Adenoid cystic carcinoma of the lacrimal gland: role of nuclear survivin (BIRC5) as a prognostic marker.

AIMS: This study aimed to evaluate the expression of nuclear survivin in adenoid cystic carcinoma (ACC) of the lacrimal gland and to determine if this expression is associated with histopathological features, markers of apoptosis and proliferation or clinical outcomes. METHODS AND RESULTS: Immunohistochemical staining for survivin, p53, Ki-67 and Bcl-2 was analyzed in 55 cases of ACC of lacrimal gland. Thirty-one cases (56.3%) expressed nuclear survivin. All cases expressed p53, Ki-67 and Bcl-2. Eleven cases (35.5%) had a high nuclear survivin score (NS-SCORE) and 20 cases (64.5%) had a low NS-SCORE. Cases with a high NS-SCORE had a shorter progression-free survival (PFS) (P < 0.0001), higher expression of Ki-67 (P < 0.005) and a solid tumour pattern >30% (P < 0.005). CONCLUSION: Nuclear expression of survivin impacts prognosis significantly and is thus a promising prognostic marker in ACC of the lacrimal gland.

KEDOP: Keratoconus early detection of progression using tomography images.

PURPOSE: To investigate a method to identification of early progression of keratoconus using deep learning neural networks. METHODS: Retrospective evaluation of medical records of patients with progressive keratoconus and had more than one followup visits. Images extracted from the single scheimplug analyzer for analysis were captured during the patient visits. The baseline progression of keratoconus is detected by a change in flat or steep K of ≥1.0D which is labeled as keratometric progression (KP) and progression detected by image based deep learning convolutional neural network (CNN) models, is labeled as latent progression (LP). Patient data consisted of model data (385 eyes of 351patients) to train and test the learning models and prediction data (1331 eyes of 828 patients) to determine the LP based on the learning models. RESULTS: The LP prediction model was able to identify progression at a mean of 11.1 months earlier than KP (p < 0.001). LP prediction model was able to identify progression earlier than KP irrespective of age category, gender, the severity of keratoconus, presenting visual acuity, astigmatism, and spherical equivalent (P < 0.001). When compared to the first visit the corrected distance visual acuity was more stable in 71% of the eyes at LP prediction visit compared to 50% at KP visit (p < 0.001). CONCLUSION: Through this study, we propose a possible solution to address the shortcomings noted in the current approaches of detecting progression relying only on KP. Avoiding bias towards feature selection from tomography images as done in the current study aids in identifying very subtle changes on the images between visits.