PurposeTo develop a fully automated procedure for multicriterial volumetric modulated arc therapy (VMAT) treatment planning (autoVMAT) for stage III/IV non-small cell lung cancer (NSCLC) patients treated with curative intent.Materials and methodsAfter configuring the developed autoVMAT system for NSCLC, autoVMAT plans were compared with manually generated clinically delivered intensity-modulated radiotherapy (IMRT) plans for 41 patients. AutoVMAT plans were also compared to manually generated VMAT plans in the absence of time pressure. For 16 patients with reduced planning target volume (PTV) dose prescription in the clinical IMRT plan (to avoid violation of organs at risk tolerances), the potential for dose escalation with autoVMAT was explored.ResultsTwo physicians evaluated 35/41 autoVMAT plans (85%) as clinically acceptable. Compared to the manually generated IMRT plans, autoVMAT plans showed statistically significant improved PTV coverage (V95% increased by 1.1% ± 1.1%), higher dose conformity (R50 reduced by 12.2% ± 12.7%), and reduced mean lung, heart, and esophagus doses (reductions of 0.9 Gy ± 1.0 Gy, 1.5 Gy ± 1.8 Gy, 3.6 Gy ± 2.8 Gy, respectively, all p < 0.001). To render the six remaining autoVMAT plans clinically acceptable, a dosimetrist needed less than 10 min hands-on time for fine-tuning. AutoVMAT plans were also considered equivalent or better than manually optimized VMAT plans. For 6/16 patients, autoVMAT allowed tumor dose escalation of 5–10 Gy.ConclusionClinically deliverable, high-quality autoVMAT plans can be generated fully automatically for the vast majority of advanced-stage NSCLC patients. For a subset of patients, autoVMAT allowed for tumor dose escalation.

We present a comparison between full field digital mammography and synthetic mammography, performed on several mammography systems from four different manufacturers. The analysis is carried out on both the digital and synthetic images of two commercially available mammography phantoms, and focuses on a set of objective metrics that encode the geometrical appearance of imaging features of diagnostic interest. In particular, we measured sizes and contrasts of several clusters of microcalcification specks, shapes and contrasts of circular masses, and the power spectrum of background regions mimicking the heterogeneous texture of the breast parenchyma. Despite the potential issues of tomosynthesis in terms of image blurring, the synthetic images do not highlight any globally significant differences in the rendering of the details of interest, when compared to the original digital mammograms: relative contrasts are generally preserved, as well as the geometry of broad structures. We conclude that, as far as the considered objective metrics are concerned, the image quality of synthetic mammography does not exhibit significant differences with respect to the one of full field digital mammography, for all the considered systems.

Quantitative analysis of biomedical images, referred to as radiomics, is emerging as a promising approach to facilitate clinical decisions and improve patient stratification. The typical radiomic workflow includes image acquisition, segmentation, feature extraction, and analysis of high-dimensional datasets. While procedures for primary radiomic analyses have been established in recent years, processing the resulting radiomic datasets remains a challenge due to the lack of specific tools for doing so. Here we present RadAR (Radiomics Analysis with R), a new software to perform comprehensive analysis of radiomic features. RadAR allows users to process radiomic datasets in their entirety, from data import to feature processing and visualization, and implements multiple statistical methods for analysis of these data. We used RadAR to analyze the radiomic profiles of more than 850 patients with cancer from publicly available datasets and showed that it was able to recapitulate expected results. These results demonstrate RadAR as a reliable and valuable tool for the radiomics community.Significance: A new computational tool performs comprehensive analysis of high-dimensional radiomic datasets, recapitulating expected results in the analysis of radiomic profiles of >850 patients with cancer from independent datasets.

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