Results:The cases of bladder endometriosis had a relatively smooth surface likely due to their submucosal nature (figure 1a, b). The mucosal lesions, namely bladder malignancy (figure 1c) and cystitis glandularis cystica (figure 1d) had a corrugated surface. Also, the angle of contact of endometriosis with the bladder wall was obtuse (figure 1e) while that of the other lesions was nearly perpendicular or acute (figure 1f). Bladder endometriosis cases had a colour score of 1 or 2 while the other lesions had a score of 2 or more. Conclusions: Grey scale and colour Doppler features of bladder endometriosis can help in differentiating it from other bladder lesions which mimic it clinically.

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Background: Ultrasound (US) is a cheap, non-invasive and well-recognized image modality for diagnosing and assessing ovarian cancer (OC). However, approximately 18% to 31% of adnexal lesions detected on US remain indeterminate. Machine learning (ML) is a promising tool for the implementation of complex multi-parametric algorithms. Despite the standardization of features capable of supporting the discrimination of ovarian masses into benign and malignant, there is the lack of accurate predictive modeling based on US examination for progression-free survival (PFS).

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