Relating anatomical variations and patient features with dose-reconstruction accuracy of a 3D dose-reconstruction approach using CT scans of recently-treated children

Purpose Reconstructing 3D dose distributions for pre-1990 pediatric 2D radiotherapy plans is challenging, but key to research on late adverse effects. We studied the relation between dosimetric accuracy, anatomical variation, and other patient features of a 3D dose-reconstruction approach using CT scans of recently-treated patients, rather than phantoms. Materials and methods CT-scans of 22 Wilms’ tumor patients (age:2.5-5.3yrs; n boys/girls:11/11) treated between 2004 and 2015 were included. Two clinical plans as applied to a 4-year-old boy and girl with a left-sided Wilms’ tumor served as references. Each plan was applied to the CT scans of the other 21 patients, adjusted to correct for anatomical differences as visible in digitally-reconstructed-radiographs, and the resulting dose was calculated. Deviations in reconstructed dose, with respect to the reference dose, in organs-at-risk (spinal cord, right kidney, liver, and spleen) were characterized by the mean dose error normalized by the prescribed dose (DEmean). Deviations in organs’ location relative to a reference point (\Delta O_loc) and in organs’ shape captured by the Dice coefficient (DC) were calculated. We estimated the Pearson’s correlation between DEmean, on the one hand, and O­loc, DC, gender, age, height, and weight, on the other hand. Results Average(range) DEmean values were: spinal cord:3(0-8)%; right kidney:6(0-20)%; liver:9(0-20)%; and spleen:23(0-80)%. DC and DEmean in the right kidney were moderately negatively correlated (r2=0.41). DEmean in the liver was uncorrelated with any o

Surrogate-free machine learning-based organ dose reconstruction for pediatric abdominal radiotherapy

To study radiotherapy-related adverse effects, detailed dose information (3D distribution) is needed for accurate dose-effect modeling. For childhood cancer survivors who underwent radiotherapy in the pre-CT era, only 2D radiographs were acquired, thus 3D dose distributions must be reconstructed. State-of-the-art methods achieve this by using 3D surrogate anatomies. These can however lack personalization and lead to coarse reconstructions. We present and validate a surrogate-free dose reconstruction method based on Machine Learning (ML). Abdominal planning CTs (n=142) of recently-treated childhood cancer patients were gathered, their organs at risk were segmented, and 300 artificial Wilms' tumor plans were sampled automatically. Each artificial plan was automatically emulated on the 142 CTs, resulting in 42,600 3D dose distributions from which dose-volume metrics were derived. Anatomical features were extracted from digitally reconstructed radiographs simulated from the CTs to resemble historical radiographs. Further, patient and radiotherapy plan features typically available from historical treatment records were collected. An evolutionary ML algorithm was then used to link features to dose-volume metrics. Besides 5-fold cross validation, a further evaluation was done on an independent dataset of five CTs each associated with two clinical plans. Cross-validation resulted in mean absolute errors ≤0.6 Gy for organs completely inside or outside the field. For organs positioned at the edge of the field, mean absolute errors ≤1.7 Gy for Dmean, ≤2.9 Gy for D2cc, and ≤13% for V5Gy and V10Gy, were obtained, without systematic bias. Similar results were found for the independent dataset. To conclude, our novel organ dose reconstruction method is not only accurate, but also efficient, as the setup of a surrogate is no longer needed

Automatic Phantom Assembling

Code to automatically assemble phantoms from DICOM CT scans and RT STRUCT files using machine learning models

Automatic generation of three-dimensional dose reconstruction data for two-dimensional radiotherapy plans for historically treated patients

Performing large-scale three-dimensional radiation dose reconstruction for patients requires a large amount of manual work. We present an image processing-based pipeline to automatically reconstruct radiation dose. The pipeline was designed for childhood cancer survivors that received abdominal radiotherapy with anterior-to-posterior and posterior-to-anterior field set-up. First, anatomical landmarks are automatically identified on two-dimensional radiographs. Second, these landmarks are used to derive parameters to emulate the geometry of the plan on a surrogate computed tomography. Finally, the plan is emulated and used as input for dose calculation. For qualitative evaluation, 100 cases of automatic and manual plan emulations were assessed by two experienced radiation dosimetrists in a blinded comparison. The two radiation dosimetrists approved 100%/100% and 92%/91% of the automatic/manual plan emulations, respectively. Similar approval rates of 100% and 94% hold when the automatic pipeline is applied on another 50 cases. Further, quantitative comparisons resulted in on average <5 mm difference in plan isocenter/borders, and <0.9 Gy in organ mean dose (prescribed dose: 14.4 Gy) calculated from the automatic and manual plan emulations. No statistically significant difference in terms of dose reconstruction accuracy was found for most organs at risk. Ultimately, our automatic pipeline results are of sufficient quality to enable effortless scaling of dose reconstruction data generation

InP single nanowire solar cells

We demonstrated single axial p-i-n junction InP nanowire (NW) solar cells. The photovoltaic behavior of the devices has been investigated by optical and electrical characterization and simulation techniques.We acknowledge the Australian Research Council (ARC) and National Natural Science Foundation of China (611760698) for financial support, and the Australian National Fabrication Facility (ANFF) ACT node for facility support