Evidence map›Paper›PMID 38003250›Full record

ArticleInternational journal of molecular sciences2023

Machine Learning Quantification of Intraepithelial Tumor-Infiltrating Lymphocytes as a Significant Prognostic Factor in High-Grade Serous Ovarian Carcinomas.

Jesús Machuca-Aguado, Antonio Félix Conde-Martín, Alejandro Alvarez-Muñoz, Enrique Rodríguez-Zarco, Alfredo Polo-Velasco, Antonio Rueda-Ramos, Rosa Rendón-García, Juan José Ríos-Martin, Miguel A Idoate

Open access · goldAbstract read
In one paragraph

Article in International journal of molecular sciences, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
0.8field-weighted citation impact, top 23% 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

5 citing papers in PubMed, 3 citations in OpenAlex.

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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

9 authors at 1 institution in 1 country.

Jesús Machuca-AguadoDepartment of Pathology, Virgen Macarena University Hospital & School of Medicine, University of Seville, 41009 Seville, Spain.ORCID 0000-0002-2398-209X
Antonio Félix Conde-MartínDepartment of Pathology, Virgen Macarena University Hospital & School of Medicine, University of Seville, 41009 Seville, Spain.
Alejandro Alvarez-MuñozDepartment of Pathology, Virgen Macarena University Hospital & School of Medicine, University of Seville, 41009 Seville, Spain.
Enrique Rodríguez-ZarcoDepartment of Pathology, Virgen Macarena University Hospital & School of Medicine, University of Seville, 41009 Seville, Spain.
Alfredo Polo-VelascoGynecology Department, Virgen Macarena University Hospital & School of Medicine, University of Seville, 41009 Seville, Spain.
Antonio Rueda-RamosOncology Department, Virgen Macarena University Hospital & School of Medicine, University of Seville, 41009 Seville, Spain.
Rosa Rendón-GarcíaDepartment of Pathology, Virgen Macarena University Hospital & School of Medicine, University of Seville, 41009 Seville, Spain.
Juan José Ríos-MartinDepartment of Pathology, Virgen Macarena University Hospital & School of Medicine, University of Seville, 41009 Seville, Spain.
Miguel A IdoateDepartment of Pathology, Virgen Macarena University Hospital & School of Medicine, University of Seville, 41009 Seville, Spain.ORCID 0000-0002-1596-1192
Hospital Universitario Virgen Macarena · ES

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The prognostic and predictive role of tumor-infiltrating lymphocytes (TILs) has been demonstrated in various neoplasms. The few publications that have addressed this topic in high-grade serous ovarian carcinoma (HGSOC) have approached TIL quantification from a semiquantitative standpoint. Clinical correlation studies, therefore, need to be conducted based on more accurate TIL quantification. We created a machine learning system based on H&E-stained sections using 76 molecularly and clinically well-characterized advanced HGSOC. This system enabled immune cell classification. These immune parameters were subsequently correlated with overall survival (OS) and progression-free survival (PFI). An intense colonization of the tumor cords by TILs was associated with a better prognosis. Moreover, the multivariate analysis showed that the intraephitelial (ie) TILs concentration was an independent and favorable prognostic factor both for OS (

Indexed as

CarcinomaOvarian NeoplasmsFemaleHumansLymphocytes, Tumor-InfiltratingPrognosisalgorithmsdigital quantificationmachine learningovarian cancertumor-infiltrating lymphocytes

Identifiers

PMID38003250
PMCPMC10671555
OpenAlexW4388446545

What OpenQuestion holds

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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.