Clinical, humanistic and economic burden associated with recurrence among patients with early-stage cancers: a systematic literature review
Bibliographic record
Abstract
Introduction: While cancer recurrences have been reported as negatively affecting patients' prognosis and imposing an economic burden to healthcare systems, there is no comprehensive summary of evidence on how frequently recurrence occurs across early-stage cancers. The goal of this study was to assess recurrence rates and their resulting clinical, humanistic and economic burden in patients with early-stage cancers. Methods: A narrative, systematic literature review was conducted including non-interventional studies evaluating adult patients diagnosed with cancer at early-stages (including: melanoma, triple negative breast cancer, non-small cell lung cancer, renal-cell carcinoma, gastric cancer, head and neck cancer, and bladder cancer). Selected studies were identified through electronic database searches, conference proceedings, and grey literature sources. Outcomes of interest included recurrence rates, post-recurrence survival, and the humanistic and economic burden associated with recurrences. Results: Among 82 studies included, 75 reported recurrence rates, eight investigated post-recurrence survival, five evaluated post-recurrence patient-reported outcomes, and seven examined the post-recurrence economic burden. Across most cancer types, recurrences occurred frequently, with later stages at diagnosis being associated with higher recurrence rates and shorter time to recurrence compared to earlier stages at diagnosis. Cancer recurrence was associated with lower survival, reduced health-related quality of life (HRQoL), worsening cancer-related symptoms and higher healthcare resource utilization. These outcomes were also more pronounced among patients diagnosed at later stages. Among cancer survivors, most patients experienced moderate fear of cancer recurrence (FCR). Patients with clinically relevant FCR had worse cancer-related symptoms and reduced HRQoL compared to those without. Direct costs in recurrent patients (predominantly in the form of inpatient and outpatient costs) were the main drivers for the total healthcare costs incurred, irrespective of the cancer types and stages. Conclusion: This study highlights the high recurrence rates experienced by patients diagnosed with early-stage cancer, particularly if diagnosed at later stages (Stage III), and their clinical, humanistic and economic impact. Cancer stage at the time of diagnosis is a key indicator of recurrence risk and post-recurrence outcomes, emphasizing the importance of earlier diagnosis and the need for therapies that prevent recurrences to better mitigate their clinical, humanistic and economic burden.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".