SurvdigitizeR: an algorithm for automated survival curve digitization.

Background: Decision analytic models and meta-analyses often rely on survival probabilities that are digitized from published Kaplan-Meier (KM) curves. However, manually extracting these probabilities from KM curves is time-consuming, expensive, and error-prone. We developed an efficient and accurate algorithm that automates extraction of survival probabilities from KM curves.

Clinical outcomes of oral epithelial dysplasia managed by observation versus excision.

Importance: Oral potentially malignant disorders, including oral epithelial dysplasia (OED), are a group of conditions with an increased risk of progression to oral cancer. Clinical management of OED is challenging and usually involves monitoring with repeated incisional biopsies or complete surgical excision.

Objective: To determine if complete surgical excision of OED impacts malignant transformation or improves survival outcomes in lesions that progress to malignancy.

Very late relapse in Hodgkin lymphoma: Characterizing an understudied population.

Background: Very late relapse (VLR) occurring >5 years after initial diagnosis is an uncommon event in the management of Hodgkin lymphoma (HL). Limited information regarding risk factors and optimal therapy is available.

Patients And Methods: We reviewed patients treated for HL at Princess Margaret Cancer Centre, Toronto, Ontario Canada between January 01, 1999 and 31 December 31, 2018.

Tightly integrated multiomics-based deep tensor survival model for time-to-event prediction.

Motivation: Multiomics cancer profiles provide essential signals for predicting cancer survival. It is challenging to reveal the complex patterns from multiple types of data and link them to survival outcomes. We aim to develop a new deep learning-based algorithm to integrate three types of high-dimensional omics data measured on the same individuals to improve cancer survival outcome prediction.