Biospecimen Use in Cancer Research Over Two Decades
Bibliographic record
Abstract
Demand for biospecimens in cancer research has increased but there are relatively few data on the trends in biospecimen usage. These data are needed to enable projection of future demand. We analyzed biospecimen usage in publications published at five-year intervals (2008, 2003, 1998, 1993, and 1988) in four cancer research journals (Cancer Research, Clinical Cancer Research, British Journal of Cancer and International Journal of Cancer). We categorized publications in three ways: 1) biospecimen utilization yes/no; 2) biospecimen cohort size; and 3) format of biospecimens used including frozen tissue, Formalin-Fixed Paraffin-Embedded (FFPE) tissue, fresh tissue, fluids, and hematological biospecimens. Biospecimens were used in 1292/3307 (39%) of publications analyzed and sufficient information was available to further classify biospecimen usage in 1228 publications. The proportion of publications in each journal using biospecimens ranged from 23% to 61% between journals, with no significant change within each journal over time. A more detailed review of tissue biospecimen use showed a significant increase in cohort sizes from 1988 to 2008 (mean 52 to 198, respectively; P < 0.0001). This reflected increased cohort sizes for both frozen and FFPE tissues from 1993 to 2008 (frozen, 59 to 119; FFPE, 66 to 194) but not fresh tissues. The relative proportion of studies using frozen or fresh tissues alone has decreased (71% to 24%) while those using FFPE alone or combined FFPE/frozen tissue cohorts has increased (24% to 72%) over this period. We conclude that the overall demand for biospecimens in cancer research has increased significantly (almost fourfold) over the past 20 years. We predict that average cohort sizes will increase by at least twofold for frozen and FFPE biospecimens over the next ten years, and that the majority of studies will be based on FFPE tissues or combined FFPE/Frozen tissue cohorts.
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 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 | 0.000 | 0.000 |
| 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) | 0.000 | 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".