FedDUS: Lung tumor segmentation on CT images through federated semi-supervised with dynamic update strategy.

BACKGROUND AND OBJECTIVE: Lung tumor annotation is a key upstream task for further diagnosis and prognosis. Although deep learning techniques have promoted automation of lung tumor segmentation, there remain challenges impeding its application in clinical practice, such as a lack of prior annotation for model training and data-sharing among centers. METHODS: In this paper, we use data from six centers to design a novel federated semi-supervised learning (FSSL) framework with dynamic model aggregation and improve segmentation performance for lung tumors. To be specific, we propose a dynamically updated algorithm to deal with model parameter aggregation in FSSL, which takes advantage of both the quality and quantity of client data. Moreover, to increase the accessibility of data in the federated learning (FL) network, we explore the FAIR data principle while the previous federated methods never involve. RESULT: The experimental results show that the segmentation performance of our model in six centers is 0.9348, 0.8436, 0.8328, 0.7776, 0.8870 and 0.8460 respectively, which is superior to traditional deep learning methods and recent federated semi-supervised learning methods. CONCLUSION: The experimental results demonstrate that our method is superior to the existing FSSL methods. In addition, our proposed dynamic update strategy effectively utilizes the quality and quantity information of client data and shows efficiency in lung tumor segmentation. The source code is released on (https://github.com/GDPHMediaLab/FedDUS).

Outcomes of Liver Resection and Transarterial Chemoembolization in Patients with Multinodular BCLC-A Hepatocellular Carcinoma.

Background: This study aimed to compare the outcomes of liver resection (LR) and transarterial chemoembolization (TACE) in patients with multinodular hepatocellular carcinoma (HCC) within the Milan criteria who were not eligible for liver transplantation. Methods: We retrospectively analyzed 483 patients with multinodular HCC within the Milan criteria, who underwent either LR or TACE as an initial therapy between 2013 and 2022. The overall survival (OS) in the entire population and recurrence-free survival (RFS) in patients who underwent LR and TACE and achieved a complete response were analyzed. Propensity score (PS) matching analysis was also used for a fair comparison of outcomes between the two groups. Results: Among the 483 patients, 107 (22.2%) and 376 (77.8%) underwent LR and TACE, respectively. The median size of the largest tumor was 2.0 cm, and 72.3% of the patients had two HCC lesions. The median OS and RFS were significantly longer in the LR group than in the TACE group (p <0.01 for both). In the multivariate analysis, TACE (adjusted hazard ratio [aHR], 1.81 and aHR, 2.41) and large tumor size (aHR, 1.43 and aHR, 1.44) were significantly associated with worse OS and RFS, respectively. The PS-matched analysis also demonstrated that the LR group had significantly longer OS and RFS than the TACE group (PS <0.05). Conclusion: In this study, LR showed better OS and RFS than TACE in patients with multinodular Barcelona Clinic Liver Cancer stage A HCC. Therefore, LR can be considered an effective treatment option for these patients.

Development and Validation of a Deep Learning Model to Reduce the Interference of Rectal Artifacts in MRI-based Prostate Cancer Diagnosis.

Purpose To develop an MRI-based model for clinically significant prostate cancer (csPCa) diagnosis that can resist rectal artifact interference. Materials and Methods This retrospective study included 2203 male patients with prostate lesions who underwent biparametric MRI and biopsy between January 2019 and June 2023. Targeted adversarial training with proprietary adversarial samples (TPAS) strategy was proposed to enhance model resistance against rectal artifacts. The automated csPCa diagnostic models trained with and without TPAS were compared using multicenter validation datasets. The impact of rectal artifacts on the diagnostic performance of each model at the patient and lesion levels was compared using the area under the receiver operating characteristic curve (AUC) and the area under the precision-recall curve (AUPRC). The AUC between models was compared using the DeLong test, and the AUPRC was compared using the bootstrap method. Results The TPAS model exhibited diagnostic performance improvements of 6% at the patient level (AUC: 0.87 vs 0.81, P < .001) and 7% at the lesion level (AUPRC: 0.84 vs 0.77, P = .007) compared with the control model. The TPAS model demonstrated less performance decline in the presence of rectal artifact-pattern adversarial noise than the control model (ΔAUC: -17% vs -19%, ΔAUPRC: -18% vs -21%). The TPAS model performed better than the control model in patients with moderate (AUC: 0.79 vs 0.73, AUPRC: 0.68 vs 0.61) and severe (AUC: 0.75 vs 0.57, AUPRC: 0.69 vs 0.59) artifacts. Conclusion This study demonstrates that the TPAS model can reduce rectal artifact interference in MRI-based csPCa diagnosis, thereby improving its performance in clinical applications. Keywords: MR-Diffusion-weighted Imaging, Urinary, Prostate, Comparative Studies, Diagnosis, Transfer Learning Clinical trial registration no. ChiCTR23000069832 Supplemental material is available for this article. Published under a CC BY 4.0 license.

The associations between fibrosis changes and liver-related events in patients with metabolic dysfunction-associated steatotic liver disease.

BACKGROUND: The prognosis of metabolic dysfunction-associated steatotic liver disease (MASLD) is associated with liver fibrosis. We investigated the associations between changes in liver stiffness measurement (LSM) over 3-year period and the development of cirrhosis or hepatocellular carcinoma (HCC) in patients with MASLD. METHODS: This study involved patients with MASLD who underwent transient elastography at baseline and 3 years after baseline from 2012 to 2020. Low (L), indeterminate (I) and high (H) LSM values were classified as <8 kPa, 8-12 kPa and >12 kPa respectively. RESULTS: Among 1738 patients, 150 (8.6%) were diagnosed with cirrhosis or HCC. The proportions of patients with L, I and H risk were 69.7%, 19.9% and 10.5% at baseline, and 78.8%, 12.8% and 8.4% at 3 years after baseline, respectively. The incidence rates of cirrhosis or HCC per 1000 person-years were 3.7 (95% confidence interval [CI], 2.4-5.5) in the L → L + I group, 23.9 (95% CI, 17.1-32.6) in the I → L + I group, 38.3 (95% CI, 22.3-61.3) in the H → L + I group, 62.5 (95% CI, 32.3-109.2) in the I → H group, 67.8 (95% CI, 18.5-173.6) in the L → H group and 93.9 (95% CI 70.1-123.1) in the H → H group. Two risk factors for the development of cirrhosis or HCC were LSM changes and low platelet counts. CONCLUSION: LSM changes could predict clinical outcomes in patients with MASLD. Thus, it is important to monitor changes in the fibrotic burden by regular assessment of LSM values.

Regorafenib plus nivolumab in unresectable hepatocellular carcinoma: the phase 2 RENOBATE trial.

Regorafenib has anti-tumor activity in patients with unresectable hepatocellular carcinoma (uHCC) with potential immunomodulatory effects, suggesting that its combination with immune checkpoint inhibitor may have clinically meaningful benefits in patients with uHCC. The multicenter, single-arm, phase 2 RENOBATE trial tested regorafenib-nivolumab as front-line treatment for uHCC. Forty-two patients received nivolumab 480 mg every 4 weeks and regorafenib 80 mg daily (3-weeks-on/1-week-off schedule). The primary endpoint was the investigator-assessed objective response rate (ORR) per Response Evaluation Criteria in Solid Tumors (RECIST) version 1.1. The secondary endpoints included safety, progression-free survival (PFS) and overall survival (OS). ORR per RECIST version 1.1 was 31.0%, meeting the primary endpoint. The most common adverse events were palmar-plantar erythrodysesthesia syndrome (38.1%), alopecia (26.2%) and skin rash (23.8%). Median PFS was 7.38 months. The 1-year OS rate was 80.5%, and the median OS was not reached. Exploratory single-cell RNA sequencing analyses of peripheral blood mononuclear cells showed that long-term responders exhibited T cell receptor repertoire diversification, enrichment of genes representing immunotherapy responsiveness in MKI67+ proliferating CD8+ T cells and a higher probability of M1-directed monocyte polarization. Our data support further clinical development of the regorafenib-nivolumab combination as front-line treatment for uHCC and provide preliminary insights on immune biomarkers of response. ClinicalTrials.gov identifier: NCT04310709 .

Improving the hepatitis C virus care cascade with the in-hospital Reflex tEsting ALarm-C (REAL-C) model.

BACKGROUND: The World Health Organization (WHO) has set targets to eliminate viral hepatitis, including hepatitis C virus (HCV) infection, by 2030. We present the results of the in-hospital Reflex tEsting ALarm-C (REAL-C) model, which incorporates reflex HCV RNA testing and sending alerts to physicians. METHODS: We conducted a retrospective study analysing the data of 1730 patients who newly tested positive for anti-HCV between March 2020 and June 2023. Three distinct periods were defined: pre-REAL-C (n = 696), incomplete REAL-C (n = 515) and complete REAL-C model periods (n = 519). The primary outcome measure was the HCV RNA testing rate throughout the study period. Additionally, we assessed the referral rate to the gastroenterology department, linkage time for diagnosis and treatment and the treatment rate. RESULTS: The rate of HCV RNA testing increased significantly from 51.0% (pre-REAL-C) to 95.6% (complete REAL-C). This improvement was consistent across clinical departments, regardless of patients' comorbidities. Among patients with confirmed HCV infection, the gastroenterology referral rate increased from 57.1% to 81.1% after the REAL-C model. The treatment rate among treatment-eligible patients was 92.4% during the study period. The mean interval from anti-HCV positivity to HCV RNA testing decreased from 45.1 to 1.9 days. The mean interval from the detection of anti-HCV positivity to direct-acting antiviral treatment also decreased from 89.5 to 49.5 days with the REAL-C model. CONCLUSION: The REAL-C model, featuring reflex testing and physician alerts, effectively increased HCV RNA testing rates and streamlined care cascades. Our model facilitated progress towards achieving WHO's elimination goals for HCV infection.

A CT-based multitask deep learning model for predicting tumor stroma ratio and treatment outcomes in patients with colorectal cancer: a multicenter cohort study.

BACKGROUND: Tumor-stroma interactions, as indicated by tumor-stroma ratio (TSR), offer valuable prognostic stratification information. Current histological assessment of TSR is limited by tissue accessibility and spatial heterogeneity. We aimed to develop a multitask deep learning (MDL) model to noninvasively predict TSR and prognosis in colorectal cancer (CRC). MATERIALS AND METHODS: In this retrospective study including 2268 patients with resected CRC recruited from four centers, we developed an MDL model using preoperative CT images for the simultaneous prediction of TSR and overall survival. Patients in the training cohort (n=956) and internal validation cohort (IVC, n=240) were randomly selected from center I. Patients in the external validation cohort1(EVC1, n=509), EVC2 (n=203), and EVC3 (n=360) were recruited from other three centers. Model performance was evaluated with respect to discrimination and calibration. Furthermore, we evaluated whether the model could predict the benefit from adjuvant chemotherapy. RESULTS: The MDL model demonstrated strong TSR discrimination, yielding areas under the receiver operating curves (AUCs) of 0.855 (95%CI, 0.800-0.910), 0.838(95% CI, 0.802-0.874), and 0.857(95% CI, 0.804-0.909) in the three validation cohorts, respectively. The MDL model was also able to predict overall survival and disease-free survival across all cohorts. In multivariable Cox analysis, the MDL score (MDLS) remained an independent prognostic factor after adjusting for clinicopathological variables (all P<0.05). For stage II and stage III disease, patients with a high MDLS benefited from adjuvant chemotherapy (hazard ratio [HR] 0.391 [95%CI, 0.230-0.666], P=0.0003; HR=0.467[95%CI, 0.331-0.659], P<0.0001, respectively), whereas those with a low MDLS did not. CONCLUSION: The multitask DL model based on preoperative CT images effectively predicted TSR status and survival in CRC patients, offering valuable guidance for personalized treatment. Prospective studies are needed to confirm its potential to select patients who might benefit from chemotherapy.

High-strength and crack-free welding of 2024 aluminium alloy via Zr-core-Al-shell wire.

The 2000 series aluminium alloys are qualified for widespread use in lightweight structures, but solidification cracking during fusion welding has been a long-standing issue. Here, we create a zirconium (Zr)-core-aluminium (Al)-shell wire (ZCASW) and employ the oscillating laser-arc hybrid welding technique to control solidification during welding, and ultimately achieve reliable and crack-free welding of 2024 aluminium alloy. We select Zr wires with an ideal lattice match to Al based on crystallographic information and wind them by the Al wires with similar chemical components to the parent material. Crack-free, equiaxed (where the length, width and height of the grains are roughly equal), fine-grained microstructures are acquired, thereby considerably increasing the tensile strength over that of conventional fusion welding joints, and even comparable to that of friction stir welding joints. This work has important engineering application value in welding of high-strength aluminum alloys.

SwinHR: Hemodynamic-powered hierarchical vision transformer for breast tumor segmentation.

Accurate and automated segmentation of breast tumors in dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) plays a critical role in computer-aided diagnosis and treatment of breast cancer. However, this task is challenging, due to random variation in tumor sizes, shapes, appearances, and blurred boundaries of tumors caused by inherent heterogeneity of breast cancer. Moreover, the presence of ill-posed artifacts in DCE-MRI further complicate the process of tumor region annotation. To address the challenges above, we propose a scheme (named SwinHR) integrating prior DCE-MRI knowledge and temporal-spatial information of breast tumors. The prior DCE-MRI knowledge refers to hemodynamic information extracted from multiple DCE-MRI phases, which can provide pharmacokinetics information to describe metabolic changes of the tumor cells over the scanning time. The Swin Transformer with hierarchical re-parameterization large kernel architecture (H-RLK) can capture long-range dependencies within DCE-MRI while maintaining computational efficiency by a shifted window-based self-attention mechanism. The use of H-RLK can extract high-level features with a wider receptive field, which can make the model capture contextual information at different levels of abstraction. Extensive experiments are conducted in large-scale datasets to validate the effectiveness of our proposed SwinHR scheme, demonstrating its superiority over recent state-of-the-art segmentation methods. Also, a subgroup analysis split by MRI scanners, field strength, and tumor size is conducted to verify its generalization. The source code is released on (https://github.com/GDPHMediaLab/SwinHR).

Response to atezolizumab plus bevacizumab specific for lung and lymph node metastases affects survival of patients with HCC.

BACKGROUND & AIMS: Tumour microenvironment heterogeneity among different organs can influence immunotherapy responses. Here, we evaluated the impact of differential organ-specific responses on survival in patients with advanced-stage hepatocellular carcinoma (HCC) treated with atezolizumab plus bevacizumab (Atezo/Bev). METHODS: We retrospectively analysed 366 consecutive patients with advanced-stage HCC treated with Atezo/Bev as first-line systemic treatment. Therapeutic response was assessed using RECIST v1.1. Patients were divided into an intention-to-treat (ITT) group (patients treated with ≥1 dose of Atezo/Bev) and a per-protocol (PP) analysis group (patients with at least one measurable lesion irrespective of location treated with ≥3 doses of Atezo/Bev). Overall response and organ-specific response at initial and best response were evaluated in the PP group. Responders were defined as patients achieving complete remission or partial response. Initial progressors were defined as patients with progressive disease after three doses of Atezo/Bev. RESULTS: The ITT and PP groups comprised 324 and 236 patients, respectively. In the PP group, the organ-specific response rate of lung and lymph node (LN) metastases at both initial and best responses were higher than those of intrahepatic lesions and macrovascular tumour thrombosis. Lung and LN-specific response rates were 21.1% and 23.5%, respectively, at initial response, and 24.7% and 31.4%, respectively, at best response. Both initial pulmonary and lymphatic progressors (adjusted hazard ratios [95% confidence intervals], 6.37 [2.10-19.3], and 8.36 [2.16-32.4], respectively) were independently associated with survival regardless of intrahepatic response. CONCLUSIONS: The response of metastatic HCC to the Atezo/Bev regimen may be used to determine whether to continue treatment or switch to second-line treatment at an early phase of therapy.

Prediction of Hepatocellular Carcinoma Development in Korean Patients after Hepatitis C Cure with Direct-Acting Antivirals.

Background/Aims: With the wide application of direct-acting antivirals (DAAs) for hepatitis C virus infection, the number of patients achieving a sustained virologic response (SVR) will continue to increase. However, no consensus has been achieved on exempting SVR-achieving patients from hepatocellular carcinoma (HCC) surveillance. Methods: Between 2013 and 2021, 873 Korean patients who achieved SVR following DAA treatment were analyzed. We evaluated the predictive performance of seven noninvasive scores (PAGE-B, modified PAGE-B, Toronto HCC risk index, fibrosis-4, aspartate aminotransferase-to-platelet ratio index, albumin-bilirubin, and age male albumin-bilirubin platelet [aMAP]) at baseline and after SVR. Results: The mean age of the 873 patients (39.3% males) was 59.1 years, and 224 patients (25.7%) had cirrhosis. During 3,542 person-years of follow-up, 44 patients developed HCC, with an annual incidence of 1.24/100 person-years. Male sex (adjusted hazard ratio [AHR], 2.21), cirrhosis (AHR, 7.93), and older age (AHR, 1.05) were associated with a significantly higher HCC risk in multivariate analysis. The performance of all scores at the time of SVR were numerically better than those at baseline as determined by the integrated area under the curve. Time-dependent area under the curves for predicting the 3-, 5-, and 7-year risk of HCC after SVR were higher in mPAGE-B (0.778, 0.746, and 0.812, respectively) and aMAP (0.776, 0.747, and 0.790, respectively) systems than others. No patients predicted as low-risk by the aMAP or mPAGE-B systems developed HCC. Conclusions: aMAP and mPAGE-B scores demonstrated the highest predictive performance for de novo HCC in DAA-treated, SVR-achieving patients. Hence, these two systems may be used to identify low-risk patients that can be exempted from HCC surveillance.

Cardiovascular risk in chronic hepatitis B patients treated with tenofovir disoproxil fumarate or tenofovir alafenamide.

BACKGROUND/AIMS: Tenofovir disoproxil fumarate (TDF) is known to have a lipid-lowering effect. This is in contrast to tenofovir alafenamide (TAF), which has a lipid-neutral effect. Therefore, concerns have been raised as to whether these differences affect long-term cardiovascular risk. Here, we aimed to evaluate the long-term risk of cardiovascular events in chronic hepatitis B (CHB) patients treated with TAF or TDF. METHODS: We retrospectively analyzed 4,124 treatment-naïve CHB patients treated with TDF (n=3,186) or TAF (n=938) between 2012 and 2022. The primary outcome was a composite endpoint of major adverse cardiovascular events (MACE), including myocardial infarction, ischemic stroke, and hospitalization for unstable angina or heart failure. Serial changes in lipid profiles between two treatments were also explored. RESULTS: The median age of the patients was 50.6 years, and 60.6% of the patients were male. At baseline, 486 (11.8%) and 637 (15.4%) of the patients had dyslipidemia and fatty liver, respectively. A total of 42 MACE occurred, with an annual incidence of 0.2%/100 person-years (PYs). At 1, 3, and 5 years, the cumulative risk of MACE was 0.4%, 0.8%, and 1.2% in patients treated with TDF, and 0.2%, 0.7%, and 0.7% in patients treated with TAF, respectively (p=0.538). No significant differences in the risk of MACE were observed between TDF and TAF. A multivariable analysis found that current smoker and a history of cardiovascular events were risk factors associated with an increased risk of MACE. CONCLUSION: Patients treated with TAF had comparable risks of cardiovascular outcomes, defined as MACE, as patients treated with TDF.

Longitudinal changes in renal function in patients with chronic hepatitis B on antiviral treatment.

BACKGROUND: Patients with chronic hepatitis B (CHB) on nucleos(t)ide analogues (NUCs) often experience renal function decline. Conflicting results regarding the impact of NUC use and renal function have recently been reported. AIM: To examine longitudinal changes in renal function according to the NUC treatment type compared with untreated patients METHODS: From 2014 to 2022, we retrospectively analysed 10,642 patients with CHB. The primary outcome was chronic kidney disease (CKD) progression, which was defined as a minimum one-stage elevation. We applied propensity score (PS) matching for outcome comparisons. RESULTS: In the PS-matched cohort of 1996 pairs, the NUC-treated group (7.6/100 person-years [PYs]) had a significantly higher CKD progression risk than the untreated group (4.4/100 PYs), with a hazard ratio (HR) of 1.70 (p < 0.001). The tenofovir disoproxil fumarate (TDF)-treated group (7.9/100 PYs) showed a 1.76-fold increased CKD progression risk compared with the untreated group (4.5/100 PYs) in the PS-matched cohort (p < 0.001). Both the entecavir- and tenofovir alafenamide (TAF)-treated groups showed CKD progression risks comparable to those of the untreated group in the PS-matched cohorts of 755 and 426 pairs, respectively (p = 0.132 and p = 0.120, respectively). No significant CKD progression risk was found between the entecavir- (6.0/100 PYs) and TAF-treated (5.2/100 PYs) groups in the PS-matched cohort of 510 pairs (p = 0.118). CONCLUSIONS: NUC-treated patients, especially those on TDF, faced a higher CKD progression risk than untreated patients. Entecavir- and TAF-treated patients had comparable CKD progression risks to untreated patients. No difference was observed between entecavir and TAF in the risk of CKD progression.

Vision Transformers for Computational Histopathology.

Computational histopathology is focused on the automatic analysis of rich phenotypic information contained in gigabyte whole slide images, aiming at providing cancer patients with more accurate diagnosis, prognosis, and treatment recommendations. Nowadays deep learning is the mainstream methodological choice in computational histopathology. Transformer, as the latest technological advance in deep learning, learns feature representations and global dependencies based on self-attention mechanisms, which is increasingly gaining prevalence in this field. This article presents a comprehensive review of state-of-the-art vision transformers that have been explored in histopathological image analysis for classification, segmentation, and survival risk regression applications. We first overview preliminary concepts and components built into vision transformers. Various recent applications including whole slide image classification, histological tissue component segmentation, and survival outcome prediction with tailored transformer architectures are then discussed. We finally discuss key challenges revolving around the use of vision transformers and envisioned future perspectives. We hope that this review could provide an elaborate guideline for readers to explore vision transformers in computational histopathology, such that more advanced techniques assisting in the precise diagnosis and treatment of cancer patients could be developed.

A risk prediction model for hepatocellular carcinoma in non-alcoholic fatty liver disease without cirrhosis: HCC prediction for non-cirrhotic NAFLD.

BACKGROUND & AIMS: Although non-alcoholic fatty liver disease (NAFLD) is becoming a leading cause of hepatocellular carcinoma (HCC), HCC risk in non-cirrhotic NAFLD received little attention. We aimed to develop and validate an HCC risk prediction model for non-cirrhotic NAFLD. METHODS: A nationwide cohort of non-cirrhotic NAFLD patients in Korea was recruited to develop a risk prediction model and validate it internally (n = 409 088). A model using a simplified point system was developed by Cox proportional hazard model. K-fold cross-validation assessed the accuracy, discrimination and calibration. The model was validated externally using a hospital cohort from Asan Medical Center (n = 8721). RESULTS: An 11-point HCC risk prediction model for non-cirrhotic NAFLD was developed using six independent factors of age, sex, diabetes, obesity, serum alanine aminotransferase level and gamma-glutamyl transferase level (c-index 0.75). The average area under receiver operating curves (AUROCs) of the model was 0.72 at 5 years and 0.75 at 10 years. In the external validation cohort, the AUROCs were 0.79 [95% confidence interval [CI], 0.59-0.95] at 5 years and 0.84 (95% CI, 0.73-0.94) at 10 years. The calibration plots showed the expected risks corresponded well with the observed risks. Risk stratification categorized patients into the low (score 0-6), moderate (7, 8) and high (9-11; estimated incidence rate >0.2%/year) risk groups. CONCLUSIONS: A novel HCC risk prediction model for non-cirrhotic NAFLD patients was developed and validated with fair performance. The model is expected to serve as a simple and reliable tool to assess HCC risk and assist precision screening of HCC.

Association between statin use and the prognosis of hepatocellular carcinoma after resection: a nationwide cohort study.

Background: The majority of patients with hepatocellular carcinoma (HCC) following hepatic resection experience tumor recurrence. Statin use is associated with a reduced risk of HCC development; however, the association between statin use and the prognosis of HCC after resection remains unclear. We aimed to investigate the effect of statin use on the prognosis after hepatic resection among patients with HCC. Methods: A nationwide cohort study was performed with data from the National Health Insurance Service Database in Korea. Among 65,101 HCC patients who underwent hepatic resection between January 2002 and December 2017, we included 21,470 patients. For validation, a hospital-based cohort of 3366 patients with very early or early-stage HCC who received curative-intent hepatic resection between January 2010 and December 2018 was analyzed. Recurrence-free survival (RFS) and overall survival (OS) was compared between statin users and non-users. Findings: Among the nationwide cohort of 21,470 patients, 2399 (11.2%) used statins and 19,071 (88.8%) did not. Among the hospital cohort of 3366 patients, 363 (10.8%) used statins and 3003 (89.2%) did not. In the propensity score-matched nationwide cohort, statin users had better RFS (hazard ratio [HR], 0.60; 95% confidence interval [CI], 0.56-0.64; P < 0.001) and OS (HR, 0.49; 95% CI, 0.45-0.53; P < 0.001), with a duration-response relationship. In the propensity score-matched validation hospital cohort, statin treatment was significantly associated with better RFS (HR, 0.73; 95% CI, 0.59-0.90; P = 0.003) and OS (HR, 0.48; 95% CI, 0.32-0.72; P < 0.001). The beneficial effects of statins were more prominent in non-cirrhotics, tumors sized ≥3 cm, tumors with microscopic vascular invasion, or early HCC recurrence (<2 years after resection). Interpretation: Statin use was associated with a better prognosis in a population-based cohort of patients with HCC after hepatic resection, which was further validated in a large hospital-based cohort. Funding: Asan Institute for Life Sciences and Corporate Relations; Korean Association for the Study of the Liver.

Atezolizumab plus bevacizumab versus active surveillance in patients with resected or ablated high-risk hepatocellular carcinoma (IMbrave050): a randomised, open-label, multicentre, phase 3 trial.

BACKGROUND: No adjuvant treatment has been established for patients who remain at high risk for hepatocellular carcinoma recurrence after curative-intent resection or ablation. We aimed to assess the efficacy of adjuvant atezolizumab plus bevacizumab versus active surveillance in patients with high-risk hepatocellular carcinoma. METHODS: In the global, open-label, phase 3 IMbrave050 study, adult patients with high-risk surgically resected or ablated hepatocellular carcinoma were recruited from 134 hospitals and medical centres in 26 countries in four WHO regions (European region, region of the Americas, South-East Asia region, and Western Pacific region). Patients were randomly assigned in a 1:1 ratio via an interactive voice-web response system using permuted blocks, using a block size of 4, to receive intravenous 1200 mg atezolizumab plus 15 mg/kg bevacizumab every 3 weeks for 17 cycles (12 months) or to active surveillance. The primary endpoint was recurrence-free survival by independent review facility assessment in the intention-to-treat population. This trial is registered with ClinicalTrials.gov, NCT04102098. FINDINGS: The intention-to-treat population included 668 patients randomly assigned between Dec 31, 2019, and Nov 25, 2021, to either atezolizumab plus bevacizumab (n=334) or to active surveillance (n=334). At the prespecified interim analysis (Oct 21, 2022), median duration of follow-up was 17·4 months (IQR 13·9-22·1). Adjuvant atezolizumab plus bevacizumab was associated with significantly improved recurrence-free survival (median, not evaluable [NE]; [95% CI 22·1-NE]) compared with active surveillance (median, NE [21·4-NE]; hazard ratio, 0·72 [adjusted 95% CI 0·53-0·98]; p=0·012). Grade 3 or 4 adverse events occurred in 136 (41%) of 332 patients who received atezolizumab plus bevacizumab and 44 (13%) of 330 patients in the active surveillance group. Grade 5 adverse events occurred in six patients (2%, two of which were treatment related) in the atezolizumab plus bevacizumab group, and one patient (<1%) in the active surveillance group. Both atezolizumab and bevacizumab were discontinued because of adverse events in 29 patients (9%) who received atezolizumab plus bevacizumab. INTERPRETATION: Among patients at high risk of hepatocellular carcinoma recurrence following curative-intent resection or ablation, recurrence-free survival was improved in those who received atezolizumab plus bevacizumab versus active surveillance. To our knowledge, IMbrave050 is the first phase 3 study of adjuvant treatment for hepatocellular carcinoma to report positive results. However, longer follow-up for both recurrence-free and overall survival is needed to assess the benefit-risk profile more fully. FUNDING: F Hoffmann-La Roche/Genentech.

Black chokeberry (Aronia melanocarpa L.) polyphenols attenuate obesity-induced colonic inflammation by regulating gut microbiota and the TLR4/NF-κB signaling pathway in high fat diet-fed rats.

This study investigated the potential benefits of black chokeberry polyphenol (BCP) supplementation on lipopolysaccharide (LPS)-stimulated inflammatory response in RAW264.7 cells and obesity-induced colonic inflammation in a high fat diet (HFD)-fed rat model. Our findings demonstrated that BCP treatment effectively reduced the production of nitric oxide (NO) and pro-inflammatory cytokines (TNF-α, IL-1ß, IL-6, and MCP-1) in LPS-induced RAW264.7 cells and concurrently mitigated oxidative stress by modulating the levels of malondialdehyde (MDA), catalase (CAT), and glutathione peroxidase (GSH-Px) in a dose-dependent manner. Furthermore, BCP supplementation significantly ameliorated HFD-induced obesity, improved glucose tolerance, and reduced systemic inflammation in HFD-fed rats. Notably, BCP treatment suppressed the mRNA expression of pro-inflammatory cytokines and alleviated intestinal barrier dysfunction by regulating the mRNA and protein expression of key tight junction proteins (ZO-1, occludin, and claudin-1), thereby inhibiting colonic inflammation caused by the TLR4/NF-κB signaling pathway. Additionally, BCP treatment altered the composition and function of the gut microbiota, leading to an increase in the total content of short-chain fatty acids (SCFAs), particularly acetic acid, propionic acid, isobutyric acid, and butyric acid. Collectively, our results highlighted the potential of BCP supplementation as a promising prebiotic strategy for treating obesity-induced colonic inflammation.

Computerized tertiary lymphoid structures density on H&E-images is a prognostic biomarker in resectable lung adenocarcinoma.

The increased amount of tertiary lymphoid structures (TLSs) is associated with a favorable prognosis in patients with lung adenocarcinoma (LUAD). However, evaluating TLSs manually is an experience-dependent and time-consuming process, which limits its clinical application. In this multi-center study, we developed an automated computational workflow for quantifying the TLS density in the tumor region of routine hematoxylin and eosin (H&E)-stained whole-slide images (WSIs). The association between the computerized TLS density and disease-free survival (DFS) was further explored in 802 patients with resectable LUAD of three cohorts. Additionally, a Cox proportional hazard regression model, incorporating clinicopathological variables and the TLS density, was established to assess its prognostic ability. The computerized TLS density was an independent prognostic biomarker in patients with resectable LUAD. The integration of the TLS density with clinicopathological variables could support individualized clinical decision-making by improving prognostic stratification.

A robust and efficient AI assistant for breast tumor segmentation from DCE-MRI via a spatial-temporal framework.

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) allows screening, follow up, and diagnosis for breast tumor with high sensitivity. Accurate tumor segmentation from DCE-MRI can provide crucial information of tumor location and shape, which significantly influences the downstream clinical decisions. In this paper, we aim to develop an artificial intelligence (AI) assistant to automatically segment breast tumors by capturing dynamic changes in multi-phase DCE-MRI with a spatial-temporal framework. The main advantages of our AI assistant include (1) robustness, i.e., our model can handle MR data with different phase numbers and imaging intervals, as demonstrated on a large-scale dataset from seven medical centers, and (2) efficiency, i.e., our AI assistant significantly reduces the time required for manual annotation by a factor of 20, while maintaining accuracy comparable to that of physicians. More importantly, as the fundamental step to build an AI-assisted breast cancer diagnosis system, our AI assistant will promote the application of AI in more clinical diagnostic practices regarding breast cancer.