Digital rectal examination remains an important screening tool for prostate cancer

Patient-derived tumour models for personalized therapeutics in urological cancers

Preclinical knowledge of dysregulated pathways and potential biomarkers for urological cancers has undergone limited translation into the clinic. Moreover, the low approval rate of new anticancer drugs and the heterogeneous drug responses in patients indicate that current preclinical models do not always reflect the complexity of malignant disease. Patient-derived tumour models used in preclinical uro-oncology research include 3D culture systems, organotypic tissue slices and patient-derived xenograft models. Technological innovations have enabled major improvements in the capacity of these tumour models to reproduce the clinical complexity of urological cancers. Each type of patient-derived model has inherent advantages and limitations that can be exploited, either alone or in combination, to gather specific knowledge on clinical challenges and address unmet clinical needs. Nevertheless, few opportunities exist for patients with urological cancers to benefit from personalized therapeutic approaches. Clinical validation of experimental data is needed to facilitate the translation and implementation of preclinical knowledge into treatment decision making.

[Study of anogenital distance as a diagnostic tool for prostate cancer]

INTRODUCTION AND OBJECTIVE

The distance from the genitals to the anus (anogenital distance [AGD]) reflects androgen concentration during prenatal development in mammals. At the present time, there is only one study suggesting the relationship between AGD and risk of prostate cancer (CaP). The goal of this study was to assess the performance and clinical utility of AGD, as a biomarker of prenatal androgenic milieu, and risk of CaP in a larger population, in CaP diagnosis.

MATERIAL AND METHODS

A case-control study was conducted on 260 men seen in a hospital outpatient clinic where underwent a physical and andrological examination and completed a brief questionnaire. CaP patients were confirmed by biopsy of the tumor. Controls were men without CaP seen in the urology outpatient clinic for routine examinations. Two variants of AGD (from the anus to the posterior base of the scrotum [AGD_AS] and to the cephalad insertion of the penis [AGD_AP]) were measured. Parametric and non-parametric tests and receiver operating characteristic (COR) analyses were used to determine relationships between AGD and presence of CaP.

RESULTS

The highest area under the curve (0.69; 95% CI 0.60 to 0.78 and 0.69; 95% CI 0.61 to 0.77) was obtained for the Gleason=7 subgroup with the AGD_AS and AGD_AP measurement, with a sensitivity and specificity of 83% and 55%, and 91% and 41%, the predictive positive value of 39% and 35% and negative value of 90% and 93% respectively.

CONCLUSION

AGD may be a useful clinical tool for the CaP diagnosis.

Robotic mechanical localization of prostate cancer correlates with magnetic resonance imaging scans

PURPOSE

To evaluate the concordance of cancer location of the tissue mapping from a mechanical pressure transducer with magnetic resonance imaging (MRI) scans.

MATERIALS AND METHODS

A total of 60 indentations were performed on 5 prostate specimens obtained after radical prostatectomy utilizing a robotic indentation system. The mechanical elastic moduli of suspected malignant lesions were calculated and mapped, and their locations were compared with suspicious areas of malignancy on MRI scans.

RESULTS

The concordance rate between the location mapping from the robotic indentation system and MRI scans results was 90.0% (54/60). The sensitivity and specificity of the robotic indentation system were 87.9% (29/33) and 92.6% (25/27), respectively. The positive predictive value and negative predictive value were 93.5% (29/31) and 93.1% (27/29), respectively.

CONCLUSION

The locations of malignant lesions derived from our robotic indentation system correlated strongly with the locations of suspected areas of malignancy on MRI scans. Our robotic system may provide a more targeted biopsy of the prostate than conventional non-targeted systemic biopsy, possibly improving the diagnostic accuracy of prostatic biopsies for cancer.

Robotic palpation-based mechanical property mapping for diagnosis of prostate cancer

PURPOSE

The aim of this study was to estimate the mechanical properties (elasticity) of normal and cancer prostate tissues and to develop a tissue elasticity map for the diagnosis and localization of prostate cancer.

MATERIALS AND METHODS

A total of 735 sites from 35 radical prostatectomy specimens were used in the experiments using a robotic palpation system, and the elasticities of the specimens were estimated by a tissue characterization algorithm. The estimated elasticities from 21 regions were separated into normal and cancer tissues using the pathological information, and a tissue elasticity map was developed using numerical functions and a nonlinear surface-fitting method.

RESULTS

The mean elastic moduli of the normal and cancer tissues were 15.25 ± 5.88 and 28.80 ± 11.20 kPa, respectively. The base region had the highest elasticity, followed by the medial and apex regions. These results demonstrated the ability to separate the cancer tissue from the normal tissue based on its elastic modulus. The tissue elasticity mapping was carried out using the estimated elasticity and nonlinear surface fitting. The proposed map showed the elasticity and was used to estimate the elastic modulus of the prostate at any given region.

CONCLUSION

Tissue elasticity may be an important indicator of prostate cancer because the pathologic changes alter the tissue properties, including cell integrity and intercellular matrix. This work provides quantitative and objective information for the diagnosis of prostate cancer. In addition, these results may have implications for the localization of prostate cancers.