Early differentiation between causes of secondary postpartum hemorrhage (PPH) can sometimes be difficult and can cause serious complications if diagnosis and treatment are delayed. The study aimed to assess the efficacy of different imaging diagnostic criteria, which would help in differentiating between gestational trophoblastic neoplasia (GTN) from indeterminate benign causes; thus, aiding in making clinical decisions in a timely fashion.

A comparative prospective study, were 33 patients complaining of 2ry PPH, with indeterminate diagnosis referred to the Radiology department female imaging unit between October 2020 and November 2022 for further assessment. Transvaginal ultrasound examination as well as functional MRI was done. The lesions were characterized and certain diagnostic criteria were evaluated.

The lesion epicenter, margin and depth of myometrial invasion detected by dynamic MRI have significant role to differentiate between GTN and other benign conditions mainly RPOC with higher sensitivity, specificity and accuracy of MRI compared to US. The p value, sensitivity and specificity as well as the accuracy of MRI were: 0.006, 50 %, 92 %, and 81.8 %; 0.000, 87.5 %, 92 % and 90.9 %; 0.002, 87.5 %, 92 % and 90.9 % compared to 0.5, 50 %, 64 % and 60.6 %; 0.01, 87.5 %, 64 % and 69.7 %; 0.2, 87.5 %, 40 % and 51.5 % by US respectively. The overall performance of MRI to reach accurate final diagnosis is higher than the US with accuracy rate of 97 % compared to 63.6 % in indeterminate cases.

MRI was found to show higher performance, compared to US in differentiating inconclusive cases of 2ry PPH.

Uterine leiomyosarcoma (uLMS) is the most common subtype of uterine sarcomas. They have a poor prognosis with high rates of recurrence and metastasis. The five-year survival for uLMS patients is between 25 and 76%, with survival rates approaching 10-15% for patients with metastatic disease at the initial diagnosis. Accumulating evidence suggests that several biological pathways are involved in uLMS pathogenesis. Notably, drugs that block abnormal functions of these pathways remarkably improve survival in uLMS patients. However, due to chemotherapy resistance, there remains a need for novel drugs that can target these pathways effectively. In this review article, we provide an overview of the recent progress in ascertaining the biological functions and regulatory mechanisms in uLMS from the perspective of aberrant biological pathways, including DNA repair, immune checkpoint blockade, protein kinase and intracellular signaling pathways, and the hedgehog pathway. We review the emerging role of epigenetics and epitranscriptome in the pathogenesis of uLMS. In addition, we discuss serum markers, artificial intelligence (AI) combined with machine learning, shear wave elastography, current management and medical treatment options, and ongoing clinical trials for patients with uLMS. Comprehensive, integrated, and deeper insights into the pathobiology and underlying molecular mechanisms of uLMS will help develop novel strategies to treat patients with this aggressive tumor.

Leiomyosarcoma (LMS), together with smooth muscle tumors of uncertain malignant potential (STUMP) and benign leiomyomas, belongs to a heterogeneous group of uterine neoplasms. According to the World Health Organization, tumors originating from uterine smooth muscle fibers are the second most frequent tumors. It is challenging to distinguish between STUMP and LMS because of an overlap of symptoms, lack of a precise definition, and unequivocal information obtained using imaging diagnostic methods. Following myomectomy or hysterectomy with laparoscopic or laparotomy surgery and a definitive histological diagnosis of STUMP, the course of treatment is determined by the need to preserve fertility. In 2014, the U.S. Food and Drug Administration published an alert that unprotected laparoscopic morcellation is correlated with a 3-fold higher likelihood of dissemination of malignant cells and disease progression. Unprotected morcellation was independently associated with a higher risk of disease recurrence after demolition or conservative surgery, with a relative risk of 2.94.

Hematoperitoneum resulting from the spontaneous rupture of a uterine tumor is a rare gynecological emergency, with very few cases reported in the last decade.

To assess the predictive ability of conventional MRI features and MRI texture features in differentiating uterine leiomyoma (LM) from uterine leiomyosarcoma (LMS).

This single-center, IRB-approved, HIPAA-compliant retrospective study included 108 patients (69 LM, 39 LMS) who had pathology, preoperative MRI, and clinical data available at our tertiary academic institution. Two radiologists independently evaluated 14 features on preoperative MRI. Texture features based on 3D segmentation were extracted from T2W-weighted MRI (T2WI) using commercially available texture software (TexRAD™, Feedback Medical Ltd., Great Britain). MRI conventional features, and clinical and MRI texture features were compared between LM and LMS groups. Dataset was randomly divided into training (86 cases) and testing (22 cases) cohorts (8:2 ratio); training cohort was further subdivided into training and validation sets using ten-fold cross-validation. Optimal radiomics model was selected out of 90 different machine learning pipelines and five models containing different combinations of MRI, clinical, and radiomics variables.

12/14 MRI conventional features and 2/2 clinical features were significantly different between LM and LMS groups. MRI conventional features had moderate to excellent inter-reader agreement for all but two features. Models combining MRI conventional and clinical features (AUC 0.956) and MRI conventional, clinical, and radiomics features (AUC 0.989) had better performance compared to models containing MRI conventional features alone (AUC 0.846 and 0.890) or radiomics features alone (0.929).

While multiple MRI and clinical features differed between LM and LMS groups, the model combining MRI, clinical, and radiomic features had the best predictive ability but was only marginally better than a model utilizing conventional MRI and clinical data alone.

The accurate discrimination of uterine leiomyosarcomas and leiomyomas in a pre-operative setting remains a current challenge. To date, the diagnosis is made by a pathologist on the excised tumor. The aim of this study was to develop a machine learning algorithm using radiomic data extracted from contrast-enhanced computed tomography (CECT) images that could accurately distinguish leiomyosarcomas from leiomyomas.

Pre-operative CECT images from patients submitted to surgery with a histological diagnosis of leiomyoma or leiomyosarcoma were used for the region of interest identification and radiomic feature extraction. Feature extraction was conducted using the PyRadiomics library, and three feature selection methods combined with the general linear model (GLM), random forest (RF), and support vector machine (SVM) classifiers were built, trained, and tested for the binary classification task (malignant vs. benign). In parallel, radiologists assessed the diagnosis with or without clinical data.

A total of 30 patients with leiomyosarcoma (mean age 59 years) and 35 patients with leiomyoma (mean age 48 years) were included in the study, comprising 30 and 51 lesions, respectively. Out of nine machine learning models, the three feature selection methods combined with the GLM and RF classifiers showed good performances, with predicted area under the curve (AUC), sensitivity, and specificity ranging from 0.78 to 0.97, from 0.78 to 1.00, and from 0.67 to 0.93, respectively, when compared to the results obtained from experienced radiologists when blinded to the clinical profile (AUC = 0.73 95%CI = 0.62-0.84), as well as when the clinical data were consulted (AUC = 0.75 95%CI = 0.65-0.85).

CECT images integrated with radiomics have great potential in differentiating uterine leiomyomas from leiomyosarcomas. Such a tool can be used to mitigate the risks of eventual surgical spread in the case of leiomyosarcoma and allow for safer fertility-sparing treatment in patients with benign uterine lesions.

Differential diagnosis between uterine leiomyomas and sarcomas is challenging. Magnetic resonance imaging (MRI) represents the second-line diagnostic method after ultrasound for the assessment of uterine masses.

To assess the accuracy of MRI in the differential diagnosis between uterine leiomyomas and sarcomas.

A systematic review and meta-analysis was performed searching five electronic databases from their inception to June 2023.

All peer-reviewed observational or randomized clinical trials that reported an unbiased postoperative histologic diagnosis of uterine leiomyoma or uterine sarcoma, which also comprehended a preoperative MRI evaluation of the uterine mass.

Sensitivity, specificity, positive and negative likelihood ratios, diagnostic odds ratio, and area under the curve on summary receiver operating characteristic of MRI in differentiating uterine leiomyomas and sarcomas were calculated as individual and pooled estimates, with 95% confidence intervals (CI).

Eight studies with 2495 women (2253 with uterine leiomyomas and 179 with uterine sarcomas), were included. MRI showed pooled sensitivity of 0.90 (95% CI 0.84-0.94), specificity of 0.96 (95% CI 0.96-0.97), positive likelihood ratio of 13.55 (95% CI 6.20-29.61), negative likelihood ratio of 0.08 (95% CI 0.02-0.32), diagnostic odds ratio of 175.13 (95% CI 46.53-659.09), and area under the curve of 0.9759.

MRI has a high diagnostic accuracy in the differential diagnosis between uterine leiomyomas and sarcomas.

Superimposed high-frequency jet ventilation (SHFJV) is suitable for respiratory motion reduction and essential for effective lung tumor ablation. Fluid filling of the target lung wing one-lung flooding (OLF) is necessary for therapeutic ultrasound applications. However, whether unilateral SHFJV allows adequate hemodynamics and gas exchange is unclear.

To compared SHFJV with pressure-controlled ventilation (PCV) during OLF by assessing hemodynamics and gas exchange in different animal positions.

SHFJV or PCV was used alternatingly to ventilate the non-flooded lungs of the 12 anesthetized pigs during OLF. The animal positions were changed from left lateral position to supine position (SP) to right lateral position (RLP) every 30 min. In each position, ventilation was maintained for 15 min in both modalities. Hemodynamic variables and arterial blood gas levels were repeatedly measured.

Unilateral SHFJV led to lower carbon dioxide removal than PCV without abnormally elevated carbon dioxide levels. SHFJV slightly decreased oxygenation in SP and RLP compared with PCV; the lowest values of PaO2 and PaO2/FiO2 ratio were found in SP [13.0; interquartile range (IQR): 12.6-5.6 and 32.5 (IQR: 31.5-38.9) kPa]. Conversely, during SHFJV, the shunt fraction was higher in all animal positions (highest in the RLP: 0.30).

In porcine model, unilateral SHFJV may provide adequate ventilation in different animal positions during OLF. Lower oxygenation and CO2 removal rates compared to PCV did not lead to hypoxia or hypercapnia. SHFJV can be safely used for lung tumor ablation to minimize ventilation-induced lung motion.

Intravenous leiomyomatosis (IVL) are rare and complex tumors, characterized by high rates of recurrences after surgical removal and the capability of multi-organ involvement including pulmonary embolization. Regarding the surgical treatment of Intracardiac Leiomiomatosis (ICL), only few articles have been published and no controlled data are available. A combined approach that involves a Team of Cardiologists, Heart Surgeons, Vascular surgeons and Radiologists seems to be successful in treating ICL.

Uterine artery embolization (UAE) is an established technique to treat benign diseases of the uterus such as uterine leiomyomata (fibroids) and adenomyosis. This article reviews the use of UAE in these conditions and summarizes the evidence regarding safety and efficacy of the technique based on the current literature.

Leiomyoma with bizarre nuclei (LBN), also known as symplastic leiomyoma, is a histological subtype of benign leiomyoma with bizarre cells and nuclear atypia. Differentiating LBN from other benign leiomyoma subtypes, uterine smooth muscle tumors of uncertain malignant potential (STUMP), or leiomyosarcoma (LMS) can be diagnostically challenging owing to overlapping features in clinical presentation and pathologic morphological analysis. The difficulty of distinguishing LBN from other lesions, especially from LMS, and the potential of LBN for subsequent malignant transformation make LBN an important topic of research. Herein, we review the definition, diagnosis, treatment, and prognosis of LBN. Histopathological examination is essential for distinguishing LBN from other diseases. Pathology sampling and morphological examination remain the key to diagnosis. The newly established ancillary immunohistochemical (IHC) and molecular genetic analysis can be useful tools for differential diagnosis. Furthermore, serum biomarkers and imaging examination may also be useful diagnostic tools. Attention should be paid to the differentiation between LBN and LMS because morphological diagnosis may still be challenging in some cases. Some IHC markers of LBN have been identified, which may be helpful for differential diagnosis. Furthermore, the use of IHC panels as diagnostic markers may be advocated. Molecular genetic studies suggest that some genes can aid with the differential diagnosis between LBN and LMS. However, increasing evidence support the idea that LBN and LMS are molecularly related, indicating that LBN may represent a potentially malignant stage of precancerous progression. At present, conservative treatment is recommended for primary LBN, especially for patients desiring to retain fertility, but close follow-up with imaging examinations is required.

Uterine body cancers (UBC) are represented by endometrial carcinoma (EC) and uterine sarcoma (USa). The clinical management of both is hindered by the complex classification of patients into risk classes. This problem could be simplified through the development of predictive models aimed at treatment tailoring based on tumor and patient characteristics. In this context, radiomics represents a method of extracting quantitative data from images in order to non-invasively acquire tumor biological and genetic information and to predict response to treatments and prognosis. Furthermore, artificial intelligence (AI) methods are an emerging field of translational research, with the aim of managing the amount of data provided by the various -omics, including radiomics, through the process of machine learning, in order to promote precision medicine.

The aim of this protocol for systematic review is to provide an overview of radiomics and AI studies on UBCs.

A systematic review will be conducted using PubMed, Scopus, and the Cochrane Library to collect papers analyzing the impact of radiomics and AI on UBCs diagnosis, prognostic classification, and clinical outcomes. The PICO strategy will be used to formulate the research questions: What is the impact of radiomics and AI on UBCs on diagnosis, prognosis, and clinical results? How could radiomics or AI improve the differential diagnosis between sarcoma and fibroids? Does Radiomics or AI have a predictive role on UBCs response to treatments? Three authors will independently screen articles at title and abstract level based on the eligibility criteria. The risk of bias and quality of the cohort studies, case series, and case reports will be based on the QUADAS 2 quality assessment tools.

PROSPERO registration number: CRD42021253535.

Uterine leiomyoma, also known as uterine fibroid, is the most common gynecological tumor, affecting almost 80% of women at some point during their lives. In the same time, other fibroid-like tumors have similar clinical presentations and about 0.5% of resected tumors of which were presumed benign fibroids in the preoperative diagnosis revealed as malignant sarcomas in the final histopathological examination. Amid the emergence of nonsurgical or minimally invasive procedures for symptomatic benign uterine fibroids, such as uterine artery embolization, high-intensity-focused ultrasound, or laparoscopic myomectomy, the preoperative diagnosis of uterine tumors through imaging becomes all the more relevant. Preoperative tissue sampling is challenging because of the variable location of the myometrial mass; thus, the preoperative evaluation of size and location is increasingly performed through magnetic resonance imaging. Features in images might also be useful for examining the full spectrum of such growths, from benign fibroids to neoplasms of uncertain behavior and malignant sarcomas. Benign fibroids include usual-type leiomyomas, myomas with degeneration, and mitotically active leiomyomas. Neoplasms of uncertain behavior include smooth muscle tumors of uncertain malignant potential, leiomyomas with bizarre nuclei, and cellular leiomyomas. Malignant sarcomas comprise leiomyosarcomas, endometrial stromal sarcomas, adenosarcomas, and carcinosarcomas. The purpose of this article is to review the spectrum of MRI findings of uterine fibroid-like tumors, from benign variants, uncertain behavior to malignant sarcomas, and update the advanced imaging modalities, including diffusion-weighted imaging, positron emission tomography/computed tomography, combining texture analysis and radiomics, to tackle this important issue.