ObjectiveThe widespread adoption of portable fundus cameras for primary care and community screening is hindered by limitations in current autofocus(AF) technologies. Image-based methods relying on sharpness evaluation require iterative searches, resulting in slow convergence, while projection-based techniques are susceptible to optical artifacts and calibration errors. To address these challenges, this study introduces a novel AF system based on direct wavefront sensing, designed to deliver simultaneous high speed, high precision, and operational robustness within the compact form factor essential for portable ophthalmic devices. MethodsOur approach fundamentally reimagines the AF process by directly measuring the ocular wavefront aberration. We developed a custom portable fundus camera integrating a miniaturized Shack-Hartmann wavefront sensor (SHWS) into the optical path. An 850 nm laser diode projects a point source onto the retina via oblique illumination to minimize corneal reflections. Light scattered from this spot carries the eye’s refractive error through the imaging optics and is directed to the SHWS, positioned at a plane optically conjugate to the primary color CMOS imaging sensor. A microlens array within the SHWS samples the incident wavefront, generating a pattern of focal spots on a CCD. Real-time centroid analysis of these spots provides a map of local wavefront slopes. These measurements are processed through a singular value decomposition (SVD) algorithm to fit a Zernike polynomial basis set, enabling real-time reconstruction of the wavefront phase. The defocus component (S) is extracted from the second-order Zernike coefficients, providing a direct, quantitative measure of the refractive error in diopters. This value serves as a precise error signal in a closed-loop control system, which commands a voice-coil actuated focusing lens to its null position in a single, deterministic step, eliminating the need for iterative search algorithms. ResultsComprehensive evaluation demonstrated the system’s high performance. Testing on a calibrated model eye (OEMI-7) established a highly linear relationship between the computed defocus S and the focusing lens position across a ±20 Diopter (D) compensation range, achievable within a 5 mm mechanical travel. The system achieved a focusing precision of 0.08 D, corresponding to an 18-fold improvement over a conventional projection spot-size method tested under identical conditions. The total focus acquisition time, encompassing wavefront measurement, computation, and lens actuation, averaged under 0.5 s. Clinical validation with 25 human volunteers (50 eyes, refractive range -15 D to +10 D) confirmed practical efficacy. The wavefront-sensing AF succeeded in 92% of attempts with a mean time of 0.5 s, substantially outperforming a projection-based benchmark which achieved only a 32% success rate with an average time of 4.25 s. The system provided instantaneous directional guidance and maintained stability during minor ocular movements. Objective assessment of image quality, via amplitude contrast of retinal vasculature, showed consistent and significant enhancement following AF correction across the entire tested diopter range. ConclusionThis work successfully implements and validates a direct wavefront-sensing autofocus paradigm for portable fundus cameras. By directly quantifying and compensating for the optical defocus aberration, this method bypasses the fundamental limitations of image-processing and projection-based techniques, enabling rapid, precise, and deterministic diopter compensation. The developed system delivers an exceptional combination of a wide operational range (±20 D), high accuracy (0.08 D), fast convergence (0.5 s), and a compact physical footprint. This technology provides a practical and high-performance focusing solution capable of enhancing the reliability, throughput, and diagnostic utility of portable retinal imaging in large-scale screening applications. Future efforts will be directed towards system cost optimization and performance adaptation for diverse ocular conditions.

ObjectiveDiscriminating atypical hepatocellular carcinoma (HCC) from other malignancies in liver nodules classified as Liver Imaging Reporting and Data System category M (LR-M) remains a significant diagnostic challenge on conventional ultrasound examination. The LR-M category, originally intended to capture non-HCC malignancies, paradoxically contains up to 63% of atypical HCCs that deviate from classic enhancement patterns, leading to potential misdiagnosis and suboptimal treatment planning. While deep learning has shown promise in HCC diagnosis, most existing models rely exclusively on single-modality ultrasound, overlooking the diagnostic benefits of integrating complementary information from multiple imaging sources. To address this gap, we propose a novel attention-weighted tri-modal ultrasound network (TUS-Net) that integrates contrast-enhanced ultrasound (CEUS), B-mode ultrasound (BUS), and time-intensity curves (TICs) to improve diagnostic accuracy for these clinically challenging lesions. MethodsOur framework incorporates a three-dimensional convolutional neural network (C3D) backbone to extract spatiotemporal features from CEUS videos, capturing dynamic vascular patterns critical for lesion characterization. To effectively fuse complementary modalities, we introduce a dual-channel feature fusion module (DCFFM) that adaptively combines features from CEUS and BUS through channel-wise attention mechanisms, allowing the model to dynamically weigh the contribution of each modality based on diagnostic relevance. Additionally, we propose a temporal intensity feature fusion module (TIFFM) that leverages quantitative hemodynamic information from TICs to guide the model’s attention toward diagnostically critical temporal phases, such as arterial wash-in and portal venous washout. The model is further enhanced by automated lesion localization using YOLOX and class activation mapping for interpretability, ensuring that predictions align with clinically meaningful imaging features. ResultsEvaluated on a tri-modal ultrasound dataset comprising 161 patients with pathologically confirmed LR-M nodules (131 atypical HCC and 30 non-HCC malignancies), our model achieved an accuracy of 86.83%, a sensitivity of 92.50%, a specificity of 75.50%, and an AUC of 89.32% in screening atypical HCC. Compared to single-modality baselines, TUS-Net demonstrated superior specificity, a clinically critical metric given the higher risk associated with misclassifying non-HCC malignancies. Ablation studies confirmed the contribution of each module, with the full model outperforming both standard C3D and 3D ResNet backbones integrated with attention mechanisms. A reader study involving junior and senior radiologists further validated the clinical utility of AI assistance, showing consistent improvements in specificity and inter-reader consistency, particularly for less experienced clinicians. ConclusionThese results surpass existing benchmark models and demonstrate the potential of our approach to enhance diagnostic precision in clinically specific cases. By intelligently fusing multi-modal ultrasound data with attention-guided mechanisms, TUS-Net offers a reliable and interpretable tool that holds promise for improving the non-invasive diagnosis of atypical HCC in challenging LR-M liver nodules.

ObjectiveDiscriminating atypical hepatocellular carcinoma (HCC) from other malignancies in liver nodules classified as Liver Imaging Reporting and Data System category M (LR-M) remains a significant diagnostic challenge on conventional ultrasound examination. The LR-M category, originally intended to capture non-HCC malignancies, paradoxically contains up to 63% of atypical HCCs that deviate from classic enhancement patterns, leading to potential misdiagnosis and suboptimal treatment planning. While deep learning has shown promise in HCC diagnosis, most existing models rely exclusively on single-modality ultrasound, overlooking the diagnostic benefits of integrating complementary information from multiple imaging sources. To address this gap, we propose a novel attention-weighted tri-modal ultrasound network (TUS-Net) that integrates contrast-enhanced ultrasound (CEUS), B-mode ultrasound (BUS), and time-intensity curves (TICs) to improve diagnostic accuracy for these clinically challenging lesions. MethodsOur framework incorporates a three-dimensional convolutional neural network (C3D) backbone to extract spatiotemporal features from CEUS videos, capturing dynamic vascular patterns critical for lesion characterization. To effectively fuse complementary modalities, we introduce a dual-channel feature fusion module (DCFFM) that adaptively combines features from CEUS and BUS through channel-wise attention mechanisms, allowing the model to dynamically weigh the contribution of each modality based on diagnostic relevance. Additionally, we propose a temporal intensity feature fusion module (TIFFM) that leverages quantitative hemodynamic information from TICs to guide the model’s attention toward diagnostically critical temporal phases, such as arterial wash-in and portal venous washout. The model is further enhanced by automated lesion localization using YOLOX and class activation mapping for interpretability, ensuring that predictions align with clinically meaningful imaging features. ResultsEvaluated on a tri-modal ultrasound dataset comprising 161 patients with pathologically confirmed LR-M nodules (131 atypical HCC and 30 non-HCC malignancies), our model achieved an accuracy of 86.83%, a sensitivity of 92.50%, a specificity of 75.50%, and an AUC of 89.32% in screening atypical HCC. Compared to single-modality baselines, TUS-Net demonstrated superior specificity, a clinically critical metric given the higher risk associated with misclassifying non-HCC malignancies. Ablation studies confirmed the contribution of each module, with the full model outperforming both standard C3D and 3D ResNet backbones integrated with attention mechanisms. A reader study involving junior and senior radiologists further validated the clinical utility of AI assistance, showing consistent improvements in specificity and inter-reader consistency, particularly for less experienced clinicians. ConclusionThese results surpass existing benchmark models and demonstrate the potential of our approach to enhance diagnostic precision in clinically specific cases. By intelligently fusing multi-modal ultrasound data with attention-guided mechanisms, TUS-Net offers a reliable and interpretable tool that holds promise for improving the non-invasive diagnosis of atypical HCC in challenging LR-M liver nodules.

ObjectivePrimary liver cancer, predominantly hepatocellular carcinoma (HCC), is a significant global health issue, ranking as the sixth most diagnosed cancer and the third leading cause of cancer-related mortality. Accurate and early diagnosis of HCC is crucial for effective treatment, as HCC and non-HCC malignancies like intrahepatic cholangiocarcinoma (ICC) exhibit different prognoses and treatment responses. Traditional diagnostic methods, including liver biopsy and contrast-enhanced ultrasound (CEUS), face limitations in applicability and objectivity. The primary objective of this study was to develop an advanced, light-weighted classification network capable of distinguishing HCC from other non-HCC malignancies by leveraging the automatic analysis of brightness changes in CEUS images. The ultimate goal was to create a user-friendly and cost-efficient computer-aided diagnostic tool that could assist radiologists in making more accurate and efficient clinical decisions. MethodsThis retrospective study encompassed a total of 161 patients, comprising 131 diagnosed with HCC and 30 with non-HCC malignancies. To achieve accurate tumor detection, the YOLOX network was employed to identify the region of interest (ROI) on both B-mode ultrasound and CEUS images. A custom-developed algorithm was then utilized to extract brightness change curves from the tumor and adjacent liver parenchyma regions within the CEUS images. These curves provided critical data for the subsequent analysis and classification process. To analyze the extracted brightness change curves and classify the malignancies, we developed and compared several models. These included one-dimensional convolutional neural networks (1D-ResNet, 1D-ConvNeXt, and 1D-CNN), as well as traditional machine-learning methods such as support vector machine (SVM), ensemble learning (EL), k-nearest neighbor (KNN), and decision tree (DT). The diagnostic performance of each method in distinguishing HCC from non-HCC malignancies was rigorously evaluated using four key metrics: area under the receiver operating characteristic (AUC), accuracy (ACC), sensitivity (SE), and specificity (SP). ResultsThe evaluation of the machine-learning methods revealed AUC values of 0.70 for SVM, 0.56 for ensemble learning, 0.63 for KNN, and 0.72 for the decision tree. These results indicated moderate to fair performance in classifying the malignancies based on the brightness change curves. In contrast, the deep learning models demonstrated significantly higher AUCs, with 1D-ResNet achieving an AUC of 0.72, 1D-ConvNeXt reaching 0.82, and 1D-CNN obtaining the highest AUC of 0.84. Moreover, under the five-fold cross-validation scheme, the 1D-CNN model outperformed other models in both accuracy and specificity. Specifically, it achieved accuracy improvements of 3.8% to 10.0% and specificity enhancements of 6.6% to 43.3% over competing approaches. The superior performance of the 1D-CNN model highlighted its potential as a powerful tool for accurate classification. ConclusionThe 1D-CNN model proved to be the most effective in differentiating HCC from non-HCC malignancies, surpassing both traditional machine-learning methods and other deep learning models. This study successfully developed a user-friendly and cost-efficient computer-aided diagnostic solution that would significantly enhances radiologists’ diagnostic capabilities. By improving the accuracy and efficiency of clinical decision-making, this tool has the potential to positively impact patient care and outcomes. Future work may focus on further refining the model and exploring its integration with multimodal ultrasound data to maximize its accuracy and applicability.

AIM To investigate the effects of Changpu Yujin Decoction(CPYJD)on striatal mitophagy and PINK1/Parkin signaling pathway in a rat model of Tourette syndrome(TS).METHODS Thirty-six SPF male SD rats were randomly assigned to the control group(n=9)and the TS modeling group(n=27).Rats in the modeling group received daily intraperitoneal injections of 3,3'-iminodipropionitrile(IDPN)(300 mg/kg)for 7 consecutive days to establish the TS model.Post-modeling,successfully induced TS rats were re-randomized into model group(no treatment),tiapride group(47.91 mg/kg)and CPYJD group(77.28 g/kg).All groups received their respective interventions via intragastric administration daily for 28 days.Following drug administration,behavioral scores were assessed in each group.Pathological alterations in the striatum were examined using HE staining,while ultrastructural changes were evaluated by transmission electron microscopy(TEM).Neuronal apoptosis was quantified via TUNEL staining,and ROS levels in striatum were measured by ELISA.Co-localization of PINK1 and LC3B was assessed using immunofluorescence(IF).Finally,mRNA and protein expressions of PINK1,Parkin,Beclin-1,P62 and LC3B(LC3B-Ⅱ/Ⅰ ratio)were analyzed by RT-qPCR and Western blot.RESULTS Compared to the control group,the model group demonstrated significantly increased behavioral scores(P＜0.01),elevated neuronal apoptosis rate and higher ROS levels in the striatum(P＜0.01);severe neuronal and mitochondrial damage in the striatum;significantly reduced mRNA and protein expressions of PINK1,Parkin,Beclin-1 and LC3B(LC3B-Ⅱ/Ⅰ ratio)in the striatum(P＜0.01);markedly upregulated P62 mRNA and protein expressions(P＜0.01).Compared to the model group,both the tiapride and CPYJD intervention groups exhibited significantly reduced behavioral scores(P＜0.01);decreased neuronal apoptosis rate and lower ROS levels(P＜0.01);improved pathological alterations in the striatal neurons and mitochondria;increased mRNA and protein expressions of PINK1,Parkin and Beclin-1 in the striatum(P＜0.05,P＜0.01);and decreased P62 mRNA and protein expressions(P＜0.01).Furthermore,the rats in the CPYJD group specifically showed elevated LC3B mRNA level and LC3B-Ⅱ/Ⅰ protein ratio in striatum(P＜0.05,P＜0.01).CONCLUSION The effect of CPYJD intervention in TS rats may involve activation of mitophagy through regulation of the PINK1/Parkin signaling pathway,improving mitochondrial function,reducing ROS levels,and thereby protecting neurons.

Bone grafting is a primary method for treating bone defects. Among various graft materials, xenogeneic bone substitutes are widely used in clinical practice due to their abundant sources, convenient processing and storage, and avoidance of secondary surgeries. With the advancement of domestic production and the limitations of imported products, an increasing number of bone filling or grafting substitute materials isentering clinical trials. Relevant experts have drafted this consensus to enhance the management of medical device clinical trials, protect the rights of participants, and ensure the scientific and effective execution of trials. It summarizes clinical experience in aspects, such as design principles, participant inclusion/exclusion criteria, observation periods, efficacy evaluation metrics, safety assessment indicators, and quality control, to provide guidance for professionals in the field.
Humans ; Bone Substitutes/therapeutic use* ; Randomized Controlled Trials as Topic/methods* ; Consensus ; Bone Transplantation ; Research Design

AIM To investigate the effects of Changpu Yujin Decoction(CPYJD)on striatal mitophagy and PINK1/Parkin signaling pathway in a rat model of Tourette syndrome(TS).METHODS Thirty-six SPF male SD rats were randomly assigned to the control group(n=9)and the TS modeling group(n=27).Rats in the modeling group received daily intraperitoneal injections of 3,3'-iminodipropionitrile(IDPN)(300 mg/kg)for 7 consecutive days to establish the TS model.Post-modeling,successfully induced TS rats were re-randomized into model group(no treatment),tiapride group(47.91 mg/kg)and CPYJD group(77.28 g/kg).All groups received their respective interventions via intragastric administration daily for 28 days.Following drug administration,behavioral scores were assessed in each group.Pathological alterations in the striatum were examined using HE staining,while ultrastructural changes were evaluated by transmission electron microscopy(TEM).Neuronal apoptosis was quantified via TUNEL staining,and ROS levels in striatum were measured by ELISA.Co-localization of PINK1 and LC3B was assessed using immunofluorescence(IF).Finally,mRNA and protein expressions of PINK1,Parkin,Beclin-1,P62 and LC3B(LC3B-Ⅱ/Ⅰ ratio)were analyzed by RT-qPCR and Western blot.RESULTS Compared to the control group,the model group demonstrated significantly increased behavioral scores(P＜0.01),elevated neuronal apoptosis rate and higher ROS levels in the striatum(P＜0.01);severe neuronal and mitochondrial damage in the striatum;significantly reduced mRNA and protein expressions of PINK1,Parkin,Beclin-1 and LC3B(LC3B-Ⅱ/Ⅰ ratio)in the striatum(P＜0.01);markedly upregulated P62 mRNA and protein expressions(P＜0.01).Compared to the model group,both the tiapride and CPYJD intervention groups exhibited significantly reduced behavioral scores(P＜0.01);decreased neuronal apoptosis rate and lower ROS levels(P＜0.01);improved pathological alterations in the striatal neurons and mitochondria;increased mRNA and protein expressions of PINK1,Parkin and Beclin-1 in the striatum(P＜0.05,P＜0.01);and decreased P62 mRNA and protein expressions(P＜0.01).Furthermore,the rats in the CPYJD group specifically showed elevated LC3B mRNA level and LC3B-Ⅱ/Ⅰ protein ratio in striatum(P＜0.05,P＜0.01).CONCLUSION The effect of CPYJD intervention in TS rats may involve activation of mitophagy through regulation of the PINK1/Parkin signaling pathway,improving mitochondrial function,reducing ROS levels,and thereby protecting neurons.

Objective:To investigate the prognosis of patients with initially resectable gastric cancer liver metastasis (GCLM) who were treated by different modalities, and analyze the influencing factors for prognosis of patients.Methods:The retrospective cohort study was conducted. The clinicopathological data of 327 patients with initially resectable GCLM who were included in the database of a nationwide multicenter retrospective cohort study on GCLM based on real-world data from January 2010 to December 2019 were collected. There were 267 males and 60 females, aged 61(54,68)years. According to the specific situations of patients, treatment modalities included radical surgery combined with systemic treatment, palliative surgery combined with systemic treatment, and systemic treatment alone. Observation indicators: (1) clinical characteristics of patients who were treated by different modalities; (2) prognostic outcomes of patients who were treated by different modalities; (3) analysis of influencing factors for prognosis of patients with initially resectable GCLM; (4) screening of potential beneficiaries in patients who were treated by radical surgery plus systemic treatment and patients who were treated by palliative surgery plus systemic treatment. Measurement data with normal distribution were represented as Mean± SD, and comparison between groups was conducted using the independent sample t test. Measurement data with skewed distribution were represented as M( Q1, Q3), and comparison between groups was conducted using the rank sum test. Count data were described as absolute numbers or percentages, and comparison between groups was conducted using the chi-square test. The Kaplan-Meier method was used to calculate survival rate and draw survival curve, and Log-Rank test was used for survival analysis. Univariate and multivariate analyses were conducted using the COX proportional hazard regression model. The propensity score matching was employed by the 1:1 nearest neighbor matching method with a caliper value of 0.1. The forest plots were utilized to evaluate potential benefits of diverse surgical combined with systemic treatments within the population. Results:(1) Clinical characteristics of patients who were treated by different modalities. Of 327 patients, there were 118 cases undergoing radical surgery plus systemic treatment, 164 cases undergoing palliative surgery plus systemic treatment, and 45 cases undergoing systemic treatment alone. There were significant differences in smoking, drinking, site of primary gastric tumor, diameter of primary gastric tumor, site of liver metastasis, and metastatic interval among the three groups of patients ( P<0.05). (2) Prognostic outcomes of patients who were treated by different modalities. The median overall survival time of the 327 pati-ents was 19.9 months (95% confidence interval as 14.9-24.9 months), with 1-, 3-year overall survival rate of 61.3%, 32.7%, respectively. The 1-year overall survival rates of patients undergoing radical surgery plus systemic treatment, palliative surgery plus systemic treatment and systemic treatment alone were 68.3%, 63.1%, 30.6%, and the 3-year overall survival rates were 41.1%, 29.9%, 11.9%, showing a significant difference in overall survival rate among the three groups of patients ( χ2=19.46, P<0.05). Results of further analysis showed that there was a significant difference in overall survival rate between patients undergoing radical surgery plus systemic treatment and patients undergoing systemic treatment alone ( hazard ratio=0.40, 95% confidence interval as 0.26-0.61, P<0.05), between patients undergoing palliative surgery plus systemic treatment and patients under-going systemic treatment alone ( hazard ratio=0.47, 95% confidence interval as 0.32-0.71, P<0.05). (3) Analysis of influencing factors for prognosis of patients with initially resectable GCLM. Results of multivariate analysis showed that the larger primary gastric tumor, poorly differentiated tumor, larger liver metastasis, multiple hepatic metastases were independent risk factors for prognosis of patients with initially resectable GCLM ( hazard ratio=1.20, 1.70, 1.20, 2.06, 95% confidence interval as 1.14-1.27, 1.25-2.31, 1.04-1.42, 1.45-2.92, P<0.05) and immunotherapy or targeted therapy, the treatment modality of radical or palliative surgery plus systemic therapy were independent protective factors for prognosis of patients with initially resectable GCLM ( hazard ratio=0.60, 0.39, 0.46, 95% confidence interval as 0.42-0.87, 0.25-0.60, 0.30-0.70, P<0.05). (4) Screening of potentinal beneficiaries in patients who were treated by radical surgery plus systemic treatment and patients who were treated by palliative surgery plus systemic treatment. Results of forest plots analysis showed that for patients with high-moderate differentiated GCLM and patients with liver metastasis located in the left liver, the overall survival rate of patients undergoing radical surgery plus systemic treatment was better than patients undergoing palliative surgery plus systemic treatment ( hazard ratio=0.21, 0.42, 95% confidence interval as 0.09-0.48, 0.23-0.78, P<0.05). Conclusions:Compared to systemic therapy alone, both radical and palliative surgery plus systemic therapy can improve the pro-gnosis of patients with initially resectable GCLM. The larger primary gastric tumor, poorly differen-tiated tumor, larger liver metastasis, multiple hepatic metastases are independent risk factors for prognosis of patients with initial resectable GCLM and immunotherapy or targeted therapy, the treatment modality of radical or palliative surgery plus systemic therapy are independent protective factors for prognosis of patients with initially resectable GCLM.

Objective:To examine the impact of varied surgical treatment strategies on the prognosis of patients with initial resectable gastric cancer liver metastases (IR-GCLM).Methods:This is a retrospective cohort study. Employing a retrospective cohort design, the study selected clinicopathological data from the national multi-center retrospective cohort study database, focusing on 282 patients with IR-GCLM who underwent surgical intervention between January 2010 and December 2019. There were 231 males and 51 males, aging ( M(IQR)) 61 (14) years (range: 27 to 80 years). These patients were stratified into radical and palliative treatment groups based on treatment decisions. Survival curves were generated using the Kaplan-Meier method and distinctions in survival rates were assessed using the Log-rank test. The Cox risk regression model evaluated HR for various factors, controlling for confounders through multivariate analysis to comprehensively evaluate the influence of surgery on the prognosis of IR-GCLM patients. A restricted cubic spline Cox proportional hazard model assessed and delineated intricate associations between measured variables and prognosis. At the same time, the X-tile served as an auxiliary tool to identify critical thresholds in the survival analysis for IR-GCLM patients. Subgroup analysis was then conducted to identify potential beneficiary populations in different surgical treatments. Results:(1) The radical group comprised 118 patients, all undergoing R0 resection or local physical therapy of primary and metastatic lesions. The palliative group comprised 164 patients, with 52 cases undergoing palliative resections for gastric primary tumors and liver metastases, 56 cases undergoing radical resections for gastric primary tumors only, 45 cases undergoing palliative resections for gastric primary tumors, and 11 cases receiving palliative treatments for liver metastases. A statistically significant distinction was observed between the groups regarding the site and the number of liver metastases (both P<0.05). (2) The median overall survival (OS) of the 282 patients was 22.7 months (95% CI: 17.8 to 27.6 months), with 1-year and 3-year OS rates were 65.4% and 35.6%, respectively. The 1-year OS rates for patients in the radical surgical group and palliative surgical group were 68.3% and 63.1%, while the corresponding 3-year OS rates were 42.2% and 29.9%, respectively. A comparison of OS between the two groups showed no statistically significant difference ( P=0.254). Further analysis indicated that patients undergoing palliative gastric cancer resection alone had a significantly worse prognosis compared to other surgical options ( HR=1.98, 95% CI: 1.21 to 3.24, P=0.006). (3) The size of the primary gastric tumor significantly influenced the patients′ prognosis ( HR=2.01, 95% CI: 1.45 to 2.79, P<0.01), with HR showing a progressively increasing trend as tumor size increased. (4) Subgroup analysis indicates that radical treatment may be more effective compared to palliative treatment in the following specific cases: well/moderately differentiated tumors ( HR=2.84, 95% CI 1.49 to 5.41, P=0.001), and patients with liver metastases located in the left lobe of the liver ( HR=2.06, 95% CI 1.19 to 3.57, P=0.010). Conclusions:In patients with IR-GCLM, radical surgery did not produce a significant improvement in the overall prognosis compared to palliative surgery. However, within specific patient subgroups (well/moderately differentiated tumors, and patients with liver metastases located in the left lobe of the liver), radical treatment can significantly improve prognosis compared to palliative approaches.

Objective:To examine the impact of varied surgical treatment strategies on the prognosis of patients with initial resectable gastric cancer liver metastases (IR-GCLM).Methods:This is a retrospective cohort study. Employing a retrospective cohort design, the study selected clinicopathological data from the national multi-center retrospective cohort study database, focusing on 282 patients with IR-GCLM who underwent surgical intervention between January 2010 and December 2019. There were 231 males and 51 males, aging ( M(IQR)) 61 (14) years (range: 27 to 80 years). These patients were stratified into radical and palliative treatment groups based on treatment decisions. Survival curves were generated using the Kaplan-Meier method and distinctions in survival rates were assessed using the Log-rank test. The Cox risk regression model evaluated HR for various factors, controlling for confounders through multivariate analysis to comprehensively evaluate the influence of surgery on the prognosis of IR-GCLM patients. A restricted cubic spline Cox proportional hazard model assessed and delineated intricate associations between measured variables and prognosis. At the same time, the X-tile served as an auxiliary tool to identify critical thresholds in the survival analysis for IR-GCLM patients. Subgroup analysis was then conducted to identify potential beneficiary populations in different surgical treatments. Results:(1) The radical group comprised 118 patients, all undergoing R0 resection or local physical therapy of primary and metastatic lesions. The palliative group comprised 164 patients, with 52 cases undergoing palliative resections for gastric primary tumors and liver metastases, 56 cases undergoing radical resections for gastric primary tumors only, 45 cases undergoing palliative resections for gastric primary tumors, and 11 cases receiving palliative treatments for liver metastases. A statistically significant distinction was observed between the groups regarding the site and the number of liver metastases (both P<0.05). (2) The median overall survival (OS) of the 282 patients was 22.7 months (95% CI: 17.8 to 27.6 months), with 1-year and 3-year OS rates were 65.4% and 35.6%, respectively. The 1-year OS rates for patients in the radical surgical group and palliative surgical group were 68.3% and 63.1%, while the corresponding 3-year OS rates were 42.2% and 29.9%, respectively. A comparison of OS between the two groups showed no statistically significant difference ( P=0.254). Further analysis indicated that patients undergoing palliative gastric cancer resection alone had a significantly worse prognosis compared to other surgical options ( HR=1.98, 95% CI: 1.21 to 3.24, P=0.006). (3) The size of the primary gastric tumor significantly influenced the patients′ prognosis ( HR=2.01, 95% CI: 1.45 to 2.79, P<0.01), with HR showing a progressively increasing trend as tumor size increased. (4) Subgroup analysis indicates that radical treatment may be more effective compared to palliative treatment in the following specific cases: well/moderately differentiated tumors ( HR=2.84, 95% CI 1.49 to 5.41, P=0.001), and patients with liver metastases located in the left lobe of the liver ( HR=2.06, 95% CI 1.19 to 3.57, P=0.010). Conclusions:In patients with IR-GCLM, radical surgery did not produce a significant improvement in the overall prognosis compared to palliative surgery. However, within specific patient subgroups (well/moderately differentiated tumors, and patients with liver metastases located in the left lobe of the liver), radical treatment can significantly improve prognosis compared to palliative approaches.