Prostate Surface Distension and Tumor Texture Descriptors From Pre-Treatment MRI Are Associated With Biochemical Recurrence Following Radical Prostatectomy: Preliminary Findings

FRONTIERS IN ONCOLOGY(2022)

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摘要
ObjectiveTo derive and evaluate the association of prostate shape distension descriptors from T2-weighted MRI (T2WI) with prostate cancer (PCa) biochemical recurrence (BCR) post-radical prostatectomy (RP) independently and in conjunction with texture radiomics of PCa. MethodsThis retrospective study comprised 133 PCa patients from two institutions who underwent 3T-MRI prior to RP and were followed up with PSA measurements for >= 3 years. A 3D shape atlas-based approach was adopted to derive prostate shape distension descriptors from T2WI, and these descriptors were used to train a random forest classifier (C-S) to predict BCR. Texture radiomics was derived within PCa regions of interest from T2WI and ADC maps, and another machine learning classifier (C-R) was trained for BCR. An integrated classifier C-S(+)R was then trained using predictions from C-S and C-R. These models were trained on D-1 (N = 71, 27 BCR+) and evaluated on independent hold-out set D-2 (N = 62, 12 BCR+). C-S(+)R was compared against pre-RP, post-RP clinical variables, and extant nomograms for BCR-free survival (bFS) at 3 years. ResultsC(S)(+)(R) resulted in a higher AUC (0.75) compared to C-R (0.70, p = 0.04) and C-S (0.69, p = 0.01) on D-2 in predicting BCR. On univariable analysis, C-S(+)R achieved a higher hazard ratio (2.89, 95% CI 0.35-12.81, p < 0.01) compared to other pre-RP clinical variables for bFS. C-S(+)R, pathologic Gleason grade, extraprostatic extension, and positive surgical margins were associated with bFS (p < 0.05). C-S(+)R resulted in a higher C-index (0.76 +/- 0.06) compared to CAPRA (0.69 +/- 0.09, p < 0.01) and Decipher risk (0.59 +/- 0.06, p < 0.01); however, it was comparable to post-RP CAPRA-S (0.75 +/- 0.02, p = 0.07). ConclusionsRadiomic shape descriptors quantifying prostate surface distension complement texture radiomics of prostate cancer on MRI and result in an improved association with biochemical recurrence post-radical prostatectomy.
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关键词
magnetic resonance imaging,prostate cancer,retrospective studies,prostatectomy,artificial intelligence,machine learning
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