title: A streamlined three-dimensional volume estimation method accurately classifies prostate tumors by volume.
abstract: Prostate tumor volume has been suggested to be an important pathologic variable that predicts for clinical significance and outcome. However, the determination of tumor volume using standard methods such as computerized planimetry or image analysis is labor intensive. We studied whether length (L), width (W), and height (number of cross sections x sectional thickness, CST) of a tumor focus could be used to estimate prostate tumor volume. We studied 1091 tumor foci from 365 selected serially sectioned radical prostatectomy specimens. We randomly divided the specimens into evaluation (182 specimens) and validation (183 specimens) groups. After analyzing the evaluation group, we derived the formula 0.4 (slope of the regression line) x L x W x CST to estimate volume. We then tested whether our three-dimensional volume estimation formula could accurately classify tumor volume for specimens in the validation set as insignificant (</=0.5 cm3) or significant (>0.5 cm3), and also into a five-category tumor volume scheme. Our three-dimensional estimate accurately classified tumors into insignificant and significant total volume categories in 94.0% of cases and into the five-category scheme in 85.8% cases. These accuracy rates were significantly better than rates for other methods. The three-dimensional estimate is an accurate and straightforward method for assessing prostate tumor volume.
mesh_terms: D001837:Body Weights and Measures / Q000379:methods*; D006801:Humans; D021621:Imaging, Three-Dimensional / Q000379:methods*; D008297:Male; D018579:Patient Selection; D011237:Predictive Value of Tests; D011467:Prostate / Q000033:anatomy & histology / Q000000981:diagnostic imaging* / Q000473:pathology* / Q000601:surgery; D011468:Prostatectomy / Q000379:methods; D011471:Prostatic Neoplasms / Q000145:classification / Q000000981:diagnostic imaging* / Q000473:pathology* / Q000601:surgery; D011859:Radiography; D011897:Random Allocation; D015203:Reproducibility of Results