English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Herein we propose a deep learning-based approach for the prediction of lunglesion response based on radiomic features extracted from clinical CT scans ofpatients in non-small cell lung cancer trials. The approach starts with theclassification of lung lesions from the set of primary and metastatic lesionsat various anatomic locations. Focusing on the lung lesions, we performautomatic segmentation to extract their 3D volumes. Radiomic features are thenextracted from the lesion on the pre-treatment scan and the first follow-upscan to predict which lesions will shrink at least 30% in diameter duringtreatment (either Pembrolizumab or combinations of chemotherapy andPembrolizumab), which is defined as a partial response by the ResponseEvaluation Criteria In Solid Tumors (RECIST) guidelines. A 5-fold crossvalidation on the training set led to an AUC of 0.84 +/- 0.03, and theprediction on the testing dataset reached AUC of 0.73 +/- 0.02 for the outcomeof 30% diameter shrinkage.

A deep learning-facilitated radiomics solution for the prediction of lung lesion shrinkage in non-small cell lung cancer trials