Objective: The CT/MRI Liver Imaging Reporting and Data System (LI-RADS) demonstrates high specificity with relatively limited sensitivity for diagnosing hepatocellular carcinoma (HCC) in high-risk patients. This study aimed to explore the possibility of improving sensitivity by combining CT/MRI LI-RADS v2018 with second-line contrast-enhanced ultrasound (CEUS) LI-RADS v2017 using sulfur hexafluoride (SHF) or perfluorobutane (PFB). Materials and Methods: This retrospective analysis of prospectively collected multicenter data included high-risk patients with treatment-naive hepatic observations. The reference standard was pathological confirmation or a composite reference standard (only for benign lesions). Each participant underwent concurrent CT/MRI, SHF-enhanced US, and PFB-enhanced US examinations. The diagnostic performances for HCC of CT/MRI LI-RADS alone and three combination strategies (combining CT/ MRI LI-RADS with either LI-RADS SHF, LI-RADS PFB, or a modified algorithm incorporating the Kupffer-phase findings for PFB [modified PFB]) were evaluated. For the three combination strategies, apart from the CT/MRI LR-5 criteria, HCC was diagnosed if CT/MRI LR-3 or LR-4 observations met the LR-5 criteria using LI-RADS SHF, LI-RADS PFB, or modified PFB. Results: In total, 281 participants (237 males; mean age, 55 ± 11 years) with 306 observations (227 HCCs, 40 non-HCC malignancies, and 39 benign lesions) were included. Using LI-RADS SHF, LI-RADS PFB, and modified PFB, 20, 23, and 31 CT/MRI LR-3/4 observations, respectively, were reclassified as LR-5, and all were pathologically confirmed as HCCs. Compared to CT/MRI LI-RADS alone (74%, 95% confidence interval [CI]: 68%–79%), the three combination strategies combining CT/MRI LI-RADS with either LI-RADS SHF, LI-RADS PFB, or modified PFB increased sensitivity (83% [95% CI: 77%–87%], 84% [95% CI: 79%–89%], 88% [95% CI: 83%–92%], respectively; all P < 0.001), while maintaining the specificity at 92% (95% CI: 84%–97%). Conclusion The combination of CT/MRI LI-RADS with second-line CEUS using SHF or PFB improved the sensitivity of HCC diagnosis without compromising specificity.

Objective: The CT/MRI Liver Imaging Reporting and Data System (LI-RADS) demonstrates high specificity with relatively limited sensitivity for diagnosing hepatocellular carcinoma (HCC) in high-risk patients. This study aimed to explore the possibility of improving sensitivity by combining CT/MRI LI-RADS v2018 with second-line contrast-enhanced ultrasound (CEUS) LI-RADS v2017 using sulfur hexafluoride (SHF) or perfluorobutane (PFB). Materials and Methods: This retrospective analysis of prospectively collected multicenter data included high-risk patients with treatment-naive hepatic observations. The reference standard was pathological confirmation or a composite reference standard (only for benign lesions). Each participant underwent concurrent CT/MRI, SHF-enhanced US, and PFB-enhanced US examinations. The diagnostic performances for HCC of CT/MRI LI-RADS alone and three combination strategies (combining CT/ MRI LI-RADS with either LI-RADS SHF, LI-RADS PFB, or a modified algorithm incorporating the Kupffer-phase findings for PFB [modified PFB]) were evaluated. For the three combination strategies, apart from the CT/MRI LR-5 criteria, HCC was diagnosed if CT/MRI LR-3 or LR-4 observations met the LR-5 criteria using LI-RADS SHF, LI-RADS PFB, or modified PFB. Results: In total, 281 participants (237 males; mean age, 55 ± 11 years) with 306 observations (227 HCCs, 40 non-HCC malignancies, and 39 benign lesions) were included. Using LI-RADS SHF, LI-RADS PFB, and modified PFB, 20, 23, and 31 CT/MRI LR-3/4 observations, respectively, were reclassified as LR-5, and all were pathologically confirmed as HCCs. Compared to CT/MRI LI-RADS alone (74%, 95% confidence interval [CI]: 68%–79%), the three combination strategies combining CT/MRI LI-RADS with either LI-RADS SHF, LI-RADS PFB, or modified PFB increased sensitivity (83% [95% CI: 77%–87%], 84% [95% CI: 79%–89%], 88% [95% CI: 83%–92%], respectively; all P < 0.001), while maintaining the specificity at 92% (95% CI: 84%–97%). Conclusion The combination of CT/MRI LI-RADS with second-line CEUS using SHF or PFB improved the sensitivity of HCC diagnosis without compromising specificity.

Objective: The CT/MRI Liver Imaging Reporting and Data System (LI-RADS) demonstrates high specificity with relatively limited sensitivity for diagnosing hepatocellular carcinoma (HCC) in high-risk patients. This study aimed to explore the possibility of improving sensitivity by combining CT/MRI LI-RADS v2018 with second-line contrast-enhanced ultrasound (CEUS) LI-RADS v2017 using sulfur hexafluoride (SHF) or perfluorobutane (PFB). Materials and Methods: This retrospective analysis of prospectively collected multicenter data included high-risk patients with treatment-naive hepatic observations. The reference standard was pathological confirmation or a composite reference standard (only for benign lesions). Each participant underwent concurrent CT/MRI, SHF-enhanced US, and PFB-enhanced US examinations. The diagnostic performances for HCC of CT/MRI LI-RADS alone and three combination strategies (combining CT/ MRI LI-RADS with either LI-RADS SHF, LI-RADS PFB, or a modified algorithm incorporating the Kupffer-phase findings for PFB [modified PFB]) were evaluated. For the three combination strategies, apart from the CT/MRI LR-5 criteria, HCC was diagnosed if CT/MRI LR-3 or LR-4 observations met the LR-5 criteria using LI-RADS SHF, LI-RADS PFB, or modified PFB. Results: In total, 281 participants (237 males; mean age, 55 ± 11 years) with 306 observations (227 HCCs, 40 non-HCC malignancies, and 39 benign lesions) were included. Using LI-RADS SHF, LI-RADS PFB, and modified PFB, 20, 23, and 31 CT/MRI LR-3/4 observations, respectively, were reclassified as LR-5, and all were pathologically confirmed as HCCs. Compared to CT/MRI LI-RADS alone (74%, 95% confidence interval [CI]: 68%–79%), the three combination strategies combining CT/MRI LI-RADS with either LI-RADS SHF, LI-RADS PFB, or modified PFB increased sensitivity (83% [95% CI: 77%–87%], 84% [95% CI: 79%–89%], 88% [95% CI: 83%–92%], respectively; all P < 0.001), while maintaining the specificity at 92% (95% CI: 84%–97%). Conclusion The combination of CT/MRI LI-RADS with second-line CEUS using SHF or PFB improved the sensitivity of HCC diagnosis without compromising specificity.

Objective: The CT/MRI Liver Imaging Reporting and Data System (LI-RADS) demonstrates high specificity with relatively limited sensitivity for diagnosing hepatocellular carcinoma (HCC) in high-risk patients. This study aimed to explore the possibility of improving sensitivity by combining CT/MRI LI-RADS v2018 with second-line contrast-enhanced ultrasound (CEUS) LI-RADS v2017 using sulfur hexafluoride (SHF) or perfluorobutane (PFB). Materials and Methods: This retrospective analysis of prospectively collected multicenter data included high-risk patients with treatment-naive hepatic observations. The reference standard was pathological confirmation or a composite reference standard (only for benign lesions). Each participant underwent concurrent CT/MRI, SHF-enhanced US, and PFB-enhanced US examinations. The diagnostic performances for HCC of CT/MRI LI-RADS alone and three combination strategies (combining CT/ MRI LI-RADS with either LI-RADS SHF, LI-RADS PFB, or a modified algorithm incorporating the Kupffer-phase findings for PFB [modified PFB]) were evaluated. For the three combination strategies, apart from the CT/MRI LR-5 criteria, HCC was diagnosed if CT/MRI LR-3 or LR-4 observations met the LR-5 criteria using LI-RADS SHF, LI-RADS PFB, or modified PFB. Results: In total, 281 participants (237 males; mean age, 55 ± 11 years) with 306 observations (227 HCCs, 40 non-HCC malignancies, and 39 benign lesions) were included. Using LI-RADS SHF, LI-RADS PFB, and modified PFB, 20, 23, and 31 CT/MRI LR-3/4 observations, respectively, were reclassified as LR-5, and all were pathologically confirmed as HCCs. Compared to CT/MRI LI-RADS alone (74%, 95% confidence interval [CI]: 68%–79%), the three combination strategies combining CT/MRI LI-RADS with either LI-RADS SHF, LI-RADS PFB, or modified PFB increased sensitivity (83% [95% CI: 77%–87%], 84% [95% CI: 79%–89%], 88% [95% CI: 83%–92%], respectively; all P < 0.001), while maintaining the specificity at 92% (95% CI: 84%–97%). Conclusion The combination of CT/MRI LI-RADS with second-line CEUS using SHF or PFB improved the sensitivity of HCC diagnosis without compromising specificity.

Objective: The CT/MRI Liver Imaging Reporting and Data System (LI-RADS) demonstrates high specificity with relatively limited sensitivity for diagnosing hepatocellular carcinoma (HCC) in high-risk patients. This study aimed to explore the possibility of improving sensitivity by combining CT/MRI LI-RADS v2018 with second-line contrast-enhanced ultrasound (CEUS) LI-RADS v2017 using sulfur hexafluoride (SHF) or perfluorobutane (PFB). Materials and Methods: This retrospective analysis of prospectively collected multicenter data included high-risk patients with treatment-naive hepatic observations. The reference standard was pathological confirmation or a composite reference standard (only for benign lesions). Each participant underwent concurrent CT/MRI, SHF-enhanced US, and PFB-enhanced US examinations. The diagnostic performances for HCC of CT/MRI LI-RADS alone and three combination strategies (combining CT/ MRI LI-RADS with either LI-RADS SHF, LI-RADS PFB, or a modified algorithm incorporating the Kupffer-phase findings for PFB [modified PFB]) were evaluated. For the three combination strategies, apart from the CT/MRI LR-5 criteria, HCC was diagnosed if CT/MRI LR-3 or LR-4 observations met the LR-5 criteria using LI-RADS SHF, LI-RADS PFB, or modified PFB. Results: In total, 281 participants (237 males; mean age, 55 ± 11 years) with 306 observations (227 HCCs, 40 non-HCC malignancies, and 39 benign lesions) were included. Using LI-RADS SHF, LI-RADS PFB, and modified PFB, 20, 23, and 31 CT/MRI LR-3/4 observations, respectively, were reclassified as LR-5, and all were pathologically confirmed as HCCs. Compared to CT/MRI LI-RADS alone (74%, 95% confidence interval [CI]: 68%–79%), the three combination strategies combining CT/MRI LI-RADS with either LI-RADS SHF, LI-RADS PFB, or modified PFB increased sensitivity (83% [95% CI: 77%–87%], 84% [95% CI: 79%–89%], 88% [95% CI: 83%–92%], respectively; all P < 0.001), while maintaining the specificity at 92% (95% CI: 84%–97%). Conclusion The combination of CT/MRI LI-RADS with second-line CEUS using SHF or PFB improved the sensitivity of HCC diagnosis without compromising specificity.

Recent advances in large models demonstrate significant prospects for transforming the field of medical imaging. These models, including large language models, large visual models, and multimodal large models, offer unprecedented capabilities in processing and interpreting complex medical data across various imaging modalities. By leveraging self-supervised pretraining on vast unlabeled datasets, cross-modal representation learning, and domain-specific medical knowledge adaptation through fine-tuning, large models can achieve higher diagnostic accuracy and more efficient workflows for key clinical tasks. This review summarizes the concepts, methods, and progress of large models in medical imaging, highlighting their potential in precision medicine. The article first outlines the integration of multimodal data under large model technologies, approaches for training large models with medical datasets, and the need for robust evaluation metrics. It then explores how large models can revolutionize applications in critical tasks such as image segmentation, disease diagnosis, personalized treatment strategies, and real-time interactive systems, thus pushing the boundaries of traditional imaging analysis. Despite their potential, the practical implementation of large models in medical imaging faces notable challenges, including the scarcity of high-quality medical data, the need for optimized perception of imaging phenotypes, safety considerations, and seamless integration with existing clinical workflows and equipment. As research progresses, the development of more efficient, interpretable, and generalizable models will be critical to ensuring their reliable deployment across diverse clinical environments. This review aims to provide insights into the current state of the field and provide directions for future research to facilitate the broader adoption of large models in clinical practice.
Humans ; Diagnostic Imaging/methods* ; Precision Medicine/methods* ; Image Processing, Computer-Assisted/methods*

Objective:To construct a fall risk prediction model for patients with hematologic malignancies and to provide a reference for the risk assessment and accurate management of falls.Methods:The prospective study design was adopted to facilitate the selection of 510 patients with hematologic malignant in Qilu Hospital of Shandong University for investigation, and relevant data such as patient demographic characteristics, disease treatment and drugs were collected. The LASSO-Logistic regression was used to screen the risk factors of falls in patients with hematologic malignancies, to construct a nomogram risk prediction model. The receiver operating characteristic curve (ROC) and calibration curve were used to evaluate the predictive performance of the model. Bootstrap resampling were used to validate internal validation of the model.Results:Among 510 patients with hematological malignancies, there were 273 males and 237 females, aged 53.0 (41.0, 63.0) years old. A total of 6 risk factors were included in the fall risk prediction model for patients with hematological malignancies, which were disease type ( OR = 0.185, 95% CI 0.061 - 0.562), body temperature ≥38 ℃ ( OR = 2.239, 95% CI 1.128 - 4.445), pain ( OR = 15.581, 95% CI 6.592 - 36.829), anemia ( OR = 4.097, 95% CI 1.536 - 10.927), days of bone marrow suppression ( OR = 3.341, 95% CI 1.619 - 6.893), and assessment of daily self-care ability ( OR = 3.160, 95% CI 1.051 - 9.506)(all P<0.05). The ROC curve of the fall risk prediction model was 0.884 (95% CI 0.841-0.927). The optimal threshold, sensitivity, and specificity of the risk prediction model were 0.248, 87.4% and 75.6%. The internal validation C statistic was 0.873. The Calibration curve was almost coincides with the ideal curve, and the model Brier score was 0.080. Conclusions:The constructed fall risk prediction model has good predictive performance, which can efficiently and objectively quantify the risk of falls, and provide a reference for the early assessment and effective prevention of falls in patients with hematological malignancies.

Objectives:To evaluate the safety and efficacy of implantation of subcutaneous implantable cardioverter defibrillator(S-ICD)after transvenous lead extraction(TLE)in ICD patients without pacing indications. Methods:All patients who underwent TLE at Peking University People's Hospital from June 2018 to October 2023 were consecutively included.TLE indication,S-ICD implantation indication,defibrillation threshold(DFT)test,complications and postoperative follow-up were collected and analyzed. Results:A total of 11 patients with TV-ICD underwent S-ICD implantation after TLE,eight patients were males and median age were 56(44,65)years.The indications for TLE were infection and lead dysfunction.Nine patients were implanted with S-ICD for secondary prevention,and the most common cause of implantation was ion channelopathies(5 cases).The operative time for S-ICD was 51(48,58)minutes and no perioperative complications were noted.Eight patients underwent DFT testing,and 100%were successful.During a median follow up of 30(9,39)months,a total of six appropriate treatments occurred in two patients,and no complications occurred,including inappropriate treatment,ineffective treatment,infection,lead malfunction and death. Conclusions:Our study provides evidence for S-ICD implantation as a replacement after TV-ICD removal.The S-ICD implantation after TLE is safe and effective.

Objectives:To assess the clinical characteristics and lead extraction in patients with venous occlusion related to infection of cardiovascular implantable electronic devices. Methods:Clinical data of 405 patients(147 men,mean age[62.4±13.2]years)who underwent lead extraction from January 2020 to January 2024 in Peking University People's Hospital were reviewed.Contrast venography of the access vein was retrospectively analyzed.The patients were divided into venous occlusion group(n=119)and non-venous occlusion group(n=286)according to the presence or absence of venous occlusion.The clinical characteristics and lead extraction of patients in two groups were analyzed. Results:Occlusion of the access vein occurred in 119 patients(29.4%).The subclavian vein was occluded in 48 cases(40.3%),brachiocephalic vein was occluded in 37 cases(31.1%),axillary vein was occluded in 30 cases(25.2%),superior vena cava was occluded in 4 cases(3.4%).There were no significant differences between venous occlusion group and non-venous occlusion group in terms of age,sex,device type,number of leads,or anticoagulation therapy(all P>0.05).Time from implant of the initial leads was significantly longer in the venous occlusion group than in the non-venous occlusion group([10.4±3.8]years vs.[5.9±4.1]years,P=0.042).Clinical extraction success rate and complications were similar between the venous occlusion group and the non-venous occlusion group(both P>0.05).Procedural duration and fluoroscopy exposure time were significantly lower in non-venous occlusion group than in the venous occlusion group(both P<0.05).Patients in the venous occlusion group required more advanced tools(such as laser sheaths,evolution sheaths,and needle's eye snares)for lead extraction compared to patients in the non-venous occlusion group(84.0%vs.67.1%,P=0.001). Conclusions:The incidence of venous occlusion related to infection of cardiovascular implantable electronic devices is 29.4%.Time from implant of the initial leads is significantly longer and lead extraction is more difficult in patients with venous occlusion,and requires more advanced tools and more time to achieve the successful lead extraction.

Objective To establish an ultra-performance liquid chromatography-tandem mass spectrometry(UPLC-MS/MS)method for the detection of 34 prohibited and restricted pesticide residues in Atractylodes,and 21 batches of commercially available Atractylodes were detected to preliminarily investigate the pesticide residues in commercial Atractylodes.Methods The samples were extracted with pure acetonitrile,the extracts were purified by Waters-HLB 3cc(60 mg)solid phase extraction column,separated by Waters-C18 column,0.1%formic acid water(containing 10 mmol/L ammonium formate)and acetonitrile were used as mobile phases(gradient elution),and quantitatively analyzed by external standard method in multi-reaction monitoring positive ion mode according to the retention time.Results The linear relationship of each component was good in their respective concentration ranges,the correlation coefficients were all greater than 0.998 0.The limits of detection were within 0.8-4.0 μg/kg,and the limits of quantification were within 2.0-10.0 μg/kg.The average recovery rate was 74.1%-97.4%,and RSD was 0.6%-6.4%(n=9).Seven pesticide residues were found in 13 batches of 21 Atractylodes macrocephalus samples.Conclusion This method is simple to operate,accurate in quantification,has high recovery rate and good repeatability,and is suitable for the detection of multiple residues of prohibited and restricted pesticides in Atractylodes.