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Performances and variability of CT radiomics for the prediction of microvascular invasion and survival in patients with HCC: a matter of chance or standardisation?

  • Gastrointestinal
  • Published:
European Radiology Aims and scope Submit manuscript

Abstract

Objectives

To measure the performance and variability of a radiomics-based model for the prediction of microvascular invasion (MVI) and survival in patients with resected hepatocellular carcinoma (HCC), simulating its sequential development and application.

Methods

This study included 230 patients with 242 surgically resected HCCs who underwent preoperative CT, of which 73/230 (31.7%) were scanned in external centres. The study cohort was split into training set (158 patients, 165 HCCs) and held-out test set (72 patients, 77 HCCs), stratified by random partitioning, which was repeated 100 times, and by a temporal partitioning to simulate the sequential development and clinical use of the radiomics model. A machine learning model for the prediction of MVI was developed with least absolute shrinkage and selection operator (LASSO). The concordance index (C-index) was used to assess the value to predict the recurrence-free (RFS) and overall survivals (OS).

Results

In the 100-repetition random partitioning cohorts, the radiomics model demonstrated a mean AUC of 0.54 (range 0.44–0.68) for the prediction of MVI, mean C-index of 0.59 (range 0.44–0.73) for RFS, and 0.65 (range 0.46–0.86) for OS in the held-out test set. In the temporal partitioning cohort, the radiomics model yielded an AUC of 0.50 for the prediction of MVI, a C-index of 0.61 for RFS, and 0.61 for OS, in the held-out test set.

Conclusions

The radiomics models had a poor performance for the prediction of MVI with a large variability in the model performance depending on the random partitioning. Radiomics models demonstrated good performance in the prediction of patient outcomes.

Clinical relevance statement

Patient selection within the training set strongly influenced the performance of the radiomics models for predicting microvascular invasion; therefore, a random approach to partitioning a retrospective cohort into a training set and a held-out set seems inappropriate.

Key Points

• The performance of the radiomics models for the prediction of microvascular invasion and survival widely ranged (AUC range 0.44–0.68) in the randomly partitioned cohorts.

• The radiomics model for the prediction of microvascular invasion was unsatisfying when trying to simulate its sequential development and clinical use in a temporal partitioned cohort imaged with a variety of CT scanners.

• The performance of the radiomics models for the prediction of survival was good with similar performances in the 100-repetition random partitioning and temporal partitioning cohorts.

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Abbreviations

CT:

Computed tomography

HCC:

Hepatocellular carcinoma

MRI:

Magnetic resonance imaging

MVI:

Microvascular invasion

OS:

Overall survival

RFS:

Recurrence-free survival

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Funding

The authors state that this work has not received any funding.

Author information

Authors and Affiliations

  1. Department of Radiology, Hôpital Beaujon, 100 Boulevard du Général Leclerc, 92110, Clichy, France

    Roberto Cannella, Maxime Ronot, Riccardo Sartoris, Valérie Vilgrain & Marco Dioguardi Burgio

  2. Section of Radiology-BiND, University Hospital ‘Paolo Giaccone’, Palermo, Italy

    Roberto Cannella

  3. Department of Health Promotion Sciences Maternal and Infant Care, Internal Medicine and Medical Specialties, PROMISE, University of Palermo, Palermo, Italy

    Roberto Cannella

  4. Champalimaud Foundation-Centre for the Unknown, 1400-038, Lisbon, Portugal

    Joao Santinha, Celso Matos & Nickolas Papanikolaou

  5. Department of Pathology, Hôpital Beaujon, Clichy, France

    Aurélie Bèaufrere

  6. Université de Paris, INSERM U1149 ‘centre de recherche sur l’inflammation’, CRI, Paris, France

    Maxime Ronot, Riccardo Sartoris, Valérie Vilgrain & Marco Dioguardi Burgio

  7. Department of HPB Surgery and Liver Transplantation, Hôpital Beaujon, Clichy, France

    Francois Cauchy

  8. Department of Digestive Oncology, Hôpital Beaujon, Clichy, France

    Mohamed Bouattour

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  1. Roberto Cannella
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Corresponding author

Correspondence to Marco Dioguardi Burgio.

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Guarantor

The scientific guarantor of this publication is Marco Dioguardi Burgio.

Conflict of interest

Maxime Ronot is a member of the European Radiology Editorial Board. They have not taken part in the review or selection process of this article.

The authors of this manuscript declare no relationships with any companies, whose products or services may be related to the subject matter of the article.

Statistics and biometry

One of the authors has significant statistical expertise.

Informed consent

Written informed consent was waived by the Institutional Review Board.

Ethical approval

Institutional Review Board approval was obtained.

Study subjects or cohorts overlap

Some study subjects or cohorts have been previously reported in the following publication: https://doi.org/10.1016/j.jhepr.2021.100380. In this previous study, the aim was to report Liver Imaging Reporting and Data System (LI-RADS)–defined imaging features of different HCC subtypes.

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• retrospective

• diagnostic or prognostic study

• performed at one institution

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Cannella, R., Santinha, J., Bèaufrere, A. et al. Performances and variability of CT radiomics for the prediction of microvascular invasion and survival in patients with HCC: a matter of chance or standardisation?. Eur Radiol 33, 7618–7628 (2023). https://doi.org/10.1007/s00330-023-09852-1

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