Dataset Information

High-throughput precision MRI assessment with integrated stack-ensemble deep learning can enhance the preoperative prediction of prostate cancer Gleason grade.

ABSTRACT:

Background

To develop and test a Prostate Imaging Stratification Risk (PRISK) tool for precisely assessing the International Society of Urological Pathology Gleason grade (ISUP-GG) of prostate cancer (PCa).

Methods

This study included 1442 patients with prostate biopsy from two centres (training, n = 672; internal test, n = 231 and external test, n = 539). PRISK is designed to classify ISUP-GG 0 (benign), ISUP-GG 1, ISUP-GG 2, ISUP-GG 3 and ISUP GG 4/5. Clinical indicators and high-throughput MRI features of PCa were integrated and modelled with hybrid stacked-ensemble learning algorithms.

Results

PRISK achieved a macro area-under-curve of 0.783, 0.798 and 0.762 for the classification of ISUP-GGs in training, internal and external test data. Permitting error ±1 in grading ISUP-GGs, the overall accuracy of PRISK is nearly comparable to invasive biopsy (train: 85.1% vs 88.7%; internal test: 85.1% vs 90.4%; external test: 90.4% vs 94.2%). PSA ≥ 20 ng/ml (odds ratio [OR], 1.58; p = 0.001) and PRISK ≥ GG 3 (OR, 1.45; p = 0.005) were two independent predictors of biochemical recurrence (BCR)-free survival, with a C-index of 0.76 (95% CI, 0.73-0.79) for BCR-free survival prediction.

Conclusions

PRISK might offer a potential alternative to non-invasively assess ISUP-GG of PCa.

SUBMITTER: Bao J

PROVIDER: S-EPMC10050457 | biostudies-literature | 2023 Mar

REPOSITORIES: biostudies-literature

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Publications

High-throughput precision MRI assessment with integrated stack-ensemble deep learning can enhance the preoperative prediction of prostate cancer Gleason grade.

Bao Jie J Hou Ying Y Qin Lang L Zhi Rui R Wang Xi-Ming XM Shi Hai-Bin HB Sun Hong-Zan HZ Hu Chun-Hong CH Zhang Yu-Dong YD

British journal of cancer 20230117 7

<h4>Background</h4>To develop and test a Prostate Imaging Stratification Risk (PRISK) tool for precisely assessing the International Society of Urological Pathology Gleason grade (ISUP-GG) of prostate cancer (PCa).<h4>Methods</h4>This study included 1442 patients with prostate biopsy from two centres (training, n = 672; internal test, n = 231 and external test, n = 539). PRISK is designed to classify ISUP-GG 0 (benign), ISUP-GG 1, ISUP-GG 2, ISUP-GG 3 and ISUP GG 4/5. Clinical indicators and hig ...[more]

PMID: 36646808

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Project description:BACKGROUND:While the new Gleason grade grouping (GGG), which started in 2016, has been widely validated in prostate cancer, it does not incorporate the concept of tertiary Gleason pattern 5. Furthermore, no study has "quantified" the individual risk of each Gleason pattern, including tertiary Gleason pattern 5, after radical prostatectomy. METHODS:We reviewed 1022 men with adjuvant-treatment-naïve prostate cancer who underwent radical prostatectomy between 2005 and 2017. The primary endpoint was biochemical recurrence-free survival, defined as two consecutive prostate-specific antigen measurements ?0.2?ng/ml after surgery. The individual quantitative risk score (IQRS) of each amount (primary/secondary/tertiary) of each Gleason pattern (3/4/5) was calculated using the Cox regression model. On the basis of the IQRS, the modified Gleason grade grouping (mGGG) model was developed. As a robustness analysis of the mGGG model, salvage treatment-free survival was also assessed. RESULTS:During a median follow-up of 45?months, 229 of 1022 (22.4%) patients developed biochemical recurrence. The IQRS of each Gleason pattern was as follows: primary 5, 1.81 points (hazard ratio [HR] 6.13); secondary 5, 1.37 points (HR 3.92); tertiary 5, 0.87 points (HR 2.39); primary 4, 1.07 points (HR 2.91); secondary 4, 0.79 points (HR 2.21); and any Gleason pattern 3, 0 points (HR 1). Based on the IQRS, the mGGG model was developed, which classified patients into the following five groups: I (3?+?3 or less); II (3?+?4); III (4?+?3); IV (3?+?4?+?t5, 4?+?3?+?t5, 3?+?5, 5?+?3, and 4?+?4); V (4?+?4?+?t5, 4?+?5, 5?+?4, and 5?+?5). The c-index for biochemical recurrence-free survival was significantly improved from 0.655 of the original GGG model to 0.672 of the mGGG model (P?<?0.05). In the robustness analysis, the c-index for salvage treatment-free survival was also significantly improved from 0.619 of the original GGG model to 0.638 of the mGGG model (P?<?0.05). CONCLUSIONS:The quantitative risk of tertiary (<?5%) Gleason pattern 5 is slightly higher than that of secondary (5-50%) Gleason pattern 4. Our newly developed mGGG model more accurately predicts outcomes after radical prostatectomy than the original GGG model.

Dataset Information

High-throughput precision MRI assessment with integrated stack-ensemble deep learning can enhance the preoperative prediction of prostate cancer Gleason grade.

Background

Methods

Results

Conclusions

Publications

High-throughput precision MRI assessment with integrated stack-ensemble deep learning can enhance the preoperative prediction of prostate cancer Gleason grade.

Similar Datasets

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