Current Perspectives on the Importance of Pathological Features in Prognostication and Guidance of Adjuvant Chemotherapy in Colon Cancer
Bibliographic record
Abstract
There is not a clear consensus on which pathological features and biomarkers are important in guiding prognosis and adjuvant therapy in colon cancer. The Pathology in Colon Cancer, Prognosis and Uptake of Adjuvant Therapy (PiCC UP) Australia and New Zealand questionnaire was distributed to colorectal surgeons, medical oncologists and pathologists after institutional board approval. The aim of this study was to understand current specialist attitudes towards pathological features in the prognostication of colon cancer and adjuvant therapy in stage II disease. A 5-scale Likert score was used to assess attitudes towards 23 pathological features for prognosis and 18 features for adjuvant therapy. Data were analysed using a rating scale and graded response model in item response theory (IRT) on STATA (Stata MP, version 15; StataCorp LP). One hundred and sixty-four specialists (45 oncologists, 86 surgeons and 33 pathologists) participated. Based on IRT modelling, the most important pathological features for prognosis in colon cancer were distant metastases, lymph node metastases and liver metastases. Other features seen as important were tumour rupture, involved margin, radial margin, CRM, lymphovascular invasion and grade of differentiation. Size of tumour, location, lymph node ratio and EGFR status were considered less important. The most important features in decision making for adjuvant therapy in stage II colon cancer were tumour rupture, lymphovascular invasion and microsatellite instability. BRAF status, size of tumour, location, tumour budding and tumour infiltrating lymphocytes were factored as lesser importance. Biomarkers such as CDX2, EGFR, KRAS and BRAF status present areas for further research to improve precision oncology. This study provides the most current status on the importance of pathological features in prognostication and recommendations for adjuvant therapy in Australia and New Zealand. Results of this nationwide study may be useful to help in guiding prognosis and adjuvant treatment in colon cancer.
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 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 | 0.034 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".