Leo Cancer Care is a trans-Atlantic company that’s pioneering the development of upright radiotherapy – a totally new take on radiation delivery in which the patient is treated in an upright position and rotated in front of a fixed treatment beam. At this week’s ASTRO 2025 meeting in San Francisco, the company introduced its first upright photon therapy system, named Grace, to an enthusiastic crowd in the ASTRO exhibit hall.

Upright treatments have a host of potential advantages over conventional radiotherapy, where patients typically lie on their back during treatment. Studies have shown that the more natural upright posture could deliver more consistent anatomical positioning and organ stability, as well as enabling more comfortable treatment positions, with patients who have experienced the technology reporting improved comfort and greater patient–therapist connection.

A fixed treatment beam also simplifies system design, reduces space and shielding requirements, and lowers infrastructure costs. And for proton therapy in particular, removing the need for a bulky and expensive gantry could help increase global access to advanced cancer treatments. Indeed, a partnership between Leo Cancer Care and Mevion Medical Systems led to the development of the MEVION S250-FIT, an ultracompact upright proton therapy system that fits inside a linac vault.

Moving on from Leo Cancer Care’s initial focus on proton therapy, the new Grace system will deliver conventional X-ray radiation therapy with patients positioned upright. Grace – named after American computer scientist and US Navy rear admiral Grace Hopper – comprises an upright patient positioning system (with six degrees of freedom and 360° continuous rotation) in front of a stationary 6 MV photon linac.

“Our future innovation, Grace, will take a proven technology, photon therapy, and rethink the way it can be delivered,” Sophie Towe, the company’s director of marketing, tells Physics World. “Upright treatment isn’t just about comfort; it’s about consistency, stability and ultimately accessibility. By integrating advanced CT imaging, faster beam delivery and a more natural patient position, we are opening the door to more adaptive and affordable care. Our goal is to show that innovation in radiotherapy doesn’t always mean bigger or more complex; it can mean smarter and more human.”

The system features a fan-beam CT scanner at the treatment isocentre, enabling planning-quality imaging throughout the entire treatment workflow. It also incorporates a large, ultrafast multileaf collimator that, in combination with the stationary photon beam delivery system, is designed to optimize dose conformity and treatment efficiency.

“Leo Cancer Care is already known for delivering upright particle therapy technology, and over the past few years we have seen a real paradigm shift as a result,” says co-founder and CEO Stephen Towe in a press statement. “Grace represents a return to our original company focus of delivering more cost-effective photon treatments to a global stage without sacrificing on treatment quality. Our technology has always been bold, but we are pioneering with purpose and that purpose is to put the patient truly back at the centre of their treatments.”

The company will install the first pre-commercial Grace systems at healthcare institutions within the Upright Photon Alliance research collaboration, which include Centre Léon Bérard, Cone Health, IHH Healthcare, Mayo Clinic and OncoRay.

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Brain metastases – cancerous lesions that have spread from elsewhere in the body – are increasingly treated using stereotactic radiotherapy (SRS), a precision technique that targets each individual lesion with a high dose of radiation. Compared with whole-brain irradiation, SRS may lead to higher local control and increased cognitive sparing, as well as a shorter overall treatment duration. But to target and treat multiple brain metastases, each lesion must first be detected on an MRI scan and accurately delineated. And this can be a complex and time-consuming task.

“There are two challenges that we face in the clinic,” explains Evrim Tezcanli, professor of radiation oncology at Acibadem Atasehir Hospital in Turkey. “First, we want to treat all the lesions. But very small lesions, particularly those under 0.1 cc, can easily be missed by untrained eyes. Larger metastases, meanwhile, are more challenging to contour – you want to cover the whole lesion without missing a pixel, but don’t want to spill radiation over into the brain tissue. It’s time-consuming work, especially if there are multiple lesions.”

To address these challenges, Siemens Healthineers has developed an AI-powered software tool that automates the contouring of brain metastases. The software – integrated into the company’s syngo.via RT Image Suite and AI-Rad Companion Organs RT packages – employs advanced deep-learning algorithms to rapidly analyse a patient’s MR images and contour and label metastatic lesions. Alongside, it delineates key organs at risk, such as the brainstem and optic structures.

“One of the main strengths of this software is that it reduces the manual workloads really well,” says Tezcanli.

Meeting clinical standards

To evaluate the accuracy and time efficiency of the new software tool, Tezcanli and her team compared AI-based delineation with the performance of two experienced radiation oncologists. The study included data from 10 patients with between three and 17 brain metastases. The radiation oncologists manually contoured all lesions (82 in total) based on patients’ contrast-enhanced MRI scans; the same images were also processed by the AI software to automatically contour the metastases.

Tezcanli reports that the software performed remarkably well. “One of the most significant findings was that the manual contours and the AI-generated contours showed strong agreement, especially for lesions larger than 0.1 cc. In terms of geometric similarity, the AI-generated boundaries were well within our clinically acceptable levels,” she says.

Comparing the manual and AI-generated contours revealed a medium Dice similarity coefficient of 0.83, increasing to 0.91 when excluding very small lesions, and a median Hausdorff distance (the maximum distance between the two contours) of 0.3 mm.

AI will definitely have a place because of the time savings and accuracy it delivers

Evrim Tezcanli

To quantify the overall time efficiency, the researchers timed the contouring process for the radiation oncologists and the AI tool. They also measured the time taken for expert review of the AI-generated results, in which a radiation oncologist checks the contours and performs any necessary adjustments before they are approved for treatment planning.

The AI software completed the contouring for each patient in just one to two minutes, reducing the workload by an average of 75%, and in some cases saving over 30 minutes per patient. “We still needed to review the AI contours, but the correction time was only three to four minutes,” says Tezcanli, emphasizing that expert review remains essential when using AI. “One case required nine minutes, but even with that patient we had a time saving of 75%.”

As well as saving time for the oncology staff, AI-based contouring has a lot to offer from the patient’s perspective. Spending less time on demanding manual contouring frees up the physician to spend more time with the patient.

Lesion detection

For their study, the researchers analysed post-contrast T1 MPRAGE sequences recorded using a 3 Tesla MRI scanner. To maximize lesion enhancement, they acquired images several minutes after contrast injection, though Tezcanli notes that this timing may vary between treatment centres. They also used image slices of 1 mm or less. “This is a very precise treatment and we want to make sure everything is accurate,” she adds.

The study deliberately included patients with varying numbers of different sized metastases, to assess the algorithms under diverse clinical scenarios. In terms of lesion detection, the software exhibited an overall sensitivity of 94% – finding 77 of the 82 metastases. The five missed lesions were extremely small, 0.01 to 0.03 cc, a volume that’s challenging even for physicians to detect. The software did, however, find three additional lesions that were not originally identified and which were later confirmed as brain metastases.

The false positive rate was 8.5%, with the software mistakenly identifying seven vascular structures as metastases. “Because the algorithms work with contrast enhancement, any vascular enhancements that mimic the tumour can be mistaken,” says Tezcanli. “Here we needed to use a dedicated MRI sequence to define whether it was a metastasis or not. That’s just one thing to be cautious about. Other than that, we were very satisfied with the software’s ability to detect small lesions and find ones that we hadn’t detected.”

Automation with HyperArc

The contours generated by the AI software are exported in DICOM RT Struct format, enabling direct transfer into the treatment planning system. At Acibadem Atasehir Hospital, this next step is performed using HyperArc, a radiosurgery-specific software module within the Eclipse treatment planning infrastructure. HyperArc performs automated treatment planning and delivery, enabling fast and efficient SRS on the Varian TrueBeam and Edge linacs.

“HyperArc has proven to be highly effective, even when treating patients with multiple brain metastases,” says Burcin Ispir, a medical physicist working alongside Tezcanli. “One of its biggest powers is its ability to perform single isocentre, automated planning for multiple targets, which significantly reduces planning time while maintaining excellent plan quality. In our experience, HyperArc-generated plans offer high conformity and steep dose gradients, which are critical for sparing normal brain tissue.”

Unlike conventional radiotherapy where homogeneity is desirable, SRS plans intentionally allow controlled heterogeneity within the target volume to improve sparing of normal tissue. HyperArc also offers automation of the beam geometry, including collimator and couch angles, ensuring consistent, fast and highly reproducible plans “For selected cases, we have found this enables a same-day workflow where contouring, planning and treatment can all be completed within a single day,” Ispir explains.

The automation in AI contouring and HyperArc planning speeds up the treatment planning process, and when compared to traditional workflows, potentially allows patients to commence radiation therapy treatments earlier. The ability to commence treatment as soon as possible after the MRI scan is imperative when treating brain metastases. Most patients will also be receiving systemic therapies, which need to be delivered on schedule. But perhaps more importantly, the high spatial precision of SRS makes the technique sensitive to even small anatomical changes within lesions. If the delay between MR imaging and radiotherapy treatment is too long, any changes occurring during that time could decrease targeting accuracy.

“We are in an era where we are using the technology to have even same-day treatments,” says Tezcanli. “We have rapid contouring with AI, a quick review of a few minutes by the expert radiation oncologist, treatment planning with HyperArc, and then a few hours later the patient is treated. This is where the technology is taking us.”

Look to the future

Continuing improvements in cancer treatment techniques mean that patients are living longer, but this also increases the likelihood of metastases developing. In addition, higher quality MRI scans and enhanced imaging protocols lead to more metastases being detected. These factors combine to increase the workload on centres treating multiple metastases with SRS.

“I think we will be treating brain metastasis more and more,” says Tezcanli. “And I think radiosurgery will be the main treatment modality in the future. AI will definitely have a place because of the time savings and accuracy it delivers. And this is only the first version of the software; I’m sure it can be improved to find even smaller lesions or differentiate vascular structures.”

Following the initial software evaluation, the team has not yet fully integrated it into their clinical routine, but Tezcanli tells Physics World that they would be happy to use the software in every one of their brain metastases treatments. “I think we will be using it routinely in the future in all of our clinical cases,” she says.

• The statements by customers of Siemens Healthineers described herein are based on results that were achieved in the customer’s unique setting. Because there is no “typical” hospital or laboratory and many variables exist (e.g., hospital size, samples mix, case mix, level of IT and/or automation adoption) there can be no guarantee that other customers will achieve the same results.
  The products/features mentioned herein are not commercially available in all countries. Their future availability cannot be guaranteed.
  Autocontouring results are generated by Siemens. The displayed renderings are created with software that is not commercially available.

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Radiotherapy is a precision cancer therapy that employs personalized treatment plans to target radiation to tumours with high accuracy. Such plans are usually created from high-resolution CT scans of the patient. But interest is growing in an alternative approach: MR simulation, in which MR images are used to generate the treatment plans – for delivery on conventional linac systems as well as the increasingly prevalent MR-guided radiotherapy systems.

One site that has transitioned to this approach is the Institut Jules Bordet in Belgium, which in 2021 acquired both an Elekta Unity MR-Linac and a Siemens MAGNETOM Aera MR-Simulator. “It was a long-term objective for our clinic to have an MR-only workflow,” says Akos Gulyban, a medical physicist at Institut Jules Bordet. “When we moved to a new campus, we decided to purchase the MR-Linac. Then we thought that if we are getting into the MR world for treatment adaptation, we also need to step up in terms of simulation.”

The move to MR simulation delivers many clinical benefits, with MR images providing the detailed anatomical information required to delineate targets and organs-at-risk with the highest precision. But it also creates new challenges for the physicists, particularly when it comes to quality assurance (QA) of MR-based systems. “The biggest concern is geometric distortion,” Gulyban explains. “If there is no distortion correction, then the usability of the machine or the sequence is very limited.”

Addressing distortion

While the magnetic field gradient is theoretically linear, and MRI is indeed extremely accurate at the imaging isocentre, moving away from the isocentre increases distortion. Images of regions 30 or 40 cm away from the isocentre – a reasonable distance for a classical linac – can differ from reality by 15 to 20 mm, says Gulyban. Thankfully, 3D correction algorithms can reduce this discrepancy down to just a couple of millimetres. But such corrections first require an accurate way to measure the distortion.

To address this task, the team at Institut Jules Bordet employ a geometric distortion phantom –the QUASAR MRID^3D Geometric Distortion Analysis System from IBA Dosimetry. Gulyban explains that the MRID^3D was chosen following discussions with experienced users, and that key selling points included the phantom’s automated software and its ability to efficiently store results for long-term traceability.

“My concern was how much time we spend cross-processing, generating reports or evaluating results,” he says. “This software is fully automated, making it much easier to perform the evaluation and less dependent on the operator.”

Gulyban adds that the team was looking for a vendor-independent solution. “I think it is a good approach to use the tools provided [by the vendor] but now we have a way to measure the same thing using a different approach. Since our new campus has a mixture of Siemens MRs and the MR-Linac, this phantom provides a vendor-independent bridge between the two worlds.”

For quality control of the MR-Simulator, the team perform distortion measurements every three months, as well as after system interventions such as shimming and following any problems arising during other routine QA procedures. “We should not consider tests as individual islands in the QA process,” says Gulyban. “For instance, the ACR image quality phantom, which is used for more frequent evaluation, also partly assesses distortion. If we see that failing, I would directly trigger measurements with the more appropriate geometric distortion phantom.”

A lightweight option

To perform MR simulation, the images used for treatment planning must encompass both the target volume and the surrounding region, to ensure accurate delineation of the tumour and nearby organs-at-risk. This requires a large field-of-view (FOV) scan – plus geometric distortion QA that covers the same large FOV.

“You’re using this image to delineate the target and also to spare the organs-at-risk, so the image must reflect reality,” explains Kawtar Lakrad, medical physicist and clinical application specialist at IBA Dosimetry. “You don’t want that image to be twisted or the target volume to appear smaller or bigger than it actually is. You want to make sure that all geometric qualities of the image align with what’s real.”

Typically, geometric distortion phantoms are grid-like, with control points spaced every 0.5 or 1 cm. The entire volume is imaged in the MR scanner and the locations of control points seen in the image compared with their actual positions. “If we apply this to a large FOV phantom, which for MRI will be filled with either water or oil, it’s going to be a very large grid and it’s going to be heavy, 40 or 50 kg,” says Lakrad.

To overcome this obstacle, IBA researchers used innovative harmonic analysis algorithms to design a lightweight geometric distortion phantom with submillimetre accuracy and a large (35 x 30 cm) FOV: the MRID^3D. The phantom comprises two concentric hollow acrylic cylinders, the only liquid being a prefilled mineral oil layer between the two shells, reducing its weight to just 21 kg.

“The idea behind the phantom was very smart because it relies on a mathematical tool,” explains Lakrad. “There is a Fourier transform for the linear signal, which is used for standard grids. But there are also spherical harmonics – and this is what’s used in the MRID^3D. The control points are all on the cylinder surface, plus one in the isocentre, creating a virtual grid that measures 3D geometric distortion.” She adds that the MRID^3D can also differentiate distortion due to the main magnetic field from gradient non-linearity distortion.

Moving into the MR world

Gulyban and his team at Institut Jules Bordet first used MR simulation for pelvic treatments, particularly prostate cancer, he tells Physics World. This was followed by abdominal tumours, such as pancreatic and liver cancers (where many patients were being treated on the MR-Linac) and more recently, cranial and head-and-neck irradiations.

Gulyban points out that the introduction of the MR-Simulator was eased by the team’s experience with the MR-Linac, which helped them “step into the MR world”. Here also, the MRID^3D phantom is used to quantify geometric distortion, both for initial commissioning and continuous QA of the MR-Linac.

“It’s like a consistency check,” he explains. “We have certain manufacturer-defined conditions that we need to meet for the MR-Linac – for instance, that distortion within a 40 mm diameter should be less than 1 mm. To ensure that these are met in a consistent fashion, we repeat the measurements with the manufacturer’s phantom and with the MRID^3D. This gives us extra peace of mind that the machine is performing under the correct conditions.”

For other cancer centres looking to integrate MR into their radiotherapy clinics, Gulyban has some key points of advice. These include starting with MR-guided radiotherapy and then adding MR simulation, identifying a suitable pathology to treat first and gain familiarity, and attending relevant courses or congresses for inspiration.

“The biggest change is actually a change in culture because you have an active MRI in the radiotherapy department,” he notes. “We are used to the radioprotection aspects of radiotherapy, wearing a dosimeter and observing radiation protection principles. MRI is even less forgiving – every possible thing that could go wrong you have to eliminate. Closing all the doors and emptying your pockets must become a reflex habit. You have to prepare mentally for that.”

“When you’re used to CT-based machines, moving to an MR workflow can be a little bit new,” adds Lakrad. “Most physicists are already familiar with the MR concept, but when it comes to the QA process, that’s the most challenging part. Some people would just repeat what’s done in radiology – but the use case is different. In radiotherapy, you have to delineate the target and surrounding volumes exactly. You’re going to be delivering dose, which means the tolerance between diagnostic and radiation therapy is different. That’s the biggest challenge.”

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