Background HER2 (human epidermal growth factor receptor 2) is one of the core biomarkers for molecular classification, prognosis evaluation, and therapeutic regimen selection in breast cancer. Intratumoral heterogeneity in breast cancer is related to HER2 expression status. Analyzing the heterogeneity within tumor subregions using habitat analysis may be valuable for predicting HER2 expression status in breast cancer. MATERIALS AND METHODS A retrospective study enrolled 502 breast cancer patients, 377 patients from Center I, and 125 patients from Center II. All cases were divided into a training set, a validation set, and a test set at a ratio of 8:1:1. Patients were stratified into HER2-zero-expressing, HER2-low-expressing and HER2-overexpressing groups based on pathological findings. Tumor habitat segmentation was performed on DCE-MRI radiomics using three clustering methods: Gaussian mixture model, K-means clustering, and quantile-based clustering. Multi-dimensional radiomic features, including first-order statistical, morphological, texture, and wavelet features, were extracted from each subregion. These radiomic signatures were further integrated with clinical variables. Multiple machine learning classifiers were constructed and trained for HER2 status prediction. Comprehensive model validation and interpretability analysis were finally conducted via receiver operating characteristic curves, confusion matrices, SHAP summary plots, and decision curve analysis to systematically evaluate model performance, feature importance, and clinical utility. RESULTS The enhancement pattern, estrogen, family history, histological grade, Ki-67 levels, long diameter, progestoge and vertical diameter differed significantly among the three-class HER2 expression groups. DCE-MRI Habitat identified distinct intratumoral subregions with heterogeneous radiological phenotypes. Habitat-derived heterogeneous features alone achieved satisfactory discriminative performance. The LightGBM model yielded a test set micro-average AUC of 0.865. Habitat texture difference, boundary complexity index, and Shannon entropy showed consistently high feature importance. Decision curve analysis confirmed the Habitat model’s prediction of HER2-low-expressing status can effectively improve the net benefit of clinical decision-making. Conclusion Habitat-derived heterogeneous features from DCE-MRI can serve as reliable non-invasive biomarkers for HER2 expression status prediction in breast cancer.

The anatomical CT patterns of pneumonia, including lobar, lobular, and interstitial, are crucial for accurate diagnosis and effective treatment. However, automatic diagnostic systems based on these patterns are rare due to overlapping features across different pneumonia types. Existing deep learning (DL) models face limitations when it comes to capturing global and long-range semantic information, which is crucial for distinguishing complex anatomical patterns. Moreover, the gap between encoder and decoder features in these models makes it difficult to fully utilize detailed feature maps in both segmentation and classification tasks. To meet these challenges, we propose an automatic diagnostic system based on a 3D multi-task DL model. The model integrates both segmentation and classification sub-networks. For segmentation, we develop a standardized preprocessing pipeline that is tailored to CT slices containing lobar, lobular, and interstitial pneumonia. To better capture global and long-range semantic features, we introduce the cross-fusion transformer block (CTB), which fuses multi-scale channel-and-spatial-wise features from different encoder stages. Additionally, we incorporate a voxel-based spatial and channel squeeze & excitation (vscSE) module to enhance local feature learning by recalibrating the feature maps. For classification, a dedicated branch attached to the segmentation sub-network classifies pneumonia patterns using the enhanced features. The model is tested on a dataset of 180 patients with pneumonia, which demonstrates significant improvements over state-of-the-art models, achieving higher accuracy, precision, sensitivity, and specificity, making it a valuable tool for aiding clinicians in pneumonia diagnosis.

Accurate segmentation of Borderline Ovarian Tumors (BOTs) on Magnetic Resonance Imaging (MRI) plays a crucial role in preoperative evaluation and surgical planning. However, traditional manual segmentation of BOT lesions is time-consuming and highly subjective. Although deep learning-based methods have achieved significant progress in medical image segmentation, their performance for BOTs remains underexplored due to the lack of dedicated datasets. Thus, in this study, we established a standardized multi-center MRI dataset of BOTs in collaboration with four major gynecologic oncology centers. Based on this dataset, we systematically evaluated several representative deep learning segmentation models, including both classical and state-of-the-art architectures, to identify the most suitable model for BOT lesion segmentation. Experimental results demonstrate that deep learning methods can achieve reliable segmentation performance for BOTs, with SegNet showing the best overall performance (DSC=0.835, mIoU=0.847). These findings provide valuable insights and references for clinical diagnosis and surgical decision-making.

While venous thromboembolism (VTE) prophylaxis is crucial following major orthopaedic surgeries including total knee arthroplasty (TKA), the impact of different prophylactic agents on postoperative hemoglobin (Hb) levels remains inadequately studied. The aim of this study was to compare the effects of aspirin, rivaroxaban, and low-molecular-weight heparin (LMWH) on early postoperative Hb changes following TKA. In this single-center retrospective cohort study, 655 primary TKAs were finally included using data from the hospital information system. Patients received either aspirin, rivaroxaban, or LMWH for VTE prophylaxis. The primary outcome was the magnitude of Hb reduction, calculated as the difference between the Hb level on the first postoperative day and the minimum postoperative Hb level before discharge. The secondary outcome was the trajectory of postoperative Hb changes within the first week. Postoperative Hb levels clearly declined within the first week, with a mean of 13.9 g/L (SD, 8.5) from postoperative day 1 in the entire cohort. In the fully adjusted linear regression model, both rivaroxaban (β = 1.5, [95

Precise execution of preoperative 3D planning is critical in total knee arthroplasty (TKA), but current verification methods—postoperative imaging, navigation, robotics—have limitations: they often assess alignment indirectly or fail to directly compare planned and actual osteotomy surfaces. An in vivo method to quantitatively evaluate plan-execution discrepancies on resected surfaces is lacking. This study aimed to introduce a novel technique for acquiring the TKA osteotomy surface using intraoperative CT, and subsequently comparing it with the preoperative plan to accurately evaluate the precision of osteotomy. Furthermore, this technique was utilized to assess the reliability of osteotomy accuracy in patient-specific instrumentation (PSI) assisted TKA procedures. In this case series study, intraoperative CT scans were acquired immediately after bone resection in 80 TKAs. 3D models of the resected surfaces were created and precisely superimposed onto the preoperative 3D planning models. Angular discrepancies between planned and achieved osteotomy planes (coronal, sagittal, axial) for femur and tibia were quantified. Outliers (> 3°) were assessed. Excellent inter- and intra-observer reliability was confirmed, with Intra-class Correlation Coefficient (ICC) ranging from 0.807 to 0.959. Intraoperative CT verification demonstrated femoral deviations of 0.91° ± 0.71° (coronal plane, 2.5

Background:Pneumonia can be anatomically classified into lobar, lobular, and interstitial types, with each type associated with different pathogens. Utilizing artificial intelligence (AI) to determine the anatomical classifications of pneumonia and assist in refining the differential diagnosis may offer a more viable and clinically relevant solution. This study aimed to develop a multi-classification model capable of identifying the occurrence of pneumonia in patients by utilizing case-specific computed tomography (CT) information, categorizing the pneumonia type (lobar, lobular, and interstitial pneumonia), and performing segmentation of the associated lesions.Methods:A total of 61 lobar pneumonia patients, 60 lobular pneumonia patients, and 60 interstitial pneumonia patients were consecutively enrolled at our local hospital from June 2020 and May 2022. All selected cases were divided into a training cohort (n=135) and an independent testing cohort (n=46). To generate the ground truth labels for the training process, manual segmentation and labeling were performed by three junior radiologists. Subsequently, the segmentations were manually reviewed and edited by a senior radiologist. AI models were developed to automatically segment the infected lung regions and classify the pneumonia. The accuracy of pneumonia lesion segmentation was analyzed and evaluated using the Dice coefficient. Receiver operating characteristic curves were plotted, and the area under the curve (AUC), accuracy, precision, sensitivity, and specificity were calculated to assess the efficacy of pneumonia classification.Results:Our AI model achieved a Dice coefficient of 0.743 [95% confidence interval (CI): 0.657-0.826] for lesion segmentation in the training set and 0.723 (95% CI: 0.602-0.845) in the test set. In the test set, our model achieved an accuracy of 0.927 (95% CI: 0.876-0.978), precision of 0.889 (95% CI: 0.827-0.951), sensitivity of 0.889 (95% CI: 0.827-0.951), specificity of 0.946 (95% CI: 0.902-0.990), and AUC of 0.989 (95% CI: 0.969-1.000) for pneumonia classification. We trained the model using labels annotated by senior physicians and compared it to a model trained using labels annotated by junior physicians. The Dice coefficient of the model's segmentation improved by 0.014, increasing from 0.709 (95% CI: 0.589-0.830) to 0.723 (95% CI: 0.602-0.845), and the AUC improved by 0.042, rising from 0.947 to 0.989.Conclusions:Our study presents a robust multi-task learning model with substantial promise in enhancing the segmentation and classification of pneumonia in medical imaging.

Erdheim-Chester Disease (ECD) is a rare form of histiocytosis characterized by xanthomatous infiltration of affected organs. We present a case of a 62-year-old man with ECD initially presenting with constrictive pericarditis. Comprehensive imaging revealed systemic involvement, including the skeleton, orbit, pituitary, lung, kidney, and retroperitoneum, despite the absence of related symptoms. The diagnosis of ECD was eventually confirmed through histopathological evidence from a CT-guided biopsy. The patient responded well to interferon-α2b treatment, with gradual symptom amelioration and improvement in imaging and laboratory findings over a 5-month follow-up period. This case highlights the importance of considering ECD in the differential diagnosis of constrictive pericarditis and the utility of multimodal imaging for accurate diagnosis and management of this rare disease. The patient's positive response to treatment also highlights the potential for effective management of ECD, particularly with early diagnosis and intervention.

Background Increasing evidence revealed that lung microbiota dysbiosis was associated with pulmonary infection in lung transplant recipients (LTRs). Pneumocystis jirovecii ( P. jirovecii ) is an opportunistic fungal pathogen that frequently causes lethal pneumonia in LTRs. However, the lung microbiota in LTRs with P. jirovecii pneumonia (PJP) remains unknow. Methods In this prospective observational study, we performed metagenomic next-generation sequencing (mNGS) on 72 bronchoalveolar lavage fluid (BALF) samples from 61 LTRs (20 with PJP, 22 with PJC, 19 time-matched stable LTRs, and 11 from LTRs after PJP recovery). We compared the lung microbiota composition of LTRs with and without P. jirovecii , and analyzed the related clinical variables. Results BALFs collected at the episode of PJP showed a more discrete distribution with a lower species diversity, and microbiota composition differed significantly compared to P. jirovecii colonization (PJC) and control group. Human gammaherpesvirus 4, Phreatobacter oligotrophus , and Pseudomonas balearica were the differential microbiota species between the PJP and the other two groups. The network analysis revealed that most species had a positive correlation, while P. jirovecii was correlated negatively with 10 species including Acinetobacter venetianus , Pseudomonas guariconensis , Paracandidimonas soli , Acinetobacter colistiniresistens , and Castellaniella defragrans , which were enriched in the control group. The microbiota composition and diversity of BALF after PJP recovery were also different from the PJP and control groups, while the main components of the PJP recovery similar to control group. Clinical variables including age, creatinine, total protein, albumin, IgG, neutrophil, lymphocyte, CD3 + CD45 + , CD3 + CD4 + and CD3 + CD8 + T cells were deeply implicated in the alterations of lung microbiota in LTRs. Conclusions This study suggests that LTRs with PJP had altered lung microbiota compared to PJC, control, and after recovery groups. Furthermore, lung microbiota is related to age, renal function, nutritional and immune status in LTRs.

目的 探讨数字乳腺体层合成(DBT)对乳腺结构扭曲的诊断效能.方法 收集160例同时行数字乳腺摄影(DM)及DBT检查的可疑为乳腺结构扭曲的患者,所有图像经2名具有专业乳腺诊断经验的医师采用双盲法阅片并取得一致.比较DM、DBT及DM+DBT 3种模式对乳腺结构扭曲的诊断效能.结果 DM和DBT发现的160例可疑乳腺结构扭曲患者中,40例影像学检查证实为非乳腺结构扭曲病变,120例影像学检查或病理检查证实为乳腺结构扭曲病变,其中致密型乳腺55例,非致密型乳腺65例.DM、DBT及DM+DBT对致密型乳腺结构扭曲的检出率比较,差异有统计学意义(P﹤0.05),其中DM+DBT成像模式检出率最高.DM+DBT对致密型乳腺结构扭曲的诊断效能优于DM和DBT,DBT对致密型乳腺结构扭曲的诊断效能优于DM(P﹤0.05).结论 DM+DBT对致密型乳腺结构扭曲的诊断效能优于DM和DBT,DBT对致密型乳腺结构扭曲的诊断效能优于DM.

目的 探讨一分钟教学法(one minute preceptor,OMP)在放射科住院医师规范化培训教学阅片中的应用效果.方法 选取北京大学第三医院放射科参加规范化培训的住院医师,将 2020 年的 15 名学生作为对照组,2021 年的 16 名学生作为试验组.在以案例为基础的教学法(case-based learning,CBL)的前提下,试验组增加 OMP.通过"问卷星"微信平台制作调查问卷,课后采用问卷调查两组学生对当前教学阅片方式的评价,同时比较两组学生影像读片和理论考试成绩.结果 试验组读片考试平均成绩为 88.25±4.36 分,显著高于对照组(82.00±6.40 分),差异有统计学意义(P<0.05);试验组理论考试平均成绩为 85.63±11.69 分,对照组为 81.33±8.06 分,差异无统计学意义(P = 0.247).结论 OMP 结合 CBL 的模式可以提高放射科住院医师规范化培训中的教学阅片效果.

[目的]探讨前参考系统全膝关节置换术(total knee arthroplasty,TKA)前侧皮质切割发生的相关因素.[方法]回顾性分析2015年10月-2020年12月在本院因原发性膝关节骨关节炎行前参考系统TKA的86例患者(105膝)连续病例资料.根据术后膝关节侧位X线片是否有前皮质切割表现,将患者分为切割组和未切割组.采用单因素分析和多因素逻辑回归分析前皮质切割发生的相关因素.[结果]105膝中,术后影像显示共28膝发生前皮质切割,占26.7％;77例未发生前皮质切割,占73.3％.前侧皮质切割者按Tayside分级,Ⅰ级13膝(12.4％),Ⅱ级10膝(9.5％),Ⅲ级5膝(4.8％),无Ⅳ级发生.单项因素比较,两组年龄、BMI、性别、侧别、前髁厚度、LDFA、MAD、前皮质屈曲角、髁间窝形态、前髁骨赘量、开髓点位置、股骨远端截骨量、后髁截骨量、通髁线辨认情况的差异均无统计学意义(P＞0.05).但是切割组术前后髁厚度显著大于未切割组(P＜0.05);切割组股骨假体选择偏小的比率显著高于未切割组(P＜0.05).多因素逻辑回归分析显示,术前影像测量后髁厚度大(OR=1.124;P＜0.05)是前皮质切割的危险因素,术中股骨假体选择偏大(OR=0.375;P＜0.05)是前皮质切割保护因素.[结论]在前参考系统TKA中,较大术前测量后髁厚度是前皮质切割的危险因素,而术中偏大的股骨假体选择是前皮质切割保护因素.

目的 探讨微信辅助以问题为导向的教学法(problem-based learning,PBL)结合以案例为基础的教学法(case-based learning,CBL)在放射科住院医师规范化培训中的应用效果.方法 选取笔者科室参加规范化培训的住院医师 38 人,随机分为实验组和对照组,实验组采用 PBL结合 CBL 的教学模式,对照组采用传统教学模式,进行 20 学时的影像诊断教学.通过"问卷星"微信平台制作调查问卷,课后采用问卷调查两组学生对当前实施教学模式的评价,同时比较两组学生影像读片的考试成绩.结果 通过 PBL结合 CBL的教学,学生的学习兴趣、文献检索能力、自学及独立思考能力、临床思维能力、影像诊断能力得到提高.此外,实验组读片考试平均成绩为 84.37±1.01 分,显著高于传统教学组 80.05±1.10 分(P<0.05);实验组理论考试平均成绩为 86.71±5.96 分,传统教学组为 82.96±7.67 分,差异无统计学意义(P =0.100).结论 PBL 结合 CBL 的教学模式可以提高放射科住院医师规范化培训的教学效果.

OBJECTIVE:This study aimed to explore the value of contrast-enhanced computed tomography texture features for predicting the risk of malignant thymic epithelial tumor. METHODS:Data of 97 patients with pathologically confirmed thymic epithelial tumors treated at in our hospital from March 2015 to October 2021 were retrospectively analyzed. Based on the World Health Organization classification of thymic epithelial tumors, patients were divided into a high-risk group (types B2, B3, and C; n = 45) and a low-risk group (types A, AB, and B1; n = 52). Texture analysis was performed using a first-order, gray-level histogram method. Six features were evaluated: mean, variance, skewness, kurtosis, energy, and entropy. The association between contrast-enhanced computed tomography texture features and the risk of malignancy in thymic epithelial tumors was analyzed. The predictive thresholds of predictive texture features were determined by receiver operating characteristics analysis. RESULTS:The mean, skewness, and entropy were significantly greater in the high-risk group than in the low-risk group ( P < 0.05); however, variance, kurtosis, and energy were comparable in the two groups ( P > 0.05). The area under curve of mean, skewness, and entropy was 0.670, 0.760, and 0.880, respectively. The optimal cutoff value of entropy for predicting risk of malignancy was 7.74, with sensitivity, specificity, and accuracy of 80.0%, 80.0%, and 75%, respectively. CONCLUSIONS:Contrast-enhanced computed tomography texture features, especially entropy, may be a useful tool to predict the risk of malignancy in thymic epithelial tumors.

Objective Three-dimensionally (3D) printed patient-specific instrumentation (PSI) might help in this regard with individual design and more accurate osteotomy, but whether the utility of such instrumentations minimizes the variability of patellar height in total knee arthroplasty (TKA) and the reasons for this effect are unknown. Our aim is to compare and analyze the variability of patellar height with PSI and conventional instrumentation (CI) in TKA. Methods Between March 2018 and November 2021, 215 patients with severe knee osteoarthritis who were treated with primary unilateral TKA were identified for this observational study. The patients were divided into the CI-TKA group and PSI-TKA group according to the osteotomy tools used in TKA. Preoperative and postoperative radiographic parameters including hip–knee–ankle angle (HKA), posterior tibial slope (PTS), Insall–Salvati ratio, modified Caton–Deschamps (mCD) ratio, anterior condylar offset (ACO), and posterior condylar offset (PCO) were evaluated. Results The groups were similar in patients' demographic data, clinical scores, and radiographic parameters preoperatively. Overall, according to the results of the Insall–Salvati ratio, postoperative patellar height reduction was noted in 140 patients (65.1%). Interestingly, the variability of patellar height was smaller in the PSI-TKA group. Radiographic evaluation revealed that the Insall–Salvati ratio after TKA had a minor change in the PSI-TKA group ( p = 0.005). Similarly, the mCD ratio after TKA also had a minor change in the PSI-TKA group ( p < 0.001). Compared to those in the CI-TKA group, the ACO ( p < 0.001) and PCO ( p = 0.011) after TKA had a minor change in the PSI-TKA group, but no minor PTS change ( p = 0.951) was achieved in the PSI-TKA group after TKA. However, even with 3D-printed patient-specific instrumentation, there were still significant reductions in patellar height, ACO, PCO, and PTS after TKA ( p < 0.001). Conclusion The variability of patellar height was sufficiently minimized with more accurate anterior and posterior femoral condyle osteotomy when 3D printed PSI was used. Furthermore, there was a trend in over-resection of the femoral anterior and posterior condyle and a marked reduction in PTS during TKA, which could lead to a change in patellar height and might result in more patellofemoral complications following TKA. Level of evidence Level II.

Objective To find out the causes of anterior femoral notching in 3D printed patient-specific patient-specific instrumentation (PSI) assisted total knee arthroplasty (TKA). Methods A retrospective analysis was carried out on the consecutive cases undergoing PSI assisted TKA in the Peking University International Hospital from January 2019 to September 2021. The clinical data of those having anterior femoral notching were collected. 3D CT scanning was performed on the knee joint after intraoperative osteotomy and the intraoperative bone fragments. Rapidform software (Version 2006) was used to reconstruct 3D images and to perform image registration and comparison analysis with the preoperatively planned 3D models. The anterior femoral notching depth was measured, and the differences in the thickness of bone fragments between preoperatively planned and intraoperative bone cutting in order to analyze the causes of the anterior femoral notching. Results A total of 86 consecutive cases (94 knees) were included and 17 cases (18/94, 19.1%) of them had anterior femoral notching. The causes of anterior femoral notching were summarized into 3 categories: abnormal position of the PSI (10 cases, 83.3%), intraoperative reduction of the femoral prosthesis size (2 cases, 16.7%), and overextension of the femoral prosthesis (1 case, 8.3%). Conclusion Abnormal femoral PSI position, intraoperative reduction of femoral prosthesis size, and preoperative femoral prosthesis overextension design are the main causes of anterior femoral notching after PSI assisted knee arthroplasty.

Objective To explore the prediction accuracy of prosthesis size of the modified 3D printed patient-specific instrumentation (PSI)-assisted total knee arthroplasty (TKA) by comparing the difference between intraoperative prosthesis size and preoperative planned prosthesis size. Methods We optimized and improved the workflow, design scheme, shape of guide, operation technology and verification method of the traditional PSI-assisted TKA. A total of 126 patients (137 knees) who received 3D printed PSI-assisted TKA based on preoperative CT images in our center were recruited in this study. The differences between the actual prosthesis size and the preoperative planned size were compared and analyzed. Results All patients completed the surgery successfully, and no serious complications occurred. For the femoral side, the difference between the actual used size and the doctor's planned size was observed in 7 cases, and the difference between the actual used size and the engineer's planned size was in 25 cases (P < 0.01), and the engineer's planned size adjusted by doctors was 19 cases (P < 0.01). For the tibial side, the actual used size and the doctor's planned size was 39 cases, the difference between the actual used size and the engineer's planned size was 44 cases, and the engineer's planned size adjusted by doctors was 5 cases. Conclusion The planned prosthesis size in Our modified 3D printed PSI-assisted TKA is consistent with the intraoperative prosthesis size, and the plan will be more consistent after adjustment by the doctors. [Key words]Objective To explore the prediction accuracy of prosthesis size of the modified 3D printed patient-specific instrumentation (PSI)-assisted total knee arthroplasty (TKA) by comparing the difference between intraoperative prosthesis size and preoperative planned prosthesis size. Methods We optimized and improved the workflow, design scheme, shape of guide, operation technology and verification method of the traditional PSI-assisted TKA. A total of 126 patients (137 knees) who received 3D printed PSI-assisted TKA based on preoperative CT images in our center were recruited in this study. The differences between the actual prosthesis size and the preoperative planned size were compared and analyzed. Results All patients completed the surgery successfully, and no serious complications occurred. For the femoral side, the difference between the actual used size and the doctor's planned size was observed in 7 cases, and the difference between the actual used size and the engineer's planned size was in 25 cases (P < 0.01), and the engineer's planned size adjusted by doctors was 19 cases (P < 0.01). For the tibial side, the actual used size and the doctor's planned size was 39 cases, the difference between the actual used size and the engineer's planned size was 44 cases, and the engineer's planned size adjusted by doctors was 5 cases. Conclusion The planned prosthesis size in Our modified 3D printed PSI-assisted TKA is consistent with the intraoperative prosthesis size, and the plan will be more consistent after adjustment by the doctors.

Objective To compare the effect of 3D printed patient-specific instrumentation (PSI) and conventional instrumentation (CI) on the patellar height and joint line position in total knee arthroplasty (TKA). Methods A retrospective case-control study was carried out on 58 patients, who underwent primary TKA in our hospital from January 2021 to October 2021. According to the osteotomy tools selected in TKA, the patients were divided into CI-TKA group and PSI-TKA group. The Insall-Salvati index, modified Caton-Deschamps (mCD) index and relative height of joint line before and after TKA were recorded and compared between the 2 groups. Results In terms of the Insall-Salvati index, the absolute difference between preoperative and postoperative score was 0.11 (0.07, 0.15) in the CI-TKA group, and 0.06 (0.03, 0.12) in the PSI-TKA group, and statistical difference was seen in the postoperative index between the 2 groups (P < 0.05). Similarly, the absolute difference between preoperative and postoperative mCD index was 0.16±0.09 in the CI-TKA group and 0.08±0.05 in the PSI-TKA group, and obvious difference was also seen in the postoperative difference between them (P < 0.01). However, the absolute difference between preoperative and postoperative joint line height was 2.53 (1.64, 4.40) mm in the CI-TKA group and 2.27 (1.53, 5.32) mm in the PSI-TKA group. There was no notable difference in postoperative joint line position between the 2 groups, but the position after TKA were significantly higher in both groups than that before TKA (P < 0.05). Conclusion The use of 3D printed PSI can help reduce the impact of TKA on the patella height, but there is no obvious advantage in maintaining the joint line position compared with conventional instrumentation.

[目的]介绍三维术前规划在全髋关节置换术(total hip arthroplasty,THA)中的应用方法和临床效果.[方法]2019年5月—2020年6月采用三维术前规划辅助人工全髋关节置换术8例.术前行双髋关节三维CT薄层扫描,建立三维立体模型,三维图像上确定解剖标志点,分析原始解剖形态及病理改变,实施模拟手术操作和假体置入.根据术前规划实施真实THA.[结果]术后髋臼杯外展角(41.63±4.63)°、前倾角(19.13±5.57)°;术后股骨前倾角(14.38±6.35)°;肢体长度改变为(-0.40±1.06)mm.三维术前规划髋臼侧假体型号6例与术前规划相同,1例相差1号,1例相差2号;股骨侧假体型号3例同术前规划,3例相差1号,2例相差2号.[结论]三维术前规划可以较精准的预估假体型号、安放假体位置..

目的:应用心血管磁共振成像(CMR)评估中国男子篮球运动员左心房的容积和功能,为科学训练和医学监督提供参考依据.方法:纳入20名中国男子篮球运动员(年龄20.5±1.5岁,技术等级为国家二级运动员)为研究组,选取15名无运动习惯者为对照组(年龄21.6±2.8岁).所有被检者行CMR扫描获得电影序列,由2名医师独立应用心血管分析软件测量心脏容积和功能,应用CMR特征追踪(CMR feature tracking,CMR-FT)技术测量左心房应变和应变率等参数,两组间对比采用独立样本t检验.结果:与对照组相比,篮球运动员的左、右心室舒张末期容积指数(LVEDVI、RVEDVI)和左心室质量指数(LVMI)明显增大(P<0.05),左、右心室射血分数(LVEF、RVEF)减低(P<0.05),左心房的最大容积指数(LAVImax)、最小容积指数(LAVImin)增大(P<0.05).在左心房功能方面,左心房的总射血分数(LATEF)、被动射血分数(LAPEF)和主动射血分数(LAAEF)在两组间的差异无统计学意义(P>0.05).篮球运动员的左心房总应变(εs)、被动应变(εe)、收缩期正向峰值应变率(SRs)、舒张早期负向峰值应变率(SRe)、舒张晚期负向峰值应变率(SRa)明显低于对照组(P<0.05),而主动应变(εa)在两组间差异无统计学意义(P>0.05).结论:中国男子篮球运动员的左心房容积增大,左心房储存功能、导管功能和泵功能减低,可能是长期高强度训练引起的生理适应性改变.CMR可用于精准评估运动员左心房容积和功能的改变,有助于科学训练的指导和医学监督.

Abstract Purpose Long-leg-radiography (LLR) is commonly used for the measurement of lower limb alignment. However, limb rotations during radiography may interfere with the alignment measurement. This study examines the effect of limb rotation on the accuracy of measurements based on the mechanical and anatomical axes of the femur and tibia, with variations in knee flexion and coronal deformity. Methods Forty-five lower limbs of 30 patients were scanned with CT. Virtual LLRs simulating five rotational positions (neutral, ± 10 $$^{\circ }$$ ∘ , and ± 20 $$^{\circ }$$ ∘ internal rotation) were generated from the CT images. Changes in the hip–knee–ankle angle (HKA) and the femorotibial angle (FTA) were measured on each image with respect to neutral values. These changes were related to knee flexion and coronal deformity under both weight- and non-weight-bearing conditions. Results The measurement errors of the HKA and FTA derived from limb rotation were up to 4.84 ± 0.66 $$^{\circ }$$ ∘ and 7.35 ± 0.88 $$^{\circ }$$ ∘ , respectively, and were correlated with knee flexion (p < 0.001) and severe coronal deformity (p < 0.001). Compared with the non-weight-bearing position, the coronal deformity measured in the weight-bearing condition was 2.62 $$^{\circ }$$ ∘ greater, the correlation coefficients between the coronal deformity and the deviation ranges of HKA and FTA were also greater. Conclusions Flexion and severe coronal deformity have a significant influence on the measurement error of lower limb alignment. Errors can be amplified in the weight-bearing condition compared with the non-weight-bearing condition. When using HKA and FTA to represent the mechanical axis and the anatomical axis on LLR, limb rotation impacts the anatomic axis more than the mechanical axis in patients with severe deformities. Considering LLR as the gold standard image modality, attention should be paid to the measurement of knee alignment. Especially for the possible errors derived from weight-bearing long-leg radiographs of patients with severe knee deformities.