Prospective evaluation of an anti-cancer drugs management programme in a dedicated oral therapy center (DICTO programme).

Oral therapies have highly modified cancer patient management and changed hospital practises. We introduce a specific Oral Therapy Centre and retrospectively review information prospectively recorded by co-ordination nurses (CNs) (the DICTO programme). We describe the roles played by CNs in the management of oral cancer therapies at Limoges Dupuytren Hospital between May 2015 and June 2018. All cancers, irrespective of stage or whether oral general chemotherapy or targeted therapy was prescribed, are included. We followed up 287 patients of median age 67 years (range 26-89 years). Of these, 76% had metastases and 44% were on first-line therapy. The vast majority (88%) of their first CN contacts occurred just after physician consultation and lasted an average of 60 min. As part of follow-up, the CNs made 2719 calls (average 10 min) to patients to educate them and to verify compliance and drug tolerance. They also received 833 calls from patients (70%) or their relatives or health professionals (30%) seeking advice on management of side effects. In addition to the initial appointments, 1069 non-scheduled follow-up visits were made to assess side effects (49.2%). The CNs devoted 5 h to each patient over 3 months of treatment (i.e. 25 min/day) and, also organised scheduled hospitalisations in the department of oncology for 51% of patients. We show the interest and real-life work in a specific oral therapy centre within oncology department with the role of CNs to facilitate the global health care of the patients.

On the new metrics for IMRT QA verification.

PURPOSE: The aim of this work is to search for new metrics that could give more reliable acceptance/rejection criteria on the IMRT verification process and to offer solutions to the discrepancies found among different conventional metrics. Therefore, besides conventional metrics, new ones are proposed and evaluated with new tools to find correlations among them. These new metrics are based on the processing of the dose-volume histogram information, evaluating the absorbed dose differences, the dose constraint fulfillment, or modified biomathematical treatment outcome models such as tumor control probability (TCP) and normal tissue complication probability (NTCP). An additional purpose is to establish whether the new metrics yield the same acceptance/rejection plan distribution as the conventional ones. METHODS: Fifty eight treatment plans concerning several patient locations are analyzed. All of them were verified prior to the treatment, using conventional metrics, and retrospectively after the treatment with the new metrics. These new metrics include the definition of three continuous functions, based on dose-volume histograms resulting from measurements evaluated with a reconstructed dose system and also with a Monte Carlo redundant calculation. The 3D gamma function for every volume of interest is also calculated. The information is also processed to obtain ΔTCP or ΔNTCP for the considered volumes of interest. These biomathematical treatment outcome models have been modified to increase their sensitivity to dose changes. A robustness index from a radiobiological point of view is defined to classify plans in robustness against dose changes. RESULTS: Dose difference metrics can be condensed in a single parameter: the dose difference global function, with an optimal cutoff that can be determined from a receiver operating characteristics (ROC) analysis of the metric. It is not always possible to correlate differences in biomathematical treatment outcome models with dose difference metrics. This is due to the fact that the dose constraint is often far from the dose that has an actual impact on the radiobiological model, and therefore, biomathematical treatment outcome models are insensitive to big dose differences between the verification system and the treatment planning system. As an alternative, the use of modified radiobiological models which provides a better correlation is proposed. In any case, it is better to choose robust plans from a radiobiological point of view. The robustness index defined in this work is a good predictor of the plan rejection probability according to metrics derived from modified radiobiological models. The global 3D gamma-based metric calculated for each plan volume shows a good correlation with the dose difference metrics and presents a good performance in the acceptance/rejection process. Some discrepancies have been found in dose reconstruction depending on the algorithm employed. Significant and unavoidable discrepancies were found between the conventional metrics and the new ones. CONCLUSIONS: The dose difference global function and the 3D gamma for each plan volume are good classifiers regarding dose difference metrics. ROC analysis is useful to evaluate the predictive power of the new metrics. The correlation between biomathematical treatment outcome models and the dose difference-based metrics is enhanced by using modified TCP and NTCP functions that take into account the dose constraints for each plan. The robustness index is useful to evaluate if a plan is likely to be rejected. Conventional verification should be replaced by the new metrics, which are clinically more relevant.