EP-1711: Effect of Noise Floor Suppression on Diffusion Kurtosis for Prostate Brachytherapy

WOS: 000467786304224

Head and neck target delineation using a novel PET automatic segmentation algorithm

Purpose To evaluate the feasibility and impact of using a novel advanced PET auto-segmentation method in Head and Neck (H&N) radiotherapy treatment (RT) planning. Methods ATLAAS, Automatic decision Tree-based Learning Algorithm for Advanced Segmentation, previously developed and validated on pre-clinical data, was applied to 18F-FDG-PET/CT scans of 20 H&N patients undergoing Intensity Modulated Radiation Therapy. Primary Gross Tumour Volumes (GTVs) manually delineated on CT/MRI scans (GTVpCT/MRI), together with ATLAAS-generated contours (GTVpATLAAS) were used to derive the RT planning GTV (GTVpfinal). ATLAAS outlines were compared to CT/MRI and final GTVs qualitatively and quantitatively using a conformity metric. Results The ATLAAS contours were found to be reliable and useful. The volume of GTVpATLAAS was smaller than GTVpCT/MRI in 70% of the cases, with an average conformity index of 0.70. The information provided by ATLAAS was used to grow the GTVpCT/MRI in 10 cases (up to 10.6 mL) and to shrink the GTVpCT/MRI in 7 cases (up to 12.3 mL). ATLAAS provided complementary information to CT/MRI and GTVpATLAAS contributed to up to 33% of the final GTV volume across the patient cohort. Conclusions ATLAAS can deliver operator independent PET segmentation to augment clinical outlining using CT and MRI and could have utility in future clinical studies

Artificial intelligence in radiation oncology: A review of its current status and potential application for the radiotherapy workforce

Objective Radiation oncology is a continually evolving speciality. With the development of new imaging modalities and advanced imaging processing techniques, there is an increasing amount of data available to practitioners. In this narrative review, Artificial Intelligence (AI) is used as a reference to machine learning, and its potential, along with current problems in the field of radiation oncology, are considered from a technical position. Key Findings AI has the potential to harness the availability of data for improving patient outcomes, reducing toxicity, and easing clinical burdens. However, problems including the requirement of complexity of data, undefined core outcomes and limited generalisability are apparent. Conclusion This original review highlights considerations for the radiotherapy workforce, particularly therapeutic radiographers, as there will be an increasing requirement for their familiarity with AI due to their unique position as the interface between imaging technology and patients. Implications for practice Collaboration between AI experts and the radiotherapy workforce are required to overcome current issues before clinical adoption. The development of educational resources and standardised reporting of AI studies may help facilitate this

Comparison of different calculation techniques for absorbed dose assessment in patient specific peptide receptor radionuclide therapy

Aim: The present work concerns the comparison of the performances of three systems for dosimetry in RPT that use different techniques for absorbed dose calculation (organ-level dosimetry, voxel-level dose kernel convolution and Monte Carlo simulations). The aim was to assess the importance of the choice of the most adequate calculation modality, providing recommendations about the choice of the computation tool. Methods: The performances were evaluated both on phantoms and patients in a multi-level approach. Different phantoms filled with a 177Lu-radioactive solution were used: a homogeneous cylindrical phantom, a phantom with organ-shaped inserts and two cylindrical phantoms with inserts different for shape and volume. A total of 70 patients with NETs treated by PRRT with 177Lu-DOTATOC were retrospectively analysed. Results: The comparisons were performed mainly between the mean values of the absorbed dose in the regions of interest. A general better agreement was obtained between Dose kernel convolution and Monte Carlo simulations results rather than between either of these two and organ-level dosimetry, both for phantoms and patients. Phantoms measurements also showed the discrepancies mainly depend on the geometry of the inserts (e.g. shape and volume). For patients, differences were more pronounced than phantoms and higher inter/intra patient variability was observed. Conclusion: This study suggests that voxel-level techniques for dosimetry calculation are potentially more accurate and personalized than organ-level methods. In particular, a voxel-convolution method provides good results in a short time of calculation, while Monte Carlo based computation should be conducted with very fast calculation systems for a possible use in clinics, despite its intrinsic higher accuracy. Attention to the calculation modality is recommended in case of clinical regions of interest with irregular shape and far from spherical geometry, in which Monte Carlo seems to be more accurate than voxel-convolution methods

Impact of different leaf velocities and dose rates on the number of monitor units and the dose-volume-histograms using intensity modulated radiotherapy with sliding-window technique

<p>Abstract</p> <p>Background</p> <p>Intensity modulated radiotherapy (IMRT) using sliding window technique utilises a leaf sequencing algorithm, which takes some control system limitations like dose rates (DR) and velocity of the leafs (LV) into account. The effect of altering these limitations on the number of monitor units and radiation dose to the organs at risk (OAR) were analysed.</p> <p>Methods</p> <p>IMRT plans for different LVs from 1.0 cm/sec to 10.0 cm/sec and different DRs from 100 MU/min to 600 MU/min for two patients with prostate cancer and two patients with squamous cell cancer of the scalp (SCCscalp) were calculated using the same "optimal fluence map". For each field the number of monitor units, the dose volume histograms and the differences in the "actual fluence maps" of the fields were analysed.</p> <p>Results</p> <p>With increase of the DR and decrease of the LV the number of monitor units increased and consequentially the radiation dose given to the OAR. In particular the serial OARs of patients with SCCscalp, which are located outside the end position of the leafs and inside the open field, received an additional dose of a higher DR and lower LV is used.</p> <p>Conclusion</p> <p>For best protection of organs at risk, a low DR and high LV should be applied. But the consequence of a low DR is both a long treatment time and also that a LV of higher than 3.0 cm/sec is mechanically not applicable. Our recommendation for an optimisation of the discussed parameters is a leaf velocity of 2.5 cm/sec and a dose rate of 300–400 MU/min (prostate cancer) and 100–200 MU/min (SCCscalp) for best protection of organs at risk, short treatment time and number of monitor units.</p