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Creating high quality treatment plans is crucial for a successfulradiotherapy treatment. However, it demands substantial effort and specialtraining for dosimetrists. Existing automated treatment planning systemstypically require either an explicit prioritization of planning objectives,human-assigned objective weights, large amounts of historic plans to train anartificial intelligence or long planning times. Many of the existingauto-planning tools are difficult to extend to new planning goals.  A new spot weight optimisation algorithm, called JulianA, was developed. Thealgorithm minimises a scalar loss function that is built only based on theprescribed dose to the tumour and organs at risk (OARs), but does not rely onhistoric plans. The objective weights in the loss function have default valuesthat do not need to be changed for the patients in our dataset. The system is aversatile tool for researchers and clinicians without specialised programmingskills. Extending it is as easy as adding an additional term to the lossfunction. JulianA was validated on a dataset of 19 patients with intra- andextracerebral neoplasms within the cranial region that had been treated at ourinstitute. For each patient, a reference plan which was delivered to the cancerpatient, was exported from our treatment database. Then JulianA created theauto plan using the same beam arrangement. The reference and auto plans weregiven to a blinded independent reviewer who assessed the acceptability of eachplan, ranked the plans and assigned the human-/machine-made labels.  The auto plans were considered acceptable in 16 out of 19 patients and atleast as good as the reference plan for 11 patients. Whether a plan was craftedby a dosimetrist or JulianA was only recognised for 9 cases. The median timefor the spot weight optimisation is approx. 2 min (range: 0.5 min - 7 min).

JulianA: An automatic treatment planning platform for intensity-modulated proton therapy and its application to intra- and extracerebral neoplasms