Radiological Advances In CRS-HIPEC Planning for Peritoneal Carcinomatosis: A Meta-Analysis Of MRI, CT, PET-CT, And Clinical Outcomes
Bibliographic record
Abstract
Background: Peritoneal carcinomatosis (PC) is a challenging manifestation of intra-abdominal malignancies historically associated with poor prognosis. The integration of cytoreductive surgery (CRS) and hyperthermic intraperitoneal chemotherapy (HIPEC) has improved survival outcomes in selected patients. Concurrently, radiological advancements such as MRI, CT, and PET-CT have played a pivotal role in refining preoperative staging, treatment planning, and postoperative monitoring. Objective: This meta-analysis aims to evaluate the impact of advanced radiological modalities in conjunction with CRS-HIPEC on clinical outcomes—specifically overall survival (OS), progression-free survival (PFS), morbidity, mortality, and recovery—in patients with peritoneal carcinomatosis and related malignancies. Methods: A systematic search was conducted across PubMed, Embase, Scopus, and Web of Science for studies published up to March 2024. Eligible studies included retrospective, prospective, and randomized controlled trials assessing imaging modalities used in CRS-HIPEC patients. Six studies involving 890 patients were included. Data on imaging modality, survival outcomes, and postoperative recovery were extracted. Risk of bias was assessed using RoB-2 and the Newcastle-Ottawa Scale. Meta-analysis was performed using RevMan 5.4 with a random-effects model. Results: MRI-based evaluations were associated with superior median OS (up to 24 months) and PFS (up to 16 months), along with reduced hospital stays and faster recovery compared to CT and PET-CT. Morbidity ranged from 20% to 30%, and mortality remained low (1%–4%). The pooled hazard ratio for MRI vs. CT was 0.75 (95% CI: 0.60–0.92; I² = 30%; p = 0.01). PET-CT showed a non-significant trend toward improved detection but lacked significant survival benefit. Conclusion: MRI demonstrates consistent advantages in enhancing preoperative staging and improving clinical outcomes for CRS-HIPEC patients. Its integration into standard assessment protocols is strongly supported. Further multicenter trials with standardized imaging and surgical criteria are warranted to validate these findings and guide evidence-based radiologic strategies in peritoneal surface malignancies.
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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 | 0.014 | 0.028 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.046 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".