Peritoneal Tuberculosis Mimicking Ovarian Cancer: Gynecologic Ultrasound Evaluation with Histopathological Confirmation

An explainable machine learning ensemble model to predict the risk of ovarian cancer in BRCA-mutated patients undergoing risk-reducing salpingo-oophorectomy

Introduction

It has been estimated that 19,880 new cases of ovarian cancer had been diagnosed in 2022. Most epithelial ovarian cancer are sporadic, while in 15%-25% of cases, there is evidence of a familial or inherited component. Approximately 20%-25% of high-grade serous carcinoma cases are caused by germline mutations in the BRCA1 and BRCA2 genes. However, owing to a lack of effective early detection methods, women with BRCA mutations are recommended to undergo bilateral risk-reducing salpingo-oophorectomy (RRSO) after childbearing. Determining the right timing for this procedure is a difficult decision. It is crucial to find a clinical signature to identify high-risk BRCA-mutated patients and determine the appropriate timing for performing RRSO.

Methods

In this work, clinical data referred to a cohort of 184 patients, of whom 7.6% were affected by adnexal tumors including invasive carcinomas and intraepithelial lesions after RSSO has been analyzed. Thus, we proposed an explainable machine learning (ML) ensemble approach using clinical data commonly collected in clinical practice to early identify BRCA-mutated patients at high risk of ovarian cancer and consequentially establish the correct timing for RRSO.

Results

The ensemble model was able to handle imbalanced data achieving an accuracy value of 83.2%, a specificity value of 85.3%, a sensitivity value of 57.1%, a G-mean value of 69.8%, and an AUC value of 71.1%.

Discussion

In agreement with the promising results achieved, the application of suitable ML techniques could play a key role in the definition of a BRCA-mutated patient-centric clinical signature for ovarian cancer risk and consequently personalize the management of these patients. As far as we know, this is the first work addressing this task from an ML perspective.

Immunogenicity and Safety of Adjuvanted Recombinant Zoster Vaccine in Rheumatoid Arthritis Patients on Anti-Cellular Biologic Agents or JAK Inhibitors: A Prospective Observational Study

Rheumatoid arthritis (RA) patients on JAK inhibitors (JAKi) have an increased HZ risk compared to those on biologic DMARDs (bDMARDs). Recently, the Adjuvanted Recombinant Zoster Vaccine (RZV) became available worldwide, showing good effectiveness in patients with inflammatory arthritis. Nevertheless, direct evidence of the immunogenicity of such a vaccine in those on JAKi or anti-cellular bDMARDs is still lacking. This prospective study aimed to assess RZV immunogenicity and safety in RA patients receiving JAKi or anti-cellular bDMARDs that are known to lead to impaired immune response. Patients with classified RA according to ACR/EULAR 2010 criteria on different JAKi or anti-cellular biologics (namely, abatacept and rituximab) followed at the RA clinic of our tertiary center were prospectively observed. Patients received two shots of the RZV. Treatments were not discontinued. At the first and second shots, and one month after the second shot, from all patients with RA, a sample was collected and RZV immunogenicity was assessed and compared between the treatment groups and healthy controls (HCs) receiving RZV for routine vaccination. We also kept track of disease activity at different follow-up times. Fifty-two consecutive RA patients, 44 females (84.61%), with an average age (±SD) of 57.46 ± 11.64 years and mean disease duration of 80.80 ± 73.06 months, underwent complete RZV vaccination between February and June 2022 at our center. At the time of the second shot (1-month follow-up from baseline), anti-VZV IgG titer increased significantly in both groups with similar magnitude (bDMARDs: 2258.76 ± 897.07 mIU/mL; JAKi: 2059.19 ± 876.62 mIU/mL, p < 0.001 for both from baseline). At one-month follow-up from the second shot, anti-VZV IgG titers remained stable in the bDMARDs group (2347.46 ± 975.47) and increased significantly in the JAKi group (2582.65 ± 821.59 mIU/mL, p = 0.03); still, no difference was observed between groups comparing IgG levels at this follow-up time. No RA flare was recorded. No significant difference was shown among treatment groups and HCs. RZV immunogenicity is not impaired in RA patients on JAKi or anti-cellular bDMARDs. A single shot of RZV can lead to an anti-VZV immune response similar to HCs without discontinuing DMARDs.

A machine learning approach applied to gynecological ultrasound to predict progression-free survival in ovarian cancer patients

In a growing number of social and clinical scenarios, machine learning (ML) is emerging as a promising tool for implementing complex multi-parametric decision-making algorithms. Regarding ovarian cancer (OC), despite the standardization of features that can support the discrimination of ovarian masses into benign and malignant, there is a lack of accurate predictive modeling based on ultrasound (US) examination for progression-free survival (PFS). This retrospective observational study analyzed patients with epithelial ovarian cancer (EOC) who were followed in a tertiary center from 2018 to 2019. Demographic features, clinical characteristics, information about the surgery and post-surgery histopathology were collected. Additionally, we recorded data about US examinations according to the International Ovarian Tumor Analysis (IOTA) classification. Our study aimed to realize a tool to predict 12 month PFS in patients with OC based on a ML algorithm applied to gynecological ultrasound assessment. Proper feature selection was used to determine an attribute core set. Three different machine learning algorithms, namely Logistic Regression (LR), Random Forest (RFF), and K-nearest neighbors (KNN), were then trained and validated with five-fold cross-validation to predict 12 month PFS. Our analysis included n. 64 patients and 12 month PFS was achieved by 46/64 patients (71.9%). The attribute core set used to train machine learning algorithms included age, menopause, CA-125 value, histotype, FIGO stage and US characteristics, such as major lesion diameter, side, echogenicity, color score, major solid component diameter, presence of carcinosis. RFF showed the best performance (accuracy 93.7%, precision 90%, recall 90%, area under receiver operating characteristic curve (AUROC) 0.92). We developed an accurate ML model to predict 12 month PFS.

Machine learning applied to MRI evaluation for the detection of lymph node metastasis in patients with locally advanced cervical cancer treated with neoadjuvant chemotherapy

PURPOSE

Concurrent cisplatin-based chemotherapy and radiotherapy (CCRT) plus brachytherapy is the standard treatment for locally advanced cervical cancer (LACC). Platinum-based neoadjuvant chemotherapy (NACT) followed by radical hysterectomy is an alternative for patients with stage IB2-IIB disease. Therefore, the correct pre-treatment staging is essential to the proper management of this disease. Pelvic magnetic resonance imaging (MRI) is the gold standard examination but studies about MRI accuracy in the detection of lymph node metastasis (LNM) in LACC patients show conflicting data. Machine learning (ML) is emerging as a promising tool for unraveling complex non-linear relationships between patient attributes that cannot be solved by traditional statistical methods. Here we investigated whether ML might improve the accuracy of MRI in the detection of LNM in LACC patients.

METHODS

We analyzed retrospectively LACC patients who underwent NACT and radical hysterectomy from 2015 to 2020. Demographic, clinical and MRI characteristics before and after NACT were collected, as well as information about post-surgery histopathology. Random features elimination wrapper was used to determine an attribute core set. A ML algorithm, namely Extreme Gradient Boosting (XGBoost) was trained and validated with tenfold cross-validation. The performances of the algorithm were assessed.

RESULTS

Our analysis included n.92 patients. FIGO stage was IB2 in n.4/92 (4.3%), IB3 in n.42/92 (45%), IIA1 in n.1/92 (1.1%), IIA2 in n.16/92 (17.4%) and IIB in n.29/92 (31.5%). Despite detected neither at pre-treatment and post-treatment MRI in any patients, LNM occurred in n.16/92 (17%) patients. The attribute core set used to train ML algorithms included grading, histotypes, age, parity, largest diameter of lesion at either pre- and post-treatment MRI, presence/absence of fornix infiltration at pre-treatment MRI and FIGO stage. XGBoost showed a good performance (accuracy 89%, precision 83%, recall 78%, AUROC 0.79).

CONCLUSIONS

We developed an accurate model to predict LNM in LACC patients in NACT, based on a ML algorithm requiring few easy-to-collect attributes.

The Application of Sonovaginography for Implementing Ultrasound Assessment of Endometriosis and Other Gynaecological Diseases

Sonovaginography is a way of assessing gynaecological diseases that can be described as cheap yet accurate and non-invasive. It consists of distention of the vagina with ultrasound gel or saline solution while performing transvaginal sonography to clearly visualize and assess a host of local cervical, as well as any vaginal, disorders. With endometriosis being a steadily growing gynaecological pathology affecting 8-15% of women of fertile age, transvaginal sonography (TVS) can be considered as one of the most accurate and comprehensive imaging techniques in its diagnosis. Nevertheless, the accuracy may vary depending on scan sites. The purpose of this narrative review is to assess the performance of sonovaginography in detecting endometriosis in those sites where TVS has a low sensitivity.

The Role of Ultrasound Guided Sampling Procedures in the Diagnosis of Pelvic Masses: A Narrative Review of the Literature

Ultrasound-guided sampling methods are usually minimally invasive techniques applied to obtain cytological specimens or tissue samples, mainly used for the diagnosis of different types of tumors. The main benefits of ultrasound guidance is its availability. It offers high flexibility in the choice of sampling approach (transabdominal, transvaginal, and transrectal) and short duration of procedure. Ultrasound guided sampling of pelvic masses represents the diagnostic method of choice in selected patients. We carried out a narrative review of literatures regarding the ultrasound-guided methods of cytological and histological evaluation of pelvic masses as well as the positive and negative predictors for the achievement of an adequate sample.