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Incorporating functional soft tissue deformations in AI model training for spatially accurate prostate cancer detection
Kovacs, Balint Zhang, Kevin Sun Baumgartner, Michael Hielscher, Thomas Bounias, Dimitrios Netzer, Nils Floca, Ralf Eith, Carolin Jäger, Paul F Meinzer, Clara
Kovacs, Balint
Zhang, Kevin Sun
Baumgartner, Michael
Hielscher, Thomas
Bounias, Dimitrios
Netzer, Nils
Floca, Ralf
Eith, Carolin
Jäger, Paul F
Meinzer, Clara
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Author
Kovacs, Balint
Zhang, Kevin Sun
Baumgartner, Michael
Hielscher, Thomas
Bounias, Dimitrios
Netzer, Nils
Floca, Ralf
Eith, Carolin
Jäger, Paul F
Meinzer, Clara
Isensee, Fabian
Gnirs, Regula
Görtz, Magdalena
Hohenfellner, Markus
Stenzinger, Albrecht
Schlemmer, Heinz-Peter
Wolf, Ivo
Maier-Hein, Klaus
Bonekamp, David
Zhang, Kevin Sun
Baumgartner, Michael
Hielscher, Thomas
Bounias, Dimitrios
Netzer, Nils
Floca, Ralf
Eith, Carolin
Jäger, Paul F
Meinzer, Clara
Isensee, Fabian
Gnirs, Regula
Görtz, Magdalena
Hohenfellner, Markus
Stenzinger, Albrecht
Schlemmer, Heinz-Peter
Wolf, Ivo
Maier-Hein, Klaus
Bonekamp, David
Supervisor
Department
Computer Vision
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Type
Journal article
Date
License
http://creativecommons.org/licenses/by/4.0/
Language
English
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Abstract
PURPOSE: To increase performance and generalization ability of artificial intelligence prostate cancer detection systems by simulating physiological size changes of the bladder and rectum and, thereby, associated deformations of the prostate and its lesions.
MATERIALS AND METHODS: This retrospective study included 1028 bi-parametric MRI examinations of men (age range: 40-90 years) performed between 2014 and 2019, divided into training/test sets (771/257). We integrated an 'anatomy-informed' transformation into the training of nnU-Net, by simulating soft-tissue deformations of the prostate resulting from size changes of the rectum and bladder. The effects of these strategies were evaluated using free-response receiver operating characteristic (FROC) to assess lesion-level performance, along with a variant: weighted alternative FROC (wAFROC), which prioritizes patient-level effects with localization criteria. Change in sensitivity was tested using a clustered McNemar test. Patient-level performance was assessed with standard and localized receiver operating characteristics (ROC/LROC) analysis.
RESULTS: On the independent test set, the anatomy-informed model simulating changes of both rectum and bladder significantly increased lesion-level detection of true positive lesions by 18.8% (from 48 to 57, p = 0.01) and demonstrated significantly higher performance in the wAFROC analysis (from 0.597 to 0.639, p < 0.01). Patient-level ROC increased slightly (from 0.779 to 0.782, p = 0.89), while LROC analysis demonstrated increased performance (from 0.471 to 0.546).
CONCLUSION: Simulation of rectum and bladder size variations during model training led to significant improvement in lesion detection performance, which may be crucial for diagnostics and therapeutic measures depending on correct lesion localization, e.g. MRI-guided biopsies or focal therapy regimes.
Citation
B. Kovacs, K.S. Zhang, M. Baumgartner, T. Hielscher, D. Bounias, N. Netzer , et al., "Incorporating functional soft tissue deformations in AI model training for spatially accurate prostate cancer detection," Magnetic Resonance Imaging, vol. 131, pp. 110698-110698, 2026, https://doi.org/10.1016/j.mri.2026.110698.
Source
Magnetic Resonance Imaging
Conference
Keywords
32 Biomedical and Clinical Sciences, 3202 Clinical Sciences, 3211 Oncology and Carcinogenesis
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Source
Publisher
Elsevier