|
1
|
Fang Y, Zhang Q, Yan J, Yu S. Application of radiomics in acute and severe non-neoplastic diseases: A literature review. J Crit Care 2025; 87:155027. [PMID: 39848114 DOI: 10.1016/j.jcrc.2025.155027] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2024] [Revised: 11/01/2024] [Accepted: 01/10/2025] [Indexed: 01/25/2025]
Abstract
Radiomics involves the integration of computer technology, big data analysis, and clinical medicine. Currently, there have been initial advancements in the fields of acute cerebrovascular disease and cardiovascular disease. The objective of radiomics is to extract quantitative features from medical images for analysis to predict the risk or treatment outcome, help in differential diagnosis, and guide clinical decisions and management. Radiomics applied research has reached a more advanced stage yet encounters several obstacles, including the need for standardization of radiomics features and alignment with treatment requirements for acute and severe illnesses. Future research should aim to seamlessly incorporate radiomics with various disciplines, leverage big data and artificial intelligence advancements, cater to the requirements of acute and critical medicine, and enhance the effectiveness of technological innovation and application in diagnosing and treating acute and critical illnesses.
Collapse
Affiliation(s)
- Yu Fang
- Department of Emergency Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430000, China; Department of Critical Care Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430000, China
| | - Qiannan Zhang
- Department of Emergency Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430000, China
| | - Jingjun Yan
- Department of Emergency Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430000, China; Department of Critical Care Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430000, China
| | - Shanshan Yu
- Department of Emergency Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430000, China; Department of Critical Care Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430000, China.
| |
Collapse
|
|
2
|
Wang J, Cao Y, Luo X, Zhuang R, Wang L, Cui K, Lu T, Hou P, Song Z, Wang Q, Li Z, Zhang Q, Hao Y. An imaging-based clinical prediction model to differentiate brucellar spondylitis from pyogenic spondylitis: a multicenter retrospective observational study. EUROPEAN SPINE JOURNAL : OFFICIAL PUBLICATION OF THE EUROPEAN SPINE SOCIETY, THE EUROPEAN SPINAL DEFORMITY SOCIETY, AND THE EUROPEAN SECTION OF THE CERVICAL SPINE RESEARCH SOCIETY 2025:10.1007/s00586-025-08905-x. [PMID: 40381013 DOI: 10.1007/s00586-025-08905-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/07/2024] [Revised: 04/09/2025] [Accepted: 04/28/2025] [Indexed: 05/19/2025]
Abstract
PURPOSE Differentiating between pyogenic (PS) and brucellar (BS) spondylitis is clinically challenging due to their similar clinical symptoms, with delayed diagnosis or misdiagnosis common, causing trouble for surgeons in selecting appropriate treatment strategies. Currently, radiology-based diagnostic models for PS and BS are lacking. This study aimed to combine magnetic resonance (MR) and radiographic imaging to elucidate the differences between PS and BS and develop a novel diagnostic model for differential diagnosis. METHODS We collected and analyzed the differences between MR and radiological images of patients with PS and BS from two medical institutions. A nomogram was constructed using least absolute shrinkage and selection operator (LASSO) regression, alongside univariate and multivariate analyses to select the best features of the predictive model. Model discrimination, calibration, and clinical utility were assessed using receiver operating characteristic, calibration, and decision curve analyses. RESULTS Among the enrolled 342 patients with PS (n = 167) or BS (n = 175), we found significant differences in MR and radiological characteristics between the two groups. LASSO regression analysis revealed that thoracic involvement, involved vertebrae number, parrot beak osteophyte presence, endplate destruction, and intervertebral disc signal strength on T1-weighted sequences were independent predictive factors for differentiating between PS and BS. The imaging-based clinical prediction model showed high accuracy in the training and validation sets, with the area under the curve achieving 0.861 and 0.908, respectively, and a significant net benefit in the threshold probability, indicating high clinical potential of the model. CONCLUSION This imaging-based model offers a useful tool for efficiently differentiating PS and BS, facilitating prompt diagnosis and treatment and mitigating incorrect or delayed diagnosis.
Collapse
Affiliation(s)
- Jin Wang
- Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, Shanghai, China
- Shandong Public Health Clinical Center, Shandong University, Jinan, China
- Department of Spinal Orthopedics, Affiliated Hospital of Shandong University of Traditional Chinese Medicine, Jinan, China
| | - Yuelong Cao
- Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Xiaoqian Luo
- Shandong Public Health Clinical Center, Shandong University, Jinan, China
| | - Ruoyu Zhuang
- Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Lijun Wang
- Shandong University of Traditional Chinese Medicine, Jinan, China
| | - Kaiying Cui
- Department of Spinal Orthopedics, Affiliated Hospital of Shandong University of Traditional Chinese Medicine, Jinan, China
| | - Tongxin Lu
- Shandong Public Health Clinical Center, Shandong University, Jinan, China
| | - Pengfei Hou
- Department of Spinal Orthopedics, Affiliated Hospital of Shandong University of Traditional Chinese Medicine, Jinan, China
| | - Zhen Song
- Shandong Public Health Clinical Center, Shandong University, Jinan, China
| | - Qing Wang
- Shandong Public Health Clinical Center, Shandong University, Jinan, China
| | - Zhaoxin Li
- Shandong University of Traditional Chinese Medicine, Jinan, China
| | - Qiang Zhang
- Shandong Public Health Clinical Center, Shandong University, Jinan, China.
| | - Yanke Hao
- Department of Spinal Orthopedics, Affiliated Hospital of Shandong University of Traditional Chinese Medicine, Jinan, China.
| |
Collapse
|
|
3
|
Lv H, Zhou J, Guo Y, Liao S, Chen H, Luo F, Xu J, Zhang Z, Zhang Z. Uniportal endoscopic decompression and debridement for infectious diseases of spine with neurological deficits: a retrospective study in China. Asian Spine J 2025; 19:205-216. [PMID: 40195635 PMCID: PMC12061602 DOI: 10.31616/asj.2025.0020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/16/2025] [Revised: 03/02/2025] [Accepted: 03/06/2025] [Indexed: 04/09/2025] Open
Abstract
STUDY DESIGN A retrospective study. PURPOSE To evaluate the clinical efficacy of uniportal endoscopic decompression and debridement (UEDD) in treating infectious diseases of the spine (IDS) with neurological deficits. OVERVIEW OF LITERATURE IDS patients with neurological deficits often require urgent surgical decompression. However, the efficacy of UEDD in this complex patient population is not well-characterized. METHODS This retrospective study analyzed 32 consecutive IDS patients who underwent UEDD surgery. Clinical features, laboratory data (erythrocyte sedimentation rate and C-reactive protein), and treatment outcomes were analyzed. RESULTS Definite microorganisms were identified in 27 patients (84.3%), with 24 (88.9%) meeting cure criteria. The cure rate was significantly higher in the detected pathogen group compared to the undetected pathogen group (88.9% vs. 80%; χ²=19.36, p<0.0001). Metagenomic next generation sequencing (mNGS) provided faster diagnosis (41.72±6.81 hours) compared to tissue culture (95.74±35.47 hours, p<0.05). The predominant causative pathogen was Mycobacterium tuberculosis, followed by Staphylococcus aureus. Significant improvements were observed in Visual Analog Scale pain scores, from a mean of 7.9 preoperatively to 1.06 at 1 year postoperatively. The Oswestry Disability Index revealed a similar trend, showing significant improvement (p<0.05). CONCLUSIONS UEDD is a viable alternative to traditional open surgery for managing IDS in high-risk patients. UEDD offers a dual therapeutic-diagnostic advantage during the initial admission phase, enabling simultaneous debridement, neurological decompression, and targeted biopsy in a single intervention. Compared with traditional tissue culture, mNGS enables rapid microbiological diagnosis and extensive pathogen coverage.
Collapse
Affiliation(s)
- Hui Lv
- Department of Spine Surgery, Jiangbei Branch of Southwest Hospital, 958th Hospital of the PLA Army, Chongqing,
China
- Department of Orthopaedic, Southwest Hospital, The First Affiliated Hospital of Army Medical University, Chongqing,
China
| | - Jianhong Zhou
- Department of Spine Surgery, Jiangbei Branch of Southwest Hospital, 958th Hospital of the PLA Army, Chongqing,
China
| | - Yuan Guo
- Department of Spine Surgery, Jiangbei Branch of Southwest Hospital, 958th Hospital of the PLA Army, Chongqing,
China
| | - Sheng Liao
- Department of Spine Surgery, Jiangbei Branch of Southwest Hospital, 958th Hospital of the PLA Army, Chongqing,
China
| | - Hui Chen
- Department of Spine Surgery, Jiangbei Branch of Southwest Hospital, 958th Hospital of the PLA Army, Chongqing,
China
| | - Fei Luo
- Department of Spine Surgery, Jiangbei Branch of Southwest Hospital, 958th Hospital of the PLA Army, Chongqing,
China
- Department of Orthopaedic, Southwest Hospital, The First Affiliated Hospital of Army Medical University, Chongqing,
China
| | - Jianzhong Xu
- Department of Spine Surgery, Jiangbei Branch of Southwest Hospital, 958th Hospital of the PLA Army, Chongqing,
China
- Department of Orthopaedic, Southwest Hospital, The First Affiliated Hospital of Army Medical University, Chongqing,
China
| | - Zhongrong Zhang
- Department of Spine Surgery, Jiangbei Branch of Southwest Hospital, 958th Hospital of the PLA Army, Chongqing,
China
- Department of Orthopaedic, Southwest Hospital, The First Affiliated Hospital of Army Medical University, Chongqing,
China
| | - Zehua Zhang
- Department of Spine Surgery, Jiangbei Branch of Southwest Hospital, 958th Hospital of the PLA Army, Chongqing,
China
- Department of Orthopaedic, Southwest Hospital, The First Affiliated Hospital of Army Medical University, Chongqing,
China
| |
Collapse
|
|
4
|
Wu JJ, Chang ZQ. Treatment of refractory thoracolumbar spine infection by thirteen times of vacuum sealing drainage: A case report. World J Orthop 2025; 16:101073. [PMID: 40124728 PMCID: PMC11924023 DOI: 10.5312/wjo.v16.i3.101073] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/03/2024] [Revised: 01/07/2025] [Accepted: 02/12/2025] [Indexed: 03/12/2025] Open
Abstract
BACKGROUND A case study of multiple distinct levels of skipped thoracolumbar spine infection was reported in which 13 successful vacuum sealing drainage (VSD) surgeries were treated. CASE SUMMARY The patient underwent a total of 13 procedures within our medical facility, including five performed under local anesthesia and eight performed under general anesthesia. The source of the ailment was ultimately identified as Enterobacter cloacae. After the last procedure, the patient's symptoms were alleviated, and the recovery process was satisfactory. Three months post-operation, the Japanese Orthopaedic Association scores had improved to 100%. Imageological examination revealed a satisfactory position of internal fixation, and the abnormal signals in the vertebral body and intervertebral space had been eliminated when compared to the pre-operative results. CONCLUSION The study demonstrates that the extreme lateral approach debridement combined with multiple VSD operations is a secure and successful method of treatment for recurrent spinal infection, providing an alternative to traditional surgery.
Collapse
Affiliation(s)
- Jun-Jie Wu
- Department of Orthopedics, 960th Hospital of PLA, Jinan 250031, Shandong Province, China
| | - Zheng-Qi Chang
- Department of Orthopedics, 960th Hospital of PLA, Jinan 250031, Shandong Province, China
| |
Collapse
|
|
5
|
Zhang QC, Lu JJ, Ma YQ, Liang B, Li J, Peng J, Zhou H, Zhang QY, Wu T, Zhou J, Zhou XG, Jiang LB, Dong J, Li XL. A diagnostic model for differentiating tuberculous spondylodiscitis from pyogenic spondylodiscitis based on pathogen-confirmed patients. EUROPEAN SPINE JOURNAL : OFFICIAL PUBLICATION OF THE EUROPEAN SPINE SOCIETY, THE EUROPEAN SPINAL DEFORMITY SOCIETY, AND THE EUROPEAN SECTION OF THE CERVICAL SPINE RESEARCH SOCIETY 2024; 33:4664-4671. [PMID: 39095489 DOI: 10.1007/s00586-024-08433-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/18/2024] [Revised: 07/14/2024] [Accepted: 07/25/2024] [Indexed: 08/04/2024]
Abstract
OBJECTIVE This study aimed to distinguish tuberculous spondylodiscitis (TS) from pyogenic spondylodiscitis (PS) based on laboratory, magnetic resonance imaging (MRI) and computed tomography (CT) findings. Further, a novel diagnostic model for differential diagnosis was developed. METHODS We obtained MRI, CT and laboratory data from TS and PS patients. Predictive models were built using binary logistic regression analysis. The receiver operating characteristic curve was analyzed. Both internal and external validation was performed. RESULTS A total of 81 patients with PS (n = 46) or TS (n = 35) were enrolled. All patients had etiological evidence from the focal lesion. Disc signal or height preservation, skip lesion or multi segment (involved segments ≥ 3) involvement, paravertebral calcification, massive sequestra formation, subligamentous bone destruction, bone erosion with osteosclerotic margin, higher White Blood Cell Count (WBC) and positive result of tuberculosis infection T cell spot test (T-SPOT.TB) were more prevalent in the TS group. A diagnostic model was developed and included four predictors: WBC<7.265 * (10^9/L), skip lesion or involved segments ≥ 3, massive sequestra formation and subligamentous bone destruction. The model showed good sensitivity, specificity, and total accuracy (91.4%, 95.7%, and 93.8%, respectively); the area under the receiver operating characteristic curve (AUC) was 0.981, similar to the results of internal validation using bootstrap resampling (1000 replicates) and external validation set, indicating good clinical predictive ability. CONCLUSIONS This study develop a good diagnostic model based on both CT and MRI, as well as laboratory findings, which may help clinicians distinguish between TS and PS.
Collapse
Affiliation(s)
- Qi-Chen Zhang
- Department of Orthopedic Surgery, Zhongshan Hospital, Fudan University, No.180, Fenglin Road, Shanghai, 200032, China
| | - Jia-Jie Lu
- Department of Orthopedic Surgery, Zhongshan Hospital, Fudan University, No.180, Fenglin Road, Shanghai, 200032, China
| | - Yi-Qun Ma
- Department of Orthopedic Surgery, Zhongshan Hospital, Fudan University, No.180, Fenglin Road, Shanghai, 200032, China
| | - Bing Liang
- Department of Orthopedic Surgery, Zhongshan Hospital, Fudan University, No.180, Fenglin Road, Shanghai, 200032, China
| | - Juan Li
- Department of Orthopaedic Surgery, Shanghai Geriatric Medical Center, Fudan University, Shanghai, China
| | - Jie Peng
- Department of Orthopedic Surgery, Zhongshan Hospital, Fudan University, No.180, Fenglin Road, Shanghai, 200032, China
| | - Hao Zhou
- Department of Orthopaedic Surgery, Xuhui Hospital, Fudan University, Shanghai, China
| | - Qian-Yi Zhang
- Department of Orthopedic Surgery, Zhongshan Hospital, Fudan University, No.180, Fenglin Road, Shanghai, 200032, China
| | - Tao Wu
- Department of Orthopedic Surgery, Zhongshan Hospital, Fudan University, No.180, Fenglin Road, Shanghai, 200032, China
| | - Jian Zhou
- Department of Orthopedic Surgery, Zhongshan Hospital, Fudan University, No.180, Fenglin Road, Shanghai, 200032, China
| | - Xiao-Gang Zhou
- Department of Orthopedic Surgery, Zhongshan Hospital, Fudan University, No.180, Fenglin Road, Shanghai, 200032, China
| | - Li-Bo Jiang
- Department of Orthopedic Surgery, Zhongshan Hospital, Fudan University, No.180, Fenglin Road, Shanghai, 200032, China.
| | - Jian Dong
- Department of Orthopedic Surgery, Zhongshan Hospital, Fudan University, No.180, Fenglin Road, Shanghai, 200032, China.
| | - Xi-Lei Li
- Department of Orthopedic Surgery, Zhongshan Hospital, Fudan University, No.180, Fenglin Road, Shanghai, 200032, China.
| |
Collapse
|
|
6
|
Li C, Xiao NS, Ke BY, Li S, Lin Y. Application of Metagenomic Next-Generation Sequencing in Suspected Spinal Infectious Diseases. World Neurosurg 2024; 185:e542-e548. [PMID: 38401756 DOI: 10.1016/j.wneu.2024.02.071] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2024] [Accepted: 02/12/2024] [Indexed: 02/26/2024]
Abstract
OBJECTIVE This study aimed to explore the clinical efficacy of metagenomic next-generation sequencing (mNGS) in diagnosing and treating suspected spinal infectious diseases. METHODS Between October 2022 to December 2023, a retrospective analysis was performed on patient records within the Department of Spinal Surgery at Guilin People's Hospital. The analysis included comprehensive data on patients with presumed spinal infectious diseases, incorporating results from mNGS tests conducted externally, conventional pathogen detection results, laboratory examination results, and imaging findings. The study aimed to assess the applicability of mNGS in the context of suspected spinal infectious lesions. RESULTS Twenty-seven patients were included in the final analysis. Pathogenic microorganisms were identified in 23 cases. The included cases encompassed 1 case of tuberculous spondylitis, 1 case of fungal infection, 3 cases of Brucella spondylitis, 3 cases of viral infection, 9 cases of bacterial infection, and 6 cases of mixed infections. Pathogenic microorganisms remained elusive in 4 cases. The application of the mNGS method demonstrated a significantly elevated positive detection rate compared to conventional methods (85.19% vs. 48.15%, P < 0.05). Moreover, the mNGS method detected a greater variety of pathogen species than traditional methods (Z = 10.69, P < 0.05). Additionally, the mNGS method exhibited a shorter detection time. CONCLUSIONS mNGS demonstrated significantly higher detection rates for bacterial, fungal, viral, and mixed infections in cases of suspected spinal infectious diseases. The clinical implementation of mNGS could further enhance the efficiency of diagnosing and treating suspected spinal infectious diseases.
Collapse
Affiliation(s)
- Cheng Li
- Department of Orthopaedics, Guilin People's Hospital, Guilin, Guangxi Province, China
| | - Nian-Su Xiao
- Department of Orthopaedics, Guilin People's Hospital, Guilin, Guangxi Province, China
| | - Bao-Yi Ke
- Department of Orthopaedics, Guilin People's Hospital, Guilin, Guangxi Province, China
| | - Sen Li
- Department of Orthopaedics, Guilin People's Hospital, Guilin, Guangxi Province, China
| | - Yang Lin
- Department of Orthopaedics, Guilin People's Hospital, Guilin, Guangxi Province, China.
| |
Collapse
|
|
7
|
Yasin P, Yimit Y, Abliz D, Mardan M, Xu T, Yusufu A, Cai X, Sheng W, Mamat M. MRI-based interpretable radiomics nomogram for discrimination between Brucella spondylitis and Pyogenic spondylitis. Heliyon 2024; 10:e23584. [PMID: 38173524 PMCID: PMC10761805 DOI: 10.1016/j.heliyon.2023.e23584] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2023] [Revised: 12/06/2023] [Accepted: 12/07/2023] [Indexed: 01/05/2024] Open
Abstract
Background Pyogenic spondylitis (PS) and Brucella spondylitis (BS) are commonly seen spinal infectious diseases. Both types can lead to vertebral destruction, kyphosis, and long-term neurological deficits if not promptly diagnosed and treated. Therefore, accurately diagnosis is crucial for personalized therapy. Distinguishing between PS and BS in everyday clinical settings is challenging due to the similarity of their clinical symptoms and imaging features. Hence, this study aims to evaluate the effectiveness of a radiomics nomogram using magnetic resonance imaging (MRI) to accurately differentiate between the two types of spondylitis. Methods Clinical and MRI data from 133 patients (2017-2022) with pathologically confirmed PS and BS (68 and 65 patients, respectively) were collected. We have divided patients into training and testing cohorts. In order to develop a clinical diagnostic model, logistic regression was utilized to fit a conventional clinical model (M1). Radiomics features were extracted from sagittal fat-suppressed T2-weighted imaging (FS-T2WI) sequence. The radiomics features were preprocessed, including scaling using Z-score and undergoing univariate analysis to eliminate redundant features. Furthermore, the Least Absolute Shrinkage and Selection Operator (LASSO) was employed to develop a radiomics score (M2). A composite model (M3) was created by combining M1 and M2. Subsequently, calibration and decision curves were generated to evaluate the nomogram's performance in both training and testing groups. The diagnostic performance of each model and the indication was assessed using the receiver operating curve (ROC) with its area under the curve (AUC). Finally, we used the SHapley Additive exPlanations (SHAP) model explanations technique to interpret the model result. Results We have finally selected 9 significant features from sagittal FS-T2WI sequences. In the differential diagnosis of PS and BS, the AUC values of M1, M2, and M3 in the testing set were 0.795, 0.859, and 0.868. The composite model exhibited a high degree of concurrence with the ideal outcomes, as evidenced by the calibration curves. The nomogram's possible clinical application values were indicated by the decision curve analysis. By using SHAP values to represent prediction outcomes, our model's prediction results are more understandable. Conclusions The implementation of a nomogram that integrates MRI and clinical data has the potential to significantly enhance the accuracy of discriminating between PS and BS within clinical settings.
Collapse
Affiliation(s)
- Parhat Yasin
- Department of Spine Surgery, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, Xinjiang, 830054, China
| | - Yasen Yimit
- Department of Radiology, The First People’s Hospital of Kashi Prefecture, Kashi, Xinjiang, 844000, China
| | - Dilxat Abliz
- Department of Orthopedic, The Eighth Affiliated Hospital of Xinjiang Medical University, Urumqi, Xinjiang, 830054, China
| | - Muradil Mardan
- Xinhua Hospital Affiliated to Shanghai Jiaotong University School of Medicine, Department of Spine Center, Shanghai, 200092, China
| | - Tao Xu
- Department of Spine Surgery, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, Xinjiang, 830054, China
| | - Aierpati Yusufu
- Department of Spine Surgery, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, Xinjiang, 830054, China
| | - Xiaoyu Cai
- Department of Spine Surgery, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, Xinjiang, 830054, China
| | - Weibin Sheng
- Department of Spine Surgery, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, Xinjiang, 830054, China
| | - Mardan Mamat
- Department of Spine Surgery, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, Xinjiang, 830054, China
| |
Collapse
|
|
8
|
Wang J, Li Z, Chi X, Chen Y, Wang H, Wang X, Cui K, Wang Q, Lu T, Zheng J, Zhang Q, Hao Y. Development of a Diagnostic Model for Differentiating Tuberculous Spondylitis and Pyogenic Spondylitis With MRI: A Multicenter Retrospective Observational Study. Spine (Phila Pa 1976) 2024; 49:34-45. [PMID: 37796171 PMCID: PMC10702692 DOI: 10.1097/brs.0000000000004848] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/14/2023] [Accepted: 10/02/2023] [Indexed: 10/06/2023]
Abstract
STUDY DESIGN Multicenter retrospective observational study. OBJECTIVE This study aimed to distinguish tuberculous spondylitis (TS) from pyogenic spondylitis (PS) using magnetic resonance imaging (MRI). Further, a novel diagnostic model for differential diagnosis was developed. SUMMARY OF BACKGROUND DATA TS and PS are the two most common spinal infections. Distinguishing between these types clinically is challenging. Delayed diagnosis can lead to deficits or kyphosis. Currently, there is a lack of radiology-based diagnostic models for TS and PS. METHODS We obtained radiologic images from MRI imaging of patients with TS and PS and applied the least absolute shrinkage and selection operator regression to select the optimal features for a predictive model. Predictive models were built using multiple logistic regression analysis. Clinical utility was determined using decision curve analysis, and internal validation was performed using bootstrap resampling. RESULTS A total of 201 patients with TS (n=105) or PS (n=96) were enrolled. We identified significant differences in MRI features between both groups. We found that noncontiguous multivertebral and single-vertebral body involvement were common in TS and PS, respectively. Vertebral bone lesions were more severe in the TS group than in the PS group (Z=-4.553, P <0.001). The patients in the TS group were also more prone to vertebral intraosseous, epidural, and paraspinal abscesses ( P <0.001). A total of 8 predictors were included in the diagnostic model. Analysis of the calibration curve and area under the receiver operating characteristic curve suggested that the model was well-calibrated with high prediction accuracy. CONCLUSIONS This is the largest study comparing MRI features in TS and PS and the first to develop an MRI-based nomogram, which may help clinicians distinguish between TS and PS.
Collapse
Affiliation(s)
- Jin Wang
- Shandong University of Traditional Chinese Medicine, Jinan, China
| | - Zhaoxin Li
- Shandong University of Traditional Chinese Medicine, Jinan, China
| | - Xiansu Chi
- Xiyuan Hospital, China Academy of Chinese Medical Sciences, Beijing, China
| | - Yungang Chen
- Department of Spinal Orthopedics, Affiliated Hospital of Shandong University of Traditional Chinese Medicine, Jinan, China
| | - Huaxin Wang
- Shandong University of Traditional Chinese Medicine, Jinan, China
| | | | - Kaiying Cui
- Department of Spinal Orthopedics, Affiliated Hospital of Shandong University of Traditional Chinese Medicine, Jinan, China
| | - Qing Wang
- Department of Orthopedics, Shandong Public Health Clinical Center Affiliated to Shandong University, Jinan, China
| | - Tongxin Lu
- Department of Orthopedics, Shandong Public Health Clinical Center Affiliated to Shandong University, Jinan, China
| | - Jianhu Zheng
- Shandong University of Traditional Chinese Medicine, Jinan, China
| | - Qiang Zhang
- Department of Orthopedics, Shandong Public Health Clinical Center Affiliated to Shandong University, Jinan, China
| | - Yanke Hao
- Department of Spinal Orthopedics, Affiliated Hospital of Shandong University of Traditional Chinese Medicine, Jinan, China
| |
Collapse
|
|
9
|
Liu YX, Lei F, Zheng LP, Yuan H, Zhou QZ, Feng DX. A diagnostic model for differentiating tuberculous spondylitis from pyogenic spondylitis: a retrospective case-control study. Sci Rep 2023; 13:10337. [PMID: 37365238 DOI: 10.1038/s41598-023-36965-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2022] [Accepted: 06/13/2023] [Indexed: 06/28/2023] Open
Abstract
The purpose of this study was to describe and compare the clinical data, laboratory examination and imaging examination of tuberculous spondylitis (TS) and pyogenic spondylitis (PS), and to provide ideas for diagnosis and treatment intervention. The patients with TS or PS diagnosed by pathology who first occurred in our hospital from September 2018 to November 2021 were studied retrospectively. The clinical data, laboratory results and imaging findings of the two groups were analyzed and compared. The diagnostic model was constructed by binary logistic regression. In addition, an external validation group was used to verify the effectiveness of the diagnostic model. A total of 112 patients were included, including 65 cases of TS with an average age of 49 ± 15 years, 47 cases of PS with an average of 56 ± 10 years. The PS group had a significantly older age than the TS group (P = 0.005). In laboratory examination, there were significant differences in WBC, neutrophil (N), lymphocyte (L), ESR, CRP, fibrinogen (FIB), serum albumin (A) and sodium (Na). The difference was also statistically significant in the comparison of imaging examinations at epidural abscesses, paravertebral abscesses, spinal cord compression, involvement of cervical, lumbar and thoracic vertebrae. This study constructed a diagnostic model, which was Y (value of TS > 0.5, value of PS < 0.5) = 1.251 * X1 (thoracic vertebrae involved = 1, thoracic vertebrae uninvolved = 0) + 2.021 * X2 (paravertebral abscesses = 1, no paravertebral abscess = 0) + 2.432 * X3 (spinal cord compression = 1, no spinal cord compression = 0) + 0.18 * X4 (value of serum A)-4.209 * X5 (cervical vertebrae involved = 1, cervical vertebrae uninvolved = 0)-0.02 * X6 (value of ESR)-0.806 * X7 (value of FIB)-3.36. Furthermore, the diagnostic model was validated using an external validation group, indicating a certain value in diagnosing TS and PS. This study puts forward a diagnostic model for the diagnosis of TS and PS in spinal infection for the first time, which has potential guiding value in the diagnosis of them and provides a certain reference for clinical work.
Collapse
Affiliation(s)
- Yu Xi Liu
- Department of Orthopaedics, The Affiliated Hospital of Southwest Medical University, No. 25 Taiping Street, Lu Zhou City, China
| | - Fei Lei
- Department of Orthopaedics, The Affiliated Hospital of Southwest Medical University, No. 25 Taiping Street, Lu Zhou City, China
| | - Li Peng Zheng
- Department of Orthopaedics, The Affiliated Hospital of Southwest Medical University, No. 25 Taiping Street, Lu Zhou City, China
| | - Hao Yuan
- Department of Orthopaedics, The Affiliated Hospital of Southwest Medical University, No. 25 Taiping Street, Lu Zhou City, China
| | - Qing Zhong Zhou
- Department of Orthopaedics, The Affiliated Hospital of Southwest Medical University, No. 25 Taiping Street, Lu Zhou City, China
| | - Da Xiong Feng
- Department of Orthopaedics, The Affiliated Hospital of Southwest Medical University, No. 25 Taiping Street, Lu Zhou City, China.
| |
Collapse
|
|
10
|
Spinal Infections. Neuroimaging Clin N Am 2023; 33:167-183. [DOI: 10.1016/j.nic.2022.07.015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
|
|
11
|
Hu X, Zhang G, Zhang H, Tang M, Liu S, Tang B, Xu D, Zhang C, Gao Q. A predictive model for early clinical diagnosis of spinal tuberculosis based on conventional laboratory indices: A multicenter real-world study. Front Cell Infect Microbiol 2023; 13:1150632. [PMID: 37033479 PMCID: PMC10080113 DOI: 10.3389/fcimb.2023.1150632] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2023] [Accepted: 03/14/2023] [Indexed: 04/11/2023] Open
Abstract
Background Early diagnosis of spinal tuberculosis (STB) remains challenging. The aim of this study was to develop a predictive model for the early diagnosis of STB based on conventional laboratory indicators. Method The clinical data of patients with suspected STB in four hospitals were included, and variables were screened by Lasso regression. Eighty-five percent of the cases in the dataset were randomly selected as the training set, and the other 15% were selected as the validation set. The diagnostic prediction model was established by logistic regression in the training set, and the nomogram was drawn. The diagnostic performance of the model was verified in the validation set. Result A total of 206 patients were included in the study, including 105 patients with STB and 101 patients with NSTB. Twelve variables were screened by Lasso regression and modeled by logistic regression, and seven variables (TB.antibody, IGRAs, RBC, Mono%, RDW, AST, BUN) were finally included in the model. AUC of 0.9468 and 0.9188 in the training and validation cohort, respectively. Conclusion In this study, we developed a prediction model for the early diagnosis of STB which consisted of seven routine laboratory indicators.
Collapse
Affiliation(s)
- Xiaojiang Hu
- Department of Spine Surgery and Orthopaedics, Xiangya Hospital, Central South University, Changsha, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China
| | - Guang Zhang
- Department of Spine Surgery and Orthopaedics, Xiangya Hospital, Central South University, Changsha, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China
| | - Hongqi Zhang
- Department of Spine Surgery and Orthopaedics, Xiangya Hospital, Central South University, Changsha, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China
| | - Mingxing Tang
- Department of Spine Surgery and Orthopaedics, Xiangya Hospital, Central South University, Changsha, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China
| | - Shaohua Liu
- Department of Spine Surgery and Orthopaedics, Xiangya Hospital, Central South University, Changsha, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China
| | - Bo Tang
- Department of Spine Surgery and Orthopaedics, Xiangya Hospital, Central South University, Changsha, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China
| | - Dongcheng Xu
- Department of Spine Surgery and Orthopaedics, Xiangya Hospital, Central South University, Changsha, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China
| | - Chengran Zhang
- Department of Spine Surgery and Orthopaedics, Xiangya Hospital, Central South University, Changsha, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China
| | - Qile Gao
- Department of Spine Surgery and Orthopaedics, Xiangya Hospital, Central South University, Changsha, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China
- *Correspondence: Qile Gao,
| |
Collapse
|
|
12
|
Wu S, Wei Y, Li H, Zhou C, Chen T, Zhu J, Liu L, Wu S, Ma F, Ye Z, Deng G, Yao Y, Fan B, Liao S, Huang S, Sun X, Chen L, Guo H, Chen W, Zhan X, Liu C. A Predictive Clinical-Radiomics Nomogram for Differentiating Tuberculous Spondylitis from Pyogenic Spondylitis Using CT and Clinical Risk Factors. Infect Drug Resist 2022; 15:7327-7338. [PMID: 36536861 PMCID: PMC9758984 DOI: 10.2147/idr.s388868] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2022] [Accepted: 12/02/2022] [Indexed: 10/30/2023] Open
Abstract
OBJECTIVE The study aimed to develop and validate a nomogram model with clinical risk factors and radiomic features for differentiating tuberculous spondylitis (TS) from pyogenic spondylitis (PS). METHODS A total of 254 patients with TS (n = 141) or PS (n = 113) were randomly divided into training (n = 180) and validation (n = 74) groups. In addition, 43 patients (TS = 22 and PS = 21) were collected to construct a test cohort. t-test analysis, de-redundancy analysis, and minimum absolute shrinkage and selection operator (lasso) algorithm were utilized on the training set to obtain the optimal radiomics features from computed tomography (CT) for constructing the radiomics model and determine the radiomics score (Rad-score). Eight clinical risk predictors were identified to develop the clinical model. Combined with clinical risk predictors and Rad-scores, a nomogram model was constructed using multivariate logistic regression analysis. RESULTS A total of 1781 features were extracted, and 12 optimal radiomic features were utilized to construct the radiomic model and determine the Rad-score. The combined clinical radiomics model revealed good discrimination performance in both the training cohort and the validation cohort (AUC = 0.891 and 0.830) and was superior to the clinical (AUC = 0.807 and 0.785) and radiomics (AUC = 0.796 and 0.811) models. The calibration curve and DCA also depicted that the nomogram had better clinical efficacy. The discriminative performance of the model is well validated in the test cohort (AUC=0.877). CONCLUSION The clinical radiomic nomogram could serve as a promising predictive tool for differentiating TS from PS, which could be helpful for clinical decision-making.
Collapse
Affiliation(s)
- Shaofeng Wu
- Department of Spine and Osteopathy Ward, The First Affiliated Hospital of Guangxi Medical University, Nanning, People’s Republic of China
| | - Yating Wei
- Department of Spine and Osteopathy Ward, The First Affiliated Hospital of Guangxi Medical University, Nanning, People’s Republic of China
| | - Hao Li
- Department of Spine and Osteopathy Ward, The First Affiliated Hospital of Guangxi Medical University, Nanning, People’s Republic of China
| | - Chenxing Zhou
- Department of Spine and Osteopathy Ward, The First Affiliated Hospital of Guangxi Medical University, Nanning, People’s Republic of China
| | - Tianyou Chen
- Department of Spine and Osteopathy Ward, The First Affiliated Hospital of Guangxi Medical University, Nanning, People’s Republic of China
| | - Jichong Zhu
- Department of Spine and Osteopathy Ward, The First Affiliated Hospital of Guangxi Medical University, Nanning, People’s Republic of China
| | - Lu Liu
- Department of Spine and Osteopathy Ward, The First Affiliated Hospital of Guangxi Medical University, Nanning, People’s Republic of China
| | - Siling Wu
- Department of Spine and Osteopathy Ward, The First Affiliated Hospital of Guangxi Medical University, Nanning, People’s Republic of China
| | - Fengzhi Ma
- Department of Spine and Osteopathy Ward, The First Affiliated Hospital of Guangxi Medical University, Nanning, People’s Republic of China
| | - Zhen Ye
- Department of Spine and Osteopathy Ward, The First Affiliated Hospital of Guangxi Medical University, Nanning, People’s Republic of China
| | - Guobing Deng
- Department of Spine and Osteopathy Ward, The First Affiliated Hospital of Guangxi Medical University, Nanning, People’s Republic of China
| | - Yuanlin Yao
- Department of Spine and Osteopathy Ward, The First Affiliated Hospital of Guangxi Medical University, Nanning, People’s Republic of China
| | - Binguang Fan
- Department of Spine and Osteopathy Ward, The First Affiliated Hospital of Guangxi Medical University, Nanning, People’s Republic of China
| | - Shian Liao
- Department of Spine and Osteopathy Ward, The First Affiliated Hospital of Guangxi Medical University, Nanning, People’s Republic of China
| | - Shengsheng Huang
- Department of Spine and Osteopathy Ward, The First Affiliated Hospital of Guangxi Medical University, Nanning, People’s Republic of China
| | - Xuhua Sun
- Department of Spine and Osteopathy Ward, The First Affiliated Hospital of Guangxi Medical University, Nanning, People’s Republic of China
| | - Liyi Chen
- Department of Spine and Osteopathy Ward, The First Affiliated Hospital of Guangxi Medical University, Nanning, People’s Republic of China
| | - Hao Guo
- Department of Spine and Osteopathy Ward, The First Affiliated Hospital of Guangxi Medical University, Nanning, People’s Republic of China
| | - Wuhua Chen
- Department of Spine and Osteopathy Ward, The First Affiliated Hospital of Guangxi Medical University, Nanning, People’s Republic of China
| | - Xinli Zhan
- Department of Spine and Osteopathy Ward, The First Affiliated Hospital of Guangxi Medical University, Nanning, People’s Republic of China
| | - Chong Liu
- Department of Spine and Osteopathy Ward, The First Affiliated Hospital of Guangxi Medical University, Nanning, People’s Republic of China
| |
Collapse
|
|
13
|
Li Z, Wu F, Hong F, Gai X, Cao W, Zhang Z, Yang T, Wang J, Gao S, Peng C. Computer-Aided Diagnosis of Spinal Tuberculosis From CT Images Based on Deep Learning With Multimodal Feature Fusion. Front Microbiol 2022; 13:823324. [PMID: 35283815 PMCID: PMC8905347 DOI: 10.3389/fmicb.2022.823324] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2021] [Accepted: 01/13/2022] [Indexed: 11/13/2022] Open
Abstract
Background Spinal tuberculosis (TB) has the highest incidence in remote plateau areas, particularly in Tibet, China, due to inadequate local healthcare services, which not only facilitates the transmission of TB bacteria but also increases the burden on grassroots hospitals. Computer-aided diagnosis (CAD) is urgently required to improve the efficiency of clinical diagnosis of TB using computed tomography (CT) images. However, classical machine learning with handcrafted features generally has low accuracy, and deep learning with self-extracting features relies heavily on the size of medical datasets. Therefore, CAD, which effectively fuses multimodal features, is an alternative solution for spinal TB detection. Methods A new deep learning method is proposed that fuses four elaborate image features, specifically three handcrafted features and one convolutional neural network (CNN) feature. Spinal TB CT images were collected from 197 patients with spinal TB, from 2013 to 2020, in the People’s Hospital of Tibet Autonomous Region, China; 3,000 effective lumbar spine CT images were randomly screened to our dataset, from which two sets of 1,500 images each were classified as tuberculosis (positive) and health (negative). In addition, virtual data augmentation is proposed to enlarge the handcrafted features of the TB dataset. Essentially, the proposed multimodal feature fusion CNN consists of four main sections: matching network, backbone (ResNet-18/50, VGG-11/16, DenseNet-121/161), fallen network, and gated information fusion network. Detailed performance analyses were conducted based on the multimodal features, proposed augmentation, model stability, and model-focused heatmap. Results Experimental results showed that the proposed model with VGG-11 and virtual data augmentation exhibited optimal performance in terms of accuracy, specificity, sensitivity, and area under curve. In addition, an inverse relationship existed between the model size and test accuracy. The model-focused heatmap also shifted from the irrelevant region to the bone destruction caused by TB. Conclusion The proposed augmentation effectively simulated the real data distribution in the feature space. More importantly, all the evaluation metrics and analyses demonstrated that the proposed deep learning model exhibits efficient feature fusion for multimodal features. Our study provides a profound insight into the preliminary auxiliary diagnosis of spinal TB from CT images applicable to the Tibetan area.
Collapse
Affiliation(s)
- Zhaotong Li
- Institute of Medical Technology, Peking University Health Science Center, Beijing, China.,School of Health Humanities, Peking University, Beijing, China
| | - Fengliang Wu
- Beijing Key Laboratory of Spinal Disease Research, Engineering Research Center of Bone and Joint Precision Medicine, Department of Orthopedics, Peking University Third Hospital, Beijing, China.,Department of Orthopedic, People's Hospital of Tibet Autonomous Region, Lhasa, China
| | - Fengze Hong
- Medical College, Tibet University, Lhasa, China
| | - Xiaoyan Gai
- Department of Respiratory and Critical Care Medicine, Peking University Third Hospital, Beijing, China
| | - Wenli Cao
- Tuberculosis Department, Beijing Geriatric Hospital, Beijing, China
| | - Zeru Zhang
- Institute of Medical Technology, Peking University Health Science Center, Beijing, China.,School of Health Humanities, Peking University, Beijing, China
| | - Timin Yang
- Department of Orthopedic, People's Hospital of Tibet Autonomous Region, Lhasa, China
| | - Jiu Wang
- Department of Orthopedic, People's Hospital of Tibet Autonomous Region, Lhasa, China
| | - Song Gao
- Institute of Medical Technology, Peking University Health Science Center, Beijing, China
| | - Chao Peng
- Department of Orthopedic, People's Hospital of Tibet Autonomous Region, Lhasa, China
| |
Collapse
|
|
14
|
Starke JR, Erkens C, Ritz N, Kitai I. Strengthening Tuberculosis Services for Children and Adolescents in Low Endemic Settings. Pathogens 2022; 11:158. [PMID: 35215101 PMCID: PMC8877840 DOI: 10.3390/pathogens11020158] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2021] [Revised: 01/21/2022] [Accepted: 01/23/2022] [Indexed: 01/22/2023] Open
Abstract
In low tuberculosis-burden countries, children and adolescents with the highest incidence of tuberculosis (TB) infection or disease are usually those who have immigrated from high-burden countries. It is, therefore, essential that low-burden countries provide healthcare services to immigrant and refugee families, to assure that their children can receive proper testing, evaluation, and treatment for TB. Active case-finding through contact tracing is a critical element of TB prevention in children and in finding TB disease at an early, easily treated stage. Passive case-finding by evaluating an ill child is often delayed, as other, more common infections and conditions are suspected initially. While high-quality laboratory services to detect Mycobacterium tuberculosis are generally available, they are often underutilized in the diagnosis of childhood TB, further delaying diagnosis in some cases. Performing research on TB disease is difficult because of the low number of cases that are spread over many locales, but critical research on the evaluation and treatment of TB infection has been an important legacy of low-burden countries. The continued education of medical providers and the involvement of educational, professional, and non-governmental organizations is a key element of maintaining awareness of the presence of TB. This article provides the perspective from North America and Western Europe but is relevant to many low-endemic settings. TB in children and adolescents will persist in low-burden countries as long as it persists throughout the rest of the world, and these wealthy countries must increase their financial commitment to end TB everywhere.
Collapse
Affiliation(s)
- Jeffrey R. Starke
- Department of Pediatrics, Division of Infectious Diseases, Baylor College of Medicine, Houston, TX 77030, USA
| | - Connie Erkens
- KNCV Tuberculosis Foundation, 2516 AB The Hague, The Netherlands;
| | - Nicole Ritz
- Department of Paediatrics and Paediatric Infectious Diseases, Children’s Hospital, Lucerne Cantonal Hospital, 6000 Lucerne, Switzerland;
- Mycobacterial and Migrant Health Research Group, Department of Clinical Research, University of Basel Children’s Hospital, University of Basel, 4031 Basel, Switzerland
| | - Ian Kitai
- Department of Pediatrics, Division of Infectious Diseases, Hospital for Sick Children, University of Toronto, Toronto, ON M5G 1X8, Canada;
| |
Collapse
|