Background: Colorectal cancer is among the most prevalent cancer entities worldwide, with every second patient developing liver metastases during their illness. For local treatment of liver metastases, a surgical approach as well as ablative treatment options, such as microwave ablation (MWA) and radiofrequency ablation (RFA), are available. The aim of this study is to evaluate the cost-effectiveness of RFA, MWA and surgery in the treatment of liver metastases of oligometastatic colorectal cancer (omCRC) that are amenable for all investigated treatment modalities. Methods: A decision analysis based on a Markov model assessed lifetime costs and quality-adjusted life years (QALY) related to the treatment strategies RFA, MWA and surgical resection. Input parameters were based on the best available and most recent evidence. Probabilistic sensitivity analyses (PSA) were performed with Monte Carlo simulations to evaluate model robustness. The percentage of cost-effective iterations was determined for different willingness-to-pay (WTP) thresholds. Results: The base-case analysis showed that surgery led to higher long-term costs compared to RFA and MWA (USD 41,848 vs. USD 36,937 vs. USD 35,234), while providing better long-term outcomes than RFA, yet slightly lower than MWA (6.80 vs. 6.30 vs. 6.95 QALYs for surgery, RFA and MWA, respectively). In PSA, MWA was the most cost-effective strategy for all WTP thresholds below USD 80,000 per QALY. Conclusions: In omCRC patients with liver metastases, MWA and surgery are estimated to provide comparable efficacy. MWA was identified as the most cost-effective strategy in intermediate resource settings and should be considered as an alternative to surgery in high resource settings.
Similar to the transformation towards personalized oncology treatment, emerging techniques for evaluating oncologic imaging are fostering a transition from traditional response assessment towards more comprehensive cancer characterization via imaging. This development can be seen as key to the achievement of truly personalized and optimized cancer diagnosis and treatment. This review gives a methodological introduction for clinicians interested in the potential of quantitative imaging biomarkers, treating of radiomics models, texture visualization, convolutional neural networks and automated segmentation, in particular. Based on an introduction to these methods, clinical evidence for the corresponding imaging biomarkers—(i) dignity and etiology assessment; (ii) tumoral heterogeneity; (iii) aggressiveness and response; and (iv) targeting for biopsy and therapy—is summarized. Further requirements for the clinical implementation of these imaging biomarkers and the synergistic potential of personalized molecular cancer diagnostics and liquid profiling are discussed.
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