Faculty, Staff and Student Publications
Language
English
Publication Date
12-12-2024
Journal
Insights into Imaging
DOI
10.1186/s13244-024-01876-5
PMID
39666257
PMCID
PMC11638435
PubMedCentral® Posted Date
12-12-2024
PubMedCentral® Full Text Version
Post-print
Abstract
Objectives: Radical prostatectomy (RP) is a common intervention in patients with localized prostate cancer (PCa), with nerve-sparing RP recommended to reduce adverse effects on patient quality of life. Accurate pre-operative detection of extraprostatic extension (EPE) remains challenging, often leading to the application of suboptimal treatment. The aim of this study was to enhance pre-operative EPE detection through multimodal data integration using explainable machine learning (ML).
Methods: Patients with newly diagnosed PCa who underwent [68Ga]Ga-PSMA-11 PET/MRI and subsequent RP were recruited retrospectively from two time ranges for training, cross-validation, and independent validation. The presence of EPE was measured from post-surgical histopathology and predicted using ML and pre-operative parameters, including PET/MRI-derived features, blood-based markers, histology-derived parameters, and demographic parameters. ML models were subsequently compared with conventional PET/MRI-based image readings.
Results: The study involved 107 patients, 59 (55%) of whom were affected by EPE according to postoperative findings for the initial training and cross-validation. The ML models demonstrated superior diagnostic performance over conventional PET/MRI image readings, with the explainable boosting machine model achieving an AUC of 0.88 (95% CI 0.87-0.89) during cross-validation and an AUC of 0.88 (95% CI 0.75-0.97) during independent validation. The ML approach integrating invasive features demonstrated better predictive capabilities for EPE compared to visual clinical read-outs (Cross-validation AUC 0.88 versus 0.71, p = 0.02).
Conclusion: ML based on routinely acquired clinical data can significantly improve the pre-operative detection of EPE in PCa patients, potentially enabling more accurate clinical staging and decision-making, thereby improving patient outcomes.
Critical relevance statement: This study demonstrates that integrating multimodal data with machine learning significantly improves the pre-operative detection of extraprostatic extension in prostate cancer patients, outperforming conventional imaging methods and potentially leading to more accurate clinical staging and better treatment decisions.
Key points: Extraprostatic extension is an important indicator guiding treatment approaches. Current assessment of extraprostatic extension is difficult and lacks accuracy. Machine learning improves detection of extraprostatic extension using PSMA-PET/MRI and histopathology.
Keywords
Prostate cancer, PSMA, PET/MRI, Machine learning, Extraprostatic extension
Published Open-Access
yes
Recommended Citation
Spielvogel, Clemens P; Ning, Jing; Kluge, Kilian; et al., "Preoperative Detection of Extraprostatic Tumor Extension in Patients With Primary Prostate Cancer Utilizing [68Ga]Ga-Psma-11 Pet/MRI" (2024). Faculty, Staff and Student Publications. 6332.
https://digitalcommons.library.tmc.edu/uthgsbs_docs/6332
Graphical Abstract
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