Evidence map›Paper›PMID 36847148›Full record

ReviewCancer control : journal of the Moffitt Cancer Center

The Use of Artificial Intelligence for Complete Cytoreduction Prediction in Epithelial Ovarian Cancer: A Narrative Review.

Giulia Parpinel, Maria Elena Laudani, Elisa Piovano, Paolo Zola, Fabrice Lecuru

Open access · goldAbstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Cancer control : journal of the Moffitt Cancer Center. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed, 2 pooled it
2.2field-weighted citation impact, top 13% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

7 citing papers in PubMed, 2 syntheses or guidelines pooled it, 8 citations in OpenAlex.

  1. Pooled it
  2. Pooled it
  3. Review
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors at 2 institutions in 2 countries.

Giulia ParpinelDepartment of Surgical Sciences, 9314University of Turin, Torino, Italy.ORCID 0000-0003-0237-2652
Maria Elena LaudaniDepartment of Surgical Sciences, 9314University of Turin, Torino, Italy.
Elisa PiovanoDepartment of Surgical Sciences, 9314University of Turin, Torino, Italy.
Paolo ZolaDepartment of Surgical Sciences, 9314University of Turin, Torino, Italy.
Fabrice LecuruBreast, Gynecology and Reconstructive Surgery Unit, 419441Curie Institute, Paris, France.
University of Turin · ITInstitut Curie · FR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionIn patients affected by epithelial ovarian cancer (EOC) complete cytoreduction (CC) has been associated with higher survival outcomes. Artificial intelligence (AI) systems have proved clinical benefice in different areas of healthcare.

objectiveTo systematically assemble and analyze the available literature on the use of AI in patients affected by EOC to evaluate its applicability to predict CC compared to traditional statistics. MATERIAL AND

methodsData search was carried out through PubMed, Scopus, Ovid MEDLINE, Cochrane Library, EMBASE, international congresses and clinical trials. The main search terms were: Artificial Intelligence AND surgery/cytoreduction AND ovarian cancer. Two authors independently performed the search by October 2022 and evaluated the eligibility criteria. Studies were included when data about Artificial Intelligence and methodological data were detailed.

resultsA total of 1899 cases were analyzed. Survival data were reported in 2 articles: 92% of 5-years overall survival (OS) and 73% of 2-years OS. The median area under the curve (AUC) resulted 0,62. The model accuracy for surgical resection reported in two articles reported was 77,7% and 65,8% respectively while the median AUC was 0,81. On average 8 variables were inserted in the algorithms. The most used parameters were age and Ca125. DISCUSSION: AI revealed greater accuracy compared against the logistic regression models data. Survival predictive accuracy and AUC were lower for advanced ovarian cancers. One study analyzed the importance of factors predicting CC in recurrent epithelial ovarian cancer and disease free interval, retroperitoneal recurrence, residual disease at primary surgery and stage represented the main influencing factors. Surgical Complexity Scores resulted to be more useful in the algorithms than pre-operating imaging.

conclusionAI showed better prognostic accuracy if compared to conventional algorithms. However further studies are needed to compare the impact of different AI methods and variables and to provide survival informations.

Indexed as

Artificial IntelligenceOvarian NeoplasmsCarcinoma, Ovarian EpithelialCytoreduction Surgical ProceduresFemaleHumansNeoplasm Recurrence, Localalgorithmartificial intelligencecytoreductionepithelial ovarian cancersurgery

Identifiers

PMID36847148
PMCPMC9972055
OpenAlexW4322495340

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.