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  • 1299 Radiomics features to predict tumor response and biomarker status in non-small cell lung cancer (NSCLC) patients treated with immunotherapy

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Regular and Young Investigator Award Abstracts
Machine Learning, Artificial Intelligence and Computational Modeling
1299 Radiomics features to predict tumor response and biomarker status in non-small cell lung cancer (NSCLC) patients treated with immunotherapy
  1. Jeeyeon Lee1,2,
  2. Maria Jose Aguilera Chuchuca1,
  3. Peter Haseok Kim3,
  4. Taegyu Um1,
  5. Madeline Jenkin1,
  6. Myungwoo Nam4,
  7. Liam Il-Young Chung1,
  8. Jisang Yu1,
  9. Trie Arni Djunadi1,
  10. Hyeonseon Kim1,
  11. Moataz Soliman1,
  12. Nicolo Gennaro1,
  13. Leeseul Kim5,
  14. Youjin Oh6,
  15. Sung Mi Yoon7,
  16. Zunairah Shah8,
  17. Soowon Lee9,
  18. Cecilia Nam10,
  19. Timothy Hong10,
  20. Yury S Velichko1 and
  21. Young Kwang Chae11
  1. 1Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
  2. 2School of Medicine, Kyungpook National University, Kyungpook National University Chilgok Hospital, Daegu, Republic of Korea
  3. 3The University of Texas at Austin, Austin, TX, USA
  4. 4Lincoln Medical and Mental Health Center, Bronx, NY, USA
  5. 5Ascension Saint Francis Hospital, Chicago, IL, USA
  6. 6Northwestern Memorial Hospital, Chicago, IL, USA
  7. 7New York City Health and Hospitals Corporation North Central Bronx/Jacobi Medical Center, The Bronx, NY, USA
  8. 8Roswell Park Comprehensive Cancer Center, Buffalo, NY, USA
  9. 9Baylor University, WACO, TX, USA
  10. 10Northwestern University, Evanston, IL, USA
  11. 11Northwestern University, Chicago, IL, USA

Abstract

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

http://creativecommons.org/licenses/by-nc/4.0/

This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See http://creativecommons.org/licenses/by-nc/4.0/.

https://doi.org/10.1136/jitc-2023-SITC2023.1299

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