Reproducibility of intratumor distribution of (18)f-fluoromisonidazole in head and neck cancer

Hypoxia is one of the main causes of the failure to achieve local control using radiotherapy. This is due to the increased radioresistance of hypoxic cells. (18)F-fluoromisonidazole ((18)F-FMISO) positron emission tomography (PET) is a noninvasive imaging technique that can assist in the identification of intratumor regions of hypoxia. The aim of this study was to evaluate the reproducibility of (18)F-FMISO intratumor distribution using two pretreatment PET scans. METHODS AND MATERIALS: We enrolled 20 head and neck cancer patients in this study. Of these, 6 were excluded from the analysis for technical reasons. All patients underwent an (18)F-fluorodeoxyglucose study, followed by two (18)F-FMISO studies 3 days apart. The hypoxic volumes were delineated according to a tumor/blood ratio >or=1.2. The (18)F-FMISO tracer distributions from the two (18)F-FMISO studies were co-registered on a voxel-by-voxel basis using the computed tomography images from the PET/computed tomography examinations. A correlation between the (18)F-FMISO intensities of the corresponding spatial voxels was derived. RESULTS: A voxel-by-voxel analysis of the (18)F-FMISO distributions in the entire tumor volume showed a strong correlation in 71% of the patients. Restraining the correlation to putatively hypoxic zones reduced the number of patients exhibiting a strong correlation to 46%. CONCLUSION: Variability in spatial uptake can occur between repeat (18)F-FMISO PET scans in patients with head and neck cancer. Blood data for one patient was not available. Of 13 patients, 6 had well-correlated intratumor distributions of (18)F-FMISO-suggestive of chronic hypoxia. More work is required to identify the underlying causes of changes in intratumor distribution before single-time-point (18)F-FMISO PET images can be used as the basis of hypoxia-targeting intensity-modulated radiotherapy

Analysing PET scans data for predicting response to chemotherapy in breast cancer patients

We discuss the use of machine learning algorithms to predict which breast cancer patients are likely to respond to (neoadjunctive) chemotherapy. A group of 96 patients from the Aberdeen Royal Infirmary had the size of their tumours assessed by Positron Emission Tomography at various stages of their chemotherapy treatment. The aim is to predict at an early stage which patients have low response to the therapy, for which alternative treatment plans should be followed. A variety of machine learning algorithms were used with this data set. Results indicate that machine learning methods outperform previous statistical approaches on the same data set