INVITED REVIEW-IMAGE REGISTRATION IN VETERINARY RADIATION ONCOLOGY: INDICATIONS, IMPLICATIONS, AND FUTURE ADVANCES

The role of artificial intelligence in veterinary radiation oncology

Veterinary radiation oncology regularly deploys sophisticated contouring, image registration, and treatment planning optimization software for patient care. Over the past decade, advances in computing power and the rapid development of neural networks, open-source software packages, and data science have been realized and resulted in new research and clinical applications of artificial intelligent (AI) systems in radiation oncology. These technologies differ from conventional software in their level of complexity and ability to learn from representative and local data. We provide clinical and research application examples of AI in human radiation oncology and their potential applications in veterinary medicine throughout the patient's care-path: from treatment simulation, deformable registration, auto-segmentation, automated treatment planning and plan selection, quality assurance, adaptive radiotherapy, and outcomes modeling. These technologies have the potential to offer significant time and cost savings in the veterinary setting; however, since the range of usefulness of these technologies have not been well studied nor understood, care must be taken if adopting AI technologies in clinical practice. Over the next several years, some practical and realizable applications of AI in veterinary radiation oncology include automated segmentation of normal tissues and tumor volumes, deformable registration, multi-criteria plan optimization, and adaptive radiotherapy. Keys in achieving success in adopting AI in veterinary radiation oncology include: establishing "truth-data"; data harmonization; multi-institutional data and collaborations; standardized dose reporting and taxonomy; adopting an open access philosophy, data collection and curation; open-source algorithm development; and transparent and platform-independent code development.

A Medical Image Registration Method Based on Progressive Images

Background

Medical image registration is an essential task for medical image analysis in various applications. In this work, we develop a coarse-to-fine medical image registration method based on progressive images and SURF algorithm (PI-SURF) for higher registration accuracy.

Methods

As a first step, the reference image and the floating image are fused to generate multiple progressive images. Thereafter, the floating image and progressive image are registered to get the coarse registration result based on the SURF algorithm. For further improvement, the coarse registration result and the reference image are registered to perform fine image registration. The appropriate progressive image has been investigated by experiments. The mutual information (MI), normal mutual information (NMI), normalized correlation coefficient (NCC), and mean square difference (MSD) similarity metrics are used to demonstrate the potential of the PI-SURF method.

Results

For the unimodal and multimodal registration, the PI-SURF method achieves the best results compared with the mutual information method, Demons method, Demons+B-spline method, and SURF method. The MI, NMI, and NCC of PI-SURF are improved by 15.5%, 1.31%, and 7.3%, respectively, while MSD decreased by 13.2% for the multimodal registration compared with the optimal result of the state-of-the-art methods.

Conclusions

The extensive experiments show that the proposed PI-SURF method achieves higher quality of registration.

Use of cone-beam computed tomography for advanced imaging of the equine patient

Access to volumetric imaging modalities, such as magnetic resonance imaging (MRI) and computed tomography (CT), has increased over the past decade and has revolutionised the way clinicians evaluate equine anatomy. More recent advancements have resulted in the development of multiple commercially available cone-beam CT (CBCT) scanners for equine use. CBCT scanners modify the traditional fan-shaped beam of ionising radiation into a three-dimensional pyramidal- or cone-shaped beam of radiation. This modification enables the scanner to acquire sufficient data to create diagnostic images of a region of interest after a single rotation of the gantry. The rapid acquisition of data and divergent X-ray beam causes some artifacts to be more prominent on CBCT images-as well as the unique cone-beam artifact-resulting in decreased contrast resolution. While the use of CT for evaluation of the equine musculoskeletal anatomy is not new, there is a paucity of literature and scientific studies on the capabilities of CBCT for equine imaging. CBCT units do not require a specialised table for imaging and in some cases are portable for imaging in the standing or anaesthetised patient. This review article summarises the basic physics of CT technology, including how CBCT imaging differs, and provides objective information about the strengths and limitations of this modality. Finally, potential future applications and techniques for imaging with CT which will need to be explored in order to fully consider the capabilities of CT imaging in the horse are discussed.

Estimation of planning organ at risk volumes for ocular structures in dogs undergoing three-dimensional image-guided periocular radiotherapy with rigid bite block immobilization

Planning organ at risk volume (PRV) estimates have been reported as methods for sparing organs at risk (OARs) during radiation therapy, especially for hypofractioned and/or dose‐escalated protocols. The objectives of this retrospective, analytical, observational study were to evaluate peri‐ocular OAR shifts and derive PRVs in a sample of dogs undergoing radiation therapy for periocular tumors. Inclusion criteria were as follows: dogs irradiated for periocular tumors, with 3D‐image‐guidance and at least four cone‐beam CTs (CBCTs) used for position verification, and positioning in a rigid bite block immobilization device. Peri‐ocular OARs were contoured on each CBCT and the systematic and random error of the shifts in relation to the planning CT position computed. The formula 1.3×Σ+0.5xσ was used to generate a PRV of each OAR in the dorsoventral, mediolateral, and craniocaudal axis. A total of 30 dogs were sampled, with 450 OARs contoured, and 2145 shifts assessed. The PRV expansion was qualitatively different for each organ (1‐4 mm for the dorsoventral and 1‐2 mm for the mediolateral and craniocaudal axes). Maximal PRV expansion was ≤4 mm and directional for the majority; most pronounced for corneas and retinas. Findings from the current study may help improve awareness of and minimization of radiation dose in peri‐ocular OARs for future canine patients. Because some OARs were difficult to visualize on CBCTs and/ or to delineate on the planning CT, authors recommend that PRV estimates be institution‐specific and applied with caution.

Volumetric tumor response assessment is inefficient without overt clinical benefit compared to conventional, manual veterinary response assessment in canine nasal tumors

Accurate assessment of tumor response to therapy is critical in guiding management of veterinary oncology patients and is most commonly performed using response evaluation criteria in solid tumors criteria. This process can be time consuming and have high intra- and interobserver variability. The primary aim of this serial measurements, secondary analysis study was to compare manual linear tumor response assessment to semi-automated, contoured response assessment in canine nasal tumors. The secondary objective was to determine if tumor measurements or clinical characteristics, such as stage, would correlate to progression-free interval. Three investigators evaluated paired CT scans of skulls of 22 dogs with nasal tumors obtained prior to and following radiation therapy. The automatically generated tumor volumes were not useful for canine nasal tumors in this study, characterized by poor intraobserver agreement between automatically generated contours and hand-adjusted contours. The radiologist's manual linear method of determining response evaluation criteria in solid tumors categorization and tumor volume is significantly faster (P < .0001) but significantly underestimates nasal tumor volume (P < .05) when compared to a contour-based method. Interobserver agreement was greater for volume determination using the contour-based method when compared to response evaluation criteria in solid tumors categorization utilizing the same method. However, response evaluation criteria in solid tumors categorization and percentage volume change were strongly correlated, providing validity to response evaluation criteria in solid tumors as a rapid method of tumor response assessment for canine nasal tumors. No clinical characteristics or tumor measurements were significantly associated with progression-free interval.

Imaging in Non-neurologic Oncologic Treatment Planning of the Head and Neck

Imaging is critical for the diagnosis and staging of veterinary oncology patients. Although cytology or biopsy is generally required for diagnosis, imaging characteristics inform the likelihood of a cancer diagnosis, can result in a prioritized list of differentials that guide further staging tests, and assist in the planning of surgery, radiation, and chemotherapy options. Advanced imaging, such as CT and MRI, can better define the extent of disease for surgical and radiation planning for head and neck cancer cases in particular. Additionally, new imaging technologies are continually being translated into veterinary fields, and they may provide more options for cancer patients as they become more widely available.

Veterinary oncology: Biology, big data and precision medicine

Despite significant advances in both the understanding and the treatment of cancer, the disease remains one of high mortality and morbidity causes in all species. Increases in survival times in human cancer have increased significantly in the past 25 years but most of these increases have been through small incremental changes. For some cancers, e.g. pancreatic cancer, survival times have not increased significantly in over 100 years. In veterinary oncology, there have been major shifts in the management of cancer in companion animals. Increased availability of specialist centres, coupled with changing attitudes in owners and veterinarians, have meant improvements in veterinary cancer care borne from market pressures and increased awareness and understanding. In this review the changing face of cancer biology over the past 25 years will be examined, and the barriers to clinical progress in veterinary medicine considered. Finally, an optimistic view of the future will be presented with the prospect of greater control over this devastating disease.