclusion: The study shows the potential of the M2-like TAM-related gene signature in EGFR-negative LUAD, explores the immune landscape based on M2-like TAM-related genes, and predict immunotherapy response of patients with EGFR-negative LUAD, providing a new insight for individualized treatment.

Abstract Objective To evaluate the feasibility, safety and efficacy of concurrent simultaneous integrated boost intensity-modulated radiotherapy (SIB-IMRT) combined with nimotuzumab in the treatment of locally advanced esophageal squamous cell cancer (ESCC). Methods Eligible patients were histologically proven to have locally advanced ESCC, and were unable to tolerate or refuse concurrent chemoradiotherapy (CCRT). Enrolled patients underwent concurrent SIB-IMRT in combination with nimotuzumab. SIB-IMRT: For the planning target volume of clinical target volume (PTV-C), the prescription dose was 50.4 Gy/28fractions, 1.8 Gy/fraction, 5fractions/week, concurrently, the planning target volume of gross tumor (PTV-G) undergone an integrated boost therapy, with a prescription dose of 63 Gy/28fractions, 2.25 Gy/fraction, 5 fractions/week. Nimotuzumab was administered concurrently with radiotherapy, 200 mg/time, on D1, 8, 15, 22, 29, and 36, with a total accumulation of 1200 mg through intravenous infusion. The primary endpoint of the study was the safety and efficacy of the combined treatment regimen, and the secondary endpoints were 1-year, 2-year, and 3-year local control and survival outcomes. Results (1) From December 2018 to August 2021, 35 patients with stage II-IVA ESCC were enrolled and 34 patients completed the full course of radiotherapy and the intravenous infusion of full-dose nimotuzumab. The overall completion rate of the protocol was 97.1%. (2) No grade 4–5 adverse events occurred in the entire group. The most common treatment-related toxicity was acute radiation esophagitis, with a total incidence of 68.6% (24/35). The incidence of grade 2 and 3 acute esophagitis was 25.7% (9/35) and 17.1% (6/35), respectively. The incidence of acute radiation pneumonitis was 8.6% (3/35), including one case each of Grades 1, 2, and 3 pneumonitis. Adverse events in other systems included decreased blood cells, hypoalbuminemia, electrolyte disturbances, and skin rash. Among these patients, five experienced grade 3 electrolyte disturbances during the treatment period (three with grade 3 hyponatremia and two with grade 3 hypokalemia). (3) Efficacy: The overall CR rate was 22.8%, PR rate was 71.4%, ORR rate was 94.2%, and DCR rate was 97.1%.(4) Local control and survival: The 1-, 2-, and 3-year local control (LC) rate, progression-free survival(PFS) rate, and overall survival(OS) rate for the entire group were 85.5%, 75.4%, and 64.9%; 65.7%, 54.1%, and 49.6%; and 77.1%, 62.9%, and 54.5%, respectively. Conclusions The combination of SIB-IMRT and nimotuzumab for locally advanced esophageal cancer demonstrated good feasibility, safety and efficacy. It offered potential benefits in local control and survival. Acute radiation esophagitis was the primary treatment-related toxicity, which is clinically manageable. This comprehensive treatment approach is worthy of further clinical exploration (ChiCTR1900027936).

Background Currently, numerous studies focus on the analysis of risk factors for lymph node metastasis in early gastric cancer, but few studies analyze the drainage patterns of metastatic lymph nodes. Methods Data was retrospectively analyzed from a database of gastric cancer resections from 2014–2018. The cohort included 786 pT1 patients with complete data. Outcomes evaluated were lymph node metastasis frequencies, survival analyses, and risk factors impacting prognosis. Results The overall lymph node metastasis rate was 23.7%. The 5-year overall survival rate (54.8% vs 95.7%; P < 0.001) and disease-free survival rate (48.4% vs 95.7%; P < 0.001) of patients with node-positive disease were significantly worse than those of patients with node-negative disease. Multivariable Cox regression identified tumor size > 2 cm (P = 0.007, < 0.001), poor differentiation (P = 0.007, < 0.001), T1b stage (all P < 0.001), lymph node metastasis (all P < 0.001), and vascular invasion (all P = 0.002, 0.016) as independent negative prognostic factors affecting 5-year OS and DFS in patients with early gastric cancer. Postoperative chemotherapy (P < 0.001, 0.019)was an independent positive prognostic factor. Conclusion This real-world observational study demonstrates that lymph node metastasis in early gastric cancer is widely and disorderly not depending on the location. Therefore, systematic lymph node dissection is necessary to cure early gastric cancer. Meanwhile its prognosis is closely related to lymph node metastasis.

To investigate clinical data and computed tomographic (CT) imaging features in differentiating gastric schwannomas (GSs) from gastric stromal tumours (GISTs) in matched patients, 31 patients with GSs were matched with 62 patients with GISTs (1:2) in sex, age, and tumour site. The clinical and imaging data were analysed. A significant (P < 0.05) difference was found in the tumour margin, enhancement pattern, growth pattern, and LD values between the 31 patients with GSs and 62 matched patients with GISTs. The GS lesions were mostly (93.5%) well defined while only 61.3% GIST lesions were well defined.The GS lesions were significantly (P = 0.036) smaller than the GIST lesions, with the LD ranging 1.5–7.4 (mean 3.67 cm) cm for the GSs and 1.0–15.30 (mean 5.09) cm for GIST lesions. The GS lesions were more significantly (P = 0.001) homogeneously enhanced (83.9% vs. 41.9%) than the GIST lesions. The GS lesions were mainly of the mixed growth pattern both within and outside the gastric wall (74.2% vs. 22.6%, P < 0.05) compared with that of GISTs. No metastasis or invasion of adjacent organs was present in any of the GS lesions, however, 1.6% of GISTs experienced metastasis and 3.2% of GISTs presented with invasion of adjacent organs. Heterogeneous enhancement and mixed growth pattern were two significant (P < 0.05) independent factors for distinguishing GS from GIST lesions. In conclusion: GS and GIST lesions may have significantly different features for differentiation in lesion margin, heterogeneous enhancement, mixed growth pattern, and longest lesion diameter, especially heterogeneous enhancement and mixed growth pattern.

Introduction The image quality of continuously acquired free-breathing Dynamic Contrast-Enhanced (DCE) golden-angle radial Magnetic Resonance Imaging (MRI) of abdomen suffers from motion artifacts and motion-related blurring. We propose a scheme by minimizing patients’ motion status from breathing as well as optimizing the acquiring parameters to improve image quality and diagnostic performance of DCE-MRI with Golden-Angle Radial Sparse Parallel (GRASP) sequence of abdomen. Methods The optimization scheme follows two principles: (1) reduce the impact on images from unpredictable and irregulate motions during examination and (2) adjust the sequence parameters to increase the number of radial views in each partition. For the assessment of image quality, signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), the severity of radial artifact, the degree of image sharpness, and a visual scoring of image quality with a 5-point scale were assessed. Results A total of 64 patients were included in this study before (16 men, 14 women, age: 54.9 ± 17.0) and after (18 men, 16 women, age: 58.6 ± 12.6) the optimization scheme was performed. The results showed that the SNR values of right and left lobe of liver in both plain phase and arterial phase were significantly increased (All P < 0.001) after the GRASP sequence been optimized. Significant improvements in CNR values were observed in the arterial phase (All P < 0.05). The significant differences in scores at each phase for visual scoring of image quality, noise of the right and left lobe of liver, radial artifact, and sharpness indicating that the image quality was significantly improved after the optimization (All P < 0.001). Conclusion Our study demonstrated that the optimized scheme significantly improved the image quality of liver DCE-MRI with GRASP sequence both in plain and arterial phases. The optimized scheme of GRASP sequence could be a superior alternative to conventional approach for the assessment of liver.

OBJECTIVE:To differentiate gastric leiomyomas (GLs) and gastric stromal tumors (GSTs) based on preoperative enhanced computed tomography characteristics. METHODS:Twenty-six pathologically confirmed GLs were propensity score-matched to 26 GSTs in a 1:1 ratio based on sex, age, tumor site, and tumor size. Tumor shape and contour, mucosal ulceration, growth pattern, enhancement pattern and degree, longest diameter, and longest diameter/vertical diameter ratio were compared between the groups. Hemorrhage, calcification, peripheral invasion, and distant metastasis were also included in the regression analysis for differentiation of the two tumors. RESULTS:Mucosal ulceration was significantly more frequent in GSTs than GLs. The enhancement degree of GSTs was significantly higher than that of GLs in the arterial and portal venous phases. Using enhancement degrees of 18 HU and 23 HU in the arterial phase and venous phase as cutoff values, respectively, we found that an enhancement degree of <18 HU in the arterial phase was an independent influential factor for diagnosis of GLs. No significant differences were found in other morphological characteristics. GLs did not metastasize or invade adjacent tissues. CONCLUSION:A low enhancement degree in GLs is the most valuable quantitative feature for differentiating these two similar tumors.

Objective: This study aims to develop and validate a novel framework, iPhantom, for automated creation of patient-specific phantoms or “digital-twins (DT)” using patient medical images. The framework is applied to assess radiation dose to radiosensitive organs in CT imaging of individual patients. Method: Given a volume of patient CT images, iPhantom segments selected anchor organs and structures (e.g., liver, bones, pancreas) using a learning-based model developed for multi-organ CT segmentation. Organs which are challenging to segment (e.g., intestines) are incorporated from a matched phantom template, using a diffeomorphic registration model developed for multi-organ phantom-voxels. The resulting digital-twin phantoms are used to assess organ doses during routine CT exams. Result: iPhantom was validated on both with a set of XCAT digital phantoms (n = 50) and an independent clinical dataset (n = 10) with similar accuracy. iPhantom precisely predicted all organ locations yielding Dice Similarity Coefficients (DSC) 0.6 - 1 for anchor organs and DSC of 0.3-0.9 for all other organs. iPhantom showed <10% errors in estimated radiation dose for the majority of organs, which was notably superior to the state-of-the-art baseline method (20-35% dose errors). Conclusion: iPhantom enables automated and accurate creation of patient-specific phantoms and, for the first time, provides sufficient and automated patient-specific dose estimates for CT dosimetry. Significance: The new framework brings the creation and application of CHPs (computational human phantoms) to the level of individual CHPs through automation, achieving wide and precise organ localization, paving the way for clinical monitoring, personalized optimization, and large-scale research.

To conduct a quantitative analysis of microcirculation blood perfusion in patients with hepatocellular carcinoma (HCC) before and after transcatheter arterial chemoembolisation (TACE) using contrast-enhanced ultrasound (CEUS). From 2013 June to 2105 October, a total of 106 HCC patients undergoing TACE were recruited. CEUS was performed before and after TACE to determine time-intensity curve (TIC) and perfusion quantitative parameters of the HCC lesions and surrounding liver parenchyma. Quantitative perfusion parameters were obtained using the region of interest method. The microcirculation blood perfusion was measured with a blood analysis system and microcirculation microscope. Tumour microvessel density (MVD) was detected by CD34 immunohistochemistry. Compared with surrounding liver parenchyma, the HCC lesions had earlier arrive time (AT), time to initial peak (TTP) and acceleration time, and faster slope of rise time (a3), but no differences were observed in mean transit time (MTT), slope of decrease to half of peak (a2), peak intensity (PI), increased signal intensity (ISI), area under the curve (AUC) and blood flow (BF). There were significant differences in PI, a3, ISI, AUC and BF in HCC lesions between before and after TACE. The high blood viscosity, low blood viscosity, plasma viscosity and integral viscosity in HCC lesions increased after TACE, but the velocity of nailfold microcirculation decreased after TACE. The MVD of well-differentiated HCC lesions was higher than that of poor-differentiated HCC lesions under a light microscope at 50x magnification. However, no significant differences were found in MVD between well-differentiated and poor-differentiated HCC lesions under a light microscope at 100x and 200x magnifications. The PI, ISI, AUC and BF of poor-differentiated HCC lesions were significant lower than those of well-differentiated HCC lesions, but there were no differences in AT, TTP, ACU, MTT, a2 and a3. In conclusion, these results indicate that quantitative CEUS perfusion parameters could be useful tools for assessing the efficacy of TACE for HCC. (C) 2016 Elsevier Ltd. All rights reserved.

Objective: To investigate the use of non-linear-blending and monochromatic dual-energy CT (DECT) images to improve the image quality of hepatic venography.Methods: 82 patients undergoing abdominal DECT in the portal venous phase were enrolled. For each patient, 31 data sets of monochromatic images and 7 data sets of non-linear-blending images were generated. The data sets of the non-linear-blending andmonochromatic images with the best contrast-to-noise ratios (CNRs) for hepatic veins were selected and compared with the images obtained at 80kVp and a simulated 120 kVp. The subjective image quality of the hepatic veins was evaluated using a fourpoint scale. The image quality of the hepatic veins was analysed using signal-to-noise ratio (SNR) and CNR values.Results: The optimal CNR between hepatic veins and the liver was obtained with the non-linear-blending images. Compared with the other three groups, there were significant differences in the maximum CNR, the SNR, the subjective ratings and the minimum background noise (p < 0.001). A comparison of the monochromatic and 80-kVp images revealed that the CNR and subjective ratings were both improved (p < 0.001). There was no significant difference in the CNR or subjective ratings between the simulated 120-kVp group and the control group (p = 0.090 and 0.053, respectively).Conclusion: The non-linear-blending technique for acquiring DECT provided the best image quality for hepatic venography.Advances in knowledge: DECT can enhance the contrast of hepatic veins and the liver, potentially allowing the wider use of low-dose contrast agents for CT examination of the liver.