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ePoster Display

1283P - Predicting the differential diagnosis of pneumonitis in stage IV non-small cell lung cancer (NSCLC) patients treated with immune checkpoint inhibitors (ICIs) by combining clinical and radiomic features

Date

16 Sep 2021

Session

ePoster Display

Topics

Management of Systemic Therapy Toxicities;  Immunotherapy;  Supportive Care and Symptom Management

Tumour Site

Non-Small Cell Lung Cancer

Presenters

Fariba Tohidinezhad

Citation

Annals of Oncology (2021) 32 (suppl_5): S949-S1039. 10.1016/annonc/annonc729

Authors

F. Tohidinezhad1, A. Traverso1, I. Zhovannik1, A. Romita1, L.E.L. Hendriks2, A.C. Dingemans2, J.G. Aerts3, G. Bootsma4, J.F. Vansteenkiste5, S. Hashemi6, E.F.F. Smit7, H. Gietema8, D. De Ruysscher1

Author affiliations

  • 1 Oncology And Developmental Biology, Maastricht University Medical Center (MUMC), 6202 AZ - Maastricht/NL
  • 2 Pulmonary Diseases, Maastricht University Medical Center (MUMC), 6202 AZ - Maastricht/NL
  • 3 Pulmonology, Erasmus University Medical Center, 3015GD - Rotterdam/NL
  • 4 Pulmonology, Zuyderland Hospital, Heerlen/NL
  • 5 Respiratory Oncology Unit (pulmonology), University Hospital KU Leuven, 3000 - Leuven/BE
  • 6 7. department Of Pulmonary Diseases, VU University Medical Center, Amsterdam/NL
  • 7 Department Of Thoracic Oncology, Netherlands Cancer Institute, Amsterdam/NL
  • 8 Radiology, Maastricht University Medical Center (MUMC), 6202 AZ - Maastricht/NL

Resources

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Abstract 1283P

Background

ICI-induced pneumonitis is a severe side effect occurring in 4-10% of the patients, requiring an appropriate diagnosis for management with steroids. The differential diagnosis between ICI-related (ICI-R) and other causes of pneumonitis (OP) remains challenging. We therefore developed a prediction model including clinical and radiomic parameters for the differential diagnosis of pneumonitis in stage IV NSCLC patients.

Methods

Patients receiving ICI were prospectively recruited from six centers. CT-based radiomic features were extracted from the segmented lungs using PyRadiomics. Multinomial regression was used for model development (reference=no pneumonitis). One center (205 patients) was used to externally validate the models.

Results

A total of 556 patients, mean age of 64.6±9.4 years and 54.3% males, were included. 64.7% and 31.5% of patients were treated with nivolumab and pembrolizumab, respectively. The incidence of ICI-R and OP was 8.1% and 9.7%, respectively. The following variables were found to be significant predictors for ICI-R pneumonitis in the combined model: number of comorbidities, type of anti-PD(L)1 medication, dyspnea, and Gray Level Co-occurrence Matrix Inverse Difference Moment Normalized (GLCM IDMN). The area under the ROC curve of the clinical, radiomic, and combined models for predicting ICI-R pneumonitis were 0.99, 0.65, and 0.99, respectively (Table). Hosmer-Lemeshow test showed fair calibration for three models. Table: 1283P

Predictive performance of the combined (clinical + radiomic) model

Predicted outcome
No pneumonitis ICI-R pneumonitis Other pneumonitis
Observed outcome No pneumonitis 174 2 2
ICI-R pneumonitis 0 5 0
Other pneumonitis 0 2 0
Sensitivity (%) 97.8 100 0
Specificity (%) 100 97.8 98.9
PPV (%) 100 55.6 0
NPV (%) 63.6 100 98.9
Accuracy (95% CI) 96.8 (93.1, 98.8)
AUC 0.994 0.99 96.5
Discrimination slope 0.917 0.699 0.183
H-L P value <0.001 0.999 0.999
Brier score 0.022 0.015 0.016

Conclusions

Clinical and radiomic features have the potency to help in the differential diagnosis of pneumonitis in patients receiving ICI.

Clinical trial identification

Editorial acknowledgement

Legal entity responsible for the study

The authors.

Funding

Bristol Myers Squibb.

Disclosure

All authors have declared no conflicts of interest.

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