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Radiomics provide an exciting approach to developing imaging biomarkers in the context of precision medicine. We focus on the preclinical arm of a co-clinical trial investigating synergy of immunotherapy combined with radiation therapy (RT) and surgical resection using a genetically engineered mouse model of sarcoma. Our protocol involves the acquisition of MRI data with T1, T2 and T1 with contrast agent. There are two MRI time points i.e. one day before RT (20Gy) and one week later. After the second MRI acquisition the primary tumor is surgically removed, and the mice are followed for up to 6 months to investigate for local recurrence or distant metastases. The tumor images are segmented using deep learning. We performed radiomics for the tumor, peritumoral rim and the combined tumor and peritumoral rim. Our first radiomics analysis was focused on determining features which are most indicative to the effects of RT. Our second analysis aimed to answer if radiomics features could predict primary tumor recurrence within this population. Top features were selected for training classifiers based on neural networks and support vector machines. Our results show that gray level radiomic features show that tumors often acquire more heterogeneous texture and that tumor volume increases one-week post RT. The results also suggest that radiomics features serve to indicate likelihood of primary tumor recurrence with the best predictive power in the combined tumor and peritumoral area in pre-RT data (AUC: 0.83). In conclusion, we have created a radiomics pipeline to serve in our current preclinical arm of the co-clinical trial.

Small animal imaging has become essential in evaluating new cancer therapies as they are translated from the preclinical to clinical domain. However, preclinical imaging is faced with unique challenges that emphasize the gap between mouse and man. One example is the difference in breathing patterns and breath-holding ability, which can dramatically affect tumor burden assessment in lung tissue. Our group is developing quantitative imaging methods for the preclinical arm of a co-clinical trial studying synergy between immunotherapy (anti-PD-1) and radiotherapy in a soft tissue sarcoma model. To mimic imaging performed in patients, primary sarcomas lesions are imaged with micro-MRI, while detection of lung metastases is performed with micro-CT. This study addresses whether respiratory gating during micro-CT acquisition improves lung tumor volume quantitation. Accuracy and precision of lung tumor measurements was determined by performing experiments involving simulations, a pocket phantom and in vivo scans with and without prospective respiratory gating. Sensitivity and precision of segmentation with and without gating was studied using simulated lung tumors. A clinically-inspired “pocket phantom” was used during in vivo mouse scanning to aid in refining and assessing the gating protocols. Finally, we performed a series of in vivo scans on tumor-bearing mice while varying the animal’s position (test-retest), and performing the analyses in triplicate to assess the effects of gating. Application of respiratory gating techniques reduced variance of repeated volume measurements and significantly improved the accuracy of tumor volume quantitation in vivo.

Small animal imaging is essential in building a bridge from basic science to the clinic by providing the confidence necessary to move new cancer therapies to patients. However, there is considerable variability in preclinical imaging, including tumor volume estimations based on tumor segmentation procedures which can be clearly user-biased. Our group is engaged in developing quantitative imaging methods which will be applied in the preclinical arm of a co-clinical trial studying synergy between anti-PD-1 treatment and radiotherapy using a genetically engineered mouse model of soft tissue sarcoma. This study focuses on a convolutional neural network (CNN)-based method for automatic tumor segmentation based on multimodal MRI images, i.e. T1 weighted, T2 weighted and T1 weighted with contrast agent. Our images were acquired on a 7.0 T Bruker Biospec small animal MRI scanner. Preliminary results show that our U-net structure and 3D patch-wise approach using both Dice and cross entropy loss functions delivers strong segmentation results. We have also compared single performance using only T2 weighted versus multimodal MR images for CNN segmentation. Our results showthat Dice similarity coefficient were higher when using multimodal versus single T2 weighted data (0.84 ± 0.05 and 0.81 ± 0.03). In conclusion, we successfully established a segmentation method for preclinical MR sarcoma data based on deep learning. This approach has the advantage of reducing user bias in tumor segmentation and improving the accuracy and precision of tumor volume estimations for co-clinical cancer trials.