Augmented Features Synergize Radiomics in Post-Operative Survival Prediction and Adjuvant Therapy Recommendation for Non-Small Cell Lung Cancer

Lawrence Wing-Chi Chan; Tong Ding; Huiling Shao; Mohan Huang; William Fuk-Yuen Hui; William Chi-Shing Cho; Sze-Chuen Cesar Wong; Ka Wai Tong; Keith Wan-Hang Chiu; Luyu Huang; Haiyu Zhou

doi:10.3389/fonc.2022.659096

Augmented Features Synergize Radiomics in Post-Operative Survival Prediction and Adjuvant Therapy Recommendation for Non-Small Cell Lung Cancer

Front Oncol. 2022 Jan 31:12:659096. doi: 10.3389/fonc.2022.659096. eCollection 2022.

Authors

Affiliations

¹ Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, Hong Kong SAR, China.
² Department of Clinical Oncology, Queen Elizabeth Hospital, Hong Kong, Hong Kong SAR, China.
³ Department of Diagnostic Radiology, University of Hong Kong, Hong Kong, Hong Kong SAR, China.
⁴ Department of Thoracic Surgery, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, School of Medicine, South China University of Technology, The Second School of Clinical Medicine, Southern Medical University, Guangzhou, China.
⁵ Department of Thoracic Surgery, Jiangxi Lung Cancer Institute, Jiangxi Cancer Hospital, Nanchang, China.

Abstract

Background: Owing to the cytotoxic effect, it is challenging for clinicians to decide whether post-operative adjuvant therapy is appropriate for a non-small cell lung cancer (NSCLC) patient. Radiomics has proven its promising ability in predicting survival but research on its actionable model, particularly for supporting the decision of adjuvant therapy, is limited.

Methods: Pre-operative contrast-enhanced CT images of 123 NSCLC cases were collected, including 76, 13, 16, and 18 cases from R01 and AMC cohorts of The Cancer Imaging Archive (TCIA), Jiangxi Cancer Hospital and Guangdong Provincial People's Hospital respectively. From each tumor region, 851 radiomic features were extracted and two augmented features were derived therewith to estimate the likelihood of adjuvant therapy. Both Cox regression and machine learning models with the selected main and interaction effects of 853 features were trained using 76 cases from R01 cohort, and their test performances on survival prediction were compared using 47 cases from the AMC cohort and two hospitals. For those cases where adjuvant therapy was unnecessary, recommendations on adjuvant therapy were made again by the outperforming model and compared with those by IBM Watson for Oncology (WFO).

Results: The Cox model outperformed the machine learning model in predicting survival on the test set (C-Index: 0.765 vs. 0.675). The Cox model consists of 5 predictors, interestingly 4 of which are interactions with augmented features facilitating the modulation of adjuvant therapy option. While WFO recommended no adjuvant therapy for only 13.6% of cases that received unnecessary adjuvant therapy, the same recommendations by the identified Cox model were extended to 54.5% of cases (McNemar's test p = 0.0003).

Conclusions: A Cox model with radiomic and augmented features could predict survival accurately and support the decision of adjuvant therapy for bettering the benefit of NSCLC patients.

Keywords: adjuvant therapy (post-operative); non-small cell lung cancer (NSCLC); patient benefit; prediction model; radiomics.