Early readout on overall survival of patients with melanoma treated with immunotherapy using a novel imaging analysis Journal Article
| Authors: | Dercle, L.; Zhao, B.; Gönen, M.; Moskowitz, C. S.; Firas, A.; Beylergil, V.; Connors, D. E.; Yang, H; Lu, L.; Fojo, T.; Carvajal, R.; Karovic, S.; Maitland, M. L.; Goldmacher, G. V.; Oxnard, G. R.; Postow, M. A.; Schwartz, L. H. |
| Article Title: | Early readout on overall survival of patients with melanoma treated with immunotherapy using a novel imaging analysis |
| Abstract: | Key Points: Question: Can machine learning identify a combination of quantitative imaging features that can predict survival with immunotherapy better than conventional size-based assessment? Findings: In a prognostic analysis of prospectively collected clinical trial data from 575 patients with a diagnosis of advanced melanoma, a random forest algorithm found that 4 computed tomography imaging features, 2 related to tumor size and 2 reflecting changes in tumor imaging phenotype, best estimated overall survival with immunotherapy. The combination of these features (signature) outperformed Response Evaluation Criteria in Solid Tumors 1.1, the standard method based on tumor diameter. Meaning: These findings suggest that radiomics and machine learning can analyze information captured by routine computed tomographic scans to improve clinical decision making for patients with melanoma treated with immunotherapy. This prognostic study assesses whether a signature on computed tomographic imaging derived from tumor size, density, and shape and its change as treatment is administered can estimate overall survival in patients with advanced melanoma. 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 th 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. |
| Keywords: | survival analysis; tomography, x-ray computed; confidence intervals; immunotherapy; roc curve; kaplan-meier estimator; retrospective design; antibodies, monoclonal -- administration and dosage; image processing, computer assisted; melanoma -- therapy; machine learning; human; melanoma -- prognosis |
| Journal Title: | JAMA Oncology |
| Volume: | 8 |
| Issue: | 3 |
| ISSN: | 2374-2437 |
| Publisher: | American Medical Association |
| Date Published: | 2022-03-01 |
| Start Page: | 385 |
| End Page: | 392 |
| Language: | English |
| DOI: | 10.1001/jamaoncol.2021.6818 |
| PROVIDER: | EBSCOhost |
| PROVIDER: | cinahl |
| PMCID: | PMC8778619 |
| PUBMED: | 35050320 |
| DOI/URL: | |
| Notes: | Accession Number: 155836129 -- Entry Date: 20220324 -- Revision Date: 20220324 -- Publication Type: Article; research; tables/charts -- Journal Subset: Peer Reviewed; USA. -- Source: Cinahl |
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