Clinical Nomogram Using Novel Computed Tomography–Based Radiomics Predicts Survival in Patients With Non–Small-Cell Lung Cancer Treated With Stereotactic Body Radiation Therapy

Abstract

PURPOSE Improved survival prediction and risk stratification in non–small-cell lung cancer (NSCLC) would lead to better prognosis counseling, adjuvant therapy selection, and clinical trial design. We propose the persistent homology (PHOM) score, the radiomic quantification of solid tumor topology, as a solution. MATERIALS AND METHODS Patients diagnosed with stage I or II NSCLC primarily treated with stereotactic body radiation therapy (SBRT) were selected (N = 554). The PHOM score was calculated for each patient's pretreatment computed tomography scan (October 2008-November 2019). PHOM score, age, sex, stage, Karnofsky Performance Status, Charlson Comorbidity Index, and post-SBRT chemotherapy were predictors in the Cox proportional hazards models for OS and cancer-specific survival. Patients were split into high– and low–PHOM score groups and compared using Kaplan-Meier curves for overall survival (OS) and cumulative incidence curves for cause-specific death. Finally, we generated a validated nomogram to predict OS, which is publicly available at Eashwarsoma.Shinyapps. RESULTS PHOM score was a significant predictor for OS (hazard ratio [HR], 1.17; 95% CI, 1.07 to 1.28) and was the only significant predictor for cancer-specific survival (1.31; 95% CI, 1.11 to 1.56) in the multivariable Cox model. The median survival for the high-PHOM group was 29.2 months (95% CI, 23.6 to 34.3), which was significantly worse compared with the low-PHOM group (45.4 months; 95% CI, 40.1 to 51.8; P < .001). The high-PHOM group had a significantly greater chance of cancer-specific death at post-treatment month 65 (0.244; 95% CI, 0.192 to 0.296) compared with the low-PHOM group (0.171; 95% CI, 0.123 to 0.218; P = .029). CONCLUSION The PHOM score is associated with cancer-specific survival and predictive of OS. Our developed nomogram can be used to inform clinical prognosis and assist in making post-SBRT treatment considerations.

Developing a clinical-radiomic prediction model for 3-year cancer-specific survival in lung cancer patients treated with stereotactic body radiation therapy

“…In the era of personalized medicine, accurately predicting prognosis is essential for guiding individualized clinical decision-making (Kang et al 2020 ). Moreover, improved survival prediction and risk stratification in NSCLC would benefit prognosis counseling, adjuvant therapy selection, and clinical trial design (Somasundaram et al 2023 ). Therefore, the prediction model developed in the study has the potential to identify lung cancer patients most likely to benefit from SBRT treatment and guide treatment strategy.…”

“…Radiomics, a non-invasive technology that converts medical images into a high-dimensional mineable feature space via high-throughput quantitative feature extraction (Bera et al 2022 ; Gillies et al 2016 ; Lambin et al 2012 , 2017 ; Reuze et al 2018 ), has been introduced for the prediction of treatment responses, patient stratification, and prognosis for lung cancer patients in recent years (Chen et al 2017 , 2023 ; Constanzo et al 2017 ; Coroller et al 2015 ; Huang et al 2016 ; Lee et al 2017 ; Li et al 2018 ; Mattonen et al 2016 ). In particular, radiomic features extracted from CT images have shown promising performance in predicting OS in NSCLC patients treated with SBRT (Jiao et al 2021 ; Li et al 2018 ; Sawayanagi et al 2022 ; Somasundaram et al 2023 ; Starkov et al 2019 ). However, most studies to date have employed OS as endpoint and there is a scarcity of studies that have integrated clinical and radiomic features to predict cancer-specific survival (CSS) in lung cancer patients undergoing SBRT.…”