Automated detection and segmentation of non-small cell lung cancer computed tomography images.

ABSTRACT: Detection and segmentation of abnormalities on medical images is highly important for patient management including diagnosis, radiotherapy, response evaluation, as well as for quantitative image research. We present a fully automated pipeline for the detection and volumetric segmentation of non-small cell lung cancer (NSCLC) developed and validated on 1328 thoracic CT scans from 8 institutions. Along with quantitative performance detailed by image slice thickness, tumor size, image interpretation difficulty, and tumor location, we report an in-silico prospective clinical trial, where we show that the proposed method is faster and more reproducible compared to the experts. Moreover, we demonstrate that on average, radiologists & radiation oncologists preferred automatic segmentations in 56%

SUBMITTER: Primakov SP 

PROVIDER: S-EPMC9198097 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature