Evidence map›Paper›PMID 41244344›Full record

ArticleBioengineering & translational medicine2025

Development and validation of a computational tool to predict treatment outcomes in cells from high-grade serous ovarian cancer patients.

Marilisa Cortesi, Dongli Liu, Elyse Powell, Ellen Barlow, Kristina Warton, Emanuele Giordano, Caroline E Ford

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Bioengineering & translational medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
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

1 citing paper in PubMed.

  1. Article
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

7 authors.

Marilisa CortesiDepartment of Electrical Electronic and Information Engineering "G.Marconi" University of Bologna Cesena Italy.ORCID https://orcid.org/0000-0002-3731-7760
Dongli LiuSchool of Clinical Medicine University of New South Wales Sydney New South Wales Australia.
Elyse PowellSchool of Clinical Medicine University of New South Wales Sydney New South Wales Australia.
Ellen BarlowSchool of Clinical Medicine University of New South Wales Sydney New South Wales Australia.
Kristina WartonSchool of Clinical Medicine University of New South Wales Sydney New South Wales Australia.
Emanuele GiordanoDepartment of Electrical Electronic and Information Engineering "G.Marconi" University of Bologna Cesena Italy.
Caroline E FordSchool of Clinical Medicine University of New South Wales Sydney New South Wales Australia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Treatment of High-grade serous ovarian cancer (HGSOC) is often ineffective due to frequent late-stage diagnosis and development of resistance to therapy. Timely selection of the most effective (combination of) drug(s) for each patient would improve outcomes, however the tools currently available to clinicians are poorly suited to the task. We here present a computational simulator capable of recapitulating cell response to treatment in ovarian cancer. The technical development of the in silico framework is described, together with its validation on both cell lines and patient- derived laboratory models. A calibration procedure to identify the parameters that best recapitulate each patient's response is also presented. Our results support the use of this tool in preclinical research, to provide relevant insights into HGSOC behavior and progression. They also provide a proof of concept for its use as a personalized medicine tool and support disease monitoring and treatment selection.

Indexed as

computational simulationdigital twinhigh grade serous ovarian cancertreatment response

Identifiers

PMID41244344
PMCPMC12617556

What Socratic holds

Textmetadata
LicenceCC BY
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 Socratic graph.