Using nuclear morphometry to predict the need for treatment among men with low grade, low stage prostate cancer enrolled in a program of expectant management with curative intent

Abstract

PURPOSE-We assessed the use of quantitative clinical and pathologic information to predict which patients would eventually require treatment for prostate cancer (CaP) in an expectant management (EM) cohort.EXPERIMENTAL DESIGN-We identified 75 men having prostate cancer with favorable initial biopsy characteristics; 30 developed an unfavorable biopsy (Gleason grade >6, >2 cores with cancer, >50% of a core with cancer, or a palpable nodule) requiring treatment and 45 maintained favorable biopsies throughout a median follow-up of 2.7years. Demographic, clinical data and quantitative tissue histomorphometry determined by digital image analysis were analyzed.RESULTS-Logistic regression (LR) modeling generated a quantitative nuclear grade (QNG) signature based on the enrollment biopsy for differentiation of Favorable and Unfavorable groups using a variable LR selection criteria of P z < 0.05. The QNG signature utilized 12 nuclear morphometric descriptors (NMDs) and had an area under the receiver operator characteristic curve (ROC-AUC) of 87% with a sensitivity of 82%, specificity of 70% and accuracy of 75%. A multivariable LR model combining QNG signature with clinical and pathological variables yielded an AUC-ROC of 88% and a sensitivity of 81%, specificity of 78% and accuracy of 79%. A LR model using prostate volume, PSA density, and number of pre-diagnosis biopsies resulted in an AUC-ROC of 68% and a sensitivity of 85%, specificity of 37% and accuracy of 56%.CONCLUSIONS-QNG using EM prostate biopsies improves the predictive accuracy of LR models based on traditional clinicopathologic variables in determining which patients will ultimately develop an unfavorable biopsy. Our QNG-based model must be rigorously, prospectively validated prior to use in the clinical arena.Keywords prostate cancer; expectant management; quantitative nuclear grade; prostate biopsy; low grade and low stage cancer

Novel Tools to Improve Patient Selection and Monitoring on Active Surveillance for Low-risk Prostate Cancer: A Systematic Review

“…The literature search identified 30 original articles that were included for review: 14 on magnetic resonance imaging (MRI) [8][9][10][11][12][13][14][15][16][17][18][19][20][21], 5 on serum markers [22][23][24][25][26], 5 on urinary markers [27][28][29][30][31], 4 on histopathology markers [32][33][34][35], and 2 on germline genetic markers [36,37]. Figure 1 presents the search strategy and study selection flowchart.…”

“…Makarov et al studied 12 nuclear morphometric descriptors (such as shape and size of nucleus) within patients in a PCa expectant management program (n = 75) [32]. Of these, 30 showed unfavourable biopsy (Gleason >6, more than two biopsy cores, >50% of cores involved) during follow-up.…”

Combined histoarchitectural and cytological biopsy grading improves grading accuracy in low‐grade prostate cancer

“…18 They developed a QNG signature based on 12 nuclear morphometric descriptors determined by digital image analysis. Retrospec- Although these data appear to be promising, the introduction into clinical routine is hampered by the complexity of the methodology.…”

“…[5][6][7][8][9][10][11][12][13] putative low-grade cancers remains a diagnostic and therapeutic dilemma when active surveillance or an organpreserving therapy comes into consideration. 15,16 Interestingly, as shown in recent studies, 17,18 grading of cytological tumor characteristics, which is not part of the Gleason grading system, seems to be of additional diagnostic value.…”

Nuclear morphometry, nucleomics and prostate cancer progression

“…5,96 Epstein et al 97 proposed a PSA density of ,0.15 ng ml 21 cm 23 and favorable diagnostic needle-biopsy characteristics (i.e., a Gleason score ,7, two or fewer cores involved with cancer, f50% of any core involved with cancer) as criteria to identify low-grade, lowstage tumors that can be followed with serial measurements of PSA and repeated biopsies without immediate intervention until reclassification requires definitive treatment. Makarov et al 98 identified 75 cases that qualified for AS (30 men required reclassification upon annual follow-up) to evaluate nuclear morphometry using the AutoCyte system and created a QNG signature of 12 nuclear morphometric descriptors. The QNG signature had an ROC-AUC of 87% with a sensitivity of 82%, specificity of 70% and accuracy of 75% to predict reclassification using the diagnostic AS biopsy.…”