AVATAR: Analysis for Visual Acuity Prediction After Eye Interventional Radiotherapy

Abstract

Aim: The aim of this study was to detect clinical factors predictive of loss of visual acuity after treatment in order to develop a predictive model to help identify patients at risk of visual loss. Patients and Methods: This was a retrospective review of patients who underwent interventional radiotherapy (brachytherapy) with 106 Ru plaque for primary uveal melanoma. A predictive nomogram for visual acuity loss at 3 years from treatment was developed. Results: A total of 152 patients were selected for the study. The actuarial probability of conservation of 20/40 vision or better was 0.74 at 1 year, 0.59 at 3 years, and 0.54 at 5 years after treatment. Factors positively correlated with loss of visual acuity included: age at start of treatment (p=0.004) and longitudinal basal diameter (p=0.057), while distance of the posterior margin of the tumor from the foveola was inversely correlated (p=0.0007). Conclusion: We identified risk factors affecting visual function and developed a predictive model and decision support tool (AVATAR nomogram).

Radiological and clinical findings in uveal melanoma treated by plaque interventional radiotherapy (brachytherapy): Visual atlas and literature review on response assessment

“…In particular, IRT may preserve both cosmesis and visual acuity (VA), which at 3 years after treatment usually reaches a stability in terms of VA loss [ 6 ].…”

Artificial intelligence (AI) and interventional radiotherapy (brachytherapy): state of art and future perspectives

“… The following characteristics can be applied in all phases of radiation treatments workflow ( Figure 1 ): First patient consultation: The application of AI in this step could result in improving patient-tailored treatment personalization, guiding clinical choices and treatment selection, comparing several kind of treatments [ 14 ], and thoroughly informing the patient on treatment tolerance and expected toxicities [ 15 , 16 ]. Delineation: Implementation of AI in this process may result in time saving and workflow efficiency optimization, with better accuracy, oncology standardization, and adequate segmentation guidelines.…”

“…First patient consultation: The application of AI in this step could result in improving patient-tailored treatment personalization, guiding clinical choices and treatment selection, comparing several kind of treatments [ 14 ], and thoroughly informing the patient on treatment tolerance and expected toxicities [ 15 , 16 ].…”