Identification of Non-Small Cell Lung Cancer Sensitive to Systemic Cancer Therapies Using Radiomics

Abstract

Purpose: Using standard-of-care CT images obtained from patients with a diagnosis of non-small cell lung cancer (NSCLC), we defined radiomics signatures predicting the sensitivity of tumors to nivolumab, docetaxel, and gefitinib.

Experimental design: Data were collected prospectively and analyzed retrospectively across multicenter clinical trials [nivolumab, n = 92, CheckMate017 (NCT01642004), CheckMate063 (NCT01721759); docetaxel, n = 50, CheckMate017; gefitinib, n = 46, (NCT00588445)]. Patients were randomized to training or validation cohorts using either a 4:1 ratio (nivolumab: 72T:20V) or a 2:1 ratio (docetaxel: 32T:18V; gefitinib: 31T:15V) to ensure an adequate sample size in the validation set. Radiomics signatures were derived from quantitative analysis of early tumor changes from baseline to first on-treatment assessment. For each patient, 1,160 radiomics features were extracted from the largest measurable lung lesion. Tumors were classified as treatment sensitive or insensitive; reference standard was median progression-free survival (NCT01642004, NCT01721759) or surgery (NCT00588445). Machine learning was implemented to select up to four features to develop a radiomics signature in the training datasets and applied to each patient in the validation datasets to classify treatment sensitivity.

Results: The radiomics signatures predicted treatment sensitivity in the validation dataset of each study group with AUC (95 confidence interval): nivolumab, 0.77 (0.55-1.00); docetaxel, 0.67 (0.37-0.96); and gefitinib, 0.82 (0.53-0.97). Using serial radiographic measurements, the magnitude of exponential increase in signature features deciphering tumor volume, invasion of tumor boundaries, or tumor spatial heterogeneity was associated with shorter overall survival.

Conclusions: Radiomics signatures predicted tumor sensitivity to treatment in patients with NSCLC, offering an approach that could enhance clinical decision-making to continue systemic therapies and forecast overall survival.

Conflict of interest statement

Disclosure of Potential Conflicts of Interest

M. Fronheiser is an employee for Bristol-Myers Squibb. S. Du is an employee for Bristol-Myers Squibb. W. Hayes is an employee for and holds ownership interest (including patents) in Bristol-Myers Squibb. D.K. Leung is an employee for Bristol-Myers Squibb. A. Roy is an employee for and holds ownership interest (including patents) in Bristol-Myers Squibb. L.H. Schwartz is a paid advisory board member for Roche and Novartis, and reports receiving commercial research grants from Merck and Boehringer Ingelheim. No potential conflicts of interest were disclosed by the other authors.

©2020 American Association for Cancer Research.

Figures

Figure 1.

Disposition of study patients. Patients could be excluded for multiple reasons. The withdrawal boxes show the number of patients excluded at each step. CT scans acquired at sites are transferred to our academic core. Image selection and quality check using a computer-aided algorithm designed by machine learning. Step 1. Segmentation of the largest measurable lung tumor on CT scan by an expert radiologist at baseline in all patient (inclusion criteria), as well as all available radiographics measurement. Steps 2–3. Tumor imaging phenotype in each patient based on imaging features extraction in the largest measurable segmented lung lesion (1,160 imaging features characterizing changes between baseline and first CT assessment). Step 4. Dimension reduction using machine learning. Identification of reproducible, nonredundant, and informative candidate imaging features for model building. Step 5. Signature building in the training set to enhance strategic decision-making and predict treatment sensitivity. Step 6. Signature validation. Step 7. Transfer of the signature features for evaluation of g and d values using serial radiographic measurements. Step 8. A subset of imaging biomarker is identified.

Figure 2.

Probability of PFS and OS over time as a function of signature score and signature features. Prolonged PFS was observed in patients with low-risk/treatment-sensitive signatures (≤0.5) in both treatment study groups (Fig. 2) using baseline and 8-week CT scans. In the nivolumab cohort (A), median PFS (95% CI) was 2.0 months (1.8–2.2) for patients whose tumors had a signature score > 0.5 (predicted insensitivity, n = 57) and 6.3 months (4.0–8.6) for patients with a signature score ≤ 0.5 (predicted sensitivity, n = 35; P < 10–4). In the docetaxel cohort (B), median PFS (95% CI) was 2.1 months (2.0–2.3) for patients whose tumors had a signature score > 0.5 (predicted insensitivity, n = 39) and 6.2 months (5.5–7.0) for signature score ≤ 0.5 (predicted sensitivity, n = 11; P < 10–4). Using serial radiographic measurements and a landmark at 8-month after drug initiation, we observed that the rate of exponential increase (g) of the radiomic features included in the signatures was associated with OS in patients from both treatment groups (C–E, pooled groups). The magnitude of exponential increase in tumor volume (gVolume, C), tumor spatial heterogeneity (gGLCM-IMC1, D), or boundary irregularity (gShape-SI4, E) was associated with shorter OS.

Figure 3.

Distribution of the rates of patients with an exponential increase (g) or decrease (d) in the eight features included in the Radiomic signatures. Using serial radiographic measurements, the eight features discovered in the three signatures (nivolumab, docetaxel, gefitinib) (A) were generalized and applied to the all cohorts. Patients with exponential increase (g values) or decrease (d values) in Radiomic features are displayed using tumor burden (B), heterogeneity (C), and boundary features (D). This is a proof of concept that AI can be trained to differentiate the simultaneous occurrence of two processes in the overwhelming majority of tumors: exponential growth of the treatment-insensitive fraction of the tumor at a rate described by a growth rate constant designated g for growth, and exponential regression of the treatment-sensitive portion of the tumor, at a rate described by a regression rate constant designated d for decay. Strikingly, the distribution is bimodal in the Gefitinib cohort suggesting a wider variability between sensitive and insensitive tumors.

Figure 4.

Distribution of the rates of patients with an exponential increase (g) or decrease (d) in the eight features included in the Radiomic signatures. Visual representation of the imaging features included in the signature. The changes in tumor imaging phenotype of the “most sensitive” patient treated with nivolumab is displayed below. Tumor was segmented, and its shape and volume are represented using volume rendering (A). As demonstrated, CT scan images are transformed to other mathematical spaces for feature extraction, e.g., CT image is transformed to LOG space for computing the entropy value (spatial heterogeneity), and tumor pixels within segmentation contour are transformed to GLCM matrix (B). Using this information, a radiomic signature predicts treatment sensitivity which is associated with patients’ OS (C).