‘One size does not fit all’: Translational health technology assessment in early breast cancer and DCIS

Overtreatment and avoidable treatment-related adverse effects not only have important health-related implications, but come at an unsustainable price – even for high-income countries. There is great potential to make oncology care more financially sustainable through de-escalation. However, this largely depends on identifying (cost)-effective prognostic and predictive tools to inform patient management, allowing certain therapies to be safely forgone in some patients and potentially intensified in others. Perhaps more crucial is ensuring the smooth adoption of these tools into routine clinical practice: through acceptance by physicians, patients, and policy makers. This PhD dissertation aims to understand the factors that may affect the use of interventions that lend themselves to de-escalating low-value care strategies for early breast cancer and ductal carcinoma in situ (DCIS). Using approaches grounded in health technology assessment (HTA), it considers clinical effectiveness of current and possible future diagnosis and treatment pathways, as well as all associated economic implications and wider implications for the patient.
The dissertation is based on three complimentary themes in the management of early-stage breast cancer. The first theme focuses on screen-detected primary DCIS, with a variety of projects focused on characterizing disease etiology, treatment and surveillance outcomes, real-world healthcare utilization, and potential of biomarkers to select low-risk women for an active surveillance strategy. Research was performed within the PRECISION (PREvent ductal Carcinoma In Situ Invasive Overtreatment Now) Consortium. The second theme focuses on treatment de-escalation for early-stage breast cancer, based on the first results of the EORTC 10041/BIG 3-04 MINDACT (Microarray in Node-Negative and 1 to 3 Positive Lymph Node Disease May Avoid Chemotherapy) phase 3 randomized control trial of the 70-gene signature. Finally, a complementary final theme and chapter highlights a promising new technology: artificial intelligence for to improve cancer detection at breast cancer screening to decrease the interval cancer rate.