Durable Response to Atezolizumab in Extensive-Stage Small-Cell Lung Cancer Leading to 60 Months Overall Survival: A Case Report
Bibliographic record
Abstract
Small-cell lung cancer (SCLC) remains a disease with poor prognosis, particularly in extensive-stage SCLC (ES-SCLC). Current standard-of-care treatment includes chemotherapy with platinum agents and etoposide plus immunotherapy with atezolizumab or durvalumab, which has achieved a mean overall survival of 12-13 months in clinical trials. However, long-term survival in ES-SCLC, even with the addition of immunotherapy, continues to be rare. We present the case of a middle-aged male patient diagnosed with ES-SCLC who was treated with four cycles of induction chemotherapy (carboplatin and etoposide) and atezolizumab, starting maintenance atezolizumab every 21 days thereafter, and thoracic radiotherapy. After 9 months, he experienced mild disease progression and was rechallenged with six cycles of carboplatin and etoposide with continued atezolizumab. Subsequent imaging showed near-complete disease resolution which has been sustained since. He has continued on maintenance atezolizumab since diagnosis and has achieved 60 months overall survival and 44 months progression-free survival. Throughout treatment, he has maintained a high functional capacity and only experienced one immune-related adverse event. Our patient is representative of a small subset who are capable of achieving durable responses to immunotherapy and his case highlights the need for further research to elucidate the clinical and biological factors driving this response.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".