Fully automated hybrid approach on conventional MRI for triaging clinically significant liver fibrosis: A multi-center cohort study.

Establishing reliable noninvasive tools to precisely diagnose clinically significant liver fibrosis (SF, ≥F2) remains an unmet need. We aimed to build a combined radiomics-clinic (CoRC) model for triaging SF and explore the additive value of the CoRC model to transient elastography-based liver stiffness measurement (FibroScan, TE-LSM). This retrospective study recruited 595 patients with biopsy-proven liver fibrosis at two centers between January 2015 and December 2021. At Center 1, the patients before December 2018 were randomly split into training (276) and internal test (118) sets, the remaining were time-independent as a temporal test set (96). Another data set (105) from Center 2 was collected for external testing. Radiomics scores were built with selected features from Deep learning-based (ResUNet) automated whole liver segmentations on MRI (T2FS and delayed enhanced-T1WI). The CoRC model incorporated radiomics scores and relevant clinical variables with logistic regression, comparing routine approaches. Diagnostic performance was evaluated by the area under the receiver operating characteristic curve (AUC). The additive value of the CoRC model to TE-LSM was investigated, considering necroinflammation. The CoRC model achieved AUCs of 0.79 (0.70, 0.86), 0.82 (0.73, 0.89), and 0.81 (0.72-0.91), outperformed FIB-4, APRI (all p < 0.05) in the internal, temporal, and external test sets and maintained the discriminatory power in G0-1 subgroups (AUCs range, 0.85-0.86; all p < 0.05). The AUCs of joint CoRC-LSM model were 0.86 (0.79-0.94), and 0.81 (0.72-0.90) in the internal and temporal sets (p = 0.01). The CoRC model was useful for triaging SF, and may add value to TE-LSM.

MRI-Based Radiomics and Deep Learning in Biological Characteristics and Prognosis of Hepatocellular Carcinoma: Opportunities and Challenges.

Hepatocellular carcinoma (HCC) is the fifth most common malignancy and the third leading cause of cancer-related death worldwide. HCC exhibits strong inter-tumor heterogeneity, with different biological characteristics closely associated with prognosis. In addition, patients with HCC often distribute at different stages and require diverse treatment options at each stage. Due to the variability in tumor sensitivity to different therapies, determining the optimal treatment approach can be challenging for clinicians prior to treatment. Artificial intelligence (AI) technology, including radiomics and deep learning approaches, has emerged as a unique opportunity to improve the spectrum of HCC clinical care by predicting biological characteristics and prognosis in the medical imaging field. The radiomics approach utilizes handcrafted features derived from specific mathematical formulas to construct various machine-learning models for medical applications. In terms of the deep learning approach, convolutional neural network models are developed to achieve high classification performance based on automatic feature extraction from images. Magnetic resonance imaging offers the advantage of superior tissue resolution and functional information. This comprehensive evaluation plays a vital role in the accurate assessment and effective treatment planning for HCC patients. Recent studies have applied radiomics and deep learning approaches to develop AI-enabled models to improve accuracy in predicting biological characteristics and prognosis, such as microvascular invasion and tumor recurrence. Although AI-enabled models have demonstrated promising potential in HCC with biological characteristics and prognosis prediction with high performance, one of the biggest challenges, interpretability, has hindered their implementation in clinical practice. In the future, continued research is needed to improve the interpretability of AI-enabled models, including aspects such as domain knowledge, novel algorithms, and multi-dimension data sources. Overcoming these challenges would allow AI-enabled models to significantly impact the care provided to HCC patients, ultimately leading to their deployment for clinical use. LEVEL OF EVIDENCE: 5 TECHNICAL EFFICACY: Stage 2.

Signal enhancement ratio of multi-phase contrast-enhanced MRI: an imaging biomarker for survival in pancreatic adenocarcinoma.

OBJECTIVES: To evaluate signal enhancement ratio (SER) for tissue characterization and prognosis stratification in pancreatic adenocarcinoma (PDAC), with quantitative histopathological analysis (QHA) as the reference standard. METHODS: This retrospective study included 277 PDAC patients who underwent multi-phase contrast-enhanced (CE) MRI and whole-slide imaging (WSI) from three centers (2015-2021). SER is defined as (SIlt - SIpre)/(SIea - SIpre), where SIpre, SIea, and SIlt represent the signal intensity of the tumor in pre-contrast, early-, and late post-contrast images, respectively. Deep-learning algorithms were implemented to quantify the stroma, epithelium, and lumen of PDAC on WSIs. Correlation, regression, and Bland-Altman analyses were utilized to investigate the associations between SER and QHA. The prognostic significance of SER on overall survival (OS) was evaluated using Cox regression analysis and Kaplan-Meier curves. RESULTS: The internal dataset comprised 159 patients, which was further divided into training, validation, and internal test datasets (n = 60, 41, and 58, respectively). Sixty-five and 53 patients were included in two external test datasets. Excluding lumen, SER demonstrated significant correlations with stroma (r = 0.29-0.74, all p < 0.001) and epithelium (r = -0.23 to -0.71, all p < 0.001) across a wide post-injection time window (range, 25-300 s). Bland-Altman analysis revealed a small bias between SER and QHA for quantifying stroma/epithelium in individual training, validation (all within ± 2%), and three test datasets (all within ± 4%). Moreover, SER-predicted low stromal proportion was independently associated with worse OS (HR = 1.84 (1.17-2.91), p = 0.009) in training and validation datasets, which remained significant across three combined test datasets (HR = 1.73 (1.25-2.41), p = 0.001). CONCLUSION: SER of multi-phase CE-MRI allows for tissue characterization and prognosis stratification in PDAC. CLINICAL RELEVANCE STATEMENT: The signal enhancement ratio of multi-phase CE-MRI can serve as a novel imaging biomarker for characterizing tissue composition and holds the potential for improving patient stratification and therapy in PDAC. KEY POINTS: Imaging biomarkers are needed to better characterize tumor tissue in pancreatic adenocarcinoma. Signal enhancement ratio (SER)-predicted stromal/epithelial proportion showed good agreement with histopathology measurements across three distinct centers. Signal enhancement ratio (SER)-predicted stromal proportion was demonstrated to be an independent prognostic factor for OS in PDAC.

Association between Liver MRI Proton Density Fat Fraction and Liver Disease Risk.

Background A better understanding of the association between liver MRI proton density fat fraction (PDFF) and liver diseases might support the clinical implementation of MRI PDFF. Purpose To quantify the genetically predicted causal effect of liver MRI PDFF on liver disease risk. Materials and Methods This population-based prospective observational study used summary-level data mainly from the UK Biobank and FinnGen. Mendelian randomization analysis was conducted using the inverse variance-weighted method to explore the causal association between genetically predicted liver MRI PDFF and liver disease risk with Bonferroni correction. The individual-level data were downloaded between August and December 2020 from the UK Biobank. Logistic regression analysis was performed to validate the association between liver MRI PDFF polygenic risk score and liver disease risk. Mediation analyses were performed using multivariable mendelian randomization. Results Summary-level and individual-level data were obtained from 32 858 participants and 378 436 participants (mean age, 57 years ± 8 [SD]; 203 108 female participants), respectively. Genetically predicted high liver MRI PDFF was associated with increased risks of malignant liver neoplasm (odds ratio [OR], 4.5; P < .001), alcoholic liver disease (OR, 1.9; P < .001), fibrosis and cirrhosis of the liver (OR, 3.0; P < .004), fibrosis of the liver (OR, 3.6; P = .002), cirrhosis of the liver (OR, 3.8; P < .001), nonalcoholic steatohepatitis (OR, 7.7; P < .001), and nonalcoholic fatty liver disease (NAFLD) (OR, 4.4; P < .001). Individual-level evidence supported these associations after grouping participants based on liver MRI PDFF polygenic risk score (all P < .004). The mediation analysis indicated that genetically predicted high-density lipoprotein cholesterol, type 2 diabetes mellitus, and waist-to-hip ratio (mediation effects, 25.1%-46.3%) were related to the occurrence of fibrosis and cirrhosis of the liver, cirrhosis of the liver, and NAFLD at liver MRI PDFF (all P < .05). Conclusion This study provided evidence of the association between genetically predicted liver MRI PDFF and liver health. © RSNA, 2023 Supplemental material is available for this article. See also the editorials by Reeder and Starekova and Monsell in this issue.

Predicting Microvascular Invasion in Hepatocellular Carcinoma Using CT-based Radiomics Model.

Background Prediction of microvascular invasion (MVI) may help determine treatment strategies for hepatocellular carcinoma (HCC). Purpose To develop a radiomics approach for predicting MVI status based on preoperative multiphase CT images and to identify MVI-associated differentially expressed genes. Materials and Methods Patients with pathologically proven HCC from May 2012 to September 2020 were retrospectively included from four medical centers. Radiomics features were extracted from tumors and peritumor regions on preoperative registration or subtraction CT images. In the training set, these features were used to build five radiomics models via logistic regression after feature reduction. The models were tested using internal and external test sets against a pathologic reference standard to calculate area under the receiver operating characteristic curve (AUC). The optimal AUC radiomics model and clinical-radiologic characteristics were combined to build the hybrid model. The log-rank test was used in the outcome cohort (Kunming center) to analyze early recurrence-free survival and overall survival based on high versus low model-derived score. RNA sequencing data from The Cancer Image Archive were used for gene expression analysis. Results A total of 773 patients (median age, 59 years; IQR, 49-64 years; 633 men) were divided into the training set (n = 334), internal test set (n = 142), external test set (n = 141), outcome cohort (n = 121), and RNA sequencing analysis set (n = 35). The AUCs from the radiomics and hybrid models, respectively, were 0.76 and 0.86 for the internal test set and 0.72 and 0.84 for the external test set. Early recurrence-free survival (P < .01) and overall survival (P < .007) can be categorized using the hybrid model. Differentially expressed genes in patients with findings positive for MVI were involved in glucose metabolism. Conclusion The hybrid model showed the best performance in prediction of MVI. © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Summers in this issue.

An imaging-based machine learning model outperforms clinical risk scores for prognosis of cirrhotic variceal bleeding.

OBJECTIVES: To develop and validate a machine learning model based on contrast-enhanced CT to predict the risk of occurrence of the composite clinical endpoint (hospital-based intervention or death) in cirrhotic patients with acute variceal bleeding (AVB). METHODS: This retrospective study enrolled 330 cirrhotic patients with AVB between January 2017 and December 2020 from three clinical centers. Contrast-enhanced CT and clinical data were collected. Centers A and B were divided 7:3 into a training set and an internal test set, and center C served as a separate external test set. A well-trained deep learning model was applied to segment the liver and spleen. Then, we extracted 106 original features of the liver and spleen separately based on the Image Biomarker Standardization Initiative (IBSI). We constructed the Liver-Spleen (LS) model based on the selected radiomics features. The performance of LS model was evaluated by receiver operating characteristics and calibration curves. The clinical utility of models was analyzed using decision curve analyses (DCA). RESULTS: The LS model demonstrated the best diagnostic performance in predicting the composite clinical endpoint of AVB in patients with cirrhosis, with an AUC of 0.782 (95% CI 0.650-0.882) and 0.789 (95% CI 0.674-0.878) in the internal test and external test groups, respectively. Calibration curves and DCA indicated the LS model had better performance than traditional clinical scores. CONCLUSION: A novel machine learning model outperforms previously known clinical risk scores in assessing the prognosis of cirrhotic patients with AVB CLINICAL RELEVANCE STATEMENT: The Liver-Spleen model based on contrast-enhanced CT has proven to be a promising tool to predict the prognosis of cirrhotic patients with acute variceal bleeding, which can facilitate decision-making and personalized therapy in clinical practice. KEY POINTS: • The Liver-Spleen machine learning model (LS model) showed good performance in assessing the clinical composite endpoint of cirrhotic patients with AVB (AUC ≥ 0.782, sensitivity ≥ 80%). • The LS model outperformed the clinical scores (AUC ≤ 0.730, sensitivity ≤ 70%) in both internal and external test cohorts.

Systems pharmacology reveals the mechanism of activity of Physalis alkekengi L. var. franchetii against lipopolysaccharide-induced acute lung injury.

Acute lung injury (ALI) is an important cause of mortality of patients with sepsis, shock, trauma, pneumonia, multiple transfusions and pancreatitis. Physalis alkekengi L. var. franchetii (Mast.) Makino (PAF) has been extensively used in Chinese folk medicine because of a good therapeutic effect in respiratory diseases. Here, an integrated approach combining network pharmacology, proton nuclear magnetic resonance-based metabolomics, histopathological analysis and biochemical assays was used to elucidate the mechanism of PAF against ALI induced by lipopolysaccharide (LPS) in a mouse model. We found that the compounds present in PAF interact with 32 targets to effectively improve the damage in the lung undergoing ALI. We predicted the putative signalling pathway involved by using the network pharmacology and then used the orthogonal signal correction partial least-squares discriminant analysis to analyse the disturbances in the serum metabolome in mouse. We also used ELISA, RT-qPCR, Western blotting, immunohistochemistry and TUNEL assay to confirm the potential signalling pathways involved. We found that PAF reduced the release of cytokines, such as TNF-α, and the accumulation of oxidation products; decreased the levels of NF-κB, p-p38, ERK, JNK, p53, caspase-3 and COX-2; and enhanced the translocation of Nrf2 from the cytoplasm to the nucleus. Collectively, PAF significantly reduced oxidative stress injury and inflammation, at the same time correcting the energy metabolism imbalance caused by ALI, increasing the amount of antioxidant-related metabolites and reducing the apoptosis of lung cells. These observations suggest that PAF may be an effective candidate preparation alleviating ALI.

Systems pharmacology reveals the mechanism of activity of Ge-Gen-Qin-Lian decoction against LPS-induced acute lung injury: A novel strategy for exploring active components and effective mechanism of TCM formulae.

Acute lung injury (ALI), a severe and life-threatening inflammation of the lung, with high morbidity and mortality, underscoring the urgent need for novel treatments. Ge-Gen-Qin-Lian decoction (GQD), a classic Chinese herbal formula, has been widely used to treat intestine-related diseases in the clinic for centuries. In recent years, a growing number of studies have found that GQD has a favorable anti-inflammatory effect. With the further study on the viscera microbiota, the link between the lungs and the gut-the gut-lung axis has been established. Based on the theory of the gut-lung axis, we used systems pharmacology to explore the effects and mechanisms of GQD treatment in ALI. Hypothesizing that GQD inhibits ALI progression, we used the experimental model of lipopolysaccharide (LPS)-induced ALI in Balb/c mice to evaluate the therapeutic potential of GQD. Our results showed that GQD exerted protective effects against LPS-induced ALI by reducing pulmonary edema and microvascular permeability. Meanwhile, GQD can downregulate the expression of LPS-induced TNF-α, IL-1ß, and IL-6 in lung tissue, bronchoalveolar lavage fluid (BLAF), and serum. To further understand the molecular mechanism of GQD in the treatment of ALI, we used the network pharmacology to predict the disease targets of the active components of GQD. Lung tissue and serum samples of the mice were separately analyzed by transcriptomics and metabolomics. KEGG pathway analysis of network pharmacology and transcriptomics indicated that PI3K/Akt signaling pathway was significantly enriched, suggesting that it may be the main regulatory pathway for GQD treatment of ALI. By immunohistochemical analysis and apoptosis detection, it was verified that GQD can inhibit ALI apoptosis through PI3K/Akt signaling pathway. Then, we used the PI3K inhibitor LY294002 to block the PI3K/Akt signaling pathway, and reversely verified that the PI3K/Akt signaling pathway is the main pathway of GQD anti-ALI. In addition, differential metabolites in mice serum samples indicate that GQD can inhibit the inflammatory process of ALI by reversing the imbalance of energy metabolism. Our study showed that, GQD did have a better therapeutic effect on ALI, and initially elucidated its molecular mechanism. Thus, GQD could be exploited to develop novel therapeutics for ALI. Moreover, our study also provides a novel strategy to explore active components and effective mechanism of TCM formula combined with TCM theory to treat ALI.

A novel and safe approach: middle cranial approach for laparoscopic right hemicolon cancer surgery with complete mesocolic excision.

BACKGROUND: Mastering right hemicolectomy techniques using laparoscopy in colorectal cancer surgery is very difficult. Although the long-term prognosis of laparoscopic right hemicolectomy (LRH) and complete mesocolic excision is unquestionable, different surgeons have their own opinions on routes of conducting LRH. OBJECTIVES: LRH surgery is very complex due to the upper abdominal anatomical structure and vascular variation. Therefore, it has been considered the most difficult of all colorectal cancer surgeries. Our innovative middle cranial approach (MCA) was developed to avoid unnecessary injuries and minimize the operative time, thereby reducing the patient's hospital stay and improving their short-term prognosis. METHODS: We compared 90 colon cancer patients who underwent the MCA between January 2016 and January 2017 with 82 patients who underwent the conventional central approach conducted by the same group of physicians (with Dr Cui as the surgeon) from 2011 to 2015. A short-term statistical analysis was performed. RESULTS: A total of 90 patients were included: 43 men and 47 women. Twenty-three patients underwent abdominal surgery (including stomach, rectum, and sigmoid colon surgery; appendectomy; and uterine attachment surgery). The median age of these patients was 62.6 (28-85) years; the median BMI was 22.9 (14.7-33.3) kg/m2; the mean bleeding volume was 53.9 (10-100) ml; the mean tumour diameter was 5.7 (0.8-9) cm, and the average number of lymph nodes detected was 19.2 (7-49). CONCLUSIONS: Our study showed that radical resection of right-sided colon cancer using the MCA was safe and feasible for the treatment of colorectal cancer patients.

Targeted metabolomic analysis of 33 amino acids and biogenic amines in human urine by ion-pairing HPLC-MS/MS: Biomarkers for tacrolimus nephrotoxicity after renal transplantation.

Calcineurin inhibitor nephrotoxicity, especially for the widely used tacrolimus, has become a major concern in post-transplant immunosuppression. Multiparametric amino acid metabolomics is useful for biomarker identification of tacrolimus nephrotoxicity, for which specific quantitative methods are highlighted as a premise. This article presents a targeted metabolomic assay to quantify 33 amino acids and biogenic amines in human urine by high-performance liquid chromatography coupled with tandem mass spectrometry. Chromatographic separation was carried out on an Agilent Zorbax SB-C18 column (3.0 × 150 mm, 5 µm) with addition of an ion-pairing agent in the mobile phase, and MS/MS detection was achieved in both the positive and negative multiple reaction monitoring modes. Good correlation coefficients (r2 > 0.98) were obtained for most analytes. Intra- and inter-day precision, stability, carryover and incurred sample reanalysis met with the acceptance criteria of the guidance of the US Food and Drug Administration. Analysis on urine from healthy volunteers and renal transplantation patients with tacrolimus nephrotoxicity confirmed symmetric dimethylarginine and serine as biomarkers for kidney injury, with AUC values of 0.95 and 0.81 in receiver operating characteristic analysis, respectively. Additionally, symmetric dimethylarginine exhibited a tight correlation with serum creatinine, and was therefore indicative of renal function. The targeted metabolomic assay was time and cost prohibitive for amino acid analysis in human urine, facilitating the biomarker identification of tacrolimus nephrotoxicity.

LC-MS/MS method for simultaneous determination of serum p-cresyl sulfate and indoxyl sulfate in patients undergoing peritoneal dialysis.

p-Cresol sulfate (pCS) and indoxyl sulfate (IS) are protein-bound uremic toxins that accumulate in patients with chronic kidney disease (CKD). They are closely associated with the mortality rate of CKD and morbidity of cardiovascular disease. In the present study, we established a rapid method for determination of pCS and IS by HPLC-MS/MS in serum samples from 205 CKD patients undergoing peritoneal dialysis. In brief, serum was extracted by acetonitrile and spiked with hydrochlorothiazide. The prepared sample was eluted through HPLC column (Agilent Zorbax SB-C18 , 3.5 µm, 2.1 × 100 mm) with a mobile phase of acetonitrile and 10 mm ammonium acetate solution (10:90, v/v) for subsequent detection of pCS and IS by MS/MS. The linearity ranged from 50 to 10,000 ng/mL for pCS (r > 0.99), and from 500 to 10,000 ng/mL for IS (r > 0.99). The lower limit of quantification was 50 ng/mL for pCS, and 500 ng/mL for IS. Relative standard deviation (RSD) of intra- and inter-day precision was within ±15%. The results showed that pCS and IS levels were partially correlated with renal function in CKD patients, and IS was directly related to serum creatinine and estimated glomerular filtration rate.

Baseline 25(OH)D level is a prognostic indicator for bariatric surgery readmission: a matched retrospective cohort study.

Introduction: Managing postsurgical complications is crucial in optimizing the outcomes of bariatric surgery, for which preoperative nutritional assessment is essential. In this study, we aimed to evaluate and validate the efficacy of vitamin D levels as an immunonutritional biomarker for bariatric surgery prognosis. Methods: This matched retrospective cohort study included adult patients who underwent bariatric surgery at a tertiary medical center in China between July 2021 and June 2022. Patients with insufficient and sufficient 25(OH)D (< 30 ng/mL) were matched in a 1:1 ratio. Follow-up records of readmission at 3 months, 6 months, and 1 year were obtained to identify prognostic indicators. Results: A matched cohort of 452 patients with a mean age of 37.14 ± 9.25 years and involving 69.47% females was enrolled. Among them, 94.25 and 5.75% underwent sleeve gastrectomy and gastric bypass, respectively. Overall, 25 patients (5.54%) were readmitted during the 1-year follow-up. The prognostic nutritional index and controlling nutritional status scores calculated from inflammatory factors did not efficiently detect malnourishment. A low 25(OH)D level (3.58 [95% CI, 1.16-11.03]) and surgery season in summer or autumn (2.68 [95% CI, 1.05-6.83]) increased the risk of 1-year readmission in both the training and validation cohorts. The area under the receiver operating characteristic curve was 0.747 (95% CI, 0.640-0.855), with a positive clinical benefit in the decision curve analyses. The relationship between 25(OH)D and 6-month readmission was U-shaped. Conclusion: Serum 25(OH)D levels have prognostic significance in bariatric surgery readmission. Hence, preferable 25(OH)D levels are recommended for patients undergoing bariatric surgery.

Identification of an Amino Acid Metabolism Reprogramming Signature for Predicting Prognosis, Immunotherapy Efficacy, and Drug Candidates in Colon Cancer.

Colon cancer ranked third among the most frequently diagnosed cancers worldwide. Amino acid metabolic reprogramming was related to the occurrence and development of colon cancer. We looked for the amino acid metabolism genes (AMGs) associated with amino acid metabolism from molecular signatures database as prognostic markers and constructed amino acid metabolism scoring model (AMS). According to AMS, the patients were divided into high AMS and low AMS groups, and the prognostic characteristics, molecular phenotypes, somatic cell mutation characteristics, immune cell infiltration characteristics, and immunotherapy effect of the two groups were systematically analyzed. Finally, the compounds targeting AMGs were also screened. We screen out 6 prognostic AMGs (P < 0.05) and construct an AMS model based on them. K-M curve indicated that OS in low AMS group was significantly higher than that in high group (P < 0.05), which were validated in multiple datasets. And different AMS groups had different molecular phenotypes, somatic cell mutation characteristics and immune cell infiltration characteristics. Low AMS group had a better effect for immunotherapy. In addition, we predicted potential therapeutic compounds that could bind to AMGs target proteins. AMS model can be used as a hierarchical tool to evaluate the prognosis, immune infiltration characteristics and immunotherapy response ability of colon cancer. And the compounds screened based on AMGs may become new anti-tumor drugs.

Prediction of preoperative microvascular invasion by dynamic radiomic analysis based on contrast-enhanced computed tomography.

PURPOSE: Microvascular invasion (MVI) is a common complication of hepatocellular carcinoma (HCC) surgery, which is an important predictor of reduced surgical prognosis. This study aimed to develop a fully automated diagnostic model to predict pre-surgical MVI based on four-phase dynamic CT images. METHODS: A total of 140 patients with HCC from two centers were retrospectively included (training set, n = 98; testing set, n = 42). All CT phases were aligned to the portal venous phase, and were then used to train a deep-learning model for liver tumor segmentation. Radiomics features were extracted from the tumor areas of original CT phases and pairwise subtraction images, as well as peritumoral features. Lastly, linear discriminant analysis (LDA) models were trained based on clinical features, radiomics features, and hybrid features, respectively. Models were evaluated by area under curve (AUC), accuracy, sensitivity, specificity, positive and negative predictive values (PPV and NPV). RESULTS: Overall, 86 and 54 patients with MVI- (age, 55.92 ± 9.62 years; 68 men) and MVI+ (age, 53.59 ± 11.47 years; 43 men) were included. Average dice coefficients of liver tumor segmentation were 0.89 and 0.82 in training and testing sets, respectively. The model based on radiomics (AUC = 0.865, 95% CI: 0.725-0.951) showed slightly better performance than that based on clinical features (AUC = 0.841, 95% CI: 0.696-0.936). The classification model based on hybrid features achieved better performance in both training (AUC = 0.955, 95% CI: 0.893-0.987) and testing sets (AUC = 0.913, 95% CI: 0.785-0.978), compared with models based on clinical and radiomics features (p-value < 0.05). Moreover, the hybrid model also provided the best accuracy (0.857), sensitivity (0.875), and NPV (0.917). CONCLUSION: The classification model based on multimodal intra- and peri-tumoral radiomics features can well predict HCC patients with MVI.

Development of a radiomics-based model to predict occult liver metastases of pancreatic ductal adenocarcinoma: a multicenter study.

BACKGROUND: Undetectable occult liver metastases block the long-term survival of pancreatic ductal adenocarcinoma (PDAC). This study aimed to develop a radiomics-based model to predict occult liver metastases and assess its prognostic capacity for survival. MATERIALS AND METHODS: Patients who underwent surgical resection and were pathologically proven with PDAC were recruited retrospectively from five tertiary hospitals between January 2015 and December 2020. Radiomics features were extracted from tumors, and the radiomics-based model was developed in the training cohort using LASSO-logistic regression. The model's performance was assessed in the internal and external validation cohorts using the area under the receiver operating curve (AUC). Subsequently, the association of the model's risk stratification with progression-free survival (PFS) and overall survival (OS) was then statistically examined using Cox regression analysis and the log-rank test. RESULTS: A total of 438 patients [mean (SD) age, 62.0 (10.0) years; 255 (58.2%) male] were divided into the training cohort ( n =235), internal validation cohort ( n =100), and external validation cohort ( n =103). The radiomics-based model yielded an AUC of 0.73 (95% CI: 0.66-0.80), 0.72 (95% CI: 0.62-0.80), and 0.71 (95% CI: 0.61-0.80) in the training, internal validation, and external validation cohorts, respectively, which were higher than the preoperative clinical model. The model's risk stratification was an independent predictor of PFS (all P <0.05) and OS (all P <0.05). Furthermore, patients in the high-risk group stratified by the model consistently had a significantly shorter PFS and OS at each TNM stage (all P <0.05). CONCLUSION: The proposed radiomics-based model provided a promising tool to predict occult liver metastases and had a great significance in prognosis.

Corrigendum to 'Predicting post-resection recurrence by integrating imaging-based surrogates of distinct vascular patterns of hepatocellular carcinoma' (JHEP Reports 5 [2023] 100806).

[This corrects the article DOI: 10.1016/j.jhepr.2023.100806.].

Deciphering spatial domains from spatially resolved transcriptomics with Siamese graph autoencoder.

BACKGROUND: Cell clustering is a pivotal aspect of spatial transcriptomics (ST) data analysis as it forms the foundation for subsequent data mining. Recent advances in spatial domain identification have leveraged graph neural network (GNN) approaches in conjunction with spatial transcriptomics data. However, such GNN-based methods suffer from representation collapse, wherein all spatial spots are projected onto a singular representation. Consequently, the discriminative capability of individual representation feature is limited, leading to suboptimal clustering performance. RESULTS: To address this issue, we proposed SGAE, a novel framework for spatial domain identification, incorporating the power of the Siamese graph autoencoder. SGAE mitigates the information correlation at both sample and feature levels, thus improving the representation discrimination. We adapted this framework to ST analysis by constructing a graph based on both gene expression and spatial information. SGAE outperformed alternative methods by its effectiveness in capturing spatial patterns and generating high-quality clusters, as evaluated by the Adjusted Rand Index, Normalized Mutual Information, and Fowlkes-Mallows Index. Moreover, the clustering results derived from SGAE can be further utilized in the identification of 3-dimensional (3D) Drosophila embryonic structure with enhanced accuracy. CONCLUSIONS: Benchmarking results from various ST datasets generated by diverse platforms demonstrate compelling evidence for the effectiveness of SGAE against other ST clustering methods. Specifically, SGAE exhibits potential for extension and application on multislice 3D reconstruction and tissue structure investigation. The source code and a collection of spatial clustering results can be accessed at https://github.com/STOmics/SGAE/.

Prevalence and associated factors of drug-drug interactions in elderly outpatients in a tertiary care hospital: a cross-sectional study based on three databases.

Background: Drug-drug interactions (DDIs) are factors of adverse drug reactions and are more common in elderly patients. Identifying potential DDIs can prevent the related risks. Fewer studies of potential DDIs in prescribing for elderly patients in outpatient clinics. This study aimed to investigate the prevalence and associated factors with potential DDIs and potentially clinically significant DDIs (csDDIs) among elderly outpatients based on 3 DDIs databases. Methods: A cross-sectional study was carried out on outpatients (≥65 years old) of a tertiary care hospital in China between January and March 2022. Patients' prescriptions, including at least 1 systemic drug, were consecutively collected. The potential DDIs were identified by Lexicomp®, Micromedex®, and DDInter. Patient-related clinical parameter recorded at the prescriptions and DDIs with higher risk rating was analyzed. Variables showing association in univariate analysis (P<0.2) were included in logistic regression analysis. Weighted kappa analysis was used to analyze the consistencies of different databases. Results: A total of 19,991 elderly outpatients were involved in the study, among whom 21,527 drug combinations including 486 drugs occurred. Lexicomp®, Micromedex®, and DDInter respectively identified 32.22%, 32.93%, and 22.62% of patients have at least one potential DDIs, meanwhile, 9.16%, 14.53%, and 4.56% of patients have at least one potential csDDIs. Under any evaluation criteria, polypharmacy and neurology visits were risk factors for csDDIs. Lexicomp® has the highest coverage rate (87.86%) for drugs. Micromedex® identified the most csDDIs (740 drug combinations). Drugs used in diabetes and psycholeptics were frequently found in the csDDIs of 2 commercial databases. The consistency between Lexicomp® and Micromedex® was moderate (weighted kappa 0.473). DDInter had fair consistencies with the other databases. Conclusions: This study showed the prevalence of potential DDIs is high in elderly outpatients and potential csDDIs were prevalent. Considering the relative risk, pre-warning of potential DDIs before outpatient prescribing is necessary. As the consistencies among identification criteria are not good, more research is needed to focus on actual adverse outcomes to promote accurate prevention of csDDIs.