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ObjectivesArtificial intelligence (AI) is seen as a major disrupting force in the future healthcare system. However, the assessment of the value of AI technologies is still unclear. Therefore, a multidisciplinary group of experts and patients developed a Model for ASsessing the value of AI (MAS-AI) in medical imaging. Medical imaging is chosen due to the maturity of AI in this area, ensuring a robust evidence-based model.MethodsMAS-AI was developed in three phases. First, a literature review of existing guides, evaluations, and assessments of the value of AI in the field of medical imaging. Next, we interviewed leading researchers in AI in Denmark. The third phase consisted of two workshops where decision makers, patient organizations, and researchers discussed crucial topics for evaluating AI. The multidisciplinary team revised the model between workshops according to comments.ResultsThe MAS-AI guideline consists of two steps covering nine domains and five process factors supporting the assessment. Step 1 contains a description of patients, how the AI model was developed, and initial ethical and legal considerations. In step 2, a multidisciplinary assessment of outcomes of the AI application is done for the five remaining domains: safety, clinical aspects, economics, organizational aspects, and patient aspects.ConclusionsWe have developed an health technology assessment-based framework to support the introduction of AI technologies into healthcare in medical imaging. It is essential to ensure informed and valid decisions regarding the adoption of AI with a structured process and tool. MAS-AI can help support decision making and provide greater transparency for all parties.

Tumor heterogeneity in breast cancer tumors is widely recognized but evolution of the breast cancer genome is complex and not yet understood. Characterizing the evolutionary landscape of breast tumors can provide biological understanding of tumor progression and metastasis may be important for directing effective treatments. We performed copy number profiling and mutation detection based on whole exome sequencing of primary tumors, lymph node and distant metastasis as well as on plasma circulating tumor DNA from twelve metastatic breast cancer patients. Preliminary data shows diverse patterns of tumor evolution. Although linear evolution with late dissemination of metastatic cells was common, parallel evolution with early dissemination from primary tumor to distant sites was evident in some cases. Comparing tissue biopsies with plasma samples, we could detect variants mirroring primary tumor and/or metastasis, depending on the time span between the progression steps. The genomic discordance between the different stages of tumor evolution in these patients not only emphasizes the importance of molecular profiling of metastatic tissue but also the possibilities of liquid biopsies for real-time tracking of tumor clonal evolution. Citation Format: Stephanie Kavan, Torben A Kruse, Lars Andersen, Martin Larsen, Malene G Hildebrandt, Anne-Marie Bak Jylling, Marianne Ewertz, Marianne Vogsen, Mads Thomassen. Mapping clonal evolution and tumor heterogeneity by whole exome sequencing of tissue and plasma circulating tumor DNA in metastatic breast cancer [abstract]. In: Proceedings of the 2021 San Antonio Breast Cancer Symposium; 2021 Dec 7-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2022;82(4 Suppl):Abstract nr P5-07-07.

Breast cancer is a spatially and temporally dynamic disease in which differently evolving genetic clones are responsible for progression and clinical outcome. We review tumor heterogeneity and clonal evolution from studies comparing primary tumors and metastasis and discuss plasma circulating tumor DNA as a powerful real-time approach for monitoring the clonal landscape of breast cancer during treatment and recurrence. We found only a few early studies exploring clonal evolution and heterogeneity through analysis of multiregional tissue biopsies of different progression steps in comparison with circulating tumor DNA (ctDNA) from blood plasma. The model of linear progression seemed to be more often reported than the model of parallel progression. The results show complex routes to metastasis, however, and plasma most often reflected metastasis more than primary tumor. The described patterns of evolution and the polyclonal nature of breast cancer have clinical consequences and should be considered during patient diagnosis and treatment selection. Current studies focusing on the relevance of clonal evolution in the clinical setting illustrate the role of liquid biopsy as a noninvasive biomarker for monitoring clonal progression and response to treatment. In the clinical setting, circulating tumor DNA may be an ideal support for tumor biopsies to characterize the genetic landscape of the metastatic disease and to improve longitudinal monitoring of disease dynamics and treatment effectiveness through detection of residual tumor after resection, relapse, or metastasis within a particular patient.