Identification of CT Imaging Phenotypes of Colorectal Liver Metastases from Radiomics Signatures-Towards Assessment of Interlesional Tumor Heterogeneity.

(1) Background: Tumoral heterogeneity (TH) is a major challenge in the treatment of metastatic colorectal cancer (mCRC) and is associated with inferior response. Therefore, the identification of TH would be beneficial for treatment planning. TH can be assessed by identifying genetic alterations. In this work, a radiomics-based approach for assessment of TH in colorectal liver metastases (CRLM) in CT scans is demonstrated. (2) Methods: In this retrospective study, CRLM of mCRC were segmented and radiomics features extracted using pyradiomics. Unsupervised k-means clustering was applied to features and lesions. Feature redundancy was evaluated by principal component analysis and reduced by Pearson correlation coefficient cutoff. Feature selection was conducted by LASSO regression and visual analysis of the clusters by radiologists. (3) Results: A total of 47 patients' (36% female, median age 64) CTs with 261 lesions were included. Five clusters were identified, and the categories small disseminated (n = 31), heterogeneous (n = 105), homogeneous (n = 64), mixed (n = 59), and very large type (n = 2) were assigned based on visual characteristics. Further statistical analysis showed correlation (p < 0.01) of clusters with sex, primary location, T- and N-status, and mutational status. Feature reduction and selection resulted in the identification of four features as a final set for cluster definition. (4) Conclusions: Radiomics features can characterize TH in liver metastases of mCRC in CT scans, and may be suitable for a better pretherapeutic classification of liver lesion phenotypes.

Economic assessment of molecular imaging in the oncology treatment process.

The development towards targeted treatments in oncology has been accompanied by significant improvements in molecular imaging. Yet, broad application of novel imaging techniques has partly been slowed down due to economical considerations. Building on the broad positive evidence of its diagnostic accuracy, modelling of effects on long-term costs and effectiveness may help to foster a broader application and acceptance of comprehensive molecular imaging techniques, such as PET/MRI. In this article, common economic evaluation techniques and cost-effectiveness analysis (CEA) evaluation methods will be introduced including Markov models and incremental cost-effectiveness ratios (ICER). This is complemented with a review of literature on recently published cost-effectiveness of molecular imaging. Additionally, the strategic relevance of CEAs for the molecular imaging community is discussed and combined with a global outlook.