A Locally Advanced NSCLC Patient Harboring a Rare KIF13A-RET Fusion Benefited from Pralsetinib: A Case Report
Bibliographic record
Abstract
The application of adjuvant treatment has significantly enhanced the survival of patients with resectable non-small cell lung cancer (NSCLC) carrying driver gene mutations. However, adjuvant-targeted therapy remains controversial for some NSCLC patients carrying rare gene mutations such as RET, as there is currently a lack of confirmed randomized controlled trials demonstrating efficacy. In this report, we describe the case of a 58-year-old man with stage IIIA NSCLC who underwent complete lobectomy with selective lymph node dissection. Postoperative next-generation sequencing revealed that the patient harbored a rare KIF13A-RET fusion. The patient elected to receive adjuvant treatment with pralsetinib monotherapy and underwent serial circulating tumor DNA (ctDNA) monitoring after surgery. During follow-up, despite experiencing dose reduction and irregular medication adherence, the patient still achieved a satisfactory disease-free survival (DFS) of 27 months. Furthermore, ctDNA predicted tumor recurrence 4 months earlier than imaging techniques. The addition of bevacizumab to the original regimen upon recurrence continued to be beneficial. Pralsetinib demonstrated promising efficacy as adjuvant therapy, while ctDNA analysis offered a valuable tool for early detection of tumor recurrence. By leveraging targeted therapies and innovative monitoring techniques, we aim to improve outcomes and quality of life for NSCLC patients in the future.
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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".