Primary breast cancer while increasing in incidence has been successfully treated with a combination of surgery and adjuvant therapies in the majority of patients. Novel treatments for primary breast cancer need to show additional benefits to existing treatments with equivalent or improved efficacy for niche groups. Photodynamic therapy (PDT) is a potential novel treatment and a Phase I/IIA, open label, non-randomised, single site trial of photodynamic therapy for the treatment of primary breast cancer was conducted. The primary aim was to identify the light dose required for 12 mm of tumour necrosis (or a plateau of necrosis) assessed by histopathology. Post-dose MRI correlation with histopathology findings in treated tumours and in normal breast tissue was sought. In addition adverse events were recorded and comparison of outcome made with matched controls. Results of the first human clinical trial with 12 patients with median follow-up of 39 months showed PDT was well tolerated, with no adverse effects and comparable outcome to control populations. Tumour necrosis increased with incremental increases in light dose, however some patients showed a poor response even at the highest light dose. Analysis suggests that there may be predictive factors for good and poor response. PDT in primary breast cancer requires further investigation to identify which patients would most benefit from this therapy.
Primary breast cancer treatment relying on surgery with the use neo-adjuvant therapies has long been established treatment. Side effects from these and varying efficacy has lead to the search for novel therapies that may result in improved results. The use of Photodynamic therapy as a novel neo-adjuvant treatment alone and in combination with agents cytotoxic to breast cancer cells was investigated.
Biomechanical modelling enables large deformation simulations of breast tissues under different loading conditions to be performed. Such simulations can be utilised to transform prone Magnetic Resonance (MR) images into a different patient position, such as upright or supine. We present a novel integration of biomechanical modelling with a surface registration algorithm which optimises the unknown material parameters of a biomechanical model and performs a subsequent regularised surface alignment. This allows deformations induced by effects other than gravity, such as those due to contact of the breast and MR coil, to be reversed. Correction displacements are applied to the biomechanical model enabling transformation of the original pre-surgical images to the corresponding target position.
The algorithm is evaluated for the prone-to-supine case using prone MR images and the skin outline of supine Computed Tomography (CT) scans for three patients. A mean target registration error (TRE) of 10:9 mm for internal structures is achieved. For the prone-to-upright scenario, an optical 3D surface scan of one patient is used as a registration target and the nipple distances after alignment between the transformed MRI and the surface are 10:1 mm and 6:3 mm respectively.