Options for metastatic colorectal cancer beyond the second line of treatment
Bibliographic record
Abstract
Colorectal cancer is the third most common cancer, with recent advances in the management of unresectable metastatic lesions. The aim of this review is to discuss the remaining options for heavily pretreated patients with unresectable metastatic colorectal cancer. Beyond second-line treatment, two epidermal growth factor receptor (EGFR) inhibitors, cetuximab and panitumumab, have a demonstrated clinical interest in patients with KRAS wild-type tumours. However, few data exist in patients pretreated with an anti-EFGR and who are being rechallenged with anti-EGFR drugs. Reintroduction of chemotherapy should be considered. In September 2012, regorafenib, a multi-kinase inhibitor was approved by the US Federal Drug Administration for patients refractory to other standard treatments. In the case of metastases limited to the liver, transarterial chemoembolization, hepatic artery infusion and radioembolization could also be discussed in selected patients. With the multiplication of therapeutic options in first-line, second-line treatment, and beyond, the concept of subsequent lines of chemotherapy should be replaced by a multiline strategy, dependent on the patient and on tumour biology. A better understanding of the tumour biology and predictive factors for the response to these therapies is needed, and further strategic trials are urgently warranted.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".