Prostate Cancer Tumor Volume Research Articles

Implementation of a map in radical prostatectomy specimen allows visual estimation of tumor volume

Introduction Tumor volume is one of the best documented prognostic factors for prostate cancer. There are several methods to gain this important parameter but unfortunately most of the clinicians in the world do not get this information in their routine practice from the pathologist. We developed a standardized method to handle radical prostatectomy specimens including a special form of mapping in order to document relevant morphological data. The aim of this study was to investigate if our model of mapping prostate cancer, which we use in routine practice, may serve for visual estimation of tumor volume. Methods We estimated the tumor volume of prostate cancer by visual estimation of 350 maps of radical prostatectomy specimens and correlated these data with established prognostic parameters and clinical outcome. Results Significant correlations between tumor volumes, as obtained from our mapping, and known prognostic parameters such as preoperative serum levels of prostatic specific antigen, loss of differentiation, histological grade, lymph node metastasis, and margins were found. In a multivariate analysis, only Gleason score and tumor stage were shown to be independent prognostic parameters. Discussion We demonstrate that mapping of prostate cancer is more than a simple method of documentation but may serve as a method for visual estimation of tumor volume of prostate cancer after radical prostatectomy. This method can further be used for a visual documentation of the tumor stage independent of changes in the TNM classification. The method is inexpensive and practicable and can therefore be applied in routine surgical pathology.

Impact of multiple biopsy cores on predicting final tumor volume in prostate cancer detected by a single microscopic focus of cancer on biopsy

Objectives To compare the impact that the number of biopsy cores have on final pathologic findings when minimal disease is detected at biopsy. Discordance has been noted between transrectal ultrasound-guided biopsy results and tumor volume even when minimal amounts of tumor are found on biopsy. Methods We identified patients who had undergone radical retropubic prostatectomy for a single microscopic focus of adenocarcinoma from a prospectively maintained surgical database. Patients were stratified into two groups: those with six biopsies or less and those with seven or more. The Gleason score, margin status, presence of extracapsular extension, and percentage of tumor volume were compared. Results A total of 102 patients in our database had a single microscopic focus of adenocarcinoma detected by needle biopsy. Of these patients, 65 underwent six or fewer biopsies and 37 underwent seven or more at transrectal ultrasonography. Of the 37 patients in group 2, 27 (73%) had a final tumor volume of less than 5% compared with 24 (37%) of 65 patients in group 1 ( P = 0.002). Of the group 2 patients, 15 (75%) with Stage T1c had an estimated tumor volume of less than 5% compared with only 11 (34%) in group 1 ( P = 0.01). No statistically significant difference was noted between the two groups for margin status, presence of extracapsular extension, or Gleason score. Conclusions A single microscopic focus of cancer obtained after multiple cores predicts for a significantly lower tumor volume on final pathologic examination across clinical stages. In the context of greater tissue sampling, the minimal disease designation may carry more predictive value and be a useful parameter in stratifying patients with Stage T1c and other good-risk factors with regard to surgical outcome.

Tumor volume in prostate cancer and serum prostate-specific antigen. Analysis from a kinetic viewpoint.

Many have observed poor correlations between serum prostate-specific antigen (PSA) and tumor volume, and this noise undoubtedly reduces the diagnostic specificity of PSA. To explore this phenomenon, we addressed the problem from a theoretical viewpoint. Specifically, we used a compartmental model and first-order kinetics to develop the mathematics necessary to relate serum PSA to tumor volume. We found that the resulting model fit well the observed kinetic data of PSA measured after biopsy or prostatectomy. The model also predicted a linear relationship between PSA and the sum of volumes of benign and malignant tissues, but the coefficients for this linear equation are more complex than previously thought. They reflect not only how much PSA may be present in each tissue but also 3 rate parameters and the volume of serum. Much of the noise in the linkage between PSA and tumor volume is due to individual patient differences in the 3 rate parameters and in serum volume. Our model predicts that without ways to directly measure the several involved rate parameters, we will not be able to accurately predict tumor volume from PSA. Nevertheless, surrogates for the missing parameters may exist and could lead to statistical models that could improve the prediction of tumor volume.

Tumor Volume in Prostate Cancer and Serum Prostate-Specific Antigen

Many have observed poor correlations between serum prostate-specific antigen (PSA) and tumor volume, and this noise undoubtedly reduces the diagnostic specificity of PSA. To explore this phenomenon, we addressed the problem from a theoretical viewpoint. Specifically, we used a compartmental model and first-order kinetics to develop the mathematics necessary to relate serum PSA to tumor volume. We found that the resulting model fit well the observed kinetic data of PSA measured after biopsy or prostatectomy. The model also predicted a linear relationship between PSA and the sum of volumes of benign and malignant tissues, but the coefficients for this linear equation are more complex than previously thought. They reflect not only how much PSA may be present in each tissue but also 3 rate parameters and the volume of serum. Much of the noise in the linkage between PSA and tumor volume is due to individual patient differences in the 3 rate parameters and in serum volume. Our model predicts that without ways to directly measure the several involved rate parameters, we will not be able to accurately predict tumor volume from PSA. Nevertheless, surrogates for the missing parameters may exist and could lead to statistical models that could improve the prediction of tumor volume.

Prostate cancer tumor volume: measurement with endorectal MR and MR spectroscopic imaging.

To determine accuracy of magnetic resonance (MR) and three-dimensional (3D) MR spectroscopic imaging in prostate cancer tumor volume measurement. Endorectal MR and 3D MR spectroscopic imaging were performed in 37 patients before radical prostatectomy. Two independent readers recorded peripheral zone tumor nodule location and volume. Results were analyzed with step-section histopathologic tumor localization and volume measurement as the standard. Accuracy of tumor volume measurement was assessed with the Pearson correlation coefficient. P values were calculated with a random effects model. Bland-Altman regression analysis was used to evaluate systematic bias between tumor volumes measured with MR imaging and true tumor volumes. Analyses were performed for all nodules and nodules greater than 0.50 cm(3). Mean volume of peripheral zone tumor nodules (n = 51) was 0.79 cm(3) (range, 0.02-3.70 cm(3)). Two readers detected 20 (65%) and 23 (74%) of 31 peripheral zone tumor nodules greater than 0.50 cm(3). For these nodules, measurements of tumor volume with MR imaging, 3D MR spectroscopic imaging, and a combination of both were all positively correlated with histopathologic volume (Pearson correlation coefficients of 0.49, 0.59, and 0.55, respectively); only measurements with 3D MR spectroscopic imaging and a combination of MR and 3D MR spectroscopic imaging demonstrated statistical significance (P <.05). Tumor volume estimation with all three methods was more accurate for higher tumor volumes. Addition of 3D MR spectroscopic imaging to MR imaging increases overall accuracy of prostate cancer tumor volume measurement, although measurement variability limits consistent quantitative tumor volume estimation, particularly for small tumors.