Neoadjuvant chemoradiotherapy in combination with deep regional hyperthermia followed by surgery for rectal cancer: a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Combining chemoradiotherapy (CRT) with deep regional hyperthermia (HT) shows promise for enhancing clinical outcomes in selected rectal cancer patients. This study aimed to integrate the evidence and evaluate the efficacy of this combined treatment approach. MATERIALS AND METHODS: A systematic search of the PubMed, Scopus, and Mendeley databases was performed. This review was conducted according to the PRISMA guidelines. The quality of studies was evaluated using the Newcastle-Ottawa scale (NOS). Random-effects meta-analyses (DerSimonian and Laird) were performed. The primary outcome was pathological complete response (pCR), and secondary endpoints were overall survival (OS), disease-free survival (DFS), local recurrence-free survival (LRFS), and toxicity. RESULTS: In total, 12 studies were included, mostly of moderate quality. Patients with locally advanced rectal cancer (LARC; n = 760) and locally recurrent rectal cancer (LRRC; n = 22) were eligible. The pooled pCR rate was 19% (95% confidence interval [CI]: 16-22%) among all 782 patients and 19% (95%CI:16-23%) among 760 LARC patients. Due to significant study heterogeneity, survival outcomes were pooled by excluding LRRC patients. The pooled 5‑year OS rate among 433 LARC patients was 87% (95%CI: 83-90%). The pooled 5‑year DFS and LRFS in LARC patients were 75% (95%CI: 70-80%) and 95% (95%CI: 92-97%), respectively. There was a lack of consistent reporting of HT treatment parameters and toxicity symptoms among the studies. CONCLUSION: The collective clinical evidence showed that neoadjuvant CRT combined with HT in rectal cancer patients is feasible, with a 19% pCR rate and excellent survival outcomes in long term follow-up.
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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 | 0.009 | 0.017 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.033 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".