A Comprehensive Evaluation of Clinicopathologic Characteristics, Molecular Features and Prognosis in Lung Adenocarcinoma with an Acinar Component
Bibliographic record
Abstract
Introduction: Lung adenocarcinoma (LUAD) is the leading cause of cancer-related mortality worldwide. Acinar is the most prevalent architectural pattern and is associated with an intermediate prognosis. Several studies have investigated the prognosis of acinar-predominant LUAD patients. Here, we aimed to move beyond the acinar-predominant classification and gain a more comprehensive understanding of how acinar minor components influence prognosis specifically when accompanying other histological patterns in LUAD. Methods: Patients were grouped by the proportion of acinar patterns in their tumors: acinar-predominant (AP), and acinar component (AC; non-acinar predominant LUAD with an acinar component of ≥5%). The clinicopathologic characteristics, recurrence-free survival (RFS), and a panel of well-characterized driver mutations, including KRAS, EGFR, BRAF, MET, and PIK3CA, were investigated in the two groups of patients. Results: Among 1263 LUAD patients, 716 (56.7%) were AP, and 547 (43.3%) were AC. In AP, the frequency of EGFR exon 19 deletions (EGFR-Del 19) was significantly higher than in AC (p = 0.014). AC demonstrated a worse RFS than AP in the unadjusted analysis (log-rank p: 0.006). In stage I, the difference in the RFS of AC in comparison to AP remained significant (p = 0.048). In the multivariable analysis, AC was significantly associated with a worse RFS in comparison to AP (hazard ratio [HR] AC vs. AP: 1.240, 95% CI: 1.103–1.312, p: 0.04), even after adjusting for other histological patterns, the mutational status, and relevant clinicopathological features. The post-recurrence survival was significantly better in patients with an acinar component of ≥5% who received EGFR tyrosine kinase inhibitors (TKIs) compared to those who did not receive TKIs (p = 0.033). Conclusions: While the predominant pattern primarily dictates prognosis in LAUD, the presence of an acinar minor component alongside other high-grade patterns may further worsen outcomes. This underscores the necessity of considering the broader histological landscape rather than focusing solely on predominant patterns, as our findings show that minor acinar components can impact RFS alongside other histological patterns.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".