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Incorporating functional soft tissue deformations in AI model training for spatially accurate prostate cancer detection

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 retrospec...

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Autori principali: Kovács, Bálint (Autore) , Zhang, Kevin Sun (Autore) , Baumgartner, Michael (Autore) , Hielscher, Thomas (Autore) , Bounias, Dimitrios (Autore) , Netzer, Nils (Autore) , Floca, Ralf (Autore) , Eith, Carolin (Autore) , Jäger, Paul F. (Autore) , Meinzer, Clara (Autore) , Isensee, Fabian (Autore) , Gnirs, Regula (Autore) , Görtz, Magdalena (Autore) , Hohenfellner, Markus (Autore) , Stenzinger, Albrecht (Autore) , Schlemmer, Heinz-Peter (Autore) , Wolf, Ivo (Autore) , Maier-Hein, Klaus H. (Autore) , Bonekamp, David (Autore)
Natura: Article (Journal)
Lingua:inglese
Pubblicazione: September 2026
In: Magnetic resonance imaging
Year: 2026, Volume: 131, Pages: 1-9
ISSN:1873-5894
DOI:10.1016/j.mri.2026.110698
Accesso online:Verlag, kostenfrei, Volltext: https://doi.org/10.1016/j.mri.2026.110698
Verlag, kostenfrei, Volltext: https://www.sciencedirect.com/science/article/pii/S0730725X26000913
Testo
Note sull'autore:Balint Kovacs, Kevin Sun Zhang, Michael Baumgartner, Thomas Hielscher, Dimitrios Bounias, Nils Netzer, Ralf Floca, Carolin Eith, Paul F. Jäger, Clara Meinzer, Fabian Isensee, Regula Gnirs, Magdalena Görtz, Markus Hohenfellner, Albrecht Stenzinger, Heinz-Peter Schlemmer, Ivo Wolf, Klaus H. Maier-Hein, David Bonekamp
Descrizione
Riassunto: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.
Descrizione del documento:Online verfügbar: 5. Mai 2026, Artikelversion: 9. Mai 2026
Gesehen am 15.06.2026
Descrizione fisica:Online Resource
ISSN:1873-5894
DOI:10.1016/j.mri.2026.110698

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