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Taddese AA, Tilahun BC, Awoke T, Atnafu A, Mamuye A, Mengiste SA. Deep-learning models for image-based gynecological cancer diagnosis: a systematic review and meta- analysis. Front Oncol 2024; 13:1216326. [PMID: 38273847 PMCID: PMC10809847 DOI: 10.3389/fonc.2023.1216326] [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: 05/03/2023] [Accepted: 11/13/2023] [Indexed: 01/27/2024] Open
Abstract
Introduction Gynecological cancers pose a significant threat to women worldwide, especially those in resource-limited settings. Human analysis of images remains the primary method of diagnosis, but it can be inconsistent and inaccurate. Deep learning (DL) can potentially enhance image-based diagnosis by providing objective and accurate results. This systematic review and meta-analysis aimed to summarize the recent advances of deep learning (DL) techniques for gynecological cancer diagnosis using various images and explore their future implications. Methods The study followed the PRISMA-2 guidelines, and the protocol was registered in PROSPERO. Five databases were searched for articles published from January 2018 to December 2022. Articles that focused on five types of gynecological cancer and used DL for diagnosis were selected. Two reviewers assessed the articles for eligibility and quality using the QUADAS-2 tool. Data was extracted from each study, and the performance of DL techniques for gynecological cancer classification was estimated by pooling and transforming sensitivity and specificity values using a random-effects model. Results The review included 48 studies, and the meta-analysis included 24 studies. The studies used different images and models to diagnose different gynecological cancers. The most popular models were ResNet, VGGNet, and UNet. DL algorithms showed more sensitivity but less specificity compared to machine learning (ML) methods. The AUC of the summary receiver operating characteristic plot was higher for DL algorithms than for ML methods. Of the 48 studies included, 41 were at low risk of bias. Conclusion This review highlights the potential of DL in improving the screening and diagnosis of gynecological cancer, particularly in resource-limited settings. However, the high heterogeneity and quality of the studies could affect the validity of the results. Further research is necessary to validate the findings of this study and to explore the potential of DL in improving gynecological cancer diagnosis.
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Affiliation(s)
- Asefa Adimasu Taddese
- Department of Health Informatics, Institute of Public Health, College of Medicine and Health Sciences, University of Gondar, Gondar, Ethiopia
- eHealthlab Ethiopia Research Center, University of Gondar, Gondar, Ethiopia
| | - Binyam Chakilu Tilahun
- Department of Health Informatics, Institute of Public Health, College of Medicine and Health Sciences, University of Gondar, Gondar, Ethiopia
- eHealthlab Ethiopia Research Center, University of Gondar, Gondar, Ethiopia
| | - Tadesse Awoke
- Department of Epidemiology and Biostatistics, Institute of Public Health, College of Medicine and Health Sciences, University of Gondar, Gondar, Ethiopia
| | - Asmamaw Atnafu
- eHealthlab Ethiopia Research Center, University of Gondar, Gondar, Ethiopia
- Department of Health Systems and Policy, Institute of Public Health, College of Medicine and Health Sciences, University of Gondar, Gondar, Ethiopia
| | - Adane Mamuye
- eHealthlab Ethiopia Research Center, University of Gondar, Gondar, Ethiopia
- School of Information Technology and Engineering, Addis Ababa University, Addis Ababa, Ethiopia
| | - Shegaw Anagaw Mengiste
- Department of Business, History and Social Sciences, University of Southeastern Norway, Vestfold, Vestfold, Norway
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Sadowski EA, Lees B, McMillian AB, Kusmirek JE, Cho SY, Barroilhet LM. Distribution of prostate specific membrane antigen (PSMA) on PET-MRI in patients with and without ovarian cancer. Abdom Radiol (NY) 2023; 48:3643-3652. [PMID: 37261441 DOI: 10.1007/s00261-023-03957-3] [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: 02/13/2023] [Revised: 05/09/2023] [Accepted: 05/11/2023] [Indexed: 06/02/2023]
Abstract
OBJECTIVES Ovarian cancer is the most lethal cancer and future research needs to focus on the early detection and exploration of new therapeutic agents. The objectives of this proof-of-concept study are to assess the feasibility of PSMA 18F-DCFPyl PET/MR imaging for detecting ovarian cancer and to evaluate the PSMA distribution in patients with and without ovarian cancer. METHODS This prospective pilot proof-of-concept study in patients with and without ovarian cancers occurred between October 2017 and January 2020. Patients were recruited from gynecologic oncology or hereditary ovarian cancer clinics, and underwent surgical removal of the uterus and ovaries for gynecologic indications. PSMA 18F-DCFPyl PET/MRI was obtained prior to standard of care surgery. RESULTS Fourteen patients were scanned: four patients with normal ovaries, six patients with benign ovarian lesions, and four patients with malignant ovarian lesions. Tracer uptake in normal ovaries (SUVmax = 2.8 ± 0.4) was greater than blood pool (SUVmax = 1.8 ± 0.5, p < 0.0001). Tracer uptake in benign ovarian lesions (2.2 ± 1.0) did not differ significantly from blood pool (p = 0.331). Tracer uptake in ovarian cancer (SUVmax = 7.8 ± 3.8) was greater than blood pool (p < 0.0001), normal ovaries (p = 0.0014), and benign ovarian lesions (p = 0.005). CONCLUSION PET/MR imaging detected PSMA uptake in ovarian cancer, with little to no uptake in benign ovarian findings. These results are encouraging and further studies in a larger patient cohort would be useful to help determine the extent and heterogeneity of PSMA uptake in ovarian cancer patients.
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Affiliation(s)
- Elizabeth A Sadowski
- Departments of Radiology, Obstetrics and Gynecology, University of Wisconsin School of Medicine and Public Health, 600 Highland Ave, E3/372, Madison, WI, 53792-3252, USA.
| | - Brittany Lees
- Atrium Health Levine Cancer Institute, 1021 Morehead Medical Drive, Suite 2100, Charlotte, NC, 28204, USA
| | - Alan B McMillian
- Department of Radiology, University of Wisconsin School of Medicine and Public Health, 1111 Highland Avenue, Rm 1139, Madison, WI, 53705, USA
| | - Joanna E Kusmirek
- Department of Radiology, University of Wisconsin School of Medicine and Public Health, 600 Highland Ave., E3/372, Madison, WI, 53792-3252, USA
| | - Steve Y Cho
- Department of Radiology, University of Wisconsin School of Medicine and Public Health, 600 Highland Ave., E3/372, Madison, WI, 53792-3252, USA
| | - Lisa M Barroilhet
- Departments of Radiology, Obstetrics and Gynecology, University of Wisconsin School of Medicine and Public Health, 600 Highland Ave, E3/372, Madison, WI, 53792-3252, USA
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3
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Shen L, Du L, Hu Y, Chen X, Hou Z, Yan Z, Wang X. MRI-based radiomics model for distinguishing Stage I endometrial carcinoma from endometrial polyp: a multicenter study. Acta Radiol 2023; 64:2651-2658. [PMID: 37291882 DOI: 10.1177/02841851231175249] [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] [Indexed: 06/10/2023]
Abstract
BACKGROUND Patients with early endometrial carcinoma (EC) have a good prognosis, but it is difficult to distinguish from endometrial polyps (EPs). PURPOSE To develop and assess magnetic resonance imaging (MRI)-based radiomics models for discriminating Stage I EC from EP in a multicenter setting. MATERIAL AND METHODS Patients with Stage I EC (n = 202) and EP (n = 99) who underwent preoperative MRI scans were collected in three centers (seven devices). The images from devices 1-3 were utilized for training and validation, and the images from devices 4-7 were utilized for testing, leading to three models. They were evaluated by the area under the receiver operating characteristic curve (AUC) and metrics including accuracy, sensitivity, and specificity. Two radiologists evaluated the endometrial lesions and compared them with the three models. RESULTS The AUCs of device 1, 2_ada, device 1, 3_ada, and device 2, 3_ada for discriminating Stage I EC from EP were 0.951, 0.912, and 0.896 for the training set, 0.755, 0.928, and 1.000 for the validation set, and 0.883, 0.956, and 0.878 for the external validation set, respectively. The specificity of the three models was higher, but the accuracy and sensitivity were lower than those of radiologists. CONCLUSION Our MRI-based models showed good potential in differentiating Stage I EC from EP and had been validated in multiple centers. Their specificity was higher than that of radiologists and may be used for computer-aided diagnosis in the future to assist clinical diagnosis.
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Affiliation(s)
- Liting Shen
- Department of Radiology, the Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, PR China
| | - Lixin Du
- Department of Medical Imaging, Shenzhen Longhua District Central Hospital, Shenzhen, PR China
| | - Yumin Hu
- Department of Radiology, Lishui Central Hospital, Zhejiang, PR China
| | - Xiaojun Chen
- Department of Radiology, Affiliated Jinhua Hospital, Zhejiang University School of Medicine, Jinhua, PR China
| | - Zujun Hou
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, PR China
| | - Zhihan Yan
- Department of Radiology, the Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, PR China
| | - Xue Wang
- Department of Radiology, the Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, PR China
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Ren J, Li Y, Liu FS, Liu C, Zhu JX, Nickel MD, Wang XY, Liu XY, Zhao J, He YL, Jin ZY, Xue HD. Comparison of a deep learning-accelerated T2-weighted turbo spin echo sequence and its conventional counterpart for female pelvic MRI: reduced acquisition times and improved image quality. Insights Imaging 2022; 13:193. [PMID: 36512158 DOI: 10.1186/s13244-022-01321-5] [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: 05/16/2022] [Accepted: 10/29/2022] [Indexed: 12/14/2022] Open
Abstract
OBJECTIVES To investigate the feasibility of a deep learning-accelerated T2-weighted turbo spin echo (TSE) sequence (T2DL) applied to female pelvic MRI, using standard T2-weighted TSE (T2S) as reference. METHODS In total, 24 volunteers and 48 consecutive patients with benign uterine diseases were enrolled. Patients in the menstrual phase were excluded. T2S and T2DL sequences in three planes were performed for each participant. Quantitative image evaluation was conducted by calculating the signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR). Image geometric distortion was evaluated by measuring the diameters in all three directions of the uterus and lesions. Qualitative image evaluation including overall image quality, artifacts, boundary sharpness of the uterine zonal layers, and lesion conspicuity were assessed by three radiologists using a 5-point Likert scale, with 5 indicating the best quality. Comparative analyses were conducted for the two sequences. RESULTS T2DL resulted in a 62.7% timing reduction (1:54 min for T2DL and 5:06 min for T2S in axial, sagittal, and coronal imaging, respectively). Compared to T2S, T2DL had significantly higher SNR (p ≤ 0.001) and CNR (p ≤ 0.007), and without geometric distortion (p = 0.925-0.981). Inter-observer agreement regarding qualitative evaluation was excellent (Kendall's W > 0.75). T2DL provided superior image quality (all p < 0.001), boundary sharpness of the uterine zonal layers (all p < 0.001), lesion conspicuity (p = 0.002, p < 0.001, and p = 0.021), and fewer artifacts (all p < 0.001) in sagittal, axial, and coronal imaging. CONCLUSIONS Compared with standard TSE, deep learning-accelerated T2-weighted TSE is feasible to reduce acquisition time of female pelvic MRI with significant improvement of image quality.
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Affiliation(s)
- Jing Ren
- Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shuai Fu Yuan Road, Dongcheng Dist., Beijing, 100730, People's Republic of China
| | - Yuan Li
- Department of Obstetrics and Gynecology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, National Clinical Research Center for Obstetric and Gynecologic Diseases, Beijing, People's Republic of China
| | - Fei-Shi Liu
- Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shuai Fu Yuan Road, Dongcheng Dist., Beijing, 100730, People's Republic of China
| | - Chong Liu
- Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shuai Fu Yuan Road, Dongcheng Dist., Beijing, 100730, People's Republic of China
| | - Jin-Xia Zhu
- MR Collaboration, Siemens Healthineers Ltd., Beijing, People's Republic of China
| | | | - Xiao-Ye Wang
- MR Clinical Marketing, Siemens Healthineers Ltd., Beijing, People's Republic of China
| | - Xin-Yu Liu
- Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shuai Fu Yuan Road, Dongcheng Dist., Beijing, 100730, People's Republic of China
| | - Jia Zhao
- Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shuai Fu Yuan Road, Dongcheng Dist., Beijing, 100730, People's Republic of China
| | - Yong-Lan He
- Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shuai Fu Yuan Road, Dongcheng Dist., Beijing, 100730, People's Republic of China.
| | - Zheng-Yu Jin
- Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shuai Fu Yuan Road, Dongcheng Dist., Beijing, 100730, People's Republic of China.
| | - Hua-Dan Xue
- Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shuai Fu Yuan Road, Dongcheng Dist., Beijing, 100730, People's Republic of China.
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Daoud T, Sardana S, Stanietzky N, Klekers AR, Bhosale P, Morani AC. Recent Imaging Updates and Advances in Gynecologic Malignancies. Cancers (Basel) 2022; 14:cancers14225528. [PMID: 36428624 PMCID: PMC9688526 DOI: 10.3390/cancers14225528] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2022] [Revised: 10/31/2022] [Accepted: 11/09/2022] [Indexed: 11/12/2022] Open
Abstract
Gynecologic malignancies are among the most common cancers in women worldwide and account for significant morbidity and mortality. Management and consequently overall patient survival is reliant upon early detection, accurate staging and early detection of any recurrence. Ultrasound, Computed Tomography (CT), Magnetic resonance imaging (MRI) and Positron Emission Tomography-Computed Tomography (PET-CT) play an essential role in the detection, characterization, staging and restaging of the most common gynecologic malignancies, namely the cervical, endometrial and ovarian malignancies. Recent advances in imaging including functional MRI, hybrid imaging with Positron Emission Tomography (PET/MRI) contribute even more to lesion specification and overall role of imaging in gynecologic malignancies. Radiomics is a neoteric approach which aspires to enhance decision support by extracting quantitative information from radiological imaging.
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PET/MR imaging in gynecologic cancer: tips for differentiating normal gynecologic anatomy and benign pathology versus cancer. Abdom Radiol (NY) 2022; 47:3189-3204. [PMID: 34687323 DOI: 10.1007/s00261-021-03264-9] [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: 04/30/2021] [Revised: 08/24/2021] [Accepted: 08/25/2021] [Indexed: 01/18/2023]
Abstract
Positron emission tomography/magnetic resonance imaging (PET/MR) is used in the pre-treatment and surveillance settings to evaluate women with gynecologic malignancies, including uterine, cervical, vaginal and vulvar cancers. PET/MR combines the excellent spatial and contrast resolution of MR imaging for gynecologic tissues, with the functional metabolic information of PET, to aid in a more accurate assessment of local disease extent and distant metastatic disease. In this review, the optimal protocol and utility of whole-body PET/MR imaging in patients with gynecologic malignancies will be discussed, with an emphasis on the advantages of PET/MR over PET/CT and how to differentiate normal or benign gynecologic tissues from cancer in the pelvis.
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7
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Yu Y, Zhang L, Sultana B, Wang B, Sun H. Diagnostic value of integrated 18F-FDG PET/MRI for staging of endometrial carcinoma: comparison with PET/CT. BMC Cancer 2022; 22:947. [PMID: 36050751 PMCID: PMC9438318 DOI: 10.1186/s12885-022-10037-0] [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: 05/11/2022] [Accepted: 08/24/2022] [Indexed: 11/21/2022] Open
Abstract
Purpose To explore the diagnostic value of integrated positron emission tomography/magnetic resonance imaging (PET/MRI) for the staging of endometrial carcinoma and to investigate the associations between quantitative parameters derived from PET/MRI and clinicopathological characteristics of endometrial carcinoma. Methods Altogether, 57 patients with endometrial carcinoma who underwent PET/MRI and PET/computed tomography (PET/CT) preoperatively were included. Diagnostic performance of PET/MRI and PET/CT for staging was compared by three readers. Associations between PET/MRI quantitative parameters of primary tumor lesions and clinicopathological characteristics of endometrial carcinoma were analyzed. Histopathological results were used as the standard. Results The overall accuracy of the International Federation of Gynecology and Obstetrics (FIGO) staging for PET/MRI and PET/CT was 86.0% and 77.2%, respectively. PET/MRI had higher accuracy in diagnosing myometrial invasion and cervical invasion and an equivalent accuracy in diagnosing pelvic lymph node metastasis against PET/CT, although without significance. All PET/MRI quantitative parameters were significantly different between stage I and stage III tumors. Only SUVmax/ADCmin were significantly different between stage I and II tumors. No parameters were significantly different between stage II and III tumors. The SUVmax/ADCmin in the receiving operating characteristic (ROC) curve had a higher area under the ROC curve for differentiating stage I tumors and other stages of endometrial carcinoma. Conclusions PET/MRI had a higher accuracy for the staging of endometrial carcinoma, mainly for FIGO stage I tumors compared to PET/CT. PET/MRI quantitative parameters, especially SUVmax/ADCmin, were associated with tumor stage and other clinicopathological characteristics. Hence, PET/MRI may be a valuable imaging diagnostic tool for preoperative staging of endometrial carcinoma.
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Affiliation(s)
- Yang Yu
- Department of Radiology, Shengjing Hospital of China Medical University, Sanhao Street No36, Heping District, Shenyang, 110004, China.,Department of Nuclear Medicine, Shengjing Hospital of China Medical University, Shenyang, 110004, China.,Liaoning Provincial Key Laboratory of Medical Imaging, Shenyang, 110004, China
| | - Le Zhang
- Department of Radiology, Qingdao Municipal Hospital, Qingdao University, Qingdao, 266011, China
| | - Bilkis Sultana
- Department of Radiology, Shengjing Hospital of China Medical University, Sanhao Street No36, Heping District, Shenyang, 110004, China
| | - Bo Wang
- Department of Nuclear Medicine, Shengjing Hospital of China Medical University, Shenyang, 110004, China
| | - Hongzan Sun
- Department of Radiology, Shengjing Hospital of China Medical University, Sanhao Street No36, Heping District, Shenyang, 110004, China. .,Department of Nuclear Medicine, Shengjing Hospital of China Medical University, Shenyang, 110004, China. .,Liaoning Provincial Key Laboratory of Medical Imaging, Shenyang, 110004, China.
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Amado Cabana S, Gallego Ojea J, Félez Carballada M. Usefulness of dynamic contrast-enhanced magnetic resonance imaging in characterizing ovarian tumors classified as indeterminate at ultrasonography. RADIOLOGIA 2022; 64:110-118. [DOI: 10.1016/j.rxeng.2020.05.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2020] [Accepted: 05/20/2020] [Indexed: 10/18/2022]
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Jain P, Aggarwal A, Ghasi RG, Malik A, Misra RN, Garg K. Role of MRI in diagnosing the primary site of origin in indeterminate cases of uterocervical carcinomas: a systematic review and meta-analysis. Br J Radiol 2022; 95:20210428. [PMID: 34623892 PMCID: PMC8722231 DOI: 10.1259/bjr.20210428] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023] Open
Abstract
OBJECTIVE To perform a literature review assessing role of MRI in predicting origin of indeterminate uterocervical carcinomas with emphasis on sequences and imaging parameters. METHODS Electronic literature search of PubMed was performed from its inception until May 2020 and PICO model used for study selection; population was female patients with known/clinical suspicion of uterocervical cancer, intervention was MRI, comparison was by histopathology and outcome was differentiation between primary endometrial and cervical cancers. RESULTS Eight out of nine reviewed articles reinforced role of MRI in uterocervical primary determination. T2 and Dynamic contrast were the most popular sequences determining tumor location, morphology, enhancement, and invasion patterns. Role of DWI and MR spectroscopy has been evaluated by even fewer studies with significant differences found in both apparent diffusion coefficient values and metabolite spectra. The four studies eligible for meta-analysis showed a pooled sensitivity of 88.4% (95% confidence interval 70.6 to 96.1%) and a pooled specificity of 39.5% (95% confidence interval 4.2 to 90.6%). CONCLUSIONS MRI plays a pivotal role in uterocervical primary determination with both conventional and newer sequences assessing important morphometric and functional parameters. Socioeconomic impact of both primaries, different management guidelines and paucity of existing studies warrants further research. Prospective multicenter trials will help bridge this gap. Meanwhile, individual patient database meta-analysis can help corroborate existing data. ADVANCES IN KNOWLEDGE MRI with its classical and functional sequences helps in differentiation of the uterine 'cancer gray zone' which is imperative as both primary endometrial and cervical tumors have different management protocols.
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Affiliation(s)
- Pooja Jain
- Department of Radiodiagnosis, VMMC and Safdarjung Hospital, New Delhi, India
| | - Ankita Aggarwal
- Department of Radiodiagnosis, VMMC and Safdarjung Hospital, New Delhi, India
| | - Rohini Gupta Ghasi
- Department of Radiodiagnosis, VMMC and Safdarjung Hospital, New Delhi, India
| | - Amita Malik
- Department of Radiodiagnosis, VMMC and Safdarjung Hospital, New Delhi, India
| | - Ritu Nair Misra
- Department of Radiodiagnosis, VMMC and Safdarjung Hospital, New Delhi, India
| | - Kanwaljeet Garg
- Department of Neurosurgery, All India Institute of Medical Sciences, New Delhi, India
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Song H, Bak S, Kim I, Woo JY, Cho EJ, Choi YJ, Rha SE, Oh SA, Youn SY, Lee SJ. An Application of Machine Learning That Uses the Magnetic Resonance Imaging Metric, Mean Apparent Diffusion Coefficient, to Differentiate between the Histological Types of Ovarian Cancer. J Clin Med 2021; 11:jcm11010229. [PMID: 35011970 PMCID: PMC8745699 DOI: 10.3390/jcm11010229] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2021] [Revised: 12/21/2021] [Accepted: 12/29/2021] [Indexed: 12/13/2022] Open
Abstract
This retrospective single-center study included patients diagnosed with epithelial ovarian cancer (EOC) using preoperative pelvic magnetic resonance imaging (MRI). The apparent diffusion coefficient (ADC) of the axial MRI maps that included the largest solid portion of the ovarian mass was analysed. The mean ADC values (ADCmean) were derived from the regions of interest (ROIs) of each largest solid portion. Logistic regression and three types of machine learning (ML) applications were used to analyse the ADCs and clinical factors. Of the 200 patients, 103 had high-grade serous ovarian cancer (HGSOC), and 97 had non-HGSOC (endometrioid carcinoma, clear cell carcinoma, mucinous carcinoma, and low-grade serous ovarian cancer). The median ADCmean of patients with HGSOC was significantly lower than that of patients without HGSOCs. Low ADCmean and CA 19-9 levels were independent predictors for HGSOC over non-HGSOC. Compared to stage I disease, stage III disease was associated with HGSOC. Gradient boosting machine and extreme gradient boosting machine showed the highest accuracy in distinguishing between the histological findings of HGSOC versus non-HGSOC and between the five histological types of EOC. In conclusion, ADCmean, disease stage at diagnosis, and CA 19-9 level were significant factors for differentiating between EOC histological types.
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Affiliation(s)
- Heekyoung Song
- Department of Obstetrics and Gynecology, Seoul St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, 222, Banpo-daero, Seocho-gu, Seoul 06591, Korea; (H.S.); (S.B.); (I.K.); (J.Y.W.); (E.J.C.); (Y.J.C.)
| | - Seongeun Bak
- Department of Obstetrics and Gynecology, Seoul St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, 222, Banpo-daero, Seocho-gu, Seoul 06591, Korea; (H.S.); (S.B.); (I.K.); (J.Y.W.); (E.J.C.); (Y.J.C.)
| | - Imhyeon Kim
- Department of Obstetrics and Gynecology, Seoul St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, 222, Banpo-daero, Seocho-gu, Seoul 06591, Korea; (H.S.); (S.B.); (I.K.); (J.Y.W.); (E.J.C.); (Y.J.C.)
| | - Jae Yeon Woo
- Department of Obstetrics and Gynecology, Seoul St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, 222, Banpo-daero, Seocho-gu, Seoul 06591, Korea; (H.S.); (S.B.); (I.K.); (J.Y.W.); (E.J.C.); (Y.J.C.)
| | - Eui Jin Cho
- Department of Obstetrics and Gynecology, Seoul St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, 222, Banpo-daero, Seocho-gu, Seoul 06591, Korea; (H.S.); (S.B.); (I.K.); (J.Y.W.); (E.J.C.); (Y.J.C.)
| | - Youn Jin Choi
- Department of Obstetrics and Gynecology, Seoul St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, 222, Banpo-daero, Seocho-gu, Seoul 06591, Korea; (H.S.); (S.B.); (I.K.); (J.Y.W.); (E.J.C.); (Y.J.C.)
| | - Sung Eun Rha
- Department of Radiology, Seoul St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, 222, Banpo-daero, Seocho-gu, Seoul 06591, Korea;
| | - Shin Ah Oh
- NAVER Clova, 246, Hwangsaeul-ro, Bundang-gu, Seongnam-si 13595, Korea;
| | - Seo Yeon Youn
- Department of Radiology, Seoul St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, 222, Banpo-daero, Seocho-gu, Seoul 06591, Korea;
- Correspondence: (S.Y.Y.); (S.J.L.)
| | - Sung Jong Lee
- Department of Obstetrics and Gynecology, Seoul St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, 222, Banpo-daero, Seocho-gu, Seoul 06591, Korea; (H.S.); (S.B.); (I.K.); (J.Y.W.); (E.J.C.); (Y.J.C.)
- Correspondence: (S.Y.Y.); (S.J.L.)
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Amado Cabana S, Gallego Ojea JC, Félez Carballada M. Usefulness of dynamic contrast-enhanced magnetic resonance imaging in characterizing ovarian tumors classified as indeterminate at ultrasonography. RADIOLOGIA 2020; 64:S0033-8338(20)30073-4. [PMID: 32650993 DOI: 10.1016/j.rx.2020.05.006] [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: 01/14/2020] [Revised: 05/11/2020] [Accepted: 05/20/2020] [Indexed: 10/23/2022]
Abstract
OBJECTIVES To determine whether there is a significant relationship between the shape of the time-intensity curve on dynamic gadolinium-enhanced magnetic resonance imaging (MRI) of ovarian tumors classified as indeterminate at ultrasonography and the type of lesion (benign, borderline, or malignant) to enable an accurate presurgical diagnosis. MATERIAL AND METHODS We used dynamic contrast-enhanced MRI to study 68 ovarian tumors that were classified as indeterminate at ultrasonography. We included only cases for which a definitive diagnosis (histologic diagnosis or ≥1 year stability on imaging tests) was available. Each case was classified as benign, borderline, or malignant. To analyze the MRI studies, we marked regions of interest in the lesion and in the myometrium (as a reference). We obtained a curve defined by the relation between the intensity of enhancement and time and classified each tumor according to four predefined curve types. We also analyzed semiquantitative parameters. Finally, we compared the results for each of the three groups of tumors. RESULTS We found significant associations (p <0.001) between the curves without early enhancement and benign and borderline lesions as well as between the curves with early enhancement and malignant lesions. Malignant lesions were significantly associated with the semiquantitative enhancement parameters: maximum (p=0.002), maximum relative (p=0.006), and relative (p=0.018). CONCLUSIONS In ovarian tumors classified as indeterminate at ultrasonography, dynamic contrast-enhanced MRI can be useful for classification as benign, borderline, or malignant because the malignant lesions are significantly associated with early enhancement curves.
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Affiliation(s)
- S Amado Cabana
- Servicio de Radiodiagnóstico, Complexo Hospitalario Universitario de Ferrol, Ferrol, A Coruña, España.
| | - J C Gallego Ojea
- Servicio de Radiodiagnóstico, Complexo Hospitalario Universitario de Ferrol, Ferrol, A Coruña, España
| | - M Félez Carballada
- Servicio de Radiodiagnóstico, Complexo Hospitalario Universitario de Ferrol, Ferrol, A Coruña, España
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Wang T, Sun H, Guo Y, Zou L. 18F-FDG PET/CT Quantitative Parameters and Texture Analysis Effectively Differentiate Endometrial Precancerous Lesion and Early-Stage Carcinoma. Mol Imaging 2020; 18:1536012119856965. [PMID: 31198089 PMCID: PMC6572902 DOI: 10.1177/1536012119856965] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022] Open
Abstract
Objective: This study evaluated the metabolic parameters and texture features of fluorodeoxyglucose positron emission tomography–computed tomography (PET/CT) for the diagnosis and differentiation of endometrial atypical hyperplasia (EAH), EAH with field cancerization (FC), and stage 1A endometrial carcinoma (EC 1a). Materials and Methods: We retrospectively analyzed the metabolic parameters of PET/CT in 170 patients with diagnoses confirmed by pathology, including 57 cases of EAH (57/170, 33.53%), 45 cases of FC (45/170, 26.47%), and 68 cases of EC 1a (68/170, 40.0%). Then, the texture features of each tumor were extracted and compared with the metabolic parameters and pathological results using nonparametric tests and linear regression analysis. The diagnostic performance was assessed by the area under the curve (AUC) values obtained from receiver operating characteristic analysis. Results: There were moderate positive correlations between the PET standardized uptake values (SUVpeak, SUVmax, and SUVmean) and postoperative pathological features with correlation coefficients (rs) of 0.663, 0.651, and 0.651, respectively (P < .001). Total lesion glycolysis showed relatively low correlation with pathological characteristics (rs = 0.476), whereas metabolic tumor volume and age showed the weakest correlations (rs = 0.186 and 0.232, respectively). To differentiate between the diagnosis of EAH and FC, SUVmax displayed the largest AUC of 0.857 (sensitivity, 82.2%; specificity, 84.2%). Five texture features were screened out as Percentile 40, Percentile 45, InverseDifferenceMoment_AllDirection_offset 1, InverseDifferenceMoment_angle 45_offset 4, and ClusterProminence_angle 135_offset 7 (P < .001) by linear model of texture analysis (AUC = 0.851; specificity = 0.692; sensitivity = 0.871). To differentiate between the diagnoses of FC and EC 1a, SUVpeak displayed the largest AUC of 0.715 (sensitivity, 67.6%; specificity, 77.8%), and 2 texture features were identified as Percentile 10 and CP_angle 135_offset 7 (AUC = 0.819; specificity = 0.871; sensitivity = 0.766; P < .001). Conclusions: SUVmax and SUVpeak had the highest diagnostic values for EAH, FC, and EC 1a compared with the other tested parameters. SUVmax, Percentile 40, Percentile 45, InverseDifferenceMoment_AllDirection_offset 1, InverseDifferenceMoment_angle 45_offset 4, and ClusterProminence_angle 135_offset 7 distinguished EAH from FC. SUVpeak, Percentile 10, and ClusterProminence_angle 135_offset 7 distinguished FC from EC 1a. This study showed that the addition of texture features provides valuable information for differentiating EAH, FC, and EC 1a diagnoses.
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Affiliation(s)
- Tong Wang
- 1 Department of Radiology, Shengjing Hospital of China Medical University, Shenyang, China
| | - Hongzan Sun
- 1 Department of Radiology, Shengjing Hospital of China Medical University, Shenyang, China
| | - Yan Guo
- 2 GE Healthcare, Beijing, China
| | - Lue Zou
- 1 Department of Radiology, Shengjing Hospital of China Medical University, Shenyang, China
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Locally advanced cervical cancer complicating pregnancy: A case of competing risks from the Catholic University of the Sacred Heart in Rome. Gynecol Oncol 2018; 150:398-405. [PMID: 30126588 DOI: 10.1016/j.ygyno.2018.06.028] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
A case of stage IB2 cervical cancer at 27 weeks of pregnancy, treated with neoadjuvant chemotherapy followed by radical Cesarean hysterectomy with full pelvic and infra-mesenteric lymphadenectomy, and adjuvant chemo-radiation is described. While she remains without disease, her baby was diagnosed with acute myelogenous leukemia. We highlight the pre-operative work-up, treatment options, safety, feasibility, and outcomes for the mother and her fetus.
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