Deep Learning Features Can Improve Radiomics-Based Prostate Cancer Aggressiveness Prediction

Detalhes bibliográficos
Autor(a) principal: Rodrigues, Nuno M.
Data de Publicação: 2024
Outros Autores: Almeida, José Guilherme de, Rodrigues, Ana, Vanneschi, Leonardo, Matos, Celso, Lisitskaya, Maria, Uysal, Aycan, Silva, Sara, Papanikolaou, Nickolas
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
Texto Completo: http://hdl.handle.net/10362/177895
Resumo: Rodrigues, N. M., Almeida, J. G. D., Rodrigues, A., Vanneschi, L., Matos, C., Lisitskaya, M., Uysal, A., Silva, S., & Papanikolaou, N. (2024). Deep Learning Features Can Improve Radiomics-Based Prostate Cancer Aggressiveness Prediction. JCO Clinical Cancer Informatics, 8, Article e2300180. https://doi.org/10.1200/CCI.23.00180 --- Supported in part by FCT, Portugal, through funding of the LASIGE Research Unit (UIDB/00408/2020 [https://doi.org/10.54499/UIDB/00408/2020], UIDP/00408/2020 [https://doi.org/10.54499/UIDP/00408/2020]), and Nuno Rodrigues PhD Grant 10.54499/2021.05322.BD (https://doi.org/10.54499/2021.05322.BD). Ana Rodrigues and José Guilherme de Almeida were supported by the European Union H2020: ProCAncer-I project (EU grant 952159). This work was supported by national funds through FCT (Fundação para a Ciéncia e a Tecnologia), under the project—UIDB/04152/2020—Centro de Investigação em Gestão de Informação (MagIC)/NOVA IMS.
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spelling Deep Learning Features Can Improve Radiomics-Based Prostate Cancer Aggressiveness PredictionMedicine(all)SDG 3 - Good Health and Well-beingRodrigues, N. M., Almeida, J. G. D., Rodrigues, A., Vanneschi, L., Matos, C., Lisitskaya, M., Uysal, A., Silva, S., & Papanikolaou, N. (2024). Deep Learning Features Can Improve Radiomics-Based Prostate Cancer Aggressiveness Prediction. JCO Clinical Cancer Informatics, 8, Article e2300180. https://doi.org/10.1200/CCI.23.00180 --- Supported in part by FCT, Portugal, through funding of the LASIGE Research Unit (UIDB/00408/2020 [https://doi.org/10.54499/UIDB/00408/2020], UIDP/00408/2020 [https://doi.org/10.54499/UIDP/00408/2020]), and Nuno Rodrigues PhD Grant 10.54499/2021.05322.BD (https://doi.org/10.54499/2021.05322.BD). Ana Rodrigues and José Guilherme de Almeida were supported by the European Union H2020: ProCAncer-I project (EU grant 952159). This work was supported by national funds through FCT (Fundação para a Ciéncia e a Tecnologia), under the project—UIDB/04152/2020—Centro de Investigação em Gestão de Informação (MagIC)/NOVA IMS.Purpose Emerging evidence suggests that the use of artificial intelligence can assist in the timely detection and optimization of therapeutic approach in patients with prostate cancer. The conventional perspective on radiomics encompassing segmentation and the extraction of radiomic features considers it as an independent and sequential process. However, it is not necessary to adhere to this viewpoint. In this study, we show that besides generating masks from which radiomic features can be extracted, prostate segmentation and reconstruction models provide valuable information in their feature space, which can improve the quality of radiomic signatures models for disease aggressiveness classification. Materials and Methods We perform 2,244 experiments with deep learning features extracted from 13 different models trained using different anatomic zones and characterize how modeling decisions, such as deep feature aggregation and dimensionality reduction, affect performance. Results While models using deep features from full gland and radiomic features consistently lead to improved disease aggressiveness prediction performance, others are detrimental. Our results suggest that the use of deep features can be beneficial, but an appropriate and comprehensive assessment is necessary to ensure that their inclusion does not harm predictive performance. Conclusion The study findings reveal that incorporating deep features derived from autoencoder models trained to reconstruct the full prostate gland (both zonal models show worse performance than radiomics only models), combined with radiomic features, often lead to a statistically significant increase in model performance for disease aggressiveness classification. Additionally, the results also demonstrate that the choice of feature selection is key to achieving good performance, with principal component analysis (PCA) and PCA + relief being the best approaches and that there is no clear difference between the three proposed latent representation extraction techniques.Information Management Research Center (MagIC) - NOVA Information Management SchoolNOVA Information Management School (NOVA IMS)RUNRodrigues, Nuno M.Almeida, José Guilherme deRodrigues, AnaVanneschi, LeonardoMatos, CelsoLisitskaya, MariaUysal, AycanSilva, SaraPapanikolaou, Nickolas2024-09-182025-09-18T00:00:00Z2024-09-18T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10362/177895eng2473-4276PURE: 93641020https://doi.org/10.1200/CCI.23.00180info:eu-repo/semantics/embargoedAccessreponame:Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)instname:FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiainstacron:RCAAP2025-02-03T01:37:19Zoai:run.unl.pt:10362/177895Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireinfo@rcaap.ptopendoar:https://opendoar.ac.uk/repository/71602025-05-28T19:41:42.391489Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) - FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiafalse
dc.title.none.fl_str_mv Deep Learning Features Can Improve Radiomics-Based Prostate Cancer Aggressiveness Prediction
title Deep Learning Features Can Improve Radiomics-Based Prostate Cancer Aggressiveness Prediction
spellingShingle Deep Learning Features Can Improve Radiomics-Based Prostate Cancer Aggressiveness Prediction
Rodrigues, Nuno M.
Medicine(all)
SDG 3 - Good Health and Well-being
title_short Deep Learning Features Can Improve Radiomics-Based Prostate Cancer Aggressiveness Prediction
title_full Deep Learning Features Can Improve Radiomics-Based Prostate Cancer Aggressiveness Prediction
title_fullStr Deep Learning Features Can Improve Radiomics-Based Prostate Cancer Aggressiveness Prediction
title_full_unstemmed Deep Learning Features Can Improve Radiomics-Based Prostate Cancer Aggressiveness Prediction
title_sort Deep Learning Features Can Improve Radiomics-Based Prostate Cancer Aggressiveness Prediction
author Rodrigues, Nuno M.
author_facet Rodrigues, Nuno M.
Almeida, José Guilherme de
Rodrigues, Ana
Vanneschi, Leonardo
Matos, Celso
Lisitskaya, Maria
Uysal, Aycan
Silva, Sara
Papanikolaou, Nickolas
author_role author
author2 Almeida, José Guilherme de
Rodrigues, Ana
Vanneschi, Leonardo
Matos, Celso
Lisitskaya, Maria
Uysal, Aycan
Silva, Sara
Papanikolaou, Nickolas
author2_role author
author
author
author
author
author
author
author
dc.contributor.none.fl_str_mv Information Management Research Center (MagIC) - NOVA Information Management School
NOVA Information Management School (NOVA IMS)
RUN
dc.contributor.author.fl_str_mv Rodrigues, Nuno M.
Almeida, José Guilherme de
Rodrigues, Ana
Vanneschi, Leonardo
Matos, Celso
Lisitskaya, Maria
Uysal, Aycan
Silva, Sara
Papanikolaou, Nickolas
dc.subject.por.fl_str_mv Medicine(all)
SDG 3 - Good Health and Well-being
topic Medicine(all)
SDG 3 - Good Health and Well-being
description Rodrigues, N. M., Almeida, J. G. D., Rodrigues, A., Vanneschi, L., Matos, C., Lisitskaya, M., Uysal, A., Silva, S., & Papanikolaou, N. (2024). Deep Learning Features Can Improve Radiomics-Based Prostate Cancer Aggressiveness Prediction. JCO Clinical Cancer Informatics, 8, Article e2300180. https://doi.org/10.1200/CCI.23.00180 --- Supported in part by FCT, Portugal, through funding of the LASIGE Research Unit (UIDB/00408/2020 [https://doi.org/10.54499/UIDB/00408/2020], UIDP/00408/2020 [https://doi.org/10.54499/UIDP/00408/2020]), and Nuno Rodrigues PhD Grant 10.54499/2021.05322.BD (https://doi.org/10.54499/2021.05322.BD). Ana Rodrigues and José Guilherme de Almeida were supported by the European Union H2020: ProCAncer-I project (EU grant 952159). This work was supported by national funds through FCT (Fundação para a Ciéncia e a Tecnologia), under the project—UIDB/04152/2020—Centro de Investigação em Gestão de Informação (MagIC)/NOVA IMS.
publishDate 2024
dc.date.none.fl_str_mv 2024-09-18
2024-09-18T00:00:00Z
2025-09-18T00:00:00Z
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