Histological patterns, subtypes and aspects of prostate cancer: different aspects, different outcomes

GATA3 Expression in Prostatic Adenosquamous Carcinoma: A Potential Diagnostic Pitfall

Urothelial carcinoma and prostatic adenocarcinoma can have overlapping histologic features and in some instances pose challenges to pathologists. GATA binding protein 3 (GATA3) immunohistochemistry (IHC) is a well-established tool to aid in this specific diagnostic dilemma as it has been shown to be a sensitive marker for urothelial carcinoma and a putatively specific marker in excluding prostatic adenocarcinoma. However, in encountering an index tumor of prostatic adenosquamous carcinoma positive for GATA3, herein we sought to investigate this potential diagnostic pitfall in a larger series of tumors. In this study, we retrospectively reviewed prostatic adenosquamous carcinomas diagnosed in 17 patients across the authors' institutions and personal consult collections in the past 10 years. GATA3 IHC was either reviewed or performed on tumors not previously tested. We also recorded other immunostains that were performed at initial diagnosis. Positivity for GATA3 was found in 9 of 17 (53%) tumors, all within squamous regions (2 tumors also showed concomitant moderate GATA3 positivity within glandular elements). The GATA3 positive tumors were all positive for p63 in the 7 tumors where p63 was also performed. Of all tumors tested, NKX3.1 was positive in 100% (13/13) of the glandular elements (3 tumors also showed NKX3.1 concomitant positivity within squamous regions). In summary, when encountering a carcinoma with mixed glandular/squamous features in which prostatic origin is being considered, awareness of GATA3 immunoreactivity in a subset of prostatic adenosquamous carcinoma is critical to avoid diagnostic pitfalls.

External Validation of a Digital Pathology-based Multimodal Artificial Intelligence Architecture in the NRG/RTOG 9902 Phase 3 Trial

BACKGROUND

Accurate risk stratification is critical to guide management decisions in localized prostate cancer (PCa). Previously, we had developed and validated a multimodal artificial intelligence (MMAI) model generated from digital histopathology and clinical features. Here, we externally validate this model on men with high-risk or locally advanced PCa treated and followed as part of a phase 3 randomized control trial.

OBJECTIVE

To externally validate the MMAI model on men with high-risk or locally advanced PCa treated and followed as part of a phase 3 randomized control trial.

DESIGN, SETTING, AND PARTICIPANTS

Our validation cohort included 318 localized high-risk PCa patients from NRG/RTOG 9902 with available histopathology (337 [85%] of the 397 patients enrolled into the trial had available slides, of which 19 [5.6%] failed due to poor image quality).

OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS

Two previously locked prognostic MMAI models were validated for their intended endpoint: distant metastasis (DM) and PCa-specific mortality (PCSM). Individual clinical factors and the number of National Comprehensive Cancer Network (NCCN) high-risk features served as comparators. Subdistribution hazard ratio (sHR) was reported per standard deviation increase of the score with corresponding 95% confidence interval (CI) using Fine-Gray or Cox proportional hazards models.

RESULTS AND LIMITATIONS

The DM and PCSM MMAI algorithms were significantly and independently associated with the risk of DM (sHR [95% CI] = 2.33 [1.60-3.38], p < 0.001) and PCSM, respectively (sHR [95% CI] = 3.54 [2.38-5.28], p < 0.001) when compared against other prognostic clinical factors and NCCN high-risk features. The lower 75% of patients by DM MMAI had estimated 5- and 10-yr DM rates of 4% and 7%, and the highest quartile had average 5- and 10-yr DM rates of 19% and 32%, respectively (p < 0.001). Similar results were observed for the PCSM MMAI algorithm.

CONCLUSIONS

We externally validated the prognostic ability of MMAI models previously developed among men with localized high-risk disease. MMAI prognostic models further risk stratify beyond the clinical and pathological variables for DM and PCSM in a population of men already at a high risk for disease progression. This study provides evidence for consistent validation of our deep learning MMAI models to improve prognostication and enable more informed decision-making for patient care.

PATIENT SUMMARY

This paper presents a novel approach using images from pathology slides along with clinical variables to validate artificial intelligence (computer-generated) prognostic models. When implemented, clinicians can offer a more personalized and tailored prognostic discussion for men with localized prostate cancer.