Deep Domain Adversarial Learning for Species-Agnostic Classification of Histologic Subtypes of Osteosarcoma

Bone Matrix-forming Tumors

Bone matrix-forming tumors are a group of neoplasms that exhibit differentiation toward any stage of osteoblast development. Their clinicopathologic features can resemble one another, yet their clinical management may vary significantly. Therefore, appropriate treatment requires accurate diagnosis, which can be challenging, especially with limited biopsy specimens. Recently, the driver genetic alterations underlying these neoplasms have been discovered, and their protein products can be targeted for diagnosis and therapy. Herein, we summarize the recent advances in our understanding of bone matrix-forming tumors and emphasize the integration of molecular genetics into their conventional clinicopathologic evaluation.

Updates in Osteosarcoma

Clinical care of osteosarcoma (OSA) in dogs has seen little change during the past 2 decades, relying on amputation and platinum-based chemotherapy for pain control and survival. Recent advancements offer hope for improved outcomes. Genomic research reveals shared genetic abnormalities between canine and human OSA. Multidimensional imaging provides valuable staging and prognostic information. Limb-sparing approaches including stereotactic body radiation therapy are routine. Ablative therapies such as microwave ablation and histotripsy show promise. Immunotherapy including cell therapy and immune checkpoint inhibition are available. Radiopharmaceuticals are tuned to target OSA cells directly. These innovations may enhance treatment and prognosis for dogs with OSA.

Review of Molecular Technologies for Investigating Canine Cancer

Genetic molecular testing is starting to gain traction as part of standard clinical practice for dogs with cancer due to its multi-faceted benefits, such as potentially being able to provide diagnostic, prognostic and/or therapeutic information. However, the benefits and ultimate success of genomic analysis in the clinical setting are reliant on the robustness of the tools used to generate the results, which continually expand as new technologies are developed. To this end, we review the different materials from which tumour cells, DNA, RNA and the relevant proteins can be isolated and what methods are available for interrogating their molecular profile, including analysis of the genetic alterations (both somatic and germline), transcriptional changes and epigenetic modifications (including DNA methylation/acetylation and microRNAs). We also look to the future and the tools that are currently being developed, such as using artificial intelligence (AI) to identify genetic mutations from histomorphological criteria. In summary, we find that the molecular genetic characterisation of canine neoplasms has made a promising start. As we understand more of the genetics underlying these tumours and more targeted therapies become available, it will no doubt become a mainstay in the delivery of precision veterinary care to dogs with cancer.

Artificial intelligence-based radiomics in bone tumors: Technical advances and clinical application

Radiomics is the extraction of predefined mathematic features from medical images for predicting variables of clinical interest. Recent research has demonstrated that radiomics can be processed by artificial intelligence algorithms to reveal complex patterns and trends for diagnosis, and prediction of prognosis and response to treatment modalities in various types of cancer. Artificial intelligence tools can utilize radiological images to solve next-generation issues in clinical decision making. Bone tumors can be classified as primary and secondary (metastatic) tumors. Osteosarcoma, Ewing sarcoma, and chondrosarcoma are the dominating primary tumors of bone. The development of bone tumor model systems and relevant research, and the assessment of novel treatment methods are ongoing to improve clinical outcomes, notably for patients with metastases. Artificial intelligence and radiomics have been utilized in almost full spectrum of clinical care of bone tumors. Radiomics models have achieved excellent performance in the diagnosis and grading of bone tumors. Furthermore, the models enable to predict overall survival, metastases, and recurrence. Radiomics features have exhibited promise in assisting therapeutic planning and evaluation, especially neoadjuvant chemotherapy. This review provides an overview of the evolution and opportunities for artificial intelligence in imaging, with a focus on hand-crafted features and deep learning-based radiomics approaches. We summarize the current application of artificial intelligence-based radiomics both in primary and metastatic bone tumors, and discuss the limitations and future opportunities of artificial intelligence-based radiomics in this field. In the era of personalized medicine, our in-depth understanding of emerging artificial intelligence-based radiomics approaches will bring innovative solutions to bone tumors and achieve clinical application.