1
|
Takeyama N, Sasaki Y, Ueda Y, Tashiro Y, Tanaka E, Nagai K, Morioka M, Ogawa T, Tate G, Hashimoto T, Ohgiya Y. Magnetic resonance imaging-based radiomics analysis of the differential diagnosis of ovarian clear cell carcinoma and endometrioid carcinoma: a retrospective study. Jpn J Radiol 2024; 42:731-743. [PMID: 38472624 PMCID: PMC11217043 DOI: 10.1007/s11604-024-01545-z] [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: 10/23/2023] [Accepted: 02/02/2024] [Indexed: 03/14/2024]
Abstract
PURPOSE To retrospectively evaluate the diagnostic potential of magnetic resonance imaging (MRI)-based features and radiomics analysis (RA)-based features for discriminating ovarian clear cell carcinoma (CCC) from endometrioid carcinoma (EC). MATERIALS AND METHODS Thirty-five patients with 40 ECs and 42 patients with 43 CCCs who underwent pretherapeutic MRI examinations between 2011 and 2022 were enrolled. MRI-based features of the two groups were compared. RA-based features were extracted from the whole tumor volume on T2-weighted images (T2WI), contrast-enhanced T1-weighted images (cT1WI), and apparent diffusion coefficient (ADC) maps. The least absolute shrinkage and selection operator (LASSO) regression with tenfold cross-validation method was performed to select features. Logistic regression analysis was conducted to construct the discriminating models. Receiver operating characteristic curve (ROC) analyses were performed to predict CCC. RESULTS Four features with the highest absolute value of the LASSO algorithm were selected for the MRI-based, RA-based, and combined models: the ADC value, absence of thickening of the uterine endometrium, absence of peritoneal dissemination, and growth pattern of the solid component for the MRI-based model; Gray-Level Run Length Matrix (GLRLM) Long Run Low Gray-Level Emphasis (LRLGLE) on T2WI, spherical disproportion and Gray-Level Size Zone Matrix (GLSZM), Large Zone High Gray-Level Emphasis (LZHGE) on cT1WI, and GLSZM Normalized Gray-Level Nonuniformity (NGLN) on ADC map for the RA-based model; and the ADC value, spherical disproportion and GLSZM_LZHGE on cT1WI, and GLSZM_NGLN on ADC map for the combined model. Area under the ROC curves of those models were 0.895, 0.910, and 0.956. The diagnostic performance of the combined model was significantly superior (p = 0.02) to that of the MRI-based model. No significant differences were observed between the combined and RA-based models. CONCLUSION Conventional MRI-based analysis can effectively distinguish CCC from EC. The combination of RA-based features with MRI-based features may assist in differentiating between the two diseases.
Collapse
Affiliation(s)
- Nobuyuki Takeyama
- Department of Radiology, Showa University School of Medicine, 1-5-8 Hatanodai, Shinagawa-Ku, Tokyo, 142-8666, Japan.
- Department of Radiology, Showa University Fujigaoka Hospital, 1-30 Fujigaoka, Aoba-Ku, Yokohama-City, 227-8501, Japan.
| | - Yasushi Sasaki
- Department of Obstetrics and Gynecology, Showa University Fujigaoka Hospital, 1-30 Fujigaoka, Aoba-Ku, Yokohama-City, Kanagawa, 227-8501, Japan
| | - Yasuo Ueda
- Department of Pathology and Laboratory Medicine, Showa University Fujigaoka Hospital, 1-30 Fujigaoka, Aoba-Ku, Yokohama-City, 227-8501, Japan
| | - Yuki Tashiro
- Department of Radiology, Showa University Fujigaoka Hospital, 1-30 Fujigaoka, Aoba-Ku, Yokohama-City, 227-8501, Japan
| | - Eliko Tanaka
- Department of Radiology, Showa University Fujigaoka Hospital, 1-30 Fujigaoka, Aoba-Ku, Yokohama-City, 227-8501, Japan
- Department of Radiology, Kawasaki Saiwai Hospital, 31-27 Ohmiya-Tyo, Saiwai-Ku, Kawasaki City, Kanagawa, 212-0014, Japan
| | - Kyoko Nagai
- Department of Radiology, Showa University Fujigaoka Hospital, 1-30 Fujigaoka, Aoba-Ku, Yokohama-City, 227-8501, Japan
| | - Miki Morioka
- Department of Obstetrics and Gynecology, Showa University Fujigaoka Hospital, 1-30 Fujigaoka, Aoba-Ku, Yokohama-City, Kanagawa, 227-8501, Japan
| | - Takafumi Ogawa
- Department of Pathology and Laboratory Medicine, Showa University Fujigaoka Hospital, 1-30 Fujigaoka, Aoba-Ku, Yokohama-City, 227-8501, Japan
| | - Genshu Tate
- Department of Pathology and Laboratory Medicine, Showa University Fujigaoka Hospital, 1-30 Fujigaoka, Aoba-Ku, Yokohama-City, 227-8501, Japan
| | - Toshi Hashimoto
- Department of Radiology, Showa University Fujigaoka Hospital, 1-30 Fujigaoka, Aoba-Ku, Yokohama-City, 227-8501, Japan
| | - Yoshimitsu Ohgiya
- Department of Radiology, Showa University School of Medicine, 1-5-8 Hatanodai, Shinagawa-Ku, Tokyo, 142-8666, Japan
| |
Collapse
|
2
|
Radzynski L, Boyer L, Kossai M, Mouraire A, Montoriol PF. Pictorial essay: MRI evaluation of endometriosis-associated neoplasms. Insights Imaging 2023; 14:144. [PMID: 37673827 PMCID: PMC10482819 DOI: 10.1186/s13244-023-01485-8] [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: 02/09/2023] [Accepted: 07/08/2023] [Indexed: 09/08/2023] Open
Abstract
Endometriosis is a frequent pathology mostly affecting women of young age. When typical aspects are present, the diagnosis can easily be made at imaging, especially at MRI. Transformation of benign endometriosis to endometriosis-associated neoplasms is rare. The physiopathology is complex and remains controversial. Endometrioid carcinoma and clear cell carcinoma are the main histological subtypes. Our goal was to review the main imaging characteristics that should point to an ovarian or extra-ovarian endometriosis-related tumor, especially at MRI, as it may be relevant prior to surgical management.Key points• Transformation of benign endometriosis to endometriosis-associated neoplasms is rare.• MRI is useful when displaying endometriosis lesions associated to an ovarian tumor.• Subtraction imaging should be used in the evaluation of complex endometriomas.
Collapse
Affiliation(s)
- Louise Radzynski
- Department of Radiology, University Hospital of Clermont-Ferrand, Clermont-Ferrand, France.
| | - Louis Boyer
- Department of Radiology, University Hospital of Clermont-Ferrand, Clermont-Ferrand, France
| | - Myriam Kossai
- Department of Pathology, Cancer Center of Clermont-Ferrand, Clermont-Ferrand, France
| | - Anne Mouraire
- Department of Pathology, Cancer Center of Clermont-Ferrand, Clermont-Ferrand, France
| | | |
Collapse
|
3
|
Sun Y, Li M, Zhang X, Xu D, Wu J, Gu X, Khan A, Shen H, Li Z. A simple and available measurement of onco-sEV dsDNA to protein ratio as a potential tumor marker. BMC Cancer 2023; 23:614. [PMID: 37400751 DOI: 10.1186/s12885-023-10886-3] [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: 01/16/2023] [Accepted: 04/25/2023] [Indexed: 07/05/2023] Open
Abstract
BACKGROUND Small extracellular vesicles (sEVs) have great potential as new biomarkers in liquid biopsy. However, due to the limitations of sEVs extraction and component analysis procedures, further clinical applications of sEVs are hampered. Carcinoembryonic antigen (CEA) is a commonly used broad-spectrum tumor marker that is strongly expressed in a variety of malignancies. RESULTS In this study, CEA+ sEVs were directly separated from serum using immunomagnetic beads, and the nucleic acid to protein ultraviolet absorption ratio (NPr) of CEA+ sEVs was determined. It was found that the NPr of CEA+ sEVs in tumor group was higher than that of healthy group. We further analyzed the sEV-derived nucleic acid components using fluorescent staining and found that the concentration ratio of double-stranded DNA to protein (dsDPr) in CEA+ sEVs was also significantly different between the two groups, with a sensitivity of 100% and a specificity of 41.67% for the diagnosis of pan-cancer. The AUC of dsDPr combined with NPr was 0.87 and the ACU of dsDPr combined with CA242 could reach 0.94, showing good diagnostic performance for pan-cancer. CONCLUSIONS This study demonstrates that the dsDPr of CEA+ sEVs can effectively distinguish sEVs derived from tumor patients and healthy individuals, which can be employed as a simple and cost-effective non-invasive screening technology to assist tumor diagnosis.
Collapse
Affiliation(s)
- Yifan Sun
- Department of Laboratory Medicine, Nanjing Drum Tower Hospital Clinical College of Jiangsu University, Nanjing, China
| | - Miao Li
- Department of Laboratory Medicine, Nanjing Drum Tower Hospital Clinical College of Jiangsu University, Nanjing, China
- Department of Laboratory Medicine, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing, China
| | - Xiaoshan Zhang
- College of Life Science, Yangtze University, Jingzhou, China
| | - Dongjie Xu
- College of Life Science, Yangtze University, Jingzhou, China
| | - Jie Wu
- Department of Laboratory Medicine, Nanjing Drum Tower Hospital Clinical College of Jiangsu University, Nanjing, China
| | - Xinrui Gu
- Department of Laboratory Medicine, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing, China
| | - Adeel Khan
- State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, National Demonstration Center for Experimental Biomedical Engineering Education (Southeast University, Southeast University, Nanjing, China
| | - Han Shen
- Department of Laboratory Medicine, Nanjing Drum Tower Hospital Clinical College of Jiangsu University, Nanjing, China.
- Department of Laboratory Medicine, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing, China.
| | - Zhiyang Li
- Department of Laboratory Medicine, Nanjing Drum Tower Hospital Clinical College of Jiangsu University, Nanjing, China.
- Department of Laboratory Medicine, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing, China.
| |
Collapse
|
4
|
Song Y, Yuan M, Wang G. Update value and clinical application of MUC16 (cancer antigen 125). Expert Opin Ther Targets 2023; 27:745-756. [PMID: 37584221 DOI: 10.1080/14728222.2023.2248376] [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: 03/01/2023] [Revised: 06/25/2023] [Accepted: 08/10/2023] [Indexed: 08/17/2023]
Abstract
INTRODUCTION The largest transmembrane mucin, mucin 16 (MUC16), contains abundant glycosylation sites on the molecular surface, allowing it to participate in various molecular pathways. When cells lose polarity and become cancerous, MUC16 is overexpressed, and more of the extracellular region (cancer antigen [CA]125) is released into serum and possibly, promote the development of diseases. Thus, MUC16 plays an indispensable role in clinical research and application. AREAS COVERED This review summarizes the update proposed role of MUC16 in carcinogenesis and metastasis. Most importantly, we prospect its potential value in targeted therapy after screening 1226 articles published within the last 10 years from PubMed. Two reviewers screened each record and each report retrieved independently. We have summarized the progress of MUC16/CA125 in basic research and clinical application, and predicted its possible future development directions. EXPERT OPINION As an important noninvasive co-factor in the diagnosis of gynecological diseases, MUC16 has been used for a long time, especially in the diagnosis and treatment of ovarian cancer. The overexpression of MUC16 plays a very obvious role in regulating inflammatory response, supporting immune suppression, and promoting the proliferation, division, and metastasis of cancer cells. In the next 20 years, there will be a luxuriant clinical application of MUC16 as a target for immune monitoring and immunotherapy.
Collapse
Affiliation(s)
- Yaan Song
- Department of Obstetrics and Gynecology, Shandong Provincial Hospital, Shandong University, Jinan, Shandong, China
- Medical Integration and Practice Center, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, China
- Gynecology Laboratory, Shandong Provincial Hospital, Jinan, Shandong, China
| | - Ming Yuan
- Department of Obstetrics and Gynecology, Shandong Provincial Hospital, Shandong University, Jinan, Shandong, China
- Gynecology Laboratory, Shandong Provincial Hospital, Jinan, Shandong, China
| | - Guoyun Wang
- Department of Obstetrics and Gynecology, Shandong Provincial Hospital, Shandong University, Jinan, Shandong, China
- Gynecology Laboratory, Shandong Provincial Hospital, Jinan, Shandong, China
| |
Collapse
|
5
|
Ren J, Mao L, Zhao J, Li XL, Wang C, Liu XY, Jin ZY, He YL, Li Y, Xue HD. Seeing beyond the tumor: computed tomography image-based radiomic analysis helps identify ovarian clear cell carcinoma subtype in epithelial ovarian cancer. LA RADIOLOGIA MEDICA 2023:10.1007/s11547-023-01666-x. [PMID: 37368228 DOI: 10.1007/s11547-023-01666-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/28/2023] [Accepted: 06/14/2023] [Indexed: 06/28/2023]
Abstract
OBJECTIVE To develop and validate a model that can preoperatively identify the ovarian clear cell carcinoma (OCCC) subtype in epithelial ovarian cancer (EOC) using CT imaging radiomics and clinical data. MATERIAL AND METHODS We retrospectively analyzed data from 282 patients with EOC (training set = 225, testing set = 57) who underwent pre-surgery CT examinations. Patients were categorized into OCCC or other EOC subtypes based on postoperative pathology. Seven clinical characteristics (age, cancer antigen [CA]-125, CA-199, endometriosis, venous thromboembolism, hypercalcemia, stage) were collected. Primary tumors were manually delineated on portal venous-phase images, and 1218 radiomic features were extracted. The F-test-based feature selection method and logistic regression algorithm were used to build the radiomic signature, clinical model, and integrated model. To explore the effects of integrated model-assisted diagnosis, five radiologists independently interpreted images in the testing set and reevaluated cases two weeks later with knowledge of the integrated model's output. The diagnostic performances of the predictive models, radiologists, and radiologists aided by the integrated model were evaluated. RESULTS The integrated model containing the radiomic signature (constructed by four wavelet radiomic features) and three clinical characteristics (CA-125, endometriosis, and hypercalcinemia), showed better diagnostic performance (AUC = 0.863 [0.762-0.964]) than the clinical model (AUC = 0.792 [0.630-0.953], p = 0.295) and the radiomic signature alone (AUC = 0.781 [0.636-0.926], p = 0.185). The diagnostic sensitivities of the radiologists were significantly improved when using the integrated model (p = 0.023-0.041), while the specificities and accuracies were maintained (p = 0.074-1.000). CONCLUSION Our integrated model shows great potential to facilitate the early identification of the OCCC subtype in EOC, which may enhance subtype-specific therapy and clinical management.
Collapse
Affiliation(s)
- Jing Ren
- Department of Radiology, Chinese Academy of Medical Sciences & Peking Union Medical College, Peking Union Medical College Hospital, Shuai Fu Yuan 1, Dongcheng District, Beijing, 100730, People's Republic of China
| | - Li Mao
- AI Lab, Deepwise Healthcare, Beijing, People's Republic of China
| | - Jia Zhao
- Department of Radiology, Chinese Academy of Medical Sciences & Peking Union Medical College, Peking Union Medical College Hospital, Shuai Fu Yuan 1, Dongcheng District, Beijing, 100730, People's Republic of China
| | - Xiu-Li Li
- AI Lab, Deepwise Healthcare, Beijing, People's Republic of China
| | - Chen Wang
- Department of Radiology, Chinese Academy of Medical Sciences & Peking Union Medical College, Peking Union Medical College Hospital, Shuai Fu Yuan 1, Dongcheng District, Beijing, 100730, People's Republic of China
| | - Xin-Yu Liu
- Department of Radiology, Chinese Academy of Medical Sciences & Peking Union Medical College, Peking Union Medical College Hospital, Shuai Fu Yuan 1, Dongcheng District, Beijing, 100730, People's Republic of China
| | - Zheng-Yu Jin
- Department of Radiology, Chinese Academy of Medical Sciences & Peking Union Medical College, Peking Union Medical College Hospital, Shuai Fu Yuan 1, Dongcheng District, Beijing, 100730, People's Republic of China
| | - Yong-Lan He
- Department of Radiology, Chinese Academy of Medical Sciences & Peking Union Medical College, Peking Union Medical College Hospital, Shuai Fu Yuan 1, Dongcheng District, Beijing, 100730, People's Republic of China.
| | - Yuan Li
- Department of Obstetrics and Gynecology, Chinese Academy of Medical Sciences & Peking Union Medical College, National Clinical Research Center for Obstetric & Gynecologic Diseases, Peking Union Medical College Hospital, Beijing, People's Republic of China.
| | - Hua-Dan Xue
- Department of Radiology, Chinese Academy of Medical Sciences & Peking Union Medical College, Peking Union Medical College Hospital, Shuai Fu Yuan 1, Dongcheng District, Beijing, 100730, People's Republic of China.
| |
Collapse
|
6
|
Lu J, Zhao S, Ma F, Li H, Li Y, Qiang J. Whole-tumor ADC histogram analysis for differentiating endometriosis-related tumors: seromucinous borderline tumor, clear cell carcinoma and endometrioid carcinoma. Abdom Radiol (NY) 2023; 48:724-732. [PMID: 36401131 DOI: 10.1007/s00261-022-03742-8] [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: 09/26/2022] [Revised: 11/03/2022] [Accepted: 11/04/2022] [Indexed: 11/21/2022]
Abstract
PURPOSE To investigate the feasibility of whole-tumor apparent diffusion coefficient (ADC) histogram analysis for improving the differentiation of endometriosis-related tumors: seromucinous borderline tumor (SMBT), clear cell carcinoma (CCC) and endometrioid carcinoma (EC). METHODS Clinical features, solid component ADC (ADCSC) and whole-tumor ADC histogram-derived parameters (volume, the ADCmean, 10th, 50th and 90th percentile ADCs, inhomogeneity, skewness, kurtosis and entropy) were compared among 22 SMBTs, 42 CCCs and 21 ECs. Statistical analyses were performed using chi-square test, one-way ANOVA or Kruskal-Wallis test, and receiver operating characteristic curves. RESULTS A significantly higher ADCSC and smaller volume were associated with SMBT than with CCC/EC. The ADCmean was significantly higher in CCC than in EC. The 10th percentile ADC was significantly lower in EC than in SMBT/CCC. The 50th and 90th percentile ADCs were significantly higher in CCC than in SMBT/EC. For differentiating SMBT from CCC, AUCs of the ADCSC, volume, and 50th and 90th percentile ADCs were 0.97, 0.86, 0.72 and 0.81, respectively. For differentiating SMBT from EC, AUCs of the ADCSC, volume and 10th percentile ADC were 0.97, 0.71 and 0.72, respectively. For differentiating CCC from EC, AUCs of the ADCmean and 10th, 50th and 90th percentile ADCs were 0.79, 0.72, 0.81 and 0.85, respectively. CONCLUSION Whole-tumor ADC histogram analysis was valuable for differentiating endometriosis-related tumors, and the 90th percentile ADC was optimal in differentiating CCC from EC.
Collapse
Affiliation(s)
- Jing Lu
- Department of Radiology, Jinshan Hospital, Fudan University, 1508 Longhang Road, Shanghai, 201508, People's Republic of China
| | - Shuhui Zhao
- Department of Radiology, Jinshan Hospital, Fudan University, 1508 Longhang Road, Shanghai, 201508, People's Republic of China.,Department of Radiology, Xinhua Hospital, Medical College of Shanghai Jiao Tong University, Shanghai, 200092, People's Republic of China
| | - Fenghua Ma
- Department of Radiology, Jinshan Hospital, Fudan University, 1508 Longhang Road, Shanghai, 201508, People's Republic of China.,Department of Radiology, Obstetrics and Gynecology Hospital, Fudan University, 419 Fangxie Road, Shanghai, 200011, People's Republic of China
| | - Haiming Li
- Department of Radiology, Jinshan Hospital, Fudan University, 1508 Longhang Road, Shanghai, 201508, People's Republic of China.,Department of Radiology, Shanghai Cancer Center, Fudan University, 270 Dongan Road, Shanghai, 200032, People's Republic of China
| | - Yong'ai Li
- Department of Radiology, Jinshan Hospital, Fudan University, 1508 Longhang Road, Shanghai, 201508, People's Republic of China.
| | - Jinwei Qiang
- Department of Radiology, Jinshan Hospital, Fudan University, 1508 Longhang Road, Shanghai, 201508, People's Republic of China.
| |
Collapse
|
7
|
He Z, Chen J, Yang F, Pan X, Liu C. Computed tomography-based texture assessment for the differentiation of benign, borderline, and early-stage malignant ovarian neoplasms. J Int Med Res 2023; 51:3000605221150139. [PMID: 36688472 PMCID: PMC9893092 DOI: 10.1177/03000605221150139] [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] [Indexed: 01/24/2023] Open
Abstract
OBJECTIVE This study was performed to examine the value of computed tomography-based texture assessment for characterizing different types of ovarian neoplasms. METHODS This retrospective study involved 225 patients with histopathologically confirmed ovarian tumors after surgical resection. Two different data sets of thick (5-mm) slices (during regular and portal venous phases) were analyzed. Raw data analysis, principal component analysis, linear discriminant analysis, and nonlinear discriminant analysis were performed to classify ovarian tumors. The radiologist's misclassification rate was compared with the MaZda (texture analysis software) findings. The results were validated with the neural network classifier. Receiver operating characteristic curves were analyzed to determine the performances of different parameters. RESULTS Nonlinear discriminant analysis had a lower misclassification rate than the other analyses. Thirty texture parameters significantly differed between the two groups. In the training set, WavEnLH_s-3 and WavEnHL_s-3 were the optimal texture features during the regular phase, while WavEnHH_s-4 and Kurtosis seemed to be the most discriminative features during the portal venous phase. In the validation test, benign versus malignant tumors and benign versus borderline lesions were well-distinguished. CONCLUSIONS Computed tomography-based texture features provide a useful imaging signature that may assist in differentiating benign, borderline, and early-stage ovarian cancer.
Collapse
Affiliation(s)
- Ziying He
- Department of Gynecologic Oncology, Guangxi Medical University Cancer Hospital, Nanning, China
| | - Jia Chen
- Department of Radiology, Guangxi Medical University Cancer Hospital, Nanning, China
| | - Fei Yang
- Department of Clinical Medical, Guangxi Medical University, Nanning, China
| | - Xinwei Pan
- Department of Gynecologic Oncology, Guangxi Medical University Cancer Hospital, Nanning, China
| | - Chanzhen Liu
- Department of Gynecologic Oncology, Guangxi Medical University Cancer Hospital, Nanning, China,Chanzhen Liu, Department of Gynecologic Oncology, Guangxi Medical University Cancer Hospital, 71 Hedi Road, Qingxiu District, Nanning 530021, China.
| |
Collapse
|
8
|
Kawahara N, Miyake R, Yamanaka S, Kobayashi H. A Novel Predictive Tool for Discriminating Endometriosis Associated Ovarian Cancer from Ovarian Endometrioma: The R2 Predictive Index. Cancers (Basel) 2021; 13:cancers13153829. [PMID: 34359728 PMCID: PMC8345171 DOI: 10.3390/cancers13153829] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2021] [Revised: 07/10/2021] [Accepted: 07/27/2021] [Indexed: 01/05/2023] Open
Abstract
Simple Summary Ovarian Endometrioma (OE) is a precancerous condition for endometriosis-associated ovarian cancer (EAOC). For many clinicians observing OE outpatients, setting the appropriate time for surgery can be a challenge because there is no suggestive milestone. Out of the fear of malignant transformation, many patients have surgery conducted according to respective faculty standards. This study aims to investigate a novel, noninvasive method not requiring an MRI device. This study partly helps to lift the above restrictions, and has the potential to suggest intervention-appropriate timing to the physician. Abstract Background: Magnetic resonance (MR) relaxometry provides a noninvasive tool to discriminate between ovarian endometrioma (OE) and endometriosis-associated ovarian cancer (EAOC), with a sensitivity and specificity of 86% and 94%, respectively. MRI models that can measure R2 values are limited, and the R2 values differ between MRI models. This study aims to extract the factors contributing to the R2 value, and to make a formula for estimating the R2 values, and to assess whether the R2 predictive index calculated by the formula could discriminate EAOC from OE. Methods: This retrospective study was conducted at our institution from November 2012 to February 2019. A total of 247 patients were included in this study. Patients with benign ovarian tumors mainly received laparoscopic surgery, and the patients suspected of having malignant tumors underwent laparotomy. Information from a chart review of the patients’ medical records was collected. Results: In the investigative cohort, among potential factors correlated with the R2 value, multiple regression analyses revealed that tumor diameter and CEA could predict the R2 value. In the validation cohort, multivariate analysis confirmed that age, CRP, and the R2 predictive index were the independent factors. Conclusions: The R2 predictive index is useful and valuable to the detection of the malignant transformation of endometrioma.
Collapse
Affiliation(s)
- Naoki Kawahara
- Correspondence: ; Tel.: +81-744-29-8877; Fax: +81-744-23-6557
| | | | | | | |
Collapse
|
9
|
Králíčková M, Vetvicka V, Fiala L, Laganà AS, Garzon S. The Search for Biomarkers in Endometriosis: a Long and Windy Road. Reprod Sci 2021; 29:1667-1673. [PMID: 34159571 DOI: 10.1007/s43032-021-00668-2] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2021] [Accepted: 06/11/2021] [Indexed: 01/29/2023]
Abstract
Endometriosis is a complex and chronic estrogen-dependent disease, affecting a significant proportion of women of reproductive age. Despite the long interest and extensive research, the pathogenesis of the disease is still debated. Although available non-invasive diagnostic methods have adequate accuracy, an invasive approach by laparoscopy is often necessary to obtain histological confirmation. In this scenario, the search for an accurate, reliable, cost-effective, clinically applicable non-invasive biomarker plays a crucial role in a potentially early diagnosis and, in this way, shape the future management of the disease. Considering these elements, the current review aims to summarize the most significant and novel results about biomarkers for the diagnosis and follow-up of women affected by endometriosis.
Collapse
Affiliation(s)
- Milena Králíčková
- Department of Histology and Embryology, Faculty of Medicine, Charles University, Pilsen, Czech Republic.,Department of Obstetrics and Gynecology, University Hospital, Faculty of Medicine, Charles University, Pilsen, Czech Republic.,Biomedical Centre, Faculty of Medicine in Plzen, Charles University, Pilsen, Czech Republic
| | - Vaclav Vetvicka
- Department of Pathology, University of Louisville, Louisville, KY, USA
| | - Luděk Fiala
- Institute of Sexology, First Faculty of Medicine, Charles University, Prague, Czech Republic
| | - Antonio Simone Laganà
- Department of Obstetrics and Gynecology, "Filippo Del Ponte" Hospital, University of Insubria, Piazza Biroldi 1, 21100, Varese, Italy.
| | - Simone Garzon
- Department of Obstetrics and Gynecology, "Filippo Del Ponte" Hospital, University of Insubria, Piazza Biroldi 1, 21100, Varese, Italy.,Department of Obstetrics and Gynecology, AOUI Verona, University of Verona, Verona, Italy
| |
Collapse
|
10
|
Nougaret S, McCague C, Tibermacine H, Vargas HA, Rizzo S, Sala E. Radiomics and radiogenomics in ovarian cancer: a literature review. Abdom Radiol (NY) 2021; 46:2308-2322. [PMID: 33174120 DOI: 10.1007/s00261-020-02820-z] [Citation(s) in RCA: 37] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2020] [Revised: 10/01/2020] [Accepted: 10/10/2020] [Indexed: 01/25/2023]
Abstract
Ovarian cancer remains one of the most lethal gynecological cancers in the world despite extensive progress in the areas of chemotherapy and surgery. Many studies have postulated that this is because of the profound heterogeneity that underpins response to therapy and prognosis. Standard imaging evaluation using CT or MRI does not take into account this tumoral heterogeneity especially in advanced stages with peritoneal carcinomatosis. As such, newly emergent fields in the assessment of tumor heterogeneity have been proposed using radiomics to evaluate the whole tumor burden heterogeneity as opposed to single biopsy sampling. This review provides an overview of radiomics, radiogenomics, and proteomics and examines the use of these newly emergent fields in assessing tumor heterogeneity and its implications in ovarian cancer.
Collapse
Affiliation(s)
- S Nougaret
- IRCM, Montpellier Cancer Research Institute, INSERM, U1194, University of Montpellier, 208 Ave des Apothicaires, 34295, Montpellier, France. .,Department of Radiology, Montpellier Cancer institute, 208 Ave des Apothicaires, 34295, Montpellier, France.
| | - Cathal McCague
- Department of Radiology, Cambridge Biomedical Campus, Box 218, Cambridge, CB2 0QQ, UK
| | - Hichem Tibermacine
- IRCM, Montpellier Cancer Research Institute, INSERM, U1194, University of Montpellier, 208 Ave des Apothicaires, 34295, Montpellier, France.,Department of Radiology, Montpellier Cancer institute, 208 Ave des Apothicaires, 34295, Montpellier, France
| | - Hebert Alberto Vargas
- Department of Radiology, Memorial Sloan Kettering Cancer Center, 1275 York Avenue, New York, NY, 10065, USA
| | - Stefania Rizzo
- Istituto di Imaging della Svizzera Italiana (IIMSI), Ente Ospedaliero Cantonale (EOC), Via Tesserete 46, 6900, Lugano, CH, Switzerland.,Facoltà di Scienze Biomediche, Università della Svizzera Italiana, Lugano, CH, Switzerland
| | - E Sala
- Department of Radiology, Cambridge Biomedical Campus, Box 218, Cambridge, CB2 0QQ, UK
| |
Collapse
|
11
|
Zhang X, Li M, Tang Z, Li X, Song T. Differentiation between endometriosis-associated ovarian cancers and non- endometriosis-associated ovarian cancers based on magnetic resonance imaging. Br J Radiol 2021; 94:20201441. [PMID: 33882252 DOI: 10.1259/bjr.20201441] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022] Open
Abstract
OBJECTIVES Endometriosis-associated ovarian cancer (EAOC) patients show different clinical characteristics compared with non-EAOC patients. However, a few studies are focused on the imaging characteristics of EAOC until now. We assessed MRI characteristics in differentiating EAOC and non-EAOC. METHODS We retrospectively analyzed clinical and MRI characteristics from 54 patients with 67 lesions diagnosed with primary epithelial ovarian carcinoma at the Third Affiliated Hospital of Guangzhou Medical University between January 2012 and October 2020. We studied MRI findings such as maximum diameter, morphology, configuration, locularity, features of mural nodules, lymphadenopathy, peritoneal implants, the presence of hyperintensity on T1WI, and hypointensity on T2WI. We also studied the clinical characteristics. Significant MRI variables in univariate analysis were selected for subsequent multivariate regression analysis. This study evaluated the diagnostic performance of the significant MRI variables in univariate analysis. RESULTS We found that the patients with EAOC, compared with those with non-EAOC, were younger, more unilateral, and had earlier FIGO stage. Univariate analysis revealed that morphology, locularity, growth pattern of mural nodules, and hypointensity on T2WI were factors that significantly differed between EAOC and non-EAOC. In the multivariate logistic regression analysis, locularity and hypointensity on T2WI were independent predictors to distinguish EAOC from non-EAOC. CONCLUSIONS EAOC typically presented as a unilocular mass with hypointensity on T2WI in cystic components. MRI could help distinguish EAOC from non-EAOC. ADVANCES IN KNOWLEDGE MRI is a promising tool for preoperative diagnosis of EAOC.
Collapse
Affiliation(s)
- Ximing Zhang
- Department of Radiology, The Third Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
| | - Min Li
- Department of Radiology, The Third Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
| | - Zhuopeng Tang
- Department of Radiology, The Third Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
| | - Xinyi Li
- Department of Radiology, The Third Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
| | - Ting Song
- Department of Radiology, The Third Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
| |
Collapse
|