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1.
Ultrasound Obstet Gynecol ; 62(4): 594-602, 2023 10.
Article in English | MEDLINE | ID: mdl-37204769

ABSTRACT

OBJECTIVE: To evaluate the performance of subjective assessment and the Assessment of Different NEoplasias in the adneXa (ADNEX) model in discriminating between benign and malignant adnexal tumors and between metastatic and primary adnexal tumors in patients with a personal history of breast cancer. METHODS: This was a retrospective single-center study including patients with a history of breast cancer who underwent surgery for an adnexal mass between 2013 and 2020. All patients had been examined with transvaginal or transrectal ultrasound using a standardized examination technique and all ultrasound reports had been stored and were retrieved for the purposes of this study. The specific diagnosis suggested by the original ultrasound examiner in the retrieved report was analyzed. For each mass, the ADNEX model risks were calculated prospectively and the highest relative risk was used to categorize each into one of five categories (benign, borderline, primary Stage I, primary Stages II-IV or metastatic ovarian cancer) for analysis of the ADNEX model in predicting the specific tumor type. The performance of subjective assessment and the ADNEX model in discriminating between benign and malignant adnexal tumors and between primary and metastatic adnexal tumors was evaluated, using final histology as the reference standard. RESULTS: Included in the study were 202 women with a history of breast cancer who underwent surgery for an adnexal mass. At histology, 93/202 (46.0%) masses were benign, 76/202 (37.6%) were primary malignancies (four borderline and 72 invasive tumors) and 33/202 (16.3%) were metastases. The original ultrasound examiner classified correctly 79/93 (84.9%) benign adnexal masses, 72/76 (94.7%) primary adnexal malignancies and 30/33 (90.9%) metastatic tumors. Subjective ultrasound evaluation had a sensitivity of 93.6%, specificity of 84.9% and accuracy of 89.6%, while the ADNEX model had higher sensitivity (98.2%) but lower specificity (78.5%), with similar accuracy (89.1%), in discriminating between benign and malignant ovarian masses. Subjective evaluation had a sensitivity of 51.5%, specificity of 88.8% and accuracy of 82.7% in distinguishing metastatic and primary tumors (including benign, borderline and invasive tumors), and the ADNEX model had a sensitivity of 63.6%, specificity of 84.6% and similar accuracy (81.2%). CONCLUSIONS: The performance of subjective assessment and the ADNEX model in discriminating between benign and malignant adnexal masses in this series of patients with history of breast cancer was relatively similar. Both subjective assessment and the ADNEX model demonstrated good accuracy and specificity in discriminating between metastatic and primary tumors, but the sensitivity was low. © 2023 International Society of Ultrasound in Obstetrics and Gynecology.


Subject(s)
Adnexal Diseases , Breast Neoplasms , Ovarian Neoplasms , Humans , Female , Breast Neoplasms/diagnostic imaging , Retrospective Studies , Sensitivity and Specificity , Adnexa Uteri/pathology , Ovarian Neoplasms/pathology , Adnexal Diseases/pathology , Ultrasonography/methods , Diagnosis, Differential
2.
Ultrasound Obstet Gynecol ; 59(2): 241-247, 2022 Feb.
Article in English | MEDLINE | ID: mdl-34225386

ABSTRACT

OBJECTIVE: To describe the clinical and ultrasound characteristics of ovarian carcinosarcoma. METHODS: This was a retrospective multicenter study. Patients with a histological diagnosis of ovarian carcinosarcoma, who had undergone preoperative ultrasound examination between 2010 and 2019, were identified from the International Ovarian Tumor Analysis (IOTA) database. Additional patients who were examined outside of the IOTA study were identified from the databases of the participating centers. The masses were described using the terms and definitions of the IOTA group. Additionally, two experienced ultrasound examiners reviewed all available images to identify typical ultrasound features using pattern recognition. RESULTS: Ninety-one patients with ovarian carcinosarcoma who had undergone ultrasound examination were identified, of whom 24 were examined within the IOTA studies and 67 were examined outside of the IOTA studies. Median age at diagnosis was 66 (range, 33-91) years and 84/91 (92.3%) patients were postmenopausal. Most patients (67/91, 73.6%) were symptomatic, with the most common complaint being pain (51/91, 56.0%). Most tumors (67/91, 73.6%) were International Federation of Gynecology and Obstetrics (FIGO) Stage III or IV. Bilateral lesions were observed on ultrasound in 46/91 (50.5%) patients. Ascites was present in 38/91 (41.8%) patients. The median largest tumor diameter was 100 (range, 18-260) mm. All ovarian carcinosarcomas contained solid components, and most were described as solid (66/91, 72.5%) or multilocular-solid (22/91, 24.2%). The median diameter of the largest solid component was 77.5 (range, 11-238) mm. Moderate or rich vascularization was found in 78/91 (85.7%) cases. Retrospective analysis of ultrasound images and videoclips using pattern recognition in 73 cases revealed that all tumors had irregular margins and inhomogeneous echogenicity of the solid components. Forty-seven of 73 (64.4%) masses appeared as a solid tumor with cystic areas. Cooked appearance of the solid tissue was identified in 28/73 (38.4%) tumors. No pathognomonic ultrasound sign of ovarian carcinosarcoma was found. CONCLUSIONS: Ovarian carcinosarcomas are usually diagnosed in postmenopausal women and at an advanced stage. The most common ultrasound appearance is a large solid tumor with irregular margins, inhomogeneous echogenicity of the solid tissue and cystic areas. The second most common pattern is a large multilocular-solid mass with inhomogeneous echogenicity of the solid tissue. © 2021 International Society of Ultrasound in Obstetrics and Gynecology.


Subject(s)
Carcinosarcoma/diagnostic imaging , Ovarian Neoplasms/diagnostic imaging , Pregnancy Complications, Neoplastic/diagnostic imaging , Adult , Ascites , Carcinosarcoma/pathology , Databases, Factual , Female , Humans , Middle Aged , Neoplasm Staging , Ovarian Neoplasms/pathology , Pregnancy , Pregnancy Complications, Neoplastic/pathology , Prognosis , Retrospective Studies , Ultrasonography, Doppler, Color/methods
4.
Mol Biosyst ; 11(6): 1717-25, 2015 Jun.
Article in English | MEDLINE | ID: mdl-25959140

ABSTRACT

The chemical composition of the cervical mucus (CM), its physical characteristics and the volume of secretion change cyclically throughout the menstrual cycle. The aim of this study was to identify the constitutive protein composition of CM of fertile women and the changes in the CM proteome throughout the menstrual cycle. Five fertile women who had a term delivery within 1 year before the study were enrolled. Proteomic analysis was performed using an Ultimate 3000 Nano/Micro-HPLC apparatus equipped with an FLM-3000-Flow manager module and coupled with an LTQ Orbitrap XL hybrid mass spectrometer; bioinformatic software was used for functional and quantitative analysis. 59, 81 and 43 proteins (mean) were respectively identified in the pre-ovulatory, ovulatory and post-ovulatory samples. 38 common proteins were identified. 42, 38 and 17 exclusive proteins were respectively identified in pre-ovulatory, ovulatory and post-ovulatory CM. The main part of CM constituents has a catalytic activity, which is mainly related to hydrolase activity. The label-free quantitative analysis of the common proteins revealed a significant reduction in the protein abundance index for antileukoproteinase, after the ovulation, and a peak of haptoglobin at ovulation. This is the first application of high-resolution MS-based proteomics for the identification of protein constituents of CM. This approach may contribute to the identification of putative biomarkers of the female reproductive tract.


Subject(s)
Cervix Mucus/chemistry , Menstrual Cycle/metabolism , Proteins/analysis , Proteome/analysis , Adult , Cervix Mucus/metabolism , Female , Humans , Proteins/chemistry , Proteins/metabolism , Proteome/chemistry , Proteome/metabolism , Proteomics
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