Tianran Li

Objectives: This study developed an AI-driven radiomics model fusing pre-radiofrequency ablation (RFA) multi-sequence MRI features and clinical variables to predict early recurrence of hepatocellular carcinoma (HCC) after RFA. Methods: A total of 169 HCC patients who underwent pre-RFA MRI (January 2015–December 2021) were retrospectively enrolled and randomly assigned to a training set ( n = 135) and an internal hold-out test set ( n = 34) at an 8:2 ratio using stratified sampling (training: 49 recurrent, 86 non-recurrent; test: 12 recurrent, 22 non-recurrent). Radiomics feature selection involved a three-step strategy of variance threshold filtering, SelectKBest, and least absolute shrinkage and selection operator (LASSO) regression. All data preprocessing, feature selection, and model training were performed exclusively within the training set to prevent information leakage. Support vector machine (SVM), logistic regression (LR), and random forest (RF) classifiers were used to construct radiomics-only, clinical-only, and integrated models. Performance was evaluated using ROC curves (AUC as primary metric), calibration curves, Brier scores, Hosmer–Lemeshow test, and decision curve analysis (DCA). The optimal threshold was determined by the Youden index. Results: Alpha-fetoprotein (AFP), platelet count (PLT), and tumor location were identified as independent clinical predictors (all VIF < 5). Of 11,320 extracted features, 61.5% ( n = 6,965) achieved ICC > 0.75, and 16 informative radiomics features were ultimately selected (inter-observer Dice = 0.85 ± 0.06). The radiomics-only models showed robust performance (test AUC: SVM = 0.826, LR = 0.830, RF = 0.826), significantly outperforming clinical-only models (test AUC: SVM = 0.688, LR = 0.706, RF = 0.724; P < 0.05). The integrated models achieved further improvement, with the RF-based integrated model demonstrating superior performance (training AUC = 0.975, test AUC = 0.909; P < 0.0083 after Bonferroni correction). Calibration showed good agreement (Brier score = 0.143, Hosmer–Lemeshow P = 0.367), and DCA demonstrated net clinical benefit across threshold probabilities of 15%–70%. At the optimal threshold (0.42), the RF-based integrated model achieved sensitivity = 83.3%, specificity = 90.9%, PPV = 83.3%, NPV = 90.9%, and accuracy = 88.2%. Conclusion: Preoperative MRI-based radiomics features combined with clinical variables effectively predict early post-RFA HCC recurrence. The integrated radiomics-clinical model outperforms both clinical-only and radiomics-only models, with the RF classifier yielding optimal performance, providing a reliable tool for individualized treatment decision-making.

Objectives: To evaluate the predictive performance of a nomogram that integrates intratumoral and peritumoral MRI-based radiomics with clinical variables for assessing the efficacy of neoadjuvant chemotherapy (NAC) in patients with osteosarcoma (OS). Methods: This retrospective study included 93 patients with pathologically confirmed OS who underwent standard NAC. Intratumoral regions were manually segmented on axial T2-weighted fat-suppressed (T2WI-FS) images using ITK-SNAP, and peritumoral regions were generated semi-automatically by isotropic expansions of 2 mm, 4 mm, and 6 mm. Random forest classifiers were constructed separately for intratumoral, peritumoral, and combined intratumoral-peritumoral radiomics features. The optimal radiomics model was incorporated with significant clinical predictors to build an individualized nomogram. Model performance was assessed through the F1 score, Delong’s test and receiver operating characteristic (ROC) curve analysis. Decision curve analysis (DCA) was applied to assess the model’s clinical utility. Results: Multivariate logistic regression identified alkaline phosphatase (ALP) (OR = 1.003, 95% CI: 1.000 ~ 1.006, P = 0.031) and pathological fracture (PF)(OR = 2.575, 95% CI: 1.036 ~ 6.401, P = 0.042) as independent predictors of NAC response. Among all radiomics models, the Model_rad-intra + peri ^2mm combination model demonstrated the best performance, achieving AUCs of 0.888 in the training set and 0.765 in the test set. The integrated nomogram further improved predictive accuracy, with AUC of 0.990 and 0.815 in the training and test sets, respectively. Conclusion: We developed and validated a nomogram that combines intratumoral and peritumoral MRI radiomics with clinical variables for predicting NAC efficacy in OS. The model demonstrated robust performance and may support early, individualized treatment evaluation and clinical decision-making in patients undergoing NAC.

Background Autologous fat grafting (AFG) is widely used to treat progressive hemifacial atrophy (PHA), but accurate measurement of tissue defects in PHA patients remains a challenge due to varied facial defects and complex anatomy. Methods This retrospective study monitored patients undergoing AFG over 12–16 months. Using 3D computed tomography (CT), the midsagittal plane of the face was scanned, mirror-image analysis (MIA) was carried out, and soft tissue volume loss was predicted across facial regions. The patients were evaluated at four intervals (preoperatively and at 3, 6, and 12 months postoperatively) by calculating the asymmetry index (AI), asymmetry rate (AR), preoperative soft tissue volume deficiency (Preop STVD), volume deficiency rate (VDR), and percentage volume maintenance (PVM). Patient characteristics, satisfaction, and outcomes were also analyzed. Results Fifteen patients (mean age 22) underwent an average of 1.9 AFG surgeries. Preoperative soft tissue defect volume was measured at 101.96 ± 82.52 cm³, with an average fat injection volume of 93.38 ± 73.71 cm³. Tissue defect rates ranged from 1.43 to 10.16% (mean 4.42 ± 3.47%), and volume maintenance varied from 35.60 to 79.16% (average 56.64 ± 13.48%). Postoperatively, defect rates ranged from 0.78 to 4.86% (mean 2.11 ± 1.66%), with volume maintenance between 52.36 and 71.74% (average 63.50 ± 8.09%). Significant improvements in facial symmetry and patient satisfaction were noted, with no complications reported. Conclusion Digitally guided precision fat grafting is a promising method for predicting soft tissue loss and improving facial symmetry in patients with progressive hemifacial atrophy. Level of Evidence IV This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266.

Immunotherapy of soft tissue sarcoma is considered an important development direction for the future. Bioinformatics analysis of genetic changes in tumors and the immune microenvironment around tumors has proven to be a mature and reliable method for predicting tumor prognosis. By mining the Cancer Genome Atlas Program database, we found immunotherapy targets of soft tissue sarcoma and analyzed their biological behavior. The data of 265 samples were downloaded to analyze the expression profile of soft tissue sarcomas. This included calculating tumor purity through the estimation of stromal and immune cells in malignant tumors using expression data, acquisition of differential genes as prognostic factors, and enrichment analysis of the differential genes. Survival analysis showed longer overall survival times for patients with higher immune scores. We obtained 83 survival-related differential genes through survival analysis, and 23 genes that could be used as independent risk factors for the prognosis of soft tissue sarcoma were obtained by multiple regression analysis of the differential genes and other recognized risk factors. Gene set enrichment analysis of the differential genes obtained immune and inflammatory gene ontology terms and signal pathways, including regulation of the T-cell apoptotic process and leukocyte transendothelial migration. After validation in an independent data set of the Gene Expression Omnibus database, 12 genes were confirmed as a result. We believe that these differential genes will be new targets for sarcoma immunotherapy and key genes for the prognosis of soft tissue sarcoma.

Anteroposterior pelvic radiographs remains the most widely employed method for diagnosing developmental dysplasia of the hip. This study aims to evaluate the accuracy of an artificial intelligence model in measuring angles in pelvic radiographs of the hip. The assessment seeks to demonstrate the efficacy of the artificial intelligence model in diagnosing both developmental dysplasia of the hip and borderline developmental dysplasia of the hip through the analysis of pelvic radiographs. A total of 1,029 patients, including 273 men and 757 women, were retrospectively included in this study. The anteroposterior pelvic radiographs were randomly divided into three sets: the training set (720 cases), the validation set (103 cases), and the test set (206 cases). Key anatomical points on the anteroposterior pelvic radiographs were identified. The Sharp, Tönnis, and Center Edge angles were calculated automatically based on the corresponding criteria. The hip development status was compared between measurements obtained from the artificial intelligence model and those defined manually by two radiologists. The area under the receiver operating characteristic curve was utilized to assess the diagnostic performance of the artificial intelligence model. The results obtained from both manual measurements and the artificial intelligence model demonstrated no significant differences in the Sharp, Tönnis, and Center edge angles (all p > 0.05). The intra-class correlation coefficients and correlation coefficient r values exceeded 0.75, indicating that both the artificial intelligence model and manual measurements exhibited good repeatability and a positive correlation. Notably, the artificial intelligence model provided measurements more faster than those conducted by radiologists (p = 0.001). The artificial intelligence model also demonstrated high diagnostic accuracy, sensitivity, and specificity for developmental dysplasia of the hip. The performance of the artificial intelligence model in diagnosing developmental dysplasia of the hip was robust. Additionally, the results from the artificial intelligence model and manual measurements were largely consistent with clinical diagnosis results (p = 0.01). The artificial intelligence model can effectively evaluate hip conditions by measuring the Sharp, Tönnis, and Center edge angles, which are consistent closely with clinical diagnosis results. The results of the artificial intelligence model measurements demonstrate a high degree of consistency with those obtained through manual measurements. The angles of Sharp, Tönnis, and Center edge, as evaluated by the deep learning-based convolutional neural network model, exhibit robust diagnostic performance in identifying both developmental dysplasia of the hip and borderline developmental dysplasia of the hip. Consequently, the artificial intelligence model has the potential to fully replace manual measurements of these critical hip angles, providing a more efficient and precise alternative for diagnosing both conditions of the hip.

The metastatic behavior of hepatocellular carcinoma (HCC) is one of the key factors that leads to poor prognosis. The aim of the current study was to determine the changes in metastasis and the proliferation potential of bone marrow mesenchymal stem cells (BMSCs) in high metastatic potential hepatocellular carcinoma (MHCC97-H) following gene silencing. The osteopontin (OPN) and transforming growth factor-beta (TGF beta (1)) genes, which are associated with metastasis and tumor proliferation, were silenced in MHCC97-H cells. Transwell assays were used to evaluate the migration of MHCC97-H cells in vitro. Additionally, a murine model of MHCC97-H lung metastasis was established. Following OPN and TGF beta (1) silencing, the migration of MHCC97-H cells was significantly reduced following BMSC intervention (P0.05). The metastasis and proliferation potential of MHCC97-H following BMSC intervention were significantly reduced in vitro and in vivo, especially in the TGF beta (1)-silenced group. The decrease in the metastatic potential in gene-silenced MHCC97-H cells was not associated with integrin alpha (v)beta (3) expression. Therefore, OPN and TGF beta (1) may be potential targets for HCC treatment, and TGF beta (1) may have a higher therapeutic potential for BMSC intervention.

Reports in Asian populations suggest that ethnic and geographical differences may influence susceptibility to multiple sclerosis (MS) and its clinical behaviors. Here, we sought to retrospectively survey clinical characteristics and MRI data in Chinese subjects with MS.

烧伤伤情的准确评估复杂而困难。一些新型皮肤成像技术, 如高光谱成像、单边磁共振成像、激光多普勒灌注成像等的出现, 已使关于烧伤伤情评估的研究取得了很大进展。随着成像技术及医学影像学的不断进步, 数字X线摄影、CT、磁共振成像及超声成像等医学影像学技术在评估烧伤伤情中发挥了不可低估的作用。然而, 有关烧伤影像学方面的研究报道仍较少, 这可能源于用于烧伤评估的成像技术及诊断经验在部分医疗机构普及还不全面, 且与烧伤相关的影像学表现复杂, 缺乏特征性。该文主要针对近年来各种成像技术在烧伤伤情评估中的应用研究进展进行综述, 旨在探求各种成像技术在烧伤伤情评估方面的应用价值。