MétaCan
Menu
Back to cohort
Record W4319982925 · doi:10.3390/cancers15041125

10 Years of Pressurized Intraperitoneal Aerosol Chemotherapy (PIPAC): A Systematic Review and Meta-Analysis

2023· review· en· W4319982925 on OpenAlexaboutno aff
Andrea Di Giorgio, Antonio Macrì, Federica Ferracci, Manuela Robella, Mario Visaloco, Giovanni De Manzoni, Paolo Sammartino, Antonio Sommariva, Daniele Biacchi, Franco Roviello, Roberta Pastorino, Denise Pires Marafon, Stefano Rotolo, Francesco Casella, Marco Vaira

Bibliographic record

VenueCancers · 2023
Typereview
Languageen
FieldMedicine
TopicIntraperitoneal and Appendiceal Malignancies
Canadian institutionsnot available
FundersAssociazione Italiana per la Ricerca sul CancroCancer Research UK
KeywordsMedicineMeta-analysisChemotherapyConventional PCIInternal medicineSurgery

Abstract

fetched live from OpenAlex

Background: Pressurized intraperitoneal aerosol chemotherapy (PIPAC) is a novel intraperitoneal drug delivery method of low-dose chemotherapy as a pressurized aerosol in patients affected by peritoneal cancer of primary or secondary origin. We performed a systematic review and meta-analysis with the aim of assessing the feasibility, safety, and efficacy of PIPAC. Methods: A systematic literature search was performed using Medline and Web of Science databases from 1 January 2011, to inception, to 31 December 2021. Data were independently extracted by two authors. The Newcastle-Ottawa Scale was used to assess the quality and risk of bias of studies. Meta-analysis was performed for pathological response, radiological response, PCI variation along treatment, and for patients undergoing three or more PIPAC. Pooled analyses were performed using the Freeman–Tukey double arcsine transformation, and 95% CIs were calculated using Clopper–Pearson exact CIs in all instances. Results: A total of 414 papers on PIPAC were identified, and 53 studies considering 4719 PIPAC procedure in 1990 patients were included for analysis. The non-access rate or inability to perform PIPAC pooled rate was 4% of the procedures performed. The overall proportion of patients who completed 3 or more cycles of PIPAC was 39%. Severe toxicities considering CTCAE 3–4 were 4% (0% to 38.5%). In total, 50 studies evaluated deaths within the first 30 postoperative days. In the included 1936 patients were registered 26 deaths (1.3%). The pooled analysis of all the studies reporting a pathological response was 68% (95% CI 0.61–0.73), with an acceptable heterogeneity (I2 28.41%, p = 0.09). In total, 10 papers reported data regarding the radiological response, with high heterogeneity and a weighted means of 15% (0% to 77.8%). PCI variation along PIPAC cycles were reported in 14 studies. PCI diminished, increased, or remained stable in eight, one and five studies, respectively, with high heterogeneity at pooled analysis. Regarding survival, there was high heterogeneity. The 12-month estimated survival from first PIPAC for colorectal cancer, gastric cancer, gynecological cancer and hepatobiliary/pancreatic cancer were, respectively, 53%, 25%, 59% and 37%. Conclusions: PIPAC may be a useful treatment option for selected patients with PM, with acceptable grade 3 and 4 toxicity and promising survival benefit. Meta-analysis showed high heterogeneity of data among up-to-date available studies. In a subset analysis per primary tumor origin, pathological tumor regression was documented in 68% of the studies with acceptable heterogeneity. Pathological regression seems, therefore, a reliable outcome for PIPAC activity and a potential surrogate endpoint of treatment response. We recommend uniform selection criteria for patients entering a PIPAC program and highlight the urgent need to standardize items for PIPAC reports and datasets.

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)0.016
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: Meta-analysis · Consensus signal: Meta-analysis
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.021
Threshold uncertainty score0.083

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0160.032
Meta-epidemiology (narrow)0.0030.002
Meta-epidemiology (broad)0.0210.046
Bibliometrics0.0070.008
Science and technology studies0.0010.001
Scholarly communication0.0030.003
Open science0.0030.002
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0040.000

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 head0.082
GPT teacher head0.357
Teacher spread0.276 · 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 designMeta-analysis
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".

Quick stats

Citations85
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueCancersSame topicIntraperitoneal and Appendiceal MalignanciesFrench-language works237,207