Advances in the management of locally advanced rectal cancer: A shift toward a patient-centred approach to balance outcomes and quality of life
Bibliographic record
Abstract
The treatment of locally advanced rectal cancer (LARC) has undergone a significant evolution in recent years, shifting toward more selective strategies that balance oncological outcomes with quality of life (QoL) preservation. Total neoadjuvant treatment (TNT) has improved local control and reduced distant metastases, but its long-term toxicities have sparked growing interest in treatment de-escalation strategies aimed at minimizing adverse effects while maintaining efficacy. This review focuses on the therapeutic advancements for LARC, analysing both established standards and emerging innovations. We discuss the increasing adoption of organ-preserving approaches, particularly the Watch-and-Wait (WW) strategy for patients achieving a clinical complete response (cCR), and potentially the selective omission of radiotherapy in well-defined cases. Additionally, we explore and examine less invasive surgical techniques that preserve function without compromising cure rates. Beyond standard treatment approaches, we highlight the role of immunotherapy, particularly its breakthrough efficacy in LARC with deficient mismatch repair/microsatellite instability (dMMR/MSI), leading to the concept of immune-ablation: achieving complete tumor regression while sparing patients from chemotherapy, radiotherapy, and surgery. Ongoing research is investigating immunotherapy's potential role also in proficient mismatch repair/microsatellite stable (pMMR/MSS) LARC. Finally, we discuss emerging predictive biomarkers, such as circulating tumor DNA (ctDNA) and radiomics, which might refine patient selection and guide treatment individualization. The future of LARC management lies in a precision-driven approach, where survival is optimized without compromising QoL. By embracing innovation and personalizing care, we are entering a new era where cure remains paramount, but never at the expense of the patient's well-being.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".