In:
Journal of Clinical Oncology, American Society of Clinical Oncology (ASCO), Vol. 35, No. 15_suppl ( 2017-05-20), p. e14534-e14534
Abstract:
e14534 Background: Nivolumab is a PD-1 inhibitor that is FDA approved for treatment of chemotherapy refractory advanced NSCLC. The current standard clinical approach to evaluating tumor response is sub-optimal in evaluating clinical benefit from immunotherapy drugs. Our study aims to explore whether changes in radiomic features of the tumor between baseline and 2-week post-treatment CT scans can predict treatment response. Methods: 41 NSCLC patients treated with nivolumab were included in this study. 22 patients with pre- and post-nivolumab CT scans were used as a learning set and the remaining 19 for independent testing. Patients who did not receive nivolumab after 2 cycles due to lack of response or progression as per RECIST were classified as ‘non-responders’, and patients who had radiological response as per RECIST, or stable disease as per RECIST and clinical improvement were classified as ‘responders’. Lung nodules on pre-treatment CT scans were annotated with 3D SLICER software by a radiologist. 312 texture features of lung nodules were extracted and investigated in the study. The percent difference of each extracted feature was calculated based on the baseline and 2 week post-therapy CT scan. In the learning set, the six features that most significantly changed between baseline and post-treatment scans and also maximally differentially expressed between responders and non-responders were identified. Unsupervised clustering was applied on the set of 6 features for the 19 patients in the test set to predict which patients did and did not respond. Results: The top 6 features predictive of response corresponded to the Haralick, Gabor and Laws texture families. Unsupervised clustering yielded an accuracy of 78.95%. Conclusions: Our results suggest that changes in certain radiomic texture features between baseline and post-treatment CT scans following nivolumab could identify early clinical response to treatment. Additional validation of these novel quantitative imaging based approaches is warranted to accurately define clinical benefit from immunotherapy.
Type of Medium:
Online Resource
ISSN:
0732-183X
,
1527-7755
DOI:
10.1200/JCO.2017.35.15_suppl.e14534
Language:
English
Publisher:
American Society of Clinical Oncology (ASCO)
Publication Date:
2017
detail.hit.zdb_id:
2005181-5
