The Development and External Validation of Artificial Intelligence-Driven MRI-Based Models to Improve Prediction of Lesion-Specific Extraprostatic Extension in Patients with Prostate Cancer.

Autor: van den Berg I; Department of Urology, St. Antonius Hospital, 3435 CM Nieuwegein, The Netherlands.; Department of Radiation Oncology, Division of Imaging & Oncology, University Medical Center Utrecht, 3584 CX Utrecht, The Netherlands.; Magnetic Detection and Imaging Group, Technical Medical Centre, University of Twente, 7522 NH Enschede, The Netherlands., Soeterik TFW; Department of Urology, St. Antonius Hospital, 3435 CM Nieuwegein, The Netherlands.; Department of Radiation Oncology, Division of Imaging & Oncology, University Medical Center Utrecht, 3584 CX Utrecht, The Netherlands., van der Hoeven EJRJ; Department of Radiology, St. Antonius Hospital, 3435 CM Nieuwegein, The Netherlands., Claassen B; Department of Radiology, Canisius Wilhelmina Hospital, 7522 NH Nijmegen, The Netherlands., Brink WM; Magnetic Detection and Imaging Group, Technical Medical Centre, University of Twente, 7522 NH Enschede, The Netherlands., Baas DJH; Department of Urology, Canisius Wilhelmina Hospital, 7522 NH Nijmegen, The Netherlands., Sedelaar JPM; Department of Urology, Radboud University Medical Center, 6525 GA Nijmegen, The Netherlands., Heine L; Quantib B.V., RadNet's AI Division, 3012 KM Rotterdam, The Netherlands., Tol J; Quantib B.V., RadNet's AI Division, 3012 KM Rotterdam, The Netherlands., van der Voort van Zyp JRN; Department of Radiation Oncology, Division of Imaging & Oncology, University Medical Center Utrecht, 3584 CX Utrecht, The Netherlands., van den Berg CAT; Department of Radiation Oncology, Division of Imaging & Oncology, University Medical Center Utrecht, 3584 CX Utrecht, The Netherlands., van den Bergh RCN; Department of Urology, St. Antonius Hospital, 3435 CM Nieuwegein, The Netherlands., van Basten JA; Department of Urology, Canisius Wilhelmina Hospital, 7522 NH Nijmegen, The Netherlands.; Department of Urology, Radboud University Medical Center, 6525 GA Nijmegen, The Netherlands., van Melick HHE; Department of Urology, St. Antonius Hospital, 3435 CM Nieuwegein, The Netherlands.
Jazyk: angličtina
Zdroj: Cancers [Cancers (Basel)] 2023 Nov 17; Vol. 15 (22). Date of Electronic Publication: 2023 Nov 17.
DOI: 10.3390/cancers15225452
Abstrakt: Adequate detection of the histopathological extraprostatic extension (EPE) of prostate cancer (PCa) remains a challenge using conventional radiomics on 3 Tesla multiparametric magnetic resonance imaging (3T mpMRI). This study focuses on the assessment of artificial intelligence (AI)-driven models with innovative MRI radiomics in predicting EPE of prostate cancer (PCa) at a lesion-specific level. With a dataset encompassing 994 lesions from 794 PCa patients who underwent robot-assisted radical prostatectomy (RARP) at two Dutch hospitals, the study establishes and validates three classification models. The models were validated on an internal validation cohort of 162 lesions and an external validation cohort of 189 lesions in terms of discrimination, calibration, net benefit, and comparison to radiology reporting. Notably, the achieved AUCs ranged from 0.86 to 0.91 at the lesion-specific level, demonstrating the superior accuracy of the random forest model over conventional radiological reporting. At the external test cohort, the random forest model was the best-calibrated model and demonstrated a significantly higher accuracy compared to radiological reporting (83% vs. 67%, p = 0.02). In conclusion, an AI-powered model that includes both existing and novel MRI radiomics improves the detection of lesion-specific EPE in prostate cancer.
Databáze: MEDLINE
Externí odkaz:
Nepřihlášeným uživatelům se plný text nezobrazuje