Evidence map›Paper›PMID 36744142›Full record

ArticleFrontiers in medicine2023

Influence of key histological characteristics on 18F-fluorodeoxyglucose /18F-choline positron emission tomography positivity in hepatocellular carcinoma: A machine learning study.

Jérôme Ghidaglia, Vincent Laurent, Mylène Sebagh, Alina Pascale, Emmanuel Durand, Nicolas Golse, Florent L Besson

Open access · goldAbstract read
In one paragraph

Article in Frontiers in medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed, 1 pooled it
0.8field-weighted citation impact, top 29% 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, 1 synthesis or guideline pooled it, 4 citations in OpenAlex.

  1. Pooled it
  2. Article
  3. Review
  4. Auxiliary Value of [Cancers · 2025
    Article
  5. Review
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 at 2 institutions in 1 country.

Jérôme GhidagliaDepartment of Biophysics and Nuclear Medicine-Molecular Imaging, Hôpitaux Universitaires Paris-Saclay, Assistance Publique-Hôpitaux de Paris, Le Kremlin-Bicêtre, France.
Vincent LaurentUniversité Paris-Saclay, Centre Borelli, Gif-sur-Yvette, France.
Mylène SebaghDepartment of Pathology, Hôpitaux Universitaires Paris-Saclay, Assistance Publique-Hôpitaux de Paris, Le Kremlin-Bicêtre, France.
Alina PascaleUniversite Paris-Saclay, Inserm, Physiopathogènése et Traitement des Maladies du Foie, UMR-S 1193, Villejuif, Île-de-France, France.
Emmanuel DurandDepartment of Biophysics and Nuclear Medicine-Molecular Imaging, Hôpitaux Universitaires Paris-Saclay, Assistance Publique-Hôpitaux de Paris, Le Kremlin-Bicêtre, France.
Nicolas GolseUniversite Paris-Saclay, Inserm, Physiopathogènése et Traitement des Maladies du Foie, UMR-S 1193, Villejuif, Île-de-France, France.
Florent L BessonDepartment of Biophysics and Nuclear Medicine-Molecular Imaging, Hôpitaux Universitaires Paris-Saclay, Assistance Publique-Hôpitaux de Paris, Le Kremlin-Bicêtre, France.
Assistance Publique – Hôpitaux de Paris · FRUniversité Paris-Saclay · FR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Purpose: To determine the characteristics influence of key histological on 18F-fluorodeoxyglucose (18F-FDG) and 18F-choline positron emission tomography (PET) positivity in hepatocellular carcinoma (HCC). Materials and methods: The 18F-FDG/18F-choline PET imaging findings of 103 histologically proven HCCs (from 62 patients, of which 47 underwent hepatectomy and 15 received liver transplantation) were retrospectively examined to assess the following key histological parameters: Grade, capsule, microvascular invasion (mVI), macrovascular invasion (MVI), and necrosis. Using a ratio of 70/30 for training and testing sets, respectively, a penalized classification model (Elastic Net) was trained using 100 repeated cross-validation procedures (10-fold cross-validation for hyperparameter optimization). The contribution of each histological parameter to the PET positivity was determined using the Shapley Additive Explanations method. Receiver operating characteristic curves with and without dimensionality reduction were finally estimated and compared. Results: Among the five key histological characteristics of HCC (Grade, capsule, mVI, MVI, and necrosis), mVI and tumor Grade (I-III) showed the highest relevance and robustness in explaining HCC uptake of 18F-FDG and 18F-choline. MVI and necrosis status both showed high instability in outcome predictions. Tumor capsule had a minimal influence on the model predictions. On retaining only mVI and Grades I-III for the final analysis, the area under the receiver operating characteristic (ROC) curve values were maintained (0.68 vs. 0.63, 0.65 vs. 0.64, and 0.65 vs. 0.64 for 18F-FDG, 18F-choline, and their combination, respectively). Conclusion: 18F-FDG/18F-choline PET positivity appears driven by both the Grade and mVI components in HCC. Consideration of the tumor microenvironment will likely be necessary to improve our understanding of multitracer PET positivity.

Indexed as

18F-choline18F-FDGhepatocellular carcinomamachine learningPET/CT

Identifiers

PMID36744142
PMCPMC9892182
OpenAlexW4317388428

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