AAPM2023_SU-300-IePD-F6-4 Purpose: Traditional methods of evaluating cardiotoxicity rely on cardiac radiation doses and do not incorporate functional imaging. Cardiac functional imaging can improve the ability to provide early prediction for clinical outcomes for lung cancer patients undergoing radiotherapy. FDG-based PET/CT imaging is routinely obtained for staging and disease assessment after treatment. Although FDG PET/CT scans are typically used to evaluate the tumor, studies have shown that the PET cardiac signal is predictive of clinical outcomes. Our study aimed to develop novel functional cardiac delta radiomics using pre and post-treatment FDG PET/CT scans to predict for overall survival (OS). Methods: We conducted a study of 109 lung cancer patients who underwent standard FDG-PET/CT scans pre- and post-radiotherapy. Data from ACRIN 6668 (N=70) and an investigator-initiated lung cancer trial (N=39) for functional avoidance radiotherapy were used. The heart was delineated, and 200 cardiac CT and PET functional radiomics features were selected. Delta radiomics was calculated as the change between pre- and post-PET/CT. The data were divided into 80%/20% training/test set, and feature reduction was performed using Wilcoxon test, hierarchical clustering, and recursive feature elimination. A Gradient Boosting Classifier machine learning model evaluated the ability of the delta PET/CT cardiac radiomics to predict for OS using 10-fold cross-validation for training and area-under-the-curve (AUC) for model assessment. Results: Median survival was 431 days (range 144 to 1640 days). 4 clinically relevant delta features were identified: pre-CT_Maximum, post-CT_Minimum, delta-CT_GLRM_Run_Variance, delta-PET_GLRM_Run_Entropy. The model showed an AUC of 0.91 on the training set and an AUC of 0.87 on the test set. Conclusion: This is the first study to evaluate functional cardiac delta radiomic features from standard PET/CT scans with data showing good predictive AUC for OS. If validated, this work provides automated methods to provide functional cardiac information for clinical outcome prediction in lung cancer patients.

1. Novel Functional Delta-Radiomics for Predicting Overall Survival in
Lung Cancer Radiotherapy using Cardiac FDG-PET Uptake
INTRODUCTION
• Traditional methods of evaluating cardiotoxicity rely on cardiac
radiation doses and do not incorporate functional imaging.
• Cardiac functional imaging can improve the ability to provide
early prediction for clinical outcomes for lung cancer patients
undergoing radiotherapy.
• FDG-based PET/CT imaging is routinely obtained for staging and
disease assessment after treatment.
• Although FDG PET/CT scans are typically used to evaluate the
tumor, studies have shown that the PET cardiac signal is predictive
of clinical outcomes.
CONCLUSIONS
• This is the first study to evaluate functional cardiac delta radiomic features from standard PET/CT scans.
• The study found that delta radiomic features can be used to predict overall survival (OS) in lung cancer patients.
• The study had a good predictive AUC for OS.
• If validated, this work could provide automated methods to provide functional cardiac information for clinical outcome prediction in
lung cancer patients
RESULTS
METHOD
Dataset
• The study included 109 patients, with data from two
cohorts.
o ACRIN 6668 (N=70)
o An investigator-initiated lung cancer trial (N=39)
• The data were divided into 80%/20% training/test set
Delineation
• The heart was manually delineated
• Auto-segmentation was also performed to evaluate
feature robustness.
o CARINA INTContour
Feature extraction
• 200 radiomics features were extracted from each image
o In-house developed radiomics workflow (N=93,
https://github.com/taznux/radiomics-tools/)
o PyRadiomics (N=107)
ACKNOWLEDGEMENTS
This project was partially supported by the Sidney Kimmel
Cancer Center Support Grant(P30CA056036).
AIM
Our study aimed to develop novel functional cardiac delta
radiomics using pre and post-treatment FDG PET/CT scans to
predict for overall survival (OS).
• The study noted that patients could have significantly
variable FDG distribution in the heart as observed on pre
and post-treatment PET/CT scans (Figure 1).
Feature reduction and selection
• Total: 1600 features
• After Wilcoxon feature reduction: 1530 features
• Hierarchical clustering : 221 features
• RFE: the top 20 features were selected (Table 1).
Model building and evaluation
• A gradient boosting machine (GBM) was selected as the
best model.
o 10-fold CV AUC: 0.83±0.07
• The model was able to accurately predict overall survival.
o Training set AUC: 0.91
o Test set AUC: 0.87
• The four clinically relevant features identified by the study
were all related to the distribution of FDG in the heart.
o Pre-CT Maximum, Post-CT Minimum, Delta-CT GLRM Run
Variance, Delta-PET GLRM Run Entropy
CONTACT INFORMATION
Wookjin Choi, PhD (wookjin.choi@Jefferson.edu)
W. Choi and Y. Vinogradskiy
Department of Radiation Oncology, Sidney Kimmel Medical College at Thomas Jefferson University, Philadelphia, PA,
Rank Image Type Name 𝝆
1 Pre-CT First order Maximum 0.14
2 Post-CT First order Minimum 0.06
3 Delta-CT GLRLM Run Variance 0.21
4 Delta-PET GLRLM Run Entropy 0.15
5 Delta-PET Shape Elongation 0.15
6 Pre-PET GLRLM Short Run High Gray Level Emphasis 0.09
7 Delta-CT GLSZM Gray Level Non Uniformity Normalized 0.01
8 Pre-PET First order Median 0.08
9 Post-CT First order Total Energy 0.01
10 Pre-CT NGTDM Coarseness 0.17
Table 1. Top 10 features selected by recursive feature elimination and their correlation (ρ) with survival.
Delta-Radiomics calculation
• The change between pre- and post-PET/CT.
o Difference Δ𝐹𝑖
= 𝐹𝑝𝑜𝑠𝑡
𝑖
− 𝐹𝑝𝑟𝑒
𝑖
o Relative difference 𝑅 Δ𝐹𝑖
=
Δ𝐹𝑖
𝐹𝑝𝑟𝑒
𝑖
𝐹: the feature vector, 𝑖: the index of the feature vector
o A total of 1600 features were selected
(2 delta sets × 4 images × 200 features).
Feature reduction
• Wilcoxon test with Bonferroni correction (adj P<0.05)
o To remove unpredictable features
• Hierarchical clustering (Spearman correlation ρ>0.7)
o To remove collinearity between the remaining features
Feature selection
• Recursive Feature Elimination (RFE) over Decision Tree
Classification
o 10-fold cross-validation was then used to select the final
feature set for training a model.
Model building
• AutoML framework (TPOT, tree-based pipeline
optimization tool) was used to find a best model with a
best feature set and optimized hyper parameters
o 100 populations and 10 generations - 10,000 pipeline
configurations were evaluated to select the best model.
o The pipelines were constructed using preprocessors (Binarizer,
ICA, PCA, Normalizer, etc.) and classifiers (Naïve Bayes,
Decision Tree, XGB, logistic regression, etc.).
0.83
0.6
0.8
1
1 2 3 4 5 6 7 8 9 10
Figure 2: Prediction performance (AUC) by the number of features, the best AUC of
0.83 was obtained with 4 features.
Figure 1: Example of patients with variable pre and post-treatment cardiac PET/CT signal.
Vinogradskiy Y, Diot Q, Jones B, et al. Evaluating Positron Emission Tomography-Based Functional Imaging Changes in the Heart After
Chemo-Radiation for Patients With Lung Cancer. International Journal of Radiation Oncology* Biology* Physics. 2020;106(5):1063-1070.