Silicone Migration from Intact Saline Breast Implants.

Breast augmentation is a widely performed surgical procedure worldwide, predominantly using silicone gel-filled implants. Concerns have primarily revolved around ruptures and the potential health risks associated with leaked silicone from silicone gel-filled implants. Cases of silicone migration from the shell of saline breast implants remain scarce. This case report introduces a unique case of a 66-year-old patient with silicone migration from intact saline breast implants. The patient presented with a range of symptoms consistent with breast implant illness. Radiological findings suggested the presence of silicone in the axillary lymph nodes, despite the integrity of the implants, thereby confirming silicone migration. Histopathological evaluation revealed a foreign body reaction and the presence of silicone in the axillary lymph nodes. Given the saline filling, the source is likely the polydimethylsiloxane shell. The rarity of documented silicone migration from intact saline breast implants, especially in patients with breast implant illness, underscores the need for more research into the health implications of leaked silicone particles from breast implants.

Endometrial Pipelle Biopsy Computer-Aided Diagnosis: A Feasibility Study.

Endometrial biopsies are important in the diagnostic workup of women who present with abnormal uterine bleeding or hereditary risk of endometrial cancer. In general, approximately 10% of all endometrial biopsies demonstrate endometrial (pre)malignancy that requires specific treatment. As the diagnostic evaluation of mostly benign cases results in a substantial workload for pathologists, artificial intelligence (AI)-assisted preselection of biopsies could optimize the workflow. This study aimed to assess the feasibility of AI-assisted diagnosis for endometrial biopsies (endometrial Pipelle biopsy computer-aided diagnosis), trained on daily-practice whole-slide images instead of highly selected images. Endometrial biopsies were classified into 6 clinically relevant categories defined as follows: nonrepresentative, normal, nonneoplastic, hyperplasia without atypia, hyperplasia with atypia, and malignant. The agreement among 15 pathologists, within these classifications, was evaluated in 91 endometrial biopsies. Next, an algorithm (trained on a total of 2819 endometrial biopsies) rated the same 91 cases, and we compared its performance using the pathologist's classification as the reference standard. The interrater reliability among pathologists was moderate with a mean Cohen's kappa of 0.51, whereas for a binary classification into benign vs (pre)malignant, the agreement was substantial with a mean Cohen's kappa of 0.66. The AI algorithm performed slightly worse for the 6 categories with a moderate Cohen's kappa of 0.43 but was comparable for the binary classification with a substantial Cohen's kappa of 0.65. AI-assisted diagnosis of endometrial biopsies was demonstrated to be feasible in discriminating between benign and (pre)malignant endometrial tissues, even when trained on unselected cases. Endometrial premalignancies remain challenging for both pathologists and AI algorithms. Future steps to improve reliability of the diagnosis are needed to achieve a more refined AI-assisted diagnostic solution for endometrial biopsies that covers both premalignant and malignant diagnoses.