Neuro-radiosurgery treatments: MRI brain tumor seeded image segmentation based on a cellular automata model

Gross Tumor Volume (GTV) segmentation on medical images is an open issue in neuro-radiosurgery. Magnetic Resonance Imaging (MRI) is the most prominent modality in radiation therapy for soft-tissue anatomical districts. Gamma Knife stereotactic neuro-radiosurgery is a mini-invasive technique used to deal with inaccessible or insufficiently treated tumors. During the planning phase, the GTV is usually contoured by radiation oncologists using a manual segmentation procedure on MR images. This methodology is certainly time-consuming and operator-dependent. Delineation result repeatability, in terms of both intra- and inter-operator reliability, is only obtained by using computer-assisted approaches. In this paper a novel semi-automatic segmentation method, based on Cellular Automata, is proposed. The developed approach allows for the GTV segmentation and computes the lesion volume to be treated. The method was evaluated on 10 brain cancers, using both area-based and distance-based metrics

Evaluation of Platinum-based Therapy Response in Non-Small Cell Lung Cancer

Aim: To evaluate the clinical value of PET imaging for an early prediction of tumor response to platinum-based therapy in patients with non-small cell lung cancer (NSCLC). In order to avoid unnecessary toxicity of ineffective chemotherapy treatment, an early identification of NSCLC patients who benefit from this therapy is mandatory. Materials and methods: Seventeen patients are enrolled prospectively: 18F-FDG-PET examinations are carried out before treatment and after the first course. The lesions with the highest uptake in each patient are evaluated according to EORTC, PERCIST and RECIST classifications to discriminate between patients who respond (complete and partial response) from those who do not respond (stable and progressive disease) to treatment. Metabolic Tumor Volume (MTV) and Total Lesion Glycolysis (TLG) are also used to evaluate therapeutic response. MTV indicates the volume of metabolically active tumors; TLG is the product between SUV mean and MTV. In literature, there are no cut-off points for therapy evaluation based on TLG or MTV variations (Δ) in sequential scans. In order to estimate cut-off values for these parameters, receiver operating characteristic (ROC) curves are used. RECIST classification is used as the outcome for the ROC analysis. Kaplan-Meier test is used to calculate Overall Survival (OS) time. OS is compared between responders and non-responders using a log-rank test. The level of statistical significance is defined as a p-value (p) of less than 0.05. Results: The ROC analysis indicates a cut-off point of -36% for ΔTLG, and -8% for ΔMTV. The Kaplan-Meier analysis shows that RECIST, ∆TLG, and ∆MTV prove to be a significant prognostic factor for predicting OS. For RECIST responder patients median OS is 595 days whereas for non-responder patients median OS is 238 days. ΔTLG shows a median OS of 492 days for responders and 238 days for non-responders. ΔMTV shows a median of 423 days for responders versus 188 days for non-responders. Conversely, EORTC and PERCIST classifications are inadequate to discriminate between responder and non-responder patients (p>0.13). For this reason, we use ROC analysis to propose an alternative PERCIST threshold of 17% for an early therapy monitoring. Conclusion: PET examinations provide an early identification of patients who benefit from platinum-based treatment. Results confirm that TLG proves a strong early prognostic factor in patients with NSCLC and could play a significant role in the field of personalized medicine, avoiding the unnecessary administration of non-curative and toxic drugs to preserve the patient’s quality of life

Psychology and the Aims of Normative Ethics

This chapter discusses the philosophical relevance of empirical research on moral cognition. It distinguishes three central aims of normative ethical theory: understanding the nature of moral agency, identifying morally right actions, and determining the justification of moral beliefs. For each of these aims, the chapter considers and rejects arguments against employing cognitive scientific research in normative inquiry. It concludes by suggesting that, whichever of the central aims one begins from, normative ethics is improved by engaging with the science of moral cognition