Anatomical Resections Improve Survival Following Lung Metastasectomy of Colorectal Cancer Harboring KRAS Mutations
Bibliographic record
Abstract
OBJECTIVE: The aim of this study was to evaluate the benefit of anatomical resection (AR) in lung metastasectomy (LM) of colorectal cancer (CRC) harboring KRAS mutations SUMMARY BACKGROUND DATA:: KRAS mutations are related to high aggressiveness in the lung metastasis of CRC. It is unknown whether AR can lead to better outcomes than can non-AR (NAR) in KRAS patients. METHODS: We retrospectively reviewed the data from 574 consecutive patients who underwent a LM for CRC. We focused on patients exhibiting 1 lung metastasis who underwent an AR (segmentectomy) or an NAR (wedge) and for whom the KRAS mutational status was known. Overall survival (OS) and time to pulmonary recurrence (TTPR) were analyzed. RESULTS: We included 168 patients, of whom 95 (56.5%) harbored KRAS mutations. An AR was performed in 74 patients (44%). The type of resection did not impact the median OS in wild-type (WT) patients (P = 0.67) but was significantly better following AR in KRAS patients (101 vs 45 months, P = 0.02) according to the multivariate analysis [hazard ratio (HR): 6.524; 95% confidence interval (CI), 2.312-18.405; P < 0.0001). TTPR was not affected by the type of resection in WT patients (P = 0.32) but was significantly better for AR in KRAS patients (50 vs 15 months, P = 0.01) in the multivariate analysis (HR: 5.273; 95% CI, 1.731-16.064; P = 0.003). The resection-margin recurrence rate was significantly higher for NAR in KRAS patients (4.8% vs 54.2%, P = 0.001) but not in WT patients (P = 0.97). CONCLUSION: AR seems to improve both the OS and TTPR in LM of CRC harboring KRAS mutations.
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 | 0.000 | 0.002 |
| 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) | 0.001 | 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".