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Regular and Young Investigator Award Abstracts
Machine Learning, Artificial Intelligence and Computational Modeling
1298 Radiomics features to predict immune checkpoint inhibitor-related pneumonitis (CIP) in NSCLC patients treated with immunotherapy
  1. Jeeyeon Lee1,2,
  2. Haseok Kim3,
  3. Maria Jose Aguilera Chuchuca1,
  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. 8Weiss Memorial Hospital, Chicago, IL, USA
  9. 9Baylor University, WACO, TX, USA
  10. 10Northwestern University, Evanston, IL, USA
  11. 11Northwestern University, Chicago, IL, USA

  • Journal for ImmunoTherapy of Cancer (JITC) preprint. The copyright holder for this preprint are the authors/funders, who have granted JITC permission to display the preprint. All rights reserved. No reuse allowed without permission.

Abstract

Background As immunotherapy is more widely used for advanced non-small cell lung cancer (NSCLC), important challenges remain with adverse events including checkpoint inhibitor-associated pneumonitis (CIP). CIP usually requires discontinuation of immunotherapy even if the tumor responds, and effective models for predicting CIP are still limited. This study aims to investigate radiomic features using artificial intelligence (AI) algorithms to predict CIP in patients with NSCLC.

Methods Data from 132 patients with stage III-IV NSCLC treated with immunotherapy were analyzed. Tumor response was evaluated based on immune-related RECIST (irRECIST) (tables 1 and 2). Patients were categorized into two groups: durable responders (including 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 from the tumor and peritumoral regions on contrast enhanced CT imaging. 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 CIP and radiation-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 CIP was more common in the non-responder group [durable responder: 8/91 (8.8%) vs non-responder: 6/41 (14.6%) (p=0.049)] (table 2). Higher platelet counts at baseline (≥400K) was more prevalent in the non-responder group (p<0.001) and patients who received chemotherapy containing regimen were more likely to be durable responders (p<0.001) (table 1).

Association between some CT-based radiomic features and the tumor size demonstrates statistically significant difference between radiation-related and CIP. Significant difference in the intercept (0.05, 95%CI[0.04, 0.05] p<0.001) and the slope (-0.01, 95%CI[-0.007, 0.012], p<0.001) of Neighboring Gray Tone Difference Matrix (NGTDM) coarseness computed for primary tumors was found with CIP (intercept difference -0.07, 95%CI[-0.1, -0.4], p<0.001 and slope difference 0.02, 95%CI[0.01, 0.02], p<0.001). For the peritumoral space, Intensity Histogram Maximum Histogram Gradient Grey Level, also showed significant difference in the intercept (30, 95%CI [19.7–40.3], p<0.001) and the slope (-2.96, 95%CI[-5.3, -0.6], p<0.015) and CIP associated difference in the intercept equal 42.7 (95%CI[12.76, -70.4], p<0.03) and slope difference -10.5 (95%CI[-15.3, -4.76], p<0.002).

Conclusions Non-responders among NSCLC patients treated with immunotherapy had a higher incidence of CIP, and CT-based radiomic features may assist in the prediction of CIP.

Ethics Approval Northwestern IRB approved: STU00207117EnterWrite to Liam Il-Young Chung

View this table:
Abstract 1298 Table 1

Clinicopathologic characteristic of 132 patient with non-small cell lung cancer (NSCLC) who recevied immunotherapy, categorized by responder and non-responder based on the immune-related RECIST (irRECIST)

View this table:
Abstract 1298 Table 2

Outcomes after immunotherapy Outcomes after immunotherapy of 132 patients with non-small cell lung cancer (NSCLC) who received immunotherapy, categorized by responder and non-responder based on the immune-related RECIST (irRECIST)

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/.

http://dx.doi.org/10.1136/jitc-2023-SITC2023.1298

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