Risk for non-sentinel metastases in the contralateral groin in patients with a unilateral positive sentinel lymph node in primary vulvar cancer- a subgroup analysis of the AGO-VOP.2 QS Vulva Study.

Unlabelled: The need for contralateral full groin dissection after bilateral sentinel node biopsy (SNB) with only unilateral detection of a macrometastasis is still a matter of debate. GROINSS-VI/II subanalysis has shown an absolute risk <3 % for contralateral groin recurrence or contralateral non-sentinel metastases, however, the individual risk varied widely dependent on the primary tumor characteristics.

Methods: The AGO VOP.

Physicochemical properties of tire-derived -phenylenediamine quinones - a comparison of experimental and computational approaches.

-phenylenediamine (PPD) compounds are added to tire rubber at percent levels to sacrificially react with oxidants for prolonged service life. Recently, the PPD transformation product -(1,3-dimethylbutyl)-'-phenyl--phenylenediamine quinone (6PPDQ) has been identified in roadway runoff as a potent toxicant for coho salmon (). As 6PPD may be phased out in favour of alternative PPDs, understanding the physicochemical properties of their corresponding quinones is important for predicting their environmental fate, distribution, and toxicity.

Anogenital high-risk HPV prevalence and screening considerations in female transplant recipients: a cross-sectional study.

Purpose: Organ transplant recipients receiving immunosuppressive therapy have a higher risk of developing anogenital HPV-related (pre)malignancies. This study aims to determine the age-dependent anogenital hrHPV prevalence in transplanted patients and to investigate various risk factors within this high-risk patient group.

Methods: Women (n = 201) after kidney (n = 98), liver (n = 93), and combined (n = 10) transplantation were tested and genotyped for hrHPV in the anal and cervical regions.

Deep learning assessment of metastatic relapse risk from digitized breast cancer histological slides.

Accurate risk stratification is critical for guiding treatment decisions in early breast cancer. We present an artificial intelligence (AI)-based tool that analyzes digitized tumor slides to predict 5-year metastasis-free survival (MFS) in patients with estrogen receptor-positive, HER2-negative (ER + /HER2 - ) early breast cancer (EBC). Our deep learning model, RlapsRisk BC, independently predicts MFS and provides significant prognostic value beyond traditional clinico-pathological variables (C-index 0.