PrecisionPro Fusion: Clinical Validation of an Automated MRI-CT Fusion System for Prostate Radiotherapy Planning
Do, D.; Conlin, C.; Baxter, M.; Christodouleas, J.; Dess, R.; Dragojevic, I.; Harisinghani, M.; Kamran, S.; Moiseenko, V.; Nagar, H.; Nakrour, N.; Nguyen, L.; Rupareliya, R.; Seyedin, S.; Song, Y.; Dale, A. M.; Seibert, T. M.
Show abstract
BackgroundAccurate image registration between magnetic resonance imaging (MRI) and computed tomography (CT) is required for precise radiation therapy of prostate cancer. Manual registration methods have been identified as a significant barrier to the implementation of advanced treatment techniques such as focal boost therapy. PurposeTo evaluate the accuracy of PrecisionPro Fusion--an automated MRI-CT registration pipeline-- compared to manual registration by experienced radiation oncologists. Materials and MethodsWe conducted a prospective, multi-institutional validation study involving six genitourinary radiation oncologists from three institutions who performed registrations on 20 patient cases. The study used a two-round design with a one-month washout period, where physicians conducted MRI-CT registrations with and without PrecisionPro Fusion. We compared PrecisionPro Fusion to practical accuracy limits of manual registration, defined by intra-physician variability (distance between a physicians two MRI-CT registrations of the same patient case) and inter-physician variability (maximum distance between a physicians registration and the physician consensus-- average of all physicians registrations of that patient case). Physician participants reported on the PrecisionPro Fusion user experience using a System Usability Scale questionnaire. ResultsIntra-physician variability for manual subspecialist registrations was median 2.9 mm (IQR: 1.9, 5.4); inter-physician variability was median: 4.7 mm (4.3, 5.7). PrecisionPro Fusion registrations had median distance from the physician consensus of 1.3 mm (IQR: 0.9, 2.7). The system received high usability scores (median 81; IQR: 74, 88). ConclusionPrecisionPro Fusion provides prostate MRI-CT registration accuracy comparable to manual physician registration. Automated MRI-CT registration could enable faster delineation of structures visible on MRI, including the urethra and intraprostatic tumors.
Matching journals
The top 2 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Clinical Impact of Contouring Variability for Prostate Cancer Tumor Boost 96%
- Comprehensive Quantitative Evaluation of Inter-observer Delineation Performance of MR-guided Delineation of Oropharyngeal Gross Tumor Volumes and High-risk Clinical Target Therapy: An R-IDEAL Stage 0 Prospective Study 96%
- Use of focal radiotherapy boost for prostate cancer and perceived barriers toward its implementation: a survey 94%
Similar papers in this journal
- Development of a High-Performance Multiparametric MRI Oropharyngeal Primary Tumor Auto-Segmentation Deep Learning Model and Investigation of Input Channel Effects: Results from a Prospective Imaging Registry 95%
- Auto-Detection and Segmentation of Involved Lymph Nodes in HPV-Associated Oropharyngeal Cancer Using a Convolutional Deep Learning Neural Network 94%
- Morphological changes after cranial fractionated photon radiotherapy: localized loss of white matter and grey matter volume with increasing dose 93%
Similar papers in this journal
- Quality Assurance Assessment of Intra-Acquisition Diffusion-Weighted and T2-Weighted Magnetic Resonance Imaging Registration and Contour Propagation for Head and Neck Cancer Radiotherapy 96%
- Evaluation of inverse treatment planning for Gamma Knife radiosurgery using fMRI brain activation maps as organs at risk 94%
- PSMA-Hornet: fully-automated, multi-target segmentation of healthy organs in PSMA PET/CT images 93%
Similar papers in this journal
- Large-scale crowdsourced radiotherapy segmentations across a variety of cancer anatomic sites: Interobserver expert/non-expert and multi-observer composite tumor and normal tissue delineation annotations from a prospective educational challenge 96%
- Segmentation of vestibular schwannoma from MRI — An open annotated dataset and baseline algorithm 96%
- Weekly Intra-Treatment Diffusion Weighted Imaging Dataset for Head and Neck Cancer Patients Undergoing MR-linac Treatment 94%
Similar papers in this journal
- Artificial Intelligence Uncertainty Quantification in Radiotherapy Applications - A Scoping Review 95%
- LITE SABR M1: a Phase I Trial of Lattice Stereotactic Body Radiotherapy for Large Tumors 92%
- Urethra contours on MRI: multidisciplinary consensus educational atlas and reference standard for artificial intelligence benchmarking 92%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.