Automated Assessment of Ki-67 Proliferation Index in Neuroendocrine Tumors by Deep Learning.

Researchers

Journal

Modalities

Models

Abstract

The Ki-67 proliferation index (PI) is a prognostic factor in neuroendocrine tumors (NETs) and defines tumor grade. Analysis of Ki-67 PI requires calculation of Ki-67 positive and negative tumor cells, which is highly subjective. To overcome this, we developed a deep learning based Ki-67 PI algorithm (KAI) that objectively calculates Ki-67 PI.Our study material consisted of NETs divided into training (n=39), testing (n=124), and validation (n=60) series. All slides were digitized and processed in the Aiforia® Create (Aiforia Technologies, Helsinki, Finland) platform.The ICC between the pathologists and the KAI was 0.89. In 46% of the tumors, the Ki-67 PIs calculated by the pathologists and the KAI were the same. In 12% of the tumors, the Ki-67 PI calculated by the KAI was 1% lower and in 42% of the tumors on average 3% higher.The DL based Ki-67 PI algorithm yields results similar to human observers. While the algorithm cannot replace the pathologist, it can assist in the laborious Ki-67 PI assessment of NETs. In the future, this approach could be useful in e.g. multi-center clinical trials where objective estimation of Ki-67 PI is crucial.This article is protected by copyright. All rights reserved.