Summary
Recent advances in the management of diffuse pleural mesothelioma (DPM) have increased
interest in prognostication and risk stratification on the basis that maximum benefit
of combination immunotherapy appears to be seen in patients who otherwise would have
the worst prognosis. Various grading schemes have been proposed, including the recently
published Mesothelioma Weighted Grading Scheme (MWGS). However, predictive modelling
using deep learning algorithms is increasingly regarded as the gold standard in prognostication.
We therefore sought to develop and validate a prognostic nomogram for DPM.
Data from 369 consecutive patients with DPM were used as independent training and
validation cohorts to develop a prognostic tool that included the following variables:
age, sex, histological type, nuclear atypia, mitotic count, necrosis, and BAP1 immunohistochemistry.
Patients were stratified into four risk groups to assess model discrimination and
calibration. To assess discrimination, the area-under-the-curve (AUC) of a receiver-operator-curve
(ROC), concordance-index (C-index), and dissimilarity index (D-index) were calculated.
Based on the 5-year ROC analysis, the AUC for our model was 0.75. Our model had a
C-index of 0.67 (95% CI 0.53–0.79) and a D-index of 2.40 (95% CI 1.69–3.43).
Our prognostic nomogram for DPM is the first of its kind, incorporates well established
prognostic markers, and demonstrates excellent predictive capability. As these factors
are routinely assessed in most pathology laboratories, it is hoped that this model
will help inform prognostication and difficult management decisions, such as patient
selection for novel therapies. This nomogram is now freely available online at: https://nomograms.shinyapps.io/Meso_Cox_ML/.
Key words
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Article info
Publication history
Published online: February 24, 2023
Accepted:
November 20,
2022
Received in revised form:
September 29,
2022
Received:
July 31,
2022
Publication stage
In Press Corrected ProofIdentification
Copyright
© 2023 Published by Elsevier B.V. on behalf of Royal College of Pathologists of Australasia.
