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Leng Y, Wang X, Zheng T, Peng F, Xiong L, Wang Y, Gong L. Development and validation of radiomics nomogram for metastatic status of epithelial ovarian cancer. Sci Rep 2024; 14:12456. [PMID: 38816463 PMCID: PMC11139946 DOI: 10.1038/s41598-024-63369-1] [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: 05/30/2023] [Accepted: 05/28/2024] [Indexed: 06/01/2024] Open
Abstract
To develop and validate an enhanced CT-based radiomics nomogram for evaluating preoperative metastasis risk of epithelial ovarian cancer (EOC). One hundred and nine patients with histologically confirmed EOC were retrospectively enrolled. The volume of interest (VOI) was delineated in preoperative enhanced CT images, and 851 radiomics features were extracted. The radiomics features were selected by the least absolute shrinkage and selection operator (LASSO), and the rad-score was calculated using the formula of the radiomics label. A clinical model, radiomics model, and combined model were constructed using the logistic regression classification algorithm. Receiver operating characteristic (ROC) curve analysis and decision curve analysis (DCA) were used to evaluate the diagnostic performance of the models. Seventy-five patients (68.8%) were histologically confirmed to have metastasis. Eleven optimal radiomics features were retained by the LASSO algorithm to develop the radiomic model. The combined model for evaluating metastasis of EOC achieved area under the curve (AUC) values of 0.929 (95% CI 0.8593-0.9996) in the training cohort and 0.909 (95% CI 0.7921-1.0000) in the test cohort. To facilitate clinical use, a radiomic nomogram was built by combining the clinical characteristics with rad-score. The DCA indicated that the nomogram had the most significant net benefit when the threshold probability exceeded 15%, surpassing the benefits of both the treat-all and treat-none strategies. Compared with clinical model and radiomics model, the radiomics nomogram has the best diagnostic performance in evaluating EOC metastasis. The nomogram is a useful and convenient tool for clinical doctors to develop personalized treatment plans for EOC patients.
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Affiliation(s)
- Yinping Leng
- Department of Radiology, The Second Affiliated Hospital of Nanchang University, Minde Road No. 1, Nanchang, 330006, Jiangxi, China
| | - Xiwen Wang
- Department of Radiology, The Second Affiliated Hospital of Nanchang University, Minde Road No. 1, Nanchang, 330006, Jiangxi, China
| | - Tian Zheng
- Department of Radiology, The Second Affiliated Hospital of Nanchang University, Minde Road No. 1, Nanchang, 330006, Jiangxi, China
| | - Fei Peng
- Department of Radiology, The Second Affiliated Hospital of Nanchang University, Minde Road No. 1, Nanchang, 330006, Jiangxi, China
| | - Liangxia Xiong
- Department of Radiology, The Second Affiliated Hospital of Nanchang University, Minde Road No. 1, Nanchang, 330006, Jiangxi, China
| | - Yu Wang
- Clinical and Technical Support, Philips Healthcare, Shanghai, 200072, Shanghai, China
| | - Lianggeng Gong
- Department of Radiology, The Second Affiliated Hospital of Nanchang University, Minde Road No. 1, Nanchang, 330006, Jiangxi, China.
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Liu L, Cai W, Tian H, Wu B, Zhang J, Wang T, Hao Y, Yue G. Ultrasound image-based nomogram combining clinical, radiomics, and deep transfer learning features for automatic classification of ovarian masses according to O-RADS. Front Oncol 2024; 14:1377489. [PMID: 38812784 PMCID: PMC11133542 DOI: 10.3389/fonc.2024.1377489] [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: 01/27/2024] [Accepted: 04/16/2024] [Indexed: 05/31/2024] Open
Abstract
Background Accurate and rapid discrimination between benign and malignant ovarian masses is crucial for optimal patient management. This study aimed to establish an ultrasound image-based nomogram combining clinical, radiomics, and deep transfer learning features to automatically classify the ovarian masses into low risk and intermediate-high risk of malignancy lesions according to the Ovarian- Adnexal Reporting and Data System (O-RADS). Methods The ultrasound images of 1,080 patients with 1,080 ovarian masses were included. The training cohort consisting of 683 patients was collected at the South China Hospital of Shenzhen University, and the test cohort consisting of 397 patients was collected at the Shenzhen University General Hospital. The workflow included image segmentation, feature extraction, feature selection, and model construction. Results The pre-trained Resnet-101 model achieved the best performance. Among the different mono-modal features and fusion feature models, nomogram achieved the highest level of diagnostic performance (AUC: 0.930, accuracy: 84.9%, sensitivity: 93.5%, specificity: 81.7%, PPV: 65.4%, NPV: 97.1%, precision: 65.4%). The diagnostic indices of the nomogram were higher than those of junior radiologists, and the diagnostic indices of junior radiologists significantly improved with the assistance of the model. The calibration curves showed good agreement between the prediction of nomogram and actual classification of ovarian masses. The decision curve analysis showed that the nomogram was clinically useful. Conclusion This model exhibited a satisfactory diagnostic performance compared to junior radiologists. It has the potential to improve the level of expertise of junior radiologists and provide a fast and effective method for ovarian cancer screening.
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Affiliation(s)
- Lu Liu
- Department of Ultrasound Medicine, South China Hospital, Medical School, Shenzhen University, Shenzhen, China
| | - Wenjun Cai
- Department of Ultrasound, Shenzhen University General Hospital, Medical School, Shenzhen University, Shenzhen, China
| | - Hongyan Tian
- Department of Ultrasound Medicine, South China Hospital, Medical School, Shenzhen University, Shenzhen, China
| | - Beibei Wu
- Department of Ultrasound Medicine, South China Hospital, Medical School, Shenzhen University, Shenzhen, China
| | - Jing Zhang
- Department of Ultrasound Medicine, South China Hospital, Medical School, Shenzhen University, Shenzhen, China
| | - Ting Wang
- Department of Ultrasound Medicine, South China Hospital, Medical School, Shenzhen University, Shenzhen, China
| | - Yi Hao
- Department of Ultrasound Medicine, South China Hospital, Medical School, Shenzhen University, Shenzhen, China
| | - Guanghui Yue
- National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen University, Shenzhen, China
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Lin Z, Ge H, Guo Q, Ren J, Gu W, Lu J, Zhong Y, Qiang J, Gong J, Li H. MRI-based radiomics model to preoperatively predict mesenchymal transition subtype in high-grade serous ovarian cancer. Clin Radiol 2024; 79:e715-e724. [PMID: 38342715 DOI: 10.1016/j.crad.2024.01.018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2023] [Revised: 01/04/2024] [Accepted: 01/12/2024] [Indexed: 02/13/2024]
Abstract
AIM To develop a magnetic resonance imaging (MRI)-based radiomics model for the preoperative identification of mesenchymal transition (MT) subtype in high-grade serous ovarian cancer (HGSOC). MATERIALS AND METHODS One hundred and eighty-nine patients with histopathologically confirmed HGSOC were enrolled retrospectively. Among the included patients, 55 patients were determined as the MT subtype and the remaining 134 were non-MT subtype. After extracting a total of 204 features from T2-weighted imaging (T2WI) and contrast-enhanced (CE)-T1WI images, the Mann-Whitney U-test, Spearman correlation test, and Boruta algorithm were adopted to select the optimal feature set. Three classifiers, including logistic regression (LR), support vector machine (SVM), and random forest (RF), were trained to develop radiomics models. The performance of established models was evaluated from three aspects: discrimination, calibration, and clinical utility. RESULTS Seven radiomics features relevant to MT subtypes were selected to build the radiomics models. The model based on the RF algorithm showed the best performance in predicting MT subtype, with areas under the curves (AUCs) of 0.866 (95 % confidence interval [CI]: 0.797-0.936) and 0.852 (95 % CI: 0.736-0.967) in the training and testing cohorts, respectively. The calibration curves, supported with Brier scores, indicated very good consistency between observation and prediction. Decision curve analysis (DCA) showed that the RF-based model could provide more net benefit, which suggested favorable utility in clinical application. CONCLUSION The RF-based radiomics model provided accurate identification of MT from the non-MT subtype and may help facilitate personalised management of HGSOC.
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Affiliation(s)
- Z Lin
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai 200032, China; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, China; Department of Radiology, Jinshan Hospital, Fudan University, Shanghai 201508, China
| | - H Ge
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, China; Department of Pathology, Fudan University Shanghai Cancer Center, Shanghai 200032, China
| | - Q Guo
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, China; Department of Gynecological Oncology, Fudan University Shanghai Cancer Center, Shanghai 200032, China
| | - J Ren
- Department of Pharmaceuticals Diagnostics, GE HealthCare, Beijing 100176, China
| | - W Gu
- Department of Pathology, Obstetrics & Gynecology Hospital, Fudan University, Shanghai 200090, China
| | - J Lu
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai 200032, China; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, China
| | - Y Zhong
- Department of Radiology, Jinshan Hospital, Fudan University, Shanghai 201508, China
| | - J Qiang
- Department of Radiology, Jinshan Hospital, Fudan University, Shanghai 201508, China.
| | - J Gong
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai 200032, China; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, China.
| | - H Li
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai 200032, China; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, China.
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Du Y, Guo W, Xiao Y, Chen H, Yao J, Wu J. Ultrasound-based deep learning radiomics model for differentiating benign, borderline, and malignant ovarian tumours: a multi-class classification exploratory study. BMC Med Imaging 2024; 24:89. [PMID: 38622546 PMCID: PMC11020982 DOI: 10.1186/s12880-024-01251-2] [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: 09/06/2023] [Accepted: 03/18/2024] [Indexed: 04/17/2024] Open
Abstract
BACKGROUND Accurate preoperative identification of ovarian tumour subtypes is imperative for patients as it enables physicians to custom-tailor precise and individualized management strategies. So, we have developed an ultrasound (US)-based multiclass prediction algorithm for differentiating between benign, borderline, and malignant ovarian tumours. METHODS We randomised data from 849 patients with ovarian tumours into training and testing sets in a ratio of 8:2. The regions of interest on the US images were segmented and handcrafted radiomics features were extracted and screened. We applied the one-versus-rest method in multiclass classification. We inputted the best features into machine learning (ML) models and constructed a radiomic signature (Rad_Sig). US images of the maximum trimmed ovarian tumour sections were inputted into a pre-trained convolutional neural network (CNN) model. After internal enhancement and complex algorithms, each sample's predicted probability, known as the deep transfer learning signature (DTL_Sig), was generated. Clinical baseline data were analysed. Statistically significant clinical parameters and US semantic features in the training set were used to construct clinical signatures (Clinic_Sig). The prediction results of Rad_Sig, DTL_Sig, and Clinic_Sig for each sample were fused as new feature sets, to build the combined model, namely, the deep learning radiomic signature (DLR_Sig). We used the receiver operating characteristic (ROC) curve and the area under the ROC curve (AUC) to estimate the performance of the multiclass classification model. RESULTS The training set included 440 benign, 44 borderline, and 196 malignant ovarian tumours. The testing set included 109 benign, 11 borderline, and 49 malignant ovarian tumours. DLR_Sig three-class prediction model had the best overall and class-specific classification performance, with micro- and macro-average AUC of 0.90 and 0.84, respectively, on the testing set. Categories of identification AUC were 0.84, 0.85, and 0.83 for benign, borderline, and malignant ovarian tumours, respectively. In the confusion matrix, the classifier models of Clinic_Sig and Rad_Sig could not recognise borderline ovarian tumours. However, the proportions of borderline and malignant ovarian tumours identified by DLR_Sig were the highest at 54.55% and 63.27%, respectively. CONCLUSIONS The three-class prediction model of US-based DLR_Sig can discriminate between benign, borderline, and malignant ovarian tumours. Therefore, it may guide clinicians in determining the differential management of patients with ovarian tumours.
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Affiliation(s)
- Yangchun Du
- Department of Ultrasound, The First Affiliated Hospital of Guangxi Medical University, No.6 Shuangyong Road, Qingxiu District, 530021, Nanning, China
- Department of Ultrasound, The People's Hospital of Guangxi Zhuang Autonomous Region & Guangxi Academy of Medical Sciences, No.6 Taoyuan Road, Qingxiu District, 530021, Nanning, China
| | - Wenwen Guo
- Department of Pathology, The People's Hospital of Guangxi Zhuang Autonomous Region & Guangxi Academy of Medical Sciences, No.6 Taoyuan Road, Qingxiu District, 530021, Nanning, China
| | - Yanju Xiao
- Department of Ultrasound, The People's Hospital of Guangxi Zhuang Autonomous Region & Guangxi Academy of Medical Sciences, No.6 Taoyuan Road, Qingxiu District, 530021, Nanning, China
| | - Haining Chen
- Department of Ultrasound, The People's Hospital of Guangxi Zhuang Autonomous Region & Guangxi Academy of Medical Sciences, No.6 Taoyuan Road, Qingxiu District, 530021, Nanning, China
| | - Jinxiu Yao
- Department of Ultrasound, The People's Hospital of Guangxi Zhuang Autonomous Region & Guangxi Academy of Medical Sciences, No.6 Taoyuan Road, Qingxiu District, 530021, Nanning, China
| | - Ji Wu
- Department of Ultrasound, The First Affiliated Hospital of Guangxi Medical University, No.6 Shuangyong Road, Qingxiu District, 530021, Nanning, China.
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Du Y, Xiao Y, Guo W, Yao J, Lan T, Li S, Wen H, Zhu W, He G, Zheng H, Chen H. Development and validation of an ultrasound-based deep learning radiomics nomogram for predicting the malignant risk of ovarian tumours. Biomed Eng Online 2024; 23:41. [PMID: 38594729 PMCID: PMC11003110 DOI: 10.1186/s12938-024-01234-y] [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: 02/05/2024] [Accepted: 04/02/2024] [Indexed: 04/11/2024] Open
Abstract
BACKGROUND The timely identification and management of ovarian cancer are critical determinants of patient prognosis. In this study, we developed and validated a deep learning radiomics nomogram (DLR_Nomogram) based on ultrasound (US) imaging to accurately predict the malignant risk of ovarian tumours and compared the diagnostic performance of the DLR_Nomogram to that of the ovarian-adnexal reporting and data system (O-RADS). METHODS This study encompasses two research tasks. Patients were randomly divided into training and testing sets in an 8:2 ratio for both tasks. In task 1, we assessed the malignancy risk of 849 patients with ovarian tumours. In task 2, we evaluated the malignancy risk of 391 patients with O-RADS 4 and O-RADS 5 ovarian neoplasms. Three models were developed and validated to predict the risk of malignancy in ovarian tumours. The predicted outcomes of the models for each sample were merged to form a new feature set that was utilised as an input for the logistic regression (LR) model for constructing a combined model, visualised as the DLR_Nomogram. Then, the diagnostic performance of these models was evaluated by the receiver operating characteristic curve (ROC). RESULTS The DLR_Nomogram demonstrated superior predictive performance in predicting the malignant risk of ovarian tumours, as evidenced by area under the ROC curve (AUC) values of 0.985 and 0.928 for the training and testing sets of task 1, respectively. The AUC value of its testing set was lower than that of the O-RADS; however, the difference was not statistically significant. The DLR_Nomogram exhibited the highest AUC values of 0.955 and 0.869 in the training and testing sets of task 2, respectively. The DLR_Nomogram showed satisfactory fitting performance for both tasks in Hosmer-Lemeshow testing. Decision curve analysis demonstrated that the DLR_Nomogram yielded greater net clinical benefits for predicting malignant ovarian tumours within a specific range of threshold values. CONCLUSIONS The US-based DLR_Nomogram has shown the capability to accurately predict the malignant risk of ovarian tumours, exhibiting a predictive efficacy comparable to that of O-RADS.
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Affiliation(s)
- Yangchun Du
- Department of Ultrasound, The People's Hospital of Guangxi Zhuang Autonomous Region and Guangxi Academy of Medical Sciences, No. 6 Taoyuan Road, Qingxiu District, Nanning, 530021, China
| | - Yanju Xiao
- Department of Ultrasound, The People's Hospital of Guangxi Zhuang Autonomous Region and Guangxi Academy of Medical Sciences, No. 6 Taoyuan Road, Qingxiu District, Nanning, 530021, China
| | - Wenwen Guo
- Department of Pathology, The People's Hospital of Guangxi Zhuang Autonomous Region and Guangxi Academy of Medical Sciences, No. 6 Taoyuan Road, Qingxiu District, Nanning, 530021, China
| | - Jinxiu Yao
- Department of Ultrasound, The People's Hospital of Guangxi Zhuang Autonomous Region and Guangxi Academy of Medical Sciences, No. 6 Taoyuan Road, Qingxiu District, Nanning, 530021, China
| | - Tongliu Lan
- Department of Ultrasound, The People's Hospital of Guangxi Zhuang Autonomous Region and Guangxi Academy of Medical Sciences, No. 6 Taoyuan Road, Qingxiu District, Nanning, 530021, China
| | - Sijin Li
- Department of Ultrasound, The People's Hospital of Guangxi Zhuang Autonomous Region and Guangxi Academy of Medical Sciences, No. 6 Taoyuan Road, Qingxiu District, Nanning, 530021, China
| | - Huoyue Wen
- Department of Ultrasound, The People's Hospital of Guangxi Zhuang Autonomous Region and Guangxi Academy of Medical Sciences, No. 6 Taoyuan Road, Qingxiu District, Nanning, 530021, China
| | - Wenying Zhu
- Department of Ultrasound, The People's Hospital of Guangxi Zhuang Autonomous Region and Guangxi Academy of Medical Sciences, No. 6 Taoyuan Road, Qingxiu District, Nanning, 530021, China
| | - Guangling He
- Department of Ultrasound, The People's Hospital of Guangxi Zhuang Autonomous Region and Guangxi Academy of Medical Sciences, No. 6 Taoyuan Road, Qingxiu District, Nanning, 530021, China
| | - Hongyu Zheng
- Department of Ultrasound, The People's Hospital of Guangxi Zhuang Autonomous Region and Guangxi Academy of Medical Sciences, No. 6 Taoyuan Road, Qingxiu District, Nanning, 530021, China.
| | - Haining Chen
- Department of Ultrasound, The People's Hospital of Guangxi Zhuang Autonomous Region and Guangxi Academy of Medical Sciences, No. 6 Taoyuan Road, Qingxiu District, Nanning, 530021, China.
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Yang EJ, Lee AJ, Hwang WY, Chang SJ, Kim HS, Kim NK, Kim Y, Kong TW, Lee EJ, Park SJ, Son JH, Suh DH, Son DH, Shim SH. Lymphadenectomy in clinically early epithelial ovarian cancer and survival analysis (LILAC): a Gynecologic Oncology Research Investigators Collaboration (GORILLA-3002) retrospective study. J Gynecol Oncol 2024; 35:35.e75. [PMID: 38497109 DOI: 10.3802/jgo.2024.35.e75] [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: 06/30/2023] [Revised: 02/13/2024] [Accepted: 02/25/2024] [Indexed: 03/19/2024] Open
Abstract
OBJECTIVE This study aimed to evaluate the therapeutic role of lymphadenectomy in patients surgically treated for clinically early-stage epithelial ovarian cancer (EOC). METHODS This retrospective, multicenter study included patients with clinically early-stage EOC based on preoperative abdominal-pelvic computed tomography or magnetic resonance imaging findings between 2007 and 2021. Oncologic outcomes and perioperative complications were compared between the lymphadenectomy and non-lymphadenectomy groups. Independent prognostic factors were determined using Cox regression analysis. Disease-free survival (DFS) was the primary outcome. Overall survival (OS) and perioperative outcomes were the secondary outcomes. RESULTS In total, 586 patients (lymphadenectomy group, n=453 [77.3%]; non-lymphadenectomy groups, n=133 [22.7%]) were eligible. After surgical staging, upstaging was identified based on the presence of lymph node metastasis in 14 (3.1%) of 453 patients. No significant difference was found in the 5-year DFS (88.9% vs. 83.4%, p=0.203) and 5-year OS (97.2% vs. 97.7%, p=0.895) between the two groups. Using multivariable analysis, lymphadenectomy was not significantly associated with DFS or OS. However, using subgroup analysis, the lymphadenectomy group with serous histology had higher 5-year DFS rates than did the non-lymphadenectomy group (86.5% vs. 74.4%, p=0.048; adjusted hazard ratio=0.281; 95% confidence interval=0.107-0.735; p=0.010). The lymphadenectomy group had longer operating time (p<0.001), higher estimated blood loss (p<0.001), and higher perioperative complication rate (p=0.004) than did the non-lymphadenectomy group. CONCLUSION In patients with clinically early-stage EOC with serous histology, lymphadenectomy was associated with survival benefits. Considering its potential harm, lymphadenectomy should be performed according to histologic subtype and subsequent chemotherapy in patients with clinically early-stage EOC. TRIAL REGISTRATION Clinical Research Information Service Identifier: KCT0007309.
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Affiliation(s)
- Eun Jung Yang
- Department of Obstetrics and Gynecology, Soonchunhyang University Cheonan Hospital, Cheonan, Korea
| | - A Jin Lee
- Department of Obstetrics and Gynecology, Research Institute of Medical Science, Konkuk University School of Medicine, Seoul, Korea
| | - Woo Yeon Hwang
- Department of Obstetrics and Gynecology, Seoul National University Bundang Hospital, Seongnam, Korea
| | - Suk-Joon Chang
- Department of Obstetrics and Gynecology, Ajou University School of Medicine, Suwon, Korea
| | - Hee Seung Kim
- Department of Obstetrics and Gynecology, Seoul National University College of Medicine, Seoul, Korea
| | - Nam Kyeong Kim
- Department of Obstetrics and Gynecology, Seoul National University Bundang Hospital, Seongnam, Korea
| | - Yeorae Kim
- Department of Obstetrics and Gynecology, Seoul National University Bundang Hospital, Seongnam, Korea
| | - Tae Wook Kong
- Department of Obstetrics and Gynecology, Ajou University School of Medicine, Suwon, Korea
| | - Eun Ji Lee
- Department of Obstetrics and Gynecology, Seoul National University College of Medicine, Seoul, Korea
| | - Soo Jin Park
- Department of Obstetrics and Gynecology, Seoul National University College of Medicine, Seoul, Korea
| | - Joo-Hyuk Son
- Department of Obstetrics and Gynecology, Ajou University School of Medicine, Suwon, Korea
| | - Dong Hoon Suh
- Department of Obstetrics and Gynecology, Seoul National University Bundang Hospital, Seongnam, Korea
| | - Dong Hee Son
- Research Coordinating Center, Konkok University Medical Center, Seoul, Korea
| | - Seung-Hyuk Shim
- Department of Obstetrics and Gynecology, Research Institute of Medical Science, Konkuk University School of Medicine, Seoul, Korea.
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Liu L, Cai W, Zhou C, Tian H, Wu B, Zhang J, Yue G, Hao Y. Ultrasound radiomics-based artificial intelligence model to assist in the differential diagnosis of ovarian endometrioma and ovarian dermoid cyst. Front Med (Lausanne) 2024; 11:1362588. [PMID: 38523908 PMCID: PMC10957533 DOI: 10.3389/fmed.2024.1362588] [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: 12/28/2023] [Accepted: 02/27/2024] [Indexed: 03/26/2024] Open
Abstract
Background Accurately differentiating between ovarian endometrioma and ovarian dermoid cyst is of clinical significance. However, the ultrasound appearance of these two diseases is variable, occasionally causing confusion and overlap with each other. This study aimed to develop a diagnostic classification model based on ultrasound radiomics to intelligently distinguish and diagnose the two diseases. Methods We collected ovarian ultrasound images from participants diagnosed as patients with ovarian endometrioma or ovarian dermoid cyst. Feature extraction and selection were performed using the Mann-Whitney U-test, Spearman correlation analysis, and the least absolute shrinkage and selection operator (LASSO) regression. We then input the final features into the machine learning classifiers for model construction. A nomogram was established by combining the radiomic signature and clinical signature. Results A total of 407 participants with 407 lesions were included and categorized into the ovarian endometriomas group (n = 200) and the dermoid cyst group (n = 207). In the test cohort, Logistic Regression (LR) achieved the highest area under curve (AUC) value (0.981, 95% CI: 0.963-1.000), the highest accuracy (94.8%), and the highest sensitivity (95.5%), while LightGBM achieved the highest specificity (97.1%). A nomogram incorporating both clinical features and radiomic features achieved the highest level of performance (AUC: 0.987, 95% CI: 0.967-1.000, accuracy: 95.1%, sensitivity: 88.0%, specificity: 100.0%, PPV: 100.0%, NPV: 88.0%, precision: 93.6%). No statistical difference in diagnostic performance was observed between the radiomic model and the nomogram (P > 0.05). The diagnostic indexes of radiomic model were comparable to that of senior radiologists and superior to that of junior radiologist. The diagnostic performance of junior radiologists significantly improved with the assistance of the model. Conclusion This ultrasound radiomics-based model demonstrated superior diagnostic performance compared to those of junior radiologists and comparable diagnostic performance to those of senior radiologists, and it has the potential to enhance the diagnostic performance of junior radiologists.
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Affiliation(s)
- Lu Liu
- Department of Ultrasound Medicine, South China Hospital, Medical School, Shenzhen University, Shenzhen, P. R. China
| | - Wenjun Cai
- Department of Ultrasound, Shenzhen University General Hospital, Medical School, Shenzhen University, Shenzhen, P. R. China
| | - Chenyang Zhou
- Department of Information, South China Hospital, Medical School, Shenzhen University, Shenzhen, P. R. China
| | - Hongyan Tian
- Department of Ultrasound Medicine, South China Hospital, Medical School, Shenzhen University, Shenzhen, P. R. China
| | - Beibei Wu
- Department of Ultrasound Medicine, South China Hospital, Medical School, Shenzhen University, Shenzhen, P. R. China
| | - Jing Zhang
- Department of Ultrasound Medicine, South China Hospital, Medical School, Shenzhen University, Shenzhen, P. R. China
| | - Guanghui Yue
- National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen University, Shenzhen, P. R. China
| | - Yi Hao
- Department of Ultrasound Medicine, South China Hospital, Medical School, Shenzhen University, Shenzhen, P. R. China
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Brandão M, Mendes F, Martins M, Cardoso P, Macedo G, Mascarenhas T, Mascarenhas Saraiva M. Revolutionizing Women's Health: A Comprehensive Review of Artificial Intelligence Advancements in Gynecology. J Clin Med 2024; 13:1061. [PMID: 38398374 PMCID: PMC10889757 DOI: 10.3390/jcm13041061] [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: 12/31/2023] [Revised: 02/04/2024] [Accepted: 02/05/2024] [Indexed: 02/25/2024] Open
Abstract
Artificial intelligence has yielded remarkably promising results in several medical fields, namely those with a strong imaging component. Gynecology relies heavily on imaging since it offers useful visual data on the female reproductive system, leading to a deeper understanding of pathophysiological concepts. The applicability of artificial intelligence technologies has not been as noticeable in gynecologic imaging as in other medical fields so far. However, due to growing interest in this area, some studies have been performed with exciting results. From urogynecology to oncology, artificial intelligence algorithms, particularly machine learning and deep learning, have shown huge potential to revolutionize the overall healthcare experience for women's reproductive health. In this review, we aim to establish the current status of AI in gynecology, the upcoming developments in this area, and discuss the challenges facing its clinical implementation, namely the technological and ethical concerns for technology development, implementation, and accountability.
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Affiliation(s)
- Marta Brandão
- Faculty of Medicine, University of Porto, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal; (M.B.); (P.C.); (G.M.); (T.M.)
| | - Francisco Mendes
- Department of Gastroenterology, São João University Hospital, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal; (F.M.); (M.M.)
- WGO Gastroenterology and Hepatology Training Center, 4200-427 Porto, Portugal
| | - Miguel Martins
- Department of Gastroenterology, São João University Hospital, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal; (F.M.); (M.M.)
- WGO Gastroenterology and Hepatology Training Center, 4200-427 Porto, Portugal
| | - Pedro Cardoso
- Faculty of Medicine, University of Porto, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal; (M.B.); (P.C.); (G.M.); (T.M.)
- Department of Gastroenterology, São João University Hospital, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal; (F.M.); (M.M.)
- WGO Gastroenterology and Hepatology Training Center, 4200-427 Porto, Portugal
| | - Guilherme Macedo
- Faculty of Medicine, University of Porto, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal; (M.B.); (P.C.); (G.M.); (T.M.)
- Department of Gastroenterology, São João University Hospital, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal; (F.M.); (M.M.)
- WGO Gastroenterology and Hepatology Training Center, 4200-427 Porto, Portugal
| | - Teresa Mascarenhas
- Faculty of Medicine, University of Porto, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal; (M.B.); (P.C.); (G.M.); (T.M.)
- Department of Obstetrics and Gynecology, São João University Hospital, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal
| | - Miguel Mascarenhas Saraiva
- Faculty of Medicine, University of Porto, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal; (M.B.); (P.C.); (G.M.); (T.M.)
- Department of Gastroenterology, São João University Hospital, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal; (F.M.); (M.M.)
- WGO Gastroenterology and Hepatology Training Center, 4200-427 Porto, Portugal
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Chen J, Liu L, He Z, Su D, Liu C. CT-Based Radiomics and Machine Learning for Differentiating Benign, Borderline, and Early-Stage Malignant Ovarian Tumors. JOURNAL OF IMAGING INFORMATICS IN MEDICINE 2024; 37:180-195. [PMID: 38343232 DOI: 10.1007/s10278-023-00903-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/16/2023] [Revised: 07/12/2023] [Accepted: 08/10/2023] [Indexed: 03/02/2024]
Abstract
To explore the value of CT-based radiomics model in the differential diagnosis of benign ovarian tumors (BeOTs), borderline ovarian tumors (BOTs), and early malignant ovarian tumors (eMOTs). The retrospective research was conducted with pathologically confirmed 258 ovarian tumor patients from January 2014 to February 2021. The patients were randomly allocated to a training cohort (n = 198) and a test cohort (n = 60). By providing a three-dimensional (3D) characterization of the volume of interest (VOI) at the maximum level of images, 4238 radiomic features were extracted from the VOI per patient. The Wilcoxon-Mann-Whitney (WMW) test, least absolute shrinkage and selection operator (LASSO), and support vector machine (SVM) were employed to select the radiomic features. Five machine learning (ML) algorithms were applied to construct three-class diagnostic models. Leave-one-out cross-validation (LOOCV) was implemented to evaluate the performance of the radiomics models. The test cohort was used to verify the generalization ability of the radiomics models. The receiver-operating characteristic (ROC) was used to evaluate diagnostic performance of radiomics model. Global and discrimination performance of five models was evaluated by average area under the ROC curve (AUC). The average ROC indicated that random forest (RF) diagnostic model in training cohort demonstrated the best diagnostic performance (micro/macro average AUC, 0.98/0.99), which was then confirmed with by LOOCV (micro/macro average AUC, 0.89/0.88) and external validation (test cohort) (micro/macro average AUC, 0.81/0.79). Our proposed CT-based radiomics diagnostic models may effectively assist in preoperatively differentiating BeOTs, BOTs, and eMOTs.
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Affiliation(s)
- Jia Chen
- Department of Radiology, Guangxi Medical University Cancer Hospital, 71 Hedi Road, Nanning, Guangxi, People's Republic of China
- Department of Radiology, Guangxi Clinical Medical Research Center of Imaging Medicine, 71 Hedi Road, Nanning, Guangxi, People's Republic of China
- Department of Medical Imaging, Guangxi Key Clinical Specialty, 71 Hedi Road, Nanning, Guangxi, People's Republic of China
- Department of Medical Imaging, Dominant Cultivation Discipline of Guangxi Medical, University Cancer Hospital, 71 Hedi Road, Nanning, Guangxi, People's Republic of China
| | - Lei Liu
- School of Computer Science and Engineering, Guilin University of Aerospace Technology, 2 Jinji Road, Guilin, Guangxi, People's Republic of China
| | - Ziying He
- Department of Gynecologic Oncology, Guangxi Medical University Cancer Hospital, 71 Hedi Road, Nanning, Guangxi, People's Republic of China
| | - Danke Su
- Department of Radiology, Guangxi Medical University Cancer Hospital, 71 Hedi Road, Nanning, Guangxi, People's Republic of China.
- Department of Radiology, Guangxi Clinical Medical Research Center of Imaging Medicine, 71 Hedi Road, Nanning, Guangxi, People's Republic of China.
- Department of Medical Imaging, Guangxi Key Clinical Specialty, 71 Hedi Road, Nanning, Guangxi, People's Republic of China.
- Department of Medical Imaging, Dominant Cultivation Discipline of Guangxi Medical, University Cancer Hospital, 71 Hedi Road, Nanning, Guangxi, People's Republic of China.
| | - Chanzhen Liu
- Department of Gynecologic Oncology, Guangxi Medical University Cancer Hospital, 71 Hedi Road, Nanning, Guangxi, People's Republic of China.
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Na I, Noh JJ, Kim CK, Lee JW, Park H. Combined radiomics-clinical model to predict platinum-sensitivity in advanced high-grade serous ovarian carcinoma using multimodal MRI. Front Oncol 2024; 14:1341228. [PMID: 38327741 PMCID: PMC10847571 DOI: 10.3389/fonc.2024.1341228] [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: 11/20/2023] [Accepted: 01/05/2024] [Indexed: 02/09/2024] Open
Abstract
Introduction We aimed to predict platinum sensitivity using routine baseline multimodal magnetic resonance imaging (MRI) and established clinical data in a radiomics framework. Methods We evaluated 96 patients with ovarian cancer who underwent multimodal MRI and routine laboratory tests between January 2016 and December 2020. The patients underwent diffusion-weighted, contrast-enhanced T1-weighted, and T2-weighted MRI. Subsequently, 293 radiomic features were extracted by manually identifying tumor regions of interest. The features were subjected to the least absolute shrinkage and selection operators, leaving only a few selected features. We built the first prediction model with a tree-based classifier using selected radiomics features. A second prediction model was built by combining the selected radiomic features with four established clinical factors: age, disease stage, initial tumor marker level, and treatment course. Both models were built and tested using a five-fold cross-validation. Results Our radiomics model predicted platinum sensitivity with an AUC of 0.65 using a few radiomics features related to heterogeneity. The second combined model had an AUC of 0.77, confirming the incremental benefits of the radiomics model in addition to models using established clinical factors. Conclusion Our combined radiomics-clinical data model was effective in predicting platinum sensitivity in patients with advanced ovarian cancer.
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Affiliation(s)
- Inye Na
- Department of Electrical and Computer Engineering, Sungkyunkwan University, Suwon, Republic of Korea
| | - Joseph J. Noh
- Gynecologic Cancer Center, Department of Obstetrics and Gynecology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Chan Kyo Kim
- Department of Radiology and Center for Imaging Science, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Jeong-Won Lee
- Gynecologic Cancer Center, Department of Obstetrics and Gynecology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Hyunjin Park
- Department of Electrical and Computer Engineering, Sungkyunkwan University, Suwon, Republic of Korea
- Center for Neuroscience Imaging Research, Institute for Basic Science, Suwon, Republic of Korea
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11
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Chen J, Yang F, Liu C, Pan X, He Z, Fu D, Jin G, Su D. Diagnostic value of a CT-based radiomics nomogram for discrimination of benign and early stage malignant ovarian tumors. Eur J Med Res 2023; 28:609. [PMID: 38115095 PMCID: PMC10729460 DOI: 10.1186/s40001-023-01561-1] [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: 09/03/2022] [Accepted: 11/30/2023] [Indexed: 12/21/2023] Open
Abstract
BACKGROUND This study aimed to identify the diagnostic value of models constructed using computed tomography-based radiomics features for discrimination of benign and early stage malignant ovarian tumors. METHODS The imaging and clinicopathological data of 197 cases of benign and early stage malignant ovarian tumors (FIGO stage I/II), were retrospectively analyzed. The patients were randomly assigned into training data set and validation data set. Radiomics features were extracted from images of plain computed tomography scan and contrast-enhanced computed tomography scan, were then screened in the training data set, and a radiomics model was constructed. Multivariate logistic regression analysis was used to construct a radiomic nomogram, containing the traditional diagnostic model and the radiomics model. Moreover, the decision curve analysis was used to assess the clinical application value of the radiomics nomogram. RESULTS Six textural features with the greatest diagnostic efficiency were finally screened. The value of the area under the receiver operating characteristic curve showed that the radiomics nomogram was superior to the traditional diagnostic model and the radiomics model (P < 0.05) in the training data set. In the validation data set, the radiomics nomogram was superior to the traditional diagnostic model (P < 0.05), but there was no statistically significant difference compared to the radiomics model (P > 0.05). The calibration curve and the Hosmer-Lemeshow test revealed that the three models all had a great degree of fit (All P > 0.05). The results of decision curve analysis indicated that utilization of the radiomics nomogram to distinguish benign and early stage malignant ovarian tumors had a greater clinical application value when the risk threshold was 0.4-1.0. CONCLUSIONS The computed tomography-based radiomics nomogram could be a non-invasive and reliable imaging method to discriminate benign and early stage malignant ovarian tumors.
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Affiliation(s)
- Jia Chen
- Department of Radiology, Guangxi Medical University Cancer Hospital, 71 Hedi Road, Nanning, Guangxi, People's Republic of China
- Department of Radiology, Guangxi Clinical Medical Research Center of Imaging Medicine, 71 Hedi Road, Nanning, Guangxi, People's Republic of China
- Department of Radiology, Guangxi Key Clinical Specialties, 71 Hedi Road, Nanning, Guangxi, People's Republic of China
- Department of Radiology, Guangxi Medical University Cancer Hospital Superiority Cultivation Discipline, 71 Hedi Road, Nanning, Guangxi, People's Republic of China
| | - Fei Yang
- Department of Clinical Medical, Guangxi Medical University, 22 Shuangyong Road, Nanning, Guangxi, People's Republic of China
| | - Chanzhen Liu
- Department of Gynecologic Oncology, Guangxi Medical University Cancer Hospital, 71 Hedi Road, Nanning, Guangxi, People's Republic of China
| | - Xinwei Pan
- Department of Gynecologic Oncology, Guangxi Medical University Cancer Hospital, 71 Hedi Road, Nanning, Guangxi, People's Republic of China
| | - Ziying He
- Department of Gynecologic Oncology, Guangxi Medical University Cancer Hospital, 71 Hedi Road, Nanning, Guangxi, People's Republic of China
| | - Danhui Fu
- Department of Radiology, Guangxi Medical University Cancer Hospital, 71 Hedi Road, Nanning, Guangxi, People's Republic of China
- Department of Radiology, Guangxi Clinical Medical Research Center of Imaging Medicine, 71 Hedi Road, Nanning, Guangxi, People's Republic of China
- Department of Radiology, Guangxi Key Clinical Specialties, 71 Hedi Road, Nanning, Guangxi, People's Republic of China
- Department of Radiology, Guangxi Medical University Cancer Hospital Superiority Cultivation Discipline, 71 Hedi Road, Nanning, Guangxi, People's Republic of China
| | - Guanqiao Jin
- Department of Radiology, Guangxi Medical University Cancer Hospital, 71 Hedi Road, Nanning, Guangxi, People's Republic of China.
- Department of Radiology, Guangxi Clinical Medical Research Center of Imaging Medicine, 71 Hedi Road, Nanning, Guangxi, People's Republic of China.
- Department of Radiology, Guangxi Key Clinical Specialties, 71 Hedi Road, Nanning, Guangxi, People's Republic of China.
- Department of Radiology, Guangxi Medical University Cancer Hospital Superiority Cultivation Discipline, 71 Hedi Road, Nanning, Guangxi, People's Republic of China.
| | - Danke Su
- Department of Radiology, Guangxi Medical University Cancer Hospital, 71 Hedi Road, Nanning, Guangxi, People's Republic of China.
- Department of Radiology, Guangxi Clinical Medical Research Center of Imaging Medicine, 71 Hedi Road, Nanning, Guangxi, People's Republic of China.
- Department of Radiology, Guangxi Key Clinical Specialties, 71 Hedi Road, Nanning, Guangxi, People's Republic of China.
- Department of Radiology, Guangxi Medical University Cancer Hospital Superiority Cultivation Discipline, 71 Hedi Road, Nanning, Guangxi, People's Republic of China.
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12
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Leng Y, Li S, Zhu J, Wang X, Luo F, Wang Y, Gong L. Application of medical imaging in ovarian cancer: a bibliometric analysis from 2000 to 2022. Front Oncol 2023; 13:1326297. [PMID: 38111527 PMCID: PMC10725957 DOI: 10.3389/fonc.2023.1326297] [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: 10/23/2023] [Accepted: 11/14/2023] [Indexed: 12/20/2023] Open
Abstract
Background Ovarian cancer (OC) is the most lethal tumor within the female reproductive system. Medical imaging plays a significant role in diagnosis and monitoring OC. This study aims to use bibliometric analysis to explore the current research hotspots and collaborative networks in the application of medical imaging in OC from 2000 to 2022. Methods A systematica search for medical imaging in OC was conducted on the Web of Science Core Collection on August 9, 2023. All reviews and articles published from January 2000 to December 2022 were downloaded, and an analysis of countries, institutions, journals, keywords, and collaborative networks was perfomed using CiteSpace and VOSviewer. Results A total of 5,958 publications were obtained, demonstrating a clear upward trend in annual publications over the study peroid. The USA led in productivity with 1,373 publications, and Harvard University emerged as the most prominent institution with 202 publications. Timmerman D was the most prolific contributor with 100 publications, and Gynecological Oncology led in the number of publications with 296. The top three keywords were "ovarian cancer" (1,256), "ultrasound" (725), and "diagnosis" (712). In addition, "pelvic masses" had the highest burst strength (25.5), followed by "magnetic resonance imaging (MRI)" (21.47). Recent emergent keywords such as "apoptosis", "nanoparticles", "features", "accuracy", and "human epididymal protein 4 (HE 4)" reflect research trends in this field and may become research hotspots in the future. Conclusion This study provides a comprehensive summary of the key contributions of OC imaging to field's development over the past 23 years. Presently, primary areas of OC imaging research include MRI, targeted therapy of OC, novel biomarker (HE 4), and artificial intelligence. These areas are expected to influence future research endeavors in this field.
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Affiliation(s)
- Yinping Leng
- Department of Radiology, the Second Affiliated Hospital of Nanchang University, Nanchang, China
| | - Shuhao Li
- Department of Radiology, the Second Affiliated Hospital of Nanchang University, Nanchang, China
| | - Jianghua Zhu
- Department of Radiology, the Second Affiliated Hospital of Nanchang University, Nanchang, China
| | - Xiwen Wang
- Department of Radiology, the Second Affiliated Hospital of Nanchang University, Nanchang, China
| | - Fengyuan Luo
- Department of Radiology, the Second Affiliated Hospital of Nanchang University, Nanchang, China
| | - Yu Wang
- Clinical and Technical Support, Philips Healthcare, Shanghai, China
| | - Lianggeng Gong
- Department of Radiology, the Second Affiliated Hospital of Nanchang University, Nanchang, China
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Travaglio Morales D, Huerga Cabrerizo C, Losantos García I, Coronado Poggio M, Cordero García JM, Llobet EL, Monachello Araujo D, Rizkallal Monzón S, Domínguez Gadea L. Prognostic 18F-FDG Radiomic Features in Advanced High-Grade Serous Ovarian Cancer. Diagnostics (Basel) 2023; 13:3394. [PMID: 37998530 PMCID: PMC10670627 DOI: 10.3390/diagnostics13223394] [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: 09/27/2023] [Revised: 11/03/2023] [Accepted: 11/05/2023] [Indexed: 11/25/2023] Open
Abstract
High-grade serous ovarian cancer (HGSOC) is an aggressive disease with different clinical outcomes and poor prognosis. This could be due to tumor heterogeneity. The 18F-FDG PET radiomic parameters permit addressing tumor heterogeneity. Nevertheless, this has been not well studied in ovarian cancer. The aim of our work was to assess the prognostic value of pretreatment 18F-FDG PET radiomic features in patients with HGSOC. A review of 36 patients diagnosed with advanced HGSOC between 2016 and 2020 in our center was performed. Radiomic features were obtained from pretreatment 18F-FDGPET. Disease-free survival (DFS) and overall survival (OS) were calculated. Optimal cutoff values with receiver operating characteristic curve/median values were used. A correlation between radiomic features and DFS/OS was made. The mean DFS was 19.6 months and OS was 37.1 months. Total Lesion Glycolysis (TLG), GLSZM_ Zone Size Non-Uniformity (GLSZM_ZSNU), and GLRLM_Run Length Non-Uniformity (GLRLM_RLNU) were significantly associated with DFS. The survival-curves analysis showed a significant difference of DSF in patients with GLRLM_RLNU > 7388.3 versus patients with lower values (19.7 months vs. 31.7 months, p = 0.035), maintaining signification in the multivariate analysis (p = 0.048). Moreover, Intensity-based Kurtosis was associated with OS (p = 0.027). Pretreatment 18F-FDG PET radiomic features GLRLM_RLNU, GLSZM_ZSNU, and Kurtosis may have prognostic value in patients with advanced HGSOC.
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Affiliation(s)
- Daniela Travaglio Morales
- Nuclear Medicine Department, La Paz University Hospital, 28046 Madrid, Spain
- Nuclear Medicine Department, Halle University Hospital, 06120 Halle, Germany
| | - Carlos Huerga Cabrerizo
- Department of Medical Physics and Radiation Protection, La Paz University Hospital, 28046 Madrid, Spain
| | | | | | | | - Elena López Llobet
- Nuclear Medicine Department, La Paz University Hospital, 28046 Madrid, Spain
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Jiang Y, Wang C, Zhou S. Artificial intelligence-based risk stratification, accurate diagnosis and treatment prediction in gynecologic oncology. Semin Cancer Biol 2023; 96:82-99. [PMID: 37783319 DOI: 10.1016/j.semcancer.2023.09.005] [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: 12/17/2022] [Revised: 08/27/2023] [Accepted: 09/25/2023] [Indexed: 10/04/2023]
Abstract
As data-driven science, artificial intelligence (AI) has paved a promising path toward an evolving health system teeming with thrilling opportunities for precision oncology. Notwithstanding the tremendous success of oncological AI in such fields as lung carcinoma, breast tumor and brain malignancy, less attention has been devoted to investigating the influence of AI on gynecologic oncology. Hereby, this review sheds light on the ever-increasing contribution of state-of-the-art AI techniques to the refined risk stratification and whole-course management of patients with gynecologic tumors, in particular, cervical, ovarian and endometrial cancer, centering on information and features extracted from clinical data (electronic health records), cancer imaging including radiological imaging, colposcopic images, cytological and histopathological digital images, and molecular profiling (genomics, transcriptomics, metabolomics and so forth). However, there are still noteworthy challenges beyond performance validation. Thus, this work further describes the limitations and challenges faced in the real-word implementation of AI models, as well as potential solutions to address these issues.
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Affiliation(s)
- Yuting Jiang
- Department of Obstetrics and Gynecology, Key Laboratory of Birth Defects and Related Diseases of Women and Children of MOE and State Key Laboratory of Biotherapy, West China Second Hospital, Sichuan University and Collaborative Innovation Center, Chengdu, Sichuan 610041, China; Department of Pulmonary and Critical Care Medicine, State Key Laboratory of Respiratory Health and Multimorbidity, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, China
| | - Chengdi Wang
- Department of Obstetrics and Gynecology, Key Laboratory of Birth Defects and Related Diseases of Women and Children of MOE and State Key Laboratory of Biotherapy, West China Second Hospital, Sichuan University and Collaborative Innovation Center, Chengdu, Sichuan 610041, China; Department of Pulmonary and Critical Care Medicine, State Key Laboratory of Respiratory Health and Multimorbidity, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, China
| | - Shengtao Zhou
- Department of Obstetrics and Gynecology, Key Laboratory of Birth Defects and Related Diseases of Women and Children of MOE and State Key Laboratory of Biotherapy, West China Second Hospital, Sichuan University and Collaborative Innovation Center, Chengdu, Sichuan 610041, China; Department of Pulmonary and Critical Care Medicine, State Key Laboratory of Respiratory Health and Multimorbidity, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, China.
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15
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Brincat MR, Mira AR, Lawrence A. Current and Emerging Strategies for Tubo-Ovarian Cancer Diagnostics. Diagnostics (Basel) 2023; 13:3331. [PMID: 37958227 PMCID: PMC10647517 DOI: 10.3390/diagnostics13213331] [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: 09/04/2023] [Revised: 10/22/2023] [Accepted: 10/27/2023] [Indexed: 11/15/2023] Open
Abstract
Tubo-ovarian cancer is the most lethal gynaecological cancer. More than 75% of patients are diagnosed at an advanced stage, which is associated with poorer overall survival. Symptoms at presentation are vague and non-specific, contributing to late diagnosis. Multimodal risk models have improved the diagnostic accuracy of adnexal mass assessment based on patient risk factors, coupled with findings on imaging and serum-based biomarker tests. Newly developed ultrasonographic assessment algorithms have standardised documentation and enable stratification of care between local hospitals and cancer centres. So far, no screening test has proven to reduce ovarian cancer mortality in the general population. This review is an update on the evidence behind ovarian cancer diagnostic strategies.
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Affiliation(s)
- Mark R. Brincat
- Department of Gynaecological Oncology, Royal London Hospital, Barts Health NHS Trust, London E1 1FR, UK
| | - Ana Rita Mira
- Department of Gynaecological Oncology, Royal London Hospital, Barts Health NHS Trust, London E1 1FR, UK
- Hospital Garcia de Orta, 2805-267 Almada, Portugal
| | - Alexandra Lawrence
- Department of Gynaecological Oncology, Royal London Hospital, Barts Health NHS Trust, London E1 1FR, UK
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Adusumilli P, Ravikumar N, Hall G, Swift S, Orsi N, Scarsbrook A. Radiomics in the evaluation of ovarian masses - a systematic review. Insights Imaging 2023; 14:165. [PMID: 37782375 PMCID: PMC10545652 DOI: 10.1186/s13244-023-01500-y] [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: 05/07/2023] [Accepted: 08/12/2023] [Indexed: 10/03/2023] Open
Abstract
OBJECTIVES The study aim was to conduct a systematic review of the literature reporting the application of radiomics to imaging techniques in patients with ovarian lesions. METHODS MEDLINE/PubMed, Web of Science, Scopus, EMBASE, Ovid and ClinicalTrials.gov were searched for relevant articles. Using PRISMA criteria, data were extracted from short-listed studies. Validity and bias were assessed independently by 2 researchers in consensus using the Quality in Prognosis Studies (QUIPS) tool. Radiomic Quality Score (RQS) was utilised to assess radiomic methodology. RESULTS After duplicate removal, 63 articles were identified, of which 33 were eligible. Fifteen assessed lesion classifications, 10 treatment outcomes, 5 outcome predictions, 2 metastatic disease predictions and 1 classification/outcome prediction. The sample size ranged from 28 to 501 patients. Twelve studies investigated CT, 11 MRI, 4 ultrasound and 1 FDG PET-CT. Twenty-three studies (70%) incorporated 3D segmentation. Various modelling methods were used, most commonly LASSO (least absolute shrinkage and selection operator) (10/33). Five studies (15%) compared radiomic models to radiologist interpretation, all demonstrating superior performance. Only 6 studies (18%) included external validation. Five studies (15%) had a low overall risk of bias, 9 (27%) moderate, and 19 (58%) high risk of bias. The highest RQS achieved was 61.1%, and the lowest was - 16.7%. CONCLUSION Radiomics has the potential as a clinical diagnostic tool in patients with ovarian masses and may allow better lesion stratification, guiding more personalised patient care in the future. Standardisation of the feature extraction methodology, larger and more diverse patient cohorts and real-world evaluation is required before clinical translation. CLINICAL RELEVANCE STATEMENT Radiomics shows promising results in improving lesion stratification, treatment selection and outcome prediction. Modelling with larger cohorts and real-world evaluation is required before clinical translation. KEY POINTS • Radiomics is emerging as a tool for enhancing clinical decisions in patients with ovarian masses. • Radiomics shows promising results in improving lesion stratification, treatment selection and outcome prediction. • Modelling with larger cohorts and real-world evaluation is required before clinical translation.
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Affiliation(s)
- Pratik Adusumilli
- Department of Radiology, Leeds Teaching Hospitals NHS Trust, Leeds, UK.
- Leeds Institute of Medical Research, University of Leeds, Leeds, UK.
- West Yorkshire Radiology Academy, Level B Clarendon Wing, Leeds General Infirmary, Great George Street, Leeds, LS1 3EX, UK.
| | - Nishant Ravikumar
- Centre for Computational Imaging and Simulation Technologies in Biomedicine, University of Leeds, Leeds, UK
| | - Geoff Hall
- Department of Medical Oncology, Leeds Teaching Hospitals NHS Trust, Leeds, UK
- Leeds Institute for Data Analytics, University of Leeds, Leeds, UK
| | - Sarah Swift
- Department of Radiology, Leeds Teaching Hospitals NHS Trust, Leeds, UK
| | - Nicolas Orsi
- Leeds Institute of Medical Research, University of Leeds, Leeds, UK
| | - Andrew Scarsbrook
- Department of Radiology, Leeds Teaching Hospitals NHS Trust, Leeds, UK
- Leeds Institute of Medical Research, University of Leeds, Leeds, UK
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Lu J, Cai S, Wang F, Wu PY, Pan X, Qiang J, Li H, Zeng M. Development of a prediction model for gross residual in high-grade serous ovarian cancer by combining preoperative assessments of abdominal and pelvic metastases and multiparametric MRI. Acad Radiol 2023; 30:1823-1831. [PMID: 36587996 DOI: 10.1016/j.acra.2022.12.019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2022] [Revised: 11/25/2022] [Accepted: 12/11/2022] [Indexed: 01/01/2023]
Abstract
RATIONALE AND OBJECTIVES To preoperatively predict residual tumor (RT) in patients with high-grade serous ovarian carcinoma (HGSOC) via a radiomic-clinical nomogram. METHODS A total of 128 patients with advanced HGSOC were enrolled (training cohort: n=106; validation cohort: n=22). Serum cancer antigen-125 (CA125), serum human epididymis protein 4 (HE-4) level, and neutrophil-to-lymphocyte ratio (NLR) were obtained from the medical records. Metastases in abdomen and pelvis (MAP) of HGSOC patients was evaluated and scored based on preoperative abdominal and pelvic enhanced CT, MRI and/or PET-CT. A volume of interest (VOI) of each tumor was manually contoured along the boundary slice-by-slice. Radiomic features were extracted from the T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI) and apparent diffusion coefficient (ADC) images. Univariate and multivariate analyses were used to determine the independent predictors of RT status. Least absolute shrinkage and selection operator (LASSO) logistic regression was performed to select optimal features and construct radiomic models. A radiomic-clinical nomogram incorporating radiomic signature and clinical parameters was developed and evaluated in training and validation cohorts. RESULTS MAP score (p = 0.002), HE-4 level (p = 0.001) and NLR (p = 0.008) were independent predictors of RT status. The final radiomic-clinical nomogram showed satisfactory prediction performance in training (AUC = 0.936), cross validation (AUC = 0.906) and separate validation cohorts (AUC = 0.900), and fitted well in calibration curves (p > 0.05). Decision curve further confirmed the clinical application value of the nomogram. CONCLUSION The proposed MRI-based radiomic-clinical nomogram achieved excellent preoperative prediction of the RT status in HGSOC.
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Affiliation(s)
- Jingjing Lu
- Department of Radiology, Zhongshan Hospital, Fudan University, No.180 Fenglin Rd, Shanghai 200032, China; Shanghai Institute of Medical Imaging
| | - Songqi Cai
- Department of Radiology, Zhongshan Hospital, Fudan University, No.180 Fenglin Rd, Shanghai 200032, China; Shanghai Institute of Medical Imaging
| | - Fang Wang
- Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd. Shanghai, 200232, China
| | - Pu-Yeh Wu
- Department of Research and Development, GE Healthcare, Beijing 100176, China
| | - Xianpan Pan
- Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd. Shanghai, 200232, China
| | - Jinwei Qiang
- Department of Radiology, Jinshan Hospital, Fudan University, 1508 Longhang Road, Shanghai 201508, China
| | - Haiming Li
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai 200032, China; Department of Oncology, Shanghai Medical College, Fudan University.
| | - Mengsu Zeng
- Department of Radiology, Zhongshan Hospital, Fudan University, No.180 Fenglin Rd, Shanghai 200032, China; Shanghai Institute of Medical Imaging
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Wei M, Feng G, Wang X, Jia J, Zhang Y, Dai Y, Qin C, Bai G, Chen S. Deep Learning Radiomics Nomogram Based on Magnetic Resonance Imaging for Differentiating Type I/II Epithelial Ovarian Cancer. Acad Radiol 2023:S1076-6332(23)00401-4. [PMID: 37643927 DOI: 10.1016/j.acra.2023.08.002] [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: 06/15/2023] [Revised: 07/27/2023] [Accepted: 08/02/2023] [Indexed: 08/31/2023]
Abstract
RATIONALE AND OBJECTIVES To develop and validate a T2-weighted magnetic resonance imaging (MRI)-based deep learning radiomics nomogram (DLRN) to differentiate between type I and type II epithelial ovarian cancer (EOC). MATERIALS AND METHODS This multicenter study incorporated 437 patients from five centers, divided into training (n = 271), internal validation (n = 68), and external validation (n = 98) sets. The deep learning (DL) model was constructed using the largest orthogonal slices of the tumor area. The extracted radiomics features were employed in building the radiomics model. The clinical model was developed based on clinical characteristics. A DLRN was built by integrating the DL signature, radiomics signature, and independent clinical predictors. Model performances were evaluated through receiver operating characteristic (ROC) analysis, Brier score, calibration curve, and decision curve analysis (DCA). The areas under the ROC curve (AUCs) were compared using the DeLong test. A two-tailed P < 0.05 was considered significantly different. RESULTS The DLRN exhibited satisfactory discrimination between type I and type II EOC with the AUC of 0.888 (95% confidence interval [CI] 0.810, 0.966) and 0.866 (95% CI 0.786, 0.946) in the internal and external validation sets, respectively. These AUCs significantly exceeded those of the clinical model (P = 0.013 and 0.043, in the internal and external validation sets, respectively). The DLRN demonstrated optimal classification accuracy and clinical application value, according to Brier scores, calibration curves, and DCA. CONCLUSION A T2-weighted MRI-based DLRN showed promising potential in differentiating between type I and type II EOC, which could offer assistance in clinical decision-making.
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Affiliation(s)
- Mingxiang Wei
- Department of Radiology, The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou Municipal Hospital, Gusu School, Nanjing Medical University, Suzhou, Jiangsu, China (M.W., X.W., S.C.)
| | - Guannan Feng
- Department of Gynecology, The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou Municipal Hospital, Gusu School, Nanjing Medical University, Suzhou, Jiangsu, China (G.F.)
| | - Xinyi Wang
- Department of Radiology, The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou Municipal Hospital, Gusu School, Nanjing Medical University, Suzhou, Jiangsu, China (M.W., X.W., S.C.)
| | - Jianye Jia
- Department of Radiology, The Affiliated Huaian No. 1 People's Hospital of Nanjing Medical University, Huaian, Jiangsu, China (J.J., G.B.)
| | - Yu Zhang
- Department of Radiology, Dushu Lake Hospital Affiliated to Soochow University, Suzhou, Jiangsu, China (Y.Z.)
| | - Yao Dai
- Department of Radiology, The First Affiliated Hospital of Soochow University, Suzhou, Jiangsu, China (Y.D.)
| | - Cai Qin
- Department of Radiology, Tumor Hospital Affiliated to Nantong University, Nantong, Jiangsu, China (C.Q.)
| | - Genji Bai
- Department of Radiology, The Affiliated Huaian No. 1 People's Hospital of Nanjing Medical University, Huaian, Jiangsu, China (J.J., G.B.)
| | - Shuangqing Chen
- Department of Radiology, The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou Municipal Hospital, Gusu School, Nanjing Medical University, Suzhou, Jiangsu, China (M.W., X.W., S.C.).
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Li H, Cai S, Deng L, Xiao Z, Guo Q, Qiang J, Gong J, Gu Y, Liu Z. Prediction of platinum resistance for advanced high-grade serous ovarian carcinoma using MRI-based radiomics nomogram. Eur Radiol 2023; 33:5298-5308. [PMID: 36995415 DOI: 10.1007/s00330-023-09552-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2022] [Revised: 01/19/2023] [Accepted: 02/13/2023] [Indexed: 03/31/2023]
Abstract
OBJECTIVE This study aimed to explore the value of a radiomics nomogram to identify platinum resistance and predict the progression-free survival (PFS) of patients with advanced high-grade serous ovarian carcinoma (HGSOC). MATERIALS AND METHODS In this multicenter retrospective study, 301 patients with advanced HGSOC underwent radiomics features extraction from the whole primary tumor on contrast-enhanced T1WI and T2WI. The radiomics features were selected by the support vector machine-based recursive feature elimination method, and then the radiomics signature was generated. Furthermore, a radiomics nomogram was developed using the radiomics signature and clinical characteristics by multivariable logistic regression. The predictive performance was evaluated using receiver operating characteristic analysis. The net reclassification index (NRI), integrated discrimination improvement (IDI), and decision curve analysis (DCA) were used to compare the clinical utility and benefits of different models. RESULTS Five features significantly correlated with platinum resistance were selected to construct the radiomics model. The radiomics nomogram, combining radiomics signatures with three clinical characteristics (FIGO stage, CA-125, and residual tumor), had a higher area under the curve (AUC) compared with the clinical model alone (AUC: 0.799 vs 0.747), with positive NRI and IDI. The net benefit of the radiomics nomogram is typically higher than clinical-only and radiomics-only models. Kaplan-Meier survival analysis showed that the radiomics nomogram-defined high-risk groups had shorter PFS compared with the low-risk groups in patients with advanced HGSOC. CONCLUSIONS The radiomics nomogram can identify platinum resistance and predict PFS. It helps make the personalized management of advanced HGSOC. KEY POINTS • The radiomics-based approach has the potential to identify platinum resistance and can help make the personalized management of advanced HGSOC. • The radiomics-clinical nomogram showed improved performance compared with either of them alone for predicting platinum-resistant HGSOC. • The proposed nomogram performed well in predicting the PFS time of patients with low-risk and high-risk HGSOC in both training and testing cohorts.
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Affiliation(s)
- Haiming Li
- Department of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, 510080, China
- Guangdong Cardiovascular Institute, Guangzhou, 510080, China
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, 200032, China
- Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou, 510080, China
| | - Songqi Cai
- Department of Radiology, Zhongshan Hospital, FudanUniversity, Shanghai, 200032, China
- Shanghai Institute of Medical Imaging, Shanghai, 200032, China
- Department of Cancer Center, Zhongshan Hospital, FudanUniversity, Shanghai, 200032, China
| | - Lin Deng
- Department of Radiology, Jinshan Hospital, FudanUniversity, Shanghai, 201508, China
| | - Zebin Xiao
- Department of Biomedical Sciences, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Qinhao Guo
- Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou, 510080, China
- Department of Gynecological Oncology, Fudan University Shanghai Cancer Center, Shanghai, 200032, China
| | - Jinwei Qiang
- Department of Radiology, Jinshan Hospital, FudanUniversity, Shanghai, 201508, China
| | - Jing Gong
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, 200032, China
- Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou, 510080, China
| | - Yajia Gu
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, 200032, China.
- Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou, 510080, China.
| | - Zaiyi Liu
- Department of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, 510080, China.
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, China.
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Huang ML, Ren J, Jin ZY, Liu XY, He YL, Li Y, Xue HD. A systematic review and meta-analysis of CT and MRI radiomics in ovarian cancer: methodological issues and clinical utility. Insights Imaging 2023; 14:117. [PMID: 37395888 DOI: 10.1186/s13244-023-01464-z] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2023] [Accepted: 06/11/2023] [Indexed: 07/04/2023] Open
Abstract
OBJECTIVES We aimed to present the state of the art of CT- and MRI-based radiomics in the context of ovarian cancer (OC), with a focus on the methodological quality of these studies and the clinical utility of these proposed radiomics models. METHODS Original articles investigating radiomics in OC published in PubMed, Embase, Web of Science, and the Cochrane Library between January 1, 2002, and January 6, 2023, were extracted. The methodological quality was evaluated using the radiomics quality score (RQS) and Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2). Pairwise correlation analyses were performed to compare the methodological quality, baseline information, and performance metrics. Additional meta-analyses of studies exploring differential diagnoses and prognostic prediction in patients with OC were performed separately. RESULTS Fifty-seven studies encompassing 11,693 patients were included. The mean RQS was 30.7% (range - 4 to 22); less than 25% of studies had a high risk of bias and applicability concerns in each domain of QUADAS-2. A high RQS was significantly associated with a low QUADAS-2 risk and recent publication year. Significantly higher performance metrics were observed in studies examining differential diagnosis; 16 such studies as well as 13 exploring prognostic prediction were included in a separate meta-analysis, which revealed diagnostic odds ratios of 25.76 (95% confidence interval (CI) 13.50-49.13) and 12.55 (95% CI 8.38-18.77), respectively. CONCLUSION Current evidence suggests that the methodological quality of OC-related radiomics studies is unsatisfactory. Radiomics analysis based on CT and MRI showed promising results in terms of differential diagnosis and prognostic prediction. CRITICAL RELEVANCE STATEMENT Radiomics analysis has potential clinical utility; however, shortcomings persist in existing studies in terms of reproducibility. We suggest that future radiomics studies should be more standardized to better bridge the gap between concepts and clinical applications.
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Affiliation(s)
- Meng-Lin Huang
- Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, People's Republic of China
| | - Jing Ren
- Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, People's Republic of China
| | - Zheng-Yu Jin
- Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, People's Republic of China
| | - Xin-Yu Liu
- Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, People's Republic of China
| | - Yong-Lan He
- Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, People's Republic of 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, People's Republic of China.
| | - Hua-Dan Xue
- Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, People's Republic of China.
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Jan YT, Tsai PS, Huang WH, Chou LY, Huang SC, Wang JZ, Lu PH, Lin DC, Yen CS, Teng JP, Mok GSP, Shih CT, Wu TH. Machine learning combined with radiomics and deep learning features extracted from CT images: a novel AI model to distinguish benign from malignant ovarian tumors. Insights Imaging 2023; 14:68. [PMID: 37093321 PMCID: PMC10126170 DOI: 10.1186/s13244-023-01412-x] [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: 01/04/2023] [Accepted: 03/20/2023] [Indexed: 04/25/2023] Open
Abstract
BACKGROUND To develop an artificial intelligence (AI) model with radiomics and deep learning (DL) features extracted from CT images to distinguish benign from malignant ovarian tumors. METHODS We enrolled 149 patients with pathologically confirmed ovarian tumors. A total of 185 tumors were included and divided into training and testing sets in a 7:3 ratio. All tumors were manually segmented from preoperative contrast-enhanced CT images. CT image features were extracted using radiomics and DL. Five models with different combinations of feature sets were built. Benign and malignant tumors were classified using machine learning (ML) classifiers. The model performance was compared with five radiologists on the testing set. RESULTS Among the five models, the best performing model is the ensemble model with a combination of radiomics, DL, and clinical feature sets. The model achieved an accuracy of 82%, specificity of 89% and sensitivity of 68%. Compared with junior radiologists averaged results, the model had a higher accuracy (82% vs 66%) and specificity (89% vs 65%) with comparable sensitivity (68% vs 67%). With the assistance of the model, the junior radiologists achieved a higher average accuracy (81% vs 66%), specificity (80% vs 65%), and sensitivity (82% vs 67%), approaching to the performance of senior radiologists. CONCLUSIONS We developed a CT-based AI model that can differentiate benign and malignant ovarian tumors with high accuracy and specificity. This model significantly improved the performance of less-experienced radiologists in ovarian tumor assessment, and may potentially guide gynecologists to provide better therapeutic strategies for these patients.
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Affiliation(s)
- Ya-Ting Jan
- Department of Biomedical Imaging and Radiological Sciences, National Yang Ming Chiao Tung University, Taipei, 112, Taiwan
- Department of Radiology, MacKay Memorial Hospital, Taipei, Taiwan
- Department of Medicine, MacKay Medical College, New Taipei City, Taiwan
- MacKay Junior College of Medicine, Nursing and Management, New Taipei City, Taiwan
| | - Pei-Shan Tsai
- Department of Biomedical Imaging and Radiological Sciences, National Yang Ming Chiao Tung University, Taipei, 112, Taiwan
- Department of Radiology, MacKay Memorial Hospital, Taipei, Taiwan
- Department of Medicine, MacKay Medical College, New Taipei City, Taiwan
- MacKay Junior College of Medicine, Nursing and Management, New Taipei City, Taiwan
| | - Wen-Hui Huang
- Department of Biomedical Imaging and Radiological Sciences, National Yang Ming Chiao Tung University, Taipei, 112, Taiwan
- Department of Radiology, MacKay Memorial Hospital, Taipei, Taiwan
- Department of Medicine, MacKay Medical College, New Taipei City, Taiwan
- MacKay Junior College of Medicine, Nursing and Management, New Taipei City, Taiwan
| | - Ling-Ying Chou
- Department of Radiology, MacKay Memorial Hospital, Taipei, Taiwan
- Department of Medicine, MacKay Medical College, New Taipei City, Taiwan
- MacKay Junior College of Medicine, Nursing and Management, New Taipei City, Taiwan
| | - Shih-Chieh Huang
- Department of Radiology, MacKay Memorial Hospital, Taipei, Taiwan
- Department of Medicine, MacKay Medical College, New Taipei City, Taiwan
- MacKay Junior College of Medicine, Nursing and Management, New Taipei City, Taiwan
| | - Jing-Zhe Wang
- Department of Radiology, MacKay Memorial Hospital, Taipei, Taiwan
- Department of Medicine, MacKay Medical College, New Taipei City, Taiwan
- MacKay Junior College of Medicine, Nursing and Management, New Taipei City, Taiwan
| | - Pei-Hsuan Lu
- Department of Radiology, MacKay Memorial Hospital, Taipei, Taiwan
- Department of Medicine, MacKay Medical College, New Taipei City, Taiwan
- MacKay Junior College of Medicine, Nursing and Management, New Taipei City, Taiwan
| | - Dao-Chen Lin
- Division of Endocrine and Metabolism, Department of Medicine, Taipei Veterans General Hospital, Taipei, Taiwan
- Department of Radiology, Taipei Veterans General Hospital, Taipei, Taiwan
- School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Chun-Sheng Yen
- Department of Biomedical Imaging and Radiological Sciences, National Yang Ming Chiao Tung University, Taipei, 112, Taiwan
| | - Ju-Ping Teng
- Department of Biomedical Imaging and Radiological Sciences, National Yang Ming Chiao Tung University, Taipei, 112, Taiwan
| | - Greta S P Mok
- Biomedical Imaging Laboratory (BIG), Department of Electrical and Computer Engineering, Faculty of Science and Technology, University of Macau, Macau, China
| | - Cheng-Ting Shih
- Department of Biomedical Imaging and Radiological Science, China Medical University, Taichung, 404, Taiwan.
| | - Tung-Hsin Wu
- Department of Biomedical Imaging and Radiological Sciences, National Yang Ming Chiao Tung University, Taipei, 112, Taiwan.
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Preoperative prediction of miliary changes in the small bowel mesentery in advanced high-grade serous ovarian cancer using MRI radiomics nomogram. Abdom Radiol (NY) 2023; 48:1119-1130. [PMID: 36651979 DOI: 10.1007/s00261-023-03802-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2022] [Revised: 01/04/2023] [Accepted: 01/05/2023] [Indexed: 01/19/2023]
Abstract
PURPOSE To develop and validate an MRI-based radiomics nomogram for the preoperative prediction of miliary changes in the small bowel mesentery (MCSBM) in advanced high-grade serous ovarian cancer (HGSOC). MATERIALS AND METHODS One hundred and twenty-eight patients with pathologically proved advanced HGSOC (training cohort: n = 91; validation cohort: n = 37) were retrospectively included. All patients were initially evaluated as MCSBM-negative by preoperative imaging modalities but were finally confirmed by surgery and histopathology (MCSBM-positive: n = 53; MCSBM-negative: n = 75). Five radiomics signatures were built based on the features from multisequence magnetic resonance images. Independent clinicoradiological factors and radiomics-fusion signature were further integrated to construct a radiomics nomogram. The performance of the nomogram was assessed using receiver operating characteristic (ROC) curves, calibration curves and clinical utility. RESULTS Radiomics signatures, ascites, and tumor size were independent predictors of MCSBM. A nomogram integrating radiomics features and clinicoradiological factors demonstrated satisfactory predictive performance with areas under the curves (AUCs) of 0.871 (95% CI 0.801-0.941) and 0.858 (95% CI 0.739-0.976) in the training and validation cohorts, respectively. The net reclassification index (NRI) and integrated discrimination improvement (IDI) revealed that the nomogram had a significantly improved ability compared with the clinical model in the training cohort (NRI = 0.343, p = 0.002; IDI = 0.299, p < 0.001) and validation cohort (NRI = 0.409, p = 0.015; IDI = 0.283, p = 0.001). CONCLUSION Our proposed nomogram has the potential to serve as a noninvasive tool for the prediction of MCSBM, which is helpful for the individualized assessment of advanced HGSOC patients.
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23
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Panico C, Avesani G, Zormpas-Petridis K, Rundo L, Nero C, Sala E. Radiomics and Radiogenomics of Ovarian Cancer. Radiol Clin North Am 2023; 61:749-760. [PMID: 37169435 DOI: 10.1016/j.rcl.2023.02.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/03/2023]
Abstract
Ovarian cancer, one of the deadliest gynecologic malignancies, is characterized by high intra- and inter-site genomic and phenotypic heterogeneity. The traditional information provided by the conventional interpretation of diagnostic imaging studies cannot adequately represent this heterogeneity. Radiomics analyses can capture the complex patterns related to the microstructure of the tissues and provide quantitative information about them. This review outlines how radiomics and its integration with other quantitative biological information, like genomics and proteomics, can impact the clinical management of ovarian cancer.
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Sheehy J, Rutledge H, Acharya UR, Loh HW, Gururajan R, Tao X, Zhou X, Li Y, Gurney T, Kondalsamy-Chennakesavan S. Gynecological cancer prognosis using machine learning techniques: A systematic review of last three decades (1990–2022). Artif Intell Med 2023; 139:102536. [PMID: 37100507 DOI: 10.1016/j.artmed.2023.102536] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2021] [Revised: 03/19/2023] [Accepted: 03/23/2023] [Indexed: 03/30/2023]
Abstract
OBJECTIVE Many Computer Aided Prognostic (CAP) systems based on machine learning techniques have been proposed in the field of oncology. The objective of this systematic review was to assess and critically appraise the methodologies and approaches used in predicting the prognosis of gynecological cancers using CAPs. METHODS Electronic databases were used to systematically search for studies utilizing machine learning methods in gynecological cancers. Study risk of bias (ROB) and applicability were assessed using the PROBAST tool. 139 studies met the inclusion criteria, of which 71 predicted outcomes for ovarian cancer patients, 41 predicted outcomes for cervical cancer patients, 28 predicted outcomes for uterine cancer patients, and 2 predicted outcomes for gynecological malignancies broadly. RESULTS Random forest (22.30 %) and support vector machine (21.58 %) classifiers were used most commonly. Use of clinicopathological, genomic and radiomic data as predictors was observed in 48.20 %, 51.08 % and 17.27 % of studies, respectively, with some studies using multiple modalities. 21.58 % of studies were externally validated. Twenty-three individual studies compared ML and non-ML methods. Study quality was highly variable and methodologies, statistical reporting and outcome measures were inconsistent, preventing generalized commentary or meta-analysis of performance outcomes. CONCLUSION There is significant variability in model development when prognosticating gynecological malignancies with respect to variable selection, machine learning (ML) methods and endpoint selection. This heterogeneity prevents meta-analysis and conclusions regarding the superiority of ML methods. Furthermore, PROBAST-mediated ROB and applicability analysis demonstrates concern for the translatability of existing models. This review identifies ways that this can be improved upon in future works to develop robust, clinically translatable models within this promising field.
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Deep learning for the ovarian lesion localization and discrimination between borderline and malignant ovarian tumors based on routine MR imaging. Sci Rep 2023; 13:2770. [PMID: 36797331 PMCID: PMC9935539 DOI: 10.1038/s41598-023-29814-3] [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: 08/27/2022] [Accepted: 02/10/2023] [Indexed: 02/18/2023] Open
Abstract
To establish a deep learning (DL) model in differentiating borderline ovarian tumor (BOT) from epithelial ovarian cancer (EOC) on conventional MR imaging. We retrospectively enrolled 201 patients of 102 pathologically proven BOTs and 99 EOCs at OB/GYN hospital Fudan University, between January 2015 and December 2017. All imaging data were reviewed on picture archiving and communication systems (PACS) server. Both T1-weighted imaging (T1WI) and T2-weighted imaging (T2WI) MR images were used for lesion area determination. We trained a U-net++ model with deep supervision to segment the lesion area on MR images. Then, the segmented regions were fed into a classification model based on DL network to categorize ovarian masses automatically. For ovarian lesion segmentation, the mean dice similarity coefficient (DSC) of the trained U-net++ model in the testing dataset achieved 0.73 [Formula: see text] 0.25, 0.76 [Formula: see text] 0.18, and 0.60 [Formula: see text] 0.24 in the sagittal T2WI, coronal T2WI, and axial T1WI images, respectively. The DL model by combined T2WI computerized network could differentiate BOT from EOC with a significantly higher AUC of 0.87, an accuracy of 83.7%, a sensitivity of 75.0% and a specificity of 87.5%. In comparison, the AUC yielded by radiologist was only 0.75, with an accuracy of 75.5%, a sensitivity of 96.0% and specificity of 54.2% (P < 0.001).The trained DL network model derived from routine MR imaging could help to distinguish BOT from EOC with a high accuracy, which was superior to radiologists' assessment.
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Koch AH, Jeelof LS, Muntinga CLP, Gootzen TA, van de Kruis NMA, Nederend J, Boers T, van der Sommen F, Piek JMJ. Analysis of computer-aided diagnostics in the preoperative diagnosis of ovarian cancer: a systematic review. Insights Imaging 2023; 14:34. [PMID: 36790570 PMCID: PMC9931983 DOI: 10.1186/s13244-022-01345-x] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2022] [Accepted: 12/05/2022] [Indexed: 02/16/2023] Open
Abstract
OBJECTIVES Different noninvasive imaging methods to predict the chance of malignancy of ovarian tumors are available. However, their predictive value is limited due to subjectivity of the reviewer. Therefore, more objective prediction models are needed. Computer-aided diagnostics (CAD) could be such a model, since it lacks bias that comes with currently used models. In this study, we evaluated the available data on CAD in predicting the chance of malignancy of ovarian tumors. METHODS We searched for all published studies investigating diagnostic accuracy of CAD based on ultrasound, CT and MRI in pre-surgical patients with an ovarian tumor compared to reference standards. RESULTS In thirty-one included studies, extracted features from three different imaging techniques were used in different mathematical models. All studies assessed CAD based on machine learning on ultrasound, CT scan and MRI scan images. Per imaging method, subsequently ultrasound, CT and MRI, sensitivities ranged from 40.3 to 100%; 84.6-100% and 66.7-100% and specificities ranged from 76.3-100%; 69-100% and 77.8-100%. Results could not be pooled, due to broad heterogeneity. Although the majority of studies report high performances, they are at considerable risk of overfitting due to the absence of an independent test set. CONCLUSION Based on this literature review, different CAD for ultrasound, CT scans and MRI scans seem promising to aid physicians in assessing ovarian tumors through their objective and potentially cost-effective character. However, performance should be evaluated per imaging technique. Prospective and larger datasets with external validation are desired to make their results generalizable.
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Affiliation(s)
- Anna H. Koch
- grid.413532.20000 0004 0398 8384Department of Gynaecology and Obstetrics and Catharina Cancer Institute, Catharina Hospital, 5623 EJ Eindhoven, Noord-Brabant, The Netherlands
| | - Lara S. Jeelof
- grid.413532.20000 0004 0398 8384Department of Gynaecology and Obstetrics and Catharina Cancer Institute, Catharina Hospital, 5623 EJ Eindhoven, Noord-Brabant, The Netherlands
| | - Caroline L. P. Muntinga
- grid.413532.20000 0004 0398 8384Department of Gynaecology and Obstetrics and Catharina Cancer Institute, Catharina Hospital, 5623 EJ Eindhoven, Noord-Brabant, The Netherlands
| | - T. A. Gootzen
- grid.413532.20000 0004 0398 8384Department of Gynaecology and Obstetrics and Catharina Cancer Institute, Catharina Hospital, 5623 EJ Eindhoven, Noord-Brabant, The Netherlands
| | - Nienke M. A. van de Kruis
- grid.413532.20000 0004 0398 8384Department of Gynaecology and Obstetrics and Catharina Cancer Institute, Catharina Hospital, 5623 EJ Eindhoven, Noord-Brabant, The Netherlands
| | - Joost Nederend
- grid.413532.20000 0004 0398 8384Department of Radiology, Catharina Hospital, 5623 EJ Eindhoven, Noord-Brabant, The Netherlands
| | - Tim Boers
- grid.6852.90000 0004 0398 8763Department of Electrical Engineering, VCA Group, University of Technology Eindhoven, 5600 MB Eindhoven, Noord-Brabant The Netherlands
| | - Fons van der Sommen
- grid.6852.90000 0004 0398 8763Department of Electrical Engineering, VCA Group, University of Technology Eindhoven, 5600 MB Eindhoven, Noord-Brabant The Netherlands
| | - Jurgen M. J. Piek
- grid.413532.20000 0004 0398 8384Department of Gynaecology and Obstetrics and Catharina Cancer Institute, Catharina Hospital, 5623 EJ Eindhoven, Noord-Brabant, The Netherlands
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Cheng M, Tan S, Ren T, Zhu Z, Wang K, Zhang L, Meng L, Yang X, Pan T, Yang Z, Zhao X. Magnetic resonance imaging radiomics to differentiate ovarian sex cord-stromal tumors and primary epithelial ovarian cancers. Front Oncol 2023; 12:1073983. [PMID: 36713500 PMCID: PMC9880468 DOI: 10.3389/fonc.2022.1073983] [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: 10/19/2022] [Accepted: 12/28/2022] [Indexed: 01/15/2023] Open
Abstract
Objective To evaluate the diagnostic ability of magnetic resonance imaging (MRI) based radiomics and traditional characteristics to differentiate between Ovarian sex cord-stromal tumors (SCSTs) and epithelial ovarian cancers (EOCs). Methods We consecutively included a total of 148 patients with 173 tumors (81 SCSTs in 73 patients and 92 EOCs in 75 patients), who were randomly divided into development and testing cohorts at a ratio of 8:2. Radiomics features were extracted from each tumor, 5-fold cross-validation was conducted for the selection of stable features based on development cohort, and we built radiomics model based on these selected features. Univariate and multivariate analyses were used to identify the independent predictors in clinical features and conventional MR parameters for differentiating SCSTs and EOCs. And nomogram was used to visualized the ultimately predictive models. All models were constructed based on the logistic regression (LR) classifier. The performance of each model was evaluated by the receiver operating characteristic (ROC) curve. Calibration and decision curves analysis (DCA) were used to evaluate the performance of models. Results The final radiomics model was constructed by nine radiomics features, which exhibited superior predictive ability with AUCs of 0.915 (95%CI: 0.869-0.962) and 0.867 (95%CI: 0.732-1.000) in the development and testing cohorts, respectively. The mixed model which combining the radiomics signatures and traditional parameters achieved the best performance, with AUCs of 0.934 (95%CI: 0.892-0.976) and 0.875 (95%CI: 0.743-1.000) in the development and testing cohorts, respectively. Conclusion We believe that the radiomics approach could be a more objective and accurate way to distinguish between SCSTs and EOCs, and the mixed model developed in our study could provide a comprehensive, effective method for clinicians to develop an appropriate management strategy.
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Affiliation(s)
- Meiying Cheng
- Department of Radiology, Third Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China
| | - Shifang Tan
- Department of Radiology, Third Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China
| | - Tian Ren
- Department of Information, Third Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China
| | - Zitao Zhu
- Medical College, Wuhan University, Wuhan, China
| | - Kaiyu Wang
- Magnetic resonance imaging (MRI) Research, GE Healthcare (China), Beijing, China
| | - Lingjie Zhang
- Department of Radiology, Third Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China
| | - Lingsong Meng
- Department of Radiology, Third Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China
| | - Xuhong Yang
- Department of Research, Huiying Medical Technology Co., Ltd., Beijing, China
| | - Teng Pan
- Department of Research, Third Affiliated Hospital of Zhengzhou University, Zhengzhou, Beijing, China
| | - Zhexuan Yang
- Department of Radiology, Third Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China
| | - Xin Zhao
- Department of Radiology, Third Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China,*Correspondence: Xin Zhao,
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Ponsiglione A, Stanzione A, Spadarella G, Baran A, Cappellini LA, Lipman KG, Van Ooijen P, Cuocolo R. Ovarian imaging radiomics quality score assessment: an EuSoMII radiomics auditing group initiative. Eur Radiol 2023; 33:2239-2247. [PMID: 36303093 PMCID: PMC9935717 DOI: 10.1007/s00330-022-09180-w] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2022] [Revised: 07/26/2022] [Accepted: 09/18/2022] [Indexed: 11/27/2022]
Abstract
OBJECTIVE To evaluate the methodological rigor of radiomics-based studies using noninvasive imaging in ovarian setting. METHODS Multiple medical literature archives (PubMed, Web of Science, and Scopus) were searched to retrieve original studies focused on computed tomography (CT), magnetic resonance imaging (MRI), ultrasound (US), or positron emission tomography (PET) radiomics for ovarian disorders' assessment. Two researchers in consensus evaluated each investigation using the radiomics quality score (RQS). Subgroup analyses were performed to assess whether the total RQS varied according to first author category, study aim and topic, imaging modality, and journal quartile. RESULTS From a total of 531 items, 63 investigations were finally included in the analysis. The studies were greatly focused (94%) on the field of oncology, with CT representing the most used imaging technique (41%). Overall, the papers achieved a median total RQS 6 (IQR, -0.5 to 11), corresponding to a percentage of 16.7% of the maximum score (IQR, 0-30.6%). The scoring was low especially due to the lack of prospective design and formal validation of the results. At subgroup analysis, the 4 studies not focused on oncological topic showed significantly lower quality scores than the others. CONCLUSIONS The overall methodological rigor of radiomics studies in the ovarian field is still not ideal, limiting the reproducibility of results and potential translation to clinical setting. More efforts towards a standardized methodology in the workflow are needed to allow radiomics to become a viable tool for clinical decision-making. KEY POINTS • The 63 included studies using noninvasive imaging for ovarian applications were mostly focused on oncologic topic (94%). • The included investigations achieved a median total RQS 6 (IQR, -0.5 to 11), indicating poor methodological rigor. • The RQS was low especially due to the lack of prospective design and formal validation of the results.
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Affiliation(s)
- Andrea Ponsiglione
- Department of Advanced Biomedical Sciences, University of Naples Federico II, Naples, Italy
| | - Arnaldo Stanzione
- Department of Advanced Biomedical Sciences, University of Naples Federico II, Naples, Italy.
| | - Gaia Spadarella
- Department of Advanced Biomedical Sciences, University of Naples Federico II, Naples, Italy
| | - Agah Baran
- Department of Diagnostic and Interventional Radiology, University of Cologne, Cologne, Germany
| | | | - Kevin Groot Lipman
- Department of Radiology, Netherlands Cancer Institute, Amsterdam, Netherlands
| | - Peter Van Ooijen
- Department of Radiation Oncology, University Medical Center Groningen, Groningen, The Netherlands
- Machine Learning Lab, Data Science Center in Health, University Medical Center Groningen, Groningen, the Netherlands
| | - Renato Cuocolo
- Department of Medicine, Surgery and Dentistry, University of Salerno, Baronissi, Italy
- Augmented Reality for Health Monitoring Laboratory (ARHeMLab), Department of Electrical Engineering and Information Technology, University of Naples "Federico II", Naples, Italy
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Ma L, Huang L, Chen Y, Zhang L, Nie D, He W, Qi X. AI diagnostic performance based on multiple imaging modalities for ovarian tumor: A systematic review and meta-analysis. Front Oncol 2023; 13:1133491. [PMID: 37152032 PMCID: PMC10160474 DOI: 10.3389/fonc.2023.1133491] [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: 12/29/2022] [Accepted: 01/30/2023] [Indexed: 05/09/2023] Open
Abstract
Background In recent years, AI has been applied to disease diagnosis in many medical and engineering researches. We aimed to explore the diagnostic performance of the models based on different imaging modalities for ovarian cancer. Methods PubMed, EMBASE, Web of Science, and Wanfang Database were searched. The search scope was all published Chinese and English literatures about AI diagnosis of benign and malignant ovarian tumors. The literature was screened and data extracted according to inclusion and exclusion criteria. Quadas-2 was used to evaluate the quality of the included literature, STATA 17.0. was used for statistical analysis, and forest plots and funnel plots were drawn to visualize the study results. Results A total of 11 studies were included, 3 of them were modeled based on ultrasound, 6 based on MRI, and 2 based on CT. The pooled AUROCs of studies based on ultrasound, MRI and CT were 0.94 (95% CI 0.88-1.00), 0.82 (95% CI 0.71-0.93) and 0.82 (95% Cl 0.78-0.86), respectively. The values of I2 were 99.92%, 99.91% and 92.64% based on ultrasound, MRI and CT. Funnel plot suggested no publication bias. Conclusion The models based on ultrasound have the best performance in diagnostic of ovarian cancer.
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Affiliation(s)
- Lin Ma
- Department of Obstetrics and Gynecology, Chengdu First People's Hospital, Chengdu, China
| | - Liqiong Huang
- Department of Ultrasound, Chengdu First People's Hospital, Chengdu, Chengdu, China
| | - Yan Chen
- Department of Obstetrics and Gynecology, Chengdu First People's Hospital, Chengdu, China
| | - Lei Zhang
- Department of Obstetrics and Gynecology, Chengdu First People's Hospital, Chengdu, China
| | - Dunli Nie
- Department of Obstetrics and Gynecology, Chengdu First People's Hospital, Chengdu, China
| | - Wenjing He
- Big Data Research Center, University of Electronic Science and Technology of China, Chengdu, China
| | - Xiaoxue Qi
- Department of Obstetrics and Gynecology, Chengdu First People's Hospital, Chengdu, China
- *Correspondence: Xiaoxue Qi,
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Li J, Li X, Ma J, Wang F, Cui S, Ye Z. Computed tomography-based radiomics machine learning classifiers to differentiate type I and type II epithelial ovarian cancers. Eur Radiol 2022:10.1007/s00330-022-09318-w. [PMID: 36515713 DOI: 10.1007/s00330-022-09318-w] [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: 05/22/2022] [Revised: 10/14/2022] [Accepted: 11/22/2022] [Indexed: 12/15/2022]
Abstract
OBJECTIVES To compare computed tomography (CT)-based radiomics for preoperatively differentiating type I and II epithelial ovarian cancers (EOCs) using different machine learning classifiers and to construct and validate the best diagnostic model. METHODS A total of 470 patients with EOCs were included retrospectively. Patients were divided into a training dataset (N = 329) and a test dataset (N = 141). A total of 1316 radiomics features were extracted from the portal venous phase of contrast-enhanced CT images for each patient, followed by dimension reduction of the features. The support vector machine (SVM), k-nearest neighbor (KNN), random forest (RF), naïve Bayes (NB), logistic regression (LR), and eXtreme Gradient Boosting (XGBoost) classifiers were trained to obtain the radiomics signatures. The performance of each radiomics signature was evaluated and compared by the area under the receiver operating characteristic curve (AUC) and relative standard deviation (RSD). The best radiomics signature was selected and combined with clinical and radiological features to establish a combined model. The diagnostic value of the combined model was assessed. RESULTS The LR-based radiomics signature performed well in the test dataset, with an AUC of 0.879 and an accuracy of 0.773. The combined model performed best in both the training and test datasets, with AUCs of 0.900 and 0.934 and accuracies of 0.848 and 0.823, respectively. CONCLUSION The combined model showed the best diagnostic performance for distinguishing between type I and II EOCs preoperatively. Therefore, it can be a useful tool for clinical individualized EOC classification. KEY POINTS • Radiomics features extracted from computed tomography (CT) could be used to differentiate type I and II epithelial ovarian cancers (EOCs). • Machine learning can improve the performance of differentiating type I and II EOCs. • The combined model exhibited the best diagnostic capability over the other models in both the training and test datasets.
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Affiliation(s)
- Jiaojiao Li
- Department of Radiology, First Affiliated Hospital of Hebei North University, No. 12, Changqing Road, Qiaoxi District, Zhangjiakou, 075000, China
- Department of Radiology, National Clinical Research Center for Cancer, Tianjin Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Huanhuxi Road, Hexi District, Tianjin, 300060, China
| | - Xubin Li
- Department of Radiology, National Clinical Research Center for Cancer, Tianjin Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Huanhuxi Road, Hexi District, Tianjin, 300060, China
| | - Juanwei Ma
- Department of Radiology, National Clinical Research Center for Cancer, Tianjin Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Huanhuxi Road, Hexi District, Tianjin, 300060, China
| | - Fang Wang
- Department of Radiology, National Clinical Research Center for Cancer, Tianjin Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Huanhuxi Road, Hexi District, Tianjin, 300060, China
| | - Shujun Cui
- Department of Radiology, First Affiliated Hospital of Hebei North University, No. 12, Changqing Road, Qiaoxi District, Zhangjiakou, 075000, China.
| | - Zhaoxiang Ye
- Department of Radiology, National Clinical Research Center for Cancer, Tianjin Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Huanhuxi Road, Hexi District, Tianjin, 300060, China.
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Xu Y, Luo HJ, Ren J, Guo LM, Niu J, Song X. Diffusion-weighted imaging-based radiomics in epithelial ovarian tumors: Assessment of histologic subtype. Front Oncol 2022; 12:978123. [PMID: 36544703 PMCID: PMC9762272 DOI: 10.3389/fonc.2022.978123] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2022] [Accepted: 11/16/2022] [Indexed: 12/12/2022] Open
Abstract
Background Epithelial ovarian tumors (EOTs) are a group of heterogeneous neoplasms. It is importance to preoperatively differentiate the histologic subtypes of EOTs. Our study aims to investigate the potential of radiomics signatures based on diffusion-weighted imaging (DWI) and apparent diffusion coefficient (ADC) maps for categorizing EOTs. Methods This retrospectively enrolled 146 EOTs patients [34 with borderline EOT(BEOT), 30 with type I and 82 with type II epithelial ovarian cancer (EOC)]. A total of 390 radiomics features were extracted from DWI and ADC maps. Subsequently, the LASSO algorithm was used to reduce the feature dimensions. A radiomics signature was established using multivariable logistic regression method with 3-fold cross-validation and repeated 50 times. Patients with bilateral lesions were included in the validation cohort and a heuristic selection method was established to select the tumor with maximum probability for final consideration. A nomogram incorporating the radiomics signature and clinical characteristics was also developed. Receiver operator characteristic, decision curve analysis (DCA), and net reclassification index (NRI) were applied to compare the diagnostic performance and clinical net benefit of predictive model. Results For distinguishing BEOT from EOC, the radiomics signature and nomogram showed more favorable discrimination than the clinical model (0.915 vs. 0.852 and 0.954 vs. 0.852, respectively) in the training cohort. In classifying early-stage type I and type II EOC, the radiomics signature exhibited superior diagnostic performance over the clinical model (AUC 0.905 vs. 0.735). The diagnostic efficacy of the nomogram was the same as that of the radiomics model with NRI value of -0.1591 (P = 0.7268). DCA also showed that the radiomics model and combined model had higher net benefits than the clinical model. Conclusion Radiomics analysis based on DWI, and ADC maps serve as an effective quantitative approach to categorize EOTs.
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Affiliation(s)
- Yi Xu
- Department of Radiology, Second Hospital of Shanxi Medical University, Taiyuan, Shanxi, China
| | - Hong-Jian Luo
- Department of Radiology, The Third Affiliated Hospital of Zunyi Medical University (The First People’s Hospital of Zunyi), Zunyi, Guizhou, China
| | | | - Li-mei Guo
- Department of Radiology, Second Hospital of Shanxi Medical University, Taiyuan, Shanxi, China
| | - Jinliang Niu
- Department of Radiology, Second Hospital of Shanxi Medical University, Taiyuan, Shanxi, China
| | - Xiaoli Song
- Department of Radiology, Second Hospital of Shanxi Medical University, Taiyuan, Shanxi, China,*Correspondence: Xiaoli Song,
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Wang M, Perucho JAU, Hu Y, Choi MH, Han L, Wong EMF, Ho G, Zhang X, Ip P, Lee EYP. Computed Tomographic Radiomics in Differentiating Histologic Subtypes of Epithelial Ovarian Carcinoma. JAMA Netw Open 2022; 5:e2245141. [PMID: 36469315 PMCID: PMC9855300 DOI: 10.1001/jamanetworkopen.2022.45141] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/12/2022] Open
Abstract
IMPORTANCE Epithelial ovarian carcinoma is heterogeneous and classified according to the World Health Organization Tumour Classification, which is based on histologic features and molecular alterations. Preoperative prediction of the histologic subtypes could aid in clinical management and disease prognostication. OBJECTIVE To assess the value of radiomics based on contrast-enhanced computed tomography (CT) in differentiating histologic subtypes of epithelial ovarian carcinoma in multicenter data sets. DESIGN, SETTING, AND PARTICIPANTS In this diagnostic study, 665 patients with histologically confirmed epithelial ovarian carcinoma were retrospectively recruited from 4 centers (Hong Kong, Guangdong Province of China, and Seoul, South Korea) between January 1, 2012, and February 28, 2022. The patients were randomly divided into a training cohort (n = 532) and a testing cohort (n = 133) with a ratio of 8:2. This process was repeated 100 times. Tumor segmentation was manually delineated on each section of contrast-enhanced CT images to encompass the entire tumor. The Mann-Whitney U test and voted least absolute shrinkage and selection operator were performed for feature reduction and selection. Selected features were used to build the logistic regression model for differentiating high-grade serous carcinoma and non-high-grade serous carcinoma. EXPOSURES Contrast-enhanced CT-based radiomics. MAIN OUTCOMES AND MEASURES Intraobserver and interobserver reproducibility of tumor segmentation were measured by Dice similarity coefficients. The diagnostic efficiency of the model was assessed by receiver operating characteristic curve and area under the curve. RESULTS In this study, 665 female patients (mean [SD] age, 53.6 [10.9] years) with epithelial ovarian carcinoma were enrolled and analyzed. The Dice similarity coefficients of intraobserver and interobserver were all greater than 0.80. Twenty radiomic features were selected for modeling. The areas under the curve of the logistic regression model in differentiating high-grade serous carcinoma and non-high-grade serous carcinoma were 0.837 (95% CI, 0.835-0.838) for the training cohort and 0.836 (95% CI, 0.833-0.840) for the testing cohort. CONCLUSIONS AND RELEVANCE In this diagnostic study, radiomic features extracted from contrast-enhanced CT were useful in the classification of histologic subtypes in epithelial ovarian carcinoma. Intraobserver and interobserver reproducibility of tumor segmentation was excellent. The proposed logistic regression model offered excellent discriminative ability among histologic subtypes.
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Affiliation(s)
- Mandi Wang
- Department of Radiology, Shenzhen People's Hospital (The Second Clinical Medical College, Jinan University; The First Affiliated Hospital, Southern University of Science and Technology), Shenzhen, Guangdong, China
- Department of Diagnostic Radiology, School of Clinical Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Jose A. U. Perucho
- Department of Radiology, Heersink School of Medicine, The University of Alabama at Birmingham, Birmingham
| | - Yangling Hu
- Department of Radiology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Moon Hyung Choi
- Department of Radiology, Eunpyeong St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea
| | - Lujun Han
- Department of Radiology, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Esther M. F. Wong
- Department of Radiology, Pamela Youde Nethersole Eastern Hospital, Hong Kong Special Administrative Region, China
| | - Grace Ho
- Department of Radiology, Queen Mary Hospital, Hong Kong Special Administrative Region, China
| | - Xiaoling Zhang
- Department of Radiology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Philip Ip
- Department of Pathology, Queen Mary Hospital, School of Clinical Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Elaine Y. P. Lee
- Department of Diagnostic Radiology, School of Clinical Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
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Lei R, Yu Y, Li Q, Yao Q, Wang J, Gao M, Wu Z, Ren W, Tan Y, Zhang B, Chen L, Lin Z, Yao H. Deep learning magnetic resonance imaging predicts platinum sensitivity in patients with epithelial ovarian cancer. Front Oncol 2022; 12:895177. [PMID: 36505880 PMCID: PMC9727155 DOI: 10.3389/fonc.2022.895177] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2022] [Accepted: 10/18/2022] [Indexed: 11/24/2022] Open
Abstract
Objective The aim of the study is to develop and validate a deep learning model to predict the platinum sensitivity of patients with epithelial ovarian cancer (EOC) based on contrast-enhanced magnetic resonance imaging (MRI). Methods In this retrospective study, 93 patients with EOC who received platinum-based chemotherapy (≥4 cycles) and debulking surgery at the Sun Yat-sen Memorial Hospital from January 2011 to January 2020 were enrolled and randomly assigned to the training and validation cohorts (2:1). Two different models were built based on either the primary tumor or whole volume of the abdomen as the volume of interest (VOI) within the same cohorts, and then a pre-trained convolutional neural network Med3D (Resnet 10 version) was transferred to automatically extract 1,024 features from two MRI sequences (CE-T1WI and T2WI) of each patient to predict platinum sensitivity. The performance of the two models was compared. Results A total of 93 women (mean age, 50.5 years ± 10.5 [standard deviation]) were evaluated (62 in the training cohort and 31 in the validation cohort). The AUCs of the whole abdomen model were 0.97 and 0.98 for the training and validation cohorts, respectively, which was better than the primary tumor model (AUCs of 0.88 and 0.81 in the training and validation cohorts, respectively). In k-fold cross-validation and stratified analysis, the whole abdomen model maintained a stable performance, and the decision function value generated by the model was a prognostic indicator that successfully discriminates high- and low-risk recurrence patients. Conclusion The non-manually segmented whole-abdomen deep learning model based on MRI exhibited satisfactory predictive performance for platinum sensitivity and may assist gynecologists in making optimal treatment decisions.
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Affiliation(s)
- Ruilin Lei
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Medical Research Center, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China,Department of Gynecological Oncology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China,Department of Medical Oncology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
| | - Yunfang Yu
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Medical Research Center, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China,Department of Medical Oncology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China,Faculty of Medicine, Macau University of Science and Technology, Macao, Macao SAR, China
| | - Qingjian Li
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Medical Research Center, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China,Department of Medical Oncology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China,Phase I Clinical Trial Centre, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
| | - Qinyue Yao
- Cells Vision Medical Technology Inc., Guangzhou, China
| | - Jin Wang
- Cells Vision Medical Technology Inc., Guangzhou, China
| | - Ming Gao
- Department of Radiology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
| | - Zhuo Wu
- Department of Radiology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
| | - Wei Ren
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Medical Research Center, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China,Department of Medical Oncology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China,Phase I Clinical Trial Centre, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
| | - Yujie Tan
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Medical Research Center, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China,Department of Medical Oncology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China,Phase I Clinical Trial Centre, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
| | - Bingzhong Zhang
- Department of Gynecological Oncology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
| | - Liliang Chen
- Cells Vision Medical Technology Inc., Guangzhou, China
| | - Zhongqiu Lin
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Medical Research Center, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China,Department of Gynecological Oncology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China,*Correspondence: Zhongqiu Lin, ; Herui Yao,
| | - Herui Yao
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Medical Research Center, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China,Department of Medical Oncology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China,Phase I Clinical Trial Centre, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China,Breast Tumor Centre, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China,*Correspondence: Zhongqiu Lin, ; Herui Yao,
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Xu HL, Gong TT, Liu FH, Chen HY, Xiao Q, Hou Y, Huang Y, Sun HZ, Shi Y, Gao S, Lou Y, Chang Q, Zhao YH, Gao QL, Wu QJ. Artificial intelligence performance in image-based ovarian cancer identification: A systematic review and meta-analysis. EClinicalMedicine 2022; 53:101662. [PMID: 36147628 PMCID: PMC9486055 DOI: 10.1016/j.eclinm.2022.101662] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/28/2022] [Revised: 08/25/2022] [Accepted: 08/30/2022] [Indexed: 11/28/2022] Open
Abstract
BACKGROUND Accurate identification of ovarian cancer (OC) is of paramount importance in clinical treatment success. Artificial intelligence (AI) is a potentially reliable assistant for the medical imaging recognition. We systematically review articles on the diagnostic performance of AI in OC from medical imaging for the first time. METHODS The Medline, Embase, IEEE, PubMed, Web of Science, and the Cochrane library databases were searched for related studies published until August 1, 2022. Inclusion criteria were studies that developed or used AI algorithms in the diagnosis of OC from medical images. The binary diagnostic accuracy data were extracted to derive the outcomes of interest: sensitivity (SE), specificity (SP), and Area Under the Curve (AUC). The study was registered with the PROSPERO, CRD42022324611. FINDINGS Thirty-four eligible studies were identified, of which twenty-eight studies were included in the meta-analysis with a pooled SE of 88% (95%CI: 85-90%), SP of 85% (82-88%), and AUC of 0.93 (0.91-0.95). Analysis for different algorithms revealed a pooled SE of 89% (85-92%) and SP of 88% (82-92%) for machine learning; and a pooled SE of 88% (84-91%) and SP of 84% (80-87%) for deep learning. Acceptable diagnostic performance was demonstrated in subgroup analyses stratified by imaging modalities (Ultrasound, Magnetic Resonance Imaging, or Computed Tomography), sample size (≤300 or >300), AI algorithms versus clinicians, year of publication (before or after 2020), geographical distribution (Asia or non Asia), and the different risk of bias levels (≥3 domain low risk or < 3 domain low risk). INTERPRETATION AI algorithms exhibited favorable performance for the diagnosis of OC through medical imaging. More rigorous reporting standards that address specific challenges of AI research could improve future studies. FUNDING This work was supported by the Natural Science Foundation of China (No. 82073647 to Q-JW and No. 82103914 to T-TG), LiaoNing Revitalization Talents Program (No. XLYC1907102 to Q-JW), and 345 Talent Project of Shengjing Hospital of China Medical University (No. M0268 to Q-JW and No. M0952 to T-TG).
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Key Words
- AI, Artificial intelligence
- AUC, Area Under the Curve
- Artificial intelligence
- CT, Computed Tomography
- DL, Deep learning
- ML, Machine learning
- MRI, Magnetic Resonance Imaging
- Medical imaging
- Meta-analysis
- OC, Ovarian cancer
- Ovarian cancer
- SE, Sensitivity
- SP, Specificity
- US, Ultrasound
- XAI, Explainable artificial intelligence
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Affiliation(s)
- He-Li Xu
- Department of Clinical Epidemiology, Shengjing Hospital of China Medical University, Shenyang, China
- Clinical Research Center, Shengjing Hospital of China Medical University, Shenyang, China
- Key Laboratory of Precision Medical Research on Major Chronic Disease, Shengjing Hospital of China Medical University, Shenyang, China
| | - Ting-Ting Gong
- Department of Obstetrics and Gynecology, Shengjing Hospital of China Medical University, Shenyang, China
| | - Fang-Hua Liu
- Department of Clinical Epidemiology, Shengjing Hospital of China Medical University, Shenyang, China
- Clinical Research Center, Shengjing Hospital of China Medical University, Shenyang, China
- Key Laboratory of Precision Medical Research on Major Chronic Disease, Shengjing Hospital of China Medical University, Shenyang, China
| | - Hong-Yu Chen
- Department of Clinical Epidemiology, Shengjing Hospital of China Medical University, Shenyang, China
- Clinical Research Center, Shengjing Hospital of China Medical University, Shenyang, China
- Key Laboratory of Precision Medical Research on Major Chronic Disease, Shengjing Hospital of China Medical University, Shenyang, China
| | - Qian Xiao
- Department of Clinical Epidemiology, Shengjing Hospital of China Medical University, Shenyang, China
| | - Yang Hou
- Department of Radiology, Shengjing Hospital of China Medical University, Shenyang, China
| | - Ying Huang
- Department of Ultrasound, Shengjing Hospital of China Medical University, Shenyang, China
| | - Hong-Zan Sun
- Department of Radiology, Shengjing Hospital of China Medical University, Shenyang, China
| | - Yu Shi
- Department of Radiology, Shengjing Hospital of China Medical University, Shenyang, China
| | - Song Gao
- Department of Obstetrics and Gynecology, Shengjing Hospital of China Medical University, Shenyang, China
| | - Yan Lou
- Department of Intelligent Medicine, China Medical University, China
| | - Qing Chang
- Department of Clinical Epidemiology, Shengjing Hospital of China Medical University, Shenyang, China
- Clinical Research Center, Shengjing Hospital of China Medical University, Shenyang, China
- Key Laboratory of Precision Medical Research on Major Chronic Disease, Shengjing Hospital of China Medical University, Shenyang, China
| | - Yu-Hong Zhao
- Department of Clinical Epidemiology, Shengjing Hospital of China Medical University, Shenyang, China
- Clinical Research Center, Shengjing Hospital of China Medical University, Shenyang, China
- Key Laboratory of Precision Medical Research on Major Chronic Disease, Shengjing Hospital of China Medical University, Shenyang, China
| | - Qing-Lei Gao
- National Clinical Research Center for Obstetrics and Gynecology, Cancer Biology Research Centre (Key Laboratory of the Ministry of Education) and Department of Gynecology and Obstetrics, Tongji Hospital, Wuhan, China
| | - Qi-Jun Wu
- Department of Clinical Epidemiology, Shengjing Hospital of China Medical University, Shenyang, China
- Clinical Research Center, Shengjing Hospital of China Medical University, Shenyang, China
- Key Laboratory of Precision Medical Research on Major Chronic Disease, Shengjing Hospital of China Medical University, Shenyang, China
- Department of Obstetrics and Gynecology, Shengjing Hospital of China Medical University, Shenyang, China
- Corresponding author at: Department of Clinical Epidemiology, Department of Obstetrics and Gynecology, Clinical Research Center, Shengjing Hospital of China Medical University, Address: No. 36, San Hao Street, Shenyang, Liaoning 110004, PR China.
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Li J, Zhang T, Ma J, Zhang N, Zhang Z, Ye Z. Machine-learning-based contrast-enhanced computed tomography radiomic analysis for categorization of ovarian tumors. Front Oncol 2022; 12:934735. [PMID: 36016613 PMCID: PMC9395674 DOI: 10.3389/fonc.2022.934735] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2022] [Accepted: 06/16/2022] [Indexed: 11/13/2022] Open
Abstract
ObjectivesThis study aims to evaluate the diagnostic performance of machine-learning-based contrast-enhanced CT radiomic analysis for categorizing benign and malignant ovarian tumors.MethodsA total of 1,329 patients with ovarian tumors were randomly divided into a training cohort (N=930) and a validation cohort (N=399). All tumors were resected, and pathological findings were confirmed. Radiomic features were extracted from the portal venous phase images of contrast-enhanced CT. The clinical predictors included age, CA-125, HE-4, ascites, and margin of tumor. Both radiomics model (including selected radiomic features) and mixed model (incorporating selected radiomic features and clinical predictors) were constructed respectively. Six classifiers [k-nearest neighbor (KNN), support vector machines (SVM), random forest (RF), logistic regression (LR), multi-layer perceptron (MLP), and eXtreme Gradient Boosting (XGBoost)] were used for each model. The mean relative standard deviation (RSD) and area under the receiver operating characteristic curve (AUC) were applied to evaluate and select the best classifiers. Then, the performances of the two models with selected classifiers were assessed in the validation cohort.ResultsThe MLP classifier with the least RSD (1.21 and 0.53, respectively) was selected as the best classifier in both radiomics and mixed models. The two models with MLP classifier performed well in the validation cohort, with the AUCs of 0.91 and 0.96 and with accuracies (ACCs) of 0.83 and 0.87, respectively. The Delong test showed that the AUC of mixed model was statistically different from that of radiomics model (p<0.001).ConclusionsMachine-learning-based CT radiomic analysis could categorize ovarian tumors with good performance preoperatively. The mixed model with MLP classifier may be a potential tool in clinical applications.
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Affiliation(s)
- Jiaojiao Li
- Department of Radiology, National Clinical Research Center for Cancer, Tianjin Key Laboratory of Cancer Prevention and Therapy, Tianjin’s Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China
- Department of Radiology, First Affiliated Hospital of Hebei North University, Zhangjiakou, China
| | - Tianzhu Zhang
- Department of Radiology, Tianjin Medical University General Hospital, Tianjin, China
| | - Juanwei Ma
- Department of Radiology, National Clinical Research Center for Cancer, Tianjin Key Laboratory of Cancer Prevention and Therapy, Tianjin’s Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China
| | - Ningnannan Zhang
- Department of Radiology, Tianjin Medical University General Hospital, Tianjin, China
| | - Zhang Zhang
- Department of Radiology, Tianjin Medical University General Hospital, Tianjin, China
- *Correspondence: Zhaoxiang Ye, ; Zhang Zhang,
| | - Zhaoxiang Ye
- Department of Radiology, National Clinical Research Center for Cancer, Tianjin Key Laboratory of Cancer Prevention and Therapy, Tianjin’s Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China
- *Correspondence: Zhaoxiang Ye, ; Zhang Zhang,
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Wei M, Zhang Y, Bai G, Ding C, Xu H, Dai Y, Chen S, Wang H. T2-weighted MRI-based radiomics for discriminating between benign and borderline epithelial ovarian tumors: a multicenter study. Insights Imaging 2022; 13:130. [PMID: 35943620 PMCID: PMC9363551 DOI: 10.1186/s13244-022-01264-x] [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: 04/27/2022] [Accepted: 07/04/2022] [Indexed: 11/16/2022] Open
Abstract
Background Preoperative differentiation between benign and borderline epithelial ovarian tumors (EOTs) is challenging and can significantly impact clinical decision making. The purpose was to investigate whether radiomics based on T2-weighted MRI can discriminate between benign and borderline EOTs preoperatively. Methods A total of 417 patients (309, 78, and 30 samples in the training and internal and external validation sets) with pathologically proven benign and borderline EOTs were included in this multicenter study. In total, 1130 radiomics features were extracted from manually delineated tumor volumes of interest on images. The following three different models were constructed and evaluated: radiomics features only (radiomics model); clinical and radiological characteristics only (clinic-radiological model); and a combination of them all (combined model). The diagnostic performances of models were assessed using receiver operating characteristic (ROC) analysis, and area under the ROC curves (AUCs) were compared using the DeLong test. Results The best machine learning algorithm to distinguish borderline from benign EOTs was the logistic regression. The combined model achieved the best performance in discriminating between benign and borderline EOTs, with an AUC of 0.86 ± 0.07. The radiomics model showed a moderate AUC of 0.82 ± 0.07, outperforming the clinic-radiological model (AUC of 0.79 ± 0.06). In the external validation set, the combined model performed significantly better than the clinic-radiological model (AUCs of 0.86 vs. 0.63, p = 0.021 [DeLong test]). Conclusions Radiomics, based on T2-weighted MRI, can provide critical diagnostic information for discriminating between benign and borderline EOTs, thus having the potential to aid personalized treatment options. Supplementary Information The online version contains supplementary material available at 10.1186/s13244-022-01264-x.
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Affiliation(s)
- Mingxiang Wei
- Department of Radiology, The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou, Jiangsu, China.,Gusu School, Nanjing Medical University, Suzhou, Jiangsu, China
| | - Yu Zhang
- Department of Radiology, Dushu Lake Hospital Affiliated to Soochow University, Suzhou, Jiangsu, China
| | - Genji Bai
- Department of Radiology, The Affiliated Huaian No. 1 People's Hospital of Nanjing Medical University, Huaian, Jiangsu, China
| | - Cong Ding
- Department of Radiology, The Affiliated Huaian No. 1 People's Hospital of Nanjing Medical University, Huaian, Jiangsu, China
| | - Haimin Xu
- Department of Radiology, Dushu Lake Hospital Affiliated to Soochow University, Suzhou, Jiangsu, China
| | - Yao Dai
- Department of Radiology, The First Affiliated Hospital of Soochow University, Suzhou, Jiangsu, China
| | - Shuangqing Chen
- Department of Radiology, The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou, Jiangsu, China. .,Gusu School, Nanjing Medical University, Suzhou, Jiangsu, China.
| | - Hong Wang
- Department of Radiology, The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou, Jiangsu, China. .,Gusu School, Nanjing Medical University, Suzhou, Jiangsu, China.
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Yao F, Ding J, Lin F, Xu X, Jiang Q, Zhang L, Fu Y, Yang Y, Lan L. Nomogram based on ultrasound radiomics score and clinical variables for predicting histologic subtypes of epithelial ovarian cancer. Br J Radiol 2022; 95:20211332. [PMID: 35612547 PMCID: PMC10162053 DOI: 10.1259/bjr.20211332] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2021] [Revised: 05/11/2022] [Accepted: 05/19/2022] [Indexed: 11/09/2022] Open
Abstract
OBJECTIVE Ovarian cancer is one of the most common causes of death in gynecological tumors, and its most common type is epithelial ovarian cancer (EOC). This study aimed to establish a radiomics signature based on ultrasound images to predict the histopathological types of EOC. METHODS Overall, 265 patients with EOC who underwent preoperative ultrasonography and surgery were eligible. They were randomly sorted into two cohorts (training cohort: test cohort = 7:3). We outlined the region of interest of the tumor on the ultrasound images of the lesion. Then, the radiomics features were extracted. Clinical, Rad-score and combined models were constructed based on the least absolute shrinkage, selection operator, and logistic regression analysis. The performance of the models was evaluated using receiver operating characteristic curves and decision curve analysis (DCA). A nomogram was formulated based on the combined prediction model. RESULTS The combined model had good performance in predicting EOC histopathological types, with an AUC of 0.83 (95% CI: 0.77-0.90) and 0.82 (95% CI: 0.71-0.93) in the training and test cohorts, respectively. The calibration curves showed that the nomogram estimation was consistent with the actual observations. DCA also verified the clinical value of the combined model. CONCLUSIONS The combined model containing clinical and ultrasound radiomics features showed an excellent performance in predicting type I and type II EOC. ADVANCES IN KNOWLEDGE This study presents the first application of ultrasound radiomics features to distinguish EOC histopathological types. The proposed clinical-radiomics nomogram could help gynecologists non-invasively identify EOC types before surgery.
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Affiliation(s)
- Fei Yao
- Department of Radiology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Jie Ding
- Department of Radiology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Feng Lin
- Department of Gynecology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Xiaomin Xu
- Department of Ultrasound imaging, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Qi Jiang
- School of First Clinical Medicine, Wenzhou Medical University, Wenzhou, China
| | - Li Zhang
- School of First Clinical Medicine, Wenzhou Medical University, Wenzhou, China
| | - Yanqi Fu
- School of First Clinical Medicine, Wenzhou Medical University, Wenzhou, China
| | - Yunjun Yang
- Department of Radiology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Li Lan
- Department of Ultrasound imaging, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
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Zheng X, Xu S, Wu J. Cervical Cancer Imaging Features Associated With ADRB1 as a Risk Factor for Cerebral Neurovascular Metastases. Front Neurol 2022; 13:905761. [PMID: 35903112 PMCID: PMC9315067 DOI: 10.3389/fneur.2022.905761] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2022] [Accepted: 06/20/2022] [Indexed: 11/13/2022] Open
Abstract
Bioinformatics tools are used to create a clinical prediction model for cervical cancer metastasis and to investigate the neurovascular-related genes that are involved in brain metastasis of cervical cancer. One hundred eighteen patients with cervical cancer were divided into two groups based on the presence or absence of metastases, and the clinical data and imaging findings of the two groups were compared retrospectively. The nomogram-based model was successfully constructed by taking into account four clinical characteristics (age, stage, N, and T) as well as one imaging characteristic (original_glszm_GrayLevelVariance Rad-score). In patients with cervical cancer, headaches and vomiting were more often reported in the brain metastasis group than in the other metastasis groups. According to the TCGA data, mRNA differential gene expression analysis of patients with cervical cancer revealed an increase in the expression of neurovascular-related gene Adrenoceptor Beta 1 (ADRB1) in the brain metastasis group. An analysis of the correlation between imaging features and ADRB1 expression revealed that ADRB1 expression was significantly higher in the low Rad-score group compared with the high Rad-score group (P = 0.025). Therefore, ADRB1 expression in cervical cancer was correlated with imaging features and was associated as a risk factor for cerebral neurovascular metastases. This study developed a nomogram prediction model for cervical cancer metastasis using age, stage, N, T and original_glszm_GrayLevelVariance. As a risk factor associated with the development of cerebral neurovascular metastases of cervical cancer, ADRB1 expression was significantly higher in brain metastases from cervical cancer.
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Affiliation(s)
- Xingju Zheng
- Department of Radiology, Guizhou Provincial People's Hospital, Guiyang, China
| | - Shilin Xu
- Department of Oncology, Xichang People's Hospital, Liangshan High-Tech Tumor Hospital, Xichang, China
| | - JiaYing Wu
- Department of Gynaecology and Obstetrics, Zhejiang Xinda Hospital, Huzhou, China
- *Correspondence: JiaYing Wu
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Zheng Y, Wang H, Li Q, Sun H, Guo L. Discriminating Between Benign and Malignant Solid Ovarian Tumors Based on Clinical and Radiomic Features of MRI. Acad Radiol 2022; 30:814-822. [PMID: 35810066 DOI: 10.1016/j.acra.2022.06.007] [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: 05/04/2022] [Revised: 05/31/2022] [Accepted: 06/08/2022] [Indexed: 12/17/2022]
Abstract
RATIONALE AND OBJECTIVES To develop and validate a combined model integrating clinical and radiomic features to non-invasive discriminate between the benign and malignant solid ovarian tumors. MATERIALS AND METHODS A total of 148 patients with 156 solid ovarian tumors (86 benign and 70 malignant tumors) were included in this study. The dataset was split into the training and the test set with a ratio of 8:2 using stratified random sampling. 12 clinical features and 1612 radiomic features were extracted from each tumor. These features were selected by least absolute shrinkage and selection operator (Lasso). Three classification models were built using extreme gradient boosting (XGB) algorithm: clinical model, radiomic model, combined model. The area under the receiver operating characteristic curve (AUC), accuracy, precision and sensitivity were analyzed to evaluate the performance of these models. RESULTS All of the three models obtained good performances in differentiating benign with malignant solid ovarian tumors in both training and test sets. The AUC, accuracy, precision, sensitivity of clinical model and radiomic model in test set were 0.847 (95% confidence interval (CI), 0.707-0.986, p <0.01), 0.774, 0.769, 0.714, and 0.807 (95%CI, 0.652-0.961, p <0.05), 0.677, 0.643, 0.643, respectively. Combined model had the best prediction results, the AUC, accuracy, precision and sensitivity were 0.954 (95%CI, 0.862-1.0, p <0.01), 0.839, 0.909 and 0.714 in test set. CONCLUSION Radiomics based on machine learning can be helpful for radiologists in differentiating the benign and malignant solid ovarian tumors.
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Affiliation(s)
- Yuemei Zheng
- School of Medical Imaging, Tianjin Medical University, No. 1 Guangdong Road, Tianjin 300203, China
| | - Hong Wang
- Department of Radiology, Tianjin First Central Hospital, Tianjin, China
| | - Qiong Li
- Department of Radiology, Tianjin Medical University General Hospital, Tianjin, China
| | - Haoran Sun
- Department of Radiology, Tianjin Medical University General Hospital, Tianjin, China
| | - Li Guo
- School of Medical Imaging, Tianjin Medical University, No. 1 Guangdong Road, Tianjin 300203, China.
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Yuan Y, Lu H, Ma X, Chen F, Zhang S, Xia Y, Wang M, Shao C, Lu J, Shen F. Is rectal filling optimal for MRI-based radiomics in preoperative T staging of rectal cancer? Abdom Radiol (NY) 2022; 47:1741-1749. [PMID: 35267070 DOI: 10.1007/s00261-022-03477-6] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2022] [Revised: 02/22/2022] [Accepted: 02/23/2022] [Indexed: 11/26/2022]
Abstract
PURPOSE To determine whether rectal filling with ultrasound gel is clinically more beneficial in preoperative T staging of patients with rectal cancer (RC) using radiomics model based on magnetic resonance imaging (MRI). METHODS A total of 94 RC patients were assigned to cohort 1 (leave-one-out cross-validation [LOO-CV] set) and 230 RC patients were assigned to cohort 2 (test set). Patients were grouped according to different pathological T stages. The radiomics features were extracted through high-resolution T2-weighted imaging for all volume of interests in the two cohorts. Optimal features were selected using the least absolute shrinkage and selection operator (LASSO) algorithm. Model 1 (without rectal filling) and model 2 (with rectal filling) were constructed. LOO-CV was adopted for radiomics model building in cohort 1. Thereafter, the cohort 2 was used to test and verify the effectiveness of the two models. RESULTS Totally, 204 patients were enrolled, including 60 cases in cohort 1 and 144 cases in cohort 2. Finally, seven optimal features with LASSO were selected to build model 1 and nine optimal features were used for model 2. The ROC curves showed an AUC of 0.806 and 0.946 for model 1 and model 2 in cohort 1, respectively, and an AUC of 0.783 and 0.920 for model 1 and model 2 in cohort 2, respectively (p = 0.021). CONCLUSION The radiomics model with rectal filling showed an advantage for differentiating T1 + 2 from T3 and had less inaccurate categories in the test cohort, suggesting that this model may be useful for T-stage evaluation.
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Affiliation(s)
- Yuan Yuan
- Department of Radiology, Changhai Hospital, No.168 Changhai Road, Shanghai, 200433, China
| | - Haidi Lu
- Department of Radiology, Changhai Hospital, No.168 Changhai Road, Shanghai, 200433, China
| | - Xiaolu Ma
- Department of Radiology, Changhai Hospital, No.168 Changhai Road, Shanghai, 200433, China
| | - Fangying Chen
- Department of Radiology, Changhai Hospital, No.168 Changhai Road, Shanghai, 200433, China
| | - Shaoting Zhang
- Department of Radiology, Changhai Hospital, No.168 Changhai Road, Shanghai, 200433, China
| | - Yuwei Xia
- Huiying Medical Technology Co., Ltd, B2, Dongsheng Science and Technology Park, HaiDian District, Beijing, China
| | - Minjie Wang
- Department of Radiology, Changhai Hospital, No.168 Changhai Road, Shanghai, 200433, China
| | - Chengwei Shao
- Department of Radiology, Changhai Hospital, No.168 Changhai Road, Shanghai, 200433, China
| | - Jianping Lu
- Department of Radiology, Changhai Hospital, No.168 Changhai Road, Shanghai, 200433, China
| | - Fu Shen
- Department of Radiology, Changhai Hospital, No.168 Changhai Road, Shanghai, 200433, China.
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Radiogenomics: A Valuable Tool for the Clinical Assessment and Research of Ovarian Cancer. J Comput Assist Tomogr 2022; 46:371-378. [DOI: 10.1097/rct.0000000000001279] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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Hu X, Liang Z, Zhang C, Wang G, Cai J, Wang P. The Diagnostic Performance of Maximum Uptake Value and Apparent Diffusion Coefficient in Differentiating Benign and Malignant Ovarian or Adnexal Masses: A Meta-Analysis. Front Oncol 2022; 12:840433. [PMID: 35223521 PMCID: PMC8864062 DOI: 10.3389/fonc.2022.840433] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2021] [Accepted: 01/17/2022] [Indexed: 12/22/2022] Open
Abstract
Objective The purpose of this meta-analysis was to provide evidence for using maximum uptake value (SUVmax) and apparent diffusion coefficient (ADC) to quantitatively differentiate benign and malignant ovarian or adnexal masses, and to indirectly compare their diagnostic performance. Material and Methods The association between SUVmax, ADC and ovarian or adnexal benign and malignant masses was searched in PubMed, Cochrane Library, and Embase databases until October 1, 2021. Two authors independently extracted the data. Studies included in the analysis were required to provide data for the construction of a 2 × 2 contingency table to evaluate the diagnostic performance of SUVmax or ADC in differentiating benign and malignant ovarian or adnexal masses. The quality of the enrolled studies was evaluated by Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) instrument, and the meta-analysis was conducted using Stata software version 14.0. Forest plots were generated according to the sensitivity and specificity of SUVmax and ADC, and meta-regression analysis was further used to assess heterogeneity between studies. Results A total of 14 studies were finally included in this meta-analysis by gradually excluding duplicate literatures, conference abstracts, guidelines, reviews, case reports, animal studies and so on. The pooled sensitivity and specificity of SUVmax for quantitative differentiation of benign and malignant ovarian or adnexal masses were 0.88 and 0.89, respectively, and the pooled sensitivity and specificity for ADC were 0.87 and 0.80, respectively. Conclusion Quantitative SUVmax and ADC values have good diagnostic performance in differentiating benign and malignant ovarian or adnexal masses, and SUVmax has higher accuracy than ADC. Future prospective studies with large sample sizes are needed for the analysis of the role of SUVmax and ADC in the differentiation of benign and malignant ovarian or adnexal masses.
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Affiliation(s)
- Xianwen Hu
- Department of Nuclear Medicine, Affiliated Hospital of Zunyi Medical University, Zunyi, China
| | - Zhigang Liang
- Department of Nuclear Medicine, Affiliated Hospital of Zunyi Medical University, Zunyi, China
| | - Chuanqin Zhang
- Department of Nuclear Medicine, Affiliated Hospital of Zunyi Medical University, Zunyi, China
| | - Guanlian Wang
- Research and Development Department, Jiangsu Yuanben Biotechnology Co., Ltd., Zunyi, China
| | - Jiong Cai
- Department of Nuclear Medicine, Affiliated Hospital of Zunyi Medical University, Zunyi, China
| | - Pan Wang
- Department of Nuclear Medicine, Affiliated Hospital of Zunyi Medical University, Zunyi, China
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Zhao J, Zhang W, Zhu YY, Zheng HY, Xu L, Zhang J, Liu SY, Li FY, Song B. Development and Validation of Noninvasive MRI-Based Signature for Preoperative Prediction of Early Recurrence in Perihilar Cholangiocarcinoma. J Magn Reson Imaging 2022; 55:787-802. [PMID: 34296802 DOI: 10.1002/jmri.27846] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2021] [Revised: 07/08/2021] [Accepted: 07/09/2021] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND Cholangiocarcinoma is a type of hepatobiliary tumor. For perihilar cholangiocarcinoma (pCCA), patients who experience early recurrence (ER) have a poor prognosis. Preoperative accurate prediction of postoperative ER can avoid unnecessary operation; however, prediction is challenging. PURPOSE To develop a novel signature based on clinical and/or MRI radiomics features of pCCA to preoperatively predict ER. STUDY TYPE Retrospective. POPULATION One hundred eighty-four patients (median age, 61.0 years; interquartile range: 53.0-66.8 years) including 115 men and 69 women. FIELD STRENGTH/SEQUENCE A 1.5 T; volumetric interpolated breath-hold examination (VIBE) sequence. ASSESSMENT The models were developed from the training set (128 patients) and validated in a separate testing set (56 patients). The contrast-enhanced arterial and portal vein phase MR images of hepatobiliary system were used for extracting radiomics features. The correlation analysis, least absolute shrinkage and selection operator (LASSO) logistic regression (LR), backward stepwise LR were mainly used for radiomics feature selection and modeling (Modelradiomic ). The univariate and multivariate backward stepwise LR were used for preoperative clinical predictors selection and modeling (Modelclinic ). The radiomics and preoperative clinical predictors were combined by multivariate LR method to construct clinic-radiomics nomogram (Modelcombine ). STATISTICAL TESTS Chi-squared (χ2 ) test or Fisher's exact test, Mann-Whitney U-test or t-test, Delong test. Two tailed P < 0.05 was considered statistically significant. RESULTS Based on the comparison of area under the curves (AUC) using Delong test, Modelclinic and Modelcombine had significantly better performance than Modelradiomic and tumor-node-metastasis (TNM) system in training set. In the testing set, both Modelclinic and Modelcombine had significantly better performance than TNM system, whereas only Modelcombine was significantly superior to Modelradiomic . However, the AUC values were not significantly different between Modelclinic and Modelcombine (P = 0.156 for training set and P = 0.439 for testing set). DATA CONCLUSION A noninvasive model combining the MRI-based radiomics signature and clinical variables is potential to preoperatively predict ER for pCCA. LEVEL OF EVIDENCE 3 TECHNICAL EFFICACY STAGE: 4.
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Affiliation(s)
- Jian Zhao
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, Sichuan, 610041, China
- Department of Radiology, Armed Police Force Hospital of Sichuan, Leshan, Sichuan, 614000, China
| | - Wei Zhang
- Department of Radiology, Armed Police Force Hospital of Sichuan, Leshan, Sichuan, 614000, China
| | - Yuan-Yi Zhu
- Department of Radiology, Armed Police Force Hospital of Sichuan, Leshan, Sichuan, 614000, China
| | - Hao-Yu Zheng
- Department of Radiology, Armed Police Force Hospital of Sichuan, Leshan, Sichuan, 614000, China
| | - Li Xu
- Department of Radiology, Armed Police Force Hospital of Sichuan, Leshan, Sichuan, 614000, China
| | - Jun Zhang
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, Sichuan, 610041, China
| | - Si-Yun Liu
- GE Healthcare (China), Beijing, 100176, China
| | - Fu-Yu Li
- Department of Biliary Surgery, West China Hospital, Sichuan University, Chengdu, Sichuan, 610041, China
| | - Bin Song
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, Sichuan, 610041, China
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Ziegelmayer S, Reischl S, Harder F, Makowski M, Braren R, Gawlitza J. Feature Robustness and Diagnostic Capabilities of Convolutional Neural Networks Against Radiomics Features in Computed Tomography Imaging. Invest Radiol 2022; 57:171-177. [PMID: 34524173 DOI: 10.1097/rli.0000000000000827] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
Abstract
MATERIALS AND METHODS Imaging phantoms were scanned twice on 3 computed tomography scanners from 2 different manufactures with varying tube voltages and currents. Phantoms were segmented, and features were extracted using PyRadiomics and a pretrained CNN. After standardization the concordance correlation coefficient (CCC), mean feature variance, feature range, and the coefficient of variant were calculated to assess feature robustness. In addition, the cosine similarity was calculated for the vectorized activation maps for an exemplary phantom. For the in vivo comparison, the radiomics and CNN features of 30 patients with hepatocellular carcinoma (HCC) and 30 patients with hepatic colon carcinoma metastasis were compared. RESULTS In total, 851 radiomics features and 256 CNN features were extracted for each phantom. For all phantoms, the global CCC of the CNN features was above 98%, whereas the highest CCC for the radiomics features was 36%. The mean feature variance and feature range was significantly lower for the CNN features. Using a coefficient of variant ≤0.2 as a threshold to define robust features and averaging across all phantoms 346 of 851 (41%) radiomics features and 196 of 256 (77%) CNN features were found to be robust. The cosine similarity was greater than 0.98 for all scanner and parameter variations. In the retrospective analysis, 122 of the 256 CNN (49%) features showed significant differences between HCC and hepatic colon metastasis. DISCUSSION Convolutional neural network features were more stable compared with radiomics features against technical variations. Moreover, the possibility of tumor entity differentiation based on CNN features was shown. Combined with visualization methods, CNN features are expected to increase reproducibility of quantitative image representations. Further studies are warranted to investigate the impact of feature stability on radiological image-based prediction of clinical outcomes.
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Affiliation(s)
- Sebastian Ziegelmayer
- From the Institute of Diagnostic and Interventional Radiology, School of Medicine, Klinikum rechts der Isar, Technical University Munich
| | - Stefan Reischl
- From the Institute of Diagnostic and Interventional Radiology, School of Medicine, Klinikum rechts der Isar, Technical University Munich
| | - Felix Harder
- From the Institute of Diagnostic and Interventional Radiology, School of Medicine, Klinikum rechts der Isar, Technical University Munich
| | - Marcus Makowski
- From the Institute of Diagnostic and Interventional Radiology, School of Medicine, Klinikum rechts der Isar, Technical University Munich
| | | | - Joshua Gawlitza
- From the Institute of Diagnostic and Interventional Radiology, School of Medicine, Klinikum rechts der Isar, Technical University Munich
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Liu X, Wang T, Zhang G, Hua K, Jiang H, Duan S, Jin J, Zhang H. Two-dimensional and three-dimensional T2 weighted imaging-based radiomic signatures for the preoperative discrimination of ovarian borderline tumors and malignant tumors. J Ovarian Res 2022; 15:22. [PMID: 35115022 PMCID: PMC8815217 DOI: 10.1186/s13048-022-00943-z] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2021] [Accepted: 12/31/2021] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Ovarian cancer is the most women malignancy in the whole world. It is difficult to differentiate ovarian cancers from ovarian borderline tumors because of some similar imaging findings.Radiomics study may help clinicians to make a proper diagnosis before invasive surgery. PURPOSE To evaluate the ability of T2-weighted imaging (T2WI)-based radiomics to discriminate ovarian borderline tumors (BOTs) from malignancies based on two-dimensional (2D) and three-dimensional (3D) lesion segmentation methods. METHODS A total of 95 patients with pathologically proven ovarian BOTs and 101 patients with malignancies were retrospectively included in this study. We evaluated the diagnostic performance of the signatures derived from T2WI-based radiomics in their ability to differentiate between BOTs and malignancies and compared the performance differences in the 2D and 3D segmentation models. The least absolute shrinkage and selection operator method (Lasso) was used for radiomics feature selection and machine learning processing. RESULTS The radiomics score between BOTs and malignancies in four types of selected T2WI-based radiomics models differed significantly at the statistical level (p < 0.0001). For the classification between BOTs and malignant masses, the 2D and 3D coronal T2WI-based radiomics models yielded accuracy values of 0.79 and 0.83 in the testing group, respectively; the 2D and 3D sagittal fat-suppressed (fs) T2WI-based radiomics models yielded an accuracy of 0.78 and 0.99, respectively. CONCLUSIONS Our results suggest that T2WI-based radiomic features were highly correlated with ovarian tumor subtype classification. 3D-sagittal MRI radiomics features may help clinicians differentiate ovarian BOTs from malignancies with high ACC.
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Affiliation(s)
- Xuefen Liu
- Department of Radiology, Obstetrics and Gynecology Hospital, Fudan University, Shanghai, P.R. China
| | - Tianping Wang
- Department of Radiology, Obstetrics and Gynecology Hospital, Fudan University, Shanghai, P.R. China
| | - Guofu Zhang
- Department of Radiology, Obstetrics and Gynecology Hospital, Fudan University, Shanghai, P.R. China
| | - Keqin Hua
- Department of Gynecology, Obstetrics and Gynecology Hospital, Fudan University, Shanghai, P.R. China
| | - Hua Jiang
- Department of Gynecology, Obstetrics and Gynecology Hospital, Fudan University, Shanghai, P.R. China
| | | | - Jun Jin
- Department of Pathology, Obstetrics and Gynecology Hospital, Fudan University, Shanghai, P.R. China
| | - He Zhang
- Department of Radiology, Obstetrics and Gynecology Hospital, Fudan University, Shanghai, P.R. China.
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46
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Wang WH, Zheng CB, Gao JN, Ren SS, Nie GY, Li ZQ. Systematic review and meta-analysis of imaging differential diagnosis of benign and malignant ovarian tumors. Gland Surg 2022; 11:330-340. [PMID: 35284306 PMCID: PMC8899432 DOI: 10.21037/gs-21-889] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2021] [Accepted: 01/20/2022] [Indexed: 12/13/2023]
Abstract
BACKGROUND With the increasing incidence of gynecological ovarian tumors, the differential diagnosis of benign and malignant ovarian tumors is of great significance for subsequent treatment. Currently, ovarian examinations commonly use computed tomography (CT) or magnetic resonance imaging (MRI). This study sought to compare the value of CT and MRI in differentiating between benign and malignant ovarian tumors. METHODS The PubMed, Cochrane Central Register of Controlled Trials, Embase, Web of Science, China National Knowledge Infrastructure, Wanfang, and Weipu databases were searched for published articles using the following terms "CT" or "Computed Tomography" or "MRI" or "Magnetic Resonance imaging" and "ovarian cancer" or "ovarian tumor" or "ovarian neoplasm" or "adnexal mass" or "adnexal lesion". The articles were screened and the data were extracted based on the inclusion and exclusion criteria. The Quality Assessment of Diagnostic Accuracy Studies-2 recommended by the Cochrane Collaboration was used to assess the methodological quality of the included studies, and the network meta-analysis was performed by Stata 15.0. RESULTS The results showed that the overall sensitivity and specificity of CT were 0.79 [95% confidence intervals (CI): 0.70-0.87] and 0.87 (95% CI: 0.80-0.92), respectively. The overall sensitivity and specificity of MRI were 0.94 (95% CI: 0.91-0.95) and 0.91 (95% CI: 0.90-0.93), respectively. The area under the curve of the CT and MRI summary receiver operating characteristics were 0.9016 and 0.9764, respectively. The positive likelihood ratio, negative likelihood ratio, and diagnostic odds ratio of CT were 5.26 (95% CI: 2.78-9.93), 0.26 (95% CI: 0.13-0.50), and 22.19 (95% CI: 7.54-65.30), respectively. The positive likelihood ratio, negative likelihood ratio, and diagnostic odds ratio of MRI were 8.69 (95% CI: 5.06-14.92), 0.07 (95% CI: 0.04-0.13), and 146.19 (95% CI: 68.88-310.24), respectively. CONCLUSIONS Compared to CT, MRI has a stronger ability to differentiate between benign and malignant ovarian tumors. It's a promising non-radiological imaging technique and a more favorable choice for patients with ovarian tumors. However, in the future, large-sample, multi-center prospective studies need to be conducted to compare the performance of MRI and CT in distinguishing between benign and malignant ovarian tumors.
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Affiliation(s)
- Wen-Huan Wang
- Department of Medical Imaging, Haikou Hospital of the Maternal and Child Health, Haikou, China
| | - Chang-Bao Zheng
- Department of Medical Imaging, Hainan Cancer Hospital, Haikou, China
| | - Jin-Niao Gao
- Department of Traditional Chinese Medicine, The First Affiliated Hospital of Hainan Medical University, Haikou, China
| | - Shang-Shang Ren
- Department of Radiology, Central South University Xiangya School of Medicine Affiliated Haikou Hospital, Haikou, China
| | - Guo-Yan Nie
- Department of Medical Imaging, Haikou Hospital of the Maternal and Child Health, Haikou, China
| | - Zhi-Qun Li
- Department of Radiology, The First Affiliated Hospital of Hainan Medical University, Haikou, China
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47
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Xiao F, Zhang L, Yang S, Peng K, Hua T, Tang G. Quantitative analysis of the MRI features in the differentiation of benign, borderline, and malignant epithelial ovarian tumors. J Ovarian Res 2022; 15:13. [PMID: 35062992 PMCID: PMC8783416 DOI: 10.1186/s13048-021-00920-y] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2021] [Accepted: 09/06/2021] [Indexed: 12/12/2022] Open
Abstract
Objective This study aims to investigate the value of the quantitative indicators of MRI in the differential diagnoses of benign, borderline, and malignant epithelial ovarian tumors (EOTs). Materials and methods The study population comprised 477 women with 513 masses who underwent MRI and operation, including benign EOTs (BeEOTs), borderline EOTs (BEOTs), and malignant EOTs (MEOTs). The clinical information and MRI findings of the three groups were compared. Then, multivariate logistic regression analysis was performed to find the independent diagnostic factors. The receiver operating characteristic (ROC) curves were also used to evaluate the diagnostic performance of the quantitative indicators of MRI and clinical information in differentiating BeEOTs from BEOTs or differentiating BEOTs from MEOTs. Results The MEOTs likely involved postmenopausal women and showed higher CA-125, HE4 levels, ROMA indices, peritoneal carcinomatosis and bilateral involvement than BeEOTs and BEOTs. Compared with BEOTs, BeEOTs and MEOTs appeared to be more frequently oligocystic (P < 0.001). BeEOTs were more likely to show mild enhancement (P < 0.001) and less ascites (P = 0.003) than BEOTs and MEOTs. In the quantitative indicators of MRI, BeEOTs usually showed thin-walled cysts and no solid component. BEOTs displayed irregular thickened wall and less solid portion. MEOTs were more frequently characterized as solid or predominantly solid mass (P < 0.001) than BeEOTs and BEOTs. The multivariate logistic regression analysis showed that volume of the solid portion (P = 0.006), maximum diameter of the solid portion (P = 0.038), enhancement degrees (P < 0.001), and peritoneal carcinomatosis (P = 0.011) were significant indicators for the differential diagnosis of the three groups. The area under the curves (AUCs) of above indicators and combination of four image features except peritoneal carcinomatosis for the differential diagnosis of BeEOTs and BEOTs, BEOTs and MEOTs ranged from 0.74 to 0.85, 0.58 to 0.79, respectively. Conclusion In this study, the characteristics of MRI can provide objective quantitative indicators for the accurate imaging diagnosis of three categories of EOTs and are helpful for clinical decision-making. Among these MRI characteristics, the volume, diameter, and enhancement degrees of the solid portion showed good diagnostic performance.
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48
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Wang T, Wang H, Wang Y, Liu X, Ling L, Zhang G, Yang G, Zhang H. MR-based radiomics-clinical nomogram in epithelial ovarian tumor prognosis prediction: tumor body texture analysis across various acquisition protocols. J Ovarian Res 2022; 15:6. [PMID: 35022079 PMCID: PMC8753904 DOI: 10.1186/s13048-021-00941-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2021] [Accepted: 12/28/2021] [Indexed: 12/16/2022] Open
Abstract
Background Epithelial ovarian cancer (EOC) is the most malignant gynecological tumor in women. This study aimed to construct and compare radiomics-clinical nomograms based on MR images in EOC prognosis prediction. Methods A total of 186 patients with pathologically proven EOC were enrolled and randomly divided into a training cohort (n = 130) and a validation cohort (n = 56). Clinical characteristics of each patient were retrieved from the hospital information system. A total of 1116 radiomics features were extracted from tumor body on T2-weighted imaging (T2WI), T1-weighted imaging (T1WI), diffusion weighted imaging (DWI) and contrast-enhanced T1-weighted imaging (CE-T1WI). Paired sequence signatures were constructed, selected and trained to build a prognosis prediction model. Radiomic-clinical nomogram was constructed based on multivariate logistic regression analysis with radiomics score and clinical features. The predictive performance was evaluated by receiver operating characteristic curve (ROC) analysis, decision curve analysis (DCA) and calibration curve. Results The T2WI radiomic-clinical nomogram achieved a favorable prediction performance in the training and validation cohort with an area under ROC curve (AUC) of 0.866 and 0.818, respectively. The DCA showed that the T2WI radiomic-clinical nomogram was better than other models with a greater clinical net benefit. Conclusion MR-based radiomics analysis showed the high accuracy in prognostic estimation of EOC patients and could help to predict therapeutic outcome before treatment. Supplementary Information The online version contains supplementary material available at 10.1186/s13048-021-00941-7.
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Affiliation(s)
- Tianping Wang
- Department of Radiology, Obstetrics and Gynecology Hospital, Fudan University, Shanghai, China
| | - Haijie Wang
- Shanghai Key Laboratory of Magnetic Resonance, East China Normal University, Shanghai, China
| | - Yida Wang
- Shanghai Key Laboratory of Magnetic Resonance, East China Normal University, Shanghai, China
| | - Xuefen Liu
- Department of Radiology, Obstetrics and Gynecology Hospital, Fudan University, Shanghai, China
| | - Lei Ling
- Department of Radiology, Obstetrics and Gynecology Hospital, Fudan University, Shanghai, China
| | - Guofu Zhang
- Department of Radiology, Obstetrics and Gynecology Hospital, Fudan University, Shanghai, China
| | - Guang Yang
- Shanghai Key Laboratory of Magnetic Resonance, East China Normal University, Shanghai, China
| | - He Zhang
- Department of Radiology, Obstetrics and Gynecology Hospital, Fudan University, Shanghai, China.
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Zhang A, Hu Q, Ma Z, Song J, Chen T. Application of enhanced computed tomography-based radiomics nomogram analysis to differentiate metastatic ovarian tumors from epithelial ovarian tumors. JOURNAL OF X-RAY SCIENCE AND TECHNOLOGY 2022; 30:1185-1199. [PMID: 36189526 DOI: 10.3233/xst-221244] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
OBJECTIVE To investigate the value of nomogram analysis based on conventional features and radiomics features of computed tomography (CT) venous phase to differentiate metastatic ovarian tumors (MOTs) from epithelial ovarian tumors (EOTs). METHODS A dataset involving 286 patients pathologically confirmed with EOTs (training cohort: 133 cases, validation cohort: 68 cases) and MOTs (training cohort: 54 cases, validation cohort: 31 cases) is assembled in this study. Radiomics features are extracted from the venous phase of CT images. Logistic regression is employed to build models based on conventional features (model 1), radiomics features (model 2), and the combination of model 1 and model 2 (model 3). Diagnostic performance is assessed and compared. Additionally, a nomogram is plotted for model 3, and decision curve analysis is applied for clinical use. RESULTS Age, abdominal metastasis, para-aortic lymph node metastasis, location, and septation are chosen to build Model 1. Ten optimal radiomics features are ultimately selected and radiomics score (rad-score) is calculated to build Model 2. Nomogram score is calculated to build model 3 that shows optimal diagnostic performance in both the training (AUC = 0.952) and validation cohorts (AUC = 0.720), followed by model 1 (AUC = 0.872 for training cohort and AUC = 0.709 for validation cohort) and model 2 (AUC = 0.833 for training cohort and AUC = 0.620 for validation cohort). Additionally, Model 3 achieves accuracy, sensitivity, and specificity of 0.893, 0.880, and 0.926 in the training cohort and 0.737, 0.853, and 0.613 in the validation cohort. CONCLUSION Model 3 demonstrates the best diagnostic performance for preoperative differentiation of MOTs from EOTs. Thus, nomogram analysis based on Model 3 may be used as a biomarker to differentiate MOTs from EOTs.
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Affiliation(s)
- Aining Zhang
- Department of Radiology, the First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Qiming Hu
- Department of Obstetrics & Gynecology, the First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Zhanlong Ma
- Department of Radiology, the First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Jiacheng Song
- Department of Radiology, the First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Ting Chen
- Department of Radiology, the First Affiliated Hospital of Nanjing Medical University, Nanjing, China
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50
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Qi L, Chen D, Li C, Li J, Wang J, Zhang C, Li X, Qiao G, Wu H, Zhang X, Ma W. Diagnosis of Ovarian Neoplasms Using Nomogram in Combination With Ultrasound Image-Based Radiomics Signature and Clinical Factors. Front Genet 2021; 12:753948. [PMID: 34650603 PMCID: PMC8505695 DOI: 10.3389/fgene.2021.753948] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2021] [Accepted: 09/13/2021] [Indexed: 12/12/2022] Open
Abstract
Objectives: To establish and validate a nomogram integrating radiomics signatures from ultrasound and clinical factors to discriminate between benign, borderline, and malignant serous ovarian tumors. Materials and methods: In this study, a total of 279 pathology-confirmed serous ovarian tumors collected from 265 patients between March 2013 and December 2016 were used. The training cohort was generated by randomly selecting 70% of each of the three types (benign, borderline, and malignant) of tumors, while the remaining 30% was included in the validation cohort. From the transabdominal ultrasound scanning of ovarian tumors, the radiomics features were extracted, and a score was calculated. The ability of radiomics to differentiate between the grades of ovarian tumors was tested by comparing benign vs borderline and malignant (task 1) and borderline vs malignant (task 2). These results were compared with the diagnostic performance and subjective assessment by junior and senior sonographers. Finally, a clinical-feature alone model and a combined clinical-radiomics (CCR) model were built using predictive nomograms for the two tasks. Receiver operating characteristic (ROC) analysis, calibration curve, and decision curve analysis (DCA) were performed to evaluate the model performance. Results: The US-based radiomics models performed satisfactorily in both the tasks, showing especially higher accuracy in the second task by successfully discriminating borderline and malignant ovarian serous tumors compared to the evaluations by senior sonographers (AUC = 0.789 for seniors and 0.877 for radiomics models in task one; AUC = 0.612 for senior and 0.839 for radiomics model in task 2). We showed that the CCR model, comprising CA125 level, lesion location, ascites, and radiomics signatures, performed the best (AUC = 0.937, 95%CI 0.905-0.969 in task 1, AUC = 0.924, 95%CI 0.876-0.971 in task 2) in the training as well as in the validation cohorts (AUC = 0.914, 95%CI 0.851-0.976 in task 1, AUC = 0.890, 95%CI 0.794-0.987 in task 2). The calibration curve and DCA analysis of the CCR model more accurately predicted the classification of the tumors than the clinical features alone. Conclusion: This study integrates novel radiomics signatures from ultrasound and clinical factors to create a nomogram to provide preoperative diagnostic information for differentiating between benign, borderline, and malignant ovarian serous tumors, thereby reducing unnecessary and risky biopsies and surgeries.
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Affiliation(s)
- Lisha Qi
- Department of Pathology, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China.,National Clinical Research Center for Cancer, Tianjin, China.,Key Laboratory of Cancer Prevention and Therapy, Tianjin, China.,Tianjin's Clinical Research Center for Cancer, Tianjin, China
| | - Dandan Chen
- Department of Pathology, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China.,National Clinical Research Center for Cancer, Tianjin, China.,Key Laboratory of Cancer Prevention and Therapy, Tianjin, China.,Tianjin's Clinical Research Center for Cancer, Tianjin, China
| | - Chunxiang Li
- National Clinical Research Center for Cancer, Tianjin, China.,Key Laboratory of Cancer Prevention and Therapy, Tianjin, China.,Tianjin's Clinical Research Center for Cancer, Tianjin, China.,Department of Ultrasonographic Diagnosis and Therapy, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China
| | - Jinghan Li
- Department of Ultrasonographic Diagnosis and Therapy, Tianjin Ninghe Hospital, Tianjin, China
| | - Jingyi Wang
- Department of Pathology, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China.,National Clinical Research Center for Cancer, Tianjin, China.,Key Laboratory of Cancer Prevention and Therapy, Tianjin, China.,Tianjin's Clinical Research Center for Cancer, Tianjin, China
| | - Chao Zhang
- National Clinical Research Center for Cancer, Tianjin, China.,Key Laboratory of Cancer Prevention and Therapy, Tianjin, China.,Tianjin's Clinical Research Center for Cancer, Tianjin, China.,Department of Bone and Soft Tissue Tumors, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China
| | - Xiaofeng Li
- National Clinical Research Center for Cancer, Tianjin, China.,Key Laboratory of Cancer Prevention and Therapy, Tianjin, China.,Tianjin's Clinical Research Center for Cancer, Tianjin, China.,Department of Molecular Imaging and Nuclear Medicine, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China
| | - Ge Qiao
- Department of Pathology, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China.,National Clinical Research Center for Cancer, Tianjin, China.,Key Laboratory of Cancer Prevention and Therapy, Tianjin, China.,Tianjin's Clinical Research Center for Cancer, Tianjin, China
| | - Haixiao Wu
- National Clinical Research Center for Cancer, Tianjin, China.,Key Laboratory of Cancer Prevention and Therapy, Tianjin, China.,Tianjin's Clinical Research Center for Cancer, Tianjin, China.,Department of Bone and Soft Tissue Tumors, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China
| | - Xiaofang Zhang
- Department of Clinical Laboratory, Tianjin Medical University General Hospital, Tianjin, China
| | - Wenjuan Ma
- National Clinical Research Center for Cancer, Tianjin, China.,Key Laboratory of Cancer Prevention and Therapy, Tianjin, China.,Tianjin's Clinical Research Center for Cancer, Tianjin, China.,Department of Breast Imaging, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China
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