RT Journal Article
SR Electronic
T1 1299 Radiomics features to predict tumor response and biomarker status in non-small cell lung cancer (NSCLC) patients treated with immunotherapy
JF Journal for ImmunoTherapy of Cancer
JO J Immunother Cancer
FD BMJ Publishing Group Ltd
SP A1444
OP A1444
DO 10.1136/jitc-2023-SITC2023.1299
VO 11
IS Suppl 1
A1 Lee, Jeeyeon
A1 Aguilera Chuchuca, Maria Jose
A1 Kim, Peter Haseok
A1 Um, Taegyu
A1 Jenkin, Madeline
A1 Nam, Myungwoo
A1 Chung, Liam Il-Young
A1 Yu, Jisang
A1 Djunadi, Trie Arni
A1 Kim, Hyeonseon
A1 Soliman, Moataz
A1 Gennaro, Nicolo
A1 Kim, Leeseul
A1 Oh, Youjin
A1 Yoon, Sung Mi
A1 Shah, Zunairah
A1 Lee, Soowon
A1 Nam, Cecilia
A1 Hong, Timothy
A1 Velichko, Yury S
A1 Chae, Young Kwang
YR 2023
UL http://jitc.bmj.com/content/11/Suppl_1/A1444.abstract
AB Background Accurately predicting response to immunotherapy in non-small cell lung cancer (NSCLC) patients is important in establishing a precision treatment strategy. This study aims to investigate chest CT-based radiomic features using artificial intelligence (AI) algorithms to predict tumor response and biomarkers in patients with NSCLC.Methods Clinicopathologic data from 132 patients with stage III-IV NSCLC treated with immunotherapy were collected, and the tumor response was evaluated based on RECIST 1.1 and immune-related RECIST (irRECIST). Patients were classified into two groups: durable responders (which include complete response [CR], partial response [PR], or stable disease [SD]) and non-responders (progressive disease [PD]). Segmentation was performed by three physicians using LIFEx software (IMIV/CEA, Orsay, France). 3D-radiomic features were collected for the tumor and peritumoral region from contrast enhanced CT scans. The lesion size was measured as a volume-of-Interest (VOI). Linear mixed-effects (LME) regression model was used to evaluate the association between radiomic features and the size of the VOI, with radiation-related and immunotherapy-related pneumonitis status specific variables for slope and intercept. The chemotherapy containing regimen was considered as a random factor. The corresponding p-values were used to assess differences in LME regression slope and/or intercept between all groups.Results Among 132 patients, 61 (46.2%) were male and 71 (53.8%) were female (median age 66.5 years [range, 29–89]). Association between some CT-based radiomic features and the tumor size demonstrates statistically significant between responders and non-responders per RECIST 1.1 and irRECIST criteria. Significant difference in the intercept (-1.09E8, 95%CI[-1.5E8, -0.7E8], p<0.001) and the slope (31.1E6, 95%CI[20.1E6, 41.4E6], p<0.001) of MORPHOLOGICAL Integrated Intensity computed for primary tumors was found with irRECIST criteria (intercept difference -6.6E7 95%CI[-11.6E7, -1.5E7], p<0.01 and slope difference (15.8E6, 95%CI[36.3E6, 28E6], p<0.01). The same radiomics feature was statistically significant between responders and non-responders per RECIST 1.1 criteria with the intercept (-1.17E8, 95%CI[-1.55E8, -0.8E8], p<0.001) and the slope (32.8E6, 95%CI[23.7E6, 41.9E6], p<0.001) with the intercept difference (-6.1E7 95%CI[-10.9E7, -1.3E7], p<0.013) and slope difference (14.7E6, 95%CI[32.3E6, 26.2E6], p<0.02). For the peritumoral space, Gray Level Size Zone Matrix (GLSZM) Large Zone Low Grey Level Emphasis, demonstrated significant difference in the intercept (-0.73, 95%CI [-1.63, 0.16], p<0.1) and the slope (0.31, 95%CI [0.9, 0.53], p<0.005) and irRECIST associated difference in the intercept was 1.9 (95%CI[0.84, 2.97], p<0.01) and slope difference was -0.5 (95%CI[-0.76, -0.25], p<0.001).Conclusions CT-based radiomic features may help predict potential responders to immunotherapy in NSCLC patients.Ethics Approval Northwestern IRB approved: STU00207117