New Research Says CT Radiomics Can Help Predict Tumor Behavior

Neale Pashley VP, Partner Services

Neale Pashley VP of Partner Services

Researchers from the H. Lee Moffitt Cancer Center and Research Institute in Tampa, FL, have found strong evidence that CT radiomics features can help predict tumor behavior in screening-detected lung cancer. PET/CT radiomics have also shown possibility to non-small cell lung cancer (NSCLC) treatment decisions.

Two CT radiomics features and a tumor volume doubling time (VDT) threshold found a high degree of accuracy for predicting survival outcomes. This was reported by first author Jaileene Pérez-Morales, PhD of Moffitt Cancer Center at the recent virtual 2020 North American Conference on Lung Cancer.

“Utilizing VDT and radiomic features, decision tree analysis identified subsets of screen-detected lung cancers associated with very poor survival outcomes suggesting such patients may [need] more aggressive treatment, such as adjuvant therapies, and more aggressive surveillance/follow-up,” the authors wrote.

How can this predict cancer outcomes?

Moffitt’s researchers generated models from cases found in lung cancer screening and looked at radiomics features and tumor volume doubling time to predict outcomes. Patient data and CT images from National Lung Screening Trial (NLST) were used. They first calculated VDT as the difference between two LDCT exams performed approximately one year apart.

Next, they took 155 intratumoral and 109 peritumoral radiomic features from the LDCT exams. After performing classification and regression tree (CART) analyses using overall survival as the main endpoint, the researchers found that the best predictive performance was achieved by using a combination of a tumor VDT threshold of 234 days and two CT radiomics features — compactness and average co-occurrence.

This isn’t Moffitt’s first major study in this field. Another study, led by Matthew Schabath, PhD, found that a deep learning algorithm could use PET/CT radiomics to identify the best treatment option for NSCLC. The researchers shared their findings in an article published online October 16 in Nature Communications.

In that project, the group retrospectively used F-18 FDG PET/CT data from two hospitals in China for training a deep-learning model to classify a NSCLC patient’s epidermal growth factor (EGFR) mutation status, an important predictor for patient treatment. Patients with an active EGFR mutation status respond better to tyrosine kinase inhibitor (TKI) treatment than immune checkpoint inhibitor (ICI) therapy.

After analyzing the images, the model generates an EGFR deep-learning score to classify their EGFR mutation status. The researchers subsequently tested the algorithm on patient data from a different hospital in China as well as Moffitt.

The deep-learning score yielded an AUC of 0.81 on the training set for discriminating between EGFR-mutant type from wild type, much higher than the AUC of 0.50 produced by the commonly used SUVmax measure, according to the authors.

Progression-free Survival Research

Progression-free survival was found to be significantly longer (p = 0.01) in TKI-treated patients who had a high EGFR deep-learning score than those who had a low deep-learning score. Equally, patients who had lower deep-learning scores and who had received immune checkpoint inhibitor treatments also had significantly longer progression-free survival than those with higher deep-learning scores (p < 0.001).

“We found that the EGFR deep-learning score was positively associated with longer progression-free survival in patients treated with tyrosine kinase inhibitors, and negatively associated with durable clinical benefit and longer progression-free survival in patients being treated with immune checkpoint inhibitor immunotherapy,” said co-author Robert Gillies, PhD, in a statement. “We would like to perform further studies but believe this model could serve as a clinical decision support tool for different treatments.”

Past studies have utilized radiomics as a noninvasive approach to predict EGFR mutation status, noted first author Wei Mu, PhD. But Moffitt’s had the best results.

“Compared to other studies, our analysis yielded among the highest accuracy to predict EGFR and had many advantages, including training, validating and testing the deep learning score with multiple cohorts from four institutions, which increased its generalizability,” she said in a statement.


By Neale Pashley,  VP of Partner Services at Collaborative Imaging