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Zhang M, Wu P, Duan YL, Jin L, Yang J, Huang S, Liu Y, Hu B, Zhai XW, Wang HS, Fu Y, Li F, Yang XM, Liu AS, Qin S, Yuan XJ, Dong YS, Liu W, Zhou JW, Zhang LP, Jia YP, Wang J, Qu LJ, Dai YP, Guan GT, Sun LR, Jiang J, Liu R, Jin RM, Wang ZJ, Wang XG, Zhang BX, Chen KL, Zhuang SQ, Zhang J, Zhou CJ, Gao ZF, Zheng MC, Zhang Y. [Mid-term efficacy of China Net Childhood Lymphoma-mature B-cell lymphoma 2017 regimen in the treatment of pediatric Burkitt lymphoma]. Zhonghua Er Ke Za Zhi 2022; 60:1011-1018. [PMID: 36207847 DOI: 10.3760/cma.j.cn112140-20220429-00390] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
Objective: To analyze the clinical characteristics of children with Burkitt lymphoma (BL) and to summarize the mid-term efficacy of China Net Childhood Lymphoma-mature B-cell lymphoma 2017 (CNCL-B-NHL-2017) regimen. Methods: Clinical features of 436 BL patients who were ≤18 years old and treated with the CNCL-B-NHL-2017 regimen from May 2017 to April 2021 were analyzed retrospectively. Clinical characteristics of patients at disease onset were analyzed and the therapeutic effects of patients with different clinical stages and risk groups were compared. Survival analysis was performed by Kaplan-Meier method, and Cox regression was used to identify the prognostic factors. Results: Among 436 patients, there were 368 (84.4%) males and 68 (15.6%) females, the age of disease onset was 6.0 (4.0, 9.0) years old. According to the St. Jude staging system, there were 4 patients (0.9%) with stage Ⅰ, 30 patients (6.9%) with stage Ⅱ, 217 patients (49.8%) with stage Ⅲ, and 185 patients (42.4%) with stage Ⅳ. All patients were stratified into following risk groups: group A (n=1, 0.2%), group B1 (n=46, 10.6%), group B2 (n=19, 4.4%), group C1 (n=285, 65.4%), group C2 (n=85, 19.5%). Sixty-three patients (14.4%) were treated with chemotherapy only and 373 patients (85.6%) were treated with chemotherapy combined with rituximab. Twenty-one patients (4.8%) suffered from progressive disease, 3 patients (0.7%) relapsed, and 13 patients (3.0%) died of treatment-related complications. The follow-up time of all patients was 24.0 (13.0, 35.0) months, the 2-year event free survival (EFS) rate of all patients was (90.9±1.4) %. The 2-year EFS rates of group A, B1, B2, C1 and C2 were 100.0%, 100.0%, (94.7±5.1) %, (90.7±1.7) % and (85.9±4.0) %, respectively. The 2-year EFS rates was higher in group A, B1, and B2 than those in group C1 (χ2=4.16, P=0.041) and group C2 (χ2=7.21, P=0.007). The 2-year EFS rates of the patients treated with chemotherapy alone and those treated with chemotherapy combined with rituximab were (79.3±5.1)% and (92.9±1.4)% (χ2=14.23, P<0.001) respectively. Multivariate analysis showed that stage Ⅳ (including leukemia stage), serum lactate dehydrogenase (LDH)>4-fold normal value, and with residual tumor in the mid-term evaluation were risk factors for poor prognosis (HR=1.38,1.23,8.52,95%CI 1.05-1.82,1.05-1.43,3.96-18.30). Conclusions: The CNCL-B-NHL-2017 regimen show significant effect in the treatment of pediatric BL. The combination of rituximab improve the efficacy further.
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Affiliation(s)
- M Zhang
- Medical Oncology Department, Pediatric Oncology Center, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing Key Laboratory of Pediatric Hematology Oncology, Key Laboratory of Major Diseases in Children, Ministry of Education, Beijing 100045, China
| | - P Wu
- Department of Hematology, Hunan Children's Hospital, Changsha 410007, China
| | - Y L Duan
- Medical Oncology Department, Pediatric Oncology Center, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing Key Laboratory of Pediatric Hematology Oncology, Key Laboratory of Major Diseases in Children, Ministry of Education, Beijing 100045, China
| | - L Jin
- Medical Oncology Department, Pediatric Oncology Center, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing Key Laboratory of Pediatric Hematology Oncology, Key Laboratory of Major Diseases in Children, Ministry of Education, Beijing 100045, China
| | - J Yang
- Medical Oncology Department, Pediatric Oncology Center, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing Key Laboratory of Pediatric Hematology Oncology, Key Laboratory of Major Diseases in Children, Ministry of Education, Beijing 100045, China
| | - S Huang
- Medical Oncology Department, Pediatric Oncology Center, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing Key Laboratory of Pediatric Hematology Oncology, Key Laboratory of Major Diseases in Children, Ministry of Education, Beijing 100045, China
| | - Y Liu
- Department of Pediatric Lymphoma, Beijing GoBroad Boren Hospital, Beijing 100070, China
| | - B Hu
- Department of Pediatric Lymphoma, Beijing GoBroad Boren Hospital, Beijing 100070, China
| | - X W Zhai
- Department of Hematology, Children's Hospital of Fudan University, National Children's Medical Center, Shanghai 201102, China
| | - H S Wang
- Department of Hematology, Children's Hospital of Fudan University, National Children's Medical Center, Shanghai 201102, China
| | - Y Fu
- Department of Hematology, Children's Hospital of Fudan University, National Children's Medical Center, Shanghai 201102, China
| | - F Li
- Hematology & Oncology Department, Children's Hospital Affiliated to Shandong University, Jinan 250022, China
| | - X M Yang
- Hematology & Oncology Department, Children's Hospital Affiliated to Shandong University, Jinan 250022, China
| | - A S Liu
- Department of Hematology & Oncology, Xi'an Children's Hospital, Xi'an 710002, China
| | - S Qin
- Department of Hematology & Oncology, Xi'an Children's Hospital, Xi'an 710002, China
| | - X J Yuan
- Department of Pediatric Hematology/Oncology, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200092, China
| | - Y S Dong
- Department of Pediatric Hematology/Oncology, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200092, China
| | - W Liu
- Department of Hematology & Oncology, Zhengzhou Children's Hospital, Zhengzhou 450018, China
| | - J W Zhou
- Department of Hematology & Oncology, Zhengzhou Children's Hospital, Zhengzhou 450018, China
| | - L P Zhang
- Department of Pediatrics, Peking University People's Hospital, Beijing 100044, China
| | - Y P Jia
- Department of Pediatrics, Peking University People's Hospital, Beijing 100044, China
| | - J Wang
- Department of Hematology & Oncology, Anhui Children's Hospital, Hefei 230022, China
| | - L J Qu
- Department of Hematology & Oncology, Anhui Children's Hospital, Hefei 230022, China
| | - Y P Dai
- Department of Pediatric Hematology & Endocrinology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan 250021, China
| | - G T Guan
- Department of Pediatric Hematology & Endocrinology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan 250021, China
| | - L R Sun
- Department of Pediatric Hematology & Oncology, the Affiliated Hospital of Qingdao University, Qingdao 266003, China
| | - J Jiang
- Department of Pediatric Hematology & Oncology, the Affiliated Hospital of Qingdao University, Qingdao 266003, China
| | - R Liu
- Department of Hematology, Children's Hospital, Capital Pediatric Research Institute, Beijing 100020, China
| | - R M Jin
- Department of Pediatrics, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China
| | - Z J Wang
- Department of Pediatrics, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China
| | - X G Wang
- Department of Hematology and Oncology, Third Affiliated Hospital of Zhengzhou University, Zhengzhou 450052
| | - B X Zhang
- Department of Pediatrics, Second Hospital of Hebei Medical University, Shijiazhuang 050004, China
| | - K L Chen
- Department of Hematology and Oncology, Wuhan Children's Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430016, China
| | - S Q Zhuang
- Department of Pediatrics, First Hospital of Quanzhou Affiliated to Fujian Medical University, Quanzhou 362002, China
| | - J Zhang
- Department of Hematology & Oncology, the First People's Hospital of Urumqi, Urumqi 830002, China
| | - C J Zhou
- Pathology Department, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing 100045, China
| | - Z F Gao
- Department of Pathology, Peking University Third Hospital, Beijing 100191, China
| | - M C Zheng
- Department of Hematology, Hunan Children's Hospital, Changsha 410007, China
| | - Yonghong Zhang
- Medical Oncology Department, Pediatric Oncology Center, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing Key Laboratory of Pediatric Hematology Oncology, Key Laboratory of Major Diseases in Children, Ministry of Education, Beijing 100045, China
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Qian L, Ren J, Liu A, Gao Y, Hao F, Zhao L, Wu H, Niu G. MR imaging of epithelial ovarian cancer: a combined model to predict histologic subtypes. Eur Radiol 2020; 30:5815-5825. [PMID: 32535738 DOI: 10.1007/s00330-020-06993-5] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2019] [Revised: 04/15/2020] [Accepted: 05/28/2020] [Indexed: 12/19/2022]
Abstract
OBJECTIVE To compare the performance of clinical features, conventional MR image features, ADC value, T2WI, DWI, DCE-MRI radiomics, and a combined multiple features model in predicting the type of epithelial ovarian cancer (EOC). METHODS In this retrospective analysis, 61 EOC patients were confirmed by histology. Significant features (p < 0.05) by multivariate logistic regression were retained to establish a clinical model, conventional MRI morphological model, ADC model, and traditional model. The radiomics model included FS-T2WI, DWI, and DCE-MRI, and also, a multisequence model was established. A total of 1070 radiomics features of each sequence were extracted; then, univariate analysis and LASSO were used to select important features. Traditional models were combined with a combined radiomics model to establish a mixed model. The predictive performance was validated by receiver operating characteristic curve (ROC) analysis, calibration curve, and decision curve analysis (DCA). A stratified analysis was conducted to compare the differences between the combined radiomics model and the traditional model in identifying early- and late-stage EOC. RESULTS Traditional models showed the highest performance (AUC = 0.96). The performance of the mixed model (AUC = 0.97) was not significantly different from that of the traditional model. The calibration curve showed that the traditional model had the highest reliability. Stratified analysis showed the potential of the combined radiomics model in the early distinction of the two tumor types. CONCLUSION The traditional model is an effective tool to distinguish EOC type I/II. Combined radiomics models have the potential to better distinguish EOC types in early FIGO stage disease. KEY POINTS • The combined radiomics model resulted in a better predictive model than that from a single sequence model. • The traditional model showed higher classification accuracy than the combined radiomics model. • Combined radiomics models have the potential to better distinguish EOC types in early FIGO stage disease.
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Affiliation(s)
- LuoDan Qian
- Department of Radiology, Affiliated Hospital of Inner Mongolia Medical University, Hohhot, 010000, China
| | - JiaLiang Ren
- GE Healthcare (Shanghai) Co., Ltd., Shanghai, 210000, China
| | - AiShi Liu
- Department of Radiology, Affiliated Hospital of Inner Mongolia Medical University, Hohhot, 010000, China
| | - Yang Gao
- Department of Radiology, Affiliated Hospital of Inner Mongolia Medical University, Hohhot, 010000, China
| | - FenE Hao
- Department of Radiology, Affiliated Hospital of Inner Mongolia Medical University, Hohhot, 010000, China
| | - Lei Zhao
- Department of Radiology, Affiliated Hospital of Inner Mongolia Medical University, Hohhot, 010000, China
| | - Hui Wu
- Department of Radiology, Affiliated Hospital of Inner Mongolia Medical University, Hohhot, 010000, China.
| | - GuangMing Niu
- Department of Radiology, Affiliated Hospital of Inner Mongolia Medical University, Hohhot, 010000, China.
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Abstract
Background Acute pulmonary embolism is one of the most common cardiovascular diseases. Computer-aided technique is widely used in chest imaging, especially for assessing pulmonary embolism. The reliability and quantitative analyses of computer-aided technique are necessary. This study aimed to evaluate the reliability of geometry-based computer-aided detection and quantification for emboli morphology and severity of acute pulmonary embolism. Material/Methods Thirty patients suspected of acute pulmonary embolism were analyzed by both manual and computer-aided interpretation of vascular obstruction index and computer-aided measurements of emboli quantitative parameters. The reliability of Qanadli and Mastora scores was analyzed using computer-aided and manual interpretation. Results The time costs of manual and computer-aided interpretation were statistically different (374.90±150.16 versus 121.07±51.76, P<0.001). The difference between the computer-aided and manual interpretation of Qanadli score was 1.83±2.19, and 96.7% (29 out of 30) of the measurements were within 95% confidence interval (intraclass correlation coefficient, ICC=0.998). The difference between the computer-aided and manual interpretation of Mastora score was 1.46±1.62, and 96.7% (29 out of 30) of the measurements were within 95% confidence interval (ICC=0.997). The emboli quantitative parameters were moderately correlated with the Qanadli and Mastora scores (all P<0.001). Conclusions Computer-aided technique could reduce the time costs, improve the and reliability of vascular obstruction index and provided additional quantitative parameters for disease assessment.
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Affiliation(s)
- Zhen-Ting Sun
- Department of Radiology, The Affiliated Hospital of Inner Mongolia Medical University, Hohhot, Inner Mongolia, China (mainland)
| | - Fen-E Hao
- Department of Radiology, The Affiliated Hospital of Inner Mongolia Medical University, Hohhot, Inner Mongolia, China (mainland)
| | - You-Min Guo
- Department of Radiology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi, China (mainland)
| | - Ai-Shi Liu
- Department of Radiology, The Affiliated Hospital of Inner Mongolia Medical University, Hohhot, Inner Mongolia, China (mainland)
| | - Lei Zhao
- Department of Radiology, The Affiliated Hospital of Inner Mongolia Medical University, Hohhot, Inner Mongolia, China (mainland)
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