# Are we really ready to use radiomics and deep learning for clinical decision support in radiology?

Guillaume Chassagnon

► To cite this version:

Guillaume Chassagnon. Are we really ready to use radiomics and deep learning for clinical decision support in radiology?. Diagnostic and Interventional Imaging, 2025, 106 (11), pp.371-372. 10.1016/j.diii.2025.07.004. hal-05364848

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Submitted on 28 Nov 2025

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Diagnostic and Interventional Imaging

Are we really ready to use radiomics and deep learning for clinical decision support in
radiology?

--Manuscript Draft--

<table><tr><td>Manuscript Number:</td><td>DIII-D-25-00553</td></tr><tr><td>Article Type:</td><td>Editorial</td></tr><tr><td>Keywords:</td><td>Artificial Intelligence; Chest imaging; Diagnostic Imaging; Radiomics; Thorax</td></tr><tr><td>Corresponding Author:</td><td>Guillaume CHASSAGNON, M.D.<br>Hopital Cochin<br>Paris, FRANCE</td></tr><tr><td>First Author:</td><td>Guillaume CHASSAGNON, M.D. Ph.D.</td></tr><tr><td>Order of Authors:</td><td>Guillaume CHASSAGNON, M.D. Ph.D.</td></tr><tr><td>Manuscript Region of Origin:</td><td>FRANCE</td></tr><tr><td>Opposed Reviewers:</td><td></td></tr></table>
# Are we really ready to use radiomics and deep learning for clinical decision support in radiology?

## Author

Guillaume Chassagnon a,b

## Affiliations

a Department of Radiology, Hôpital Cochin, Assistance Publique-Hôpitaux de Paris, 75014, Paris, France

b Université Paris Cité, Faculté de Médecine, 75006 Paris, France
# Are we really ready to use radiomics and deep learning for clinical decision support in radiology?

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**Keywords:** Artificial Intelligence; Chest imaging; Diagnostic Imaging; Radiomics; Thorax

## Abbreviations

AI, Artificial intelligence

AUC, Area under the receiver operating characteristic curve

CT, Computed tomography

In this issue of *Diagnostic & Interventional Imaging*, Mahiou *et al*. investigated the potential of artificial intelligence (AI) to distinguish different types of nodular pulmonary infections on chest computed tomography (CT) images in immunocompromised patients [1]. Using radiomics, the authors developed and validated a classification model to distinguish between aspergillosis, tuberculosis, and mucormycosis/nocardiosis. The results are promising; the radiomic model achieved a mean area under the curve (AUC) of 0.84, outperforming the clinical/semantic model (mean AUC, 0.66), especially in the identification of tuberculosis (AUC, 0.89 vs. 0.62; *P* = 0.046) [1]. It should be noted that no expert radiologist was asked make an explicit diagnosis. Although the clinical/semantic model incorporates indices that can be used in clinical practice, it may not fully capture the nuanced reasoning of an experienced radiologist. Interestingly, a combined model integrating both radiomics and clinical/semantic features did not outperform the radiomic model alone [1]. However, a combined model
integrating both radiomics and clinical/semantic features did not outperform the radiomic model alone [1].

These findings raise the timely and broader question of whether we are ready to use AI for clinical decision support in radiology for lesion characterization. The potential is clear. In many diagnostic scenarios where radiologists face overlapping imaging findings, such as here with nodular pulmonary lesions in immunocompromised patients, AI could help unearth subtle details that are beyond human perception. In the study by Mahiou *et al.*, radiomics features, especially texture-derived features, exhibited greater discriminative power than clinical or semantic variables [1]. However, decision-making is not straightforward when the tool's predictions cannot be verified. Indeed, even when AI models demonstrate high performance, they may not always help radiologists make better decisions. In a recent study by Yu *et al.*, 107 radiologists were asked to interpret chest X-rays using an AI computer-aided detection tool [2]. Their performance did not systematically improve, and in some cases, it even decreased [2]. This highlights a key issue. While AI has the potential to enhance human performance, it can also diminish it. For example, AI can create cognitive friction or encourage over-dependence. In fact, a key point is that radiologists must be aware of how the tools they use perform. They must also be aware of the conditions that can affect its performance levels.

To date, the primary use of AI in routine thoracic imaging is to aid in the detection of lesions, including lung nodules [3] and pulmonary embolisms [4] on chest CT examinations, as well as many other types of lesions on chest X-rays [5]. Only a few tools are available for lesion characterization, and most of them target lung nodules. This discrepancy likely reflects both the technical difficulties in developing robust tools for clinical use and the need to demonstrate the benefits of using them compared to the standard approach.

However, several hurdles remain to be overcome before AI can become a reliable assistant in such tasks. Most radiomics studies, including this one, are retrospective and
conducted in a single center. These studies rely on handcrafted features extracted from semi-
manually segmented lesions. Deep learning approaches, which could facilitate more
generalizable, end-to-end models, necessitate larger, well-curated datasets, which are
challenging to compile. There are also questions about interpretability and medico-legal
responsibility when it comes to clinical implementation [6].

Are we really ready to use tools whose predictions we are unable to verify?
Technologically, the answer is yes for some applications. However, clinically and culturally,
the answer may still be "not yet." The next step is to demonstrate the safety and clinical benefits
of using these kinds of tools prospectively. Several ongoing trials (NCT05389774 in the United
Kingdom and NCT05704920 in France) are assessing the utility of AI for lung nodule
characterization. Their results will be critical in determining if clinical decision support by AI
for lesion characterization in thoracic imaging can transition from research to practice.

Human rights

Not applicable for editorials.

Informed consent and patient details

Not applicable for editorials.

Author contribution

The author attests that he meets the current International Committee of Medical Journal Editors
(ICMJE) criteria for authorship.

Declaration of competing interest

The author has no competing interest to declare in relation with this editorial.

Funding

This editorial received no specific funding
References

1. Mahiou Y, Duron L, Decoux A, Alanio A, Denis B, Arnoux A, et al. Differentiation of nodular pulmonary infections in immunocompromised patients using CT-based radiomics. Diagn Interv Imaging 2025: doi: 10.1016/j.diii.2025.05.004.

2. Yu F, Moehring A, Banerjee O, Salz T, Agarwal N, Rajpurkar P. Heterogeneity and predictors of the effects of AI assistance on radiologists. Nat Med 2024;30:837-849.

3. de Margerie-Mellon C, Chassagnon G. Artificial intelligence: a critical review of applications for lung nodule and lung cancer. Diagn Interv Imaging 2023;104:11-17.

4. Djahnine A, Lazarus C, Lederlin M, Mulé S, Wiemker R, Si-Mohamed S, et al. Detection and severity quantification of pulmonary embolism with 3D CT data using an automated deep learning-based artificial solution. Diagn Interv Imaging 2024;105:97-103.

5. Bennani S, Regnard NE, Ventre J, Lassalle L, Nguyen T, Ducarouge A, et al. Using AI to improve radiologist performance in detection of abnormalities on chest radiographs. Radiology 2023;309:e230860.

6. Guenoun D, Zins M, Champsaur P, Thomassin-Naggara I; DRIM France AI Study Group. French community grid for the evaluation of radiological artificial intelligence solutions (DRIM France Artificial Intelligence Initiative). Diagn Interv Imaging 2024;105:74-81.