Radiomics of NSCLC: Quantitative CT Image Feature Characterization and Tumor Shrinkage Prediction

Radiomics is the high-throughput extraction and analysis of quantitative image features. For non-small cell lung cancer (NSCLC) patients, radiomics can be applied to standard of care computed tomography (CT) images to improve tumor diagnosis, staging, and response assessment. The first objective of this work was to show that CT image features extracted from pre-treatment NSCLC tumors could be used to predict tumor shrinkage in response to therapy. This is important since tumor shrinkage is an important cancer treatment endpoint that is correlated with probability of disease progression and overall survival. Accurate prediction of tumor shrinkage could also lead to individually customized treatment plans. To accomplish this objective, 64 stage NSCLC patients with similar treatments were all imaged using the same CT scanner and protocol. Quantitative image features were extracted and principal component regression with simulated annealing subset selection was used to predict shrinkage. Cross validation and permutation tests were used to validate the results. The optimal model gave a strong correlation between the observed and predicted shrinkages with . The second objective of this work was to identify sets of NSCLC CT image features that are reproducible, non-redundant, and informative across multiple machines. Feature sets with these qualities are needed for NSCLC radiomics models to be robust to machine variation and spurious correlation. To accomplish this objective, test-retest CT image pairs were obtained from 56 NSCLC patients imaged on three CT machines from two institutions. For each machine, quantitative image features with concordance correlation coefficient values greater than 0.90 were considered reproducible. Multi-machine reproducible feature sets were created by taking the intersection of individual machine reproducible feature sets. Redundant features were removed through hierarchical clustering. The findings showed that image feature reproducibility and redundancy depended on both the CT machine and the CT image type (average cine 4D-CT imaging vs. end-exhale cine 4D-CT imaging vs. helical inspiratory breath-hold 3D CT). For each image type, a set of cross-machine reproducible, non-redundant, and informative image features was identified. Compared to end-exhale 4D-CT and breath-hold 3D-CT, average 4D-CT derived image features showed superior multi-machine reproducibility and are the best candidates for clinical correlation.

Partial volume correction incorporating Rb-82 positron range for quantitative myocardial perfusion PET based on systolic-diastolic activity ratios and phantom measurements.

BACKGROUND: Quantitative myocardial PET perfusion imaging requires partial volume corrections. METHODS: Patients underwent ECG-gated, rest-dipyridamole, myocardial perfusion PET using Rb-82 decay corrected in Bq/cc for diastolic, systolic, and combined whole cycle ungated images. Diastolic partial volume correction relative to systole was determined from the systolic/diastolic activity ratio, systolic partial volume correction from phantom dimensions comparable to systolic LV wall thicknesses and whole heart cycle partial volume correction for ungated images from fractional systolic-diastolic duration for systolic and diastolic partial volume corrections. RESULTS: For 264 PET perfusion images from 159 patients (105 rest-stress image pairs, 54 individual rest or stress images), average resting diastolic partial volume correction relative to systole was 1.14 ± 0.04, independent of heart rate and within ±1.8% of stress images (1.16 ± 0.04). Diastolic partial volume corrections combined with those for phantom dimensions comparable to systolic LV wall thickness gave an average whole heart cycle partial volume correction for ungated images of 1.23 for Rb-82 compared to 1.14 if positron range were negligible as for F-18. CONCLUSION: Quantitative myocardial PET perfusion imaging requires partial volume correction, herein demonstrated clinically from systolic/diastolic absolute activity ratios combined with phantom data accounting for Rb-82 positron range.