Time and again, we see 3D printing used in the medical field to reduce procedure times, save money, and make surgery safer. Using a combination of magnetic resonance imaging (MRI) and CT scans, a group of researchers from Hospital Cruz Roja in Córdoba, Spain, used a hybrid 3D printing technique to create lung cancer models in an effort to reduce surgery time for chest tumors.
In an award-winning poster presentation for the 2018 European Congress of Radiation (ECR), the researchers documented exactly how to make the hybrid models, and explained the advantages they provide in guiding tumor removal.
“[Hybrid] 3D printing constitutes a novel and potentially useful technique available for treatment planning and learning improvement. Although its impact is still to be proved, this procedure could improve surgery planning, especially in complex operations,” Dr. Jordi Broncano, one of the hospital presenters, wrote.
According to Dr. Broncano and his colleagues, numerous studies have been completed that provide evidence of the immense value that 3D printed models present for implantation of devices and prosthetics, anatomic chest studies, and surgical planning and guiding of chest procedures. Typically, the models are 3D representations of MRIs or CT scans taken during chest evaluations.
CT scans are commonly performed when a patient initially presents with lung cancer, because the anatomic detail of the lungs is so great in this method. However, rather than using CT scans for staging lung cancer and detecting metastases, the researchers said that clinicians normally favor MR images.
This is the route that the hospital research team went, but instead of choosing one or the other, they went with both types of imaging to take advantage of the strengths offered by each. After acquiring chest CT scans and functional MRIs of several lung cancer patients, they fused them together to make a hybrid 3D printed model of a tumor-ridden lung.
First, the team acquired contrast-enhanced CT scans with a small field of view, which would help “increase the spatial resolution of the scans.” An iterative reconstruction algorithm on the CT scans served to reduce image noise. Then, to delineate distinct scan areas, they followed vendor-based, semi-automatic segmentation protocols.
Collected MR images of the patients’ lungs were then merged with the corresponding CT scans through the IntelliSpace Portal advanced visualization platform from Philips Healthcare, in order to fuse them all into a single STL file for hybrid 3D printing. In order to properly align the CT scans with high b-value diffusion-weighted MR images, the team had to use a rigid registration algorithm – this helped match field-of-view for datasets from both forms of imaging, and allowed them to manually fix any positioning errors in advance.
Finally, the researchers modified the STL file using open source MeshLab software, and 3D printed the hybrid models with a ProJet MJP 5600 from 3D Systems. This allowed the team to represent the whole tumor, node, and metastasis staging of a single tumor in a 3D printed model.
Dr. Broncano and the rest of the hospital team completed a case study evaluating how much a patient-specific, hybrid 3D printed model of a lung and its tumors would actually help with guiding surgeons in the removal of the cancer. Spoiler alert – they were absolutely able to lower the amount of time the surgery took to remove the cancer by using these hybrid models intraoperatively.
After using the hybrid 3D printed models as a guide during five partial lobectomies, the team compared their results with the outcomes of 10 similar surgeries where the surgeons did not have the use of models.
The team concluded, “Despite the limited statistical power, it seems that 3D printing may have a potential and beneficial impact in the surgical treatment of lung cancer.”
By applying 3D printing technology to this type of surgery, intraoperative time was reduced, on average, by 30.6 minutes, when compared with the surgeries that did not use 3D printed models. In addition, intubation time was reduced by 30.5 minutes (p < 0.05) and recovery time in the hospital was lowered by 1.9 days.
Dr. Broncano noted, “We are registering short- and midterm follow-up [of patient postoperative recovery] at one month and three months and increasing the power of our cohort to obtain better results.”
However, while the team did not report a “statistically significant difference” between the amount of time patients spent in intensive care and their spirometric parameters with either of the surgical techniques, the potential benefits shown from this project have encouraged them to continue the study, and expand the population of it. This time, they will be evaluating any post-treatment effects that come from the intraoperative use of 3D printed models.
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