# Letter to the Editor: Comment on the recent article titled "Diagnostic and economic evaluation of matrix-assisted laser desorption/ionization time-of-flight mass spectrometry with machine learning for screening of Johne's disease from dairy cow serum"

Thibaut Lurier, Marie-Anne Arcangioli

► To cite this version:

Thibaut Lurier, Marie-Anne Arcangioli. Letter to the Editor: Comment on the recent article titled "Diagnostic and economic evaluation of matrix-assisted laser desorption/ionization time-of-flight mass spectrometry with machine learning for screening of Johne's disease from dairy cow serum". Journal of Dairy Science, 2026, 109 (3), pp.2069. 10.3168/jds.2025-27813. hal-05525454

HAL Id: hal-05525454

https://hal.inrae.fr/hal-05525454v1

Submitted on 24 Feb 2026

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_1.png" /></figure>

Distributed under a Creative Commons CC BY 4.0 - Attribution - International License
<figure><img src="image_2.png" /></figure>

# Letter to the Editor: Comment on the recent article titled “Diagnostic and economic evaluation of matrix-assisted laser desorption/ionization time-of-flight mass spectrometry with machine learning for screening of Johne’s disease from dairy cow serum”

Thibaut Lurier<fnref n="1" />,<fnref n="2" />* and Marie-Anne Arcangioli<fnref n="3" />

We read with great interest the recent article evaluating MALDI-TOF MS combined with machine learning for Johne's disease screening (Sarkar et al., 2026). Although the study is innovative, we believe that both the diagnostic and economic assessments rely on assumptions that substantially undermine the validity of the conclusions.

From a methodological standpoint, the diagnostic evaluation suffers from several weaknesses: the limited sample size associated with the risk of overfitting when applying machine learning to complex proteomic profiles, and the lack of clarity on animal selection, which raises concerns about representativeness relative to the intended clinical context. Moreover, the study reports only relative sensitivity (Se) and specificity (Sp) of MALDI-TOF compared with the ELISA, not to the true infection status.

Despite this, the economic model assumes absolute Se and Sp of 80% and 83% for MALDI-TOF, and 50% and 99% for the ELISA. These assumptions concerning the MALDI-TOF are unjustified. Given that the MALDI-TOF Se and Sp are only relative to ELISA, its true Se and Sp should—at best—be the product of both: approximately 0.8 × 0.5 = 40% for Se and 0.83 × 0.99 ≈ 82% for Sp. Even this computation presumes that the 27% MALDI-TOF “false positives” are indeed false, and not animals misclassified by the imperfect ELISA reference.

Furthermore, the economic scenarios assume conditional independence between tests, including scenario with repeated MALDI-TOF applications. Such independence is not justified and highly unlikely at least concerning the repeated use of MALDI-TOF.

In summary, the diagnostic interpretation and, in particular, the economic conclusions presented in this paper are not merely fragile—they are fundamentally flawed and should not be used to support any policy or field decision making.

**Nonstandard abbreviations used:** Se = sensitivity; Sp = specificity.

## REFERENCE

Sarkar, S., B. A. Young, C. S. Daniels, and J. E. Thompson. 2026. Diagnostic and economic evaluation of matrix-assisted laser desorption/ionization time-of-flight mass spectrometry with machine learning for screening of Johne's disease from dairy cow serum. J. Dairy Sci. 109:513–526. https://doi.org/10.3168/jds.2025-27394.

## ORCIDS

Thibaut Lurier https://orcid.org/0000-0001-6463-3796

Marie-Anne Arcangioli https://orcid.org/0000-0001-5830-2915

<footnote n="1">¹INRAE, VetAgro Sup, UMR EPIA, University of Lyon, 69280 Marcy l'Etoile, France</footnote>

<footnote n="2">²INRAE, VetAgro Sup, UMR EPIA, University of Clermont Auvergne, 63122 Saint-Genès-Champanelle, France</footnote>

<footnote n="3">³VetAgro Sup, Université de Lyon, UMR Mycoplasmoses Animales, 69280 Marcy l'Etoile, France</footnote>