Radiomics Response Signature for Identification of Metastatic Colorectal Cancer Sensitive to Therapies Targeting EGFR Pathway

Laurent Dercle, MD, PhD; Lin Lu, PhD; Lawrence H. Schwartz, MD; Min Qian, PhD; Sabine Tejpar, MD, PhD; Peter Eggleton, MB; Binsheng Zhao, DSc; Hubert Piessevaux, MD, PhD

Disclosures

J Natl Cancer Inst. 2020;112(9):902-912. 

In This Article

Abstract and Introduction

Abstract

Background: The authors sought to forecast survival and enhance treatment decisions for patients with liver metastatic colorectal cancer by using on-treatment radiomics signature to predict tumor sensitiveness to irinotecan, 5-fluorouracil, and leucovorin (FOLFIRI) alone (F) or in combination with cetuximab (FC).

Methods: We retrospectively analyzed 667 metastatic colorectal cancer patients treated with F or FC. Computed tomography quality was classified as high (HQ) or standard (SD). Four datasets were created using the nomenclature (treatment) – (quality). Patients were randomly assigned (2:1) to training or validation sets: FCHQ: 78:38, FCSD: 124:62, FHQ: 78:51, FSD: 158:78. Four tumor-imaging biomarkers measured quantitative radiomics changes between standard of care computed tomography scans at baseline and 8 weeks. Using machine learning, the performance of the signature to classify tumors as treatment sensitive or treatment insensitive was trained and validated using receiver operating characteristic (ROC) curves. Hazard ratio and Cox regression models evaluated association with overall survival (OS).

Results: The signature (area under the ROC curve [95% confidence interval (CI)]) used temporal decrease in tumor spatial heterogeneity plus boundary infiltration to successfully predict sensitivity to antiepidermal growth factor receptor therapy (FCHQ: 0.80 [95% CI = 0.69 to 0.94], FCSD: 0.72 [95% CI = 0.59 to 0.83]) but failed with chemotherapy (FHQ: 0.59 [95% CI = 0.44 to 0.72], FSD: 0.55 [95% CI = 0.43 to 0.66]). In cetuximab-containing sets, radiomics signature outperformed existing biomarkers (KRAS-mutational status, and tumor shrinkage by RECIST 1.1) for detection of treatment sensitivity and was strongly associated with OS (two-sided P < .005).

Conclusions: Radiomics response signature can serve as an intermediate surrogate marker of OS. The signature outperformed known biomarkers in providing an early prediction of treatment sensitivity and could be used to guide cetuximab treatment continuation decisions.

Introduction

Colorectal cancer (CRC) is a leading cause of cancer death globally. Liver metastasis affects more than one-half of CRC patients.[1] Anti–epidermal growth factor receptor (EGFR) therapies, including tyrosine kinase inhibitors and monoclonal antibodies, demonstrate activity in both CRC and other tumor types. In metastatic CRC (mCRC) patients, the assessment of anti-EGFR monoclonal antibody efficacy relies on computed tomography (CT) scan response endpoints. The on-treatment shrinkage of metastases on CT scans is considered a hallmark of EGFR signaling pathway dependency[2–5] and of treatment sensitivity.[6] In unresectable mCRC, tumor shrinkage guides the clinical decision to pursue a curative opportunity (downstage to resection) or palliative treatment to maximize overall survival (OS) and improve quality of life by symptom relief.[1] As the decision to continue EGFR-targeted therapy must balance the risks and potential rewards of treatment, there is a pressing need for biomarkers that can estimate the likelihood of clinical benefit in individual patients.

We sought to meet this need for alternative biomarkers by using artificial intelligence (AI). We utilized machine learning to create an AI signature that evaluated a change in tumor phenotype between baseline and 8 weeks on CT-scan images to predict clinical outcome (OS). Radiology is undergoing rapid change because of remarkable advances in the field of AI, particularly algorithms for machine learning and deep learning, which enable automated, high-throughput quantification of medical images as a set of quantitative features. Traditional radiomic features are identified according to a priori definitions (eg, tumor diameter or density), and artificial neural networks adaptively define deep learning features as spatial hierarchies of representations. The dataset of radiomic and deep learning features can be mined by machine learning to identify statistically significant associations between variables of interest, such as clinical outcome or tumor mutational status. This big data approach is applicable to any imaging modality but requires large and consistent datasets, which CT scanning currently best provides because of its use as a standard of care in oncology. In particular, the widespread adoption of CT measurement of tumor diameter (eg, RECIST 1.1) as an endpoint in clinical trials has generated an invaluable resource for AI research. The current study leverages this resource by analyzing data from the multicenter clinical trial NCT00154102.

AI techniques allow objective and reproducible analysis of CT image characteristics, including imaging features not apparent to the human eye. The overall goal of this study is to determine whether AI techniques can offer oncologists additional clinical information regarding response assessment. Our test case is the decision to continue anti-EGFR treatment. We utilized data from mCRC patients treated with cetuximab in combination with FOLFIRI (irinotecan, 5-fluorouracil, and leucovorin) in the randomized multicenter CRYSTAL trial (NCT00154102). Data from this trial were used to obtain regulatory approval for the clinical use of cetuximab in mCRC. Pretreatment selection is currently based on a biopsy sample (primary tumor or metastasis) or on liquid biopsy. In approximately one-half of mCRCs patients,[7] a RAS wild-type mutational status (KRAS and NRAS exons 2, 3, 4) guides the clinical use of cetuximab in addition to either FOLFIRI[8–11] or FOLFOX.[12] This treatment has been found to prolong median OS by 8 months in RAS wild type[10] and increase overall response rate, but also to increase the rate of adverse events.[8] However, several tumor biopsy–driven clinical trials have shown the limitations of predicting clinical benefit from EGFR-targeted therapies in mCRC and other solid tumors based on a single biomarker.[13] The exception is the presence of EGFR mutation in non-small cell lung cancer, which does predict a higher response rate and prolonged survival.[13]

To determine whether an AI-driven signature could guide clinicians to continue EGFR-targeted therapies on an individualized basis, we aimed to develop and validate an on-treatment signature detecting mCRC patients sensitive to FOLFIRI+cetuximab (FC) using quantitative assessment of tumor changes between baseline and 8-week CT images.

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