Quality of Life of Lung Cancer Patients with Immune-Related Endocrinopathies During Immunotherapy: A Prospective Study Based on the EORTC QLQ-C30 and QLQ-LC13 Questionnaires in Romania
Bibliographic record
Abstract
(1) Background: Globally, lung cancer is the leading cause of cancer death, but immunotherapy has impressively improved the outcomes, generating longer progression-free survival and overall survival. Endocrine immune-related adverse events (irAEs) are mostly irreversible and need life-long hormonal substitution therapy. The evaluation of the quality of life of lung cancer patients experiencing endocrine toxicities during immune checkpoint inhibitor (ICI) treatment is relevant for both patients and healthcare providers. (2) Methods: This was a prospective cohort study of lung cancer patients treated with immune checkpoint inhibitors in a tertiary-level hospital in Romania from 1 December 2021 to 30 September 2024. Quality of life was assessed using versions of the EORTC QLQ-C30 and EORTC QLQ-LC-13 validated and translated into the Romanian language. We investigated several clinical variables to evaluate their impact on QoL. (3) Results: Fifty-nine lung cancer patients were evaluated for their QoL before ICI initiation and at the end of the study. Endocrine-irAEs occurred in 17 lung cancer patients (28.8%). Quality of life as assessed using the EORTC questionnaires was statistically significantly improved, even in patients experiencing endocrine-irAEs. (4) Conclusions: Our prospective cohort study succeeded in delivering the proof of concept of an increased QoL in lung cancer patients who had developed endocrine-irAEs under immunotherapy. Despite toxicities, especially rather frequent endocrine-irAEs, ICIs enabled durable disease control and symptom relief, improving the QoL of the overall trial population. As more lung cancer patients undergo immunotherapy in metastatic or early stages, we draw attention to this particular patient population with autoimmune endocrinopathies, as they will live longer and require life-long hormonal therapy.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".