Automatic Patient-level Diagnosis of Prostate Disease with Fused 3D MRI and Tabular Clinical Data

Oleksii Bashkanov, Marko Rak, Lucas Engelage, Christianus Hansen
Medical Imaging with Deep Learning, PMLR 227:1225-1238, 2024.

Abstract

Computer-aided diagnosis systems for automatic prostate cancer diagnosis can provide radiologists with decision support during image reading. However, in this case, patient-relevant information often remains unexploited due to the greater focus on the image recognition side, with various imaging devices and modalities, while omitting other potentially valuable clinical data. Therefore, our work investigates the performance of recent methods for the fusion of rich image data and heterogeneous tabular data. Those data may include patient demographics as well as laboratory data, e.g., prostate-specific antigen (PSA). Experiments on the large dataset (3800 subjects) indicated that when using the fusion method with demographic data in clinically significant prostate cancer (csPCa) detection tasks, the mean area under the receiver operating characteristic curve (ROC AUC) has improved significantly from 0.736 to 0.765. We also observed that the naïve concatenation performs similarly or even better than the \mbox{state-of-the-art} fusion modules. We also achieved better prediction quality in grading prostate disease by including more samples from longitudinal PSA profiles in the tabular feature set. Thus, by including the three last PSA samples per patient, the best-performing model has reached AUC of 0.794 and a quadratic weighted kappa score (QWK) of 0.464, which constitutes a significant improvement compared with the image-only method, with ROC AUC of 0.736 and QWK of 0.342.

Cite this Paper

BibTeX
@InProceedings{pmlr-v227-bashkanov24a, title = {Automatic Patient-level Diagnosis of Prostate Disease with Fused 3D MRI and Tabular Clinical Data}, author = {Bashkanov, Oleksii and Rak, Marko and Engelage, Lucas and Hansen, Christianus}, booktitle = {Medical Imaging with Deep Learning}, pages = {1225--1238}, year = {2024}, editor = {Oguz, Ipek and Noble, Jack and Li, Xiaoxiao and Styner, Martin and Baumgartner, Christian and Rusu, Mirabela and Heinmann, Tobias and Kontos, Despina and Landman, Bennett and Dawant, Benoit}, volume = {227}, series = {Proceedings of Machine Learning Research}, month = {10--12 Jul}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v227/bashkanov24a/bashkanov24a.pdf}, url = {https://proceedings.mlr.press/v227/bashkanov24a.html}, abstract = {Computer-aided diagnosis systems for automatic prostate cancer diagnosis can provide radiologists with decision support during image reading. However, in this case, patient-relevant information often remains unexploited due to the greater focus on the image recognition side, with various imaging devices and modalities, while omitting other potentially valuable clinical data. Therefore, our work investigates the performance of recent methods for the fusion of rich image data and heterogeneous tabular data. Those data may include patient demographics as well as laboratory data, e.g., prostate-specific antigen (PSA). Experiments on the large dataset (3800 subjects) indicated that when using the fusion method with demographic data in clinically significant prostate cancer (csPCa) detection tasks, the mean area under the receiver operating characteristic curve (ROC AUC) has improved significantly from 0.736 to 0.765. We also observed that the naïve concatenation performs similarly or even better than the \mbox{state-of-the-art} fusion modules. We also achieved better prediction quality in grading prostate disease by including more samples from longitudinal PSA profiles in the tabular feature set. Thus, by including the three last PSA samples per patient, the best-performing model has reached AUC of 0.794 and a quadratic weighted kappa score (QWK) of 0.464, which constitutes a significant improvement compared with the image-only method, with ROC AUC of 0.736 and QWK of 0.342.} }
Endnote
%0 Conference Paper %T Automatic Patient-level Diagnosis of Prostate Disease with Fused 3D MRI and Tabular Clinical Data %A Oleksii Bashkanov %A Marko Rak %A Lucas Engelage %A Christianus Hansen %B Medical Imaging with Deep Learning %C Proceedings of Machine Learning Research %D 2024 %E Ipek Oguz %E Jack Noble %E Xiaoxiao Li %E Martin Styner %E Christian Baumgartner %E Mirabela Rusu %E Tobias Heinmann %E Despina Kontos %E Bennett Landman %E Benoit Dawant %F pmlr-v227-bashkanov24a %I PMLR %P 1225--1238 %U https://proceedings.mlr.press/v227/bashkanov24a.html %V 227 %X Computer-aided diagnosis systems for automatic prostate cancer diagnosis can provide radiologists with decision support during image reading. However, in this case, patient-relevant information often remains unexploited due to the greater focus on the image recognition side, with various imaging devices and modalities, while omitting other potentially valuable clinical data. Therefore, our work investigates the performance of recent methods for the fusion of rich image data and heterogeneous tabular data. Those data may include patient demographics as well as laboratory data, e.g., prostate-specific antigen (PSA). Experiments on the large dataset (3800 subjects) indicated that when using the fusion method with demographic data in clinically significant prostate cancer (csPCa) detection tasks, the mean area under the receiver operating characteristic curve (ROC AUC) has improved significantly from 0.736 to 0.765. We also observed that the naïve concatenation performs similarly or even better than the \mbox{state-of-the-art} fusion modules. We also achieved better prediction quality in grading prostate disease by including more samples from longitudinal PSA profiles in the tabular feature set. Thus, by including the three last PSA samples per patient, the best-performing model has reached AUC of 0.794 and a quadratic weighted kappa score (QWK) of 0.464, which constitutes a significant improvement compared with the image-only method, with ROC AUC of 0.736 and QWK of 0.342.
APA
Bashkanov, O., Rak, M., Engelage, L. & Hansen, C.. (2024). Automatic Patient-level Diagnosis of Prostate Disease with Fused 3D MRI and Tabular Clinical Data. Medical Imaging with Deep Learning, in Proceedings of Machine Learning Research 227:1225-1238 Available from https://proceedings.mlr.press/v227/bashkanov24a.html.

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