Early Readout on Overall Survival of Patients With Melanoma Treated With Immunotherapy Using a Novel Imaging Analysis

Abstract

Importance: Existing criteria to estimate the benefit of a therapy in patients with cancer rely almost exclusively on tumor size, an approach that was not designed to estimate survival benefit and is challenged by the unique properties of immunotherapy. More accurate prediction of survival by treatment could enhance treatment decisions.

Objective: To validate, using radiomics and machine learning, the performance of a signature of quantitative computed tomography (CT) imaging features for estimating overall survival (OS) in patients with advanced melanoma treated with immunotherapy.

Design, setting, and participants: This prognostic study used radiomics and machine learning to retrospectively analyze CT images obtained at baseline and first follow-up and their associated clinical metadata. Data were prospectively collected in the KEYNOTE-002 (Study of Pembrolizumab [MK-3475] Versus Chemotherapy in Participants With Advanced Melanoma; 2017 analysis) and KEYNOTE-006 (Study to Evaluate the Safety and Efficacy of Two Different Dosing Schedules of Pembrolizumab [MK-3475] Compared to Ipilimumab in Participants With Advanced Melanoma; 2016 analysis) multicenter clinical trials. Participants included 575 patients with a diagnosis of advanced melanoma who were randomly assigned to training and validation sets. Data for the present study were collected from November 20, 2012, to June 3, 2019, and analyzed from July 1, 2019, to September 15, 2021.

Interventions: KEYNOTE-002 featured trial groups testing intravenous pembrolizumab, 2 mg/kg or 10 mg/kg every 2 or every 3 weeks based on randomization, or investigator-choice chemotherapy; KEYNOTE-006 featured trial groups testing intravenous ipilimumab, 3 mg/kg every 3 weeks and intravenous pembrolizumab, 10 mg/kg every 2 or 3 weeks based on randomization.

Main outcomes and measures: The performance of the signature CT imaging features for estimating OS at the month 6 posttreatment landmark in patients who received pembrolizumab was measured using an area under the time-dependent receiver operating characteristics curve (AUC).

Results: A random forest model combined 25 imaging features extracted from tumors segmented on CT images to identify the combination (signature) that best estimated OS with pembrolizumab in 575 patients. The signature combined 4 imaging features, 2 related to tumor size and 2 reflecting changes in tumor imaging phenotype. In the validation set (287 patients treated with pembrolizumab), the signature reached an AUC for estimation of OS status of 0.92 (95% CI, 0.89-0.95). The standard method, Response Evaluation Criteria in Solid Tumors 1.1, achieved an AUC of 0.80 (95% CI, 0.75-0.84) and classified tumor outcomes as partial or complete response (93 of 287 [32.4%]), stable disease (90 of 287 [31.3%]), or progressive disease (104 of 287 [36.2%]).

Conclusions and relevance: The findings of this prognostic study suggest that the radiomic signature discerned from conventional CT images at baseline and on first follow-up may be used in clinical settings to provide an accurate early readout of future OS probability in patients with melanoma treated with single-agent programmed cell death 1 blockade.

Figure 1.. Study Participants

IO indicates immunotherapy.

Figure 2.. Prediction of Overall Survival (OS) Using Radiomics Signature vs Response Evaluation Criteria in Solid Tumors 1.1 (RECIST 1.1) in the Validation Set

The regulatory end point defined progression-free survival and overall response rate. In the pembrolizumab validation set (n = 287), trichotomized RECIST 1.1 response classified patients at 3 months as having a complete or partial response (93 of 287 [32.4%]), stable disease (90 of 287, [31.3%]), and progressive disease (104 of 287 [36.2%]). The signature was trichotomized from best to worse predicted prognosis using the same distribution as RECIST 1.1 (classified patients into 3 groups, including 32.4%, 31.3%, and 36.2% of patients as cutoffs to divide the signature results into top, middle, and bottom groups). The area under the time-dependent receiver operating characteristics curve for prediction of survival status at 6 months posttreatment landmark was 0.92 (95% CI, 0.89-0.95) for the continuous signature, 0.89 (95% CI, 0.86-0.92) for the trichotomized signature, and 0.80 (95% CI, 0.74-0.84) the trichotomized RECIST 1.1.

Figure 3.. Signature vs Response Evaluation Criteria in Solid Tumors 1.1 (RECIST 1.1) in the Validation (Pembrolizumab Only) Cohort

In the pembrolizumab validation set (n = 287), we further observed that the radiomics signature provides a better prognostic separation than trichotomized response per RECIST 1.1 at month 3. We used Kaplan-Meier curves. The signature as well as the 4 features were trichotomized using the same distribution as RECIST 1.1, ranging from best to worst predicted prognosis: complete or partial response (CR/PR; 93 of 287 [32.4%]) to stable disease (SD; 90 of 287 [31.3%]) and progressive disease (PD; 104 of 287 [36.2%]). This comparison was intended to show that the radiomics signature is unambiguous and parsimonious and provides a better separability of classification. Shaded areas indicate 95% CIs.