Challenges in diagnosis and biomarker testing for RET-altered lung and thyroid cancer care: an international mixed-method study
Bibliographic record
Abstract
BACKGROUND: The introduction of new targeted therapies for RET-altered lung and thyroid cancers (LC/TC) has impacted pathologists' practice by making genomic testing more relevant. Variations in health systems and treatment access result in distinct clinical challenges and barriers. This study aimed to assess practice gaps and challenges experienced by pathologists involved in the diagnosis of RET-altered LC/TC, including biomarker testing, to inform educational solutions. METHODS: Pathologists in Germany, Japan, the UK, and US participated in this ethics-approved mixed-methods study, which included interviews and surveys (data collected January-March 2020). Qualitative data was thematically analysed, quantitative data was analysed with chi-square and Kruskal-Wallis H-tests, and both were triangulated. RESULTS: A total of 107 pathologists took part in this study. Knowledge gaps were reported regarding genomic testing for LC/TC in Japan (79/60%), the UK (73/66%), and the US (53/30%). Skill gaps were reported when selecting genomic biomarker tests to diagnose TC in Japan (79%), the UK (73%) and US (57%) and when performing specific biomarker tests, especially in Japan (82% for RET) and in the UK (75% for RET). Japanese participants (80%) reported uncertainty about what information to share with the multidisciplinary team to ensure optimal patient-centered care. At the time of data collection, pathologists in Japan faced access barriers to using RET biomarker tests: only 28% agreed that there are relevant RET genomic biomarker tests available in Japan, versus 67% to 90% in other countries. CONCLUSIONS: This study identified areas where pathologists need additional continuing professional development opportunities to enhance their competencies and better support delivery of care to patients with RET-altered lung or thyroid tumours. Addressing identified gaps and improving competencies of pathologists in this field should be emphasised in continuing medical education curricula and through quality improvement initiatives. Strategies deployed on an institutional and health system level should aim to improve interprofessional communication and genetic biomarker testing expertise.
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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 | 0.076 | 0.072 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".