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Record W4389057068 · doi:10.1097/sla.0000000000006161

Surgical Outcomes in Total Neoadjuvant Therapy for Rectal Cancer Versus Standard Long-course Chemoradiation

2023· review· en· W4389057068 on OpenAlexaff
Wenjie Lin, Christine Li, Elizabeth Clement, Carl J. Brown, Manoj J. Raval, Ahmer Karimuddin, Amandeep Ghuman, P. Terry Phang

Bibliographic record

VenueAnnals of Surgery · 2023
Typereview
Languageen
FieldMedicine
TopicColorectal Cancer Surgical Treatments
Canadian institutionsSt. Paul's Hospital
Fundersnot available
KeywordsMedicineNeoadjuvant therapyChemoradiotherapyColorectal cancerMeta-analysisRadiation therapySurgeryOncologyCancerInternal medicineBreast cancer

Abstract

fetched live from OpenAlex

OBJECTIVE: This systematic review and meta-analysis seeks to evaluate the impact of total neoadjuvant therapy (TNT) for rectal cancers on surgical complications and surgical pathology when compared with standard long-course chemoradiotherapy (LCRT). BACKGROUND: The oncological benefits of TNT are well published in previous meta-analyses, but there is little synthesized information on how it affects surgical outcomes. A recent study has suggested an increase in local recurrence and higher rates of breached total mesorectal excision (TME) plane in TNT patients. METHODS: This study conformed to the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. A search was performed in Medline (via PubMed), Cochrane databases, EMBASE and CINAHL to identify relevant randomized controlled trials (RCTs) comparing outcomes between TNT and LCRT. Meta-analyses of pooled proportions between TNT and LCRT were performed, comparing primary outcomes of surgical mortality, morbidity and all reported complications; surgical-pathology differences, namely mesorectal quality, R0 resection rates, circumferential resection margin positive rates, and sphincter preservation rates. Death and progression of disease during neoadjuvant treatment period was also compared. Risk of bias of RCTs was performed using the Cochrane risk-of-bias tool by 2 independent reviewers. RESULTS: A total of 3185 patients with rectal cancer from 11 RCTs were included in the analysis: 1607 received TNT and 1578 received LCRT, of which 1422 (TNT arm) and 1391 (LCRT arm) underwent surgical resection with curative intent. There was no significant difference in mortality [risk ratio (RR)=0.86, 95% CI: 0.13-5.52, P =0.88, I2 =52%] or major complications (RR=1.04, 95% CI: 0.86-1.26, P =0.70, I2 =0%) between TNT and LCRT. There was a significantly higher risk of breached TME in TNT group on pooled analysis (RR=1.49, 95% CI: 1.03-12.16, P =0.03, I2 =0%), and on subgroup analysis there is higher risk of breached TME in those receiving extended duration of neoadjuvant treatment (>17 weeks from start of treatment to surgery) when compared with LCRT (RR=1.61, 95% CI: 1.06-2.44, P =0.03). No difference in R0 resection rates (RR=0.85, 95% CI: 0.66-1.10, P =0.21, I2 =15%), circumferential resection margin positive rates (RR=0.87, 95% CI: 0.65-1.16, P =0.35, I2 =10%) or sphincter preservation rates (RR=1.02, 95% CI: 0.83-1.25, P =0.88, I2 =57%) were observed. There was a significantly lower risk of progression of disease to an unresectable stage during the neoadjuvant treatment period in TNT patients (RR=0.60, 95% CI: 0.39-0.92, P =0.03, I2 =18%). On subgroup analysis, it appears to favor those receiving extended duration of neoadjuvant treatment (RR=0.44, 95% CI: 0.26-0.80, P =0.002), and those receiving induction-type chemotherapy in TNT (RR=0.25, 95% CI: 0.07-0.88, P =0.03). CONCLUSIONS: TNT increases rates of breached TME which can contribute to higher local recurrence rates. TNT, however, improves systemic control by reducing early progression of disease during neoadjuvant treatment period. Further research is warranted to identify patients that will benefit from this 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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.015
metaresearch head score (Gemma)0.032
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.017
Threshold uncertainty score0.077

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0150.032
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0170.043
Bibliometrics0.0050.005
Science and technology studies0.0000.001
Scholarly communication0.0030.002
Open science0.0020.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.472
GPT teacher head0.506
Teacher spread0.035 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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".

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Citations20
Published2023
Admission routes1
Has abstractyes

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