Diagnostic accuracy of MRI in the differential diagnosis between uterine leiomyomas and sarcomas: A systematic review and meta-analysis

Background: 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. Objectives: To assess the accuracy of MRI in the differential diagnosis between uterine leiomyomas and sarcomas. Search Strategy: A systematic review and meta-analysis was performed searching five electronic databases from their inception to June 2023. Selection Criteria: 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. Data Collection and Analysis: 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). Results: 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. Conclusions: MRI has a high diagnostic accuracy in the differential diagnosis between uterine leiomyomas and sarcomas

Haematological and biochemical abnormalities in hunting dogs infected with Acanthocheilonema reconditum, associated risk factors, and a European overview

Acanthocheilonema reconditum is a filarial parasite transmitted by arthropods (fleas, lice, and ticks) that infect dogs. There is minimal published data available to date on potential haematological and biochemical changes associated with this parasitic infection. Study aims were (i) provide an overview of A. reconditum in Europe, (ii) define A. reconditum prevalence and risk factors in a specific dog population (hunting) from southern Italy, and (iii) assess the frequency of haemato-biochemical abnormalities associated with infection. Blood samples collected from 3020 dogs were tested by a modified Knott’s technique to count and identify microfilariae. Eighty-four dogs were infected by A. reconditum (2.78%; 95% CI 2.19–3.37%). Microfilariae ranged from 1 to 212/ml. Based on clinical examination, all but six dogs with non-specific symptoms were healthy. Haematological abnormalities included leucocytosis (n = 15), with eosinophilia (n = 14) and monocytosis (n = 13). Serum biochemical abnormalities included increased total serum proteins (n = 19), albumins (n = 7), total globulins (n = 14), ALT (n = 1), and ALP (n = 1); one dog was hypoalbuminemic, and BUN was mildly increased in 2 dogs. Risk factors included the province origin (Napoli, OR=5.4, 95%CI: 2.1–14.0; Caserta, OR=5.1, 95%CI: 2.5–10.6), hunting wild mammals (OR=2.8, 95% 95%CI: 1.6–4.8), and ectoparasite infestation (OR=1.9, 95%CI: 1.1–3.1). There was a negative correlation between microfilaraemic load and decreased albumin level (−0.37; p=0.021). Our results showed that A. reconditum circulates within the hunting dog population of southern Italy, with seemingly low pathogenic potential

Endometrial Cancer Arising in Adenomyosis (EC-AIA): A Systematic Review

Endometrial cancer arising in adenomyosis (EC-AIA) is a rare uterine disease characterized by the malignant transformation of the ectopic endometrium within the adenomyotic foci. Clinicopathological and survival data are mostly limited to case reports and a few cohort studies. We aimed to assess the clinicopathological features and survival outcomes of women with EC-AIA through a systematic review of the literature. Six electronic databases were searched, from 2002 to 2022, for all peer-reviewed studies that reported EC-AIA cases. Thirty-seven EC-AIA patients from 27 case reports and four case series were included in our study. In our analysis, EC-AIA appeared as a rare disease that mainly occurs in menopausal women, shares symptoms with endometrial cancer, and is challenging to diagnose preoperatively. Differently from EC, it shows a higher prevalence of the non-endometrioid histotype, advanced FIGO stages, and p53-signature, which might be responsible for its worse prognosis. Future studies are necessary, to confirm our findings and further investigate this rare condition

Is preoperative ultrasound tumor size a prognostic factor in endometrial carcinoma patients?

Objective: We aimed to assess the prognostic value of preoperative ultrasound tumor size in EC through a single center, observational, retrospective, cohort study. Methods: Medical records and electronic clinical databases were searched for all consecutive patients with EC, preoperative ultrasound scans available to ad hoc estimate tumor size, and a follow-up of at least 2-year, at our Institution from January 2010 to June 2018. Patients were divided into two groups based on different dimensional cut-offs for the maximum tumor diameter: 2, 3 and 4 cm. Differences in overall survival (OS), disease specific survival (DSS) and progression-free survival (PFS) were assessed among the groups by using the Kaplan–Meier estimator and the log-rank test. Results: 108 patients were included in the study. OS, DSS and PFS did not significantly differ between the groups based on the different tumor diameter cut-offs. No significant differences were found among the groups sub-stratified by age, BMI, FIGO stage, FIGO grade, lymphovascular space invasion status, myometrial invasion, lymph nodal involvement, histotype, and adjuvant treatment. Conclusions: Preoperative ultrasound tumor size does not appear as a prognostic factor in EC women

Distribution and risk factors of canine haemotropic mycoplasmas in hunting dogs from southern Italy

Mycoplasma haemocanis (Mhc) and “Candidatus Mycoplasma haematoparvum” (CMhp) are the main haemoplasma species known to infect dogs. The aim of this study was to determine the prevalence of haemoplasma species infections in hunting dogs from southern Italy and assess related risk factors. 1,433 hunting dogs living in Campania region were tested by qPCR assay. The prevalence was 19.9 %; 13.1 % for Mhc and 11.4 % for CMhp; 4.6 % showed a coinfection with both haemoplasma species. Statistical analysis revealed living in Salerno province (Mhc: OR 3.72; CMhp: OR 2.74), hound (Mhc: OR 5.26; CMhp: OR 8.46) and mixed breed (Mhc: OR 3.38; CMhp: OR 2.80), rural environment (Mhc: OR 12.58; CMhp: OR 10.38), wild mammal hunting (Mhc: OR 8.73; CMhp: OR 8.32), cohabitation with other animals (Mhc: OR 2.82; CMhp: OR 2.78) and large pack size (Mhc: OR 2.96; CMhp: OR 1.61) as risk factors for haemoplasmas. Male gender (OR 1.44) and tick infestation history (OR 1.40) represented risk factors only for Mhc, while adult age (2 7 years - OR 2.01; > 7 years - OR 1.84) and large body size (OR 1.48) were associated only to CMhp. Mhc infection was significantly associated to Babesia vogeli (p < 0.05) and Hepatozoon canis (p < 0.001), while CMhp with H. canis (p < 0.001). This study adds information on haemoplasma species distribution in hunting dogs in southern Italy. Outdoor lifestyle and contact with wild fauna, through greater exposure to tick infestation, or possibly wounds acquired during hunting or fighting, could be factors contributing to haemoplasma infections

Hepatozoon canis in hunting dogs from Southern Italy: distribution and risk factors

Hepatozoon canis is a hemoprotozoan organism that infects domestic and wild carnivores throughout much of Europe. The parasite is mainly transmitted through the ingestion of infected ticks containing mature oocysts. The aims of the present survey were to determine the prevalence of H. canis in hunting dogs living in Southern Italy and to assess potential infection risk factors. DNA extracted from whole blood samples, collected from 1433 apparently healthy dogs living in the Napoli, Avellino, and Salerno provinces of Campania region (Southern Italy), was tested by a quantitative real-time polymerase chain reaction (qPCR) assay to amplify H. canis. Furthermore, the investigated dog population was also screened by qPCR for the presence of Ehrlichia canis, a major tick-borne pathogen in Southern Italy, in order to assess possible co-infections. Two hundred dogs were H. canis PCR-positive, resulting in an overall prevalence of 14.0% (CI 12.2–15.9). Breed category (P &lt; 0.0001), hair coat length (P = 0.015), and province of residence (P &lt; 0.0001) represented significant risk factors for H. canis infection. The presence ofH. canis DNA was also significantly associated with E. canis PCR positivity (P &lt; 0.0001). Hunting dogs in Campania region (Southern Italy) are frequently exposed to H. canis, and the infection is potentially associated with close contact with wildlife. Further studies are needed to assess the pathogenic potential of H. canis, as well as the epidemiological relationships between hunting dogs and wild animal populations sharing the same habitats in Southern Italy

Distribution and risk factors associated with Babesia spp. infection in hunting dogs from Southern Italy

Canine babesiosis is caused by haemoprotozoan organisms of the genus Babesia which are transmitted by the bite of a hard tick. The aim of this survey was to determine the prevalence and risk factors associated with Babesia species infections in hunting dogs from Southern Italy. Blood samples were collected from 1311 healthy dogs in the Napoli, Avellino and Salerno provinces of the Campania region of Southern Italy. Serological testing was performed using two enzyme-linked immunosorbent assays (ELISA), with one designed to detect B. canis and B. vogeli antibodies, and the other designed to detect B. gibsoni antibodies. Blood samples were also tested by quantitative real-time polymerase chain reaction (qPCR) assays for amplification of B. canis, B. vogeli and B. gibsoni DNA. The overall seroprevalence for B. canis/B. vogeli was 14.0%, compared to 0.2% for B. gibsoni. B. canis and B. vogeli PCR positive rates were 0.15% and 1.1%, respectively. B. gibsoni DNA was not amplified by qPCR. Male gender (OR 1.85), increased age (OR 1.01), long hair coat (OR 1.61) and living in Salerno province (OR 1.71) represented risk factors for B. canis/B. vogeli seroreactivity. Hunting dogs in Southern Italy are often exposed to B. canis/B. vogeli, however Babesia spp. infection was infrequently detected using qPCR. Further studies are needed to determine the extent to which Babesia spp. cause clinical disease in hunting dogs, and to evaluate the potential epidemiological relationships between hunting dogs and wild animal populations sharing the same area

Detection and Classification of Hysteroscopic Images Using Deep Learning

Simple Summary This article discusses the potential of deep learning (DL) models in aiding the diagnosis of endometrial pathologies through hysteroscopic images. While hysteroscopy with endometrial biopsy is currently the gold standard for diagnosis, it heavily relies on the expertise of gynecologists. The study aims to develop a DL model for automated detection and classification of endometrial pathologies. Conducted as a monocentric observational retrospective cohort study, it reviewed records and videos of hysteroscopies from patients with confirmed intrauterine lesions. The DL model was trained using these images, with or without incorporating clinical factors. Results indicate that while the DL model showed promising results, its diagnostic performance remained relatively low, even with the inclusion of clinical data. The best performance was achieved when clinical factors were included, with precision, recall, specificity, and F1 scores ranging from 80 to 90% for classification and 85 to 93% for identification tasks. Despite slight improvements in clinical data, further refinement of DL models is warranted for more accurate diagnosis of endometrial pathologies.Abstract Background: Although hysteroscopy with endometrial biopsy is the gold standard in the diagnosis of endometrial pathology, the gynecologist experience is crucial for a correct diagnosis. Deep learning (DL), as an artificial intelligence method, might help to overcome this limitation. Unfortunately, only preliminary findings are available, with the absence of studies evaluating the performance of DL models in identifying intrauterine lesions and the possible aid related to the inclusion of clinical factors in the model. Aim: To develop a DL model as an automated tool for detecting and classifying endometrial pathologies from hysteroscopic images. Methods: A monocentric observational retrospective cohort study was performed by reviewing clinical records, electronic databases, and stored videos of hysteroscopies from consecutive patients with pathologically confirmed intrauterine lesions at our Center from January 2021 to May 2021. Retrieved hysteroscopic images were used to build a DL model for the classification and identification of intracavitary uterine lesions with or without the aid of clinical factors. Study outcomes were DL model diagnostic metrics in the classification and identification of intracavitary uterine lesions with and without the aid of clinical factors. Results: We reviewed 1500 images from 266 patients: 186 patients had benign focal lesions, 25 benign diffuse lesions, and 55 preneoplastic/neoplastic lesions. For both the classification and identification tasks, the best performance was achieved with the aid of clinical factors, with an overall precision of 80.11%, recall of 80.11%, specificity of 90.06%, F1 score of 80.11%, and accuracy of 86.74 for the classification task, and overall detection of 85.82%, precision of 93.12%, recall of 91.63%, and an F1 score of 92.37% for the identification task. Conclusion: Our DL model achieved a low diagnostic performance in the detection and classification of intracavitary uterine lesions from hysteroscopic images. Although the best diagnostic performance was obtained with the aid of clinical data, such an improvement was slight