Back

Discordance in pleural mesothelioma response classification and modelling of impact on clinical trials

Cowell, G. W.; Roche, J.; Noble, C.; Stobo, D. B.; Papanastasiou, A.; Kidd, A. C.; Tsim, S.; Blyth, K. G.

2026-03-20 oncology
10.64898/2026.03.18.26348731 medRxiv
Show abstract

Introduction Agreement between radiologists regarding treatment response in Pleural Mesothelioma (PM) is acknowledged to be poor, but downstream effects in clinical trials have not been quantified. Methods We performed a mixed methods study, composed of a multicentre, retrospective cohort study and in silico modelling. CT images and data were retrieved from 4 UK centres regarding chemotherapy-treated patients. Expert radiologists classified response using modified Response Evaluation Criteria In Solid Tumours criteria (mRECIST) v1.1, generating discordance rate (%) and agreement. In silico modelling simulated two-arm trials of an active therapy with intended 80% power and confidence intervals for four endpoints (objective response rate (ORR), disease control rate (DCR), progression-free survival (PFS), overall survival (OS)) covering 95% of the true effect. Actual power and endpoint coverage were modelled against mRECIST misclassification rate (a single reporter equivalent of discordance rate). Consecutive simulations varied misclassification rate from 0-100% in 1% increments, each repeated 10,000 times. Results 172 cases were included. Discordance rate was 35% (60/172), kappa=0.456. In silico modelling demonstrated reduced power and endpoint precision with increasing misclassification. At 17% misclassification, corresponding to the observed 35% discordance, power dropped from 80% to 55% for ORR, 53% for DCR, 65% for PFS and 66% for OS, with endpoint coverage reduced to 88%, 89%, 92% and 92%, respectively. 50/60 (83%) discordances reflected interpretation or measurement differences intrinsic to mRECIST. Discordance was not associated with tumour volume. Conclusions Inconsistent response classification is common in PM and substantially reduces statistical power and endpoint precision in clinical trials.

Matching journals

The top 10 journals account for 50% of the predicted probability mass.

1
Clinical Cancer Research
58 papers in training set
Top 0.1%
8.4%
2
BMJ Open
554 papers in training set
Top 3%
6.4%
3
JCO Precision Oncology
14 papers in training set
Top 0.1%
6.4%
4
Thorax
32 papers in training set
Top 0.1%
4.9%
5
British Journal of Cancer
42 papers in training set
Top 0.2%
4.9%
6
PLOS ONE
4510 papers in training set
Top 36%
4.0%
7
JAMA Network Open
127 papers in training set
Top 0.8%
4.0%
8
JCO Clinical Cancer Informatics
18 papers in training set
Top 0.2%
4.0%
9
Journal of Clinical Epidemiology
28 papers in training set
Top 0.1%
4.0%
10
Frontiers in Oncology
95 papers in training set
Top 1%
3.9%
50% of probability mass above
11
Scientific Reports
3102 papers in training set
Top 37%
3.6%
12
Nature Communications
4913 papers in training set
Top 39%
3.6%
13
Cancers
200 papers in training set
Top 2%
3.6%
14
JNCI Cancer Spectrum
10 papers in training set
Top 0.2%
2.1%
15
Annals of Oncology
13 papers in training set
Top 0.4%
1.9%
16
European Journal of Cancer
10 papers in training set
Top 0.2%
1.7%
17
npj Precision Oncology
48 papers in training set
Top 0.5%
1.7%
18
eLife
5422 papers in training set
Top 43%
1.7%
19
Neuro-Oncology
30 papers in training set
Top 0.4%
1.7%
20
BMC Research Notes
29 papers in training set
Top 0.2%
1.3%
21
BMC Cancer
52 papers in training set
Top 2%
1.3%
22
JNCI: Journal of the National Cancer Institute
16 papers in training set
Top 0.4%
1.3%
23
Diagnostics
48 papers in training set
Top 1%
1.2%
24
International Journal of Radiation Oncology*Biology*Physics
21 papers in training set
Top 0.3%
1.2%
25
PeerJ
261 papers in training set
Top 11%
0.9%
26
Leukemia
39 papers in training set
Top 0.7%
0.9%
27
EClinicalMedicine
21 papers in training set
Top 0.7%
0.9%
28
BMJ Health & Care Informatics
13 papers in training set
Top 0.7%
0.9%
29
Journal of Medical Imaging
11 papers in training set
Top 0.3%
0.9%
30
BMC Bioinformatics
383 papers in training set
Top 7%
0.7%