Prostate tumor eccentricity predicts Gleason score better than prostate tumor volume

被引:5
|
作者
Mayer, Rulon [1 ,2 ]
Simone, Charles B., II [3 ]
Turkbey, Baris [4 ]
Choyke, Peter [4 ]
机构
[1] Univ Penn, Philadelphia, PA 19104 USA
[2] Oncoscore, 10702 Keswick St, Garrett Pk, MD 20896 USA
[3] New York Proton Ctr, New York, NY USA
[4] NIH, Bldg 10, Bethesda, MD 20892 USA
关键词
Tumor morphology; prostate cancer (PCa); multi-parametric MRI; Gleason score; histology of wholemount prostatectomy; SEMINAL-VESICLE INVASION; RADICAL PROSTATECTOMY; CANCER VOLUME; ADENOCARCINOMA; MRI; BIOPSY; ALGORITHMS; SYSTEM;
D O I
10.21037/qims-21-466
中图分类号
R8 [特种医学]; R445 [影像诊断学];
学科分类号
1002 ; 100207 ; 1009 ;
摘要
Background: Prostate tumor volume predicts biochemical recurrence, metastases, and tumor proliferation. A recent study showed that prostate tumor eccentricity (elongation or roundness) correlated with Gleason score. No studies examined the relationship among the prostate tumor's shape, volume, and potential aggressiveness. Methods: Of the 26 patients that were analyzed, 18 had volumes >1 cc for the histology-based study, and 25 took up contrast material for the MRI portion of this study. This retrospective study quantitatively compared tumor eccentricity and volume measurements from pathology assessment sectioned wholemount prostates and multi-parametric MRI to Gleason scores. Multi-parametric MRI (T1, T2, diffusion, dynamic contrast enhanced images) were resized, translated, and stitched to form spatially registered multi-parametric cubes. Multi-parametric signatures that characterize prostate tumors were inserted into a target detection algorithm (Adaptive Cosine Estimator, ACE). Various detection thresholds were applied to discriminate tumor from normal tissue. Pixel-based blobbing, and labeling were applied to digitized pathology slides and threshold ACE images. Tumor volumes were measured by counting voxels within the blob. Eccentricity calculation used moments of inertia from the blobs. Results: From wholemount prostatectomy slides, fitting two sets of independent variables, prostate tumor eccentricity (largest blob eccentricity, weighted eccentricity, filtered weighted eccentricity) and tumor volume (largest blob volume, average blob volume, filtered average blob volume) to Gleason score in a multivariate analysis, yields correlation coefficient R=0.798 to 0.879 with P<0.01. The eccentricity t-statistic exceeded the volume t-statistic. Fitting histology-based total prostate tumor volume against Gleason score yields R=0.498, P=0.0098. From multi-parametric MRI, the correlation coefficient R between the Gleason score and the largest blob eccentricity for varying thresholds (0.30 to 0.55) ranged from R=-0.51 to -0.672 (P-values <0.01). For varying thresholds (0.60 to 0.80) for MRI detection, the R between the largest blob volume eccentricity against the Gleason score ranged from R=0.46 to 0.50 (P-values <0.03). Combining tumor eccentricity and tumor volume in multivariate analysis failed to increase Gleason score prediction. Conclusions: Prostate tumor eccentricity, determined by histology or MRI, more accurately predicted Gleason score than prostate tumor volume. Combining tumor eccentricity with volume from histology based analysis enhanced Gleason score prediction, unlike MRI.
引用
收藏
页码:1096 / 1108
页数:13
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