Deep learning on CT scans to predict checkpoint inhibitor treatment outcomes in advanced melanoma
Rattachement africain : nl, us. Niveau de preuve : code pays fourni par la source.
Le résumé fourni par la source
Abstract Introduction Checkpoint 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. Methods Adult 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. Results A 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. Discussion Although 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.
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Le contrôle bibliographique ouvert
DOI retrouvé dans Crossref DOI retrouvé ; titre concordant.
- Titre Crossref
- Deep learning on CT scans to predict checkpoint inhibitor treatment outcomes in advanced melanoma
- Date Crossref
- 27/07/2023
- Éditeur
- openRxiv
- Type
- posted-content
Ce recoupement confirme des métadonnées liées au DOI. Il ne confirme ni la méthode ni les conclusions de l’étude, et il ne compte pas comme une seconde source scientifique indépendante.
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Utrecht University pays non établi dans la noticeUniversité ou école supérieure
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University Medical Center Utrecht Image Sciences Institute pays non établi dans la noticeÉtablissement de santé
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Eindhoven University of Technology Department of Biomedical Engineering pays non établi dans la noticeUniversité ou école supérieure
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Mayo Clinic Department of Radiology pays non établi dans la noticeÉtablissement de santé
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Zuyderland Medisch Centrum pays non établi dans la noticeÉtablissement de santé
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Radboud University Nijmegen Department of Medical Oncology pays non établi dans la noticeUniversité ou école supérieure
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Isala pays non établi dans la noticeÉtablissement de santé
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Amsterdam University Medical Centers pays non établi dans la noticeÉtablissement de santé
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Vrije Universiteit Amsterdam Department of Medical Oncology pays non établi dans la noticeUniversité ou école supérieure
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University of Groningen Department of Medical Oncology pays non établi dans la noticeUniversité ou école supérieure
Utrecht University, Image Sciences Institute — University Medical Center Utrecht et Department of Biomedical Engineering — Eindhoven University of Technology, avec 9 autres affiliations.
Une affiliation ne permet pas de déduire la nationalité d’un auteur.