Evidence map›Paper›PMID 36672451›Full record

ArticleCancers2023

Concordance of Radiological, Laparoscopic and Laparotomic Scoring to Predict Complete Cytoreduction in Women with Advanced Ovarian Cancer.

Mariano Catello Di Donna, Giuseppe Cucinella, Giulia Zaccaria, Giuseppe Lo Re, Agata Crapanzano, Sergio Salerno, Vincenzo Giallombardo, Giulio Sozzi, Anna Fagotti, Giovanni Scambia and 2 more

Open access · goldAbstract read
In one paragraph

Article in Cancers, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed, 1 pooled it
6.3field-weighted citation impact, top 3% 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

10 citing papers in PubMed, 1 synthesis or guideline pooled it, 23 citations in OpenAlex.

  1. Pooled it
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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

12 authors at 3 institutions in 1 country.

Mariano Catello Di DonnaUnit of Gynecologic Oncology, ARNAS "Civico-Di Cristina-Benfratelli", 90127 Palermo, Italy.ORCID 0000-0002-8830-1865
Giuseppe CucinellaUnit of Gynecologic Oncology, ARNAS "Civico-Di Cristina-Benfratelli", 90127 Palermo, Italy.ORCID 0000-0001-8574-7346
Giulia ZaccariaUnit of Gynecologic Oncology, ARNAS "Civico-Di Cristina-Benfratelli", 90127 Palermo, Italy.
Giuseppe Lo ReDepartment of Biomedicine, Neuroscience and Advanced Diagnostics, University of Palermo, 90133 Palermo, Italy.ORCID 0000-0002-7479-948X
Agata CrapanzanoDepartment of Biomedicine, Neuroscience and Advanced Diagnostics, University of Palermo, 90133 Palermo, Italy.
Sergio SalernoDepartment of Biomedicine, Neuroscience and Advanced Diagnostics, University of Palermo, 90133 Palermo, Italy.
Vincenzo GiallombardoUnit of Gynecologic Oncology, ARNAS "Civico-Di Cristina-Benfratelli", 90127 Palermo, Italy.ORCID 0000-0003-2337-7081
Giulio SozziUnit of Gynecologic Oncology, ARNAS "Civico-Di Cristina-Benfratelli", 90127 Palermo, Italy.ORCID 0000-0001-5847-8822
Anna FagottiGynecologic Oncology Unit, Department of Woman and Child Health and Public Health, Fondazione Policlinico Universitario A. Gemelli IRCCS, Largo Francesco Vito 1, 00168 Rome, Italy.
Giovanni ScambiaGynecologic Oncology Unit, Department of Woman and Child Health and Public Health, Fondazione Policlinico Universitario A. Gemelli IRCCS, Largo Francesco Vito 1, 00168 Rome, Italy.
Antonio Simone LaganàUnit of Gynecologic Oncology, ARNAS "Civico-Di Cristina-Benfratelli", 90127 Palermo, Italy.ORCID 0000-0003-1543-2802
Vito ChianteraUnit of Gynecologic Oncology, ARNAS "Civico-Di Cristina-Benfratelli", 90127 Palermo, Italy.
Azienda di Rilievo Nazionale ed Alta Specializzazione · ITUniversity of Palermo · ITUniversità Cattolica del Sacro Cuore · IT

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveTo identify the best method among the radiologic, laparoscopic and laparotomic scoring assessment to predict the outcomes of cytoreductive surgery in patients with advanced ovarian cancer (AOC).

methodsPatients with AOC who underwent pre-operative computed tomography (CT) scan, laparoscopic evaluation, and cytoreductive surgery between August 2016 and February 2021 were retrospectively reviewed. Predictive Index (PI) score and Peritoneal Cancer Index (PCI) scores were used to estimate the tumor load and predict the residual disease in the primary debulking surgery (PDS) and interval debulking surgery (IDS) after neoadjuvant chemotherapy (NACT) groups. Concordance percentages were calculated between the two scores.

resultsAmong 100 eligible patients, 69 underwent PDS, and 31 underwent NACT and IDS. Complete cytoreduction was achieved in 72.5% of patients in the PDS group and 77.4% in the IDS. In patients undergoing PDS, the laparoscopic PI and the laparotomic PCI had the best accuracies for complete cytoreduction (R0) [area under the curve (AUC) = 0.78 and AUC = 0.83, respectively]. In the IDS group, the laparotomic PI (AUC = 0.75) and the laparoscopic PCI (AUC= 0.87) were associated with the best accuracy in R0 prediction. Furthermore, radiological assessment, through PI and PCI, was associated with the worst accuracy in either PDS or IDS group (PI in PDS: AUC = 0.64; PCI in PDS: AUC = 0.64; PI in IDS: AUC = 0.46; PCI in IDS: AUC = 0.47).

conclusionThe laparoscopic score assessment had high accuracy for optimal cytoreduction in AOC patients undergoing PDS or IDS. Integrating diagnostic laparoscopy in the decision-making algorithm to accurately triage AOC patients to different treatment strategies seems necessary.

Indexed as

cytoreductive surgeryinterval debulking surgeryneoadjuvant chemotherapyovarian cancerperitoneal cancer index scoreprediction modelpredictive index scoreprimary debulking surgery

Identifiers

PMID36672451
PMCPMC9856465
OpenAlexW4315864897

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.