A collaborative workflow between pathologists and deep learning for the evaluation of tumour cellularity in lung adenocarcinoma.

ABSTRACT:

Aims

The reporting of tumour cellularity in cancer samples has become a mandatory task for pathologists. However, the estimation of tumour cellularity is often inaccurate. Therefore, we propose a collaborative workflow between pathologists and artificial intelligence (AI) models to evaluate tumour cellularity in lung cancer samples and propose a protocol to apply it to routine practice.

Methods and results

We developed a quantitative model of lung adenocarcinoma that was validated and tested on 50 cases, and a collaborative workflow where pathologists could access the AI results and adjust their original tumour cellularity scores (adjusted-score) that we tested on 151 cases. The adjusted-score was validated by comparing them with a ground truth established by manual annotati

SUBMITTER: Sakamoto T 

PROVIDER: S-EPMC9826135 | biostudies-literature | 2022 Dec

REPOSITORIES: biostudies-literature