Predicting neoadjuvant chemotherapy benefit using deep learning from stromal histology in breast cancer.

Neoadjuvant chemotherapy (NAC) is a standard treatment option for locally advanced breast cancer. However, not all patients benefit from NAC; some even obtain worse outcomes after therapy. Hence, predictors of treatment benefit are crucial for guiding clinical decision-making.

Deep learning-based predictive biomarker of pathological complete response to neoadjuvant chemotherapy from histological images in breast cancer.

Background: Pathological complete response (pCR) is considered a surrogate endpoint for favorable survival in breast cancer patients treated with neoadjuvant chemotherapy (NAC). Predictive biomarkers of treatment response are crucial for guiding treatment decisions. With the hypothesis that histological information on tumor biopsy images could predict NAC response in breast cancer, we proposed a novel deep learning (DL)-based biomarker that predicts pCR from images of hematoxylin and eosin (H&E)-stained tissue and evaluated its predictive performance.

Uncommon primary Group A streptococcal erector spinae pyomyositis diagnosed as muscle strain in a fit young male.

Acute lower back pain in a fit healthy male with recent strenuous physical activity is often attributed to muscle strain. We report a rare case of erector spinae pyomyositis developing in a young and otherwise healthy young adult male that was almost misdiagnosed as muscle strain. Despite admission and close monitoring, diagnosis was only confirmed on a magnetic resonance imaging of the spine later during the hospital stay.

[Progression in diagnostic pathology; development of virtual microscopy and its applications].

Many systems have already been designed and successfully used for clinical laboratory and pathological examination. The evolution of image analysis was enabled when analog images of the original glass slides could be transferred to digital images with the rapid development of virtual microscopy and virtual slides depended upon computer technologies. Today, whole slide can be acquired by virtual microscopes.