1
|
Wang ZG, Yang FL, Liu CY, Wang F, Xiong Y, Zhang Q, Chen MN, Lai H. Predicting intraoperative hemorrhage during curettage treatment of cesarean scar pregnancy using free-breathing GRASP DCE-MRI. BMC Pregnancy Childbirth 2024; 24:22. [PMID: 38172701 PMCID: PMC10763255 DOI: 10.1186/s12884-023-06188-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2023] [Accepted: 12/11/2023] [Indexed: 01/05/2024] Open
Abstract
OBJECTIVE To explore the feasibility of the golden-angle radial sparse parallel (GRASP) dynamic magnetic resonance imaging (MRI) technique in predicting the intraoperative bleeding risk of scar pregnancy. METHODS A total of 49 patients with cesarean scar pregnancy (CSP) who underwent curettage and GRASP-MRI imaging were retrospectively selected between January 2021 and July 2022. The pharmacokinetic parameters, including Wash-in, Wash-out, time to peck (TTP), initial area under the curve (iAUC), the transfer rate constant (Ktrans), constant flow rate (Kep), and volume of extracellular space (Ve), were calculated. The amount of intraoperative bleeding was recorded by a gynecologist who performed surgery, after which patients were divided into non-hemorrhage (blood loss ≤ 200 mL) and hemorrhage (blood loss > 200 mL) groups. The measured pharmacokinetic parameters were statistically compared using the t-test or Mann-Whitney U test with a significant level set to be p < 0.05. The receiver operating characteristic (ROC) curve was constructed, and the area under the curve (AUC) was calculated to evaluate each parameter's capability in intraoperative hemorrhage subgroup classification. RESULTS Twenty patients had intraoperative hemorrhage (blood loss > 200 mL) during curettage. The hemorrhage group had larger Wash-in, iAUC, Ktrans, Ve, and shorter TTP than the non-hemorrhage group (all P > 0.05). Wash-in had the highest AUC value (0.90), while Ktrans had the lowest value (0.67). Wash-out and Kep were not significantly different between the two groups. CONCLUSION GRASP DCE-MRI has the potential to forecast intraoperative hemorrhage during curettage treatment of CSP, with Wash-in exhibiting the highest predictive performance. This data holds promise for advancing personalized treatment. However, further study is required to compare its effectiveness with other risk factors identified through anatomical MRI and ultrasound.
Collapse
Affiliation(s)
- Zhi-Gang Wang
- Department of Radiology, Chengdu Women's and Children's Central Hospital, School of Medicine, University of Electronic Science and Technology of China, No.1617 of Riyue Avenue, Qingyang District, Chengdu, 610091, China
| | - Feng-Leng Yang
- Department of Radiology, Chengdu Women's and Children's Central Hospital, School of Medicine, University of Electronic Science and Technology of China, No.1617 of Riyue Avenue, Qingyang District, Chengdu, 610091, China
| | - Chun-Ying Liu
- Department of Radiology, Chengdu Women's and Children's Central Hospital, School of Medicine, University of Electronic Science and Technology of China, No.1617 of Riyue Avenue, Qingyang District, Chengdu, 610091, China
| | - Fang Wang
- Department of Radiology, Chengdu Women's and Children's Central Hospital, School of Medicine, University of Electronic Science and Technology of China, No.1617 of Riyue Avenue, Qingyang District, Chengdu, 610091, China
| | - Ying Xiong
- Department of Gynecology, Chengdu Women's and Children's Central Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Qiang Zhang
- Department of Gynecology, Chengdu Women's and Children's Central Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Mei-Ning Chen
- Department of MR Scientific Marketing, Siemens Healthineers, Shanghai, China
| | - Hua Lai
- Department of Radiology, Chengdu Women's and Children's Central Hospital, School of Medicine, University of Electronic Science and Technology of China, No.1617 of Riyue Avenue, Qingyang District, Chengdu, 610091, China.
| |
Collapse
|
2
|
Lin M, Xie Y, Hong L, Wu S, Deng X, Chen Y, Tan X. Abnormal adiposity indices are associated with an increased risk of diabetes in a non-obese Asian population. Public Health 2024; 226:144-151. [PMID: 38064777 DOI: 10.1016/j.puhe.2023.11.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2023] [Revised: 10/11/2023] [Accepted: 11/07/2023] [Indexed: 01/15/2024]
Abstract
OBJECTIVES The aim of this study was to evaluate the association between adiposity indices and the risk of incident diabetes and to compare their predictive ability in non-obese healthy individuals. STUDY DESIGN Population-based cohort study. METHODS Data were taken from the NAGALA research study, which enrolled Japanese adults aged 18-79 years. Cox regression was used to evaluate the association between adiposity indices (including waist circumference [WC], waist-to-height ratio [WHtR], lipid accumulation product index [LAP], body roundness index [BRI], visceral adiposity index [VAI] and Chinese visceral adiposity index [CVAI]) and diabetes risk. The performance of the indices for predicting diabetes was explored using area under the receiver operating characteristic curve (AUC). A Chinese community-based population was used for validation. RESULTS A total of 12,940 healthy Japanese individuals with normal body mass index and glycaemic levels were included and were followed up for a median of 6 years. Multivariable Cox models revealed a positive and significant association between all indices and incident diabetes, with the hazard ratios for the highest quartile of the indices ranging from 1.89 to 2.90 (all P-values < 0.01). A non-linear association between WC, BRI and VAI and a linear association between WHtR, LAP and CVAI and diabetes risk were observed. CVAI, VAI and LAP had comparable ability in predicting diabetes, with the highest AUC being 0.733 for CVAI. Data from 10,830 Chinese individuals confirmed these results. CONCLUSIONS Adiposity indices are associated with incident diabetes in healthy non-obese individuals. Participants in the highest quartile of WC, WHtR, LAP, BRI, VAI and CVAI had an increased risk of developing diabetes.
Collapse
Affiliation(s)
- Mengyue Lin
- Department of Cardiology, First Affiliated Hospital of Shantou University Medical College, Shantou, China
| | - Yandan Xie
- Department of Cardiology, First Affiliated Hospital of Shantou University Medical College, Shantou, China
| | - Liekai Hong
- Department of Cardiology, First Affiliated Hospital of Shantou University Medical College, Shantou, China
| | - Shiwan Wu
- Department of Cardiology, First Affiliated Hospital of Shantou University Medical College, Shantou, China
| | - Xiulian Deng
- Department of Cardiology, First Affiliated Hospital of Shantou University Medical College, Shantou, China
| | - Yequn Chen
- Department of Cardiology, First Affiliated Hospital of Shantou University Medical College, Shantou, China; Clinical Research Center, First Affiliated Hospital of Shantou University Medical College, Shantou, China.
| | - Xuerui Tan
- Department of Cardiology, First Affiliated Hospital of Shantou University Medical College, Shantou, China; Clinical Research Center, First Affiliated Hospital of Shantou University Medical College, Shantou, China.
| |
Collapse
|
3
|
Kuang Y, Li R, Jia P, Ye W, Zhou R, Zhu R, Wang J, Lin S, Pang P, Ji W. MRI-Based Radiomics: Nomograms predicting the short-term response after transcatheter arterial chemoembolization (TACE) in hepatocellular carcinoma patients with diameter less than 5 cm. Abdom Radiol (NY) 2021; 46:3772-3789. [PMID: 33713159 DOI: 10.1007/s00261-021-02992-2] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2020] [Revised: 02/05/2021] [Accepted: 02/11/2021] [Indexed: 02/06/2023]
Abstract
PURPOSE To construct MRI radiomics nomograms that can predict short-term response after TACE in HCC patients with diameter less than 5 cm. METHODS MRI images and clinical data of 153 cases with tumor diameter less than 5 cm before TACE from 3 hospitals were collected retrospectively and divided into 1 internal training set and 1 external validation set. The T2-weighted imaging (T2WI) and dynamic contrast-enhanced MRI arterial phase (DCE-MR AP) images were studied. Multivariable logistic regression was used to construct Radiomics models, Clinics models, and Nomograms based on T2WI and DCE-MR AP, respectively. The receiver characteristic curve (ROC) was used to evaluate the predictive performance of each model. RESULTS In this study, 113 eligible cases in Hospital 1 were collected as the training set, and 40 eligible cases in other hospitals were used as the verification set. 11 T2WI features and 11 DCE-MRI AP features with the most predictive value were finally screened. 3 models based on T2WI and 3 models based on DCE-MRI AP were established, respectively. The area under curve (AUC) value of Nomogram based on T2WI of training set and validation set was 0.83 and 0.81, respectively. The AUC value of the models based on T2WI and models based on AP was almost equal, and Nomograms were the most effective models among all three types of models. CONCLUSION MRI-based Nomogram has greater predictive efficacy to predict the response after TACE than Radiomics and Clinics models alone, and the efficacy of T2WI-based models and DCE-MRI AP-based models was almost equal.
Collapse
Affiliation(s)
- Yani Kuang
- Taizhou Hospital of Zhejiang Province affiliated to Wenzhou Medical University, No. 150 Ximen Street, Linhai, Zhejiang, China
| | - Renzhan Li
- Sanmen People's Hospital, Taizhou, China
| | - Peng Jia
- First People's Hospital of Taizhou city, Zhejiang, China
| | - Wenhai Ye
- Sanmen People's Hospital, Taizhou, China
| | - Rongzhen Zhou
- Taizhou Hospital of Zhejiang Province affiliated to Wenzhou Medical University, No. 150 Ximen Street, Linhai, Zhejiang, China
| | - Rui Zhu
- Taizhou Hospital of Zhejiang Province affiliated to Wenzhou Medical University, No. 150 Ximen Street, Linhai, Zhejiang, China
| | - Jian Wang
- Taizhou Hospital of Zhejiang Province affiliated to Wenzhou Medical University, No. 150 Ximen Street, Linhai, Zhejiang, China
| | - Shuangxiang Lin
- Taizhou Hospital of Zhejiang Province affiliated to Wenzhou Medical University, No. 150 Ximen Street, Linhai, Zhejiang, China
| | | | - Wenbin Ji
- Taizhou Hospital of Zhejiang Province affiliated to Wenzhou Medical University, No. 150 Ximen Street, Linhai, Zhejiang, China.
| |
Collapse
|
4
|
Galán RJ, Bernal-Vasquez AM, Jebsen C, Piepho HP, Thorwarth P, Steffan P, Gordillo A, Miedaner T. Early prediction of biomass in hybrid rye based on hyperspectral data surpasses genomic predictability in less-related breeding material. Theor Appl Genet 2021; 134:1409-1422. [PMID: 33630103 PMCID: PMC8081675 DOI: 10.1007/s00122-021-03779-1] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/21/2020] [Accepted: 01/19/2021] [Indexed: 05/15/2023]
Abstract
Hyperspectral data is a promising complement to genomic data to predict biomass under scenarios of low genetic relatedness. Sufficient environmental connectivity between data used for model training and validation is required. The demand for sustainable sources of biomass is increasing worldwide. The early prediction of biomass via indirect selection of dry matter yield (DMY) based on hyperspectral and/or genomic prediction is crucial to affordably untap the potential of winter rye (Secale cereale L.) as a dual-purpose crop. However, this estimation involves multiple genetic backgrounds and genetic relatedness is a crucial factor in genomic selection (GS). To assess the prospect of prediction using reflectance data as a suitable complement to GS for biomass breeding, the influence of trait heritability ([Formula: see text]) and genetic relatedness were compared. Models were based on genomic (GBLUP) and hyperspectral reflectance-derived (HBLUP) relationship matrices to predict DMY and other biomass-related traits such as dry matter content (DMC) and fresh matter yield (FMY). For this, 270 elite rye lines from nine interconnected bi-parental families were genotyped using a 10 k-SNP array and phenotyped as testcrosses at four locations in two years (eight environments). From 400 discrete narrow bands (410 nm-993 nm) collected by an uncrewed aerial vehicle (UAV) on two dates in each environment, 32 hyperspectral bands previously selected by Lasso were incorporated into a prediction model. HBLUP showed higher prediction abilities (0.41 - 0.61) than GBLUP (0.14 - 0.28) under a decreased genetic relationship, especially for mid-heritable traits (FMY and DMY), suggesting that HBLUP is much less affected by relatedness and [Formula: see text]. However, the predictive power of both models was largely affected by environmental variances. Prediction abilities for DMY were further enhanced (up to 20%) by integrating both matrices and plant height into a bivariate model. Thus, data derived from high-throughput phenotyping emerges as a suitable strategy to efficiently leverage selection gains in biomass rye breeding; however, sufficient environmental connectivity is needed.
Collapse
Affiliation(s)
- Rodrigo José Galán
- State Plant Breeding Institute, University of Hohenheim, 70593, Stuttgart, Germany
| | | | | | - Hans-Peter Piepho
- Biostatistics Unit, Institute of Crop Science, University of Hohenheim, 70593, Stuttgart, Germany
| | - Patrick Thorwarth
- State Plant Breeding Institute, University of Hohenheim, 70593, Stuttgart, Germany
- KWS SAAT SE, Grimsehlstraße 31, 37574, Einbeck, Germany
| | - Philipp Steffan
- KWS LOCHOW GMBH, Ferdinand-von-Lochow Straße 5, 29303, Bergen, Germany
| | - Andres Gordillo
- KWS LOCHOW GMBH, Ferdinand-von-Lochow Straße 5, 29303, Bergen, Germany
| | - Thomas Miedaner
- State Plant Breeding Institute, University of Hohenheim, 70593, Stuttgart, Germany.
| |
Collapse
|
5
|
Yang J, Wang F, Wang J, Han X, Hu H, Yu C, Yuan J, Yao P, Miao X, Wei S, Wang Y, Chen W, Liang Y, Guo H, Zhang X, Zheng D, Tang Y, Yang H, He M. Using different anthropometric indices to assess prediction ability of type 2 diabetes in elderly population: a 5 year prospective study. BMC Geriatr 2018; 18:218. [PMID: 30223783 PMCID: PMC6142386 DOI: 10.1186/s12877-018-0912-2] [Citation(s) in RCA: 38] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2018] [Accepted: 09/10/2018] [Indexed: 01/19/2023] Open
Abstract
BACKGROUND Emerging studies have investigated the association between different anthropometric indices with diabetes risk but the results were inconsistent. The aims of the study were to examine the associations of different anthropometric indices with incident diabetes risk and whether novel anthropometric indices improve diabetes prediction beyond traditional indices among elderly Chinese. METHODS Nine thousand nine hundred sixty-two elderly individuals (age ≥ 60 years old) derived from the prospective Dongfeng-Tongji cohort were included. Hazard ratio (HR) and corresponding 95% confidence interval (CI) were evaluated by Cox proportional hazard model to examine the associations between traditional anthropometric indices (body mass index [BMI], waist circumference [WC], waist-to-height ratio [WHtR]), novel anthropometric indices (visceral adiposity index [VAI], a body shape index [ABSI], body roundness index [BRI]) and diabetes risk. Receiver operating characteristic (ROC) curve and area under curve (AUC) were applied to compare the novel anthropometric indices with the traditional indices in diabetes prediction. RESULTS During mean 4.6 years of follow-up, 614 incident cases of type 2 diabetes (T2D) were identified. Significant positive associations were detected between BMI, WC, WHtR, VAI and BRI and incident T2D risk. For ABSI, no significant association was observed in either men or women. BMI was the strongest predictor in diabetes in men (AUC = 0.655) comparable with the other anthropometric indices (P < 0.05). Similar as men, BMI was the strongest predictor (AUC = 0.635) in women. Except for WC, the AUC of BMI was larger than WHtR, VAI, and BRI. In contrast, ABSI was not a good predictor in either men (AUC = 0.507) or women (AUC = 0.503). CONCLUSIONS In elderly Chinese, BMI, WC, WHtR, VAI and BRI were positively associated with incident T2D risk. Among them, BMI was the strongest predictor in both men and women.
Collapse
Affiliation(s)
- Jing Yang
- Department of Occupational and Environmental Health and State Key Laboratory of Environmental Health for Incubating, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, 13 Hangkong Rd, Wuhan, 430030, Hubei, China
| | - Fei Wang
- Department of Occupational and Environmental Health and State Key Laboratory of Environmental Health for Incubating, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, 13 Hangkong Rd, Wuhan, 430030, Hubei, China
| | - Jing Wang
- Department of Occupational and Environmental Health and State Key Laboratory of Environmental Health for Incubating, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, 13 Hangkong Rd, Wuhan, 430030, Hubei, China
| | - Xu Han
- Department of Occupational and Environmental Health and State Key Laboratory of Environmental Health for Incubating, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, 13 Hangkong Rd, Wuhan, 430030, Hubei, China
| | - Hua Hu
- Department of Occupational and Environmental Health and State Key Laboratory of Environmental Health for Incubating, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, 13 Hangkong Rd, Wuhan, 430030, Hubei, China
| | - Caizheng Yu
- Department of Occupational and Environmental Health and State Key Laboratory of Environmental Health for Incubating, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, 13 Hangkong Rd, Wuhan, 430030, Hubei, China
| | - Jing Yuan
- Department of Occupational and Environmental Health and State Key Laboratory of Environmental Health for Incubating, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, 13 Hangkong Rd, Wuhan, 430030, Hubei, China
| | - Ping Yao
- Department of Occupational and Environmental Health and State Key Laboratory of Environmental Health for Incubating, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, 13 Hangkong Rd, Wuhan, 430030, Hubei, China
| | - Xiaoping Miao
- Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Sheng Wei
- Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Youjie Wang
- Department of Occupational and Environmental Health and State Key Laboratory of Environmental Health for Incubating, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, 13 Hangkong Rd, Wuhan, 430030, Hubei, China
| | - Weihong Chen
- Department of Occupational and Environmental Health and State Key Laboratory of Environmental Health for Incubating, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, 13 Hangkong Rd, Wuhan, 430030, Hubei, China
| | - Yuan Liang
- Department of Occupational and Environmental Health and State Key Laboratory of Environmental Health for Incubating, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, 13 Hangkong Rd, Wuhan, 430030, Hubei, China
| | - Huan Guo
- Department of Occupational and Environmental Health and State Key Laboratory of Environmental Health for Incubating, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, 13 Hangkong Rd, Wuhan, 430030, Hubei, China
| | - Xiaomin Zhang
- Department of Occupational and Environmental Health and State Key Laboratory of Environmental Health for Incubating, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, 13 Hangkong Rd, Wuhan, 430030, Hubei, China
| | - Dan Zheng
- Department of Occupational and Environmental Health and State Key Laboratory of Environmental Health for Incubating, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, 13 Hangkong Rd, Wuhan, 430030, Hubei, China
| | - Yuhan Tang
- Department of Occupational and Environmental Health and State Key Laboratory of Environmental Health for Incubating, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, 13 Hangkong Rd, Wuhan, 430030, Hubei, China
| | - Handong Yang
- Dongfeng Central Hospital, Dongfeng Motor Corporation and Hubei University of Medicine, Shiyan, Hubei, China
| | - Meian He
- Department of Occupational and Environmental Health and State Key Laboratory of Environmental Health for Incubating, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, 13 Hangkong Rd, Wuhan, 430030, Hubei, China.
| |
Collapse
|