Prediction of multi-criteria optimization (MCO) parameter efficiency in volumetric modulated arc therapy (VMAT) treatment planning using machine learning (ML).

PURPOSE: To predict the impact of optimization parameter changes on dosimetric plan quality criteria in multi-criteria optimized volumetric-modulated-arc therapy (VMAT) planning prior to optimization using machine learning (ML). METHODS: A data base comprising a total of 21,266 VMAT treatment plans for 44 cranial and 18 spinal patient geometries was generated. The underlying optimization algorithm is governed by three highly composite parameters which model a combination of important aspects of the solution. Patient geometries were parametrized via volume- and shape properties of the voxel objects and overlap-volume histograms (OVH) of the planning-target-volume (PTV) and a relevant organ-at-risk (OAR). The impact of changes in one of the three optimization parameters on the maximally achievable value range of five dosimetric properties of the resulting dose distributions was studied. To predict the extent of this impact based on patient geometry, treatment site, and current parameter settings prior to optimization, three different ML-models were trained and tested. Precision-recall curves, as well as the area-under-curve (AUC) of the resulting receiver-operator-characteristic (ROC) curves were analyzed for model assessment. RESULTS: Successful identification of parameter regions resulting in a high variability of dosimetric plan properties depended on the choice of geometry features, the treatment indication and the plan property under investigation. AUC values between 0.82 and 0.99 could be achieved. The best average-precision (AP) values obtained from the corresponding precision/recall curves ranged from 0.71 to 0.99. CONCLUSIONS: Machine learning models trained on a database of pre-optimized treatment plans can help finding relevant optimization parameter ranges prior to optimization.

Approximation of dose quality indicator values in multi-criteria optimized (MCO) volumetric modulated arc therapy (VMAT) treatment planning using trilinear dose interpolation.

PURPOSE: To approximate dose-volume histogram (DVH) based quality indicators in volumetric modulated arc therapy (VMAT) planning using multi-criteria optimization (MCO) with a low number of composite optimization parameters. METHODS: The solution space for VMAT optimization with a low number of composite optimization parameters is approximated by trilinear dose inter- polation and prediction of dose-volume-histogram (DVH) based plan quality indicator values. To assess the approximation quality a diverse dataset of 44 cranial and 18 spine patient geometries was chosen. Optimization results are governed by three composite parameters focusing on target-organ-at-risk- (OAR)-trade-off, overall healthy tissue sparing, and delivery/quality assurance complexity. 21,266 optimized dose distributions were pre-calculated and the numerical values for a choice of 10 DVH points, referred to as plan quality indicators, were stored to serve as ground truth. Using a subset of 8 and 27 pre-calculated optimization results, dose distributions for unknown parameter values were approximated by trilinear interpolation. The resulting quality indicator values were compared to the previously obtained exact solutions. RESULTS: The magnitude of the deviation between exact and approximated values varied largely with respect to patient geometry and the criterion under investigation. Approximation with 27 pre-calculated results yielded lower deviations than approximation with 8 results, at the cost of a higher pre-calculation workload. CONCLUSIONS: Solution space approximation via trilinear dose interpolation in VMAT treatment planning governed by composite optimization parameters is possible without further knowledge of the internal implementation of the underlying optimizer. Maximum average deviations between approxi- mation and actual values of characteristic dose quality indicators below 1% (cranial) and 8% (spine) allow for a quick qualitative assessment of the possible solution landscape.

Loss of Kindlin-1 causes skin atrophy and lethal neonatal intestinal epithelial dysfunction.

Kindler Syndrome (KS), characterized by transient skin blistering followed by abnormal pigmentation, skin atrophy, and skin cancer, is caused by mutations in the FERMT1 gene. Although a few KS patients have been reported to also develop ulcerative colitis (UC), a causal link to the FERMT1 gene mutation is unknown. The FERMT1 gene product belongs to a family of focal adhesion proteins (Kindlin-1, -2, -3) that bind several beta integrin cytoplasmic domains. Here, we show that deleting Kindlin-1 in mice gives rise to skin atrophy and an intestinal epithelial dysfunction with similarities to human UC. This intestinal dysfunction results in perinatal lethality and is triggered by defective intestinal epithelial cell integrin activation, leading to detachment of this barrier followed by a destructive inflammatory response.