Tailored Approaches and Patient-centered Care: The Current Landscape of Neoadjuvant Therapy in Rectal Cancer
Bibliographic record
Abstract
Colorectal cancer (CRC) is the third most diagnosed cancer in Canada and worldwide. Although mortality rates have declined, it remains the second most lethal malignancy worldwide. For patients with locally advanced rectal cancer (LARC), several new concepts have been introduced in recent years for treatment sequencing and de-escalation. The use of pelvic magnetic resonance imaging (MRI) for initial staging and neoadjuvant therapy response assessment has become a key part of the workup for LARC, utilizing the expertise of specialist radiologists. High-volume rectal cancer centers have adopted total neoadjuvant therapy (TNT) as a preferred approach for many patients with LARC. There is rising interest in shortening the duration of chemotherapy or radiation, or even omitting radiation altogether for select patients, to reduce the burden of long-term toxicities. For patients who achieve clinical complete or near-complete responses (cCR or nCR) to neoadjuvant therapies, nonoperative management (NOM) has emerged as an option to avoid the complications of a total mesorectal excision (TME). This paradigm shift has resulted in numerous treatment options for many patients with rectal cancer, enabling a more individualized, multidisciplinary approach to care. Clinicians must understand how to interpret the evidence around these new concepts to successfully implement them into clinical practice. This review summarizes the recent evidence for neoadjuvant therapy approaches in rectal cancer to provide a context for this paradigm shift to a tailored therapeutic strategy.
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".