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Tan X, Yang X, Hu S, Chen X, Sun Z. A nomogram for predicting postoperative complications based on tumor spectral CT parameters and visceral fat area in gastric cancer patients. Eur J Radiol 2023; 167:111072. [PMID: 37666073 DOI: 10.1016/j.ejrad.2023.111072] [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: 05/17/2023] [Revised: 08/12/2023] [Accepted: 08/28/2023] [Indexed: 09/06/2023]
Abstract
PURPOSE To construct a nomogram combining tumor spectral CT parameters and visceral fat area (VFA) to predict postoperative complications (POCs) in patients with gastric cancer (GC). METHOD This retrospective study included 101 GC patients who underwent preoperative abdominal spectral CT scan and were divided into two groups (37 with POCs and 64 without POCs) according to the Clavien-Dindo classification standard. Logistic regression was used to establish spectral, VFA, and combined models for predicting POCs. The combined prediction model was presented as a nomogram, and the diagnostic performance of each model was evaluated using receiver operating characteristic (ROC) curve analysis. RESULTS The AUCs of the VFA and spectral model were 0.71 (95% CI: 0.62-0.80) and 0.81 (95% CI: 0.72-0.88), respectively. VFA, the slope of spectral curve (λ) in venous phase (λ-VP) and tumor Hounsfield units on monoenergetic images 40 keV in VP (MonoE40keV-VP) were independent predictors of POCs in GC. The nomogram yielded an AUC of 0.89 (95% CI: 0.81-0.94). The combined model was superior to the VFA or spectral models by comparing their AUCs (P = 0.000 and 0.022). CONCLUSIONS The nomogram based on two tumor spectral parameters (λ-VP, MonoE40keV-VP) and VFA could serve as a convenient tool for predicting the POCs of GC patients.
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Affiliation(s)
- Xiaoying Tan
- Department of Radiology, Affiliated Hospital of Jiangnan University, Wuxi City 214062, Jiangsu Province, China
| | - Xiao Yang
- Department of Radiology, Affiliated Hospital of Jiangnan University, Wuxi City 214062, Jiangsu Province, China
| | - Shudong Hu
- Department of Radiology, Affiliated Hospital of Jiangnan University, Wuxi City 214062, Jiangsu Province, China
| | - Xingbiao Chen
- Department of Clinical Science, Philips Healthcare, Shanghai 200233, China
| | - Zongqiong Sun
- Department of Radiology, Affiliated Hospital of Jiangnan University, Wuxi City 214062, Jiangsu Province, China.
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Zheng Y, Geng D, Yu T, Xia W, She D, Liu L, Yin B. Prognostic value of pretreatment MRI texture features in breast cancer brain metastasis treated with Gamma Knife radiosurgery. Acta Radiol 2021; 62:1208-1216. [PMID: 32910684 DOI: 10.1177/0284185120956296] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
BACKGROUND Gamma Knife radiosurgery (GKS) was recommended for treating patients with breast cancer brain metastasis (BCBM), but predictions of the existing prognostic models for therapeutic responsiveness vary substantially. PURPOSE To investigate the prognostic value of pretreatment clinical, MRI radiologic, and texture features in patients with BCBM undergoing GKS. MATERIAL AND METHODS The data of 81 BCBMs in 44 patients were retrospectively reviewed. Progressive disease was defined as an increase of at least 20% in the longest diameter of the target lesion or the presence of new intracranial lesions on contrast-enhanced T1-weighted (CE-T1W) imaging. Radiomic features were extracted from pretreatment CE-T1W images, T2-weighted (T2W) images, and ADC maps. Cox proportional hazard analyses were performed to identify independent predictors associated with BCBM-specific progression-free survival (PFS). A nomogram was constructed and its calibration ability was assessed. RESULTS The cumulative BCBM-specific PFS was 52.27% at six months and 11.36% at one year, respectively. Age (hazard ratio [HR] 1.04; 95% confidence interval [CI] 1.01-1.06; P = 0.004) and CE-T1W-based kurtosis (HR 0.72; 95% CI 0.57-0.92; P = 0.008) were the independent predictors. The combination of CE-T1W-based kurtosis and age displayed a higher C-index (C-index 0.70; 95% CI 0.63-0.77) than did CE-T1W-based kurtosis (C-index 0.65; 95% CI 0.57-0.73) or age (C-index 0.63; 95% CI 0.56-0.70) alone. The nomogram based on the combinative model provided a better performance over age (P < 0.05). The calibration curves elucidated good agreement between prediction and observation for the probability of 7- and 12-month BCBM-specific PFS. CONCLUSION Pretreatment CE-T1W-based kurtosis combined with age could improve prognostic ability in patients with BCBM undergoing GKS.
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Affiliation(s)
- Yingyan Zheng
- Department of Radiology, Huashan Hospital, Fudan University, Shanghai, PR China
| | - Daoying Geng
- Department of Radiology, Huashan Hospital, Fudan University, Shanghai, PR China
- Institute of Functional and Molecular Medical Imaging, Fudan University, Shanghai, PR China
| | - Tonggang Yu
- Department of Radiology, Shanghai Gamma Hospital, Huashan Hospital, Fudan University, Shanghai, PR China
| | - Wei Xia
- Department of Radiology, Huashan Hospital, Fudan University, Shanghai, PR China
- Academy for Engineering and Technology, Fudan University, Shanghai, PR China
- Department of Medical Imaging, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, Jiangsu, PR China
| | - Dejun She
- Department of Radiology, Huashan Hospital, Fudan University, Shanghai, PR China
| | - Li Liu
- Department of Radiology, Shanghai Cancer Center, Fudan University, Shanghai, PR China
| | - Bo Yin
- Department of Radiology, Huashan Hospital, Fudan University, Shanghai, PR China
- Institute of Functional and Molecular Medical Imaging, Fudan University, Shanghai, PR China
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Xu Z, Zhao K, Han L, Li P, Shi Z, Huang X, Han C, Wang H, Chen M, Liu C, Liang Y, Li S, Huang Y, Chen X, Liang C, Cao W, Liu Z. Combining quantitative and qualitative magnetic resonance imaging features to differentiate anorectal malignant melanoma from low rectal cancer. PRECISION CLINICAL MEDICINE 2021; 4:119-128. [PMID: 35694154 PMCID: PMC8982618 DOI: 10.1093/pcmedi/pbab011] [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] [Subscribe] [Scholar Register] [Received: 03/24/2021] [Revised: 04/22/2021] [Accepted: 04/26/2021] [Indexed: 02/05/2023] Open
Abstract
BACKGROUND Distinguishing anorectal malignant melanoma from low rectal cancer remains challenging because of the overlap of clinical symptoms and imaging findings. We aim to investigate whether combining quantitative and qualitative magnetic resonance imaging (MRI) features could differentiate anorectal malignant melanoma from low rectal cancer. METHODS Thirty-seven anorectal malignant melanoma and 98 low rectal cancer patients who underwent pre-operative rectal MRI from three hospitals were retrospectively enrolled. All patients were divided into the primary cohort (N = 84) and validation cohort (N = 51). Quantitative image analysis was performed on T1-weighted (T1WI), T2-weighted (T2WI), and contrast-enhanced T1-weighted imaging (CE-T1WI). The subjective qualitative MRI findings were evaluated by two radiologists in consensus. Multivariable analysis was performed using stepwise logistic regression. The discrimination performance was assessed by the area under the receiver operating characteristic curve (AUC) with a 95% confidence interval (CI). RESULTS The skewness derived from T2WI (T2WI-skewness) showed the best discrimination performance among the entire quantitative image features for differentiating anorectal malignant melanoma from low rectal cancer (primary cohort: AUC = 0.852, 95% CI 0.788-0.916; validation cohort: 0.730, 0.645-0.815). Multivariable analysis indicated that T2WI-skewness and the signal intensity of T1WI were independent factors, and incorporating both factors achieved good discrimination performance in two cohorts (primary cohort: AUC = 0.913, 95% CI 0.868-0.958; validation cohort: 0.902, 0.844-0.960). CONCLUSIONS Incorporating T2WI-skewness and the signal intensity of T1WI achieved good performance for differentiating anorectal malignant melanoma from low rectal cancer. The quantitative image analysis helps improve diagnostic accuracy.
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Affiliation(s)
| | | | | | - Pinxiong Li
- Department of Radiology, the Affiliated Hospital of Southwest Medical University, Luzhou 646000, China
| | - Zhenwei Shi
- Department of Radiology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou 510080, China
| | - Xiaomei Huang
- Department of Radiology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou 510080, China
- The Second School of Clinical Medicine, Southern Medical University, Guangzhou 510080, China
| | - Chu Han
- Department of Radiology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou 510080, China
| | - Huihui Wang
- Department of Radiology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou 510080, China
- Shantou University Medical College, Shantou 515041, China
| | - Minglei Chen
- Department of Radiology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou 510080, China
- Shantou University Medical College, Shantou 515041, China
| | - Chen Liu
- Department of Radiology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou 510080, China
- The Second School of Clinical Medicine, Southern Medical University, Guangzhou 510080, China
| | - Yanting Liang
- Department of Radiology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou 510080, China
- Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou 510080, China
| | - Suyun Li
- School of Medicine, South China University of Technology, Guangzhou 510006, China
- Department of Radiology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou 510080, China
| | - Yanqi Huang
- Department of Radiology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou 510080, China
- The Second School of Clinical Medicine, Southern Medical University, Guangzhou 510080, China
| | - Xin Chen
- Department of Radiology, Guangzhou First People's Hospital, the Second Affiliated Hospital of South China University of Technology, Guangzhou 510180, China
| | - Changhong Liang
- Department of Radiology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou 510080, China
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Evaluating survival in subjects with astrocytic brain tumors by dynamic susceptibility-weighted perfusion MR imaging. PLoS One 2021; 16:e0244275. [PMID: 33406116 PMCID: PMC7787526 DOI: 10.1371/journal.pone.0244275] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2020] [Accepted: 12/07/2020] [Indexed: 12/02/2022] Open
Abstract
Purpose Studies have evaluated the application of perfusion MR for predicting survival in patients with astrocytic brain tumors, but few of them statistically adjust their results to reflect the impact of the variability of treatment administered in the patients. Our aim was to analyze the association between the perfusion values and overall survival time, with adjustment for various clinical factors, including initial treatments and follow-up treatments. Materials and methods This study consisted of 51 patients with astrocytic brain tumors who underwent perfusion-weighted MRI with MultiHance® at a dose of 0.1 mmol/kg prior to initial surgery. We measured the mean rCBV, the 5% & 10% maximum rCBV, and the variation of rCBV in the tumors. Comparisons were made between patients with and without 2-year survival using two-sample t-test or Wilcoxon rank-sum test for the continuous data, or chi-square and Fisher exact tests for categorical data. The multivariate cox-proportional hazard regression was fit to evaluate the association between rCBV and overall survival time, with adjustment for clinical factors. Results Patients who survived less than 2 years after diagnosis had a higher mean and maximum rCBV and a larger variation of rCBV. After adjusting for clinical factors including therapeutic measures, we found no significant association of overall survival time within 2 years with any of these rCBV values. Conclusions Although patients who survived less than 2 years had a higher mean and maximum rCBV and a larger variation of rCBV, rCBV itself may not be used independently for predicting 2-year survival of patients with astrocytic brain tumors.
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Radiomics prognostication model in glioblastoma using diffusion- and perfusion-weighted MRI. Sci Rep 2020; 10:4250. [PMID: 32144360 PMCID: PMC7060336 DOI: 10.1038/s41598-020-61178-w] [Citation(s) in RCA: 47] [Impact Index Per Article: 11.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2018] [Accepted: 02/19/2020] [Indexed: 01/30/2023] Open
Abstract
We aimed to develop and validate a multiparametric MR radiomics model using conventional, diffusion-, and perfusion-weighted MR imaging for better prognostication in patients with newly diagnosed glioblastoma. A total of 216 patients with newly diagnosed glioblastoma were enrolled from two tertiary medical centers and divided into training (n = 158) and external validation sets (n = 58). Radiomic features were extracted from contrast-enhanced T1-weighted imaging, fluid-attenuated inversion recovery, diffusion-weighted imaging, and dynamic susceptibility contrast imaging. After radiomic feature selection using LASSO regression, an individualized radiomic score was calculated. A multiparametric MR prognostic model was built using the radiomic score and clinical predictors. The results showed that the multiparametric MR prognostic model (radiomics score + clinical predictors) exhibited good discrimination (C-index, 0.74) and performed better than a conventional MR radiomics model (C-index, 0.65, P < 0.0001) or clinical predictors (C-index, 0.66; P < 0.0001). The multiparametric MR prognostic model also showed robustness in external validation (C-index, 0.70). Our results indicate that the incorporation of diffusion- and perfusion-weighted MR imaging into an MR radiomics model to improve prognostication in glioblastoma patients improved its performance over that achievable using clinical predictors alone.
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Prediction of survival in patients affected by glioblastoma: histogram analysis of perfusion MRI. J Neurooncol 2018; 139:455-460. [DOI: 10.1007/s11060-018-2887-4] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2017] [Accepted: 04/08/2018] [Indexed: 01/20/2023]
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Park B, Choi BS, Sung YS, Woo DC, Shim WH, Kim KW, Choi YS, Pae SJ, Suh JY, Cho H, Kim JK. Influence of B 1-Inhomogeneity on Pharmacokinetic Modeling of Dynamic Contrast-Enhanced MRI: A Simulation Study. Korean J Radiol 2017; 18:585-596. [PMID: 28670153 PMCID: PMC5447634 DOI: 10.3348/kjr.2017.18.4.585] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2016] [Accepted: 11/18/2016] [Indexed: 01/16/2023] Open
Abstract
OBJECTIVE To simulate the B1-inhomogeneity-induced variation of pharmacokinetic parameters on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). MATERIALS AND METHODS B1-inhomogeneity-induced flip angle (FA) variation was estimated in a phantom study. Monte Carlo simulation was performed to assess the FA-deviation-induced measurement error of the pre-contrast R1, contrast-enhancement ratio, Gd-concentration, and two-compartment pharmacokinetic parameters (Ktrans, ve, and vp). RESULTS B1-inhomogeneity resulted in -23-5% fluctuations (95% confidence interval [CI] of % error) of FA. The 95% CIs of FA-dependent % errors in the gray matter and blood were as follows: -16.7-61.8% and -16.7-61.8% for the pre-contrast R1, -1.0-0.3% and -5.2-1.3% for the contrast-enhancement ratio, and -14.2-58.1% and -14.1-57.8% for the Gd-concentration, respectively. These resulted in -43.1-48.4% error for Ktrans, -32.3-48.6% error for the ve, and -43.2-48.6% error for vp. The pre-contrast R1 was more vulnerable to FA error than the contrast-enhancement ratio, and was therefore a significant cause of the Gd-concentration error. For example, a -10% FA error led to a 23.6% deviation in the pre-contrast R1, -0.4% in the contrast-enhancement ratio, and 23.6% in the Gd-concentration. In a simulated condition with a 3% FA error in a target lesion and a -10% FA error in a feeding vessel, the % errors of the pharmacokinetic parameters were -23.7% for Ktrans, -23.7% for ve, and -23.7% for vp. CONCLUSION Even a small degree of B1-inhomogeneity can cause a significant error in the measurement of pharmacokinetic parameters on DCE-MRI, while the vulnerability of the pre-contrast R1 calculations to FA deviations is a significant cause of the miscalculation.
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Affiliation(s)
- Bumwoo Park
- Department of Radiology, Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul 05505, Korea.,Center for Bioimaging of New Drug Development, Asan Institute for Life Sciences, Asan Medical Center, University of Ulsan College of Medicine, Seoul 05505, Korea
| | - Byung Se Choi
- Department of Radiology, Seoul National University College of Medicine, Seoul National University Bundang Hospital, Seongnam 13620, Korea
| | - Yu Sub Sung
- Department of Radiology, Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul 05505, Korea.,Center for Bioimaging of New Drug Development, Asan Institute for Life Sciences, Asan Medical Center, University of Ulsan College of Medicine, Seoul 05505, Korea
| | - Dong-Cheol Woo
- Department of Radiology, Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul 05505, Korea.,Center for Bioimaging of New Drug Development, Asan Institute for Life Sciences, Asan Medical Center, University of Ulsan College of Medicine, Seoul 05505, Korea
| | - Woo Hyun Shim
- Department of Radiology, Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul 05505, Korea.,Center for Bioimaging of New Drug Development, Asan Institute for Life Sciences, Asan Medical Center, University of Ulsan College of Medicine, Seoul 05505, Korea
| | - Kyung Won Kim
- Department of Radiology, Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul 05505, Korea.,Center for Bioimaging of New Drug Development, Asan Institute for Life Sciences, Asan Medical Center, University of Ulsan College of Medicine, Seoul 05505, Korea
| | - Yoon Seok Choi
- Department of Radiology, Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul 05505, Korea.,Center for Bioimaging of New Drug Development, Asan Institute for Life Sciences, Asan Medical Center, University of Ulsan College of Medicine, Seoul 05505, Korea
| | - Sang Joon Pae
- Department of Surgery, National Health Insurance Service Ilsan Hospital, Goyang 10444, Korea
| | - Ji-Yeon Suh
- Department of Radiology, Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul 05505, Korea.,Center for Bioimaging of New Drug Development, Asan Institute for Life Sciences, Asan Medical Center, University of Ulsan College of Medicine, Seoul 05505, Korea
| | - Hyungjoon Cho
- Biomedical Engineering, Ulsan National Institute of Science and Technology, Ulsan 44919, Korea
| | - Jeong Kon Kim
- Department of Radiology, Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul 05505, Korea.,Center for Bioimaging of New Drug Development, Asan Institute for Life Sciences, Asan Medical Center, University of Ulsan College of Medicine, Seoul 05505, Korea
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