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Liu J, Dong L, Zhang X, Wu Q, Yang Z, Zhang Y, Xu C, Wu Q, Wang M. Radiomics analysis for prediction of lymph node metastasis after neoadjuvant chemotherapy based on pretreatment MRI in patients with locally advanced cervical cancer. Front Oncol 2024; 14:1376640. [PMID: 38779088 PMCID: PMC11109452 DOI: 10.3389/fonc.2024.1376640] [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: 02/15/2024] [Accepted: 04/17/2024] [Indexed: 05/25/2024] Open
Abstract
Background This study aims to develop and validate a pretreatment MRI-based radiomics model to predict lymph node metastasis (LNM) following neoadjuvant chemotherapy (NACT) in patients with locally advanced cervical cancer (LACC). Methods Patients with LACC who underwent NACT from two centers between 2013 and 2022 were enrolled retrospectively. Based on the lymph node (LN) status determined in the pathology reports after radical hysterectomy, patients were categorized as LN positive or negative. The patients from center 1 were assigned as the training set while those from center 2 formed the validation set. Radiomics features were extracted from pretreatment sagittal T2-weighted imaging (Sag-T2WI), axial diffusion-weighted imaging (Ax-DWI), and the delayed phase of dynamic contrast-enhanced sagittal T1-weighted imaging (Sag-T1C) for each patient. The K-best and least absolute shrinkage and selection operator (LASSO) methods were employed to reduce dimensionality, and the radiomics features strongly associated with LNM were selected and used to construct three single-sequence models. Furthermore, clinical variables were incorporated through multivariate regression analysis and fused with the selected radiomics features to construct the clinical-radiomics combined model. The diagnostic performance of the models was assessed using receiver operating characteristic (ROC) curve analysis. The clinical utility of the models was evaluated by the area under the ROC curve (AUC) and decision curve analysis (DCA). Results A total of 282 patients were included, comprising 171 patients in the training set, and 111 patients in the validation set. Compared to the Sag-T2WI model (AUC, 95%CI, training set, 0.797, 0.722-0.782; validation set, 0.648, 0.521-0.776) and the Sag-T1C model (AUC, 95%CI, training set, 0.802, 0.723-0.882; validation set, 0.630, 0.505-0.756), the Ax-DWI model exhibited the highest diagnostic performance with AUCs of 0.855 (95%CI, 0.791-0.919) in training set, and 0.753 (95%CI, 0.638-0.867) in validation set, respectively. The combined model, integrating selected features from three sequences and FIGO stage, surpassed predictive ability compared to the single-sequence models, with AUC of 0.889 (95%CI, 0.833-0.945) and 0.859 (95%CI, 0.781-0.936) in the training and validation sets, respectively. Conclusions The pretreatment MRI-based radiomics model, integrating radiomics features from three sequences and clinical variables, exhibited superior performance in predicting LNM following NACT in patients with LACC.
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Affiliation(s)
- Jinjin Liu
- Department of Medical Imaging, People’s Hospital of Zhengzhou University (Henan Provincial People’s Hospital), Zhengzhou, Henan, China
| | - Linxiao Dong
- Department of Medical Imaging, People’s Hospital of Henan University (Henan Provincial People’s Hospital), Zhengzhou, Henan, China
| | - Xiaoxian Zhang
- Department of Radiology, the Affiliated Cancer Hospital of Zhengzhou University, Henan Cancer Hospital, Zhengzhou, Henan, China
| | - Qingxia Wu
- Beijing United Imaging Research Institute of Intelligent Imaging, United Imaging Intelligence Co., Ltd., Beijing, China
| | - Zihan Yang
- Department of Medical Imaging, People’s Hospital of Zhengzhou University (Henan Provincial People’s Hospital), Zhengzhou, Henan, China
| | - Yuejie Zhang
- Department of Medical Imaging, People’s Hospital of Henan University (Henan Provincial People’s Hospital), Zhengzhou, Henan, China
| | - Chunmiao Xu
- Department of Radiology, the Affiliated Cancer Hospital of Zhengzhou University, Henan Cancer Hospital, Zhengzhou, Henan, China
| | - Qingxia Wu
- Department of Medical Imaging, People’s Hospital of Zhengzhou University (Henan Provincial People’s Hospital), Zhengzhou, Henan, China
- Department of Medical Imaging, People’s Hospital of Henan University (Henan Provincial People’s Hospital), Zhengzhou, Henan, China
| | - Meiyun Wang
- Department of Medical Imaging, People’s Hospital of Zhengzhou University (Henan Provincial People’s Hospital), Zhengzhou, Henan, China
- Department of Medical Imaging, People’s Hospital of Henan University (Henan Provincial People’s Hospital), Zhengzhou, Henan, China
- Laboratory of Brain Science and Brain-Like Intelligence Technology, Institute for Integrated Medical Science and Engineering, Henan Academy of Science, Zhengzhou, Henan, China
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Wang H, Yu C, Ding H, Zhang L, Chen X, He L. Computed Tomography-Based Radiomics Signature for Predicting Segmental Chromosomal Aberrations at 1p36 and 11q23 in Pediatric Neuroblastoma. J Comput Assist Tomogr 2024; 48:472-479. [PMID: 38013242 DOI: 10.1097/rct.0000000000001564] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2023]
Abstract
OBJECTIVE This study aimed to develop and assess the precision of a radiomics signature based on computed tomography imaging for predicting segmental chromosomal aberrations (SCAs) status at 1p36 and 11q23 in neuroblastoma. METHODS Eighty-seven pediatric patients diagnosed with neuroblastoma and with confirmed genetic testing for SCAs status at 1p36 and 11q23 were enrolled and randomly stratified into a training set and a test set. Radiomics features were extracted from 3-phase computed tomography images and analyzed using various statistical methods. An optimal set of radiomics features was selected using a least absolute shrinkage and selection operator regression model to calculate the radiomics score for each patient. The radiomics signature was validated using receiver operating characteristic curves to obtain the area under the curve and 95% confidence interval (CI). RESULTS Eight radiomics features were carefully selected and used to compute the radiomics score, which demonstrated a statistically significant distinction between the SCAs and non-SCAs groups in both sets. The radiomics signature achieved an area under the curve of 0.869 (95% CI, 0.788-0.943) and 0.883 (95% CI, 0.753-0.978) in the training and test sets, respectively. The accuracy of the radiomics signature was 0.817 and 0.778 in the training and test sets, respectively. The Hosmer-Lemeshow test confirmed that the radiomics signature was well calibrated. CONCLUSIONS Computed tomography-based radiomics signature has the potential to predict SCAs at 1p36 and 11q23 in neuroblastoma.
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Affiliation(s)
- Haoru Wang
- From the Department of Radiology, Children's Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Pediatrics, Chongqing, China
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Monthatip K, Boonnag C, Muangmool T, Charoenkwan K. A machine learning-based prediction model of pelvic lymph node metastasis in women with early-stage cervical cancer. J Gynecol Oncol 2024; 35:e17. [PMID: 37921601 PMCID: PMC10948976 DOI: 10.3802/jgo.2024.35.e17] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2023] [Revised: 09/03/2023] [Accepted: 10/03/2023] [Indexed: 11/04/2023] Open
Abstract
OBJECTIVE To develop a novel machine learning-based preoperative prediction model for pelvic lymph node metastasis (PLNM) in early-stage cervical cancer by combining the clinical findings and preoperative computerized tomography (CT) of the whole abdomen and pelvis. METHODS Patients diagnosed with International Federation of Gynecology and Obstetrics stage IA2-IIA1 squamous cell carcinoma, adenocarcinoma, and adenosquamous carcinoma of the cervix who had primary radical surgery with bilateral pelvic lymphadenectomy from January 1, 2003 to December 31, 2020, were included. Seven supervised machine learning algorithms, including logistic regression, random forest, support vector machine, adaptive boosting, gradient boosting, extreme gradient boosting, and category boosting, were used to evaluate the risk of PLNM. RESULTS PLNM was found in 199 (23.9%) of 832 patients included. Younger age, larger tumor size, higher stage, no prior conization, tumor appearance, adenosquamous histology, and vaginal metastasis as well as the CT findings of larger tumor size, parametrial metastasis, pelvic lymph node enlargement, and vaginal metastasis, were significantly associated with PLNM. The models' predictive performance, including accuracy (89.1%-90.6%), area under the receiver operating characteristics curve (86.9%-91.0%), sensitivity (77.4%-82.4%), specificity (92.1%-94.3%), positive predictive value (77.0%-81.7%), and negative predictive value (93.0%-94.4%), appeared satisfactory and comparable among all the algorithms. After optimizing the model's decision threshold to enhance the sensitivity to at least 95%, the 'highly sensitive' model was obtained with a 2.5%-4.4% false-negative rate of PLNM prediction. CONCLUSION We developed prediction models for PLNM in early-stage cervical cancer with promising prediction performance in our setting. Further external validation in other populations is needed with potential clinical applications.
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Affiliation(s)
- Kamonrat Monthatip
- Department of Obstetrics and Gynecology, Faculty of Medicine, Chiang Mai University, Chiang Mai, Thailand
| | - Chiraphat Boonnag
- Biomedical Informatics Center, Faculty of Medicine, Chiang Mai University, Chiang Mai, Thailand
| | - Tanarat Muangmool
- Department of Obstetrics and Gynecology, Faculty of Medicine, Chiang Mai University, Chiang Mai, Thailand
| | - Kittipat Charoenkwan
- Department of Obstetrics and Gynecology, Faculty of Medicine, Chiang Mai University, Chiang Mai, Thailand.
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Liu S, Zhang A, Xiong J, Su X, Zhou Y, Li Y, Zhang Z, Li Z, Liu F. The application of radiomics machine learning models based on multimodal MRI with different sequence combinations in predicting cervical lymph node metastasis in oral tongue squamous cell carcinoma patients. Head Neck 2024; 46:513-527. [PMID: 38108536 DOI: 10.1002/hed.27605] [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: 08/21/2023] [Revised: 11/15/2023] [Accepted: 12/06/2023] [Indexed: 12/19/2023] Open
Abstract
BACKGROUND The purpose of this study was to explore preliminary the performance of radiomics machine learning models based on multimodal MRI to predict the risk of cervical lymph node metastasis (CLNM) for oral tongue squamous cell carcinoma (OTSCC) patients. METHODS A total of 400 patients were enrolled in this study and divided into six groups according to the different combinations of MRI sequences. Group I consisted of patients with T1-weighted images (T1WI) and FS-T2WI (fat-suppressed T2-weighted images), group II consisted of patients with T1WI, FS-T2WI, and contrast enhanced MRI (CE-MRI), group III consisted of patients with T1WI, FS-T2WI, and T2-weighted images (T2WI), group IV consisted of patients with T1WI, FS-T2WI, CE-MRI, and T2WI, group V consisted of patients with T1WI, FS-T2WI, T2WI, and apparent diffusion coefficient map (ADC), and group VI consisted of patients with T1WI, FS-T2WI, CE-MRI, T2WI, and ADC. Machine learning models were constructed. The performance of the models was compared in each group. RESULTS The machine learning model in group IV including T1WI, FS-T2WI, T2WI, and CE-MRI presented best prediction performance, with AUCs of 0.881 and 0.868 in the two sets. The models with CE-MRI performed better than the models without CE-MRI(I vs. II, III vs. IV, V vs. VI). CONCLUSIONS The radiomics machine learning models based on CE-MRI showed great accuracy and stability in predicting the risk of CLNM for OTSCC patients.
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Affiliation(s)
- Sheng Liu
- Department of Oromaxillofacial-Head and Neck Surgery, Department of Oral and Maxillofacial Surgery, School and Hospital of Stomatology, China Medical University, Liaoning Provincial Key Laboratory of Oral Diseases, Shenyang, China
| | - Aihua Zhang
- Department of Oromaxillofacial-Head and Neck Surgery, Department of Oral and Maxillofacial Surgery, School and Hospital of Stomatology, China Medical University, Liaoning Provincial Key Laboratory of Oral Diseases, Shenyang, China
| | - Jianjun Xiong
- Department of Oromaxillofacial-Head and Neck Surgery, Department of Oral and Maxillofacial Surgery, School and Hospital of Stomatology, China Medical University, Liaoning Provincial Key Laboratory of Oral Diseases, Shenyang, China
| | - Xingzhou Su
- Department of Oromaxillofacial-Head and Neck Surgery, Department of Oral and Maxillofacial Surgery, School and Hospital of Stomatology, China Medical University, Liaoning Provincial Key Laboratory of Oral Diseases, Shenyang, China
| | - Yuhang Zhou
- Department of Oromaxillofacial-Head and Neck Surgery, Department of Oral and Maxillofacial Surgery, School and Hospital of Stomatology, China Medical University, Liaoning Provincial Key Laboratory of Oral Diseases, Shenyang, China
| | - Yang Li
- Department of Oromaxillofacial-Head and Neck Surgery, Department of Oral and Maxillofacial Surgery, School and Hospital of Stomatology, China Medical University, Liaoning Provincial Key Laboratory of Oral Diseases, Shenyang, China
| | - Zheng Zhang
- Department of Radiology, The First Affiliated Hospital of China Medical University, Shenyang, China
| | - Zhenning Li
- Department of Oromaxillofacial-Head and Neck Surgery, Department of Oral and Maxillofacial Surgery, School and Hospital of Stomatology, China Medical University, Liaoning Provincial Key Laboratory of Oral Diseases, Shenyang, China
| | - Fayu Liu
- Department of Oromaxillofacial-Head and Neck Surgery, Department of Oral and Maxillofacial Surgery, School and Hospital of Stomatology, China Medical University, Liaoning Provincial Key Laboratory of Oral Diseases, Shenyang, China
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Qin F, Sun X, Tian M, Jin S, Yu J, Song J, Wen F, Xu H, Yu T, Dong Y. Prediction of lymph node metastasis in operable cervical cancer using clinical parameters and deep learning with MRI data: a multicentre study. Insights Imaging 2024; 15:56. [PMID: 38411729 PMCID: PMC10899556 DOI: 10.1186/s13244-024-01618-7] [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] [Received: 06/13/2023] [Accepted: 12/09/2023] [Indexed: 02/28/2024] Open
Abstract
OBJECTIVES To develop and validate a magnetic resonance imaging-based (MRI) deep multiple instance learning (D-MIL) model and combine it with clinical parameters for preoperative prediction of lymph node metastasis (LNM) in operable cervical cancer. METHODS A total of 392 patients with cervical cancer were retrospectively enrolled. Clinical parameters were analysed by logistical regression to construct a clinical model (M1). A ResNet50 structure is applied to extract features at the instance level without using manual annotations about the tumour region and then construct a D-MIL model (M2). A hybrid model (M3) was constructed by M1 and M2 scores. The diagnostic performance of each model was evaluated by the area under the receiver operating characteristic curve (AUC) and compared using the Delong method. Disease-free survival (DFS) was evaluated by the Kaplan‒Meier method. RESULTS SCC-Ag, maximum lymph node short diameter (LNmax), and tumour volume were found to be independent predictors of M1 model. For the diagnosis of LNM, the AUC of the training/internal/external cohort of M1 was 0.736/0.690/0.732, the AUC of the training/internal/external cohort of M2 was 0.757/0.714/0.765, and the AUC of the training/internal/external cohort of M3 was 0.838/0.764/0.835. M3 showed better performance than M1 and M2. Through the survival analysis, patients with higher hybrid model scores had a shorter time to reach DFS. CONCLUSION The proposed hybrid model could be used as a personalised non-invasive tool, which is helpful for predicting LNM in operable cervical cancer. The score of the hybrid model could also reflect the DFS of operable cervical cancer. CRITICAL RELEVANCE STATEMENT Lymph node metastasis is an important factor affecting the prognosis of cervical cancer. Preoperative prediction of lymph node status is helpful to make treatment decisions, improve prognosis, and prolong survival time. KEY POINTS • The MRI-based deep-learning model can predict the LNM in operable cervical cancer. • The hybrid model has the highest diagnostic efficiency for the LNM prediction. • The score of the hybrid model can reflect the DFS of operable cervical cancer.
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Affiliation(s)
- Fengying Qin
- Department of Radiology, Cancer Hospital of Dalian University of Technology (Liaoning Cancer Hospital & Institute), Shenyang, Liaoning, 110042, China
| | - Xinyan Sun
- Department of Radiology, Cancer Hospital of Dalian University of Technology (Liaoning Cancer Hospital & Institute), Shenyang, Liaoning, 110042, China
| | - Mingke Tian
- Department of Radiology, Cancer Hospital of Dalian University of Technology (Liaoning Cancer Hospital & Institute), Shenyang, Liaoning, 110042, China
| | - Shan Jin
- School of Biomedical Engineering, Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian, 116081, China
| | - Jian Yu
- Department of Radiology, Huludao Center Hospital, Huludao, 125001, China
| | - Jing Song
- Department of Radiology, Shengjing Hospital of China Medical University, Shenyang, 110801, China
| | - Feng Wen
- Department of Radiology, Shengjing Hospital of China Medical University, Shenyang, 110801, China
| | - Hongming Xu
- School of Biomedical Engineering, Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian, 116081, China
| | - Tao Yu
- Department of Radiology, Cancer Hospital of Dalian University of Technology (Liaoning Cancer Hospital & Institute), Shenyang, Liaoning, 110042, China
| | - Yue Dong
- Department of Radiology, Cancer Hospital of Dalian University of Technology (Liaoning Cancer Hospital & Institute), Shenyang, Liaoning, 110042, China.
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Guan W, Wang Y, Zhao H, Lu H, Zhang S, Liu J, Shi B. Prediction models for lymph node metastasis in cervical cancer based on preoperative heart rate variability. Front Neurosci 2024; 18:1275487. [PMID: 38410157 PMCID: PMC10894972 DOI: 10.3389/fnins.2024.1275487] [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: 08/10/2023] [Accepted: 01/15/2024] [Indexed: 02/28/2024] Open
Abstract
Background The occurrence of lymph node metastasis (LNM) is one of the critical factors in determining the staging, treatment and prognosis of cervical cancer (CC). Heart rate variability (HRV) is associated with LNM in patients with CC. The purpose of this study was to validate the feasibility of machine learning (ML) models constructed with preoperative HRV as a feature of CC patients in predicting CC LNM. Methods A total of 292 patients with pathologically confirmed CC admitted to the Department of Gynecological Oncology of the First Affiliated Hospital of Bengbu Medical University from November 2020 to September 2023 were included in the study. The patient' preoperative 5-min electrocardiogram data were collected, and HRV time-domain, frequency-domain and non-linear analyses were subsequently performed, and six ML models were constructed based on 32 parameters. Model performance was assessed using the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity. Results Among the 6 ML models, the random forest (RF) model showed the best predictive performance, as specified by the following metrics on the test set: AUC (0.852), accuracy (0.744), sensitivity (0.783), and specificity (0.785). Conclusion The RF model built with preoperative HRV parameters showed superior performance in CC LNM prediction, but multicenter studies with larger datasets are needed to validate our findings, and the physiopathological mechanisms between HRV and CC LNM need to be further explored.
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Affiliation(s)
- Weizheng Guan
- School of Medical Imaging, Bengbu Medical University, Bengbu, Anhui, China
| | - Yuling Wang
- Department of Gynecologic Oncology, The First Affiliated Hospital, Bengbu Medical University, Bengbu, Anhui, China
| | - Huan Zhao
- School of Medical Imaging, Bengbu Medical University, Bengbu, Anhui, China
| | - Hui Lu
- School of Medical Imaging, Bengbu Medical University, Bengbu, Anhui, China
| | - Sai Zhang
- School of Medical Imaging, Bengbu Medical University, Bengbu, Anhui, China
| | - Jian Liu
- Department of Gynecologic Oncology, The First Affiliated Hospital, Bengbu Medical University, Bengbu, Anhui, China
| | - Bo Shi
- School of Medical Imaging, Bengbu Medical University, Bengbu, Anhui, China
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Wu F, Zhang R, Li F, Qin X, Xing H, Lv H, Li L, Ai T. Radiomics analysis based on multiparametric magnetic resonance imaging for differentiating early stage of cervical cancer. Front Med (Lausanne) 2024; 11:1336640. [PMID: 38371508 PMCID: PMC10869616 DOI: 10.3389/fmed.2024.1336640] [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: 11/11/2023] [Accepted: 01/15/2024] [Indexed: 02/20/2024] Open
Abstract
Objective To investigate the performance of multiparametric magnetic resonance imaging (MRI)-based radiomics models in differentiating early stage of cervical cancer (Stage I-IIa vs. IIb-IV). Methods One hundred patients with cervical cancer who underwent preoperative MRI between June 2020 and March 2022 were retrospectively enrolled. Training (n = 70) and testing cohorts (n = 30) were assigned by stratified random sampling. The clinical and pathological features, including age, histological subtypes, tumor grades, and node status, were compared between the two cohorts by t-test or chi-square test. Radiomics features were extracted from each volume of interest (VOI) on T2-weighted images (T2WI) and apparent diffusion coefficient (ADC) maps. The data balance of the training cohort was resampled by synthesizing minority oversampling techniques. Subsequently, the adiomics signatures were constructed by the least absolute shrinkage and selection operator algorithm and minimum-redundancy maximum-relevance with 10-fold cross-validation. Logistic regression was applied to predict the cervical cancer stages (low [I-IIa]) and (high [IIb-IV] FIGO stages). The receiver operating characteristic curve (area under the curve [AUC]) and decision curve analysis were used to assess the performance of the radiomics model. Results The characteristics of age, histological subtypes, tumor grades, and node status were not significantly different between the low [I-IIa] and high [IIb-IV] FIGO stages (p > 0.05 for both the training and test cohorts). Three models based on T2WI, ADC maps, and the combined were developed based on six radiomics features from T2WI and three radiomics features from ADC maps, with AUCs of 0.855 (95% confidence interval [CI], 0.777-0.934) and 0.823 (95% CI, 0.727-0.919), 0.861 (95% CI, 0.785-0.936) and 0.81 (95% CI, 0.701-0.918), 0.934 (95% CI, 0.884-0.984) and 0.902 (95% CI, 0.832-0.972) in the training and test cohorts. Conclusion The radiomics models combined T2W and ADC maps had good predictive performance in differentiating the early stage from locally advanced cervical cancer.
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Affiliation(s)
- Feng Wu
- Department of Radiology, Xiangyang Central Hospital, Affiliated Hospital of Hubei University of Arts and Science, Xiangyang, China
| | - Rui Zhang
- Department of Radiology, Xiangyang Central Hospital, Affiliated Hospital of Hubei University of Arts and Science, Xiangyang, China
| | - Feng Li
- Department of Radiology, Xiangyang Central Hospital, Affiliated Hospital of Hubei University of Arts and Science, Xiangyang, China
| | - Xiaomin Qin
- Department of Obstetrics and Gynaecology, Xiangyang Central Hospital, Affiliated Hospital of Hubei University of Arts and Science Xiangyang, China
| | - Hui Xing
- Department of Obstetrics and Gynaecology, Xiangyang Central Hospital, Affiliated Hospital of Hubei University of Arts and Science Xiangyang, China
| | - Huabing Lv
- Department of Obstetrics and Gynaecology, Xiangyang Central Hospital, Affiliated Hospital of Hubei University of Arts and Science Xiangyang, China
| | - Lin Li
- Department of Obstetrics and Gynaecology, Xiangyang Central Hospital, Affiliated Hospital of Hubei University of Arts and Science Xiangyang, China
| | - Tao Ai
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
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Small C, Prior P, Nasief H, Zeitlin R, Saeed H, Paulson E, Morrow N, Rownd J, Erickson B, Bedi M. A general framework to develop a radiomic fingerprint for progression-free survival in cervical cancer. Brachytherapy 2023; 22:728-735. [PMID: 37574352 DOI: 10.1016/j.brachy.2023.06.004] [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: 02/27/2023] [Revised: 05/11/2023] [Accepted: 06/06/2023] [Indexed: 08/15/2023]
Abstract
PURPOSE Treatment of locally advanced cervical cancer patients includes chemoradiation followed by brachytherapy. Our aim is to develop a delta radiomics (DRF) model from MRI-based brachytherapy treatment and assess its association with progression free survival (PFS). MATERIALS AND METHODS A retrospective analysis of FIGO stage IB- IV cervical cancer patients between 2012 and 2018 who were treated with definitive chemoradiation followed by MRI-based intracavitary brachytherapy was performed. Clinical factors together with 18 radiomic features extracted from different radiomics matrices were analyzed. The delta radiomic features (DRFs) were extracted from MRI on the first and last brachytherapy fractions. Support Vector Machine (SVM) models were fitted to combinations of 2-3 DRFs found significant after Spearman correlation and Wilcoxon rank sum test statistics. Additional models were tested that included clinical factors together with DRFs. RESULTS A total of 39 patients were included in the analysis with a median patient age of 52 years. Progression occurred in 20% of patients (8/39). The significant DRFs using two DRF feature combinations was a model using auto correlation (AC) and sum variance (SV). The best performing three feature model combined mean, AC & SV. Additionally, the inclusion of FIGO stages with the 2- and 3 DRF combination model(s) improved performance compared to models with only DRFs. However, all the clinical factor + DRF models were not significantly different from one another (all AUCs were 0.77). CONCLUSIONS Our study shows promising evidence that radiomics metrics are associated with progression free survival in cervical cancer.
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Affiliation(s)
- Christina Small
- Department of Radiation Oncology, Medical College of Wisconsin, Milwaukee, WI.
| | - Phillip Prior
- Department of Radiation Oncology, Medical College of Wisconsin, Milwaukee, WI
| | - Haidy Nasief
- Department of Radiation Oncology, Medical College of Wisconsin, Milwaukee, WI
| | - Ross Zeitlin
- Department of Radiation Oncology, John H Stroger, Jr. Hospital of Cook County, Chicago, IL
| | - Hina Saeed
- Department of Radiation Oncology, Lynn Cancer Institute, Baptist Health South Florida, Boynton Beach, FL
| | - Eric Paulson
- Department of Radiation Oncology, Medical College of Wisconsin, Milwaukee, WI
| | - Natalya Morrow
- Department of Radiation Oncology, Medical College of Wisconsin, Milwaukee, WI
| | - Jason Rownd
- Department of Radiation Oncology, Medical College of Wisconsin, Milwaukee, WI
| | - Beth Erickson
- Department of Radiation Oncology, Medical College of Wisconsin, Milwaukee, WI
| | - Meena Bedi
- Department of Radiation Oncology, Medical College of Wisconsin, Milwaukee, WI
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Wagner‐Larsen KS, Hodneland E, Fasmer KE, Lura N, Woie K, Bertelsen BI, Salvesen Ø, Halle MK, Smit N, Krakstad C, Haldorsen IS. MRI-based radiomic signatures for pretreatment prognostication in cervical cancer. Cancer Med 2023; 12:20251-20265. [PMID: 37840437 PMCID: PMC10652318 DOI: 10.1002/cam4.6526] [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: 05/10/2023] [Revised: 08/16/2023] [Accepted: 08/31/2023] [Indexed: 10/17/2023] Open
Abstract
BACKGROUND Accurate pretherapeutic prognostication is important for tailoring treatment in cervical cancer (CC). PURPOSE To investigate whether pretreatment MRI-based radiomic signatures predict disease-specific survival (DSS) in CC. STUDY TYPE Retrospective. POPULATION CC patients (n = 133) allocated into training(T) (nT = 89)/validation(V) (nV = 44) cohorts. FIELD STRENGTH/SEQUENCE T2-weighted imaging (T2WI) and diffusion-weighted imaging (DWI) at 1.5T or 3.0T. ASSESSMENT Radiomic features from segmented tumors were extracted from T2WI and DWI (high b-value DWI and apparent diffusion coefficient (ADC) maps). STATISTICAL TESTS Radiomic signatures for prediction of DSS from T2WI (T2rad ) and T2WI with DWI (T2 + DWIrad ) were constructed by least absolute shrinkage and selection operator (LASSO) Cox regression. Area under time-dependent receiver operating characteristics curves (AUC) were used to evaluate and compare the prognostic performance of the radiomic signatures, MRI-derived maximum tumor size ≤/> 4 cm (MAXsize ), and 2018 International Federation of Gynecology and Obstetrics (FIGO) stage (I-II/III-IV). Survival was analyzed using Cox model estimating hazard ratios (HR) and Kaplan-Meier method with log-rank tests. RESULTS The radiomic signatures T2rad and T2 + DWIrad yielded AUCT /AUCV of 0.80/0.62 and 0.81/0.75, respectively, for predicting 5-year DSS. Both signatures yielded better or equal prognostic performance to that of MAXsize (AUCT /AUCV : 0.69/0.65) and FIGO (AUCT /AUCV : 0.77/0.64) and were significant predictors of DSS after adjusting for FIGO (HRT /HRV for T2rad : 4.0/2.5 and T2 + DWIrad : 4.8/2.1). Adding T2rad and T2 + DWIrad to FIGO significantly improved DSS prediction compared to FIGO alone in cohort(T) (AUCT 0.86 and 0.88 vs. 0.77), and FIGO with T2 + DWIrad tended to the same in cohort(V) (AUCV 0.75 vs. 0.64, p = 0.07). High radiomic score for T2 + DWIrad was significantly associated with reduced DSS in both cohorts. DATA CONCLUSION Radiomic signatures from T2WI and T2WI with DWI may provide added value for pretreatment risk assessment and for guiding tailored treatment strategies in CC.
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Affiliation(s)
- Kari S. Wagner‐Larsen
- Mohn Medical Imaging and Visualization Centre (MMIV), Department of RadiologyHaukeland University HospitalBergenNorway
- Section for Radiology, Department of Clinical MedicineUniversity of BergenBergenNorway
| | - Erlend Hodneland
- Mohn Medical Imaging and Visualization Centre (MMIV), Department of RadiologyHaukeland University HospitalBergenNorway
- Department of MathematicsUniversity of BergenBergenNorway
| | - Kristine E. Fasmer
- Mohn Medical Imaging and Visualization Centre (MMIV), Department of RadiologyHaukeland University HospitalBergenNorway
- Section for Radiology, Department of Clinical MedicineUniversity of BergenBergenNorway
| | - Njål Lura
- Mohn Medical Imaging and Visualization Centre (MMIV), Department of RadiologyHaukeland University HospitalBergenNorway
- Section for Radiology, Department of Clinical MedicineUniversity of BergenBergenNorway
| | - Kathrine Woie
- Department of Obstetrics and GynecologyHaukeland University HospitalBergenNorway
| | | | - Øyvind Salvesen
- Clinical Research Unit, Department of Clinical and Molecular MedicineNorwegian University of Science and TechnologyTrondheimNorway
| | - Mari K. Halle
- Department of Obstetrics and GynecologyHaukeland University HospitalBergenNorway
- Centre for Cancer Biomarkers (CCBIO), Department of Clinical ScienceUniversity of BergenBergenNorway
| | - Noeska Smit
- Mohn Medical Imaging and Visualization Centre (MMIV), Department of RadiologyHaukeland University HospitalBergenNorway
- Department of InformaticsUniversity of BergenBergenNorway
| | - Camilla Krakstad
- Department of Obstetrics and GynecologyHaukeland University HospitalBergenNorway
- Centre for Cancer Biomarkers (CCBIO), Department of Clinical ScienceUniversity of BergenBergenNorway
| | - Ingfrid S. Haldorsen
- Mohn Medical Imaging and Visualization Centre (MMIV), Department of RadiologyHaukeland University HospitalBergenNorway
- Section for Radiology, Department of Clinical MedicineUniversity of BergenBergenNorway
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Ye L, Wang Y, Xiang W, Yao J, Liu J, Song B. Radiomic Analysis of Quantitative T2 Mapping and Conventional MRI in Predicting Histologic Grade of Bladder Cancer. J Clin Med 2023; 12:5900. [PMID: 37762841 PMCID: PMC10531568 DOI: 10.3390/jcm12185900] [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: 08/20/2023] [Accepted: 09/04/2023] [Indexed: 09/29/2023] Open
Abstract
We explored the added value of a radiomic strategy based on quantitative transverse relaxation (T2) mapping and conventional magnetic resonance imaging (MRI) to evaluate the histologic grade of bladder cancer (BCa) preoperatively. Patients who were suspected of BCa underwent pelvic MRI (including T2 mapping and diffusion-weighted imaging (DWI) before any treatment. All patients with histological-proved urothelial BCa were included. We constructed different prediction models using the mean signal values and radiomic features from both T2 mapping and apparent diffusion coefficient (ADC) maps. The diagnostic performance of each model or parameter was assessed using receiver operating characteristic curves. In total, 92 patients were finally included (training cohort, n = 64; testing cohort, n = 28); among these, 71 had high-grade BCa. In the testing cohort, the T2-mapping radiomic model achieved the highest prediction performance (area under the curve (AUC), 0.87; 95% confidence interval (CI), 0.73-1.0) compared with the ADC radiomic model (AUC, 0.77; 95%CI, 0.56-0.97), and the joint radiomic model of 0.78 (95%CI, 0.61-0.96). Our results demonstrated that radiomic mapping could provide more information than direct evaluation of T2 and ADC values in differentiating histological grades of BCa. Additionally, among the radiomic models, the T2-mapping radiomic model outperformed the ADC and joint radiomic models.
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Affiliation(s)
- Lei Ye
- Department of Radiology, West China Hospital, Sichuan University, Chengdu 610041, China; (L.Y.); (Y.W.); (W.X.); (B.S.)
| | - Yayi Wang
- Department of Radiology, West China Hospital, Sichuan University, Chengdu 610041, China; (L.Y.); (Y.W.); (W.X.); (B.S.)
- Department of Ophthalmology, West China Hospital, Sichuan University, Chengdu 610041, China
| | - Wanxin Xiang
- Department of Radiology, West China Hospital, Sichuan University, Chengdu 610041, China; (L.Y.); (Y.W.); (W.X.); (B.S.)
| | - Jin Yao
- Department of Radiology, West China Hospital, Sichuan University, Chengdu 610041, China; (L.Y.); (Y.W.); (W.X.); (B.S.)
| | - Jiaming Liu
- Department of Urology, Institute of Urology, West China Hospital, Sichuan University, Chengdu 610041, China
| | - Bin Song
- Department of Radiology, West China Hospital, Sichuan University, Chengdu 610041, China; (L.Y.); (Y.W.); (W.X.); (B.S.)
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11
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Ai C, Zhang L, Ding W, Zhong S, Li Z, Li M, Zhang H, Zhang L, Zhang L, Hu H. A nomogram-based optimized Radscore for preoperative prediction of lymph node metastasis in patients with cervical cancer after neoadjuvant chemotherapy. Front Oncol 2023; 13:1117339. [PMID: 37655103 PMCID: PMC10466037 DOI: 10.3389/fonc.2023.1117339] [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: 12/06/2022] [Accepted: 07/24/2023] [Indexed: 09/02/2023] Open
Abstract
Purpose To construct a superior single-sequence radiomics signature to assess lymphatic metastasis in patients with cervical cancer after neoadjuvant chemotherapy (NACT). Methods The first half of the study was retrospectively conducted in our hospital between October 2012 and December 2021. Based on the history of NACT before surgery, all pathologies were divided into the NACT and surgery groups. The incidence rate of lymphatic metastasis in the two groups was determined based on the results of pathological examination following lymphadenectomy. Patients from the primary and secondary centers who received NACT were enrolled for radiomics analysis in the second half of the study. The patient cohorts from the primary center were randomly divided into training and test cohorts at a ratio of 7:3. All patients underwent magnetic resonance imaging after NACT. Segmentation was performed on T1-weighted imaging (T1WI), T2-weighted imaging, contrast-enhanced T1WI (CET1WI), and diffusion-weighted imaging. Results The rate of lymphatic metastasis in the NACT group (33.2%) was significantly lower than that in the surgery group (58.7%, P=0.007). The area under the receiver operating characteristic curve values of Radscore_CET1WI for predicting lymph node metastasis and non-lymphatic metastasis were 0.800 and 0.797 in the training and test cohorts, respectively, exhibiting superior diagnostic performance. After combining the clinical variables, the tumor diameter on magnetic resonance imaging was incorporated into the Rad_clin model constructed using Radscore_CET1WI. The Hosmer-Lemeshow test of the Rad_clin model revealed no significant differences in the goodness of fit in the training (P=0.594) or test cohort (P=0.748). Conclusions The Radscore provided by CET1WI may achieve a higher diagnostic performance in predicting lymph node metastasis. Superior performance was observed with the Rad_clin model.
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Affiliation(s)
- Conghui Ai
- Department of Radiology, The Third Affiliated Hospital of Kunming Medical University, Kunming, Yunnan, China
| | - Lan Zhang
- Department of Radiation Oncology, The Third Affiliated Hospital of Kunming Medical University (Yunnan Cancer Hospital, Yunnan Cancer Center), Kunming, China
| | - Wei Ding
- 920th Hospital of Joint Logistics Support Force, Kunming, Yunnan, China
| | - Suixing Zhong
- Department of Radiology, The Third Affiliated Hospital of Kunming Medical University, Kunming, Yunnan, China
| | - Zhenhui Li
- Department of Radiology, The Third Affiliated Hospital of Kunming Medical University, Kunming, Yunnan, China
| | - Miaomiao Li
- Department of Radiology, The Third Affiliated Hospital of Kunming Medical University, Kunming, Yunnan, China
| | - Huimei Zhang
- Department of Radiology, The Third Affiliated Hospital of Kunming Medical University, Kunming, Yunnan, China
| | - Lan Zhang
- Department of Radiology, The First Affiliated Hospital of Henan University of Chinese Medicine, Zhengzhou, Henan, China
| | - Lei Zhang
- Department of Gynecology, Yunnan Tumor Hospital & The Third Affiliated Hospital of Kunming Medical University, Kunming, Yunnan, China
| | - Hongyan Hu
- Department of Pathology, The Third Affiliated Hospital of Kunming Medical University (Yunnan Cancer Center), Kunming, China
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Avesani G, Perazzolo A, Amerighi A, Celli V, Panico C, Sala E, Gui B. The Utility of Contrast-Enhanced Magnetic Resonance Imaging in Uterine Cervical Cancer: A Systematic Review. Life (Basel) 2023; 13:1368. [PMID: 37374150 DOI: 10.3390/life13061368] [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: 05/04/2023] [Revised: 06/03/2023] [Accepted: 06/07/2023] [Indexed: 06/29/2023] Open
Abstract
Correct staging of cervical cancer is essential to establish the best therapeutic procedure and prognosis for the patient. MRI is the best imaging modality for local staging and follow-up. According to the latest ESUR guidelines, T2WI and DWI-MR sequences are fundamental in these settings, and CE-MRI remains optional. This systematic review, according to the PRISMA 2020 checklist, aims to give an overview of the literature regarding the use of contrast in MRI in cervical cancer and provide more specific indications of when it may be helpful. Systematic searches on PubMed and Web Of Science (WOS) were performed, and 97 papers were included; 1 paper was added considering the references of included articles. From our literature review, it emerged that many papers about the use of contrast in cervical cancer are dated, especially about staging and detection of tumor recurrence. We did not find strong evidence suggesting that CE-MRI is helpful in any clinical setting for cervical cancer staging and detection of tumor recurrence. There is growing evidence that perfusion parameters and perfusion-derived radiomics models might have a role as prognostic and predictive biomarkers, but the lack of standardization and validation limits their use in a research setting.
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Affiliation(s)
- Giacomo Avesani
- Department of Diagnostic Imaging, Oncological Radiotherapy and Hematology, Fondazione Policlinico Universitario "A. Gemelli" IRCCS, 00168 Rome, Italy
| | - Alessio Perazzolo
- Department of Radiological and Hematological Sciences, Università Cattolica del Sacro Cuore, 00168 Rome, Italy
| | - Andrea Amerighi
- Department of Radiological and Hematological Sciences, Università Cattolica del Sacro Cuore, 00168 Rome, Italy
| | - Veronica Celli
- Department of Diagnostic Imaging, Oncological Radiotherapy and Hematology, Fondazione Policlinico Universitario "A. Gemelli" IRCCS, 00168 Rome, Italy
| | - Camilla Panico
- Department of Diagnostic Imaging, Oncological Radiotherapy and Hematology, Fondazione Policlinico Universitario "A. Gemelli" IRCCS, 00168 Rome, Italy
| | - Evis Sala
- Department of Diagnostic Imaging, Oncological Radiotherapy and Hematology, Fondazione Policlinico Universitario "A. Gemelli" IRCCS, 00168 Rome, Italy
| | - Benedetta Gui
- Department of Diagnostic Imaging, Oncological Radiotherapy and Hematology, Fondazione Policlinico Universitario "A. Gemelli" IRCCS, 00168 Rome, Italy
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13
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Zhang Z, Wan X, Lei X, Wu Y, Zhang J, Ai Y, Yu B, Liu X, Jin J, Xie C, Jin X. Intra- and peri-tumoral MRI radiomics features for preoperative lymph node metastasis prediction in early-stage cervical cancer. Insights Imaging 2023; 14:65. [PMID: 37060378 PMCID: PMC10105820 DOI: 10.1186/s13244-023-01405-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2022] [Accepted: 03/16/2023] [Indexed: 04/16/2023] Open
Abstract
BACKGROUND Noninvasive and accurate prediction of lymph node metastasis (LNM) is very important for patients with early-stage cervical cancer (ECC). Our study aimed to investigate the accuracy and sensitivity of radiomics models with features extracted from both intra- and peritumoral regions in magnetic resonance imaging (MRI) with T2 weighted imaging (T2WI) and diffusion weighted imaging (DWI) for predicting LNM. METHODS A total of 247 ECC patients with confirmed lymph node status were enrolled retrospectively and randomly divided into training (n = 172) and testing sets (n = 75). Radiomics features were extracted from both intra- and peritumoral regions with different expansion dimensions (3, 5, and 7 mm) in T2WI and DWI. Radiomics signature and combined radiomics models were constructed with selected features. A nomogram was also constructed by combining radiomics model with clinical factors for predicting LNM. RESULTS The area under curves (AUCs) of radiomics signature with features from tumors in T2WI and DWI were 0.841 vs. 0.791 and 0.820 vs. 0.771 in the training and testing sets, respectively. Combining radiomics features from tumors in the T2WI, DWI and peritumoral 3 mm expansion in T2WI achieved the best performance with an AUC of 0.868 and 0.846 in the training and testing sets, respectively. A nomogram combining age and maximum tumor diameter (MTD) with radiomics signature achieved a C-index of 0.884 in the prediction of LNM for ECC. CONCLUSIONS Radiomics features extracted from both intra- and peritumoral regions in T2WI and DWI are feasible and promising for the preoperative prediction of LNM for patients with ECC.
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Affiliation(s)
- Zhenhua Zhang
- Department of Radiology, The 1st Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Xiaojie Wan
- Department of Obstetrics and Gynecology, Women's Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Xiyao Lei
- Department of Radiotherapy Center, The 1st Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Yibo Wu
- Department of Radiotherapy Center, The 1st Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Ji Zhang
- Department of Radiotherapy Center, The 1st Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Yao Ai
- Department of Radiotherapy Center, The 1st Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Bing Yu
- Department of Radiotherapy Center, The 1st Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Xinmiao Liu
- School of Laboratory Medicine and Life Sciences, Wenzhou Medical University, Wenzhou, China
| | - Juebin Jin
- Department of Medical Engineering, The 1st Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Congying Xie
- Department of Radiation and Medical Oncology, The 2nd Affiliated Hospital of Wenzhou Medical University, Wenzhou, China.
| | - Xiance Jin
- Department of Radiotherapy Center, The 1st Affiliated Hospital of Wenzhou Medical University, Wenzhou, China.
- School of Basic Medical Science, Wenzhou Medical University, Wenzhou, China.
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14
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Yu W, Xu H, Chen F, Shou H, Chen Y, Jia Y, Zhang H, Ding J, Xiong H, Wang Y, Song T. Development and validation of a radiomics-based nomogram for the prediction of postoperative malnutrition in stage IB1-IIA2 cervical carcinoma. Front Nutr 2023; 10:1113588. [PMID: 36819703 PMCID: PMC9936189 DOI: 10.3389/fnut.2023.1113588] [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: 12/01/2022] [Accepted: 01/12/2023] [Indexed: 02/05/2023] Open
Abstract
Objective In individuals with stage IB1-IIA2 cervical cancer (CC) who received postoperative radiotherapy ± chemotherapy (PORT/CRT), the interaction between sarcopenia and malnutrition remains elusive, let alone employing a nomogram model based on radiomic features of psoas extracted at the level of the third lumbar vertebra (L3). This study was set to develop a radiomics-based nomogram model to predict malnutrition as per the Patient-Generated Subjective Global Assessment (PG-SGA) for individuals with CC. Methods In total, 120 individuals with CC underwent computed tomography (CT) scans before PORT/CRT. The radiomic features of psoas at L3 were obtained from non-enhanced CT images. Identification of the optimal features and construction of the rad-score formula were conducted utilizing the least absolute shrinkage and selection operator (LASSO) logistic regression to predict malnutrition in the training dataset (radiomic model). Identification of the major clinical factors in the clinical model was performed by means of binary logistic regression analysis. The radiomics-based nomogram was further developed by integrating radiomic signatures and clinical risk factors (combined model). The receiver operating characteristic (ROC) curves and decision curves analysis (DCA) were employed for the evaluation and comparison of the three models in terms of their predictive performance. Results Twelve radiomic features in total were chosen, and the rad-score was determined with the help of the non-zero coefficient from LASSO regression. Multivariate analysis revealed that besides rad-score, age and Eastern Cooperative Oncology Group performance status could independently predict malnutrition. As per the data of this analysis, a nomogram prediction model was constructed. The area under the ROC curves (AUC) values of the radiomic and clinical models were 0.778 and 0.847 for the training and 0.776 and 0.776 for the validation sets, respectively. An increase in the AUC was observed up to 0.972 and 0.805 in the training and validation sets, respectively, in the combined model. DCA also confirmed the clinical benefit of the combined model. Conclusion This radiomics-based nomogram model depicted potential for use as a marker for predicting malnutrition in stage IB1-IIA2 CC patients who underwent PORT/CRT and required further investigation with a large sample size.
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Affiliation(s)
- Wenke Yu
- Department of Radiology, Qingchun Hospital of Zhejiang Province, Hangzhou, China
| | - Hong’en Xu
- Cancer Center, Department of Radiation Oncology, Zhejiang Provincial People’s Hospital, Affiliated People’s Hospital, Hangzhou Medical College, Hangzhou, Zhejiang, China
| | - Fangjie Chen
- Department of Outpatient Nursing, Zhejiang Provincial People’s Hospital, Affiliated People’s Hospital, Hangzhou Medical College, Hangzhou, Zhejiang, China
| | - Huafeng Shou
- Department of Gynecology, Zhejiang Provincial People’s Hospital, Affiliated People’s Hospital, Hangzhou Medical College, Hangzhou, Zhejiang, China
| | - Ying Chen
- Department of Clinical Nutrition, Zhejiang Provincial People’s Hospital, Affiliated People’s Hospital, Hangzhou Medical College, Hangzhou, Zhejiang, China
| | - Yongshi Jia
- Cancer Center, Department of Radiation Oncology, Zhejiang Provincial People’s Hospital, Affiliated People’s Hospital, Hangzhou Medical College, Hangzhou, Zhejiang, China
| | - Hongwei Zhang
- Department of Radiology, Qingchun Hospital of Zhejiang Province, Hangzhou, China
| | - Jieni Ding
- Cancer Center, Department of Radiation Oncology, Zhejiang Provincial People’s Hospital, Affiliated People’s Hospital, Hangzhou Medical College, Hangzhou, Zhejiang, China
| | - Hanchu Xiong
- Cancer Center, Department of Radiation Oncology, Zhejiang Provincial People’s Hospital, Affiliated People’s Hospital, Hangzhou Medical College, Hangzhou, Zhejiang, China
| | - Yiwen Wang
- Department of Clinical medical engineering, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Tao Song
- Cancer Center, Department of Radiation Oncology, Zhejiang Provincial People’s Hospital, Affiliated People’s Hospital, Hangzhou Medical College, Hangzhou, Zhejiang, China,*Correspondence: Tao Song, ✉
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Wei Q, Yuan W, Jia Z, Chen J, Li L, Yan Z, Liao Y, Mao L, Hu S, Liu X, Chen W. Preoperative MR radiomics based on high-resolution T2-weighted images and amide proton transfer-weighted imaging for predicting lymph node metastasis in rectal adenocarcinoma. Abdom Radiol (NY) 2023; 48:458-470. [PMID: 36460837 DOI: 10.1007/s00261-022-03731-x] [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: 07/21/2022] [Revised: 10/26/2022] [Accepted: 10/26/2022] [Indexed: 12/04/2022]
Abstract
OBJECTIVES Lymph node (LN) metastasis is an important prognostic factor in rectal cancer (RC). However, accurate identification of LN metastasis can be challenged for radiologists. The aim of our study was to assess the utility of MRI radiomics based on T2-weighted images (T2WI) and amide proton transfer-weighted (APTw) images for predicting LN metastasis in RC preoperatively. METHODS A total of 125 patients with pathologically confirmed rectal adenocarcinoma (RA) from January 2019 to June 2021 who underwent preoperative MR were enrolled in this retrospective study. Radiomics features were extracted from high-resolution T2WI and APTw images of primary tumor. The most relevant radiomics and clinical features were selected using correlation and multivariate logistic analysis. Radiomics models were built using five machine learning algorithms including support vector machine (SVM), logical regression (LR), k- nearest neighbor (KNN), naive bayes (NB), and random forest (RF). The best algorithm was selected for further establish the clinical- radiomics model. The receiver operating characteristic curve (ROC) analysis was used to assess the performance of radiomics and clinical-radiomics model for predicting LN metastasis. RESULTS The LR classifier had the best prediction performance, with AUCs of 0.983 (95% CI 0.957-1.000), 0.864 (95% CI 0.729-0.972), 0.851 (95% CI 0.713-0.940) on the training set, validation, and test sets, respectively. In terms of prediction, the clinical-radiomics combined model outperformed the radiomics model. The AUCs of the clinical-radiomics combined model in the validation and test sets were 0.900 (95% CI 0.785-0.986), and 0.929 (95% CI 0.721-0.943), respectively. CONCLUSION The radiomics model based on high-resolution T2WI and APTw images can predict LN metastasis accurately in patients with RA.
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Affiliation(s)
- Qiurong Wei
- Department of Radiology, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, 510120, Guangdong Province, China
| | - Wenjing Yuan
- Department of Radiology, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, 510120, Guangdong Province, China
| | - Ziqi Jia
- Department of Radiology, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, 510120, Guangdong Province, China
| | - Jialiang Chen
- Department of Radiology, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, 510120, Guangdong Province, China
| | - Ling Li
- Department of Radiology, The Second People's Hospital of Shaanxi Province, Xi'an, 710000, Shaanxi province, China
| | - Zhaoxian Yan
- Department of Radiology, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, 510120, Guangdong Province, China
| | - Yuting Liao
- GE Healthcare, Guangzhou, 510623, Guangdong Province, China
| | - Liting Mao
- Department of Radiology, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, 510120, Guangdong Province, China
| | - Shaowei Hu
- Department of Pathology, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, 510120, Guangdong Province, China
| | - Xian Liu
- Department of Radiology, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, 510120, Guangdong Province, China
| | - Weicui Chen
- Department of Radiology, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, 510120, Guangdong Province, China.
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Li H, Chen XL, Liu H, Lu T, Li ZL. MRI-based multiregional radiomics for predicting lymph nodes status and prognosis in patients with resectable rectal cancer. Front Oncol 2023; 12:1087882. [PMID: 36686763 PMCID: PMC9846353 DOI: 10.3389/fonc.2022.1087882] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2022] [Accepted: 12/06/2022] [Indexed: 01/06/2023] Open
Abstract
Purpose To establish and evaluate multiregional T2-weighted imaging (T2WI)-based clinical-radiomics model for predicting lymph node metastasis (LNM) and prognosis in patients with resectable rectal cancer. Methods A total of 346 patients with pathologically confirmed rectal cancer from two hospitals between January 2019 and December 2021 were prospectively enrolled. Intra- and peritumoral features were extracted separately, and least absolute shrinkage and selection operator regression was applied for feature selection. Radiomics signatures were built using the selected features from different regions. The clinical-radiomic nomogram was developed by combining the intratumoral and peritumoral radiomics signatures score (radscore) and the most predictive clinical parameters. The diagnostic performances of the nomogram and clinical model were evaluated using the area under the receiver operating characteristic curve (AUC). The prognostic model for 3-year recurrence-free survival (RFS) was constructed using univariate and multivariate Cox analysis. Results The intratumoral radscore (radscore 1) included four features, the peritumoral radscore (radscore 2) included five features, and the combined intratumoral and peritumoural radscore (radscore 3) included ten features. The AUCs for radscore 3 were higher than that of radscore 1 in training cohort (0.77 vs. 0.71, P=0.182) and internal validation cohort (0.76 vs. 0.64, P=0.041). The AUCs for radscore 3 were higher than that of radscore 2 in training cohort (0.77 vs. 0.74, P=0.215) and internal validation cohort (0.76 vs. 0.68, P=0.083). A clinical-radiomic nomogram showed a higher AUC compared with the clinical model in training cohort (0.84 vs. 0.67, P<0.001) and internal validation cohort (0.78 vs. 0.64, P=0.038) but not in external validation (0.72 vs. 0.76, P=0.164). Multivariate Cox analysis showed MRI-reported extramural vascular invasion (EMVI) (HR=1.099, 95%CI: 0.462-2.616; P=0.031) and clinical-radiomic nomogram-based LNM (HR=2.232, 95%CI:1.238-7.439; P=0.017) were independent risk factors for assessing 3-year RFS. Combined clinical-radiomic nomogram based LNM and MRI-reported EMVI showed good performance in training cohort (AUC=0.748), internal validation cohort (AUC=0.706) and external validation (AUC=0.688) for predicting 3-year RFS. Conclusion A clinical-radiomics nomogram exhibits good performance for predicting preoperative LNM. Combined clinical-radiomic nomogram based LNM and MRI-reported EMVI showed clinical potential for assessing 3-year RFS.
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Affiliation(s)
- Hang Li
- Department of Radiology, Sichuan Academy of Medical Sciences and Sichuan Provincial People’s Hospital, Chengdu, Sichuan, China
| | - Xiao-li Chen
- Department of Radiology, Affiliated Cancer Hospital of Medical School, University of Electronic Science and Technology of China, Sichuan Cancer Hospital, Chengdu, China
| | | | - Tao Lu
- Department of Radiology, Sichuan Academy of Medical Sciences and Sichuan Provincial People’s Hospital, Chengdu, Sichuan, China,*Correspondence: Tao Lu, ; Zhen-lin Li,
| | - Zhen-lin Li
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, China,*Correspondence: Tao Lu, ; Zhen-lin Li,
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Qin S, Jiang Y, Wang F, Tang L, Huang X. Development and validation of a combined model based on dual-sequence MRI radiomics for predicting the efficacy of high-intensity focused ultrasound ablation for hysteromyoma. Int J Hyperthermia 2022; 40:2149862. [PMID: 36535929 DOI: 10.1080/02656736.2022.2149862] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022] Open
Abstract
OBJECTIVES To determine the value of dual-sequence magnetic resonance imaging (MRI)-based radiomics in predicting the efficacy of high-intensity focused ultrasound (HIFU) ablation for hysteromyoma. METHODS A total of 142 patients with 172 hysteromyomas (95 hysteromyomas from the sufficient ablation group, and 77 hysteromyomas from the insufficient ablation group) were enrolled in the study. The clinical-radiological model was constructed with independent clinical-radiological risk factors, the radiomics model was constructed based on the optimal radiomics features of hysteromyoma from dual sequences, and the two groups of features were incorporated to construct the combined model. A fivefold cross validation procedure was adopted to validate these models. A nomogram was constructed, applying the combined model in the training cohort. The models were assessed with receiver operating characteristic (ROC) curves and integrated discrimination improvement (IDI). An independent test cohort comprising 40 patients was used to evaluate the performance of the optimal model. RESULTS Among the three models, the average areas under the ROC curves (AUC) of the radiomics model and combined model were 0.803 (95% confidence interval (CI): 0.726-0.881) and 0.841 (95% CI: 0.772-0.909), which were better than the clinical-radiological model in the training cohort. The IDI showed that the combined model had the best prediction accuracy. The combined model also showed good discrimination in both the validation cohort (AUC = 0.834) and the independent test cohort (AUC = 0.801). CONCLUSION The combined model based on the dual-sequence MRI radiomics is the most promising tool from our study to assist clinicians in predicting HIFU ablation efficacy.
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Affiliation(s)
- Shize Qin
- Department of Radiology, Affiliated Hospital of North Sichuan Medical College, Nanchong, China
| | - Yu Jiang
- Department of Radiology, Affiliated Hospital of North Sichuan Medical College, Nanchong, China
| | - Fang Wang
- Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China
| | - Lingling Tang
- Department of Radiology, Affiliated Hospital of North Sichuan Medical College, Nanchong, China
| | - Xiaohua Huang
- Department of Radiology, Affiliated Hospital of North Sichuan Medical College, Nanchong, China
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Ren J, Li Y, Liu XY, Zhao J, He YL, Jin ZY, Xue HD. Diagnostic performance of ADC values and MRI-based radiomics analysis for detecting lymph node metastasis in patients with cervical cancer: A systematic review and meta-analysis. Eur J Radiol 2022; 156:110504. [PMID: 36108474 DOI: 10.1016/j.ejrad.2022.110504] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2022] [Revised: 08/16/2022] [Accepted: 08/25/2022] [Indexed: 11/21/2022]
Abstract
OBJECTIVE To evaluate and compare the diagnostic performance of apparent diffusion coefficient (ADC) values and MRI-based radiomics analysis for lymph node metastasis (LNM) detection in patients with cervical cancer (CC). METHODS We searched relevant databases for studies on ADC values and MRI-based radiomics analysis for LNM detection in CC between January 2001 and December 2021. Methodological quality assessment of risk of bias using Quality Assessment of Diagnostic Accuracy Studies 2 and radiomics quality score (RQS) of the studies was conducted. The pooled sensitivity, specificity, positive likelihood ratio (LR+), negative likelihood ratio (LR-), diagnostic odds ratio (DOR), and area under the curve (AUC) were calculated. Diagnostic performance was compared between the two quantitative analyses using a two-sample Z-test. RESULTS In total, 22 studies including 2314 patients were included. Unclear risk of bias was observed in 4.5-36.4% of the studies. The 8 radiomics studies exhibited a median (interquartile range) RQS of 13.5 (5.5-15.75). The pooled sensitivity, specificity, LR+, LR-, DOR, and AUC of the ADC values vs radiomics analysis were 0.86 vs 0.84, 0.85 vs 0.73, 5.7 vs 3.1, 0.17 vs 0.22, 34 vs 14, and 0.91 vs 0.86, respectively. There was no threshold effect or publication bias, but significant heterogeneity existed among the studies. No significant difference was detected in the diagnostic performance of the two quantitative analyses using the Z-test. CONCLUSION ADC values are more clinically promising because they are more easily accessible and widely applied, and exhibit a non-statistically significant trend to outperform radiomics analysis.
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Affiliation(s)
- Jing Ren
- Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, PR China.
| | - Yuan Li
- Department of Obstetrics and Gynecology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, National Clinical Research Center for Obstetric & Gynecologic Diseases, Beijing, PR China.
| | - Xin-Yu Liu
- Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, PR China.
| | - Jia Zhao
- Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, PR China.
| | - Yong-Lan He
- Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, PR China.
| | - Zheng-Yu Jin
- Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, PR China.
| | - Hua-Dan Xue
- Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, PR China.
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Jiang X, Song J, Zhang A, Cheng W, Duan S, Liu X, Chen T. Preoperative Assessment of MRI-Invisible Early-Stage Endometrial Cancer With MRI-Based Radiomics Analysis. J Magn Reson Imaging 2022. [PMID: 36259352 DOI: 10.1002/jmri.28492] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2022] [Revised: 10/07/2022] [Accepted: 10/07/2022] [Indexed: 12/20/2022] Open
Abstract
BACKGROUND Radiomics-based analyses have demonstrated impact on studies of endometrial cancer (EC). However, there have been no radiomics studies investigating preoperative assessment of MRI-invisible EC to date. PURPOSE To develop and validate radiomics models based on sagittal T2-weighted images (T2WI) and T1-weighted contrast-enhanced images (T1CE) for the preoperative assessment of MRI-invisible early-stage EC and myometrial invasion (MI). STUDY TYPE Retrospective. POPULATION One hundred fifty-eight consecutive patients (mean age 50.7 years) with MRI-invisible endometrial lesions were enrolled from June 2016 to March 2022 and randomly divided into the training (n = 110) and validation cohort (n = 48) using a ratio of 7:3. FIELD STRENGTH/SEQUENCE 3-T, T2WI, and T1CE sequences, turbo spin echo. ASSESSMENT Two radiologists performed image segmentation and extracted features. Endometrial lesions were histopathologically classified as benign, dysplasia, and EC with or without MI. In the training cohort, 28 and 20 radiomics features were selected to build Model 1 and Model 2, respectively, generating rad-score 1 (RS1) and rad-score 2 (RS2) for evaluating MRI-invisible EC and MI. STATISTICAL TESTS The least absolute shrinkage and selection operator logistic regression method was used to select radiomics features. Mann-Whitney U tests and Chi-square test were used to analyze continuous and categorical variables. Receiver operating characteristic curve (ROC) and decision curve analysis were used for performance evaluation. The area under the ROC curve (AUC), accuracy, sensitivity, specificity, positive predictive value, and negative predictive value were calculated. A P-value <0.05 was considered statistically significant. RESULTS Model 1 had good performance for preoperative detecting of MRI-invisible early-stage EC in the training and validation cohorts (AUC: 0.873 and 0.918). In addition, Model 2 had good performance in assessment of MI of MRI-invisible endometrial lesions in the training and validation cohorts (AUC: 0.854 and 0.834). DATA CONCLUSION MRI-based radiomics models may provide good performance for detecting MRI-invisible EC and MI. EVIDENCE LEVEL 3 TECHNICAL EFFICACY: Stage 2.
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Affiliation(s)
- Xiaoting Jiang
- Department of Radiology, First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Jiacheng Song
- Department of Radiology, First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Aining Zhang
- Department of Radiology, First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Wenjun Cheng
- Department of Gynaecology and Obstetrics, First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Shaofeng Duan
- GE Healthcare, Precision Health Institution, Shanghai, China
| | - Xisheng Liu
- Department of Radiology, First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Ting Chen
- Department of Radiology, First Affiliated Hospital of Nanjing Medical University, Nanjing, China
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20
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Jiang X, Song J, Duan S, Cheng W, Chen T, Liu X. MRI radiomics combined with clinicopathologic features to predict disease-free survival in patients with early-stage cervical cancer. Br J Radiol 2022; 95:20211229. [PMID: 35604668 PMCID: PMC10162065 DOI: 10.1259/bjr.20211229] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2021] [Revised: 03/21/2022] [Accepted: 05/06/2022] [Indexed: 11/05/2022] Open
Abstract
OBJECTIVE To establish a comprehensive model including MRI radiomics and clinicopathological features to predict post-operative disease-free survival (DFS) in early-stage (pre-operative FIGO Stage IB-IIA) cervical cancer. METHODS A total of 183 patients with early-stage cervical cancer admitted to our Jiangsu Province Hospital underwent radical hysterectomy were enrolled in this retrospective study from January 2013 to June 2018 and their clinicopathology and MRI information were collected. They were then divided into training cohort (n = 129) and internal validation cohort (n = 54). The radiomic features were extracted from the pre-operative T1 contrast-enhanced (T1CE) and T2 weighted image of each patient. Least absolute shrinkage and selection operator regression and multivariate Cox proportional hazard model were used for feature selection, and the rad-score (RS) of each patient were evaluated individually. The clinicopathology model, T1CE_RS model, T1CE + T2_RS model, and clinicopathology combined with T1CE_RS model were established and compared. Patients were divided into high- and low-risk groups according to the optimum cut-off values of four models. RESULTS T1CE_RS model showed better performance on DFS prediction of early-stage cervical cancer than clinicopathological model (C-index: 0.724 vs 0.659). T1CE+T2_RS model did not improve predictive performance (C-index: 0.671). The combination of T1CE_RS and clinicopathology features showed more accurate predictive ability (C-index=0.773). CONCLUSION The combination of T1CE_RS and clinicopathology features showed more accurate predictive performance for DFS of patients with early-stage (pre-operative IB-IIA) cervical cancer which can aid in the design of individualised treatment strategies and regular follow-up. ADVANCES IN KNOWLEDGE A radiomics signature composed of T1CE radiomic features combined with clinicopathology features allowed differentiating patients at high or low risk of recurrence.
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Affiliation(s)
- Xiaoting Jiang
- Department of Radiology, First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Jiacheng Song
- Department of Radiology, First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Shaofeng Duan
- GE Healthcare, Precision Health Institution, Shanghai, China
| | - Wenjun Cheng
- Department of Gynaecology and Obstetrics, First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Ting Chen
- Department of Radiology, First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Xisheng Liu
- Department of Radiology, First Affiliated Hospital of Nanjing Medical University, Nanjing, China
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Zhang Y, Zhang KY, Jia HD, Fang X, Lin TT, Wei C, Qian LT, Dong JN. Feasibility of Predicting Pelvic Lymph Node Metastasis Based on IVIM-DWI and Texture Parameters of the Primary Lesion and Lymph Nodes in Patients with Cervical Cancer. Acad Radiol 2022; 29:1048-1057. [PMID: 34654623 DOI: 10.1016/j.acra.2021.08.026] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2021] [Revised: 08/22/2021] [Accepted: 08/27/2021] [Indexed: 12/14/2022]
Abstract
RATIONALE AND OBJECTIVES To investigate the feasibility and value of intravoxel incoherent motion diffusion weighted imaging (IVIM-DWI) and texture parameters of primary lesions and lymph nodes for predicting pelvic lymph node metastasis in patients with cervical cancer. MATERIALS AND METHODS A total of 143 patients with cervical cancer confirmed by surgical pathology were analyzed retrospectively and 125 patients were enrolled in primary lesions study, 83 patients and 134 lymph nodes were enrolled in lymph nodes study. Patients and lymph nodes were randomly divided into training group and test group at a ratio of 2: 1. The IVIM-DWI parameters and 3D texture features of primary lesions and lymph nodes of all patients were measured. The least absolute shrinkage and selection operator algorithm, spearman's correlation analysis, independent two-sample t-test and Mann-Whitney U-test were used to select texture parameters. Multivariate Logistic regression analysis and receiver operating characteristic curves were used to model and evaluate diagnostic performances. RESULTS In primary lesions study, model 1 was constructed by combining f value, original_shape_Sphericity and original_firstorder_Mean of primary lesions. In lymph nodes study, model 2 was constructed by combining short diameter, circular enhancement and rough margin of lymph nodes. Model 3 was constructed by combining ADC, f value and original_glszm_Small Area Emphasis of lymph nodes. The areas under curve of model 1, 2 and 3 in training group and test group were 0.882, 0.798, 0.907 and 0.862, 0.771, 0.937 respectively. CONCLUSION Models based on IVIM-DWI and texture parameters of primary lesions and lymph nodes both performed well in diagnosing pelvic lymph node metastasis of cervical cancer and were superior to morphological features of lymph nodes. Especially, parameters of lymph nodes showed higher diagnostic efficiency and clinical significance.
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22
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Yang S, Liu C, Li C, Hua K. Nomogram Predicting Lymph Node Metastasis in the Early-Stage Cervical Cancer. Front Med (Lausanne) 2022; 9:866283. [PMID: 35847788 PMCID: PMC9280490 DOI: 10.3389/fmed.2022.866283] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2022] [Accepted: 06/09/2022] [Indexed: 12/24/2022] Open
Abstract
Background Accurately predicting the risk level of lymph node metastasis is essential for the treatment of patients with early cervical cancer. The purpose of this study is to construct a new nomogram based on 2-deoxy-2-fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) and clinical characteristics to assess early-stage cervical cancer patients’ risk of lymph node metastasis. Materials and Methods From January 2019 to November 2020, the records of 234 patients with stage IA-IIA [International Federation of Gynecology and Obstetrics (FIGO) 2018] cervical cancer who had undergone PET/CT examination within 30 days before surgery were retrospectively reviewed. A nomogram to predict the risk of lymph node metastasis was constructed based on it. The nomogram was developed and validated by internal and external validation. The validation cohorts included 191 cervical cancer patients from December 2020 to October 2021. Results Four factors [squamous cell carcinoma associated antigen (SCCA), maximum standardized uptake value of lymph node (nSUVmax), uterine corpus invasion in PET/CT and tumor size in PET/CT] were finally determined as the predictors of the nomogram. At the area under the receiver operating characteristic curve cohort was 0.926 in the primary and was 0.897 in the validation cohort. The calibration curve shows good agreement between the predicted probability and the actual probability. The decision curve analysis showed the clinical utility of the nomogram. Conclusion We had established and verified a simple and effective nomogram, which can be used to predict the lymph node metastasis of cervical cancer patients before surgery.
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Affiliation(s)
- Shimin Yang
- Department of Obstetrics and Gynecology, Obstetrics and Gynecology Hospital, Fudan University, Shanghai, China
| | - Chunli Liu
- Shanghai Universal Medical Imaging Diagnostic Center, Shanghai, China
| | - Chunbo Li
- Department of Obstetrics and Gynecology, Obstetrics and Gynecology Hospital, Fudan University, Shanghai, China
- *Correspondence: Chunbo Li,
| | - Keqin Hua
- Department of Obstetrics and Gynecology, Obstetrics and Gynecology Hospital, Fudan University, Shanghai, China
- Keqin Hua,
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Li L, Zhang J, Zhe X, Tang M, Zhang X, Lei X, Zhang L. A meta-analysis of MRI-based radiomic features for predicting lymph node metastasis in patients with cervical cancer. Eur J Radiol 2022; 151:110243. [DOI: 10.1016/j.ejrad.2022.110243] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2021] [Revised: 02/22/2022] [Accepted: 03/05/2022] [Indexed: 12/23/2022]
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Liu B, Sun Z, Xu ZL, Zhao HL, Wen DD, Li YA, Zhang F, Hou BX, Huan Y, Wei LC, Zheng MW. Predicting Disease-Free Survival With Multiparametric MRI-Derived Radiomic Signature in Cervical Cancer Patients Underwent CCRT. Front Oncol 2022; 11:812993. [PMID: 35145910 PMCID: PMC8821662 DOI: 10.3389/fonc.2021.812993] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2021] [Accepted: 12/30/2021] [Indexed: 11/13/2022] Open
Abstract
Prognostic biomarkers that can reliably predict the disease-free survival (DFS) of locally advanced cervical cancer (LACC) are needed for identifying those patients at high risk for progression, who may benefit from a more aggressive treatment. In the present study, we aimed to construct a multiparametric MRI-derived radiomic signature for predicting DFS of LACC patients who underwent concurrent chemoradiotherapy (CCRT).
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Affiliation(s)
- Bing Liu
- Department of Radiology, Xijing Hospital, Airforce Military Medical University, Xi’an, China
| | - Zhen Sun
- Department of Orthopaedics, Xijing Hospital, Airforce Military Medical University, Xi’an, China
| | - Zi-Liang Xu
- Department of Radiology, Xijing Hospital, Airforce Military Medical University, Xi’an, China
| | - Hong-Liang Zhao
- Department of Radiology, Xijing Hospital, Airforce Military Medical University, Xi’an, China
| | - Di-Di Wen
- Department of Radiology, Xijing Hospital, Airforce Military Medical University, Xi’an, China
| | - Yong-Ai Li
- Department of Radiology, Jinshan Hospital, Fudan University, Shanghai, China
| | - Fan Zhang
- Department of Radiology, Shanxi Traditional Chinese Medical Hospital, Taiyuan, China
| | - Bing-Xin Hou
- Department of Radiation Oncology, Xijing Hospital, Airforce Military Medical University, Xi’an, China
| | - Yi Huan
- Department of Radiology, Xijing Hospital, Airforce Military Medical University, Xi’an, China
- *Correspondence: Yi Huan, ; Li-Chun Wei, ; Min-Wen Zheng,
| | - Li-Chun Wei
- Department of Radiation Oncology, Xijing Hospital, Airforce Military Medical University, Xi’an, China
- *Correspondence: Yi Huan, ; Li-Chun Wei, ; Min-Wen Zheng,
| | - Min-Wen Zheng
- Department of Radiology, Xijing Hospital, Airforce Military Medical University, Xi’an, China
- *Correspondence: Yi Huan, ; Li-Chun Wei, ; Min-Wen Zheng,
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Liu Y, Song T, Dong TF, Zhang W, Wen G. MRI-based radiomics analysis to evaluate the clinicopathological characteristics of cervical carcinoma: a multicenter study. Acta Radiol 2021; 64:395-403. [PMID: 34918963 DOI: 10.1177/02841851211065142] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/09/2022]
Abstract
BACKGROUND Preoperative prediction of clinical pathological indicators of cervical cancer (CC) is of great significance to the formulation of personalized treatment plans for CC. PURPOSE To investigate magnetic resonance imaging (MRI) radiomics analysis for the evaluation of pathological types, tumor grade, FIGO stage, and lymph node metastasis (LNM) of CC. MATERIAL AND METHODS A total of 235 patients with CC from three institutes were enrolled in the study. All patients underwent T2/SPAIR and contrast-enhanced T1-weighted (CE-T1WI) imaging scans before radical hysterectomy by pelvic lymph node dissection surgery. Radiomics features extracted from T2/SPAIR and CE-T1WI imaging were selected by the least absolute shrinkage and selection operator (LASSO) methods for further radiomics signature calculation. These radiomic features were used to construct regression and decision tree models to evaluate the performance of radiomic features in distinguishing clinicopathological indicators. RESULTS The area under the curve (AUC) of T2/SPAIR and CE-T1WI imaging were 0.777 and 0.750, respectively, for differentiating between adenocarcinoma and squamous cell carcinoma. From the two sequences, the AUC of the verification group that distinguished low FIGO stage from high FIGO stage was 0.716 and 0.676, respectively. The AUC for moderately well and poorly differentiated tumors were 0.729 on T2/SPAIR and 0.749 on CE-T1WI imaging. The AUC of the verification groups for LNM was 0.730 and 0.618 on T2/SPAIR and CE-T1WI imaging, respectively. CONCLUSION MRI radiomics features can be used as a non-invasive method to evaluate the clinicopathological indexes of CC and provide an important auxiliary examination method for patients to determine individualized treatment plans before operation.
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Affiliation(s)
- Yi Liu
- Medical Imaging Center, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong, PR China
| | - Ting Song
- Department of Radiology, The Third Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong, PR China
| | - Tian-Fa Dong
- Department of Radiology, The Third Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong, PR China
| | - Wei Zhang
- Department of Radiology, The Third Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong, PR China
| | - Ge Wen
- Medical Imaging Center, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong, PR China
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Ran C, Sun J, Qu Y, Long N. Clinical value of MRI, serum SCCA, and CA125 levels in the diagnosis of lymph node metastasis and para-uterine infiltration in cervical cancer. World J Surg Oncol 2021; 19:343. [PMID: 34886853 PMCID: PMC8656033 DOI: 10.1186/s12957-021-02448-3] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2021] [Accepted: 11/13/2021] [Indexed: 12/13/2022] Open
Abstract
Background Cervical cancer shows great differences in depth of invasion, metastasis, and other biological behaviors. The location of the lesion is special, so it is usually difficult to determine the clinical stage. This study aimed to explore the clinical value of magnetic resonance imaging (MRI) and tumor serum markers for the preoperative diagnosis of cervical cancer lymph node metastasis and para-uterine invasion. Methods A total of 200 patients with cervical cancer admitted to our hospital from January 2019 to January 2020 were collected as the research subjects. Comparing the diagnosis results of preoperative MRI scan, serum tumor markers, and postoperative pathological examination using single factor comparison, we determined the MRI scan results, the comprehensive matching rate between serum tumor markers (squamous cell carcinoma antigen (SCCA), carbohydrate antigen 125 (CA125)) and postoperative pathological results, and the differences of sensitivity, specificity, and accuracy in the prediction of lymph node metastasis and para-uterine infiltration of cervical cancer. Results The levels of SCCA and CA125 in patients with para-uterine invasion and lymph node metastasis were higher than those of patients without invasion and metastasis. Among them, the level of SCCA was significantly different (P<0.05). The level of CA125 was not statistically significant (P>0.05), so MRI combined with serum SCCA was selected for combined diagnosis in the later period. The sensitivity, specificity, and accuracy of MRI diagnosis of cervical cancer and para-uterine infiltrating lymph node metastasis and metastasis were 55.2, 91.6, and 89.5% and 55.2, 91.6, and 89.5%, respectively. These data in MRI combined with serum SCCA were 76.3, 95.3, and 94.3% and 63.2, 96.0, and 95.1%, respectively. The accuracy of tumor markers combined with MRI in the diagnosis of cervical cancer lymph node metastasis and para-uterine invasion was higher than that of MRI. Conclusions MRI combined with serum SCCA can more accurately identify cervical cancer lymph node metastasis and para-uterine invasion compared with MRI alone. Tumor marker combined with MRI diagnosis is an important auxiliary method for cervical cancer treatment and can provide comprehensive and reliable clinical evidence for evaluation before cervical cancer surgery.
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Affiliation(s)
- Chao Ran
- Department of Medical Imaging, Affiliated Yantai Yuhuangding Hospital of Qingdao University, No.20 Yuhuangding East Road, Zhifu District, Yantai, 264000, China
| | - Jian Sun
- Department of Medical Imaging, Affiliated Yantai Yuhuangding Hospital of Qingdao University, No.20 Yuhuangding East Road, Zhifu District, Yantai, 264000, China
| | - Yunhui Qu
- Department of Medical Imaging, Affiliated Yantai Yuhuangding Hospital of Qingdao University, No.20 Yuhuangding East Road, Zhifu District, Yantai, 264000, China
| | - Na Long
- Department of Medical Imaging, Affiliated Yantai Yuhuangding Hospital of Qingdao University, No.20 Yuhuangding East Road, Zhifu District, Yantai, 264000, China.
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Wang J, Liu J, Gao L, Li G, Sun Y, Shi B. Heart Rate Variability is an Independent Predictor of Lymph Node Metastasis in Patients with Cervical Cancer. Cancer Manag Res 2021; 13:8821-8830. [PMID: 34853536 PMCID: PMC8627856 DOI: 10.2147/cmar.s336268] [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: 08/26/2021] [Accepted: 11/11/2021] [Indexed: 01/19/2023] Open
Abstract
Purpose Heart rate variability (HRV) has been reported as a useful biomarker for prognostic factors in a variety of cancers. The purpose of this study was to explore the predictive value of preoperative HRV for lymph node metastasis (LNM) in patients with cervical cancer (CC). Patients and Methods A total of 77 patients with CC were included, including 18 LNM and 59 non-LNM patients. A five-minute resting electrocardiogram (ECG) was collected before surgery for the analysis of HRV time domain, frequency domain and Poincaré plot parameters (ie, SDNN, RMSSD, LF, HF, LF/HF, SD1, SD2 and SD2/SD1). Student’s t-tests and logistic regression were performed to determine the relationship between HRV and LNM. Results The LNM group had significantly lower SDNN, LF, and SD2 than the non-LNM group (all p < 0.05; all Cohen’s d > 0.5). Binary logistic regression analysis indicated that SDNN, LF and SD2 were still significantly associated with LNM. Specifically, for each 1 ms decrease in SDNN and SD2 and each 1 logarithmic unit decrease in ln (LF), the odds of LNM increased by 12%, 9%, and 86%, respectively (all p < 0.05). Conclusion These findings suggest an association between HRV and CC LNM, and HRV could be a potential noninvasive biomarker for the prediction of LNM in CC patients.
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Affiliation(s)
- Jingfeng Wang
- School of Medical Imaging, Bengbu Medical College, Bengbu, Anhui, 233030, People's Republic of China.,Anhui Key Laboratory of Computational Medicine and Intelligent Health, Bengbu Medical College, Bengbu, Anhui, 233030, People's Republic of China
| | - Jian Liu
- Department of Gynecologic Oncology, First Affiliated Hospital, Bengbu Medical College, Bengbu, Anhui, 233004, People's Republic of China
| | - Longfei Gao
- Department of Gynecologic Oncology, First Affiliated Hospital, Bengbu Medical College, Bengbu, Anhui, 233004, People's Republic of China
| | - Guangqiao Li
- School of Medical Imaging, Bengbu Medical College, Bengbu, Anhui, 233030, People's Republic of China.,Anhui Key Laboratory of Computational Medicine and Intelligent Health, Bengbu Medical College, Bengbu, Anhui, 233030, People's Republic of China
| | - Yilin Sun
- Department of Gynecologic Oncology, First Affiliated Hospital, Bengbu Medical College, Bengbu, Anhui, 233004, People's Republic of China
| | - Bo Shi
- School of Medical Imaging, Bengbu Medical College, Bengbu, Anhui, 233030, People's Republic of China.,Anhui Key Laboratory of Computational Medicine and Intelligent Health, Bengbu Medical College, Bengbu, Anhui, 233030, People's Republic of China
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Wang M, Perucho JA, Vardhanabhuti V, Ip P, Ngan HY, Lee EY. Radiomic Features of T2-weighted Imaging and Diffusion Kurtosis Imaging in Differentiating Clinicopathological Characteristics of Cervical Carcinoma. Acad Radiol 2021; 29:1133-1140. [PMID: 34583867 DOI: 10.1016/j.acra.2021.08.018] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2021] [Revised: 07/28/2021] [Accepted: 08/12/2021] [Indexed: 01/06/2023]
Abstract
RATIONALE AND OBJECTIVES Clinicopathological characteristics including histological subtypes, tumour grades and International Federation of Gynecology and Obstetrics (FIGO) stages are crucial factors in the clinical decision for cervical carcinoma (CC). The purpose of this study was to evaluate the ability of T2-weighted imaging (T2WI) and diffusion kurtosis imaging (DKI) radiomics in differentiating clinicopathological characteristics of CC. MATERIALS AND METHODS One hundred and seventeen histologically confirmed CC patients (mean age 56.5 ± 14.0 years) with pre-treatment magnetic resonance imaging were retrospectively reviewed. DKI was acquired with 4 b-values (0-1500 s/mm2). Volumes of interest were contoured around the tumours on T2WI and DKI. Radiomic features including shape, first-order and grey-level co-occurrence matrix with wavelet transforms were extracted. Intraclass correlation coeffient between 2 radiologists was used for features reduction. Feature selection was achieved by elastic net and minimum redundancy maximum relevance. Selected features were used to build random forest (RF) models. The performances for differentiating histological subtypes, tumour grades and FIGO stages were assessed by receiver operating characteristic analysis. RESULTS Area under the curves (AUCs) for T2WI-only RF models for discriminating histological subtypes, tumour grades and FIGO stages were 0.762, 0.686, and 0.719. AUCs for DWI-only models were 0.663, 0.645, and 0.868, respectively. AUCs of the combined T2WI and DKI models were 0.823, 0.790, and 0.850, respectively. CONCLUSION T2WI and DKI radiomic features could differentiate the clinicopathological characteristics of CC. A combined model showed excellent diagnostic discrimination for histological subtypes, while a DKI-only model presented the best performance in differentiating FIGO stages.
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Sakai K. [2. Radiomics of MRI]. Nihon Hoshasen Gijutsu Gakkai Zasshi 2021; 77:866-875. [PMID: 34421076 DOI: 10.6009/jjrt.2021_jsrt_77.8.866] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Affiliation(s)
- Koji Sakai
- Department of Radiology, Graduate School of Medical Science, Kyoto Prefectural University of Medicine
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Li H, Zhu M, Jian L, Bi F, Zhang X, Fang C, Wang Y, Wang J, Wu N, Yu X. Radiomic Score as a Potential Imaging Biomarker for Predicting Survival in Patients With Cervical Cancer. Front Oncol 2021; 11:706043. [PMID: 34485139 PMCID: PMC8415417 DOI: 10.3389/fonc.2021.706043] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2021] [Accepted: 07/19/2021] [Indexed: 01/07/2023] Open
Abstract
OBJECTIVES Accurate prediction of prognosis will help adjust or optimize the treatment of cervical cancer and benefit the patients. We aimed to investigate the incremental value of radiomics when added to the FIGO stage in predicting overall survival (OS) in patients with cervical cancer. METHODS This retrospective study included 106 patients with cervical cancer (FIGO stage IB1-IVa) between October 2017 and May 2019. Patients were randomly divided into a training cohort (n = 74) and validation cohort (n = 32). All patients underwent contrast-enhanced computed tomography (CT) prior to treatment. The ITK-SNAP software was used to delineate the region of interest on pre-treatment standard-of-care CT scans. We extracted 792 two-dimensional radiomic features by the Analysis Kit (AK) software. Pearson correlation coefficient analysis and Relief were used to detect the most discriminatory features. The radiomic signature (i.e., Radscore) was constructed via Adaboost with Leave-one-out cross-validation. Prognostic models were built by Cox regression model using Akaike information criterion (AIC) as the stopping rule. A nomogram was established to individually predict the OS of patients. Patients were then stratified into high- and low-risk groups according to the Youden index. Kaplan-Meier curves were used to compare the survival difference between the high- and low-risk groups. RESULTS Six textural features were identified, including one gray-level co-occurrence matrix feature and five gray-level run-length matrix features. Only the FIGO stage and Radscore were independent risk factors associated with OS (p < 0.05). The C-index of the FIGO stage in the training and validation cohorts was 0.703 (95% CI: 0.572-0.834) and 0.700 (95% CI: 0.526-0.874), respectively. Correspondingly, the C-index of Radscore was 0.794 (95% CI: 0.707-0.880) and 0.754 (95% CI: 0.623-0.885). The incorporation of the FIGO stage and Radscore achieved better performance, with a C-index of 0.830 (95% CI: 0.738-0.922) and 0.772 (95% CI: 0.615-0.929), respectively. The nomogram based on the FIGO stage and Radscore could individually predict the OS probability with good discrimination and calibration. The high-risk patients had shorter OS compared with the low-risk patients (p < 0.05). CONCLUSION Radiomics has the potential for noninvasive risk stratification and may improve the prediction of OS in patients with cervical cancer when added to the FIGO stage.
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Affiliation(s)
- Handong Li
- Department of Radiology, Hunan Cancer Hospital, The Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, China
| | - Miaochen Zhu
- Central Laboratory, Hunan Cancer Hospital, The Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, China
| | - Lian Jian
- Department of Radiology, Hunan Cancer Hospital, The Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, China
| | - Feng Bi
- Department of Radiology, Hunan Cancer Hospital, The Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, China
| | - Xiaoye Zhang
- Central Laboratory, Hunan Cancer Hospital, The Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, China
| | - Chao Fang
- Department of Clinical Pharmaceutical Research Institution, Hunan Cancer Hospital, Affiliated Tumor Hospital of Xiangya Medical School of Central South University, Changsha, China
| | - Ying Wang
- Central Laboratory, Hunan Cancer Hospital, The Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, China
| | - Jing Wang
- Gynecological Oncology Clinical Research Center, Hunan Cancer Hospital, Affiliated Tumor Hospital of Xiangya Medical School of Central South University, Changsha, China
| | - Nayiyuan Wu
- Central Laboratory, Hunan Cancer Hospital, The Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, China
| | - Xiaoping Yu
- Department of Radiology, Hunan Cancer Hospital, The Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, China
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Olthof EP, van der Aa MA, Adam JA, Stalpers LJA, Wenzel HHB, van der Velden J, Mom CH. The role of lymph nodes in cervical cancer: incidence and identification of lymph node metastases-a literature review. Int J Clin Oncol 2021; 26:1600-1610. [PMID: 34241726 DOI: 10.1007/s10147-021-01980-2] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2021] [Accepted: 06/24/2021] [Indexed: 01/15/2023]
Abstract
Correct identification of patients with lymph node metastasis from cervical cancer prior to treatment is of great importance, because it allows more tailored therapy. Patients may be spared unnecessary surgery or extended field radiotherapy if the nodal status can be predicted correctly. This review captures the existing knowledge on the identification of lymph node metastases in cervical cancer. The risk of nodal metastases increases per 2009 FIGO stage, with incidences in the pelvic region ranging from 2% (stage IA2) to 14-36% (IB), 38-51% (IIA) and 47% (IIB); and in the para-aortic region ranging from 2 to 5% (stage IB), 10-20% (IIA), 9% (IIB), 13-30% (III) and 50% (IV). In addition, age, tumor size, lymph vascular space invasion, parametrial invasion, depth of stromal invasion, histological type, and histological grade are reported to be independent prognostic factors for the risk of nodal metastases. Furthermore, biomarkers can contribute to predict a patient's nodal status, of which the squamous cell carcinoma antigen (SCC-Ag) is currently the most widely used in squamous cell cervical cancer. Still, pre-treatment lymph node assessment is primarily performed by imaging, of which diffusion-weighted magnetic resonance imaging has the highest sensitivity and 2-deoxy-2-[18F]fluoro-D-glucose positron emission computed tomography the highest specificity. Imaging results can be combined with clinical parameters in nomograms to increase the accuracy of predicting positives nodes. Despite all the progress regarding pre-treatment prediction of lymph node metastases in cervical cancer in recent years, prediction rates are not robust enough to safely abandon surgical staging of the pelvic or para-aortic region yet.
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Affiliation(s)
- Ester P Olthof
- Department of Research and Development, Netherlands Comprehensive Cancer Organisation, Godebaldkwartier 419, 3511 DT, Utrecht, The Netherlands.
- Department of Research and Development, Netherlands Comprehensive Cancer Organisation, Postbus 19079, 3501 DB, Utrecht, The Netherlands.
| | - Maaike A van der Aa
- Department of Research and Development, Netherlands Comprehensive Cancer Organisation, Godebaldkwartier 419, 3511 DT, Utrecht, The Netherlands
| | - Judit A Adam
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical Centre, University of Amsterdam, Meibergdreef 9, 1105 AZ, Amsterdam, The Netherlands
| | - Lukas J A Stalpers
- Department of Radiation Oncology, Amsterdam University Medical Centre, University of Amsterdam, Meibergdreef 9, 1105 AZ, Amsterdam, The Netherlands
| | - Hans H B Wenzel
- Department of Research and Development, Netherlands Comprehensive Cancer Organisation, Godebaldkwartier 419, 3511 DT, Utrecht, The Netherlands
| | - Jacobus van der Velden
- Department of Gynaecological Oncology, Amsterdam University Medical Centre, University of Amsterdam, Meibergdreef 9, 1105 AZ, Amsterdam, The Netherlands
| | - Constantijne H Mom
- Department of Gynaecological Oncology, Amsterdam University Medical Centre, University of Amsterdam, Meibergdreef 9, 1105 AZ, Amsterdam, The Netherlands
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Laliscia C, Gadducci A, Mattioni R, Orlandi F, Giusti S, Barcellini A, Gabelloni M, Morganti R, Neri E, Paiar F. MRI-based radiomics: promise for locally advanced cervical cancer treated with a tailored integrated therapeutic approach. TUMORI JOURNAL 2021; 108:376-385. [PMID: 34235995 DOI: 10.1177/03008916211014274] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
Abstract
OBJECTIVE To assess prognostic factors by analyzing clinical and radiomic data of patients with locally advanced cervical cancer (LACC) treated with definitive concurrent cisplatin-based chemoradiotherapy (CCRT) using magnetic resonance imaging (MRI). METHODS We analyzed radiomic features from MRI in 60 women with FIGO (International Federation of Gynecology and Obstetrics) stage IB2-IVA cervical cancer who underwent definitive CCRT 45-50.4 Gy (in 25-28 fractions). Thirty-nine (65.0%) received EBRT sequential boost (4-20 Gy) on primary tumor site and 56 (93.3%) received high-dose-rate brachytherapy boost (6-28 Gy) (daily fractions of 5-7 Gy). Moreover, 71.7% of patients received dose-dense neoadjuvant chemotherapy for 6 cycles. The gross tumor volume was defined on T2-weighted sequences and 29 features were extracted from each MRI performed before and after CCRT, using dedicated software, and their prognostic value was correlated with clinical information. RESULTS In univariate analysis, age ⩾60 years and FIGO stage IB2-IIB had significantly better progression-free survival (PFS) (p = 0.022 and p = 0.009, respectively). There was a trend for significance for worse overall survival (OS) in patients with positive nodes (p = 0.062). In multivariate analysis, only age ⩾60 years and FIGO stage IB2-IIB reached significantly better PFS (p = 0.020 and p = 0.053, respectively). In radiomic dataset, in multivariate analysis, pregray level p75 was significantly associated with PFS (p = 0.047), pre-D3D value with OS (p = 0.049), and preinformation measure of correlation value with local control (p = 0.031). CONCLUSION The combination of clinical and radiomics features can provide information to predict behavior and prognosis of LACC and to make more accurate treatment decisions.
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Affiliation(s)
- Concetta Laliscia
- Department of New Technologies and Translational Research, Division of Radiation Oncology, University of Pisa, Pisa, Italy
| | - Angiolo Gadducci
- Department of Experimental and Clinical Medicine, Division of Gynecology and Obstetrics, University of Pisa, Pisa, Italy
| | - Roberto Mattioni
- Department of New Technologies and Translational Research, Division of Radiation Oncology, University of Pisa, Pisa, Italy
| | - Francesca Orlandi
- Department of New Technologies and Translational Research, Division of Radiation Oncology, University of Pisa, Pisa, Italy
| | - Sabina Giusti
- Department of New Technologies and Translational Research, Division of Radiology, University of Pisa, Pisa, Italy
| | - Amelia Barcellini
- National Center of Oncological Hadrontherapy (Fondazione CNAO), Pavia, Italy
| | - Michela Gabelloni
- Department of New Technologies and Translational Research, Division of Radiology, University of Pisa, Pisa, Italy
| | - Riccardo Morganti
- Department of Clinical and Experimental Medicine, Section of Statistics, University of Pisa, Pisa, Italy
| | - Emanuele Neri
- Department of New Technologies and Translational Research, Division of Radiology, University of Pisa, Pisa, Italy
| | - Fabiola Paiar
- Department of New Technologies and Translational Research, Division of Radiation Oncology, University of Pisa, Pisa, Italy
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Yuan HL, Zhang X, Li Y, Guan Q, Chu WW, Yu HP, Liu L, Zheng YQ, Lu JJ. A Nomogram for Predicting Risk of Thromboembolism in Gastric Cancer Patients Receiving Chemotherapy. Front Oncol 2021; 11:598116. [PMID: 34123774 PMCID: PMC8187914 DOI: 10.3389/fonc.2021.598116] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2020] [Accepted: 02/22/2021] [Indexed: 12/15/2022] Open
Abstract
Purpose: The aims of this study were to develop and validate a novel nomogram to predict thromboembolism (TE) in gastric cancer (GC) patients receiving chemotherapy and to test its predictive ability. Methods: This retrospective study included 544 GC patients who received chemotherapy as the initial treatment at two medical centers. Among the 544 GC patients who received chemotherapy, 275 and 137 patients in the First Affiliated Hospital of Nanchang University from January 2014 to March 2019 were enrolled in the training cohort and the validation cohort, respectively. A total of 132 patients in the Beilun branch of the First Affiliated Hospital of Zhejiang University from January 2015 to August 2019 were enrolled in external validation cohorts. The nomogram was based on parameters determined by univariate and multivariate logistic analyses. The prediction performance of the nomogram was measured by the area under the receiver operating characteristic curve (AUROC), the calibration curve, and decision curve analysis (DCA). The applicability of the nomogram was internally and independently validated. Results: The predictors included the Eastern Cooperative Oncology Group Performance Status (ECOG), presence of an active cancer (AC), central venous catheter (CVC), and D-dimer levels. These risk factors are shown on the nomogram and verified. The nomogram demonstrated good discrimination and fine calibration with an AUROC of 0.875 (0.832 in internal validation and 0.807 in independent validation). The DCA revealed that the nomogram had a high clinical application value. Conclusions: We propose the nomogram for predicting TE in patients with GC receiving chemotherapy, which can help in making timely personalized clinical decisions for different risk populations.
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Affiliation(s)
- Hai-Liang Yuan
- Department of Gastroenterology, Beilun Branch of the First Affiliated Hospital of Zhejiang University, Ningbo, China.,Department of Gastroenterology Oncology, The First Affiliated Hospital of Nanchang University, Nanchang, China
| | - Xiang Zhang
- Department of Gastroenterology Oncology, The First Affiliated Hospital of Nanchang University, Nanchang, China
| | - Yan Li
- Department of Gastroenterology, Beilun Branch of the First Affiliated Hospital of Zhejiang University, Ningbo, China
| | - Qing Guan
- Department of Gastroenterology, Beilun Branch of the First Affiliated Hospital of Zhejiang University, Ningbo, China
| | - Wei-Wei Chu
- Department of Gastroenterology, Beilun Branch of the First Affiliated Hospital of Zhejiang University, Ningbo, China
| | - Hai-Ping Yu
- Department of Gastroenterology, Beilun Branch of the First Affiliated Hospital of Zhejiang University, Ningbo, China
| | - Lian Liu
- Department of Gastroenterology, Beilun Branch of the First Affiliated Hospital of Zhejiang University, Ningbo, China
| | - Yun-Quan Zheng
- Department of Gastroenterology, Beilun Branch of the First Affiliated Hospital of Zhejiang University, Ningbo, China
| | - Jing-Jing Lu
- Department of Gastroenterology, Beilun Branch of the First Affiliated Hospital of Zhejiang University, Ningbo, China
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Li C, Yin J. Radiomics Based on T2-Weighted Imaging and Apparent Diffusion Coefficient Images for Preoperative Evaluation of Lymph Node Metastasis in Rectal Cancer Patients. Front Oncol 2021; 11:671354. [PMID: 34041033 PMCID: PMC8141802 DOI: 10.3389/fonc.2021.671354] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2021] [Accepted: 04/12/2021] [Indexed: 12/24/2022] Open
Abstract
Purpose To develop and validate a radiomics nomogram based on T2-weighted imaging (T2WI) and apparent diffusion coefficient (ADC) features for the preoperative prediction of lymph node (LN) metastasis in rectal cancer patients. Materials and Methods One hundred and sixty-two patients with rectal cancer confirmed by pathology were retrospectively analyzed, who underwent T2WI and DWI sequences. The data sets were divided into training (n = 97) and validation (n = 65) cohorts. For each case, a total of 2,752 radiomic features were extracted from T2WI, and ADC images derived from diffusion-weighted imaging. A two-sample t-test was used for prefiltering. The least absolute shrinkage selection operator method was used for feature selection. Three radiomics scores (rad-scores) (rad-score 1 for T2WI, rad-score 2 for ADC, and rad-score 3 for the combination of both) were calculated using the support vector machine classifier. Multivariable logistic regression analysis was then used to construct a radiomics nomogram combining rad-score 3 and independent risk factors. The performances of three rad-scores and the nomogram were evaluated using the area under the receiver operating characteristic curve (AUC). Decision curve analysis (DCA) was used to assess the clinical usefulness of the radiomics nomogram. Results The AUCs of the rad-score 1 and rad-score 2 were 0.805, 0.749 and 0.828, 0.770 in the training and validation cohorts, respectively. The rad-score 3 achieved an AUC of 0.879 in the training cohort and an AUC of 0.822 in the validation cohort. The radiomics nomogram, incorporating the rad-score 3, age, and LN size, showed good discrimination with the AUC of 0.937 for the training cohort and 0.884 for the validation cohort. DCA confirmed that the radiomics nomogram had clinical utility. Conclusions The radiomics nomogram, incorporating rad-score based on features from the T2WI and ADC images, and clinical factors, has favorable predictive performance for preoperative prediction of LN metastasis in patients with rectal cancer.
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Affiliation(s)
- Chunli Li
- Department of Radiology, Shengjing Hospital of China Medical University, Shenyang, China.,Department of Biomedical Engineering, School of Fundamental Sciences, China Medical University, Shenyang, China
| | - Jiandong Yin
- Department of Radiology, Shengjing Hospital of China Medical University, Shenyang, China
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Michalet M, Azria D, Tardieu M, Tibermacine H, Nougaret S. Radiomics in radiation oncology for gynecological malignancies: a review of literature. Br J Radiol 2021; 94:20210032. [PMID: 33882246 DOI: 10.1259/bjr.20210032] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022] Open
Abstract
Radiomics is the extraction of a significant number of quantitative imaging features with the aim of detecting information in correlation with useful clinical outcomes. Features are extracted, after delineation of an area of interest, from a single or a combined set of imaging modalities (including X-ray, US, CT, PET/CT and MRI). Given the high dimensionality, the analytical process requires the use of artificial intelligence algorithms. Firstly developed for diagnostic performance in radiology, it has now been translated to radiation oncology mainly to predict tumor response and patient outcome but other applications have been developed such as dose painting, prediction of side-effects, and quality assurance. In gynecological cancers, most studies have focused on outcomes of cervical cancers after chemoradiation. This review highlights the role of this new tool for the radiation oncologists with particular focus on female GU oncology.
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Affiliation(s)
- Morgan Michalet
- University Federation of Radiation Oncology of Mediterranean Occitanie, Montpellier Cancer Institute, Univ Montpellier, Montpellier, France.,INSERM U1194 IRCM, Montpellier, France
| | - David Azria
- University Federation of Radiation Oncology of Mediterranean Occitanie, Montpellier Cancer Institute, Univ Montpellier, Montpellier, France.,INSERM U1194 IRCM, Montpellier, France
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Liu D, Zhang X, Zheng T, Shi Q, Cui Y, Wang Y, Liu L. Optimisation and evaluation of the random forest model in the efficacy prediction of chemoradiotherapy for advanced cervical cancer based on radiomics signature from high-resolution T2 weighted images. Arch Gynecol Obstet 2021; 303:811-820. [PMID: 33394142 PMCID: PMC7960581 DOI: 10.1007/s00404-020-05908-5] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2020] [Accepted: 11/17/2020] [Indexed: 12/28/2022]
Abstract
PURPOSE Our objective was to establish a random forest model and to evaluate its predictive capability of the treatment effect of neoadjuvant chemotherapy-radiation therapy. METHODS This retrospective study included 82 patients with locally advanced cervical cancer who underwent scanning from March 2013 to May 2018. The random forest model was established and optimised based on the open source toolkit scikit-learn. Byoptimising of the number of decision trees in the random forest, the criteria for selecting the final partition index and the minimum number of samples partitioned by each node, the performance of random forest in the prediction of the treatment effect of neoadjuvant chemotherapy-radiation therapy on advanced cervical cancer (> IIb) was evaluated. RESULTS The number of decision trees in the random forests influenced the model performance. When the number of decision trees was set to 10, 25, 40, 55, 70, 85 and 100, the performance of random forest model exhibited an increasing trend first and then a decreasing one. The criteria for the selection of final partition index showed significant effects on the generation of decision trees. The Gini index demonstrated a better effect compared with information gain index. The area under the receiver operating curve for Gini index attained a value of 0.917. CONCLUSION The random forest model showed potential in predicting the treatment effect of neoadjuvant chemotherapy-radiation therapy based on high-resolution T2WIs for advanced cervical cancer (> IIb).
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Affiliation(s)
- Defeng Liu
- Department of Magnetic Resonance Imaging, Qinhuangdao Municipal No. 1 Hospital, Qinhuangdao, People's Republic of China
| | - Xiaohang Zhang
- State Grid Information & Telecommunication Group Co., Ltd., Beijing, People's Republic of China
| | - Tao Zheng
- Department of Magnetic Resonance Imaging, Qinhuangdao Municipal No. 1 Hospital, Qinhuangdao, People's Republic of China
| | - Qinglei Shi
- Scientific Clinical Specialist, Siemens Ltd., Beijing, People's Republic of China
| | - Yujie Cui
- Department of Magnetic Resonance Imaging, Qinhuangdao Municipal No. 1 Hospital, Qinhuangdao, People's Republic of China
| | - Yongji Wang
- Cooperative Innovation Center, Institute of Software, Chinese Academy of Sciences, Beijing, People's Republic of China
- University of Chinese Academy of Sciences, Beijing, People's Republic of China
- State Key Laboratory of Computer Science (Institute of Software, The Chinese Academy of Sciences), Beijing, People's Republic of China
| | - Lanxiang Liu
- Department of Magnetic Resonance Imaging, Qinhuangdao Municipal No. 1 Hospital, Qinhuangdao, People's Republic of China.
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Feasibility of MRI-based radiomics features for predicting lymph node metastases and VEGF expression in cervical cancer. Eur J Radiol 2020; 134:109429. [PMID: 33290975 DOI: 10.1016/j.ejrad.2020.109429] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2020] [Revised: 10/09/2020] [Accepted: 11/18/2020] [Indexed: 12/29/2022]
Abstract
PURPOSE To investigate the predictive value of MRI-based radiomics features for lymph node metastasis (LNM) and vascular endothelial growth factor (VEGF) expression in patients with cervical cancer. METHOD A total of 163 patients with cervical cancer were enrolled in this study. A total of 134 patients were included for LNM differentiation, and 118 were included for VEGF expression discrimination. The patients were randomly assigned to the training group or test group at a ratio of 2:1. Radiomics features were extracted from T1WI enhanced and T2WI MRI scans of each patient, and tumor stage was also documented according to the International Federation of Gynecology and Obstetrics (FIGO) guidelines. The least absolute shrinkage and selection operator algorithm was used for feature selection. The results of 5-fold cross validation were applied to select the best classification models. The performances of the constructed models were further evaluated with the test group. RESULTS Sixteen radiomics features and the FIGO stage were selected to construct the LNM discrimination model. The LNM prediction model achieved the best diagnostic performance, with areas under the receiver operating curve (AUCs) of 0.95 and 0.88 in the training group and test group, respectively. Nine radiomics characteristics were screened to build the VEGF prediction model, with AUCs of 0.82 and 0.70 in the training group and test group, respectively. Decision curve analysis confirmed their clinical usefulness. CONCLUSIONS The presented radiomics prediction models demonstrated potential to noninvasively differentiate LNM and VEGF expression in cervical cancer.
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