Diagnosis Of Ovarian Tumors Research Articles

EP30.11: Two‐step strategy for optimising the diagnosis of borderline ovarian tumours

EP30.11: Two‐step strategy for optimising the diagnosis of borderline ovarian tumours

The Contribution of Lipidomics in Ovarian Cancer Management: A Systematic Review.

Lipidomics is a comprehensive study of all lipid components in living cells, serum, plasma, or tissues, with the aim of discovering diagnostic, prognostic, and predictive biomarkers for diseases such as malignant tumors. This systematic review evaluates studies, applying lipidomics to the diagnosis, prognosis, prediction, and differentiation of malignant and benign ovarian tumors. A literature search was performed in PubMed, Science Direct, and SciFinder. Only publications written in English after 2012 were included. Relevant citations were identified from the reference lists of primary included studies and were also included in our list. All studies included referred to the application of lipidomics in serum/plasma samples from human cases of OC, some of which also included tumor tissue samples. In some of the included studies, metabolome analysis was also performed, in which other metabolites were identified in addition to lipids. Qualitative data were assessed, and the risk of bias was determined using the ROBINS-I tool. A total of twenty-nine studies were included, fifteen of which applied non-targeted lipidomics, seven applied targeted lipidomics, and seven were reviews relevant to our objectives. Most studies focused on the potential application of lipidomics in the diagnosis of OC and showed that phospholipids and sphingolipids change most significantly during disease development. In conclusion, this systematic review highlights the potential contribution of lipids as biomarkers in OC management.

Germ Cell Tumors Ovary “Dysgerminoma” with Mayer-Rokitansky-Kuster-Hauser Syndrome

Background: Ovarian Germ Cell Tumors originate from primitive germinal cells and can be either malignant or benign. MRKH syndrome is characterized by congenital hypoplasia of the uterus and upper vagina and can occur due to disrupted fusion of the Mullerian ducts. Diagnosis of ovarian tumors in MRKH patients is difficult but can be characterized by abdominal pain and distended.Objective: The aim of this case report is to explain the diagnostic methods and interventions performed in patients with ovarian tumors and MRKH syndrome.Case Presentation: A 25-year-old female came to the hospital with complaints of a lower abdominal lump three months ago. It was followed by severe pain, weight loss, shortness of breath, and yellowish vaginal discharge. Physical examination showed anemia, obesity, and a vagina size of 7cm with a probe. Chest X-ray showed a mass in the mediastinum and pleural effusion, and USG showed suspicion of an ovarian cyst and uterine agenesis.Conclusion: Ovarian tumor with MRKH syndrome is a rare case. Diagnosis was based on the patient’s history, clinical findings, radiologic examination, and confirmed with laparotomy and histopathology. Regular examinations are recommended to prevent and identify genital tract problems and pelvic diseases in women, especially adolescents.

Mayer–Rokitansky–Kuster–Hauser syndrome (MRKH) and Ovarian Cyst : A Rare Case

Background: In the most recent publications on Mayer-Rokitansky-Küster-Hauser (MRKH) syndrome, the uterine remnants and ovaries in patients may develop uterine remnant leiomyoma, adenomyosis, or ovarian tumor, and this can lead to problems in differential diagnosis. Here we summarize the diagnosis methods and available interventions for ovarian tumor in MRKH syndrome, with emphasis on the relevant clinical findings and illustrative relevant case. According to the clinical findings and illustrative relevant case, with the help of imaging techniques, ovarian tumors can be detected in the pelvis in patients with MRKH syndrome and evaluated in terms of size. Laparoscopy could further differentiate ovarian tumors into different pathological types. In addition, laparoscopic surgery not only is helpful for the diagnosis of MRKH combined ovarian tumor, but also has a good treatment role for excising ovarian tumor at the same time. Moreover, laparoscopic removals of ovarian tumor can be considered as a safe and reliable treatment for conservative management.Case report: 25-year-old nulliparous woman with female phenotype and normal secondary sex development presented with complaints of primary amenorrhea and difficulty in sexual intercourse. Physical examination revealed that there were no vaginal and uterus structure. A transvaginal ultrasound examination showed absent of uterine . normal right ovariesand cystic mass in left adnexa . Laparoscopy cystectomy and amniograft vaginoplasty  was performed in this patient. Histopatology result showed Cystadenoma srous ovarii with borderline serous ovarii.Conclusion: Rarely ovarian cyst was associated with MRKH . Although malformation are associated with MRKH syndrome. Association  beetween MRKH syndrome and ovarian cyst can be foundÂ

Preliminary Results of Deep Learning Approach for Preoperative Diagnosis of Ovarian Cancer Based on Pelvic MRI Scans.

To predict the pathological diagnosis of ovarian tumors using preoperative MRI images, using deep learning models. A total of 185 patients were enrolled, including 40 with ovarian cancers, 25 with borderline malignant tumors, and 120 with benign tumors. Using sagittal and horizontal T2-weighted images (T2WI), we constructed the pre-trained convolutional neural networks to predict pathological diagnoses. The performance of the model was assessed by precision, recall, and F1-score on macro-average with 95% confidence interval (95%CI). The accuracy and area under the curve (AUC) were also assessed after binary transformation by the division into benign and non-benign groups. The macro-average accuracy in the three-class classification was 0.523 (95%CI=0.504-0.544) for sagittal images and 0.426 (95%CI=0.404-0.446) for horizontal images. The model achieved a precision of 0.63 (95%CI=0.61-0.66), recall of 0.75 (95%CI=0.72-0.78), and F1 score of 0.69 (95%CI=0.67-0.71) for benign tumor. Regarding the discrimination between benign and non-benign tumors, the accuracy in the binary-class classification was 0.628 (95%CI=0.592-0.662) for sagittal images and AUC was 0.529 (95%CI=0.500-0.557). Using deep learning, we could perform pathological diagnosis from preoperative MRI images.

Radiomics in the CT diagnosis of ovarian cystic malignancies - a pilot study.

The conventional computed tomography (CT) appearance of ovarian cystic masses is often insufficient to adequately differentiate between benign and malignant entities. This study aims to investigate whether texture analysis of the fluid component can augment the CT diagnosis of ovarian cystic tumors. Eighty-four patients with adnexal cystic lesions who underwent CT examinations were retrospectively included. All patients had a final diagnosis that was established by histological analysis in forty four cases. The texture features of the lesions content were extracted using dedicated software and further used for comparing benign and malignant lesions, primary tumors and metastases, malignant and borderline lesions, and benign and borderline lesions. Texture features' discriminatory ability was evaluated through univariate and receiver operating characteristics analysis and also by the use of the k-nearest-neighbor classifier. The univariate analysis showed statistically significant results when comparing benign and malignant lesions (the Difference Variance parameter, p=0.0074) and malignant and borderline tumors (the Correlation parameter, p=0.488). The highest accuracy (83.33%) was achieved by the classifier when discriminating primary tumors from ovarian metastases. Texture parameters were able to successfully discriminate between different types of ovarian cystic lesions based on their content, but it is not entirely clear whether these differences are a result of the physical properties of the fluids or their appartenance to a particular histopathological group. If further validated, radiomics can offer a rapid and non-invasive alternative in the diagnosis of ovarian cystic tumors.

Peculiarities of differential diagnosis of malignant and benign ovarian tumors in girls (case report of immature teratoma of the ovary)

Germ cell tumors are a heterogeneous group of tumors differentiating from primary germ cells of embryonic gonads and their derivatives. This case report demonstrates the diagnostic algorithm and diagnostic tactics of a rarely encountered histological type of ovarian germ cell tumor.

Comparison of the diagnostic accuracy of HE4 with CA125 and validation of the ROMA index in differentiating malignant and benign epithelial ovarian tumours among patients in Lagos, Nigeria.

This prospective cross-sectional study compared the diagnostic accuracy of human epididymal protein 4 (HE4) with cancer antigen 125 (CA 125) and validates the risk of malignancy algorithm (ROMA) in differentiating benign from malignant ovarian tumours. The study population included 112 women with an ultrasound diagnosis of an adnexal mass, out of whom 49 women had a diagnosis of ovarian cancer following optimal debulking surgery, and 63 women had a diagnosis of benign ovarian tumour. All diagnosis was confirmed by histopathological analysis. Serum HE4 and CA 125 were assessed preoperatively according to the manufacturer's instructions. CA 125 and HE4 cut-offs were 35 U/mL and 70 pM/L respectively. Serum CA 125 and HE4 were significantly higher in ovarian cancer patients compared to those with benign ovarian tumours (p < 0.001 and p < 0.000, respectively). HE4 had higher sensitivity (77.5% versus 69.4%), specificity (96.8% versus 82.5%), positive predictive value (PPV) (95% versus 75.6%) and negative predictive value (84.7% versus 77.6%) than CA 125. When the two markers were combined with each other in the ROMA index, Specificity and PPV reached 100% each. In the receiver operative characteristics analysis, the area under the curve for CA 125 was 0.679 (95% CI 0.566-0.791, p = 0.001), HE4 was 0.845 (95% CI 0.760-0.930, p = 0.000) and ROMA was 0.902 (95% CI 0.851-0.998, p = 0.000) and this was statistically significant (p < 0.001). Conclusively, HE4 performed better than CA 125 in differentiating benign from malignant ovarian tumours and the combination of the two biomarkers improved the detection of ovarian cancer. In addition, the cut off values corresponding to the highest accuracy for CA 125 and HE4 were 126 U/mL and 42 pM/L respectively in this study. The value for CA 125 is much higher while that of HE4 is much lower than the reference values obtained predominantly from the white population.

The role of five plasma proteins in the diagnosis of ovarian tumors

Ovarian cancer (OC) is one of the leading causes of deaths of women in the world. In Bulgaria, about 800 women are diagnosed with OC every year. With early detection, OC is treatable and has a high survival rate. Unfortunately, there are no effective strategies for disease prevention. This necessitates the search for effective screening methods to improve early diagnosis by identifying biomarkers with maximum specificity and sensitivity. Many serum tumor markers are known, but most are not tumor specific. This brief overview aimed to briefly describe biomarkers and summarize data from various studies that suggest that appropriately combined plasma concentrations can be used to increase the efficiency of OC detection: carcinoma antigen (CA 125; a protein produced by ovarian tumor cells), transthyretin (TTR; a transport protein for retinol-binding protein and thyroxine), Apo-A1 lipoprotein (the main protein component of HDL lipoproteins) and transferrin (plays a central role in the metabolism of iron and β2-microglobulin, belongs to the HLA system, is related to immune responses in some tissue-dependent processes). The study of these proteins will reveal the potential of the five biomarkers in a panel for the prediction and timely diagnosis of ovarian malignancy.

CASE REPORT- ADOLESCENT BORDERLINE OVARIAN TUMOUR WITH TORSION: A SURGICAL MANAGEMENT.

Background: Ovarian cancer is one of the common and the leading cause of death among women with gynecological malignancies. Ovarian tumor may sometimes present with torsion which is a gynecological emergency.Case presentation: We report a case of 16 years old girl who presented with acute pain in right sided lower abdomen with vomiting. Ultrasonography suggestive of bilateral complex ovarian cysts with right ovarian cyst torsion. The patient underwent right sided salpingo-oophorectomy and left sided multiple cystectomy with removal of excrescence. The conrmed diagnosis of serous borderline ovarian tumor with torsion was made retrospectively with pathology report. Ultrasonography, clinical presentation and pat Conclusions: hological examination play important role in diagnosis and hence in treatment of ovarian tumors

Clinical Value of Combined Multi-Indicator Tests in Diagnosis of Benign Ovarian.

To investigate the existence and degree of correlation between benign ovarian tumors and physiological indicators such as reproductive hormones and tumor markers. A total of 150 patients with benign ovarian tumors admitted to Jiaxing First Hospital between January 1, 2019, and May 30, 2021, were enrolled as research subjects, while 104 healthy women were enrolled in the control group. Comparative analysis of the correlation between the reproductive hormones LH, FSH, T, E2, and the tumor indicators AMH, AFP, CEA, CA125, and CA199 between the groups was performed. There was no statistical difference in LH, FSH, T, AMH, and CEA expression levels between the experimental and control groups (p≥0.05); E2, CA125, and CA199 levels were higher significantly in the experimental group than in the control group (P<0.001); AFP levels were significantly lower in the experimental group than in the control group (P<0.05). CA125 (0.762) had the highest AUC when diagnosing the value of each index of E2, CA125, and CA199 for benign ovarian tumors. CA125 had the highest sensitivity (56.7%), followed by E2 (50.0%); CA199 had the highest specificity (84.5%), followed by CA125 (83.7%). The combined diagnosis of benign ovarian tumors was performed using different combinations of the indicators. When the two indicators were combined for diagnosis, the combination of E2 + CA199 had the highest sensitivity (82.6%), whereas the combination of CA125 + CA199 had the largest AUC (0.783) and the highest specificity (86.4%). The combined diagnosis of E2+CA125+CA199 had a higher AUC than the combined diagnosis of the two indicators (0.805), with a sensitivity of 77.2%, and a specificity of 70.9%. The most relevant factors for benign ovarian tumors are E2, CA125, and CA199 and the combination of these three indicators has the highest AUC for disease prediction while increasing the detection rate of benign ovarian tumors.

AI diagnostic performance based on multiple imaging modalities for ovarian tumor: A systematic review and meta-analysis.

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. 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. 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. The models based on ultrasound have the best performance in diagnostic of ovarian cancer.

Abstract 5549: Normalized platelet splicing junction count is a novel biomarker for diagnosis of ovarian tumors

Abstract Owing to a lack of disease-specific symptoms and effective screening tools, most ovarian cancers are diagnosed at an advanced stage, resulting in high recurrence and mortality rates. Recently, there has been an effort to diagnose ovarian cancer utilizing platelets, which are crucial in the immunology of oncogenesis. These transcriptome-wise techniques, however, had two significant drawbacks: utilizing a normalization method that can be altered depending on the content of the samples in the dataset and using an excessive number of features. We, therefore, present a method for assessing the existence of ovarian cancer or benign tumors by utilizing a fewer features with a sampling invariant normalization technique. First, we downloaded platelet transcriptome data of patients with ovarian cancer (n=28), benign ovarian tumor (n=17), and their healthy counterparts (n=204) from GEO (GSE158508 and GSE89843 - PRJNA353588 and PRJNA353588). Second, for external validation, we prospectively enrolled patients with ovarian cancer (n=4) and benign ovarian tumors (n=9), and healthy women (n=14) at Seoul National University Hospital (SNUH), collected their blood samples, and obtained platelet transcriptome data. The normalization was performed using commonly and invariantly existing 20 splice junctions in the isolated platelet of tumor and normal samples. Herein, 319 splice junctions were selected as features for the SVM model diagnosing the existence of an ovarian tumor. Feature selection and model development were conducted using a training set (n=152) and model performance was assessed from a separate test set (n=97). SVM classifier that utilized our splice junction-based biomarkers (20 for normalization, 319 for tumor classification) demonstrated 93.8% (71.7 - 98.9%) sensitivity, 100.0% (95.5 - 100.0%) specificity, and 1.0 of AUC in the test data set with a predetermined 0.5 cut-off value. Considering the difference of sequencing raw data between the open source and newly collected sample in terms of read length and read types (single-end vs paired-end), the SVM model from the open source was not suitable to the independent validation set. Thus, we train new SVM models using the same splice junctions with LOOCV in SNUH dataset. As the results, the model showed its diagnostic performance with 92.3% (66.7 - 98.6%) sensitivity and 92.9% (68.6 - 98.7%) specificity. We found novel splice junction-based biomarkers for the early detection or diagnosis of ovarian tumors. Although possible confounding factors such as ethnic variance may affect the performance of the models using these biomarkers, since the data preprocessing procedure is sampling invariant, the inclusion of samples from the same population would not affect our classification results. Furthermore, suggested biomarkers can be utilized during medical checkups of women without any symptoms to find women with ovarian tumors, whether benign or malignant. Citation Format: Eunyong Ahn, Se Ik Kim, Sungmin Park, Sarah Kim, Seung Jin Yang, Yeochan Kim, Dong Won Hwang, Heeyeon Kim, HyunA Jo, Untack Cho, Juwon Lee, Yong-Sang Song, TaeJin Ahn. Normalized platelet splicing junction count is a novel biomarker for diagnosis of ovarian tumors. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 5549.

Therapeutic Management and Prognostic Factors for Ovarian Malignant Tumours in Adolescents: A Comprehensive Review of Current Guidelines.

Background: Ovarian malignant tumours are rarely diagnosed in adolescents but may have a significant impact on their survival, future fertility and quality of life. The management of such cases is rather complex and requires expertise and careful planning according to scarce existing evidence and recommendations. Objective: The aim of this study was to review and compare recommendations from published guidelines regarding the diagnosis, prognosis and treatment of malignant ovarian tumours in adolescents. Evidence acquisition: A comparative descriptive/narrative review of guidelines issued by L'Observatoire des Tumeurs Malignes Rares Gynécologiques, the British Society for Paediatric & Adolescent Gynaecology, the European Society for Medical Oncology, the European Society of Gynecological Oncology-European Society for Paediatric Oncology and the European Cooperative Study Group for Pediatric Rare Tumors was conducted. Results: All guidelines recommend a thorough diagnostic work-up, consisting of both imaging tests and serum tumour marker measurement, as well as the use of immunohistochemical methods to confirm the diagnosis and complete surgical staging prior to constructing the treatment plan. There is a lack of recommendations regarding the assessment of prognostic factors, with only one guideline providing detailed information. Treatment strategies, as suggested by the majority of guidelines and with only a few discrepancies between them, should include both surgery and adjuvant therapies, mainly chemotherapy, with great emphasis on fertility preservation when it is considered oncologically safe and on the significance of regular and long-term follow-up. Conclusions: There is a significant degree of agreement among recommendations of existing guidelines. The reported differences, although limited, highlight the need for the adoption of an international consensus in order to further improve the management of adolescent ovarian cancer.

Advances in diagnosis, clinical management and molecular characterization of ovarian Brenner tumors

Brenner tumors is a very uncommon ovarian epithelial neoplasm, morphologically resembling the transitional cell neoplasm of urinary tract. It is further classified into three subtypes as a disease spectrum based...

Histological Pattern of Ovarian Tumor in Reproductive age group in a Tertiary Care Hospital

Objectives: To find out the frequency of histological pattern of ovarian tumor in reproductive age group in a tertiary care Hospital. Study Design: Cross sectional study. Place &amp; duration of study: Department of Obstetrics &amp; Gynecology, Liaquat University of medical and health sciences hospital, Hyderabad from October 2021 to September 2022. Methodology: All patients with ovarian tumor in reproductive age admitted during study periods were included in study. The diagnosis of ovarian tumors was suspected form clinical features and ultrasound findings and confirmed on histopathology. Data analysis was performed on SPSS version 24. Chi-Square test of significance (chi square test) was applied to see association among categorical variables. P value ≤ 0.05 was taken as significant. Results: Similarly pattern of ovarian tumors was as followed; such as cord stromal, germ cell and epithelial ovarian 50%, 33.96% and 16.04% respectively among 106 women. High incidence of sex cord and epithelial tumors was observed in older age (above 40 years) group 25% and 68.8% respectively but germ cell tumor was observed mostly in younger age group 92.6% in 21-30 years as compared to older age women and 6.3% in above 40 years. Practical Implication: Our research establishes the basis for future studies on ovarian tumor in Pakistan, allowing for better health policy decisions to be made based on this information. Conclusion: Histopathological examination shows that epithelial and germ cell tumors collectively constitute large number of cases specifically in older age. Histological examination in early stage of life is important therapeutically and prognostically. Keywords: Ovarian tumors, Malignancies, Histological pattern, Reproductive age, Parity

Machine Learning Approach for Comparative Analysis of De-Noising Techniques in Ultrasound Images of Ovarian Tumors

Ovarian abnormalities such ovarian cysts, tumors, and polycystic ovaries are one of the serious disorders affecting women's health. In ultrasound imaging of ovarian abnormalities, noise during capturing of the image and its transmission process frequently corrupts the image. In order to make the best judgments possible at the appropriate moment, ovarian cysts in females must be accurately detected. In computer aided diagnosis of ovarian tumors, preprocessing is a very important step. In preprocessing, de-noising of medical images is a particularly a difficult task since it must be done while maintaining image features that are essential for diagnosis. In this research work we are using various denoising filters on ultrasound images of ovarian tumors. For different noise denoising techniques, performance measures like MSE, PSNR, SSIM, and UQI etc. are calculated. According to experimental findings, Block matching 3-D filter outperforms all other methods. Radiologists can better diagnose the condition with the use of this computer-assisted system.

Combined use of CA125, neutrophil/lymphocyte ratio and platelet/lymphocyte ratio for the diagnosis of borderline and malignant epithelial ovarian tumors

BackgroundThe mortality rate of ovarian cancer ranks first among three common gynecological malignant tumors due to insidious onset and lack of effective early diagnosis methods. Borderline epithelial ovarian tumor (BEOT) is a type of low malignant potential tumor that is typically associated with better outcomes than ovarian cancer. However, BEOTs are easily confused with benign and malignant epithelial ovarian tumors (EOTs) due to similar clinical symptoms and lack of specific tumor biomarkers and imaging examinations. Notably, a small subset of BEOTs will transform into low-grade serous ovarian carcinoma with a poor prognosis. Therefore, searching for potential biomarkers that can be easily obtained and accurately identify malignant epithelial ovarian tumors (MEOTs) as well as BEOTs is essential for the clinician. Cancer antigen 125 (CA125) is a commonly used biomarker for the diagnosis of EOTs in the preoperative scenario but has low sensitivity and specificity. Nowadays, inflammatory biomarkers including inflammatory cell counts and derived ratios such as neutrophil/lymphocyte ratio (NLR) and platelet/lymphocyte ratio (PLR) have been proved to be associated with tumor progression and poor prognosis, and were considered to be the most economically potential surrogate biomarkers for various malignancies. The purpose of this study was to find appropriate combinations of inflammatory and tumor biomarkers to improve the diagnostic efficiency of EOTs, especially the BEOTs.ResultsCA125, NLR and PLR increased steadily among benign, borderline and malignant EOTs and tended to be higher in advanced (stage III-IV) and lymph node metastasis MEOT groups than in early stage (stage I-II) and non-lymph node metastasis MEOT groups. CA125, NLR and PLR could be used separately in the differentiation of EOTs but could not take into account both sensitivity and specificity. The combined use of CA125, NLR and PLR was evaluated to be more efficient, especially in the identification of BEOTs, with both high sensitivity and high specificity.ConclusionsThe levels of CA125, NLR and PLR were closely related to the nature of EOTs and malignant progression of MEOTs. The combination of CA125, NLR and PLR was more accurate in identifying the nature of EOTs than either alone or double combination, especially for BEOTs.

Improving the Segmentation Accuracy of Ovarian-Tumor Ultrasound Images Using Image Inpainting.

Diagnostic results can be radically influenced by the quality of 2D ovarian-tumor ultrasound images. However, clinically processed 2D ovarian-tumor ultrasound images contain many artificially recognized symbols, such as fingers, crosses, dashed lines, and letters which assist artificial intelligence (AI) in image recognition. These symbols are widely distributed within the lesion's boundary, which can also affect the useful feature-extraction-utilizing networks and thus decrease the accuracy of lesion classification and segmentation. Image inpainting techniques are used for noise and object elimination from images. To solve this problem, we observed the MMOTU dataset and built a 2D ovarian-tumor ultrasound image inpainting dataset by finely annotating the various symbols in the images. A novel framework called mask-guided generative adversarial network (MGGAN) is presented in this paper for 2D ovarian-tumor ultrasound images to remove various symbols from the images. The MGGAN performs to a high standard in corrupted regions by using an attention mechanism in the generator to pay more attention to valid information and ignore symbol information, making lesion boundaries more realistic. Moreover, fast Fourier convolutions (FFCs) and residual networks are used to increase the global field of perception; thus, our model can be applied to high-resolution ultrasound images. The greatest benefit of this algorithm is that it achieves pixel-level inpainting of distorted regions without clean images. Compared with other models, our model achieveed better results with only one stage in terms of objective and subjective evaluations. Our model obtained the best results for 256 × 256 and 512 × 512 resolutions. At a resolution of 256 × 256, our model achieved 0.9246 for SSIM, 22.66 for FID, and 0.07806 for LPIPS. At a resolution of 512 × 512, our model achieved 0.9208 for SSIM, 25.52 for FID, and 0.08300 for LPIPS. Our method can considerably improve the accuracy of computerized ovarian tumor diagnosis. The segmentation accuracy was improved from 71.51% to 76.06% for the Unet model and from 61.13% to 66.65% for the PSPnet model in clean images.

Computed Tomography Indicators for Differentiating Stage 1 Borderline Ovarian Tumors from Stage I Malignant Epithelial Ovarian Tumors.

Preoperative diagnosis of borderline ovarian tumors (BOTs) is of increasing concern. This study aimed to determine computed tomography (CT) features in differentiating stage 1 BOTs from stage I malignant epithelial ovarian tumors (MEOTs). A total of 170 ovarian masses (97 BOTs and 73 MEOTs) from 141 consecutive patients who underwent preoperative CT imaging were retrospectively analyzed. Two readers independently and retrospectively reviewed quantitative and qualitative CT features. Multivariate logistic analysis demonstrated that a larger tumor size (p = 0.0284 for reader 1, p = 0.0391 for reader 2) and a smaller solid component (p = 0.0007 for reader 1, p = 0.0003 for reader 2) were significantly associated with BOTs compared with MEOTs. In the subanalysis of cases with a solid component, smaller (p = 0.0092 for reader 1, p = 0.0014 for reader 2) and ill-defined (p = 0.0016 for reader 1, p = 0.0414 for reader 2) solid component was significantly associated with BOTs compared with MEOTs. Tumor size and the size and margin of the solid component were useful for differentiating stage 1 BOTs from stage 1 MEOTs on CT images.