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Petrila O, Nistor I, Romedea NS, Negru D, Scripcariu V. Can the ADC Value Be Used as an Imaging "Biopsy" in Endometrial Cancer? Diagnostics (Basel) 2024; 14:325. [PMID: 38337842 PMCID: PMC10855861 DOI: 10.3390/diagnostics14030325] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2023] [Revised: 01/07/2024] [Accepted: 01/26/2024] [Indexed: 02/12/2024] Open
Abstract
BACKGROUND The tumor histological grade is closely related to the prognosis of patients with endometrial cancer (EC). Multiparametric MRI, including diffusion-weighted imaging (DWI), provides information about the cellular density that may be useful to differentiate between benign and malignant uterine lesions. However, correlations between apparent diffusion coefficient (ADC) values and histopathological grading in endometrial cancer remain controversial. MATERIAL AND METHODS We retrospectively evaluated 92 patients with endometrial cancers, including both endometrioid adenocarcinomas (64) and non-endometrioid adenocarcinomas (28). All patients underwent DWI procedures, and mean ADC values were calculated in a region of interest. These values were then correlated with the tumor grading offered by the histopathological examination, which was considered the gold standard. In this way, the patients were divided into three groups (G1, G2, and G3). The ADC values were then compared to the results offered by the biopsy to see if the DWI sequence and ADC map could replace this procedure. We also compared the mean ADC values to the myometrial invasion (>50%) and lymphovascular space invasion. RESULTS We have divided the ADC values into three categories corresponding to three grades: >0.850 × 10-3 mm2/s (ADC1), 0.730-0.849 × 10-3 mm2/s (ADC2) and <0.730 × 10-3 mm2/s (ADC3). The diagnostic accuracy of the ADC value was 85.71% for ADC1, 75.76% for ADC2, and 91.66% for ADC3. In 77 cases out of 92, the category in which they were placed using the ADC value corresponded to the result offered by the histopathological exam with an accuracy of 83.69%. For only 56.52% of patients, the biopsy result included the grading system. For each grading category, the mean ADC value showed better results than the biopsy; for G1 patients, the mean ADC value had an accuracy of 85.71% compared to 66.66% in the biopsy, G2 had 75.76% compared to 68.42%, and G3 had 91.66 compared to 75%. For both deep myometrial invasion and lymphovascular space invasion, there is a close, inversely proportional correlation with the mean ADC value. CONCLUSIONS Mean endometrial tumor ADC on MR-DWI is inversely related to the histological grade, deep myometrial invasion and lymphovascular space invasion. Using this method, the patients could be better divided into risk categories for personalized treatment.
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Affiliation(s)
- Octavia Petrila
- Faculty of General Medicine, The University of Medicine and Pharmacy “Grigore T. Popa”, 700115 Iasi, Romania (V.S.)
- Department of Radiology, “Dr. C.I. Parhon” Clinical Hospital, 700503 Iasi, Romania
| | - Ionut Nistor
- Faculty of General Medicine, The University of Medicine and Pharmacy “Grigore T. Popa”, 700115 Iasi, Romania (V.S.)
- Department of Nephrology, “Dr. C.I. Parhon” Clinical Hospital, 700503 Iasi, Romania
| | - Narcis Sandy Romedea
- Department of Surgery, “Dr. Iacob Czihac” Clinical Emergency Hospital, 700506 Iasi, Romania;
| | | | - Viorel Scripcariu
- Faculty of General Medicine, The University of Medicine and Pharmacy “Grigore T. Popa”, 700115 Iasi, Romania (V.S.)
- Department of Surgery, Regional Oncology Institute, 700483 Iasi, Romania
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Li J, Zhou H, Lu X, Wang Y, Pang H, Cesar D, Liu A, Zhou P. Preoperative prediction of cervical cancer survival using a high-resolution MRI-based radiomics nomogram. BMC Med Imaging 2023; 23:153. [PMID: 37821840 PMCID: PMC10568765 DOI: 10.1186/s12880-023-01111-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2023] [Accepted: 09/25/2023] [Indexed: 10/13/2023] Open
Abstract
BACKGROUND Cervical cancer patients receiving radiotherapy and chemotherapy require accurate survival prediction methods. The objective of this study was to develop a prognostic analysis model based on a radiomics score to predict overall survival (OS) in cervical cancer patients. METHODS Predictive models were developed using data from 62 cervical cancer patients who underwent radical hysterectomy between June 2020 and June 2021. Radiological features were extracted from T2-weighted (T2W), T1-weighted (T1W), and diffusion-weighted (DW) magnetic resonance images prior to treatment. We obtained the radiomics score (rad-score) using least absolute shrinkage and selection operator (LASSO) regression and Cox's proportional hazard model. We divided the patients into low- and high-risk groups according to the critical rad-score value, and generated a nomogram incorporating radiological features. We evaluated the model's prediction performance using area under the receiver operating characteristic (ROC) curve (AUC) and classified the participants into high- and low-risk groups based on radiological characteristics. RESULTS The 62 patients were divided into high-risk (n = 43) and low-risk (n = 19) groups based on the rad-score. Four feature parameters were selected via dimensionality reduction, and the scores were calculated after modeling. The AUC values of ROC curves for prediction of 3- and 5-year OS using the model were 0.84 and 0.93, respectively. CONCLUSION Our nomogram incorporating a combination of radiological features demonstrated good performance in predicting cervical cancer OS. This study highlights the potential of radiomics analysis in improving survival prediction for cervical cancer patients. However, further studies on a larger scale and external validation cohorts are necessary to validate its potential clinical utility.
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Affiliation(s)
- Jia Li
- Department of Radiology, The Affiliated Hospital of Southwest Medical University, Luzhou, China
| | - Hao Zhou
- Department of Cardiology, West China Hospital, Sichuan University/West China School of Nursing, Sichuan University, Chengdu, China
| | - Xiaofei Lu
- Department of Radiology, The Affiliated Hospital of Southwest Medical University, Luzhou, China
| | - Yiren Wang
- School of Nursing, Southwest Medical University, Luzhou, China
| | - Haowen Pang
- Department of Oncology, The Affiliated Hospital of Southwest Medical University, Luzhou, China
| | - Daniel Cesar
- Department of Gynecology Oncology, National Cancer Institute, Rio de Janeiro, Brazil
| | - Aiai Liu
- Department of Radiology, The Affiliated Hospital of Southwest Medical University, Luzhou, China.
| | - Ping Zhou
- Department of Radiology, The Affiliated Hospital of Southwest Medical University, Luzhou, China.
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A nomogram for preoperative risk stratification based on MR morphological parameters in patients with endometrioid adenocarcinoma. Eur J Radiol 2023; 163:110789. [PMID: 37068415 DOI: 10.1016/j.ejrad.2023.110789] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2022] [Revised: 03/02/2023] [Accepted: 03/14/2023] [Indexed: 03/19/2023]
Abstract
PURPOSE To develop and validate a nomogram based on MRI morphological parameters to preoperatively discriminate between low-risk and non-low-risk patients with endometrioid endometrial carcinoma (EEC). METHODS Two hundred eighty-one women with histologically confirmed EEC were divided into training (1.5-T MRI, n = 182) and validation cohorts (3.0-T MRI, n = 99). According to the European Society of Medical Oncology guidelines, the patients were divided into four risk groups: low, intermediate, high-intermediate, and high. Binary classification models were developed (low-risk vs. non-low-risk). Univariate logistic regression (LR) analyses were used to determine which variables to select to build the predictive models. Five classification models were constructed, and the best model was selected. The area under the receiver operating characteristic curve (AUC) was calculated to evaluate the performance of the prediction model and nomogram. P < 0.05 indicated a statistically significant difference. RESULTS Age and four morphological parameters (tumor size, tumor volume, maximum anteroposterior tumor diameter on sagittal T2-weighted images (APsag), and tumor area ratio (TAR)) were selected, and the LR model was used to construct an MRI morphological nomogram. The AUCs for the nomogram in predicting a non-low-risk of EEC among patients in the training and validation cohorts were 0.856 (sensitivity = 75.0%, specificity = 83.1%) and 0.849 (sensitivity = 74.6%, specificity = 85.0%), respectively. CONCLUSION An MRI morphological nomogram was developed and achieved high diagnostic performance for classifying low-risk and non-low-risk EEC preoperatively, which could provide support for therapeutic decision-making. Furthermore, our findings indicate that this nomogram is robust in the clinical application of various field strength data.
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Liu XF, Yan BC, Li Y, Ma FH, Qiang JW. Radiomics nomogram in aiding preoperatively dilatation and curettage in differentiating type II and type I endometrial cancer. Clin Radiol 2023; 78:e29-e36. [PMID: 36192204 DOI: 10.1016/j.crad.2022.08.139] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2022] [Revised: 08/23/2022] [Accepted: 08/24/2022] [Indexed: 01/21/2023]
Abstract
AIM To established a radiomics nomogram for improving the dilatation and curettage (D&C) result in differentiating type II from type I endometrial cancer (EC) preoperatively. MATERIAL AND METHODS EC patients (n=875) were enrolled retrospectively and divided randomly into a training cohort (n=437) and a test cohort (n=438), according to the ratio of 1:1. Radiomics signatures were extracted and selected from apparent diffusion coefficient (ADC) maps. A multivariate logistic regression analysis was used to identify the independent clinical risk factors. An ADC based-radiomics nomogram was built by integrating the selected radiomics signatures and the independent clinical risk factors. Decision curve analysis (DCA) was conducted to determine the clinical usefulness of the radiomics nomogram. The net reclassification index (NRI) and total integrated discrimination index (IDI) were calculated to compare the discrimination performances between the radiomics nomogram and the D&C result. RESULTS Receiver operating characteristic (ROC) curves showed that the clinical risk factors, the D&C, and the ADC based-radiomics nomogram yielded areas under the ROC curves (AUCs) of 0.70 (95% CI: 0.64-0.76), 0.85 (95% CI: 0.80-0.89), and 0.93 (95% CI: 0.90-0.96) in the training cohort and 0.64 (95% CI: 0.57-0.71), 0.82 (95% CI: 0.77-0.87) and 0.91 (95% CI: 0.87-0.95) in the test cohort, respectively. The DCA, NRI, and IDI demonstrated the clinically usefulness of the ADC based-radiomics nomogram. CONCLUSION The ADC-based radiomics nomogram could be used to improve the D&C result in differentiating type II from type I EC preoperatively.
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Affiliation(s)
- X-F Liu
- Department of Radiology, Jinshan Hospital, Fudan University, 201508, Shanghai, China
| | - B-C Yan
- Department of Radiology, Jinshan Hospital, Fudan University, 201508, Shanghai, China
| | - Y Li
- Department of Radiology, Jinshan Hospital, Fudan University, 201508, Shanghai, China.
| | - F-H Ma
- Departments of Radiology, Obstetrics & Gynecology Hospital, Fudan University, 200090, Shanghai, China
| | - J-W Qiang
- Department of Radiology, Jinshan Hospital, Fudan University, 201508, Shanghai, China.
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Zhang K, Zhang Y, Fang X, Dong J, Qian L. MRI-based radiomics and ADC values are related to recurrence of endometrial carcinoma: a preliminary analysis. BMC Cancer 2021; 21:1266. [PMID: 34819042 PMCID: PMC8611883 DOI: 10.1186/s12885-021-08988-x] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2021] [Accepted: 11/10/2021] [Indexed: 01/13/2023] Open
Abstract
Background To identify predictive value of apparent diffusion coefficient (ADC) values and magnetic resonance imaging (MRI)-based radiomics for all recurrences in patients with endometrial carcinoma (EC). Methods One hundred and seventy-four EC patients who were treated with operation and followed up in our institution were retrospectively reviewed, and the patients were divided into training and test group. Baseline clinicopathological features and mean ADC (ADCmean), minimum ADC (ADCmin), and maximum ADC (ADCmax) were analyzed. Radiomic parameters were extracted on T2 weighted images and screened by logistic regression, and then a radiomics signature was developed to calculate the radiomic score (radscore). In training group, Kaplan–Meier analysis was performed and a Cox regression model was used to evaluate the correlation between clinicopathological features, ADC values and radscore with recurrence, and verified in the test group. Results ADCmean showed inverse correlation with recurrence, while radscore was positively associated with recurrence. In univariate analyses, FIGO stage, pathological types, myometrial invasion, ADCmean, ADCmin and radscore were associated with recurrence. In the training group, multivariate Cox analysis showed that pathological types, ADCmean and radscore were independent risk factors for recurrence, which were verified in the test group. Conclusions ADCmean value and radscore were independent predictors of recurrence of EC, which can supplement prognostic information in addition to clinicopathological information and provide basis for individualized treatment and follow-up plan. Supplementary Information The online version contains supplementary material available at 10.1186/s12885-021-08988-x.
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Affiliation(s)
- Kaiyue Zhang
- Department of Radiation Oncology, Anhui Provincial Hospital Affiliated to Anhui Medical University, Hefei, 230001, China
| | - Yu Zhang
- Department of Radiation Oncology, Anhui Provincial Hospital Affiliated to Anhui Medical University, Hefei, 230001, China
| | - Xin Fang
- Department of Radiology, First Affiliated Hospital of University of Science and Technology of China, Anhui Provincial Cancer Hospital, Hefei, 230031, China
| | - Jiangning Dong
- Department of Radiation Oncology, Anhui Provincial Hospital Affiliated to Anhui Medical University, Hefei, 230001, China. .,Department of Radiology, First Affiliated Hospital of University of Science and Technology of China, Anhui Provincial Cancer Hospital, Hefei, 230031, China.
| | - Liting Qian
- Department of Radiation Oncology, Anhui Provincial Hospital Affiliated to Anhui Medical University, Hefei, 230001, China. .,Department of Radiation Oncology, First Affiliated Hospital of University of Science and Technology of China, Hefei, 230001, China.
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Chen J, Fan W, Gu H, Wang Y, Liu Y, Chen X, Ren S, Wang Z. The value of the apparent diffusion coefficient in differentiating type II from type I endometrial carcinoma. Acta Radiol 2021; 62:959-965. [PMID: 32727213 DOI: 10.1177/0284185120944913] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
BACKGROUND Diagnostic type II endometrial carcinoma (EC) is considered more aggressive and has a poorer prognosis than type I EC; differentiation between them is helpful for preoperative clinical decision-making. However, the diagnostic value of the apparent diffusion coefficient (ADC) in differentiating them remains unclear. PURPOSE To investigate the value of ADC in differentiating type II EC from type I EC. MATERIAL AND METHODS Ninety-four patients with EC who underwent diffusion-weighted imaging (DWI) were retrospectively included and divided into type I and type II subgroups, based on the postoperative pathologic results. We analyzed the clinical characteristics, conventional magnetic resonance imaging manifestations, and ADC mean values (ADCmean), ADC minimum values (ADCmin), and ADC max values (ADCmax). Receiver operating characteristic (ROC) curve analysis was further used to assess the predictive performance. RESULTS The ADCmean, ADCmin, and tumor size differed significantly between the two subtypes. The area under the ROC curve (AUC) for ADCmean and ADCmin was 0.787 (95% confidence interval [CI] = 0.692-0.88) and 0.835 (95% CI = 0.751-0.919) for predicting type II EC, respectively. The optimal cut-off value of ADCmean for prediction was 0.757 × 10-3 mm2/s with a sensitivity of 91%, a specificity of 58%, and an accuracy of 74%, while for ADCmin was 0.637 × 10-3 mm2/s with a sensitivity of 82%, a specificity of 73%, and an accuracy of 75%. CONCLUSION EC with lower ADCmean and ADCmin values derived from DWI, and a larger size, are indicative of type II EC.
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Affiliation(s)
- Jingya Chen
- Department of Radiology, Jiangsu Province Hospital of Chinese Medicine, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, Jiangsu Province, PR China
| | - Weimin Fan
- Department of Clinical Laboratory, Women’s Hospital of Nanjing Medical University (Nanjing Maternity and Child Health Care Hospital), Nanjing, Jiangsu Province, PR China
| | - Hailei Gu
- Department of Radiology, Women’s Hospital of Nanjing Medical University (Nanjing Maternity and Child Health Care Hospital), Nanjing, Jiangsu Province, PR China
| | - Yaohui Wang
- Department of Pathology, Jiangsu Province Hospital of Chinese Medicine, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, Jiangsu Province, PR China
| | - Yuting Liu
- Department of Pathology, Children’s Hospital of Nanjing Medical University, Nanjing, Jiangsu Province, PR China
| | - Xiao Chen
- Department of Radiology, Jiangsu Province Hospital of Chinese Medicine, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, Jiangsu Province, PR China
| | - Shuai Ren
- Department of Radiology, Jiangsu Province Hospital of Chinese Medicine, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, Jiangsu Province, PR China
| | - Zhongqiu Wang
- Department of Radiology, Jiangsu Province Hospital of Chinese Medicine, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, Jiangsu Province, PR China
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