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Akçay A, Gültekin MA, Altıntaş F, Peker AA, Balsak S, Atasoy B, Toluk Ö, Toprak H. Updated endometrial cancer FIGO staging: the role of MRI in determining newly included histopathological criteria. Abdom Radiol (NY) 2024:10.1007/s00261-024-04398-2. [PMID: 38836884 DOI: 10.1007/s00261-024-04398-2] [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: 04/08/2024] [Revised: 05/16/2024] [Accepted: 05/17/2024] [Indexed: 06/06/2024]
Abstract
PURPOSE Endometrial cancer (EC) is among the prevalent malignancies in gynecology, showing an increasing occurrence and mortality rate. The updated 2023 FIGO staging integrates both histopathological and molecular analyses, which significantly impact the prognosis and treatment approaches. This research aims to examine the effectiveness of MRI in identifying essential histopathological tumor features, including histological subtype, grade, and lymphovascular space invasion. METHODS A total of 106 patients diagnosed with EC from February 2018 to December 2023 underwent preoperative pelvic MRI. Surgical procedures followed ESMO guidelines, with histopathological assessments using FIGO 2009 criteria. Two radiologists independently evaluated MRI images, measuring maximum tumor size, minimum tumor ADC value (using a free-hand ROI technique), and ADC tumor/myometrium ratio. MRI findings were compared with histopathological data. RESULTS Peritoneal implant presence and tumor size exhibited significant differences between endometrioid adenocarcinoma (EAC) and non-endometrioid endometrial carcinoma (NEEC), with p values of < 0.001 and 0.003, respectively. Significant differences in age, tumor size, ADC tumor, and ADC tumor/myometrium between low-grade and high-grade tumors were observed, with p values of < 0.001, 0.004, 0.006, and 0.011, respectively. Increased tumor size, reduced ADC tumor, ADC tumor/myometrium, and pelvic peritoneal implant presence were significantly associated with LVSI, with p values of < 0.001, 0.001, 0.002, and 0.001, respectively. The AUC values for tumor size, ADC tumor, and ADC tumor/myometrium were 0.842, 0.781 and 0.747, respectively, in distinguishing between low and high-grade endometrial tumors. Similarly, obtained AUC values for predicting LVSI were 0.836, 0.719, and 0.696, respectively. CONCLUSION Our study emphasizes MRI's role in predicting tumor characteristics such as histological subtype, grade, and LVSI based on updated FIGO criteria. By highlighting the potential of MRI, this research contributes to our comprehension of improving diagnostic and clinical management for EC. Further multicenter studies are warranted to validate these findings and establish MRI's role in EC management.
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Affiliation(s)
- Ahmet Akçay
- Department of Radiology, Faculty of Medicine, Bezmialem Vakif University, +34093, Istanbul, Turkey.
| | - Mehmet Ali Gültekin
- Department of Radiology, Faculty of Medicine, Bezmialem Vakif University, +34093, Istanbul, Turkey
| | - Fazılhan Altıntaş
- Department of Radiology, Faculty of Medicine, Bezmialem Vakif University, +34093, Istanbul, Turkey
| | - Abdusselim Adil Peker
- Department of Radiology, Faculty of Medicine, Bezmialem Vakif University, +34093, Istanbul, Turkey
| | - Serdar Balsak
- Department of Radiology, Faculty of Medicine, Bezmialem Vakif University, +34093, Istanbul, Turkey
| | - Bahar Atasoy
- Department of Radiology, Faculty of Medicine, Bezmialem Vakif University, +34093, Istanbul, Turkey
| | - Özlem Toluk
- Department of Biostatistics, Faculty of Medicine, Bezmialem Vakif University, Istanbul, Turkey
| | - Hüseyin Toprak
- Department of Radiology, Faculty of Medicine, Bezmialem Vakif University, +34093, Istanbul, Turkey
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Li HJ, Cao K, Li XT, Zhu HT, Zhao B, Gao M, Song X, Sun YS. A comparative study of mono-exponential and advanced diffusion-weighted imaging in differentiating stage IA endometrial carcinoma from benign endometrial lesions. J Cancer Res Clin Oncol 2024; 150:141. [PMID: 38504026 PMCID: PMC10951008 DOI: 10.1007/s00432-024-05668-8] [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: 11/07/2023] [Accepted: 02/25/2024] [Indexed: 03/21/2024]
Abstract
PURPOSE The purpose of the current investigation is to compare the efficacy of different diffusion models and diffusion kurtosis imaging (DKI) in differentiating stage IA endometrial carcinoma (IAEC) from benign endometrial lesions (BELs). METHODS Patients with IAEC, endometrial hyperplasia (EH), or a thickened endometrium confirmed between May 2016 and August 2022 were retrospectively enrolled. All of the patients underwent a preoperative pelvic magnetic resonance imaging (MRI) examination. The apparent diffusion coefficient (ADC) from the mono-exponential model, pure diffusion coefficient (D), pseudo-diffusion coefficient (D*), perfusion fraction (f) from the bi-exponential model, distributed diffusion coefficient (DDC), water molecular diffusion heterogeneity index from the stretched-exponential model, diffusion coefficient (Dk) and diffusion kurtosis (K) from the DKI model were calculated. Receiver operating characteristic (ROC) analysis was used to evaluate the diagnostic efficiency. RESULTS A total of 90 patients with IAEC and 91 patients with BELs were enrolled. The values of ADC, D, DDC and Dk were significantly lower and D* and K were significantly higher in cases of IAEC (p < 0.05). Multivariate analysis showed that K was the only predictor. The area under the ROC curve of K was 0.864, significantly higher compared with the ADC (0.601), D (0.811), D* (0.638), DDC (0.743) and Dk (0.675). The sensitivity, specificity and accuracy of K were 78.89%, 85.71% and 80.66%, respectively. CONCLUSION Advanced diffusion-weighted imaging models have good performance for differentiating IAEC from EH and endometrial thickening. Among all of the diffusion parameters, K showed the best performance and was the only independent predictor. Diffusion kurtosis imaging was defined as the most valuable model in the current context.
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Affiliation(s)
- Hai-Jiao Li
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiology, Peking University Cancer Hospital and Institute, No. 52 Fu Cheng Road, Hai Dian District, Beijing, 100142, China
| | - Kun Cao
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiology, Peking University Cancer Hospital and Institute, No. 52 Fu Cheng Road, Hai Dian District, Beijing, 100142, China
| | - Xiao-Ting Li
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiology, Peking University Cancer Hospital and Institute, No. 52 Fu Cheng Road, Hai Dian District, Beijing, 100142, China
| | - Hai-Tao Zhu
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiology, Peking University Cancer Hospital and Institute, No. 52 Fu Cheng Road, Hai Dian District, Beijing, 100142, China
| | - Bo Zhao
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiology, Peking University Cancer Hospital and Institute, No. 52 Fu Cheng Road, Hai Dian District, Beijing, 100142, China
| | - Min Gao
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Gynecological Oncology, Peking University Cancer Hospital and Institute, No. 52 Fu Cheng Road, Hai Dian District, Beijing, 100142, China
| | - Xiang Song
- Siemens Healthineers Digital Technology (Shanghai) Co., Ltd, Customer Services CRM, No.7 Wangjing Zhonghuan Nanlu, Beijing, 100102, China
| | - Ying-Shi Sun
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiology, Peking University Cancer Hospital and Institute, No. 52 Fu Cheng Road, Hai Dian District, Beijing, 100142, China.
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Ma X, Xu L, Ma F, Zhang J, Zhang G, Qiang J. Whole-tumor apparent diffusion coefficient histogram analysis for preoperative risk stratification in endometrial endometrioid adenocarcinoma. Int J Gynaecol Obstet 2024; 164:1174-1183. [PMID: 37925611 DOI: 10.1002/ijgo.15226] [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: 07/07/2023] [Revised: 10/15/2023] [Accepted: 10/17/2023] [Indexed: 11/06/2023]
Abstract
OBJECTIVE To investigate the application of whole-tumor apparent diffusion coefficient (ADC) histogram metrics for preoperative risk stratification in endometrial endometrioid adenocarcinoma (EEA). METHODS Preoperative MRI of 502 EEA patients were retrospectively analyzed. Whole tumor ADC histogram analysis was performed with regions of interest drawn on all tumor slices of diffusion-weighted imaging scans. Risk stratification was based on ESMO-ESTRO-ESP guidelines: low-, intermediate-, high-intermediate-, and high-risk. Univariable analysis was used to compare ADC histogram metrics (tumor volume, minADC, maxADC, and meanADC; 10th, 25th, 50th, 75th, and 90th percentiles of ADC [recorded as P10, P25, P50, P75, and P90 ADC, respectively]; skewness; and kurtosis) between different risk EEAs, and multivariable logistic regression analysis to determine the optimal metric or combined model for risk stratifications. Receiver operating characteristic curve analysis with the area under the curve (AUC) was used for diagnostic performance evaluation. RESULTS A decreasing tendency in multiple ADC values was observed from the low- to high-intermediate-risk EEAs. The (low + intermediate)-risk EEAs and low-risk EEAs had significantly smaller tumor volumes and higher minADCs, meanADCs, P10, P25, P50, P75, and P90 ADCs than the (high-intermediate + high)-risk EEAs and non-low-risk EEAs (all P < 0.05), respectively. The combined models of the (meanADC + volume) and the (P75 ADC + volume) yielded the largest AUCs of 0.775 and 0.780 in identifying the (low + intermediate)- and the low-risk EEAs from the other EEAs, respectively. CONCLUSION Whole-tumor ADC histogram metrics might be helpful for preoperatively identifying low- and (low + intermediate)-risk EEAs, facilitating personalized therapeutic planning.
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Affiliation(s)
- Xiaoliang Ma
- Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai, People's Republic of China
- Department of Radiology, Jinshan Hospital, Fudan University, Shanghai, People's Republic of China
| | - Limin Xu
- Department of Ultrasound, Lishui People's Hospital, Zhejiang Province, Lishui, People's Republic of China
| | - Fenghua Ma
- Department of Radiology, Obstetrics and Gynecology Hospital, Fudan University, Shanghai, People's Republic of China
| | - Jialiang Zhang
- Department of Radiology, Jinshan Hospital, Fudan University, Shanghai, People's Republic of China
| | - Guofu Zhang
- Department of Radiology, Obstetrics and Gynecology Hospital, Fudan University, Shanghai, People's Republic of China
| | - Jinwei Qiang
- Department of Radiology, Jinshan Hospital, Fudan University, Shanghai, People's Republic of China
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Ma C, Zhao Y, Song Q, Meng X, Xu Q, Tian S, Chen L, Wang N, Song Q, Lin L, Wang J, Liu A. Multi-parametric MRI-based radiomics for preoperative prediction of multiple biological characteristics in endometrial cancer. Front Oncol 2023; 13:1280022. [PMID: 38188296 PMCID: PMC10768555 DOI: 10.3389/fonc.2023.1280022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2023] [Accepted: 11/15/2023] [Indexed: 01/09/2024] Open
Abstract
Purpose To develop and validate multi-parametric MRI (MP-MRI)-based radiomics models for the prediction of biological characteristics in endometrial cancer (EC). Methods A total of 292 patients with EC were divided into LVSI (n = 208), DMI (n = 292), MSI (n = 95), and Her-2 (n = 198) subsets. Total 2316 radiomics features were extracted from MP-MRI (T2WI, DWI, and ADC) images, and clinical factors (age, FIGO stage, differentiation degree, pathological type, menopausal state, and irregular vaginal bleeding) were included. Intra-class correlation coefficient (ICC), spearman's rank correlation test, univariate logistic regression, and least absolute shrinkage and selection operator (LASSO) were used to select radiomics features; univariate and multivariate logistic regression were used to identify clinical independent risk factors. Five classifiers were applied (logistic regression, random forest, decision tree, K-nearest neighbor, and Bayes) to construct radiomics models for predicting biological characteristics. The clinical model was built based on the clinical independent risk factors. The combined model incorporating the radiomics score (radscore) and the clinical independent risk factors was constructed. The model was evaluated by ROC curve, calibration curve (H-L test), and decision curve analysis (DCA). Results In the training cohort, the RF radiomics model performed best among the five classifiers for the three subsets (MSI, LVSI, and DMI) according to AUC values (AUCMSI: 0.844; AUCLVSI: 0.952; AUCDMI: 0.840) except for Her-2 subset (Decision tree: AUC=0.714), and the combined model had higher AUC than the clinical model in each subset (MSI: AUCcombined =0.907, AUCclinical =0.755; LVSI: AUCcombined =0.959, AUCclinical =0.835; DMI: AUCcombined = 0.883, AUCclinical =0.796; Her-2: AUCcombined =0.812, AUCclinical =0.717; all P<0.05). Nevertheless, in the validation cohort, significant differences between the two models (combined vs. clinical model) were found only in the DMI and LVSI subsets (DMI: AUCcombined =0.803, AUCclinical =0.698; LVSI: AUCcombined =0.926, AUCclinical =0.796; all P<0.05). Conclusion The radiomics analysis based on MP-MRI and clinical independent risk factors can potentially predict multiple biological features of EC, including DMI, LVSI, MSI, and Her-2, and provide valuable guidance for clinical decision-making.
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Affiliation(s)
- Changjun Ma
- Department of Radiology, First Affiliated Hospital, Dalian Medical University, Dalian, China
- Medical Imaging Articial Intelligence Engineering Technology Research Center, Dalian, China
| | - Ying Zhao
- Department of Radiology, First Affiliated Hospital, Dalian Medical University, Dalian, China
- Medical Imaging Articial Intelligence Engineering Technology Research Center, Dalian, China
| | - Qingling Song
- Department of Radiology, First Affiliated Hospital, Dalian Medical University, Dalian, China
- Medical Imaging Articial Intelligence Engineering Technology Research Center, Dalian, China
| | - Xing Meng
- Dalian Women and Children’s Medical Group, Dalian, China
| | - Qihao Xu
- Department of Radiology, First Affiliated Hospital, Dalian Medical University, Dalian, China
- Medical Imaging Articial Intelligence Engineering Technology Research Center, Dalian, China
| | - Shifeng Tian
- Department of Radiology, First Affiliated Hospital, Dalian Medical University, Dalian, China
- Medical Imaging Articial Intelligence Engineering Technology Research Center, Dalian, China
| | - Lihua Chen
- Department of Radiology, First Affiliated Hospital, Dalian Medical University, Dalian, China
- Medical Imaging Articial Intelligence Engineering Technology Research Center, Dalian, China
| | - Nan Wang
- Department of Radiology, First Affiliated Hospital, Dalian Medical University, Dalian, China
- Medical Imaging Articial Intelligence Engineering Technology Research Center, Dalian, China
| | - Qingwei Song
- Department of Radiology, First Affiliated Hospital, Dalian Medical University, Dalian, China
- Medical Imaging Articial Intelligence Engineering Technology Research Center, Dalian, China
| | - Liangjie Lin
- Clinical & Technical Support, Philips Healthcare, Beijing, China
| | - Jiazheng Wang
- Clinical & Technical Support, Philips Healthcare, Beijing, China
| | - Ailian Liu
- Department of Radiology, First Affiliated Hospital, Dalian Medical University, Dalian, China
- Medical Imaging Articial Intelligence Engineering Technology Research Center, Dalian, China
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Li X, Tian S, Ma C, Chen L, Qin J, Wang N, Lin L, Liu A. Multimodal MRI for Estimating Her-2 Gene Expression in Endometrial Cancer. Bioengineering (Basel) 2023; 10:1399. [PMID: 38135990 PMCID: PMC10740753 DOI: 10.3390/bioengineering10121399] [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: 10/16/2023] [Revised: 11/15/2023] [Accepted: 11/27/2023] [Indexed: 12/24/2023] Open
Abstract
PURPOSE To assess the value of multimodal MRI, including amide proton transfer-weighted imaging (APT), diffusion kurtosis imaging (DKI), and T2 mapping sequences for estimating human epidermal growth factor receptor-2 (Her-2) expression in patients with endometrial cancer (EC). METHODS A total of 54 patients with EC who underwent multimodal pelvic MRI followed by biopsy were retrospectively selected and divided into the Her-2 positive (n = 24) and Her-2 negative (n = 30) groups. Her-2 expression was confirmed by immunohistochemistry (IHC). Two observers measured APT, mean kurtosis (MK), mean diffusivity (MD), and T2 values for EC lesions. RESULTS The Her-2 (+) group showed higher APT values and lower MD and T2 values than the Her-2 (-) group (all p < 0.05); there was no significant difference in MK values (p > 0.05). The area under the receiver operating characteristic curve (AUC) of APT, MD, T2, APT + T2, APT + MD, T2 + MD, and APT + MD + T2 models to identify the two groups of cases were 0.824, 0.695, 0.721, 0.824, 0.858, 0.782, and 0.860, respectively, and the diagnostic efficacy after combined APT + MD + T2 value was significantly higher than those of MD and T2 values individually (p = 0.018, 0.028); the diagnostic efficacy of the combination of APT + T2 values was significantly higher than that of T2 values separately (p = 0.028). Weak negative correlations were observed between APT and T2 values (r = -0.365, p = 0.007), moderate negative correlations between APT and MD values (r = -0.560, p < 0.001), and weak positive correlations between MD and T2 values (r = 0.336, p = 0.013). The APT values were independent predictors for assessing Her-2 expression in EC patients. CONCLUSION The APT, DKI, and T2 mapping sequences can be used to preoperatively assess the Her-2 expression in EC, which can contribute to more precise treatment for clinical preoperative.
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Affiliation(s)
- Xiwei Li
- Department of Radiology, The First Affiliated Hospital of Dalian Medical University, Dalian 116011, China; (X.L.)
| | - Shifeng Tian
- Department of Radiology, The First Affiliated Hospital of Dalian Medical University, Dalian 116011, China; (X.L.)
| | - Changjun Ma
- Department of Radiology, The First Affiliated Hospital of Dalian Medical University, Dalian 116011, China; (X.L.)
| | - Lihua Chen
- Department of Radiology, The First Affiliated Hospital of Dalian Medical University, Dalian 116011, China; (X.L.)
| | - Jingwen Qin
- Department of Pathology, The First Affiliated Hospital of Dalian Medical University, Dalian 116011, China
| | - Nan Wang
- Department of Radiology, The First Affiliated Hospital of Dalian Medical University, Dalian 116011, China; (X.L.)
| | - Liangjie Lin
- Clinical and Technical Support, Philips Healthcare, Beijing 100016, China
| | - Ailian Liu
- Department of Radiology, The First Affiliated Hospital of Dalian Medical University, Dalian 116011, China; (X.L.)
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Shifeng T, Yue W, Wen Z, Lihua C, Nan W, Liangjie L, Ailian L. The value of multimodal functional magnetic resonance imaging in differentiating p53abn from p53wt endometrial carcinoma. Acta Radiol 2023; 64:2948-2956. [PMID: 37661630 DOI: 10.1177/02841851231198911] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/05/2023]
Abstract
BACKGROUND Endometrial carcinoma (EC) is the sixth most common cancer in women. P53 gene expression in patients with endometrial cancer can predict the efficacy and prognosis of patients with neoadjuvant therapy. PURPOSE To explore the value of multimodal magnetic resonance imaging (MRI) in differentiating p53 abnormal (p53abn) from p53 wild-type (p53wt) EC. MATERIAL AND METHODS Data from 47 EC patients, including 14 p53abn cases and 33 p53wt cases, were retrospectively analyzed. The preoperative MRI sequences included amide proton transfer weighted (APTw) imaging, T2 mapping, mDIXON-Quant imaging and diffusion-weighted imaging (DWI). After post-processing, APT, T2, transverse relaxation rate (R2*), fat fraction (FF) and apparent diffusion coefficient (ADC) maps were obtained. The APT, T2, R2*, FF and ADC values for lesions of the two groups of cases were measured by two observers who were blind to the pathological data. RESULTS The APT value and R2* value in the p53abn group were higher than those in the p53wt group, while the ADC value was lower (all P < 0.05). There was no statistically significant difference in T2 value and FF value between the two groups (all P > 0.05). The area under curve of APT, R2*, ADC and combined APT + R2*+ADC values for identification of p53abn and p53wt EC were 0.739, 0.689, 0.718 and 0.820, respectively (all P > 0.05). CONCLUSION APTw, mDIXON-Quant and DWI techniques can be usedfor quantitative identification of p53abn and p53wt EC. The multimodal MRI provides a new way for preoperative quantitative evaluation of EC molecular typing, which has certain clinical application value.
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Affiliation(s)
- Tian Shifeng
- Department of Radiology, the First Affiliated Hospital of Dalian Medical University, Dalian, China
| | - Wang Yue
- Department of Radiology, the First Affiliated Hospital of Dalian Medical University, Dalian, China
| | - Zhu Wen
- Department of Pathology, the First Affiliated Hospital of Dalian Medical University, Dalian, China
| | - Chen Lihua
- Department of Radiology, the First Affiliated Hospital of Dalian Medical University, Dalian, China
| | - Wang Nan
- Department of Radiology, the First Affiliated Hospital of Dalian Medical University, Dalian, China
| | - Lin Liangjie
- Philips (China) Investment Co., Ltd, Dalian, China
| | - Liu Ailian
- Department of Radiology, the First Affiliated Hospital of Dalian Medical University, Dalian, China
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