Predicting liver SBRT eligibility and plan quality for VMAT and 4π plans

Abstract Background It is useful to predict planned dosimetry and determine the eligibility of a liver cancer patient for SBRT treatment using knowledge based planning (KBP). We compare the predictive accuracy using the overlap volume histogram (OVH) and statistical voxel dose learning (SVDL) KBP prediction models for coplanar VMAT to non-coplanar 4π radiotherapy plans. Methods In this study, 21 liver SBRT cases were selected, which were initially treated using coplanar VMAT plans. They were then re-planned using 4π IMRT plans with 20 inversely optimized non-coplanar beams. OVH was calculated by expanding the planning target volume (PTV) and then plotting the percent overlap volume v with the liver vs. r v , the expansion distance. SVDL calculated the distance to the PTV for all liver voxels and bins the voxels of the same distance. Their dose information is approximated by either taking the median or using a skew-normal or non-parametric fit, which was then applied to voxels of unknown dose for each patient in a leave-one-out test. The liver volume receiving less than 15 Gy (V<15Gy), DVHs, and 3D dose distributions were predicted and compared between the prediction models and planning methods. Results On average, V<15Gy was predicted within 5%. SVDL was more accurate than OVH and able to predict DVH and 3D dose distributions. Median SVDL yielded predictive errors similar or lower than the fitting methods and is more computationally efficient. Prediction of the 4π dose was more accurate compared to VMAT for all prediction methods, with significant (p < 0.05) results except for OVH predicting liver V<15Gy (p = 0.063). Conclusions In addition to evaluating plan quality, KBP is useful to automatically determine the patient eligibility for liver SBRT and quantify the dosimetric gains from non-coplanar 4π plans. The two here analyzed dose prediction methods performed more accurately for the 4π plans than VMAT

16p11.2–p12.2 duplication syndrome; a genomic condition differentiated from euchromatic variation of 16p11.2

Chromosome 16 contains multiple copy number variations (CNVs) that predispose to genomic disorders. Here, we differentiate pathogenic duplications of 16p11.2–p12.2 from microscopically similar euchromatic variants of 16p11.2. Patient 1 was a girl of 18 with autism, moderate intellectual disability, behavioural difficulties, dysmorphic features and a 7.71-Mb (megabase pair) duplication (16:21?521?005–29?233?146). Patient 2 had a 7.81-Mb duplication (16:21?382?561–29?191?527), speech delay and obsessional behaviour as a boy and, as an adult, short stature, macrocephaly and mild dysmorphism. The duplications contain 65 coding genes of which Polo-like kinase 1 (PLK1) has the highest likelihood of being haploinsufficient and, by implication, a triplosensitive gene. An additional 1.11-Mb CNV of 10q11.21 in Patient 1 was a possible modifier containing the G-protein-regulated inducer of neurite growth 2 (GPRIN2) gene. In contrast, the euchromatic variants in Patients 3 and 4 were amplifications from a 945-kb region containing non-functional immunoglobulin heavy chain (IGHV), hect domain pseudogene (HERC2P4) and TP53-inducible target gene 3 (TP53TG3) loci in proximal 16p11.2 (16:31?953?353–32?898?635). Paralogous pyrosequencing gave a total copy number of 3–8 in controls and 8 to &gt;10 in Patients 3 and 4. The 16p11.2–p12.2 duplication syndrome is a recurrent genomic disorder with a variable phenotype including developmental delay, dysmorphic features, mild to severe intellectual disability, autism, obsessive or stereotyped behaviour, short stature and anomalies of the hands and fingers. It is important to differentiate pathogenic 16p11.2–p12.2 duplications from harmless, microscopically similar euchromatic variants of proximal 16p11.2, especially at prenatal diagnosi