Haploidentical HSCT with post transplantation cyclophosphamide versus unrelated donor HSCT in pediatric patients affected by acute leukemia.

T-cell replete hematopoietic stem cell transplantation (HSCT) from a haploidentical donor followed by high doses of cyclophosphamide has been demonstrated to provide the best chances of a cure for many children in need of an allograft but who lack both a sibling and an unrelated donor. In this study we retrospectively compared the outcome of pediatric patients undergoing T-replete haploidentical HSCT (Haplo) for acute leukemia with those undergoing transplantation from unrelated HLA-matched donor (MUD) and HLA mismatched unrelated donor (MMUD) from 2012 to 2017 at our Center. Both univariable and multivariable analyses showed similar 5-year overall survival rates for MUD, MMUD, and Haplo patients: 71% (95% CI 56-86), 72% (95% CI 55-90), and 75% (95% CI 54-94), respectively (p = 0.97). Haplo patients showed reduced event-free survival rates compared to MUD and MMUD patients: 30% (95% CI 12-49) versus 70% (95% CI 55-84) versus 53% (95% CI 35-73), respectively (p = 0.007), but these data were not confirmed by a multivariable analysis. Non-relapse mortality (NRM) and relapse incidence (RI) were similar for the three groups. Therefore, our data confirm that Haplo is a suitable clinical option for pediatric patients needing HSCT when lacking both an MUD and an MMUD donor.

Radiotherapy of Hodgkin and Non-Hodgkin Lymphoma: A Nonrigid Image-Based Registration Method for Automatic Localization of Prechemotherapy Gross Tumor Volume.

PURPOSE: To improve the contouring of clinical target volume for the radiotherapy of neck Hodgkin/non-Hodgkin lymphoma by localizing the prechemotherapy gross target volume onto the simulation computed tomography using [18F]-fluorodeoxyglucose positron emission tomography/computed tomography. MATERIAL AND METHODS: The gross target volume delineated on prechemotherapy [18F]-fluorodeoxyglucose positron emission tomography/computed tomography images was warped onto simulation computed tomography using deformable image registration. Fifteen patients with neck Hodgkin/non-Hodgkin lymphoma were analyzed. Quality of image registration was measured by computing the Dice similarity coefficient on warped organs at risk. Five radiation oncologists visually scored the localization of automatic gross target volume, ranking it from 1 (wrong) to 5 (excellent). Deformable registration was compared to rigid registration by computing the overlap index between the automatic gross target volume and the planned clinical target volume and quantifying the V95 coverage. RESULTS: The Dice similarity coefficient was 0.80 ± 0.07 (median ± quartiles). The physicians' survey had a median score equal to 4 (good). By comparing the rigid versus deformable registration, the overlap index increased from a factor of about 4 and the V95 (percentage of volume receiving the 95% of the prescribed dose) went from 0.84 ± 0.38 to 0.99 ± 0.10 (median ± quartiles). CONCLUSION: This study demonstrates the impact of using deformable registration between prechemotherapy [18F]-fluorodeoxyglucose positron emission tomography/computed tomography and simulation computed tomography, in order to automatically localize the gross target volume for radiotherapy treatment of patients with Hodgkin/non-Hodgkin lymphoma.

Validation of automatic contour propagation for 4D treatment planning using multiple metrics.

The aim of this work is to provide insights into multiple metrics clinical validation of deformable image registration and contour propagation methods in 4D lung radiotherapy planning. The following indices were analyzed and compared: Volume Difference (VD), Dice Similarity Coefficient (DSC), Positive Predictive Value (PPV) and Surface Distances (SD). The analysis was performed on three patient datasets, using as reference a ground-truth volume generated by means of Simultaneous Truth And Performance Level Estimation (STAPLE) algorithm from the outlines of five experts. Significant discrepancies in the quality assessment provided by the different metrics in all the examined cases were found. Metrics sensitivity was more evident in presence of image artifacts and particularly for tubular anatomical structures, such as esophagus or spinal cord. Volume Differences did not account for position and DSC exhibited criticalities due to its intrinsic symmetry (i.e. over- and under-estimation of the reference contours cannot be discriminated) and dependency on the total volume of the structure. PPV analysis showed more robust performance, as each voxel concurs to the classification of the propagation, but was not able to detect inclusion of propagated and ground-truth volumes. Mesh distances could interpret the actual shape of the structures, but might report higher mismatches in case of large local differences in the contour surfaces. According to our study, the combination of VD and SD for the validation of contour propagation algorithms in 4D could provide the necessary failure detection accuracy.

Genetic evolutionary taboo search for optimal marker placement in infrared patient setup.

In infrared patient setup adequate selection of the external fiducial configuration is required for compensating inner target displacements (target registration error, TRE). Genetic algorithms (GA) and taboo search (TS) were applied in a newly designed approach to optimal marker placement: the genetic evolutionary taboo search (GETS) algorithm. In the GETS paradigm, multiple solutions are simultaneously tested in a stochastic evolutionary scheme, where taboo-based decision making and adaptive memory guide the optimization process. The GETS algorithm was tested on a group of ten prostate patients, to be compared to standard optimization and to randomly selected configurations. The changes in the optimal marker configuration, when TRE is minimized for OARs, were specifically examined. Optimal GETS configurations ensured a 26.5% mean decrease in the TRE value, versus 19.4% for conventional quasi-Newton optimization. Common features in GETS marker configurations were highlighted in the dataset of ten patients, even when multiple runs of the stochastic algorithm were performed. Including OARs in TRE minimization did not considerably affect the spatial distribution of GETS marker configurations. In conclusion, the GETS algorithm proved to be highly effective in solving the optimal marker placement problem. Further work is needed to embed site-specific deformation models in the optimization process.

A neural network based method for optical patient set-up registration in breast radiotherapy.

Patient set-up optimization is required in breast-cancer radiotherapy to fill the accuracy gap between personalized treatment planning and uncertainties in the irradiation set-up. Opto-electronic systems allow implementing automatic procedures to minimize the positional mismatches of light-reflecting markers located on the patient surface with respect to a corresponding reference configuration. The same systems are used to detect the position of the irradiated body surface by means of laser spots; patient set-up is then corrected by matching the control points onto a CT based reference model through surface registration algorithms. In this paper, a non-deterministic approach based on Artificial Neural Networks is proposed for the automatic, real-time verification of geometrical set-up of breast irradiation. Unlike iterative surface registration methods, no passive fiducials are used and true real-time performance is obtained. Moreover, the non-deterministic modeling performed by the neural algorithm minimizes sensitivity to intra-fractional and inter-fractional non-rigid motion of the breast. The technique was validated through simulated activities by using reference CT data acquired on four subjects. Results show that the procedure is able to detect and reduce simulated set-up errors and revealed high reliability in patient position correction, even when the surface deformation is included in testing conditions.