In:
International Urology and Nephrology, Springer Science and Business Media LLC, Vol. 55, No. 11 ( 2023-08-09), p. 2703-2715
Abstract:
To evaluate the feasibility of using mpMRI image features predicted by AI algorithms in the prediction of clinically significant prostate cancer (csPCa). Materials and methods This study analyzed patients who underwent prostate mpMRI and radical prostatectomy (RP) at the Affiliated Hospital of Jiaxing University between November 2017 and December 2022. The clinical data collected included age, serum prostate-specific antigen (PSA), and biopsy pathology. The reference standard was the prostatectomy pathology, and a Gleason Score (GS) of 3 + 3 = 6 was considered non-clinically significant prostate cancer (non-csPCa), while a GS ≥ 3 + 4 was considered csPCa. A pre-trained AI algorithm was used to extract the lesion on mpMRI, and the image features of the lesion and the prostate gland were analyzed. Two logistic regression models were developed to predict csPCa: an MR model and a combined model. The MR model used age, PSA, PSA density (PSAD), and the AI-predicted MR image features as predictor variables. The combined model used biopsy pathology and the aforementioned variables as predictor variables. The model’s effectiveness was evaluated by comparing it to biopsy pathology using the area under the curve (AUC) of receiver operation characteristic (ROC) analysis. Results A total of 315 eligible patients were enrolled with an average age of 70.8 ± 5.9. Based on RP pathology, 18 had non-csPCa, and 297 had csPCa. PSA, PSAD, biopsy pathology, and ADC value of the prostate outside the lesion (ADC prostate ) varied significantly across different ISUP grade groups of RP pathology ( P 〈 0.001). Other clinical variables and image features did not vary significantly across different ISUP grade groups ( P 〉 0.05). The MR model included PSAD, the ratio of ADC value between the lesion and the prostate outside the lesion (ADC lesion/prostate ), the signal intensity ratio of DWI between the lesion and the prostate outside the lesion (DWI lesion/prostate ), and the ratio of DWI lesion/prostate to ADC lesion/prostate . The combined model included biopsy pathology, ADC lesion/prostate , mean signal intensity of the lesion on DWI (DWI lesion ), DWI signal intensity of the prostate outside the lesion (DWI prostate ), and signal intensity ratio of DWI between the lesion and the prostate outside the lesion (DWI lesion/prostate ). The AUC of the MR model (0.830, 95% CI 0.743, 0.916) was not significantly different from that of biopsy pathology (0.820, 95% CI 0.728, 0.912, P = 0.884). The AUC of the combined model (0.915, 95% CI 0.849, 0.980) was higher than that of the biopsy pathology ( P = 0.042) and MR model ( P = 0.031). Conclusion The aggressiveness of prostate cancer can be effectively predicted using AI-extracted image features from mpMRI images, similar to biopsy pathology. The prediction accuracy was improved by combining the AI-extracted mpMRI image features with biopsy pathology, surpassing the performance of biopsy pathology alone.
Type of Medium:
Online Resource
ISSN:
1573-2584
DOI:
10.1007/s11255-023-03722-x
Language:
English
Publisher:
Springer Science and Business Media LLC
Publication Date:
2023
detail.hit.zdb_id:
2015547-5
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