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Objectives: This study aimed to construct a radiomics nomogram and validate its performance in the preoperative differentiation between early-stage (I and II) serous borderline ovarian tumors (SBOTs) and serous malignant ovarian tumors (SMOTs). Methods: Data were collected from 80 patients with early-stage SBOTs and 102 with early-stage SMOTs (training set: n = 127; validation set: n = 55). Univariate and multivariate analyses were performed to identify the independent clinicoradiological factors. A radiomics signature model was constructed using radiomics features extracted from multidetector computed tomography images of the venous phase, in which the least absolute shrinkage and selection operator regression was employed to lessen the dimensionality of the data and choose the radiomics features. A nomogram model was established by combining independent clinicoradiological factors with the radiomics signature. The performance of nomogram calibration, discrimination, and clinical usefulness was evaluated using training and validation sets. Results: In terms of clinicoradiological characteristics, age (p = 0.001), the diameter of the solid component (p = 0.009), and human epididymis protein 4 level (p < 0.001) were identified as the independent risk factors of SMOT, for which the area under the curves (AUCs) were calculated to be 0.850 and 0.836 in the training and validation sets, respectively. Nine features were finally selected to construct the radiomics signature model, which exhibited AUCs of 0.879 and 0.826 for the training and validation sets, respectively. The nomogram model demonstrated considerable calibration and discrimination with AUCs of 0.940 and 0.909 for the training and validation sets, respectively. The nomogram model displayed more prominent clinical usefulness than the clinicoradiological and radiomics signature models according to the decision curve analysis. Conclusions: The nomogram model can be employed as an individualized preoperative non-invasive tool for differentiating early-stage SBOTs from SMOTs. [ABSTRACT FROM AUTHOR]

Simple Summary: Uterine sarcomas are the second most common unexpected malignancy diagnosed after surgery. It is worrisome, as its preoperative diagnosing can impact the choice of the treatment method, including surgery. Therefore, nowadays, many researchers are trying to find innovative methods to differentiate benign and malignant lesions of the uterus preoperatively. The review of the current literature showed that the use of more than one parameter in specific diagnostic scales is one of the most effective methods. Moreover, machine learning models and artificial intelligence (AI) are hope-giving directions, which may help in preoperative ULMS and ULM distinguishment. In order to collect a large amount of ULMS patients, multicenter databases seem necessary. The distinguishing of uterine leiomyosarcomas (ULMS) and uterine leiomyomas (ULM) before the operation and histopathological evaluation of tissue is one of the current challenges for clinicians and researchers. Recently, a few new and innovative methods have been developed. However, researchers are trying to create different scales analyzing available parameters and to combine them with imaging methods with the aim of ULMs and ULM preoperative differentiation ULMs and ULM. Moreover, it has been observed that the technology, meaning machine learning models and artificial intelligence (AI), is entering the world of medicine, including gynecology. Therefore, we can predict the diagnosis not only through symptoms, laboratory tests or imaging methods, but also, we can base it on AI. What is the best option to differentiate ULM and ULMS preoperatively? In our review, we focus on the possible methods to diagnose uterine lesions effectively, including clinical signs and symptoms, laboratory tests, imaging methods, molecular aspects, available scales, and AI. In addition, considering costs and availability, we list the most promising methods to be implemented and investigated on a larger scale. [ABSTRACT FROM AUTHOR]

Objectives In young patients with borderline tumors the fertility-sparing treatment is indicated, thus the preoperative investigation is important. The aim of this study was to perform a comparative assessment of sensitivity and specificity of selected ultrasonographic and clinical parameters for the diagnoses of borderline tumors and ovarian cancers. Methods: We retrospectively analyzed 57 patients who underwent surgical treatment in the Maria Sklodowska-Curie Memorial Cancer Center from Jan 01, 2008 to Dec 31, 2009. Ovarian cancers were diagnosed in 41 patients, and borderline ovarian tumors in 16 patients. Statistical model was developed to determine independent predictive factors that would be useful in preoperative differentiation between both tumors. The model included the following factors: menopausal status, tumor morphology, wall thickness (including outgrowths), septal thickness, echogenicity, resistive index, serum CA-125 level, and free fluid in the peritoneal cavity. Results: Based on the statistical model developed, independent predictive factors in the differentiation between ovarian cancers and borderline tumors included the menopausal status ( P = 0.005), tumor echogenicity ( P = 0.047) and the presence of free fluid in the Douglas pouch ( P = 0.043). With the cutoff value of 13 (with scores below 13 indicating a borderline ovarian tumor, and scores of ≥13 indicating ovarian cancer), sensitivity was 90.2 % and specificity was 87 %. Conclusions: Our proposed model of preoperative evaluation has a sensitivity of 90 % in the differentiation between ovarian cancers and borderline tumors. When combined with intraoperative findings, it allows optimal surgical therapeutic decisions to be made in patients with borderline ovarian tumors. [ABSTRACT FROM AUTHOR]

Assessment of adnexal masses focuses on the accurate discrimination between benign and malignant lesions. In our tertiary referral center, we evaluated the discriminative power of expert sonography, serum CA-125 measurement, risk malignancy index (RMI) by Jacobs, and 2 preoperative triage strategies (combination of CA-125 measurement and RMI assessment with expert sonography).From 2002 to 2008, a total of 1362 surgical explorations with indication of an adnexal mass from our department were included in this study. Preoperative workup in all patients comprised a gynecologic examination, expert sonography, and serum CA-125 measurement. We calculated sensitivity, specificity, positive and negative predictive value (PPV and NPV), and Cohen κ (prevalence-adjusted measurement) to evaluate the discriminative power of each diagnostic test.Discriminative power of the evaluated tests differed depending on patients' menopausal state. In the premenopause, expert sonography reached the highest discriminative power with a κ value of 0.53, a PPV of 0.45, and an NPV of 0.99. In the postmemopause, the combinations of expert sonography with CA-125 serum measurement or RMI assessment achieved the highest discriminative power: The combination of CA-125 and expert sonography reached a PPV of 0.89 and an NPV of 0.97; κ yielded 0.84. The RMI combined with expert sonography as a triage strategy showed comparable results with a PPV of 0.89, an NPV of 0.96, and a κ value of 0.82.Preoperative assessment of an adnexal mass may be guided by the patient's menopausal state. In premenopausal patients, expert sonography is helpful for preoperative differentiation between benign and malignant lesions; in postmenopausal patients, the use of triage strategies of either CA-125 serum measurement or RMI combined with expert sonography can be recommended. [ABSTRACT FROM AUTHOR]