MRI versus Dual-Energy CT in Local-Regional Staging of Gastric Cancer.

Background Preoperative local-regional tumor staging of gastric cancer (GC) is critical for appropriate treatment planning. The comparative accuracy of multiparametric MRI (mpMRI) versus dual-energy CT (DECT) for staging of GC is not known. Purpose To compare the diagnostic accuracy of personalized mpMRI with that of DECT for local-regional T and N staging in patients with GC receiving curative surgical intervention. Materials and Methods Patients with GC who underwent gastric mpMRI and DECT before gastrectomy with lymphadenectomy were eligible for this single-center prospective noninferiority study between November 2021 and September 2022. mpMRI comprised T2-weighted imaging, multiorientational zoomed diffusion-weighted imaging, and extradimensional volumetric interpolated breath-hold examination dynamic contrast-enhanced imaging. Dual-phase DECT images were reconstructed at 40 keV and standard 120 kVp-like images. Using gastrectomy specimens as the reference standard, the diagnostic accuracy of mpMRI and DECT for T and N staging was compared by six radiologists in a pairwise blinded manner. Interreader agreement was assessed using the weighted κ and Kendall W statistics. The McNemar test was used for head-to-head accuracy comparisons between DECT and mpMRI. Results This study included 202 participants (mean age, 62 years ± 11 [SD]; 145 male). The interreader agreement of the six readers for T and N staging of GC was excellent for both mpMRI (κ = 0.89 and 0.85, respectively) and DECT (κ = 0.86 and 0.84, respectively). Regardless of reader experience, higher accuracy was achieved with mpMRI than with DECT for both T (61%-77% vs 50%-64%; all P < .05) and N (54%-68% vs 51%-58%; P = .497-.005) staging, specifically T1 (83% vs 65%) and T4a (78% vs 68%) tumors and N1 (41% vs 24%) and N3 (64% vs 45%) nodules (all P < .05). Conclusion Personalized mpMRI was superior in T staging and noninferior or superior in N staging compared with DECT for patients with GC. Clinical trial registration no. NCT05508126 © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Méndez and Martín-Garre in this issue.

Deep learning-accelerated T2WI: image quality, efficiency, and staging performance against BLADE T2WI for gastric cancer.

PURPOSE: The purpose of our study is to investigate image quality, efficiency, and diagnostic performance of a deep learning-accelerated single-shot breath-hold (DLSB) against BLADE for T2-weighted MR imaging (T2WI) for gastric cancer (GC). METHODS: 112 patients with GCs undergoing gastric MRI were prospectively enrolled between Aug 2022 and Dec 2022. Axial DLSB-T2WI and BLADE-T2WI of stomach were scanned with same spatial resolution. Three radiologists independently evaluated the image qualities using a 5-scale Likert scales (IQS) in terms of lesion delineation, gastric wall boundary conspicuity, and overall image quality. Signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were calculated in measurable lesions. T staging was conducted based on the results of both sequences for GC patients with gastrectomy. Pairwise comparisons between DLSB-T2WI and BLADE-T2WI were performed using the Wilcoxon signed-rank test, paired t-test, and chi-squared test. Kendall's W, Fleiss' Kappa, and intraclass correlation coefficient values were used to determine inter-reader reliability. RESULTS: Against BLADE, DLSB reduced total acquisition time of T2WI from 495 min (mean 4:42 per patient) to 33.6 min (18 s per patient), with better overall image quality that produced 9.43-fold, 8.00-fold, and 18.31-fold IQS upgrading against BALDE, respectively, in three readers. In 69 measurable lesions, DLSB-T2WI had higher mean SNR and higher CNR than BLADE-T2WI. Among 71 patients with gastrectomy, DLSB-T2WI resulted in comparable accuracy to BLADE-T2WI in staging GCs (P > 0.05). CONCLUSIONS: DLSB-T2WI demonstrated shorter acquisition time, better image quality, and comparable staging accuracy, which could be an alternative to BLADE-T2WI for gastric cancer imaging.

Dual-energy CT quantitative parameters to evaluate occult peritoneal metastasis in advanced gastric cancer preoperatively.

PURPOSE: To explore whether dual-energy CT (DECT) quantitative parameters could provide analytic value for the diagnosis of patients with occult peritoneal metastasis (OPM) in advanced gastric cancer preoperatively. MATERIALS AND METHODS: This retrospective study included 219 patients with advanced gastric cancer and DECT scans. The patient's clinical data and DECT related iodine concentration (IC) parameters and effective atomic number (Zeff) were collated and analyzed among noun-peritoneal metastasis (NPM), OPM and radiologically peritoneal metastasis (RPM) groups. The predictive performance of the DECT parameters was compared with that of the conventional CT features and clinical characteristics through evaluating area under curve of the precision-recall (AUC-PR), F1 score, balanced accuracy, sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV). RESULTS: Borrmann IV type diagnosed on CT and serum tumor indicator CA125 index were statistically different between the NPM and OPM groups. DECT parameters included IC, normalized IC (NIC), and Zeff of PM group were lower than the NPM group. The DECT predictive nomogram combined three independent DECT parameters produced a better diagnostic performance than the conventional CT feature Borrmann IV type and serum CA125 index in AUC-PR with 0.884 vs 0.368 vs 0.189, but similar to the combined indicator which was based on the DECT parameters, the conventional CT feature, and serum CA125 index in AUC-PR with 0.884 vs 0.918. CONCLUSION: The lower quantitative NIC, IC ratio, and Zeff on DECT was associated with peritoneal metastasis in advanced gastric cancer and was promising to identify patients with OPM noninvasively.

Utility of Combined Use of Imaging Features From Abdominopelvic CT and CA 125 to Identify Presence of CT Occult Peritoneal Metastases in Advanced Gastric Cancer.

OBJECTIVE: The purpose of this study is to identify the presence of occult peritoneal metastasis (OPM) in patients with advanced gastric cancer (AGC) by using clinical characteristics and abdominopelvic computed tomography (CT) features. METHODS: This retrospective study included 66 patients with OPM and 111 patients without peritoneal metastasis (non-PM [NPM]) who underwent preoperative contrast-enhanced CT between January 2020 and December 2021. Occult PMs means PMs that are missed by CT but later diagnosed by laparoscopy or laparotomy. Patients with NPM means patients have neither PM nor other distant metastases, indicating there is no evidence of distant metastases in patients with AGC. Patients' clinical characteristics and CT features such as tumor marker, Borrmann IV, enhancement patterns, and pelvic ascites were observed by 2 experienced radiologists. Computed tomography features and clinical characteristics were combined to construct an indicator for identifying the presence of OPM in patients with AGC based on a logistic regression model. Receiver operating characteristic curves and the area under the receiver operating characteristic curve (AUC) were generated to assess the diagnostic performance of the combined indicator. RESULTS: Four independent predictors (Borrmann IV, pelvic ascites, carbohydrate antigen 125, and normalized arterial CT value) differed significantly between OPM and NPM and performed outstandingly in distinguishing patients with OPM from those without PM (AUC = 0.643-0.696). The combined indicator showed a higher AUC value than the independent risk factors (0.820 vs 0.643-0.696). CONCLUSIONS: The combined indicator based on abdominopelvic CT features and carbohydrate antigen 125 may assist clinicians in identifying the presence of CT OPMs in patients with AGC.

IDR-targeting compounds suppress HPV genome replication via disruption of phospho-BRD4 association with DNA damage response factors.

Compounds binding to the bromodomains of bromodomain and extra-terminal (BET) family proteins, particularly BRD4, are promising anticancer agents. Nevertheless, side effects and drug resistance pose significant obstacles in BET-based therapeutics development. Using high-throughput screening of a 200,000-compound library, we identified small molecules targeting a phosphorylated intrinsically disordered region (IDR) of BRD4 that inhibit phospho-BRD4 (pBRD4)-dependent human papillomavirus (HPV) genome replication in HPV-containing keratinocytes. Proteomic profiling identified two DNA damage response factors-53BP1 and BARD1-crucial for differentiation-associated HPV genome amplification. pBRD4-mediated recruitment of 53BP1 and BARD1 to the HPV origin of replication occurs in a spatiotemporal and BRD4 long (BRD4-L) and short (BRD4-S) isoform-specific manner. This recruitment is disrupted by phospho-IDR-targeting compounds with little perturbation of the global transcriptome and BRD4 chromatin landscape. The discovery of these protein-protein interaction inhibitors (PPIi) not only demonstrates the feasibility of developing PPIi against phospho-IDRs but also uncovers antiviral agents targeting an epigenetic regulator essential for virus-host interaction and cancer development.

Anti-PD-1 plus anti-angiogenesis combined with chemotherapy in patients with HER2-negative advanced or metastatic gastric cancer: a multi-institutional retrospective study.

Background: Immunotherapy plus chemotherapy have been confirmed to be effective in treating advanced or metastatic gastric cancer (GC). Anti- programmed death-1 (PD-1) plus antiangiogenic agents have shown promising activity and tolerant toxicity in subsequent therapy of late-stage gastric cancer. The aim of this study was to assess the efficacy and safety of anti-PD-1 plus anti-angiogenic agents and chemotherapy in advanced or metastatic GC and to explore the potential biomarkers associated with response. Methods: We retrospectively reviewed thirty human epidermal growth factor receptor 2 (HER2)-negative advanced or metastatic GC patients who received PD-1 plus anti-angiogenic drugs and chemotherapy. Conversion therapy was defined when the patients could undergo resection post combination therapy. Clinical data were retrieved from medical records. We conducted exploratory biomarker analysis of baseline gene mutations and tumor mutation burden (TMB) using the next-generation sequencing (NGS), PD-L1 by immunohistochemistry (IHC), and the tumor immune microenvironment (TIME) by multiplex immunofluorescence. Results: A total of 30 patients received anti-PD-1plus anti-angiogenic drugs and chemotherapy during the study period. The objective response rate (ORR) was 76.7% [95% confidence interval (CI): 57.7-90.1%] and disease control rate (DCR) was 86.7% (95% CI: 69.3-96.2%). A total of 11 patients (36.7%) achieved conversion therapy and underwent surgery. The R0 resection rate was 90.9%. Of the 11 patients, 9 (81.8%) responded to the treatment, 1 with a pathological complete response (pCR) and 8 with a major pathological response (MPR). No adverse events of grade 3 or higher occurred. Neither PD-L1 expression nor TMB was significantly correlated with treatment response. Analysis of TIME revealed that the fraction of CD8+ T cell in the invasive margin was higher in responders than non-responders before treatment. TAM2 in the tumor center and CD8+ T cell in the invasive margin was significantly increased after combination therapy, which suggested that combination therapy promoted infiltration of CD8+ T cells, thereby exerting an antitumor effect. Conclusions: Immunotherapy plus anti-angiogenic drugs and chemotherapy is a promising treatment strategy for advanced or metastatic GC patients. Tumor infiltration CD8+ T cells may serve as potential predictive biomarker.

Preoperative Assessment of MRI-Invisible Early-Stage Endometrial Cancer With MRI-Based Radiomics Analysis.

BACKGROUND: Radiomics-based analyses have demonstrated impact on studies of endometrial cancer (EC). However, there have been no radiomics studies investigating preoperative assessment of MRI-invisible EC to date. PURPOSE: To develop and validate radiomics models based on sagittal T2-weighted images (T2WI) and T1-weighted contrast-enhanced images (T1CE) for the preoperative assessment of MRI-invisible early-stage EC and myometrial invasion (MI). STUDY TYPE: Retrospective. POPULATION: One hundred fifty-eight consecutive patients (mean age 50.7 years) with MRI-invisible endometrial lesions were enrolled from June 2016 to March 2022 and randomly divided into the training (n = 110) and validation cohort (n = 48) using a ratio of 7:3. FIELD STRENGTH/SEQUENCE: 3-T, T2WI, and T1CE sequences, turbo spin echo. ASSESSMENT: Two radiologists performed image segmentation and extracted features. Endometrial lesions were histopathologically classified as benign, dysplasia, and EC with or without MI. In the training cohort, 28 and 20 radiomics features were selected to build Model 1 and Model 2, respectively, generating rad-score 1 (RS1) and rad-score 2 (RS2) for evaluating MRI-invisible EC and MI. STATISTICAL TESTS: The least absolute shrinkage and selection operator logistic regression method was used to select radiomics features. Mann-Whitney U tests and Chi-square test were used to analyze continuous and categorical variables. Receiver operating characteristic curve (ROC) and decision curve analysis were used for performance evaluation. The area under the ROC curve (AUC), accuracy, sensitivity, specificity, positive predictive value, and negative predictive value were calculated. A P-value <0.05 was considered statistically significant. RESULTS: Model 1 had good performance for preoperative detecting of MRI-invisible early-stage EC in the training and validation cohorts (AUC: 0.873 and 0.918). In addition, Model 2 had good performance in assessment of MI of MRI-invisible endometrial lesions in the training and validation cohorts (AUC: 0.854 and 0.834). DATA CONCLUSION: MRI-based radiomics models may provide good performance for detecting MRI-invisible EC and MI. EVIDENCE LEVEL: 3 TECHNICAL EFFICACY: Stage 2.

SurvivalCNN: A deep learning-based method for gastric cancer survival prediction using radiological imaging data and clinicopathological variables.

Radiological images have shown promising effects in patient prognostication. Deep learning provides a powerful approach for in-depth analysis of imaging data and integration of multi-modal data for modeling. In this work, we propose SurvivalCNN, a deep learning structure for cancer patient survival prediction using CT imaging data and non-imaging clinical data. In SurvivalCNN, a supervised convolutional neural network is designed to extract volumetric image features, and radiomics features are also integrated to provide potentially different imaging information. Within SurvivalCNN, a novel multi-thread multi-layer perceptron module, namely, SurvivalMLP, is proposed to perform survival prediction from censored survival data. We evaluate the proposed SurvivalCNN framework on a large clinical dataset of 1061 gastric cancer patients for both overall survival (OS) and progression-free survival (PFS) prediction. We compare SurvivalCNN to three different modeling methods and examine the effects of various sets of data/features when used individually or in combination. With five-fold cross validation, our experimental results show that SurvivalCNN achieves averaged concordance index 0.849 and 0.783 for predicting OS and PFS, respectively, outperforming the compared state-of-the-art methods and the clinical model. After future validation, the proposed SurvivalCNN model may serve as a clinical tool to improve gastric cancer patient survival estimation and prognosis analysis.

MRI radiomics combined with clinicopathologic features to predict disease-free survival in patients with early-stage cervical cancer.

OBJECTIVE: To establish a comprehensive model including MRI radiomics and clinicopathological features to predict post-operative disease-free survival (DFS) in early-stage (pre-operative FIGO Stage IB-IIA) cervical cancer. METHODS: A total of 183 patients with early-stage cervical cancer admitted to our Jiangsu Province Hospital underwent radical hysterectomy were enrolled in this retrospective study from January 2013 to June 2018 and their clinicopathology and MRI information were collected. They were then divided into training cohort (n = 129) and internal validation cohort (n = 54). The radiomic features were extracted from the pre-operative T1 contrast-enhanced (T1CE) and T2 weighted image of each patient. Least absolute shrinkage and selection operator regression and multivariate Cox proportional hazard model were used for feature selection, and the rad-score (RS) of each patient were evaluated individually. The clinicopathology model, T1CE_RS model, T1CE + T2_RS model, and clinicopathology combined with T1CE_RS model were established and compared. Patients were divided into high- and low-risk groups according to the optimum cut-off values of four models. RESULTS: T1CE_RS model showed better performance on DFS prediction of early-stage cervical cancer than clinicopathological model (C-index: 0.724 vs 0.659). T1CE+T2_RS model did not improve predictive performance (C-index: 0.671). The combination of T1CE_RS and clinicopathology features showed more accurate predictive ability (C-index=0.773). CONCLUSION: The combination of T1CE_RS and clinicopathology features showed more accurate predictive performance for DFS of patients with early-stage (pre-operative IB-IIA) cervical cancer which can aid in the design of individualised treatment strategies and regular follow-up. ADVANCES IN KNOWLEDGE: A radiomics signature composed of T1CE radiomic features combined with clinicopathology features allowed differentiating patients at high or low risk of recurrence.

Development and validation of a dual-energy CT-based model to estimate the malignant probability of distal gastric wall thickening.

Background: This study developed and validated a viable model for the preoperative diagnosis of malignant distal gastric wall thickening based on dual-energy spectral computed tomography (DEsCT). Methods: The imaging data of 208 patients who were diagnosed with distal gastric wall thickening using DEsCT were retrospectively collected and divided into a training cohort (n=151) and a testing cohort (n=57). The patient's clinical data and pathological information were collated. The multivariable logistic regression model was built using 5 selected features, and subsequently, a 10-fold cross-validation was performed to identify the optimal model. A nomogram was established based on the training cohort. Finally, the diagnostic performance of the best model was compared to the existing conventional CT scheme through evaluating the discrimination ability in the testing cohort in terms of the receiver operating characteristic curve (ROC), calibration, and clinical usefulness. Results: Stepwise regression analysis identified 5 candidate variables with the smallest Akaike information criteria (AIC), namely, the venous phase spectral curve [VP_ SC; odds ratio (OR) 8.419], focal enhancement (OR 3.741), arterial phase mixed (OR 1.030), tumor site (OR 0.573), and diphasic shape change (DP_shape change; OR 2.746). The best regression model with 10-fold cross-validation consisting of VP_SC and focal enhancement was built using the 5 candidate variables. The average area under the ROC curve (AUC) of the model from the 10-fold cross-validation was 0.803 (sensitivity of 69.2%, specificity of 94.1%, and accuracy of 74.8%). In the testing cohort, the DEsCT model identified using the regression model performed better (AUC 0.905, sensitivity 81.3%, specificity 85.4%, and accuracy 84.2%) than did the conventional CT scheme (AUC 0.852, sensitivity 80.0%, specificity 76.6%, and accuracy 77.2%). The nomogram based on the DEsCT model showed good calibration and provided a better net benefit for predicting malignancy of distal gastric wall thickening. Conclusions: Comprehensive assessment with the DEsCT-based model can be used to facilitate the individualized diagnosis of malignancy risk in patients presenting with distal gastric wall thickening.

Efficacy and safety of camrelizumab plus apatinib during the perioperative period in resectable hepatocellular carcinoma: a single-arm, open label, phase II clinical trial.

OBJECTIVE: This study aimed to assess the efficacy and safety of camrelizumab plus apatinib in patients with resectable hepatocellular carcinoma (HCC) as neoadjuvant therapy. METHODS: Initially, 20 patients with HCC were screened and 18 patients with resectable HCC were enrolled in this open-label, single-arm, phase II clinical trial. Patients received three cycles of neoadjuvant therapy including three doses of camrelizumab concurrent with apatinib for 21 days followed by surgery. Four to 8 weeks after surgery, patients received eight cycles of adjuvant therapy with camrelizumab in combination with apatinib. Major pathological reactions (MPR), complete pathological reactions (pCR), objective response rate (ORR), relapse-free survival (RFS), and adverse events (AE) were assessed. In addition, cancer tissue and plasma samples were collected before and after treatment, and genetic differences between responding and non-responding lesions were compared by tumor immune microenvironment (TIME) analysis, circulating tumor DNA (ctDNA) analysis and proteomics analysis. RESULTS: In 18 patients with HCC who completed neoadjuvant therapy, 3 (16.7%) and 6 (33.3%) patients with HCC reached ORR based on Response Evaluation Criteria in Solid Tumors (RECIST) V.1.1 and modified RECIST criteria, respectively. Of the 17 patients with HCC who received surgical resection, 3 (17.6%) patients with HCC reported MPR and 1 (5.9%) patient with HCC achieved pCR. The 1-year RFS rate of the enrolled patients was 53.85% (95% CI: 24.77% to 75.99%). Grade 3/4 AEs were reported in 3 (16.7%) of the 18 patients, with the most common AEs being rash (11.1%), hypertension (5.6%), drug-induced liver damage (5.6%), and neutropenia (5.6%) in the preoperative phase. The 289 NanoString panel RNA sequencing showed that TIME cell infiltration especially dendritic cells (DCs) infiltration was better in responding tumors than in non-responding tumors. Our results of ctDNA revealed a higher positive rate (100%) among patients with HCC with stage IIb-IIIa disease. When comparing patients with pCR/MPR and non-MPR, we observed more mutations in patients who achieved pCR/MPR at baseline (6 mutations vs 2.5 mutations, p=0.025). Patients who were ctDNA positive after adjuvant therapy presented a trend of shorter RFS than those who were ctDNA negative. Proteomic analysis suggested that abnormal glucose metabolism in patients with multifocal HCC might be related to different sensitivity of treatment in different lesions. CONCLUSION: Perioperative camrelizumab plus apatinib displays a promising efficacy and manageable toxicity in patients with resectable HCC. DCs infiltration might be a predictive marker of response to camrelizumab and apatinib as well as patients' recurrence. ctDNA as a compose biomarker can predict pathological response and relapse. Abnormal glucose metabolism in patients with multifocal HCC may be related to different sensitivity of treatment in different lesions. TRIAL REGISTRATION NUMBER: NCT04297202.

Preoperative application of systemic inflammatory biomarkers combined with MR imaging features in predicting microvascular invasion of hepatocellular carcinoma.

PURPOSE: To investigate whether systemic inflammatory biomarkers compared with the imaging features interpreted by radiologists can offer complementary value for predicting the risk of microvascular invasion (MVI) in patients with hepatocellular carcinoma (HCC). METHODS: A total of 156 patients with histologically confirmed HCC between Jan 2018 and Dec 2020 were retrospectively enrolled in the primary cohort. Preoperative clinical-inflammatory biomarkers and MR imaging of the patients were recorded and then evaluated as an inflammatory score (Inflam-score) and imaging feature score (Radio-score). Six Inflam-scores and 12 Radio-scores were determined from each patient by univariate analysis. Logistic regression was performed to select risk factors for MVI and establish a predictive nomogram. Decision curve analysis was applied to estimate the incremental value of the Inflam-score to the Radio-score for predicting MVI. RESULTS: Four Radio-scores and 2 Inflam-scores, namely, larger tumor size, non-smooth tumor margin, presence of satellite nodules, presence of peritumoral enhance, higher neutrophil-lymphocyte ratio (NLR), and lower prognostic nutritional index (PNI), were significantly associated with MVI (p < 0.05). An MVI risk prediction nomogram was then constructed with an area under the curve (AUC) of 0.868 (95% CI 0.806-0.931). Adding Inflam-scores to Radio-scores improved the sensitivity of the model from 60.9 to 80.4% in receiver operating characteristic (ROC) curve analysis and led to a net benefit in decision curve analysis. CONCLUSION: Systemic inflammatory biomarkers are complementary tools that provide additional benefit to conventional imaging estimation for predicting MVI in HCC patients.

Comparison of Computed Tomography Features of Gastric and Small Bowel Gastrointestinal Stromal Tumors With Different Risk Grades.

OBJECTIVE: This study aimed to compare the computed tomography (CT) features of gastric and small bowel gastrointestinal stromal tumors (GISTs) and further identify the predictors for risk stratification of them, respectively. METHODS: According to the modified National Institutes of Health criteria, patients were classified into low-malignant potential group and high-malignant potential group. Two experienced radiologists reviewed the CT features including the difference of CT values between arterial phase and portal venous phase (PVPMAP) by consensus. The CT features of gastric and small bowel GISTs were compared, and the association of CT features with risk grades was analyzed, respectively. Determinant CT features were used to construct corresponding models. RESULTS: Univariate analysis showed that small bowel GISTs tended to present with irregular contour, mixed growth pattern, ill-defined margin, severe necrosis, ulceration, tumor vessels, heterogeneous enhancement, larger size, and marked enhancement compared with gastric GISTs. According to multivariate analysis, tumor size (P < 0.001; odds ratio [OR], 3.279), necrosis (P = 0.008; OR, 2.104) and PVPMAP (P = 0.045; OR, 0.958) were the independent influencing factors for risk stratification of gastric GISTs. In terms of small bowel GISTs, the independent predictors were tumor size (P < 0.001; OR, 3.797) and ulceration (P = 0.031; OR, 4.027). Receiver operating characteristic curve indicated that the CT models for risk stratification of gastric and small bowel GISTs both achieved the best predictive performance. CONCLUSIONS: Computed tomography features of gastric and small bowel GISTs are different. Furthermore, the qualitative and quantitative CT features of GISTs may be favorable for preoperative risk stratification.

Prediction of the Ki-67 expression level and prognosis of gastrointestinal stromal tumors based on CT radiomics nomogram.

PURPOSE: To build and validate a radiomics nomogram integrated with the radiomics signature and subjective CT characteristics to predict the Ki-67 expression level of gastrointestinal stromal tumors (GISTs). Moreover, the purpose was to compare the performance of pathological Ki-67 expression level with predicted Ki-67 expression level in estimating the prognosis of GISTs patients. METHODS: According to pathological results, patients were classified into high-Ki-67 labeling index group (Ki-67 LI ≥ 5%) and low-Ki-67 LI group (Ki-67 LI < 5%). Radiomics features extracted from contrast-enhanced CT(CECT) images were selected and classified to build a radiomics signature. A combined model was built by incorporating radiomics signature and determinant subjective CT characteristics using multivariate logistic regression analysis. The diagnostic performance of the radiomics signature, subjective CT model and combined model were explored by receiver operating characteristic (ROC) curve analysis and Delong test. The model with best diagnostic performance was then set up for the prediction nomogram. Recurrence-free survival (RFS) rates were compared utilizing Kaplan-Meier curve. RESULTS: The generated combined model yielded the best diagnostic performance with area under the curve (AUC) values of 0.738 [95% confidence interval (CI): 0.669-0.807] and 0.772 (95% CI 0.683-0.860) in the training set and testing set respectively. The nomogram based on the combined model demonstrated good calibration in the training set and testing set (both P > 0.05). Patients of high-Ki-67 LI group predicted by our nomogram had a poorer RFS than patients of low-Ki-67 LI group (P < 0.0001). CONCLUSION: This radiomics nomogram based on CECT had a satisfactory performance in predicting both the Ki-67 expression level and prognosis noninvasively in patients with GISTs, which may serve as an effective imaging tool that can assist in guiding personalized clinical treatment.

Radiomics analysis of pretreatment MRI in predicting tumor response and outcome in hepatocellular carcinoma with transarterial chemoembolization: a two-center collaborative study.

BACKGROUND AND OBJECTIVE: To develop a machine-learning model by integrating clinical and imaging modalities for predicting tumor response and survival of hepatocellular carcinoma (HCC) with transarterial chemoembolization (TACE). METHODS: 140 HCC patients with TACE were retrospectively included from two centers. Tumor response were evaluated using modified Response Evaluation Criteria in Solid Tumors (mRECIST) criteria. Response-related radiomics scores (Rad-scores) were constructed on T2-weighted images (T2WI) and dynamic contrast-enhanced (DCE) imaging separately, and then integrated with conventional clinic-radiological variables into a logistic regression (LR) model for predicting tumor response. LR model was trained in 94 patients in center 1 and independently tested in 46 patients in center 2. RESULTS: Among 4 MRI sequences, T2WI achieved better performance than DCE (area under the curve [AUC] 0.754 vs 0.602 to 0.752). LR model by combining Rad-score on T2WI with Barcelona Clinic Liver Cancer (BCLC) stage and albumin-bilirubin (ALBI) grade resulted in an AUC of 0.813 in training and 0.781 in test for predicting tumor response. In survival analysis, progression-free survival (PFS) and overall survival (OS) presented significant difference between LR-predicted responders and non-responders. The ALBI grade and BCLC stage were independent predictors of PFS; and LR-predicted response, ALBI grade, satellite node, and BCLC stage were independent predictors of OS. The resulting Cox model produced concordance-indexes of 0.705 and 0.736 for predicting PFS and OS, respectively. CONCLUSIONS: The model combined MRI radiomics with clinical factors demonstrated favorable performance for predicting tumor response and clinical outcomes, thus may help personalized clinical management.

Prognostic aspects of lymphovascular invasion in localized gastric cancer: new insights into the radiomics and deep transfer learning from contrast-enhanced CT imaging.

OBJECTIVES: Lymphovascular invasion (LVI) is a factor significantly impacting treatment and outcome of patients with gastric cancer (GC). We aimed to investigate prognostic aspects of a preoperative LVI prediction in GC using radiomics and deep transfer learning (DTL) from contrast-enhanced CT (CECT) imaging. METHODS: A total of 1062 GC patients (728 training and 334 testing) between Jan 2014 and Dec 2018 undergoing gastrectomy were retrospectively included. Based on CECT imaging, we built two gastric imaging (GI) markers, GI-marker-1 from radiomics and GI-marker-2 from DTL features, to decode LVI status. We then integrated demographics, clinical data, GI markers, radiologic interpretation, and biopsies into a Gastric Cancer Risk (GRISK) model for predicting LVI. The performance of GRISK model was tested and applied to predict survival outcomes in GC patients. Furthermore, the prognosis between LVI (+) and LVI (-) patients was compared in chemotherapy and non-chemotherapy cohorts, respectively. RESULTS: GI-marker-1 and GI-marker-2 yield similar performance in predicting LVI in training and testing dataset. The GRISK model yields the diagnostic performance with AUC of 0.755 (95% CI 0.719-0.790) and 0.725 (95% CI 0.669-0.781) in training and testing dataset. Patients with LVI (+) trend toward lower progression-free survival (PFS) and overall survival (OS). The difference of prognosis between LVI (+) and LVI (-) was more noticeable in non-chemotherapy than that in chemotherapy group. CONCLUSION: Radiomics and deep transfer learning features on CECT demonstrate potential power for predicting LVI in GC patients. Prospective use of a GRISK model can help to optimize individualized treatment decisions and predict survival outcomes.

ATF6 promotes liver fibrogenesis by regulating macrophage-derived interleukin-1α expression.

Macrophages contribute to liver fibrogenesis by the production of a large variety of cytokines. ATF6 is associated with the activation of macrophages. The present study aimed to investigate the role of ATF6 in the expression of macrophage-derived cytokines and liver fibrogenesis after acute liver injury. Following thioacetamide (TAA)-induced acute liver injury, the characteristics of the occurrence of liver fibrosis and the secretion of cytokines by macrophages were first described. Then, the role of various cytokines secreted by macrophages in activating hepatic stellate cells (HSCs) was tested in vitro. Finally, endoplasmic reticulum stress (ER-stress) signals in macrophages were detected following liver injury. siRNA was used to interfere with the expression of ATF6 in macrophages to verify the influence of ATF6 on cytokine expression and liver fibrogenesis after liver injury. A single intraperitoneal injection of TAA induced acute liver injury. The depletion of macrophages attenuated acute liver injury, while it inhibited liver fibrogenesis. During acute liver injury, macrophages secrete a variety of cytokines. Most of these cytokines promoted the activation of HSCs, but the effect of IL-1α was most significant. In the early stage of acute liver injury, ER-stress signals in macrophages were activated. Interference of ATF6 expression suppressed the secretion of cytokines by macrophages and attenuated liver fibrogenesis. Overall, in the early stage of acute liver injury, ATF6 signals promoted the expression of macrophage-derived cytokines to participate in liver fibrogenesis, and IL-1α exhibited the most significant role in promoting the activation of HSCs and liver fibrogenesis.

MondoA-Thioredoxin-Interacting Protein Axis Maintains Regulatory T-Cell Identity and Function in Colorectal Cancer Microenvironment.

BACKGROUND & AIMS: The metabolic features and function of intratumoral regulatory T cells (Tregs) are ambiguous in colorectal cancer. Tumor-infiltrating Tregs are reprogrammed to exhibit high glucose-depleting properties and adapt to the glucose-restricted microenvironment. The glucose-responsive transcription factor MondoA is highly expressed in Tregs. However, the role of MondoA in colorectal cancer-infiltrating Tregs in response to glucose limitation remains to be elucidated. METHODS: We performed studies using mice, in which MondoA was conditionally deleted in Tregs, and human colorectal cancer tissues. Seahorse and other metabolic assays were used to assess Treg metabolism. To study the role of Tregs in antitumor immunity, we used a subcutaneous MC38 colorectal cancer model and induced colitis-associated colorectal cancer in mice by azoxymethane and dextran sodium sulfate. RESULTS: Our analysis of single-cell RNA sequencing data of patients with colorectal cancer revealed that intratumoral Tregs featured low activity of the MondoA-thioredoxin-interacting protein (TXNIP) axis and increased glucose uptake. Although MondoA-deficient Tregs were less immune suppressive and selectively promoted T-helper (Th) cell type 1 (Th1) responses in a subcutaneous MC38 tumor model, Treg-specific MondoA knockout mice were more susceptible to azoxymethane-DSS-induced colorectal cancer. Mechanistically, suppression of the MondoA-TXNIP axis promoted glucose uptake and glycolysis, induced hyperglycolytic Th17-like Tregs, which facilitated Th17 inflammation, promoted interleukin 17A-induced of CD8+ T-cell exhaustion, and drove colorectal carcinogenesis. Blockade of interleukin 17A reduced tumor progression and minimized the susceptibility of MondoA-deficient mice to colorectal carcinogenesis. CONCLUSIONS: The MondoA-TXNIP axis is a critical metabolic regulator of Treg identity and function in the colorectal cancer microenvironment and a promising target for cancer therapy.

Impaired AGO2/miR-185-3p/NRP1 axis promotes colorectal cancer metastasis.

Increasing evidence suggests that global downregulation of miRNA expression is a hallmark of human cancer, potentially due to defects in the miRNA processing machinery. In this study, we found that the protein expression of Argonaute 2 (AGO2), a key regulator of miRNA processing, was downregulated in colorectal cancer (CRC) tissues, which was also consistent with the findings of the Clinical Proteomic Tumor Analysis Consortium (CPTAC). Furthermore, the correlation between the levels of AGO2 and epithelial-mesenchymal transition (EMT) markers (E-cadherin and vimentin) indicated that reduced levels of AGO2 promoted EMT in CRC. Low expression of AGO2 was an indicator of a poor prognosis among CRC patients. Knockdown of AGO2 in CRC cells promoted migration, invasion and metastasis formation in vitro and in vivo but had no influence on proliferation. To provide detailed insight into the regulatory roles of AGO2, we performed integrated transcriptomic, quantitative proteomic and microRNA sequencing (miRNA-seq) analyses of AGO2 knockdown cells and the corresponding wild-type cells and identified neuropilin 1 (NRP1) as a new substrate of AGO2 via miR-185-3p. Our data provided evidence that knockdown of AGO2 resulted in a reduction of miR-185-3p expression, leading to the upregulation of the expression of NRP1, which is a direct target of miR-185-3p, and elevated CRC cell metastatic capacity. Inhibition of NRP1 or treatment with a miR-185-3p mimic successfully rescued the phenotypes of impaired AGO2, which suggested that therapeutically targeting the AGO2/miR-185-3p/NRP1 axis may be a potential treatment approach for CRC.

Identifying Prognostic Markers From Clinical, Radiomics, and Deep Learning Imaging Features for Gastric Cancer Survival Prediction.

BACKGROUND: Gastric cancer is one of the leading causes of cancer death in the world. Improving gastric cancer survival prediction can enhance patient prognostication and treatment planning. METHODS: In this study, we performed gastric cancer survival prediction using machine learning and multi-modal data of 1061 patients, including 743 for model learning and 318 independent patients for evaluation. A Cox proportional-hazard model was trained to integrate clinical variables and CT imaging features (extracted by radiomics and deep learning) for overall and progression-free survival prediction. We further analyzed the prediction effects of clinical, radiomics, and deep learning features. Concordance index (c-index) was used as the model performance metric, and the predictive effects of multi-modal features were measured by hazard ratios (HRs) at pre- and post-operative settings. RESULTS: Among 318 patients in the independent testing group, the hazard predicted by Cox from multi-modal features is associated with their survival. The highest c-index was 0.783 (95% CI, 0.782-0.783) and 0.770 (95% CI, 0.769-0.771) for overall and progression-free survival prediction, respectively. The post-operative variables are significantly (p<0.001) more predictive than the pre-operative variables. Pathological tumor stage (HR=1.336 [overall survival]/1.768 [progression-free survival], p<0.005), pathological lymph node stage (HR=1.665/1.433, p<0.005), carcinoembryonic antigen (CEA) (HR=1.632/1.522, p=0.02), chemotherapy treatment (HR=0.254/0.287, p<0.005), radiomics signature [HR=1.540/1.310, p<0.005], and deep learning signature [HR=1.950/1.420, p<0.005]) are significant survival predictors. CONCLUSION: Our study showed that CT radiomics and deep learning imaging features are significant pre-operative predictors, providing additional prognostic information to the pathological staging markers. Lower CEA levels and chemotherapy treatments also increase survival chances. These findings can enhance gastric cancer patient prognostication and inform treatment planning.