Academic Journal
Deep learning on CT scans to predict checkpoint inhibitor treatment outcomes in advanced melanoma
| العنوان: | Deep learning on CT scans to predict checkpoint inhibitor treatment outcomes in advanced melanoma |
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| المؤلفون: | Ter Maat, Laurens S., De Mooij, Rob A.J., Van Duin, Isabella A.J., Verhoeff, Joost J.C., Elias, Sjoerd G., Leiner, Tim, van Amsterdam, Wouter A.C., Troenokarso, Max F., Arntz, Eran R.A.N., Van den Berkmortel, Franchette W.P.J., Boers-Sonderen, Marye J., Boomsma, Martijn F., Van den Eertwegh, Fons J.M., de Groot, Jan Willem, Hospers, Geke A.P., Piersma, Djura, Vreugdenhil, Art, Westgeest, Hans M., Kapiteijn, Ellen, De Wit, Ardine A., Blokx, Willeke A.M., Van Diest, Paul J., De Jong, Pim A., Pluim, Josien P.W., Suijkerbuijk, Karijn P.M., Veta, Mitko |
| المصدر: | Ter Maat , L S , De Mooij , R A J , Van Duin , I A J , Verhoeff , J J C , Elias , S G , Leiner , T , van Amsterdam , W A C , Troenokarso , M F , Arntz , E R A N , Van den Berkmortel , F W P J , Boers-Sonderen , M J , Boomsma , M F , Van den Eertwegh , F J M , de Groot , J W , Hospers , G A P , Piersma , D , Vreugdenhil , .... |
| سنة النشر: | 2024 |
| المجموعة: | University of Groningen research database |
| الوصف: | 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. |
| نوع الوثيقة: | article in journal/newspaper |
| وصف الملف: | application/pdf |
| اللغة: | English |
| DOI: | 10.1038/s41598-024-81188-2 |
| الاتاحة: | https://hdl.handle.net/11370/0fd15d05-9259-46b0-afbc-7b2992188984 https://research.rug.nl/en/publications/0fd15d05-9259-46b0-afbc-7b2992188984 https://doi.org/10.1038/s41598-024-81188-2 https://pure.rug.nl/ws/files/1189511583/s41598-024-81188-2.pdf http://www.scopus.com/inward/record.url?scp=85213701722&partnerID=8YFLogxK |
| Rights: | info:eu-repo/semantics/openAccess |
| رقم الانضمام: | edsbas.986316AE |
| قاعدة البيانات: | BASE |
| DOI: | 10.1038/s41598-024-81188-2 |
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