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Introduction Predicting checkpoint inhibitors treatment outcomes in melanoma is a relevant task, due to the unpredictable and potentially fatal toxicity and high costs for society. However, accurate biomarkers for treatment outcomes are lacking. Radiomics are a technique to quantitatively capture tumor characteristics on readily available computed tomography (CT) imaging. The purpose of this study was to investigate the added value of radiomics for predicting durable clinical benefit from checkpoint inhibitors in melanoma in a large, multicenter cohort. Methods Patients who received first-line anti-PD1 with or without anti-CTLA4 treatment for advanced cutaneous melanoma were retrospectively identified from nine participating hospitals. For every patient, up to five representative lesions were segmented on baseline CT and radiomics features were extracted. A machine learning pipeline was trained on the radiomics features to predict durable clinical benefit, defined as stable disease for more than six months or response per RECIST 1.1 criteria. This approach was evaluated using a leave-one-center-out cross validation and compared to a model based on previously discovered clinical predictors. Lastly, a combination model was built on the radiomics and clinical model. Results A total of 620 patients were included, of which 59.2% experienced durable clinical benefit. The radiomics model achieved an area under the receiver operator characteristic curve (AUROC) of 0.607 [95%CI 0.562-0.652], lower than that of the clinical model (AUROC=0.646 [95%CI 0.600-0.692]). The combination model yielded no improvement over the clinical model in terms of discrimination (AUROC=0.636 [95%CI 0.592-0.680]) or calibration. The output of the radiomics model was significantly correlated with three out of five input variables of the clinical model (p < 0.001). Discussion The radiomics model achieved a moderate predictive value of durable clinical benefit, which was statistically significant. However, a radiomics approach was unable to add value to a simpler clinical model, most likely due to the overlap in predictive information learned by both models. Future research should focus on the application of deep learning, spectral CT derived radiomics and a multimodal approach for accurately predicting benefit to checkpoint inhibitor treatment in advanced melanoma.

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IntroductionCheckpoint inhibitor treatment has proven successful for advanced melanoma. However, a significant fraction of patients does not experience benefit from this treatment, that is also associated with potentially severe toxicity and high costs. Previous research has not yet resulted in adequate biomarkers that can predict treatment outcomes. The present work is the first to investigate the value of deep learning on computed tomography (CT) imaging of melanoma lesions for predicting checkpoint inhibitor treatment outcomes in advanced melanoma.MethodsAdult patients that were treated with first line anti-PD1 ± anti-CTLA4 therapy for unresectable stage IIIC or stage IV melanoma were retrospectively identified from ten participating centers. Up to five representative lesions were segmented volumetrically on baseline CT; a deep learning model (DLM) was trained on the corresponding volumes to predict clinical benefit, defined as stable disease for a minimum of six months, or response at any time during follow-up. Optimal hyperparameters and model types (Densenet, Efficientnet, Squeeze-Excitation ResNet, ResNeXt) were iteratively explored. The DLM was compared to a model of previously identified clinical predictors (presence of liver and brain metastasis, level of lactate dehydrogenase, performance status and number of affected organs), and a combination model consisting of both clinical predictors and the DLM.ResultsA total of 730 eligible patients with 2722 lesions were included. Rate of clinical benefit was 59.6%. The selected deep learning model was a Squeeze-Excitation ResNet with random initialization, trained with the Adam optimizer. 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 0.592 – 0.678]. The combination model reached an AUROC of 0.635 [95% CI 0.595 – 0.676]. None of the differences in AUROC were statistically significant. The output of the DLM was significantly correlated with four of the five input variables of the clinical model.DiscussionAlthough the DLM reached a statistically significant discriminative value, it was unable to improve over previously identified clinical predictors. The most likely cause is that the DLM learns to detect a lesion’s size and organ location, which is information that is already present in the clinical model. Given the substantial sample size and extensive hyperparameter optimization, this indicates that the predictive value of CT imaging of lesions for checkpoint inhibitor response in melanoma is likely limited. 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.