Reactive Stroma and Acinar Morphology in Prostate Cancer: Implications for Progression and Prognostic Assessment
Bibliographic record
Abstract
INTRODUCTION: Prostate cancer (PC) remains a significant global health concern, with prognostic assessments largely reliant on the Gleason Classification System. While it has proven effective, subjectivity in interpretation persists, prompting the need for complementary approaches. Reactive stroma (RS) has emerged as a potential candidate for enhancing PC characterization, as it reflects intricate interactions among stromal, epithelial, and extracellular matrix components. To shed light on this, we conducted a comprehensive study. METHODS: Two expert pathologists independently analyzed consecutive prostate biopsies (n = 120 patients), categorized into four groups based on Gleason scores. Four acinar patterns were described, denoted as A, B, C, and D. Our study uncovered a noteworthy presence of RS, predominantly within poorly differentiated tumors. Stromogenic tumors, characterized by high RS content, were particularly associated with Gleason scores of 4 + 3 and ≥ 8. Intriguingly, acinar patterns, including the distinctive B and D patterns, exhibited strong correlations with stromogenic tumors. Incorporating quantitative imaging techniques (Second Harmonic Generation and Two-Photon Excitation Fluorescence Microscopy), we examined collagen fiber density within the stroma. RESULTS: Our analysis revealed a direct relationship between RS intensity and collagen fiber counts, particularly prominent in patterns B and D. These findings suggest that the stromal reaction in PC is closely linked to acinar morphology and collagen deposition. Moreover, rudimentary microacini at the tumor periphery, associated with intense RS and patterns B and D, may signify an unfavorable prognosis. CONCLUSION: Our study highlights the potential of RS as an additional prognostic factor in PC. It underscores the intricate interplay between acinar patterns, RS intensity, and collagen fiber density, providing valuable insights for future prognostic assessments and therapeutic strategies. Further exploration of these relationships is essential for a comprehensive understanding of PC progression and management.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".