PSMA PET Imaging and Therapy in Adenoid Cystic Carcinoma and Other Salivary Gland Cancers: A Systematic Review

Adenoid cystic carcinoma (ACC) and other salivary gland cancers (SGCs) are rare tumors where application of prostate specific membrane antigen (PSMA) positron emission tomography (PET) and PSMA radioligand therapy have yet to be studied extensively. This review explores the role of PSMA PET imaging and therapy as a theranostic tool for ACC and other SGCs based on current literature. A comprehensive literature search on PubMed and Embase was performed. All relevant studies containing information on PSMA PET imaging in ACC and SGC were included. Ten studies (one prospective, three retrospective, five case reports and one review paper) were included. For ACC, the mean maximum standardized uptake value (SUVmax) for local recurrence and distant metastases ranged from 2.41 to 13.8 and 2.04 to 14.9, respectively. In SGC, the meanSUVmax ranged from 1.2–12.50. Most studies observed PSMA expression positivity on immunohistochemistry (IHC) when there was PSMA PET uptake. PSMA PET was able to detect lesions not detected on standard imaging. Despite the small number of studies and wide intra-patient and inter-tumor variation of PSMA uptake in ACC and SGC, 68Gallium (68Ga)-PSMA PET has promising prospects as a diagnostic and radioligand therapeutic option. Further studies to answer the various theranostics considerations are required to guide its use in the real-world setting

Generative adversarial networks in ophthalmology: what are these and how can they be used?

PURPOSE OF REVIEW: The development of deep learning (DL) systems requires a large amount of data, which may be limited by costs, protection of patient information and low prevalence of some conditions. Recent developments in artificial intelligence techniques have provided an innovative alternative to this challenge via the synthesis of biomedical images within a DL framework known as generative adversarial networks (GANs). This paper aims to introduce how GANs can be deployed for image synthesis in ophthalmology and to discuss the potential applications of GANs-produced images. RECENT FINDINGS: Image synthesis is the most relevant function of GANs to the medical field, and it has been widely used for generating 'new' medical images of various modalities. In ophthalmology, GANs have mainly been utilized for augmenting classification and predictive tasks, by synthesizing fundus images and optical coherence tomography images with and without pathologies such as age-related macular degeneration and diabetic retinopathy. Despite their ability to generate high-resolution images, the development of GANs remains data intensive, and there is a lack of consensus on how best to evaluate the outputs produced by GANs. SUMMARY: Although the problem of artificial biomedical data generation is of great interest, image synthesis by GANs represents an innovation with yet unclear relevance for ophthalmology

Generative Artificial Intelligence Through ChatGPT and Other Large Language Models in Ophthalmology: Clinical Applications and Challenges

The rapid progress of large language models (LLMs) driving generative artificial intelligence applications heralds the potential of opportunities in health care. We conducted a review up to April 2023 on Google Scholar, Embase, MEDLINE, and Scopus using the following terms: “large language models,” “generative artificial intelligence,” “ophthalmology,” “ChatGPT,” and “eye,” based on relevance to this review. From a clinical viewpoint specific to ophthalmologists, we explore from the different stakeholders’ perspectives—including patients, physicians, and policymakers—the potential LLM applications in education, research, and clinical domains specific to ophthalmology. We also highlight the foreseeable challenges of LLM implementation into clinical practice, including the concerns of accuracy, interpretability, perpetuating bias, and data security. As LLMs continue to mature, it is essential for stakeholders to jointly establish standards for best practices to safeguard patient safety. Financial Disclosure(s): Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article

Artificial intelligence and deep learning in ophthalmology

Artificial intelligence (AI) based on deep learning (DL) has sparked tremendous global interest in recent years. DL has been widely adopted in image recognition, speech recognition and natural language processing, but is only beginning to impact on healthcare. In ophthalmology, DL has been applied to fundus photographs, optical coherence tomography and visual fields, achieving robust classification performance in the detection of diabetic retinopathy and retinopathy of prematurity, the glaucoma-like disc, macular oedema and age-related macular degeneration. DL in ocular imaging may be used in conjunction with telemedicine as a possible solution to screen, diagnose and monitor major eye diseases for patients in primary care and community settings. Nonetheless, there are also potential challenges with DL application in ophthalmology, including clinical and technical challenges, explainability of the algorithm results, medicolegal issues, and physician and patient acceptance of the AI 'black-box' algorithms. DL could potentially revolutionise how ophthalmology is practised in the future. This review provides a summary of the state-of-the-art DL systems described for ophthalmic applications, potential challenges in clinical deployment and the path forward

Genomic landscape of lung adenocarcinoma in East Asians

Lung cancer is the world’s leading cause of cancer death and shows strong ancestry disparities. By sequencing and assembling a large genomic and transcriptomic dataset of lung adenocarcinoma (LUAD) in individuals of East Asian ancestry (EAS; n = 305), we found that East Asian LUADs had more stable genomes characterized by fewer mutations and fewer copy number alterations than LUADs from individuals of European ancestry. This difference is much stronger in smokers as compared to nonsmokers. Transcriptomic clustering identified a new EAS-specific LUAD subgroup with a less complex genomic profile and upregulated immune-related genes, allowing the possibility of immunotherapy-based approaches. Integrative analysis across clinical and molecular features showed the importance of molecular phenotypes in patient prognostic stratification. EAS LUADs had better prediction accuracy than those of European ancestry, potentially due to their less complex genomic architecture. This study elucidated a comprehensive genomic landscape of EAS LUADs and highlighted important ancestry differences between the two cohorts

Acceptance and Perception of Artificial Intelligence Usability in Eye Care (APPRAISE) for Ophthalmologists: A Multinational Perspective

Background: Many artificial intelligence (AI) studies have focused on development of AI models, novel techniques, and reporting guidelines. However, little is understood about clinicians' perspectives of AI applications in medical fields including ophthalmology, particularly in light of recent regulatory guidelines. The aim for this study was to evaluate the perspectives of ophthalmologists regarding AI in 4 major eye conditions: diabetic retinopathy (DR), glaucoma, age-related macular degeneration (AMD) and cataract. Methods: This was a multi-national survey of ophthalmologists between March 1st, 2020 to February 29th, 2021 disseminated via the major global ophthalmology societies. The survey was designed based on microsystem, mesosystem and macrosystem questions, and the software as a medical device (SaMD) regulatory framework chaired by the Food and Drug Administration (FDA). Factors associated with AI adoption for ophthalmology analyzed with multivariable logistic regression random forest machine learning. Results: One thousand one hundred seventy-six ophthalmologists from 70 countries participated with a response rate ranging from 78.8 to 85.8% per question. Ophthalmologists were more willing to use AI as clinical assistive tools (88.1%, n = 890/1,010) especially those with over 20 years' experience (OR 3.70, 95% CI: 1.10–12.5, p = 0.035), as compared to clinical decision support tools (78.8%, n = 796/1,010) or diagnostic tools (64.5%, n = 651). A majority of Ophthalmologists felt that AI is most relevant to DR (78.2%), followed by glaucoma (70.7%), AMD (66.8%), and cataract (51.4%) detection. Many participants were confident their roles will not be replaced (68.2%, n = 632/927), and felt COVID-19 catalyzed willingness to adopt AI (80.9%, n = 750/927). Common barriers to implementation include medical liability from errors (72.5%, n = 672/927) whereas enablers include improving access (94.5%, n = 876/927). Machine learning modeling predicted acceptance from participant demographics with moderate to high accuracy, and area under the receiver operating curves of 0.63–0.83. Conclusion: Ophthalmologists are receptive to adopting AI as assistive tools for DR, glaucoma, and AMD. Furthermore, ML is a useful method that can be applied to evaluate predictive factors on clinical qualitative questionnaires. This study outlines actionable insights for future research and facilitation interventions to drive adoption and operationalization of AI tools for Ophthalmology

Efficacy of targeted therapies for oncogene-driven lung cancer in early single-arm versus late phase randomized clinical trials: A comparative analysis.

There is an expanding number of approved targeted therapies for oncogene-driven lung cancer and many emerging therapies with promising efficacy data. Regulatory approvals are increasingly based on early phase trials (often single-arm phase II trials), in which the primary endpoint is objective response rate (ORR) or progression-free survival (PFS). Efficacy outcomes from early phase trials may not always correlate with those observed in later-phase randomized trials. In the precision oncology era with effective targeted therapies however, there are arguments for greater confidence in the efficacy outcomes from non-randomized single-arm trials. Nevertheless, there remain numerous challenges in understanding and interpreting efficacy outcomes for novel targeted therapies in trials that may have dose finding and safety as the primary objective and lack a standard-of-care control arm. Therefore, we sought to review the efficacy outcomes in early versus late phase clinical trials for approved targeted therapies in lung cancer - to better understand the interpretation of preliminary measures of clinical benefit. Nine pairs of early and late phase trials were identified, according to line of therapy for six targeted therapies in lung cancer (afatinib, ceritinib, crizotinib, dacomitinib, lorlatinib and osimertinib). Key efficacy outcomes, including ORR, PFS and overall survival (OS) were compared. Importantly, we found that in oncogene-driven lung cancer, early phase trial outcomes have historically been consistent with subsequent late phase trials. This suggests efficacy outcomes from early phase trials of targeted therapies in lung cancer may translate reliably to larger randomized trials. This has many potential implications for drug development in lung cancer, with regards to regulatory approvals and the design and conduct of clinical trials

Biological rationale and current clinical experience with anti-insulin-like growth factor 1 receptor monoclonal antibodies in treating sarcoma: twenty years from the bench to the bedside.

Two decades have elapsed since insulin-like growth factor-1 receptor (IGF-1R) signaling was initially implicated in sarcoma biology to the first clinical experience of IGF-1R blockade in sarcoma. During these 21 years, the IGF pathway and its key mediator IGF-1R have been implicated in the genesis, growth, proliferation, metastasis, and resistance to conventional treatment in several sarcoma subtypes. In addition, IGF-1R has been validated, both in vitro and in vivo, as a target for the treatment of sarcoma. Several radiologic and clinical responses to IGF-1R monoclonal antibodies have been reported in Ewing sarcoma patients enrolled in early clinical studies. Furthermore, these therapies were well tolerated, and thus far severe toxicity has been rare. The early clinical evidence of antitumor activity has supported the initiation of various phase II clinical trials in Ewing and other sarcoma subtypes, the results of which are eagerly awaited, as well as studies assessing IGF-1R monoclonal antibodies in combination with traditional cytotoxics or other targeted therapies. Despite these encouraging results, not all patients benefit from IGF-1R inhibition and consequently there is an urgent need for the identification of predictive markers of response