On the feasibility of automatically selecting similar patients in highly individualized radiotherapy dose reconstruction for historic data of pediatric cancer survivors

Purpose: The aim of this study is to establish the first step toward a novel and highly individualized three-dimensional (3D) dose distribution reconstruction method, based on CT scans and organ delineations of recently treated patients. Specifically, the feasibility of automatically selecting the CT scan of a recently treated childhood cancer patient who is similar to a given historically treated child who suffered from Wilms' tumor is assessed.Methods: A cohort of 37 recently treated children between 2- and 6-yr old are considered. Five potential notions of ground-truth similarity are proposed, each focusing on different anatomical aspects. These notions are automatically computed from CT scans of the abdomen and 3D organ delineations (liver, spleen, spinal cord, external body contour). The first is based on deformable image registration, the second on the Dice similarity coefficient, the third on the Hausdorff distance, the fourth on pairwise organ distances, and the last is computed by means of the overlap volume histogram. The relationship between typically available features of historically treated patients and the proposed ground-truth notions of similarity is studied by adopting state-of-the-art machine learning techniques, including random forest. Also, the feasibility of automatically selecting the most similar patient is assessed by comparing ground-truth rankings of similarity with predicted rankings.Results: Similarities (mainly) based on the external abdomen shape and on the pairwise organ distances are highly correlated (Pearson rp ≥ 0.70) and are successfully modeled with random forests based on historically recorded features (pseudo-R2 ≥ 0.69). In contrast, similarities based on the shape of internal organs cannot be modeled. For the similarities that random forest can reliably model, an estimation of feature relevance indicates that abdominal diameters and weight are the most important. Experiments on automatically selecting similar patients lead to coarse, yet quite robust results: the most similar patient is retrieved only 22% of the times, however, the error in worst-case scenarios is limited, with the fourth most similar patient being retrieved.Conclusions: Results demonstrate that automatically selecting similar patients is feasible when focusing on the shape of the external abdomen and on the position of internal organs. Moreover, whereas the common practice in phantom-based dose reconstruction is to select a representative phantom using age, height, and weight as discriminant factors for any treatment scenario, our analysis on abdominal tumor treatment for children shows that the most relevant features are weight and the anterior-posterior and left-right abdominal diameters

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

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