Deep learning on CT scans to predict checkpoint inhibitor treatment outcomes in advanced melanoma.

Immune checkpoint inhibitor (ICI) treatment has proven successful for advanced melanoma, but is associated with potentially severe toxicity and high costs. Accurate biomarkers for response are lacking. The present work is the first to investigate the value of deep learning on CT imaging of metastatic lesions for predicting ICI treatment outcomes in advanced melanoma. Adult patients that were treated with ICI for advanced melanoma were retrospectively identified from ten participating centers. A deep learning model (DLM) was trained on volumes of lesions on baseline CT to predict clinical benefit. The DLM was compared to and combined with a model of known clinical predictors (presence of liver and brain metastasis, level of lactate dehydrogenase, performance status and number of affected organs). A total of 730 eligible patients with 2722 lesions were included. The DLM reached an area under the receiver operating characteristic (AUROC) of 0.607 [95%CI 0.565-0.648]. In comparison, a model of clinical predictors reached an AUROC of 0.635 [95%CI 0.59 -0.678]. The combination model reached an AUROC of 0.635 [95% CI 0.595-0.676]. Differences in AUROC were not statistically significant. The output of the DLM was significantly correlated with four of the five input variables of the clinical model. The DLM reached a statistically significant discriminative value, but was unable to improve over known clinical predictors. The present work shows that the assessment over known clinical predictors is an essential step for imaging-based prediction and brings important nuance to the almost exclusively positive findings in this field.
Competing Interests: Declarations. Competing interests: AvdE has advisory relationships with Bristol-Myers Squibb, MSD Oncology, Amgen, Roche, Novartis, Sanofi, Pfizer, Ipsen, Merck, Pierre Fabre and has received research study grants not related to this paper from Sanofi, Bristol-Myers Squibb, TEVA, Idera and has received travel expenses MSD Oncology, Roche, Pfizer, Sanofi, Pierre Fabre and has received speaker honoraria from BMS and Novartis. JdG has consultancy/advisory relationships with Bristol Myers Squibb, Pierre Fabre, Servier, MSD, Novartis. PJ has a research collaboration with Philips Healthcare and Vifor Pharma. MBS has consultancy/advisory relationships with Pierre Fabre, MSD and Novartis, none related to current work and paid to institute. EK has consultancy/advisory relationships with Bristol Myers Squibb, Novartis, Merck, Pierre Fabre, Lilly, Bayer, EISAI and Ipsen paid to the institute, and received research grants not related to this paper from Bristol Myers Squibb, Delcath, Novartis and Pierre Fabre. PD has consultancy/advisory relationships with Paige, Pantarei and Samantree paid to the institution and research grants from Pfizer, none related to current work and paid to institute. KS has advisory relationships with Bristol Myers Squibb, Novartis, MSD, Pierre Fabre, AbbVie, Sairopsa and received honoraria from Novartis and MSD and research funding from Bristol Myers Squibb, TigaTx and Philips. TL has received research funding from Philips. GH consultancy/advisory relationships with Amgen, Bristol-Myers Squibb, Roche, MSD, Pfizer, Novartis, Sanofi, Pierre Fabre and has received research grants from Bristol-Myers Squibb, Seerave. All payments to the Institution. HW received honoraria from Merck, Astellas, Roche and travel expenses from Ipsen and Astellas. All remaining authors have declared no conflicts of interest.
(© 2024. The Author(s).)