Highlights from the 13th St Gallen International Breast Cancer Conference 2013. Access to innovation for patients with breast cancer: how to speed it up?

The recognition that early breast cancer is a spectrum of diseases each requiring a specific systemic therapy guided the 13th St Gallen International Breast Cancer Consensus Conference [1]. The meeting assembled 3600 participants from nearly 90 countries worldwide. Educational content has been centred on the primary and multidisciplinary treatment approach of early breast cancer. The meeting culminated on the final day, with the St Gallen Breast Cancer Treatment Consensus, established by 40-50 of the world's most experienced opinion leaders in the field of breast cancer treatment. The major issue that arose during the consensus conference was the increasing gap between what is theoretically feasible in patient risk stratification, in treatment, and in daily practice management. We need to find new paths to access innovations to clinical research and daily practice. To ensure that continued innovation meets the needs of patients, the therapeutic alliance between patients and academic-led research should to be extended to include relevant pharmaceutical companies and drug regulators with a unique effort to bring innovation into clinical practice. We need to bring together major players from the world of breast cancer research to map out a coordinated strategy on an international scale, to address the disease fragmentation, to share financial resources, and to integrate scientific data. The final goal will be to improve access to an affordable, best standard of care for all patients in each country.

How to beat the Rayleigh limit?

The problem of achieving super-resolution, i.e. resolution beyond the classical Rayleigh distance of half a wavelength, is a real challenge in several imaging problems. The development of computer-assisted instruments and the possibility of inverting the recorded data has clearly modified the traditional concept of resolving power of an instrument. We show that, in the framework of inverse problem theory, the achievable resolution limit arises no longer from a universal rule but instead from a practical limitation due to noise amplification in the data inversion process. We analyze under what circumstances super-resolution can be achieved and we show how to assess the actual resolution limits in a given experiment, as a function of the noise level and of the available a priori knowledge about the object function. We emphasize the importance of the a priori knowledge of its effective support and we show that significant super-resolution can be achieved for "subwavelength sources", i.e. objects which are smaller than the probing wavelength.