Whole mount histopathological correlation with prostate MRI in Grade I and II prostatectomy patients

Exploring the potential of ex-vivo 7-T magnetic resonance imaging on patients with clinically significant prostate cancer: visibility and size perspective

Background

Despite progress in multiparametric magnetic resonance imaging (MRI), issues of prostate cancer invisibility and underestimated tumor burden persist. This study investigates the potential of an ultra-high field MRI at 7-T in an ex-vivo setting to address these limitations.

Methods

This prospective study included 54 tumors from 20 treatment-naïve clinically significant prostate cancer patients, confirmed by biopsy, despite negative findings on preoperative 3-T MRI. Ex-vivo 7-T MRI of resected prostates was performed, with assessment on tumor visibility and size. Factors influencing visibility were analyzed using logistic regression analyses.

Results

Tumor visibility was confirmed in 80% of patients, and 48% of all tumors on ex-vivo imaging. Gleason pattern 4 percentage (odds ratio 1.09) and tumor size on pathology (odds ratio 1.36) were significantly associated with visibility (P < 0.05). Mean MRI-visible and invisible tumor sizes were 10.5 mm and 5.3 mm, respectively. The size discrepancy between MRI and pathology was 2.7 mm.

Conclusion

Tumor visibility on ex-vivo 7-T MRI was influenced by tumor grade and size. The notable tumor visibility initially overlooked on 3-T MRI, along with small size discrepancy with pathology, suggests potential improvements in resolution.

Prostate zones and cancer: lost in transition?

Localized prostate cancer shows great clinical, genetic and environmental heterogeneity; however, prostate cancer treatment is currently guided solely by clinical staging, serum PSA levels and histology. Increasingly, the roles of differential genomics, multifocality and spatial distribution in tumorigenesis are being considered to further personalize treatment. The human prostate is divided into three zones based on its histological features: the peripheral zone (PZ), the transition zone (TZ) and the central zone (CZ). Each zone has variable prostate cancer incidence, prognosis and outcomes, with TZ prostate tumours having better clinical outcomes than PZ and CZ tumours. Molecular and cell biological studies can improve understanding of the unique molecular, genomic and zonal cell type features that underlie the differences in tumour progression and aggression between the zones. The unique biology of each zonal tumour type could help to guide individualized treatment and patient risk stratification.

Artificial Intelligence Combined With Big Data to Predict Lymph Node Involvement in Prostate Cancer: A Population-Based Study

Background

A more accurate preoperative prediction of lymph node involvement (LNI) in prostate cancer (PCa) would improve clinical treatment and follow-up strategies of this disease. We developed a predictive model based on machine learning (ML) combined with big data to achieve this.

Methods

Clinicopathological characteristics of 2,884 PCa patients who underwent extended pelvic lymph node dissection (ePLND) were collected from the U.S. National Cancer Institute’s Surveillance, Epidemiology, and End Results (SEER) database from 2010 to 2015. Eight variables were included to establish an ML model. Model performance was evaluated by the receiver operating characteristic (ROC) curves and calibration plots for predictive accuracy. Decision curve analysis (DCA) and cutoff values were obtained to estimate its clinical utility.

Results

Three hundred and forty-four (11.9%) patients were identified with LNI. The five most important factors were the Gleason score, T stage of disease, percentage of positive cores, tumor size, and prostate-specific antigen levels with 158, 137, 128, 113, and 88 points, respectively. The XGBoost (XGB) model showed the best predictive performance and had the highest net benefit when compared with the other algorithms, achieving an area under the curve of 0.883. With a 5%~20% cutoff value, the XGB model performed best in reducing omissions and avoiding overtreatment of patients when dealing with LNI. This model also had a lower false-negative rate and a higher percentage of ePLND was avoided. In addition, DCA showed it has the highest net benefit across the whole range of threshold probabilities.

Conclusions

We established an ML model based on big data for predicting LNI in PCa, and it could lead to a reduction of approximately 50% of ePLND cases. In addition, only ≤3% of patients were misdiagnosed with a cutoff value ranging from 5% to 20%. This promising study warrants further validation by using a larger prospective dataset.

Correlation of Prostate-Imaging Reporting and Data Scoring System scoring on multiparametric prostate magnetic resonance imaging with histopathological factors in radical prostatectomy material in Turkish prostate cancer patients: a multicenter study of the Urooncology Association

Background

Histopathological features after radical prostatectomy (RP) provide important information for the prognosis of prostate cancer (PCa). The possible correlations between Prostate-Imaging Reporting and Data Scoring System (PIRADS) scores in multiparametric magnetic resonance imaging (mpMRI) may also be predictive for prognosis. In this study, we aimed to evaluate the correlation of PIRADS scores with histopathological data.

Methods

A total of 177 patients who underwent preoperative mpMRI and RP for PCa from eight institutions were included in the study. Correlation of PIRADS score in preoperative mpMRI with adverse histopathological factors in RP specimen was investigated using univariate and multivariate analyses.

Results

The relationship between PIRADS score and postoperative extracapsular extension, lymphovascular invasion, and seminal vesicle involvement was significant (P < 0.001, P = 0.032, and P = 0.007, respectively). Although the PIRADS score was significantly correlated with the number of dissected lymph nodes (p = 0.026), it had no significant correlation with the number of positive nodes (P = 0.611). Total Gleason score, extracapsular extension, seminal vesicle invasion, and number of lymph nodes were found to be independent factors, which correlated with high PIRADS scores in ordinal logistic regression analysis.

Conclusion

PIRADS scoring system in mpMRI showed a statistically significant correlation with adverse histopathological factors in RP specimen. A higher PIRADS score may help to predict a higher Gleason score, indicating clinically important PCa as well as poor prognotic factors such as extracapsular extension, lymphovascular invasion, and seminal vesicle invasion that may indicate a higher risk of recurrence and the need for additional treatment.

The Tumor Microenvironments of Lethal Prostate Cancer

Localized prostate cancer (confined to the gland) generally is considered curable, with nearly a 100% 5-year-survival rate. When the tumor escapes the prostate capsule, leading to metastasis, there is a poorer prognosis and higher mortality rate, with 5-year survival dropping to less than 30%. A major research question has been to understand the transition from indolent (low risk) disease to aggressive (high risk) disease. In this chapter, we provide details of the changing tumor microenvironments during prostate cancer invasion and their role in the progression and metastasis of lethal prostate cancer. Four microenvironments covered here include the muscle stroma, perineural invasion, hypoxia, and the role of microvesicles in altering the extracellular matrix environment. The adaptability of prostate cancer to these varied microenvironments and the cues for phenotypic changes are currently understudied areas. Model systems for understanding smooth muscle invasion both in vitro and in vivo are highlighted. Invasive human needle biopsy tissue and mouse xenograft tumors both contain smooth muscle invasion. In combination, the models can be used in an iterative process to validate molecular events for smooth muscle invasion in human tissue. Understanding the complex and interacting microenvironments in the prostate holds the key to early detection of high-risk disease and preventing tumor invasion through escape from the prostate capsule.