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Himoto Y, Nishio M, Yamanoi K, Matsumoto YK. Reply to Letter to the Editor: Nodal infiltration in endometrial cancer: a prediction model using best subset regression. Eur Radiol 2024:10.1007/s00330-024-10861-x. [PMID: 38922450 DOI: 10.1007/s00330-024-10861-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2024] [Revised: 03/18/2024] [Accepted: 03/22/2024] [Indexed: 06/27/2024]
Affiliation(s)
- Yuki Himoto
- Department of Diagnostic Imaging and Nuclear Medicine, Kyoto University Graduate School of Medicine, Kyoto, Japan.
| | - Mizuho Nishio
- Department of Radiology, Kobe University Graduate School of Medicine, Kobe, Japan
| | - Koji Yamanoi
- Department of Gynecology and Obstetrics, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Yuka Kuriyama Matsumoto
- Department of Diagnostic Imaging and Nuclear Medicine, Kyoto University Graduate School of Medicine, Kyoto, Japan
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Ma X, Cai S, Lu J, Rao S, Zhou J, Zeng M, Pan X. The Added Value of ADC-based Nomogram in Assessing the Depth of Myometrial Invasion of Endometrial Endometrioid Adenocarcinoma. Acad Radiol 2024; 31:2324-2333. [PMID: 38016822 DOI: 10.1016/j.acra.2023.11.016] [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: 08/24/2023] [Revised: 10/28/2023] [Accepted: 11/08/2023] [Indexed: 11/30/2023]
Abstract
RATIONALE AND OBJECTIVES To explore the potential value of the apparent diffusion coefficient (ADC)-based nomogram models in preoperatively assessing the depth of myometrial invasion of endometrial endometrioid adenocarcinoma (EEA). MATERIALS AND METHODS Preoperative magnetic resonance imaging (MRI) of 210 EEA patients were retrospectively analyzed. ADC histogram metrics derive from the whole-tumor regions of interest. Univariate and multivariate analyses were used to screen the ADC histogram metrics and clinical characteristics for nomogram model building. The diagnostic sensitivity, specificity, accuracy, positive predictive value (PPV), and negative predictive value (NPV) of two radiologists without and with the assistance of models were calculated and compared. RESULTS Two nomogram models were developed for predicting no myometrial invasion (NMI) and deep myometrial invasion (DMI) with area under the curves of 0.85 and 0.82, respectively. With the assistance of models, the overall accuracies were significantly improved [radiologist_1, 73.3% vs 86.2% (p = 0.001); radiologist_2, 80.0% vs 91.0% (p = 0.002)]. In determining NMI, the sensitivity and PPV were greatly improved but not significant for radiologist_1 (51.9% vs 77.8% and 46.7% vs 75.0%, p = 0.229 and 0.511), and under/near the significance level for radiologist_2 (59.3% vs 88.9% and 57.1% vs 82.8%, p = 0.041 and 0.065), while the specificity, accuracy, and NPV were significantly improved (all p < 0.001). In determining DMI, all sensitivity, specificity, accuracy, PPV, and NPV were significantly improved (all p < 0.001). CONCLUSION The ADC-based nomogram models can improve the diagnostic performance of radiologist in preoperatively assessing the depth of myometrial invasion and facilitate optimizing clinical individualized treatment decisions.
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Affiliation(s)
- Xiaoliang Ma
- Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai, People's Republic of China (X.M., S.C., J.L., S.R., J.Z., MZ.)
| | - Songqi Cai
- Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai, People's Republic of China (X.M., S.C., J.L., S.R., J.Z., MZ.)
| | - Jingjing Lu
- Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai, People's Republic of China (X.M., S.C., J.L., S.R., J.Z., MZ.)
| | - Shengxiang Rao
- Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai, People's Republic of China (X.M., S.C., J.L., S.R., J.Z., MZ.)
| | - Jianjun Zhou
- Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai, People's Republic of China (X.M., S.C., J.L., S.R., J.Z., MZ.)
| | - Mengsu Zeng
- Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai, People's Republic of China (X.M., S.C., J.L., S.R., J.Z., MZ.)
| | - Xiaoping Pan
- Department of Radiology, Lishui People's Hospital, Dazhong Road, Zhejiang, People's Republic of China (X.P.).
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Matsumoto YK, Himoto Y, Nishio M, Kikkawa N, Otani S, Ito K, Yamanoi K, Kato T, Fujimoto K, Kurata Y, Moribata Y, Yoshida H, Minamiguchi S, Mandai M, Kido A, Nakamoto Y. Nodal infiltration in endometrial cancer: a prediction model using best subset regression. Eur Radiol 2024; 34:3375-3384. [PMID: 37882835 DOI: 10.1007/s00330-023-10310-1] [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: 01/16/2023] [Revised: 07/17/2023] [Accepted: 08/17/2023] [Indexed: 10/27/2023]
Abstract
OBJECTIVES To build preoperative prediction models with and without MRI for regional lymph node metastasis (r-LNM, pelvic and/or para-aortic LNM (PENM/PANM)) and for PANM in endometrial cancer using established risk factors. METHODS In this retrospective two-center study, 364 patients with endometrial cancer were included: 253 in the model development and 111 in the external validation. For r-LNM and PANM, respectively, best subset regression with ten-time fivefold cross validation was conducted using ten established risk factors (4 clinical and 6 imaging factors). Models with the top 10 percentile of area under the curve (AUC) and with the fewest variables in the model development were subjected to the external validation (11 and 4 candidates, respectively, for r-LNM and PANM). Then, the models with the highest AUC were selected as the final models. Models without MRI findings were developed similarly, assuming the cases where MRI was not available. RESULTS The final r-LNM model consisted of pelvic lymph node (PEN) ≥ 6 mm, deep myometrial invasion (DMI) on MRI, CA125, para-aortic lymph node (PAN) ≥ 6 mm, and biopsy; PANM model consisted of DMI, PAN, PEN, and CA125 (in order of correlation coefficient β values). The AUCs were 0.85 (95%CI: 0.77-0.92) and 0.86 (0.75-0.94) for the external validation, respectively. The model without MRI for r-LNM and PANM showed AUC of 0.79 (0.68-0.89) and 0.87 (0.76-0.96), respectively. CONCLUSIONS The prediction models created by best subset regression with cross validation showed high diagnostic performance for predicting LNM in endometrial cancer, which may avoid unnecessary lymphadenectomies. CLINICAL RELEVANCE STATEMENT The prediction risks of lymph node metastasis (LNM) and para-aortic LNM can be easily obtained for all patients with endometrial cancer by inputting the conventional clinical information into our models. They help in the decision-making for optimal lymphadenectomy and personalized treatment. KEY POINTS •Diagnostic performance of lymph node metastases (LNM) in endometrial cancer is low based on size criteria and can be improved by combining with other clinical information. •The optimized logistic regression model for regional LNM consists of lymph node ≥ 6 mm, deep myometrial invasion, cancer antigen-125, and biopsy, showing high diagnostic performance. •Our model predicts the preoperative risk of LNM, which may avoid unnecessary lymphadenectomies.
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Affiliation(s)
- Yuka Kuriyama Matsumoto
- Department of Diagnostic Imaging and Nuclear Medicine, Kyoto University Graduate School of Medicine, 54 Kawaharacho, Shogoin, Sakyo-Ku, Kyoto, 606-8507, Japan
| | - Yuki Himoto
- Department of Diagnostic Imaging and Nuclear Medicine, Kyoto University Graduate School of Medicine, 54 Kawaharacho, Shogoin, Sakyo-Ku, Kyoto, 606-8507, Japan.
| | - Mizuho Nishio
- Department of Radiology, Kobe University Graduate School of Medicine, Kobe, Japan
| | - Nao Kikkawa
- Department of Diagnostic Radiology, National Cancer Center Hospital, Tokyo, Japan
| | - Satoshi Otani
- Department of Diagnostic Imaging and Nuclear Medicine, Kyoto University Graduate School of Medicine, 54 Kawaharacho, Shogoin, Sakyo-Ku, Kyoto, 606-8507, Japan
| | - Kimiteru Ito
- Department of Diagnostic Radiology, National Cancer Center Hospital, Tokyo, Japan
| | - Koji Yamanoi
- Department of Gynecology and Obstetrics, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Tomoyasu Kato
- Department of Gynecology, National Cancer Center Hospital, Tokyo, Japan
| | - Koji Fujimoto
- Department of Diagnostic Imaging and Nuclear Medicine, Kyoto University Graduate School of Medicine, 54 Kawaharacho, Shogoin, Sakyo-Ku, Kyoto, 606-8507, Japan
| | - Yasuhisa Kurata
- Department of Diagnostic Imaging and Nuclear Medicine, Kyoto University Graduate School of Medicine, 54 Kawaharacho, Shogoin, Sakyo-Ku, Kyoto, 606-8507, Japan
| | - Yusaku Moribata
- Department of Diagnostic Imaging and Nuclear Medicine, Kyoto University Graduate School of Medicine, 54 Kawaharacho, Shogoin, Sakyo-Ku, Kyoto, 606-8507, Japan
| | - Hiroshi Yoshida
- Department of Diagnostic Pathology, National Cancer Center Hospital, Tokyo, Japan
| | - Sachiko Minamiguchi
- Department of Diagnostic Pathology, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Masaki Mandai
- Department of Gynecology and Obstetrics, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Aki Kido
- Department of Diagnostic Imaging and Nuclear Medicine, Kyoto University Graduate School of Medicine, 54 Kawaharacho, Shogoin, Sakyo-Ku, Kyoto, 606-8507, Japan
| | - Yuji Nakamoto
- Department of Diagnostic Imaging and Nuclear Medicine, Kyoto University Graduate School of Medicine, 54 Kawaharacho, Shogoin, Sakyo-Ku, Kyoto, 606-8507, Japan
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Chen J, Wang X, Xu Q, Zhang W, Chen H, Gu H, Tang W, Tian Y, Wang Z. Development and external validation of a nomogram for predicting overall survival of patients with non-endometrioid endometrial cancer: A population-based analysis. Heliyon 2024; 10:e28864. [PMID: 38596036 PMCID: PMC11002679 DOI: 10.1016/j.heliyon.2024.e28864] [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: 11/15/2022] [Revised: 03/25/2024] [Accepted: 03/26/2024] [Indexed: 04/11/2024] Open
Abstract
Objectives The main objective of this study was to identify the key predictors and construct a nomogram that can be used to predict the overall survival of individuals with non-endometrioid endometrial cancer. Methods A total of 2686 non-endometrioid endometrial cancer patients confirmed between 1988 and 2018 were selected from the Surveillance, Epidemiology, and End Results database. They were divided into a training cohort and an internal validation cohort. Independent risk factors were chosen by Cox regression analyses. A predictive nomogram model for overall survival was constructed based on above factors. A Chinese cohort of 41 patients was collected to be an external validation cohort. Results Eight variables were estimated as independent predictors for overall survival. A nomogram was established using these factors. The C-index for predicting the overall survival of patients with non-endometrioid endometrial cancer from the nomogram was 0.734, 0.700, and 0.767 in training, internal, and external validation cohort, respectively. Calibration plots and decision curve analysis showed that the nomogram was valuable for further clinical application. Conclusion We constructed a nomogram which can be used as an effective tool to predict the 3- and 5-year overall survival of Non-endometrioid endometrial cancer patients.
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Affiliation(s)
- Jingya Chen
- Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, China
| | | | - Qinfeng Xu
- Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, China
| | - Wei Zhang
- Affiliated Hospital of Integrated Traditional Chinese and Western Medicine, Nanjing University of Chinese Medicine, Nanjing, China
| | - Hu Chen
- Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, China
| | - Hailei Gu
- Women's Hospital of Nanjing Medical University, Nanjing Maternity and Child Health Care Hospital, Nanjing, China
| | - Wenwei Tang
- Women's Hospital of Nanjing Medical University, Nanjing Maternity and Child Health Care Hospital, Nanjing, China
| | - Ying Tian
- Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, China
| | - Zhongqiu Wang
- Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, China
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Fang R, Lin N, Weng S, Liu K, Chen X, Cao D. Multiparametric MRI radiomics improves preoperative diagnostic performance for local staging in patients with endometrial cancer. Abdom Radiol (NY) 2024; 49:875-887. [PMID: 38189937 DOI: 10.1007/s00261-023-04149-9] [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: 08/01/2023] [Revised: 11/28/2023] [Accepted: 11/29/2023] [Indexed: 01/09/2024]
Abstract
PURPOSE To determine whether multiparametric magnetic resonance imaging (MRI) radiomics-based machine learning methods can improve preoperative local staging in patients with endometrial cancer (EC). METHODS Data of patients with histologically confirmed EC who underwent preoperative MRI were retrospectively analyzed and divided into a training or test set. Radiomic features extracted from multiparametric MR images were used to train and test the prediction of deep myometrial invasion (DMI) and cervical stromal invasion (CSI). Two radiologists assessed the presence of DMI and CSI on conventional MR images. A combined model incorporating a radiomic signature and conventional MR images was constructed and presented as a nomogram. Performance of the predictive models was assessed using the area under curve (AUC) in the receiver operating curve analysis and pairwise comparison using DeLong's test with Bonferroni correction. RESULTS This study included 198 women (training set = 138, test set = 60). Conventional MRI achieved AUCs of 0.837 and 0.799 for detecting DMI and 0.825 and 0.858 for detecting CSI in the training and test sets, respectively. The nomogram achieved AUCs of 0.928 and 0.869 for detecting DMI and 0.913 and 0.937 for detecting CSI in the training and test sets, respectively. The ability of the nomogram to detect DMI and CSI in the two sets was superior to that of conventional MRI (adjusted p < 0.05), except for the ability to detect CSI in the test set (adjusted p > 0.05). CONCLUSION A nomogram incorporating radiomics signature into conventional MRI improved the efficacy of preoperative local staging of EC.
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Affiliation(s)
- Ruqi Fang
- Department of Radiology, The First Affiliated Hospital of Fujian Medical University, 20 Cha-Zhong Road, Fuzhou, 350005, Fujian, People's Republic of China
- Department of Radiology, Fujian Provincial Maternity and Children's Hospital, Fuzhou, 350001, Fujian, People's Republic of China
- Department of Radiology, Fujian Provincial Obstetrics and Gynecology Hospital, Fuzhou, 350011, Fujian, People's Republic of China
| | - Na Lin
- Department of Radiology, The First Affiliated Hospital of Fujian Medical University, 20 Cha-Zhong Road, Fuzhou, 350005, Fujian, People's Republic of China
| | - Shuping Weng
- Department of Radiology, Fujian Provincial Maternity and Children's Hospital, Fuzhou, 350001, Fujian, People's Republic of China
- Department of Radiology, Fujian Provincial Obstetrics and Gynecology Hospital, Fuzhou, 350011, Fujian, People's Republic of China
| | - Kaili Liu
- Department of Radiology, Fujian Provincial Maternity and Children's Hospital, Fuzhou, 350001, Fujian, People's Republic of China
- Department of Radiology, Fujian Provincial Obstetrics and Gynecology Hospital, Fuzhou, 350011, Fujian, People's Republic of China
| | - Xiaping Chen
- Department of Radiology, Fujian Provincial Maternity and Children's Hospital, Fuzhou, 350001, Fujian, People's Republic of China
- Department of Radiology, Fujian Provincial Obstetrics and Gynecology Hospital, Fuzhou, 350011, Fujian, People's Republic of China
| | - Dairong Cao
- Department of Radiology, The First Affiliated Hospital of Fujian Medical University, 20 Cha-Zhong Road, Fuzhou, 350005, Fujian, People's Republic of China.
- Department of Radiology, Fujian Key Laboratory of Precision Medicine for Cancer, The First Affiliated Hospital, Fujian Medical University, Fuzhou, 350005, Fujian, People's Republic of China.
- Department of Radiology, National Regional Medical Center, Binhai Campus of the First Affiliated Hospital, Fujian Medical University, Fuzhou, 350212, Fujian, People's Republic of China.
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Huang ML, Ren J, Jin ZY, Liu XY, Li Y, He YL, Xue HD. Application of magnetic resonance imaging radiomics in endometrial cancer: a systematic review and meta-analysis. LA RADIOLOGIA MEDICA 2024; 129:439-456. [PMID: 38349417 DOI: 10.1007/s11547-024-01765-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/18/2023] [Accepted: 01/03/2024] [Indexed: 03/16/2024]
Abstract
PURPOSE We aimed to systematically assess the methodological quality and clinical potential application of published magnetic resonance imaging (MRI)-based radiomics studies about endometrial cancer (EC). METHODS Studies of EC radiomics analyses published between 1 January 2000 and 19 March 2023 were extracted, and their methodological quality was evaluated using the radiomics quality score (RQS) and Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2). Pairwise correlation analyses and separate meta-analyses of studies exploring differential diagnoses and risk prediction were also performed. RESULTS Forty-five studies involving 3 aims were included. The mean RQS was 13.77 (range: 9-22.5); publication bias was observed in the areas of 'index test' and 'flow and timing'. A high RQS was significantly associated with therapy selection-aimed studies, low QUADAS-2 risk, recent publication year, and high-performance metrics. Raw data from 6 differential diagnosis and 34 risk prediction models were subjected to meta-analysis, revealing diagnostic odds ratios of 23.81 (95% confidence interval [CI] 8.48-66.83) and 18.23 (95% CI 13.68-24.29), respectively. CONCLUSION The methodological quality of radiomics studies involving patients with EC is unsatisfactory. However, MRI-based radiomics analyses showed promising utility in terms of differential diagnosis and risk prediction.
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Affiliation(s)
- Meng-Lin Huang
- Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shuai Fu Yuan 1#, Dongcheng Dist., Beijing, 100730, People's Republic of China
| | - Jing Ren
- Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shuai Fu Yuan 1#, Dongcheng Dist., Beijing, 100730, People's Republic of China
| | - Zheng-Yu Jin
- Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shuai Fu Yuan 1#, Dongcheng Dist., Beijing, 100730, People's Republic of China
| | - Xin-Yu Liu
- Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shuai Fu Yuan 1#, Dongcheng Dist., Beijing, 100730, People's Republic of China
| | - Yuan Li
- Department of Obstetrics and Gynecology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, National Clinical Research Center for Obstetric and Gynecologic Diseases, Shuai Fu Yuan 1#, Dongcheng Dist., Beijing, 100730, People's Republic of China.
| | - Yong-Lan He
- Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shuai Fu Yuan 1#, Dongcheng Dist., Beijing, 100730, People's Republic of China.
| | - Hua-Dan Xue
- Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shuai Fu Yuan 1#, Dongcheng Dist., Beijing, 100730, People's Republic of China.
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Leo E, Stanzione A, Miele M, Cuocolo R, Sica G, Scaglione M, Camera L, Maurea S, Mainenti PP. Artificial Intelligence and Radiomics for Endometrial Cancer MRI: Exploring the Whats, Whys and Hows. J Clin Med 2023; 13:226. [PMID: 38202233 PMCID: PMC10779496 DOI: 10.3390/jcm13010226] [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: 12/02/2023] [Revised: 12/23/2023] [Accepted: 12/23/2023] [Indexed: 01/12/2024] Open
Abstract
Endometrial cancer (EC) is intricately linked to obesity and diabetes, which are widespread risk factors. Medical imaging, especially magnetic resonance imaging (MRI), plays a major role in EC assessment, particularly for disease staging. However, the diagnostic performance of MRI exhibits variability in the detection of clinically relevant prognostic factors (e.g., deep myometrial invasion and metastatic lymph nodes assessment). To address these challenges and enhance the value of MRI, radiomics and artificial intelligence (AI) algorithms emerge as promising tools with a potential to impact EC risk assessment, treatment planning, and prognosis prediction. These advanced post-processing techniques allow us to quantitatively analyse medical images, providing novel insights into cancer characteristics beyond conventional qualitative image evaluation. However, despite the growing interest and research efforts, the integration of radiomics and AI to EC management is still far from clinical practice and represents a possible perspective rather than an actual reality. This review focuses on the state of radiomics and AI in EC MRI, emphasizing risk stratification and prognostic factor prediction, aiming to illuminate potential advancements and address existing challenges in the field.
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Affiliation(s)
- Elisabetta Leo
- Department of Advanced Biomedical Sciences, University of Naples “Federico II”, 80131 Naples, Italy
| | - Arnaldo Stanzione
- Department of Advanced Biomedical Sciences, University of Naples “Federico II”, 80131 Naples, Italy
| | - Mariaelena Miele
- Department of Advanced Biomedical Sciences, University of Naples “Federico II”, 80131 Naples, Italy
| | - Renato Cuocolo
- Department of Medicine, Surgery and Dentistry, University of Salerno, 84081 Baronissi, Italy
| | - Giacomo Sica
- Department of Radiology, Monaldi Hospital, Azienda Ospedaliera dei Colli, 80131 Naples, Italy
| | - Mariano Scaglione
- Department of Medicine, Surgery and Pharmacy, University of Sassari, 07100 Sassari, Italy
| | - Luigi Camera
- Department of Advanced Biomedical Sciences, University of Naples “Federico II”, 80131 Naples, Italy
| | - Simone Maurea
- Department of Advanced Biomedical Sciences, University of Naples “Federico II”, 80131 Naples, Italy
| | - Pier Paolo Mainenti
- Institute of Biostructures and Bioimaging of the National Council of Research (CNR), 80131 Naples, Italy
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Kido A, Himoto Y, Kurata Y, Minamiguchi S, Nakamoto Y. Preoperative Imaging Evaluation of Endometrial Cancer in FIGO 2023. J Magn Reson Imaging 2023. [PMID: 38146775 DOI: 10.1002/jmri.29161] [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: 06/14/2023] [Revised: 11/07/2023] [Accepted: 11/09/2023] [Indexed: 12/27/2023] Open
Abstract
The staging of endometrial cancer is based on the International Federation of Gynecology and Obstetrics (FIGO) staging system according to the examination of surgical specimens, and has revised in 2023, 14 years after its last revision in 2009. Molecular and histological classification has incorporated to new FIGO system reflecting the biological behavior and prognosis of endometrial cancer. Nonetheless, the basic role of imaging modalities including ultrasound, computed tomography (CT), magnetic resonance imaging (MRI), and positron emission tomography, as a preoperative assessment of the tumor extension and also the evaluation points in CT and MRI imaging are not changed, other than several point of local tumor extension. In the field of radiology, it has also undergone remarkable advancement through the rapid progress of computational technology. The application of deep learning reconstruction techniques contributes the benefits of shorter acquisition time or higher quality. Radiomics, which extract various quantitative features from the images, is also expected to have the potential for the quantitative prediction of risk factors such as histological types and lymphovascular space invasion, which is newly included in the new FIGO system. This article reviews the preoperative imaging diagnosis in new FIGO system and recent advances in imaging analysis and their clinical contributions in endometrial cancer. EVIDENCE LEVEL: 4 TECHNICAL EFFICACY: Stage 3.
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Affiliation(s)
- Aki Kido
- Department Radiology, Toyama University Hospital, Toyama, Japan
- Department of Diagnostic Imaging and Nuclear Medicine, Kyoto University Hospital, Kyoto, Japan
| | - Yuki Himoto
- Department of Diagnostic Imaging and Nuclear Medicine, Kyoto University Hospital, Kyoto, Japan
| | - Yasuhisa Kurata
- Department of Diagnostic Imaging and Nuclear Medicine, Kyoto University Hospital, Kyoto, Japan
| | | | - Yuji Nakamoto
- Department of Diagnostic Imaging and Nuclear Medicine, Kyoto University Hospital, Kyoto, Japan
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Ren Z, Chen B, Hong C, Yuan J, Deng J, Chen Y, Ye J, Li Y. The value of machine learning in preoperative identification of lymph node metastasis status in endometrial cancer: a systematic review and meta-analysis. Front Oncol 2023; 13:1289050. [PMID: 38173835 PMCID: PMC10761539 DOI: 10.3389/fonc.2023.1289050] [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: 09/05/2023] [Accepted: 12/06/2023] [Indexed: 01/05/2024] Open
Abstract
Background The early identification of lymph node metastasis status in endometrial cancer (EC) is a serious challenge in clinical practice. Some investigators have introduced machine learning into the early identification of lymph node metastasis in EC patients. However, the predictive value of machine learning is controversial due to the diversity of models and modeling variables. To this end, we carried out this systematic review and meta-analysis to systematically discuss the value of machine learning for the early identification of lymph node metastasis in EC patients. Methods A systematic search was conducted in Pubmed, Cochrane, Embase, and Web of Science until March 12, 2023. PROBAST was used to assess the risk of bias in the included studies. In the process of meta-analysis, subgroup analysis was performed according to modeling variables (clinical features, radiomic features, and radiomic features combined with clinical features) and different types of models in various variables. Results This systematic review included 50 primary studies with a total of 103,752 EC patients, 12,579 of whom had positive lymph node metastasis. Meta-analysis showed that among the machine learning models constructed by the three categories of modeling variables, the best model was constructed by combining radiomic features with clinical features, with a pooled c-index of 0.907 (95%CI: 0.886-0.928) in the training set and 0.823 (95%CI: 0.757-0.890) in the validation set, and good sensitivity and specificity. The c-index of the machine learning model constructed based on clinical features alone was not inferior to that based on radiomic features only. In addition, logistic regression was found to be the main modeling method and has ideal predictive performance with different categories of modeling variables. Conclusion Although the model based on radiomic features combined with clinical features has the best predictive efficiency, there is no recognized specification for the application of radiomics at present. In addition, the logistic regression constructed by clinical features shows good sensitivity and specificity. In this context, large-sample studies covering different races are warranted to develop predictive nomograms based on clinical features, which can be widely applied in clinical practice. Systematic review registration https://www.crd.york.ac.uk/PROSPERO, identifier CRD42023420774.
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Affiliation(s)
- Zhonglian Ren
- Department of Obstetrics and Gynecology, Chengdu Shuangliu Distract Maternal and Child Health Hospital, Chengdu, China
| | - Banghong Chen
- Data Science R&D Center of Yanchang Technology, Chengdu, China
| | - Changying Hong
- Department of Obstetrics and Gynecology, Chengdu Shuangliu Distract Maternal and Child Health Hospital, Chengdu, China
| | - Jiaying Yuan
- Department of Obstetrics and Gynecology, Chengdu Shuangliu Distract Maternal and Child Health Hospital, Chengdu, China
| | - Junying Deng
- Department of Obstetrics and Gynecology, Chengdu Shuangliu Distract Maternal and Child Health Hospital, Chengdu, China
| | - Yan Chen
- Department of Obstetrics and Gynecology, Chengdu Shuangliu Distract Maternal and Child Health Hospital, Chengdu, China
| | - Jionglin Ye
- Department of Obstetrics and Gynecology, Chengdu Shuangliu Distract Maternal and Child Health Hospital, Chengdu, China
| | - Yanqin Li
- Department of Obstetrics and Gynecology, Chengdu Shuangliu Distract Maternal and Child Health Hospital, Chengdu, China
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10
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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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Ding SX, Sun YF, Meng H, Wang JN, Xue LY, Gao BL, Yin XP. Radiomics model based on multi-sequence MRI for preoperative prediction of ki-67 expression levels in early endometrial cancer. Sci Rep 2023; 13:22052. [PMID: 38086918 PMCID: PMC10716186 DOI: 10.1038/s41598-023-49540-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2023] [Accepted: 12/09/2023] [Indexed: 12/18/2023] Open
Abstract
To validate a radiomics model based on multi-sequence magnetic resonance imaging (MRI) in predicting the ki-67 expression levels in early-stage endometrial cancer, 131 patients with early endometrial cancer who had undergone pathological examination and preoperative MRI scan were retrospectively enrolled and divided into two groups based on the ki-67 expression levels. The radiomics features were extracted from the T2 weighted imaging (T2WI), dynamic contrast enhanced T1 weighted imaging (DCE-T1WI), and apparent diffusion coefficient (ADC) map and screened using the Pearson correlation coefficients (PCC). A multi-layer perceptual machine and fivefold cross-validation were used to construct the radiomics model. The receiver operating characteristic (ROC) curves analysis, calibration curves, and decision curve analysis (DCA) were used to assess the models. The combined multi-sequence radiomics model of T2WI, DCE-T1WI, and ADC map showed better discriminatory powers than those using only one sequence. The combined radiomics models with multi-sequence fusions achieved the highest area under the ROC curve (AUC). The AUC value of the validation set was 0.852, with an accuracy of 0.827, sensitivity of 0.844, specificity of 0.773, and precision of 0.799. In conclusion, the combined multi-sequence MRI based radiomics model enables preoperative noninvasive prediction of the ki-67 expression levels in early endometrial cancer. This provides an objective imaging basis for clinical diagnosis and treatment.
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Affiliation(s)
- Si-Xuan Ding
- Department of Radiology, Affiliated Hospital of Hebei University, Hebei Key Laboratory of Precise Imaging of Inflammation Related Tumors, No. 212 Eastern Yuhua Road, Baoding City, 071000, Hebei Province, People's Republic of China
| | - Yu-Feng Sun
- College of Quality and Technical Supervision, Hebei University, No. 180, Wu Si East Road, Baoding City, 071000, Hebei Province, People's Republic of China
| | - Huan Meng
- Department of Radiology, Affiliated Hospital of Hebei University, Hebei Key Laboratory of Precise Imaging of Inflammation Related Tumors, No. 212 Eastern Yuhua Road, Baoding City, 071000, Hebei Province, People's Republic of China
| | - Jia-Ning Wang
- Department of Radiology, Affiliated Hospital of Hebei University, Hebei Key Laboratory of Precise Imaging of Inflammation Related Tumors, No. 212 Eastern Yuhua Road, Baoding City, 071000, Hebei Province, People's Republic of China
| | - Lin-Yan Xue
- College of Quality and Technical Supervision, Hebei University, No. 180, Wu Si East Road, Baoding City, 071000, Hebei Province, People's Republic of China.
| | - Bu-Lang Gao
- Department of Radiology, Affiliated Hospital of Hebei University, Hebei Key Laboratory of Precise Imaging of Inflammation Related Tumors, No. 212 Eastern Yuhua Road, Baoding City, 071000, Hebei Province, People's Republic of China
| | - Xiao-Ping Yin
- Department of Radiology, Affiliated Hospital of Hebei University, Hebei Key Laboratory of Precise Imaging of Inflammation Related Tumors, No. 212 Eastern Yuhua Road, Baoding City, 071000, Hebei Province, People's Republic of China.
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Petrila O, Stefan AE, Gafitanu D, Scripcariu V, Nistor I. The Applicability of Artificial Intelligence in Predicting the Depth of Myometrial Invasion on MRI Studies-A Systematic Review. Diagnostics (Basel) 2023; 13:2592. [PMID: 37568955 PMCID: PMC10416838 DOI: 10.3390/diagnostics13152592] [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: 06/28/2023] [Revised: 07/22/2023] [Accepted: 07/28/2023] [Indexed: 08/13/2023] Open
Abstract
(1) Objective: Artificial intelligence (AI) has become an important tool in medicine in diagnosis, prognosis, and treatment evaluation, and its role will increase over time, along with the improvement and validation of AI models. We evaluated the applicability of AI in predicting the depth of myometrial invasion in MRI studies in women with endometrial cancer. (2) Methods: A systematic search was conducted in PubMed, SCOPUS, Embase, and clinicaltrials.gov databases for research papers from inception to May 2023. As keywords, we used: "endometrial cancer artificial intelligence", "endometrial cancer AI", "endometrial cancer MRI artificial intelligence", "endometrial cancer machine learning", and "endometrial cancer machine learning MRI". We excluded studies that did not evaluate myometrial invasion. (3) Results: Of 1651 screened records, eight were eligible. The size of the dataset was between 50 and 530 participants among the studies. We evaluated the models by accuracy scores, area under the curve, and sensitivity/specificity. A quantitative analysis was not appropriate for this study due to the high heterogeneity among studies. (4) Conclusions: High accuracy, sensitivity, and specificity rates were obtained among studies using different AI systems. Overall, the existing studies suggest that they have the potential to improve the accuracy and efficiency of the myometrial invasion evaluation of MRI images in endometrial cancer patients.
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Affiliation(s)
- Octavia Petrila
- Faculty of Medicine, University of Medicine and Pharmacy “Grigore T. Popa”, 700115 Iasi, Romania; (O.P.); (D.G.); (V.S.); (I.N.)
- Department of Radiology, “Sfantul Spiridon” Hospital, 700111 Iasi, Romania
| | - Anca-Elena Stefan
- Faculty of Medicine, University of Medicine and Pharmacy “Grigore T. Popa”, 700115 Iasi, Romania; (O.P.); (D.G.); (V.S.); (I.N.)
- Department of Nephrology, “Dr. C.I. Parhon” Hospital, 700503 Iasi, Romania
| | - Dumitru Gafitanu
- Faculty of Medicine, University of Medicine and Pharmacy “Grigore T. Popa”, 700115 Iasi, Romania; (O.P.); (D.G.); (V.S.); (I.N.)
- Department of Obstetrics and Gynecology, “Elena Doamna” Hospital, 700398 Iasi, Romania
| | - Viorel Scripcariu
- Faculty of Medicine, University of Medicine and Pharmacy “Grigore T. Popa”, 700115 Iasi, Romania; (O.P.); (D.G.); (V.S.); (I.N.)
- Regional Institute of Oncology, 700483 Iasi, Romania
| | - Ionut Nistor
- Faculty of Medicine, University of Medicine and Pharmacy “Grigore T. Popa”, 700115 Iasi, Romania; (O.P.); (D.G.); (V.S.); (I.N.)
- Department of Nephrology, “Dr. C.I. Parhon” Hospital, 700503 Iasi, Romania
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Zheng T, Pan J, Du D, Liang X, Yi H, Du J, Wu S, Liu L, Shi G. Preoperative assessment of high-grade endometrial cancer using a radiomic signature and clinical indicators. Future Oncol 2023; 19:587-601. [PMID: 37097730 DOI: 10.2217/fon-2022-0631] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/26/2023] Open
Abstract
Aim: To develop and validate a radiomics-based combined model (ModelRC) to predict the pathological grade of endometrial cancer. Methods: A total of 403 endometrial cancer patients from two independent centers were enrolled as training, internal validation and external validation sets. Radiomic features were extracted from T2-weighted images, apparent diffusion coefficient map and contrast-enhanced 3D volumetric interpolated breath-hold examination images. Results: Compared with the clinical model and radiomics model, ModelRC showed superior performance; the areas under the receiver operating characteristic curves were 0.920 (95% CI: 0.864-0.962), 0.882 (95% CI: 0.779-0.955) and 0.881 (95% CI: 0.815-0.939) for the training, internal validation and external validation sets, respectively. Conclusion: ModelRC, which incorporated clinical and radiomic features, exhibited excellent performance in the prediction of high-grade endometrial cancer.
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Affiliation(s)
- Tao Zheng
- Department of Radiology, The Fourth Hospital of Hebei Medical University, Shijiazhuang, 050000, PR China
| | - Jiangyang Pan
- Department of Radiology, The Fourth Hospital of Hebei Medical University, Shijiazhuang, 050000, PR China
| | - Dan Du
- Department of Medical Imaging Center, The First Hospital of Qinhuangdao, Qinhuangdao, 066000, PR China
| | - Xin Liang
- Department of Medical Imaging Center, The First Hospital of Qinhuangdao, Qinhuangdao, 066000, PR China
| | - Huiling Yi
- Department of Medical Imaging Center, The First Hospital of Qinhuangdao, Qinhuangdao, 066000, PR China
| | - Juan Du
- Department of Medical Imaging Center, The First Hospital of Qinhuangdao, Qinhuangdao, 066000, PR China
| | - Shuo Wu
- Department of Medical Imaging Center, The First Hospital of Qinhuangdao, Qinhuangdao, 066000, PR China
| | - Lanxiang Liu
- Department of Medical Imaging Center, The First Hospital of Qinhuangdao, Qinhuangdao, 066000, PR China
| | - Gaofeng Shi
- Department of Radiology, The Fourth Hospital of Hebei Medical University, Shijiazhuang, 050000, PR China
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Role of Machine Learning (ML)-Based Classification Using Conventional 18F-FDG PET Parameters in Predicting Postsurgical Features of Endometrial Cancer Aggressiveness. Cancers (Basel) 2023; 15:cancers15010325. [PMID: 36612321 PMCID: PMC9818853 DOI: 10.3390/cancers15010325] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2022] [Revised: 12/23/2022] [Accepted: 12/26/2022] [Indexed: 01/05/2023] Open
Abstract
PURPOSE to investigate the preoperative role of ML-based classification using conventional 18F-FDG PET parameters and clinical data in predicting features of EC aggressiveness. METHODS retrospective study, including 123 EC patients who underwent 18F-FDG PET (2009-2021) for preoperative staging. Maximum standardized uptake value (SUVmax), SUVmean, metabolic tumour volume (MTV), and total lesion glycolysis (TLG) were computed on the primary tumour. Age and BMI were collected. Histotype, myometrial invasion (MI), risk group, lymph-nodal involvement (LN), and p53 expression were retrieved from histology. The population was split into a train and a validation set (80-20%). The train set was used to select relevant parameters (Mann-Whitney U test; ROC analysis) and implement ML models, while the validation set was used to test prediction abilities. RESULTS on the validation set, the best accuracies obtained with individual parameters and ML were: 61% (TLG) and 87% (ML) for MI; 71% (SUVmax) and 79% (ML) for risk groups; 72% (TLG) and 83% (ML) for LN; 45% (SUVmax; SUVmean) and 73% (ML) for p53 expression. CONCLUSIONS ML-based classification using conventional 18F-FDG PET parameters and clinical data demonstrated ability to characterize the investigated features of EC aggressiveness, providing a non-invasive way to support preoperative stratification of EC patients.
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Feng Y, Wang Z, Xiao M, Li J, Su Y, Delvoux B, Zhang Z, Dekker A, Xanthoulea S, Zhang Z, Traverso A, Romano A, Zhang Z, Liu C, Gao H, Wang S, Qian L. An Applicable Machine Learning Model Based on Preoperative Examinations Predicts Histology, Stage, and Grade for Endometrial Cancer. Front Oncol 2022; 12:904597. [PMID: 35712473 PMCID: PMC9196302 DOI: 10.3389/fonc.2022.904597] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2022] [Accepted: 05/02/2022] [Indexed: 11/23/2022] Open
Abstract
Purpose To build a machine learning model to predict histology (type I and type II), stage, and grade preoperatively for endometrial carcinoma to quickly give a diagnosis and assist in improving the accuracy of the diagnosis, which can help patients receive timely, appropriate, and effective treatment. Materials and Methods This study used a retrospective database of preoperative examinations (tumor markers, imaging, diagnostic curettage, etc.) in patients with endometrial carcinoma. Three algorithms (random forest, logistic regression, and deep neural network) were used to build models. The AUC and accuracy were calculated. Furthermore, the performance of machine learning models, doctors’ prediction, and doctors with the assistance of models were compared. Results A total of 329 patients were included in this study with 16 features (age, BMI, stage, grade, histology, etc.). A random forest algorithm had the highest AUC and Accuracy. For histology prediction, AUC and accuracy was 0.69 (95% CI=0.67-0.70) and 0.81 (95%CI=0.79-0.82). For stage they were 0.66 (95% CI=0.64-0.69) and 0.63 (95% CI=0.61-0.65) and for differentiation grade 0.64 (95% CI=0.63-0.65) and 0.43 (95% CI=0.41-0.44). The average accuracy of doctors for histology, stage, and grade was 0.86 (with AI) and 0.79 (without AI), 0.64 and 0.53, 0.5 and 0.45, respectively. The accuracy of doctors’ prediction with AI was higher than that of Random Forest alone and doctors’ prediction without AI. Conclusion A random forest model can predict histology, stage, and grade of endometrial cancer preoperatively and can help doctors in obtaining a better diagnosis and predictive results.
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Affiliation(s)
- Ying Feng
- Department of Ultrasound, Beijing Friendship Hospital, Capital Medical University, Beijing, China
| | - Zhixiang Wang
- Department of Radiation Oncology (Maastro), GROW School for Oncology and Reproduction, Maastricht University Medical Centre+, Maastricht, Netherlands
| | - Meizhu Xiao
- Department of Obstetrics and Gynecology, Beijing Chao-Yang Hospital, Capital Medical University, Beijing, China
| | - Jinfeng Li
- Department of Obstetrics and Gynecology, Beijing Chao-Yang Hospital, Capital Medical University, Beijing, China
| | - Yuan Su
- Department of Ultrasound, Beijing Friendship Hospital, Capital Medical University, Beijing, China
| | - Bert Delvoux
- Department of Obstetrics and Gynecology, GROW-School for Oncology and Developmental Biology, Maastricht University Medical Centre, Maastricht, Netherlands
| | - Zhen Zhang
- Department of Radiation Oncology (Maastro), GROW School for Oncology and Reproduction, Maastricht University Medical Centre+, Maastricht, Netherlands
| | - Andre Dekker
- Department of Radiation Oncology (Maastro), GROW School for Oncology and Reproduction, Maastricht University Medical Centre+, Maastricht, Netherlands
| | - Sofia Xanthoulea
- Department of Obstetrics and Gynecology, GROW-School for Oncology and Developmental Biology, Maastricht University Medical Centre, Maastricht, Netherlands
| | - Zhiqiang Zhang
- Department of Obstetrics and Gynecology, Beijing Chao-Yang Hospital, Capital Medical University, Beijing, China
| | - Alberto Traverso
- Department of Radiation Oncology (Maastro), GROW School for Oncology and Reproduction, Maastricht University Medical Centre+, Maastricht, Netherlands
| | - Andrea Romano
- Department of Obstetrics and Gynecology, GROW-School for Oncology and Developmental Biology, Maastricht University Medical Centre, Maastricht, Netherlands
| | - Zhenyu Zhang
- Department of Obstetrics and Gynecology, Beijing Chao-Yang Hospital, Capital Medical University, Beijing, China
| | - Chongdong Liu
- Department of Obstetrics and Gynecology, Beijing Chao-Yang Hospital, Capital Medical University, Beijing, China
| | - Huiqiao Gao
- Department of Obstetrics and Gynecology, Beijing Chao-Yang Hospital, Capital Medical University, Beijing, China
| | - Shuzhen Wang
- Department of Obstetrics and Gynecology, Beijing Chao-Yang Hospital, Capital Medical University, Beijing, China
| | - Linxue Qian
- Department of Ultrasound, Beijing Friendship Hospital, Capital Medical University, Beijing, China
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İnce O, Yıldız H, Kisbet T, Ertürk ŞM, Önder H. Classification of retinoblastoma-1 gene mutation with machine learning-based models in bladder cancer. Heliyon 2022; 8:e09311. [PMID: 35520623 PMCID: PMC9061624 DOI: 10.1016/j.heliyon.2022.e09311] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2021] [Revised: 01/19/2022] [Accepted: 04/20/2022] [Indexed: 11/19/2022] Open
Abstract
Purpose This study aims to evaluate the potential of machine learning algorithms built with radiomics features from computed tomography urography (CTU) images that classify RB1 gene mutation status in bladder cancer. Method The study enrolled CTU images of 18 patients with and 54 without RB1 mutation from a public database. Image and data preprocessing were performed after data augmentation. Feature selection steps were consisted of filter and wrapper methods. Pearson’s correlation analysis was the filter, and a wrapper-based sequential feature selection algorithm was the wrapper. Models with XGBoost, Random Forest (RF), and k-Nearest Neighbors (kNN) algorithms were developed. Performance metrics of the models were calculated. Models’ performances were compared by using Friedman’s test. Results 8 features were selected from 851 total extracted features. Accuracy, sensitivity, specificity, precision, recall, F1 measure and AUC were 84%, 80%, 88%, 86%, 80%, 0.83 and 0.84, for XGBoost; 72%, 80%, 65%, 67%, 80%, 0.73 and 0.72 for RF; 66%, 53%, 76%, 67%, 53%, 0.60 and 0.65 for kNN, respectively. XGBoost model had outperformed kNN model in Friedman’s test (p = 0.006). Conclusions Machine learning algorithms with radiomics features from CTU images show promising results in classifying bladder cancer by RB1 mutation status non-invasively.
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Affiliation(s)
- Okan İnce
- Health Sciences University Prof. Dr. Cemil Tascioglu City Hospital, Department of Radiology, Turkey
- Corresponding author.
| | - Hülya Yıldız
- Health Sciences University Prof. Dr. Cemil Tascioglu City Hospital, Department of Radiology, Turkey
| | - Tanju Kisbet
- Health Sciences University Prof. Dr. Cemil Tascioglu City Hospital, Department of Radiology, Turkey
| | - Şükrü Mehmet Ertürk
- Istanbul University Istanbul Medical Faculty, Department of Radiology, Turkey
| | - Hakan Önder
- Health Sciences University Prof. Dr. Cemil Tascioglu City Hospital, Department of Radiology, Turkey
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