Malignant Ovarian Tumors Research Articles

Developing a deep learning model for predicting ovarian cancer in Ovarian-Adnexal Reporting and Data System Ultrasound (O-RADS US) Category 4 lesions: A multicenter study

PurposeTo develop a deep learning (DL) model for differentiating between benign and malignant ovarian tumors of Ovarian-Adnexal Reporting and Data System Ultrasound (O-RADS US) Category 4 lesions, and validate its diagnostic performance.MethodsA retrospective analysis of 1619 US images obtained from three centers from December 2014 to March 2023. DeepLabV3 and YOLOv8 were jointly used to segment, classify, and detect ovarian tumors. Precision and recall and area under the receiver operating characteristic curve (AUC) were employed to assess the model performance.ResultsA total of 519 patients (including 269 benign and 250 malignant masses) were enrolled in the study. The number of women included in the training, validation, and test cohorts was 426, 46, and 47, respectively. The detection models exhibited an average precision of 98.68% (95% CI: 0.95–0.99) for benign masses and 96.23% (95% CI: 0.92–0.98) for malignant masses. Moreover, in the training set, the AUC was 0.96 (95% CI: 0.94–0.97), whereas in the validation set, the AUC was 0.93(95% CI: 0.89–0.94) and 0.95 (95% CI: 0.91–0.96) in the test set. The sensitivity, specificity, accuracy, positive predictive value, and negative predictive values for the training set were 0.943,0.957,0.951,0.966, and 0.936, respectively, whereas those for the validation set were 0.905,0.935, 0.935,0.919, and 0.931, respectively. In addition, the sensitivity, specificity, accuracy, positive predictive value, and negative predictive value for the test set were 0.925, 0.955, 0.941, 0.956, and 0.927, respectively.ConclusionThe constructed DL model exhibited high diagnostic performance in distinguishing benign and malignant ovarian tumors in O-RADS US category 4 lesions.

Analysis of Diagnostic Efficacy of the International Ovarian Tumor Analysis ADNEX Model and the ACR O-RADS US (Ovarian-Adnexal Reporting and Data System) for Benign and Malignant Ovarian Tumors: A Retrospective Study in a Tumor Center in Northeast China.

This study is to analyze and compare the diagnostic efficacy of the ADNEX model and O-RADS in Northeast China for benign and malignant ovarian-adnexal tumors. From July 2020 to February 2022, ultrasound images of 312 ovarian-adnexal masses included in the study were analyzed retrospectively, and the properties of these masses were identified using the ADNEX model and O-RADS. The diagnostic efficiency of the ADNEX model and O-RADS was analyzed using a ROC curve, and the capacities of the two models in differentiating benign and malignant ovarian masses at the optimum cutoff value were compared, as well as the consistency of their diagnosis results was evaluated. The study included 312 ovarian-adnexal masses, including 145 malignant masses and 167 benign masses from 287 patients with an average age of (46.8 ± 11.3) years. The AUC of the ADNEX model was 0.974, and the optimum cutoff value was the risk value > 24.2%, with the corresponding sensitivity and specificity being 97.93 and 86.83, respectively. The AUC of the O-RADS was 0.956, and the optimum cutoff value was > O-RADS 3, with the corresponding sensitivity and specificity being 97.24 and 85.03, respectively. The AUCs of the two models were 0.924 and 0.911 at the optimum cutoff values, with no statistical differences between them (P = 0.284). Consistency analysis: the kappa values of the two models for the determination and pathological results of masses were 0.840 and 0.815, respectively, and that for the diagnostic outcomes was 0.910. Both the ADNEX model and O-RADS had good diagnostic performance in people from Northeast China. Their diagnostic capabilities were similar, and diagnostic results were highly consistent at the optimum cutoff values.

Endometrioid Carcinoma Four Years After Hysterectomy: A Rare Case Report

It is quite a fascinating case as we have recently experienced in Mugda Medical College Hospital, a tertiary level teaching hospital in Dhaka, Bangladesh, that a woman was diagnosed and treated for endometrioid adenocarcinoma four years after her vaginal hysterectomy had been done. Our patient was presented with abdominal pain for nearly 2 weeks along with nausea, vomiting and generalized weakness. Ultrasound evaluation revealed a complex pelvic mass (8.6cm × 4.9cm). Chest x-ray revealed a left sided pleural effusion, while CT scan of the abdomen revealed heterogeneously enhancing complex pelvic mass and mild ascites. After a clinical correlation with all the pathological investigations, the patient was advised for a core biopsy. Ultrasonogram guided core biopsy was done. Histopathological examination showed a malignant ovarian epithelial tumor. Then she underwent an interventional or surgical procedure, which included an exploratory laparotomy, bilateral salpingo-oophorectomy with bilateral pelvic lymph node dissection and infracolic omentectomy done under general anesthesia. The final histopathological examination of the removed mass showed an endometrioid adenocarcinoma (Grade 2). Meanwhile, she was referred for radiation therapy (both vaginal brachytherapy and external pelvic radiation) after the patient had recovered from surgery. We will discuss this case as a part of our clinical interest and continuing education for our clinicians and residents. Mugda Med Coll J. 2024; 7(1): 53-55

Comparison of overall and patterns of care in patients with a malignant ovarian germ cell tumor by age in the United States: a National Cancer Database (2004–2016) analysis

BackgroundWomen aged ≥40 years diagnosed with a malignant ovarian germ cell tumor are more likely to have poor outcomes than their younger counterparts (aged 15–39 years).ObjectiveWe used the National Cancer...

Laparoscopic-assisted Fertility-sparing Surgery for Growing Teratoma Syndrome of the Ovary: Experience from a Tertiary Center

Study ObjectiveThe main objective is to evaluate the feasibility of laparoscopic fertility-sparing surgery in women with growing teratoma syndrome. DesignRetrospective cohort study. SettingChinese tertiary university hospital. PatientsPatients with growing teratoma syndrome who underwent fertility-sparing surgery between January 2015 and August 2023. InterventionsBaseline characteristics and surgical outcomes were evaluated, including clinical information, surgical procedures, operative time, intraoperative blood loss, complications, length of hospital stay, and follow-up information. Measurement and Main ResultsTwenty-six patients with ovarian growing teratoma syndrome underwent fertility-sparing surgery: 12 had laparoscopic surgery and 14 underwent laparotomic surgery. In the laparoscopic group, the median age of the patients during initial management of immature teratoma or mixed malignant ovarian germ cell tumor was 14.0 years (interquartile range: 13.0-24.5 years). Eleven patients were nulliparous. The primary ovarian tumor was pure immature teratoma in 10 patients and mixed ovarian germ cell tumor in 2 patients. Complete laparoscopic tumor resection was achieved in 11 patients. Patients in laparoscopic group had shorter median operative time (76.5 vs 180.0 minutes, p = 0.001), lower estimated blood loss (20.0 vs 400.0 ml, p < 0.001), decreased postoperative hospital stay (2.0 vs 7.0 days, p < 0.001), compared with laparotomic surgery. There was no conversion to laparotomy and no perioperative complications. Histological examination confirmed mature teratoma in all cases. During a median follow-up of 21.9 months (interquartile range: 7.6-44.9 months), 11 patients were alive without disease and 1 was alive with disease. One pregnancy was achieved postoperatively. ConclusionLaparoscopic fertility-sparing surgery may represent a feasible option in well-selected patients with ovarian growing teratoma syndrome. Surgery should be performed in gynecologic oncology centers by experienced staff trained in endoscopic procedures. More research and long-time follow-up are needed to determine the oncological outcomes and safety of laparoscopic surgery in this population.

Comparing the Accuracy, Sensitivity, Specificity, and Predictive Values of Two Biomarkers in Detecting Malignant Ovarian Tumors

Introduction: Ovarian cancer presents a significant challenge in oncology due to its high mortality rates primarily stemming from late-stage diagnoses. Early detection through reliable biomarkers such as CA-125 and IL-6 is crucial for improving patient outcomes. Aim of the study: This study aimed to compare the accuracy, sensitivity, specificity, and predictive values of CA-125 and IL-6 in detecting malignant ovarian tumors. Methods: A cross-sectional analytical study was conducted at the Department of Gynecological Oncology, BSMMU, and NICRH, Dhaka, Bangladesh. A total of 94 women with suspected ovarian tumors underwent preoperative assessment of CA-125 and IL-6 levels. Receiver-operator characteristic (ROC) curves were utilized to determine optimal cut-off values. Result: In this study of 94 women with ovarian tumors, we evaluated the diagnostic performance of CA-125 and IL-6 biomarkers. CA-125 showed a sensitivity of 83.0% and specificity of 51.2% at a cut-off of ≥89.0 u/ml, while IL-6 exhibited 84.9% sensitivity and 80.5% specificity at ≥9.5 pg/ml. Combining CA-125 and IL-6 improved specificity to 95.1%, maintaining a sensitivity of 77.4%. These findings underscore the potential of biomarker combinations in enhancing diagnostic accuracy for detecting malignant ovarian tumors. Conclusion: IL-6 exhibited higher sensitivity and specificity compared to CA-125 in detecting malignant ovarian tumors. The combined use of both biomarkers improved specificity while maintaining reasonable sensitivity, suggesting their potential utility in clinical practice for early detection and management of ovarian malignancies.

Integration of a Comprehensive Ultrasound Assessment in the Prognostic Modeling of benign and Malignant Ovarian Tumors

Ovarian tumors can be challenging to diagnose and treat, especially in areas with limited access to advanced medical care. During routine prenatal ultrasounds, abnormal growths in the ovaries can be unexpectedly discovered. In clinical practice, it is essential to accurately differentiate between benign and malignant ovarian tumors in order to develop appropriate treatment plans. Ultrasound imaging has become the preferred diagnostic tool. The aim of this article is to examine the ultrasound characteristics of ovarian and adnexal growths and to explore how these characteristics, along with other clinical factors, can aid in predicting the likelihood of ovarian cancer.

Trastuzumab deruxtecan (DS-8201a), a HER2-targeting antibody-drug conjugate, demonstrates in vitro and in vivo antitumor activity against primary and metastatic ovarian tumors overexpressing HER2.

High-grade serous ovarian cancer (HGSOC) and ovarian clear cell carcinoma (CC), are biologically aggressive tumors endowed with the ability to rapidly metastasize to the abdominal cavity and distant organs. About 10% of HGSOC and 30% of CC demonstrate HER2 IHC 3 + receptor over-expression. We evaluated the efficacy of trastuzumab deruxtecan (T-DXd; DS-8201a), a novel HER2-targeting antibody-drug conjugate (ADC) to an ADC isotype control (CTL ADC) against multiple HGSOC and CC tumor models. Eleven ovarian cancer cell lines including a matched primary and metastatic cell line established from the same patient, were evaluated for HER2 expression by immunohistochemistry and flow cytometry, and gene amplification by fluorescence in situ hybridization assays. In vitro experiments demonstrated T-DXd to be significantly more effective against HER2 3 + HGSOC and CC cell lines when compared to CTL ADC (p < 0.0001). T-DXd induced efficient bystander killing of HER2 non-expressing tumor cells when admixed with HER2 3 + cells. In vivo activity of T-DXd was studied in HER2 IHC 3 + HGSOC and CC mouse xenograft models. We found T-DXd to be significantly more effective than CTL ADC against HER2 3 + HGSOC (KR(CH)31) and CC (OVA10) xenografts with a significant difference in tumor growth starting at day 8 (p = 0.0003 for KR(CH)31, p < 0.0001 for OVA10). T-DXd also conferred a survival advantage in both xenograft models. T-DXd may represent an effective ADC against primary and metastatic HER2-overexpressing HGSOC and CC.

Nutritional and Inflammatory Indicators Aid in Differentiating Benign from Malignant Ovarian Neoplasms: Development and Validation of a Nomogram

Nutritional and Inflammatory Indicators Aid in Differentiating Benign from Malignant Ovarian Neoplasms: Development and Validation of a Nomogram

Primary ovarian cancer combined with primary fallopian tube cancer: A case report

BACKGROUND Low grade serous carcinoma of the ovary (LGSOC) is a rare type of epithelial ovarian cancer with a low incidence rate. The origin of ovarian cancer has always been a hot topic in gynecological oncology research, and some scholars believe that the origin of ovarian malignant tumors is the fallopian tubes. Primary fallopian tube cancer is the lowest incidence of malignant tumors in the female reproductive system. There are only a few reports in the literature, but the mortality rate is very high. But in clinical practice, fallopian tube cancer is very common, but in most cases, it is classified as ovarian cancer. CASE SUMMARY We report a 54 years old postmenopausal woman who was hospitalized with a lower abdominal mass and underwent surgical treatment. The final pathological confirmation was low-grade serous carcinoma of the right ovary and low-grade serous carcinoma of the left fallopian tube. No special treatment was performed after the surgery, and the patient was instructed to undergo regular follow-up without any signs of disease progression. CONCLUSION The prognosis of LGSOC is relatively good, over 80% of patients still experience disease recurrence.

Clinicopathological analysis of giant ovarian tumors

ObjectiveThis study aims to analyze giant ovarian tumors' clinical and pathological characteristics. Material and MethodsThis was an analytical observational study. Medical records of all patients with giant ovarian tumors who underwent surgery between January 2020 and June 2022 at Dr. Soetomo Academic Hospital, Surabaya, Indonesia, were analyzed. ResultsWe analyzed 63 patients with ovarian tumors measuring > 20 cm who underwent surgery at Dr. Soetomo Academic Hospital, Surabaya, Indonesia. The mean tumor size was 25.9 cm (largest size was 41 cm). There was no significant difference in tumor size between benign and malignant giant ovarian tumors (p = 0.261). Based on histopathological results, 66.67 % of giant ovarian tumors were malignant, 26.98 % were benign, and 6.35 % were borderline. Among the malignant tumors, the epithelial type accounted for 69 % of cases. Most giant ovarian tumors originated in the left adnexa (68.25 %). There was no significant difference in patient age (p = 0.511), tumor size (p = 0.168), malignancy (p = 0.303), and histopathological type (p = 0.232) regardless of adnexal side. CA125 levels did not differ significantly between malignant and benign giant ovarian tumors (p = 0.604). There was no correlation between malignant ovarian tumor size and CA125 levels, while there was a significant difference between CA125 levels and the adnexal side (p = 0.010). ConclusionsMost giant ovarian tumors were malignant, diagnosed at an early stage, and predominantly epithelial type. CA125 levels did not correlate with the size of malignant ovarian tumors. Most giant ovarian tumors originate in the left adnexa.

MR Relaxometry for Discriminating Malignant Ovarian Cystic Tumors: A Prospective Multicenter Cohort Study.

Endometriosis-associated ovarian cancer (EAOC) is a well-known type of cancer that arises from ovarian endometrioma (OE). OE contains iron-rich fluid in its cysts due to repeated hemorrhages in the ovaries. However, distinguishing between benign and malignant tumors can be challenging. We conducted a retrospective study on magnetic resonance (MR) relaxometry of cyst fluid to distinguish EAOC from OE and reported that this method showed good accuracy. The purpose of this study is to evaluate the accuracy of a non-invasive method in re-evaluating pre-surgical diagnosis of malignancy by a prospective multicenter cohort study. After the standard diagnosis process, the R2 values were obtained using a 3T system. Data on the patients were then collected through the Case Report Form (CRF). Between December 2018 and March 2023, six hospitals enrolled 109 patients. Out of these, 81 patients met the criteria required for the study. The R2 values calculated using MR relaxometry showed good discriminating ability with a cut-off of 15.74 (sensitivity 80.6%, specificity 75.0%, AUC = 0.750, p < 0.001) when considering atypical or borderline tumors as EAOC. When atypical and borderline cases were grouped as OE, EAOC could be distinguished with a cut-off of 16.87 (sensitivity 87.0%, specificity 61.1%). MR relaxometry has proven to be an effective tool for discriminating EAOC from OE. Regular use of this method is expected to provide significant insights for clinical practice.

Management of Bilateral Borderline Ovarian Tumor in a 26-year-old Patient in a Resource-Limited Setting

&amp;lt;i&amp;gt;Background&amp;lt;/i&amp;gt;: borderline ovarian tumors are rare epithelial tumors, more common before the age of 40, with challenging preoperative diagnosis. Our aim is to describe the management of a case in a resource-limited context in Burkina Faso. &amp;lt;i&amp;gt;Case report:&amp;lt;/i&amp;gt; a 26-year-old primigravida with a 4-year-old child presented with an abdominal mass evolving over one year in the context of secondary amenorrhea. Clinical examination revealed weight loss, cutaneous-mucosal pallor, increased abdominal volume with tender deep palpation, and an irregular, mildly firm mass around the umbilicus extending to the iliac fossae, with a depressed area along the midline. Speculum examination was normal. On vaginal examination, the cervix was unremarkable, the uterus was of normal size, but the lateral fornices and Douglas pouch were filled. Further investigations revealed microcytic hypochromic anemia and a borderline normal CA 125 level. The rest of the laboratory tests were normal. Abdominal and pelvic ultrasound and CT revealed a strong suspicion of a bilateral ovarian tumour, with no signs of secondary localisation. MRI was not available. The diagnosis of probably malignant bilateral ovarian tumour was retained, and laparotomy was indicated. When the parietal peritoneum was opened, two ovarian tumours with a budding appearance occupied the entire lower half of the abdomen. A part of the omentum was in contact with both masses. Bilateral adnexectomy, total hysterectomy and partial omentectomy were performed in the absence of an extemporaneous histological study. Histological study of the surgical specimens concluded that the tumour was borderline ovarian. Follow-up at 3 years showed no signs of recurrence or tumour extension. &amp;lt;i&amp;gt;Conclusion&amp;lt;/i&amp;gt;: borderline ovarian tumours are managed surgically, and the choice of conservative or radical surgery is not easy when there is a desire to have children, and when the ovaries are bilaterally involved. Fortunately, the vital prognosis is favourable.

Molecular and pathologic data to guide selection of patients with endometrioid endometrial cancer for ovarian preservation

ObjectivesTo investigate the association of molecular and pathologic factors with concurrent or recurrent ovarian disease to guide ovarian preservation in endometrioid endometrial cancer.MethodsPatients with endometrial cancer ≤50 years of age...

Frequency of exhausted and senescent CD4+ T cells in peripheral blood, specific to antigen-associated tumors (TPI1, PRDX2, A1AT, AHSG AND PPIA) and its impact and its association with the diagnosis of benign and malignant ovarian tumors

Frequency of exhausted and senescent CD4+ T cells in peripheral blood, specific to antigen-associated tumors (TPI1, PRDX2, A1AT, AHSG AND PPIA) and its impact and its association with the diagnosis of benign and malignant ovarian tumors

Research progress in endometriosis-associated ovarian cancer.

Endometriosis-associated ovarian cancer (EAOC) is a unique subtype of ovarian malignant tumor originating from endometriosis (EMS) malignant transformation, which has gradually become one of the hot topics in clinical and basic research in recent years. According to clinicopathological and epidemiological findings, precancerous lesions of ovarian clear cell carcinoma (OCCC) and ovarian endometrioid carcinoma (OEC) are considered as EMS. Given the large number of patients with endometriosis and its long time window for malignant transformation, sufficient attention should be paid to EAOC. At present, the pathogenesis of EAOC has not been clarified, no reliable biomarkers have been found in the diagnosis, and there is still a lack of basis and targets for stratified management and precise treatment in the treatment. At the same time, due to the long medical history of patients, the fast growth rate of cancer cells, and the possibility of eliminating the earliest endometriosis-associated ovarian cancer, it is difficult to find the corresponding histological evidence. As a result, few patients are finally diagnosed with EAOC, which increases the difficulty of in-depth study of EAOC. This article reviews the epidemiology, pathogenesis, risk factors, clinical diagnosis, new treatment strategies and prognosis of endometriosis-associated ovarian cancer, and prospects the future direction of basic research and clinical transformation, in order to achieve stratified management and personalized treatment of ovarian cancer patients.

Profiling of Tumour-Infiltrating Lymphocytes and Tumour-Associated Macrophages in Ovarian Epithelial Cancer-Relation to Tumour Characteristics and Impact on Prognosis.

Early evidence suggests a strong impact of tumour-infiltrating lymphocytes (TILs) on both the prognosis and clinical behaviour of ovarian cancer. Proven associations, however, have not yet translated to successful immunotherapies and further work in the field is urgently needed. We aimed to analyse the tumour microenvironment of a well-characterised cohort of ovarian cancer samples. Tumour markers were selected owing to their comparative underrepresentation in the current literature. Paraffin-embedded, formalin-fixed tumour tissue blocks of 138 patients representative of the population and including early stage disease were identified, stained for CD3, CD20, CD68 and CD163 and analysed for both the stromal and intertumoral components. Data were statistically analysed in relation to clinical details, histological subtype, borderline vs. malignant status, survival and management received. Mean stromal CD3, total CD3 count, mean stromal CD20 and total CD20 count all correlated negatively with survival. Malignant ovarian tumours consistently demonstrated significantly higher infiltration of all analysed immune cells than borderline tumours. Assessment of the stromal compartment produced a considerably higher proportion of significant results when compared to the intra-tumoural infiltrates. Customary assessment of solely intra-tumoural cells in advanced stage disease patients undergoing primary debulking surgery should be challenged, with recommendations for future scoring systems provided.

Ultrasound-based deep learning radiomics model for differentiating benign, borderline, and malignant ovarian tumours: a multi-class classification exploratory study.

Accurate preoperative identification of ovarian tumour subtypes is imperative for patients as it enables physicians to custom-tailor precise and individualized management strategies. So, we have developed an ultrasound (US)-based multiclass prediction algorithm for differentiating between benign, borderline, and malignant ovarian tumours. We randomised data from 849 patients with ovarian tumours into training and testing sets in a ratio of 8:2. The regions of interest on the US images were segmented and handcrafted radiomics features were extracted and screened. We applied the one-versus-rest method in multiclass classification. We inputted the best features into machine learning (ML) models and constructed a radiomic signature (Rad_Sig). US images of the maximum trimmed ovarian tumour sections were inputted into a pre-trained convolutional neural network (CNN) model. After internal enhancement and complex algorithms, each sample's predicted probability, known as the deep transfer learning signature (DTL_Sig), was generated. Clinical baseline data were analysed. Statistically significant clinical parameters and US semantic features in the training set were used to construct clinical signatures (Clinic_Sig). The prediction results of Rad_Sig, DTL_Sig, and Clinic_Sig for each sample were fused as new feature sets, to build the combined model, namely, the deep learning radiomic signature (DLR_Sig). We used the receiver operating characteristic (ROC) curve and the area under the ROC curve (AUC) to estimate the performance of the multiclass classification model. The training set included 440 benign, 44 borderline, and 196 malignant ovarian tumours. The testing set included 109 benign, 11 borderline, and 49 malignant ovarian tumours. DLR_Sig three-class prediction model had the best overall and class-specific classification performance, with micro- and macro-average AUC of 0.90 and 0.84, respectively, on the testing set. Categories of identification AUC were 0.84, 0.85, and 0.83 for benign, borderline, and malignant ovarian tumours, respectively. In the confusion matrix, the classifier models of Clinic_Sig and Rad_Sig could not recognise borderline ovarian tumours. However, the proportions of borderline and malignant ovarian tumours identified by DLR_Sig were the highest at 54.55% and 63.27%, respectively. The three-class prediction model of US-based DLR_Sig can discriminate between benign, borderline, and malignant ovarian tumours. Therefore, it may guide clinicians in determining the differential management of patients with ovarian tumours.

Development and validation of an ultrasound-based deep learning radiomics nomogram for predicting the malignant risk of ovarian tumours

BackgroundThe timely identification and management of ovarian cancer are critical determinants of patient prognosis. In this study, we developed and validated a deep learning radiomics nomogram (DLR_Nomogram) based on ultrasound (US) imaging to accurately predict the malignant risk of ovarian tumours and compared the diagnostic performance of the DLR_Nomogram to that of the ovarian-adnexal reporting and data system (O-RADS).MethodsThis study encompasses two research tasks. Patients were randomly divided into training and testing sets in an 8:2 ratio for both tasks. In task 1, we assessed the malignancy risk of 849 patients with ovarian tumours. In task 2, we evaluated the malignancy risk of 391 patients with O-RADS 4 and O-RADS 5 ovarian neoplasms. Three models were developed and validated to predict the risk of malignancy in ovarian tumours. The predicted outcomes of the models for each sample were merged to form a new feature set that was utilised as an input for the logistic regression (LR) model for constructing a combined model, visualised as the DLR_Nomogram. Then, the diagnostic performance of these models was evaluated by the receiver operating characteristic curve (ROC).ResultsThe DLR_Nomogram demonstrated superior predictive performance in predicting the malignant risk of ovarian tumours, as evidenced by area under the ROC curve (AUC) values of 0.985 and 0.928 for the training and testing sets of task 1, respectively. The AUC value of its testing set was lower than that of the O-RADS; however, the difference was not statistically significant. The DLR_Nomogram exhibited the highest AUC values of 0.955 and 0.869 in the training and testing sets of task 2, respectively. The DLR_Nomogram showed satisfactory fitting performance for both tasks in Hosmer–Lemeshow testing. Decision curve analysis demonstrated that the DLR_Nomogram yielded greater net clinical benefits for predicting malignant ovarian tumours within a specific range of threshold values.ConclusionsThe US-based DLR_Nomogram has shown the capability to accurately predict the malignant risk of ovarian tumours, exhibiting a predictive efficacy comparable to that of O-RADS.

Metastatic Ovarian Tumor from Incidental Renal Cell Carcinoma: A Case Report and Literature Review

Metastatic Ovarian Tumor from Incidental Renal Cell Carcinoma: A Case Report and Literature Review