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Radiomics is an advanced image-processing framework, which extracts image features and considers them as biomarkers towards personalized medicine. Applications include disease detection, diagnosis, prognosis, and therapy response assessment/prediction. As radiation therapy aims for further individualized treatments, radiomics could play a critical role in various steps before, during and after treatment. Elucidation of the concept of radiomics-guided radiation therapy (RGRT) is the aim of this review, attempting to highlight opportunities and challenges underlying the use of radiomics to guide clinicians and physicists towards more effective radiation treatments. This work identifies the value of RGRT in various steps of radiotherapy from patient selection to follow-up, and subsequently provides recommendations to improve future radiotherapy using quantitative imaging features.

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Derived from 2 yr of deliberations and community engagement, Medical Physics 3.0 (MP3.0) is an effort commissioned by the American Association of Physicists in Medicine (AAPM) to devise a framework of strategies by which medical physicists can maintain and improve their integral roles in, and contributions to, health care and its innovation under conditions of rapid change and uncertainty. Toward that goal, MP3.0 advocates a broadened and refreshed model of sustainable excellence by which medical physicists can and should contribute to health care. The overarching conviction of MP3.0 is that every healthcare facility can benefit from medical physics and every patient's care can be improved by a medical physicist. This large and expansive challenge necessitates a range of strategies specific to each area of medical physics: clinical practice, research, product development, and education. The present paper offers a summary of the Phase 1 deliberations of the MP3.0 initiative pertaining to strategic directions of the discipline primarily but not exclusively oriented toward the clinical practice of medical physics in the United States.

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PURPOSE: To develop planning and delivery capabilities for linear accelerator-based nonisocentric trajectory modulated arc therapy (TMAT) and to evaluate the benefit of TMAT for accelerated partial breast irradiation (APBI) with the patient in prone position. METHODS AND MATERIALS: An optimization algorithm for volumetrically modulated arc therapy (VMAT) was generalized to allow for user-defined nonisocentric TMAT trajectories combining couch rotations and translations. After optimization, XML scripts were automatically generated to program and subsequently deliver the TMAT plans. For 10 breast patients in the prone position, TMAT and 6-field noncoplanar intensity modulated radiation therapy (IMRT) plans were generated under equivalent objectives and constraints. These plans were compared with regard to whole breast tissue volume receiving more than 100%, 80%, 50%, and 20% of the prescription dose. RESULTS: For TMAT APBI, nonisocentric collision-free horizontal arcs with large angular span (251.5 ± 7.9°) were optimized and delivered with delivery time of ∼4.5 minutes. Percentage changes of whole breast tissue volume receiving more than 100%, 80%, 50%, and 20% of the prescription dose for TMAT relative to IMRT were -10.81% ± 6.91%, -27.81% ± 7.39%, -14.82% ± 9.67%, and 39.40% ± 10.53% (P≤.01). CONCLUSIONS: This is a first demonstration of end-to-end planning and delivery implementation of a fully dynamic APBI TMAT. Compared with IMRT, TMAT resulted in marked reduction of the breast tissue volume irradiated at high doses.

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PURPOSE: A novel 4D volumetric modulated are therapy (4D-VMAT) planning system is presented where radiation sparing of organs at risk (OARs) is enhanced by exploiting respiratory motion of tumor and healthy tissues. METHODS: In conventional radiation therapy, a motion encompassing margin is normally added to the clinical target volume (CTV) to ensure the tumor receives the planned treatment dose. This results in a substantial increase in dose to the OARs. Our 4D-VMAT algorithm aims to reduce OAR dose by incorporating 4D volumetric target and OAR motions directly into the optimization process. During optimization, phase correlated beam samples are progressively added throughout the full range of gantry rotation. The resulting treatment plans have respiratory phase-optimized apertures whose deliveries are synchronized to the patient's respiratory cycle. 4D-VMAT plans reduce dose to the OAR by: (1) eliminating the motion margin, (2) selectively redistributing OAR dose over the OAR volume, and (3) timing larger dose contributions (MU) to respiratory phases where greater separations between the target and OAR occur. Our 4D-VMAT algorithm was tested by simulating a variety of tumor motion amplitudes (0.5-2 cm) in the superior/inferior and anterior/ posterior directions. 4D-VMAT's performance was compared against 3D-VMAT, gated VMAT and dynamic multileaf collimator (DMLC) ideal-tracking VMAT. RESULTS: Results show that OAR sparing of 4D-VMAT was greater than 3D-VMAT in all cases due to the smaller PTV margin. Compared to DMLC ideal-tracking VMAT, 4D-VMAT's OAR sparing is superior only when the relative distance between the PTV and OAR is changing. For gated VMAT, results compared to 4D-VMAT are phantom dependent. There was negligible difference in plan qualities for the tested case of motion along the anterior/posterior axis. For motions along the superior/inferior axis, gated VMAT's narrow beam-on window reduces the OAR volume directly irradiated by the linac but also allows higher dose accumulation in the exposed OAR. In contrast, 4D-VMAT can reduce the OAR volume exposed to high doses but at the cost of redistributing the OAR dose over a larger volume. Finally for 4D-VMAT, an increase in tumor motion no longer resulted in greater irradiation of the OAR as seen in conventional 3D radiation therapy. OAR dose levels were preserved for increasing target motion along the anterior/posterior axis. For increasing superior/inferior motion, the volume of OAR exposed to high doses actually decreased due to dose redistribution. CONCLUSIONS: Our investigation demonstrated that the 4D-VMAT system has the potential to improve radiation therapy of periodically moving tumors over 3D-VMAT, gating or tracking methods.

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Local learning methods, such as local linear regression and nearest neighbor classifiers, base estimates on nearby training samples, neighbors. Usually, the number of neighbors used in estimation is fixed to be a global "optimal" value, chosen by cross validation. This paper proposes adapting the number of neighbors used for estimation to the local geometry of the data, without need for cross validation. The term enclosing neighborhood is introduced to describe a set of neighbors whose convex hull contains the test point when possible. It is proven that enclosing neighborhoods yield bounded estimation variance under some assumptions. Three such enclosing neighborhood definitions are presented: natural neighbors, natural neighbors inclusive, and enclosing k-NN. The effectiveness of these neighborhood definitions with local linear regression is tested for estimating lookup tables for color management. Significant improvements in error metrics are shown, indicating that enclosing neighborhoods may be a promising adaptive neighborhood definition for other local learning tasks as well, depending on the density of training samples.

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