Artificial intelligence- image learning and its applications in neurooncology: a review.

Image learning involves using artificial intelligence (AI) to analyse radiological images. Various machine and deeplearning- based techniques have been employed to process images and extract relevant features. These can later be used to detect tumours early and predict their survival based on their grading and classification. Radiomics is now also used to predict genetic mutations and differentiate between tumour progression and treatment-related side effects. These were once completely dependent on invasive procedures like biopsy and histopathology. The use and feasibility of these techniques are now widely being explored in neurooncology to devise more accurate management plans and limit morbidity and mortality. Hence, the future of oncology lies in the exploration of AI-based image learning techniques, which can be applied to formulate management plans based on less invasive diagnostic techniques, earlier detection of tumours, and prediction of prognosis based on radiomic features. In this review, we discuss some of these applications of image learning in current medical dynamics.

Navigating the Complexities of Artificial Intelligence-Enabled Real-World Data Collection for Oncology Pharmacovigilance.

This new editorial discusses the promise and challenges of successful integration of natural language processing methods into electronic health records for timely, robust, and fair oncology pharmacovigilance.

Applications of artificial intelligence in urologic oncology.

PURPOSE: With the recent rising interest in artificial intelligence (AI) in medicine, many studies have explored the potential and usefulness of AI in urological diseases. This study aimed to comprehensively review recent applications of AI in urologic oncology. MATERIALS AND METHODS: We searched the PubMed-MEDLINE databases for articles in English on machine learning (ML) and deep learning (DL) models related to general surgery and prostate, bladder, and kidney cancer. The search terms were a combination of keywords, including both "urology" and "artificial intelligence" with one of the following: "machine learning," "deep learning," "neural network," "renal cell carcinoma," "kidney cancer," "urothelial carcinoma," "bladder cancer," "prostate cancer," and "robotic surgery." RESULTS: A total of 58 articles were included. The studies on prostate cancer were related to grade prediction, improved diagnosis, and predicting outcomes and recurrence. The studies on bladder cancer mainly used radiomics to identify aggressive tumors and predict treatment outcomes, recurrence, and survival rates. Most studies on the application of ML and DL in kidney cancer were focused on the differentiation of benign and malignant tumors as well as prediction of their grade and subtype. Most studies suggested that methods using AI may be better than or similar to existing traditional methods. CONCLUSIONS: AI technology is actively being investigated in the field of urological cancers as a tool for diagnosis, prediction of prognosis, and decision-making and is expected to be applied in additional clinical areas soon. Despite technological, legal, and ethical concerns, AI will change the landscape of urological cancer management.

Virtual consultations: the experience of oncology and palliative care healthcare professionals.

OBJECTIVES: To maintain continuity of care during the Covid-19 pandemic, virtual consultations (VC) became the mainstay of patient-healthcare practitioner interactions. The aim of this study was to explore the views of oncology and palliative care healthcare professionals (HCPs) regarding the medium of VC. METHOD: A cross sectional mixed methodology observational study of oncology and palliative care HCPs, analysed via an inductive thematic approach. This was undertaken in accordance with relevant guidelines and regulations. RESULTS: 87 surveys were completed. Three master themes were identified. Personal, professional, and familial factors including patient age, illness and VC skillset all influenced practitioner's experience of VC. Relationships and connection were highlighted by survey respondents as important influences, with a perception that VC could reduce usual relationships with patients, compared to previous face-to-face consults. There was a perceived loss in these domains with VC. Sharing bad news and having challenging conversations was seen as particularly difficult via VC. Many survey respondents emphasized that they preferred to have first time consultations face-to-face, and not virtually. Within the domain of logistical and practical implications reduced travel and increased accessibility were seen as a significant benefit of VC. The inability to examine patients and concerns regarding missing clinical signs was emphasised as a significant worry, alongside the challenges faced with occasionally failing technology. CONCLUSION: VC were felt to have a role for those patients who are already known to professionals, where there was an established relationship. VC for difficult discussions and for unstable patients were felt to be inadequate. Triaging patient suitability prior to offering VC, with emphasis on the importance of patient choice, was seen as a priority in this new era of VC.

High-throughput molecular assays for inclusion in personalised oncology trials - State-of-the-art and beyond.

In the last decades, the development of high-throughput molecular assays has revolutionised cancer diagnostics, paving the way for the concept of personalised cancer medicine. This progress has been driven by the introduction of such technologies through biomarker-driven oncology trials. In this review, strengths and limitations of various state-of-the-art sequencing technologies, including gene panel sequencing (DNA and RNA), whole-exome/whole-genome sequencing and whole-transcriptome sequencing, are explored, focusing on their ability to identify clinically relevant biomarkers with diagnostic, prognostic and/or predictive impact. This includes the need to assess complex biomarkers, for example microsatellite instability, tumour mutation burden and homologous recombination deficiency, to identify patients suitable for specific therapies, including immunotherapy. Furthermore, the crucial role of biomarker analysis and multidisciplinary molecular tumour boards in selecting patients for trial inclusion is discussed in relation to various trial concepts, including drug repurposing. Recognising that today's exploratory techniques will evolve into tomorrow's routine diagnostics and clinical study inclusion assays, the importance of emerging technologies for multimodal diagnostics, such as proteomics and in vivo drug sensitivity testing, is also discussed. In addition, key regulatory aspects and the importance of patient engagement in all phases of a clinical trial are described. Finally, we propose a set of recommendations for consideration when planning a new precision cancer medicine trial.

Agent-based approaches for biological modeling in oncology: A literature review.

CONTEXT: Computational modeling involves the use of computer simulations and models to study and understand real-world phenomena. Its application is particularly relevant in the study of potential interactions between biological elements. It is a promising approach to understand complex biological processes and predict their behavior under various conditions. METHODOLOGY: This paper is a review of the recent literature on computational modeling of biological systems. Our study focuses on the field of oncology and the use of artificial intelligence (AI) and, in particular, agent-based modeling (ABM), between 2010 and May 2023. RESULTS: Most of the articles studied focus on improving the diagnosis and understanding the behaviors of biological entities, with metaheuristic algorithms being the models most used. Several challenges are highlighted regarding increasing and structuring knowledge about biological systems, developing holistic models that capture multiple scales and levels of organization, reproducing emergent behaviors of biological systems, validating models with experimental data, improving computational performance of models and algorithms, and ensuring privacy and personal data protection are discussed.

Precision Cardio-oncology: Update on Omics-Based Diagnostic Methods.

OPINION STATEMENT: Cardio-oncology is an emerging interdisciplinary field dedicated to the early detection and treatment of adverse cardiovascular events associated with anticancer treatment, and current clinical management of anticancer-treatment-related cardiovascular toxicity (CTR-CVT) remains limited by a lack of detailed phenotypic data. However, the promise of diagnosing CTR-CVT using deep phenotyping has emerged with the development of precision medicine, particularly the use of omics-based methodologies to discover sensitive biomarkers of the disease. In the future, combining information produced by a variety of omics methodologies could expand the clinical practice of cardio-oncology. In this review, we demonstrate how omics approaches can improve our comprehension of CTR-CVT deep phenotyping, discuss the positive and negative aspects of available omics approaches for CTR-CVT diagnosis, and outline how to integrate multiple sets of omics data into individualized monitoring and treatment. This will offer a reliable technical route for lowering cardiovascular morbidity and mortality in cancer patients and survivors.

Endocrine-metabolic assessment checklist for cancer patients treated with immunotherapy: A proposal by the Italian Association of Medical Oncology (AIOM), Italian Association of Medical Diabetologists (AMD), Italian Society of Diabetology (SID), Italian Society of Endocrinology (SIE) and Italian Society of Pharmacology (SIF) multidisciplinary group.

Immunotherapy with immune checkpoint inhibitors (ICI) is increasingly employed in oncology. National and international endocrine and oncologic scientific societies have provided guidelines for the management of endocrine immune-related adverse events. However, guidelines recommendations differ according to the specific filed, particularly pertaining to recommendations for the timing of endocrine testing. In this position paper, a panel of experts of the Italian Association of Medical Oncology (AIOM), Italian Association of Medical Diabetologists (AMD), Italian Society of Diabetology (SID), Italian Society of Endocrinology (SIE), and Italian Society of Pharmacology (SIF) offers a critical multidisciplinary consensus for a clear, simple, useful, and easily applicable endocrine-metabolic assessment checklist for cancer patients on immunotherapy.

Addressing transportation barriers in oncology: existing programs and new solutions.

Transportation is an underrecognized, but modifiable barrier to accessing cancer care, especially for clinical trials. Clinicians, insurers, and health systems can screen patients for transportation needs and link them to transportation. Direct transportation services (i.e., ride-sharing, insurance-provided transportation) have high rates of patient satisfaction and visit completion. Patient financial reimbursements provide necessary funds to counteract the effects of transportation barriers, which can lead to higher trial enrollment, especially for low socioeconomic status and racially and ethnically diverse patients. Expanding transportation interventions to more cancer patients, and addressing knowledge, service, and system gaps, can help more patients access needed cancer care.

Innovation and Discovery: A 30-Year Journey in Advancing Cancer Care.

Since the inaugural issue of Current Oncology was published 30 years ago, we have witnessed significant advancements in cancer research and care [...].

Oncology's trial and error: Analysis of the FDA withdrawn accelerated approvals.

Launched in 1992, the FDA accelerated approval program grants drugs indicated in rare/life threatening diseases the ability to be marketed at a faster pace than through the traditional track. Each manufacturing company presents its drug to the FDA, and within 60 days it will determine if the drug is eligible for this path. Many drugs that were initially approved through this route, subsequently did not demonstrate their clinical benefits. With cancer being a leading cause of death, a vast majority of drugs that have been approved/withdrawn from this pathway are indicated within oncology. There are a wide variety of cancer subtypes and therapeutic target sites that these drugs have been evaluated for. Herein, is an overview of the 17 oncology drugs, spanning 22 cancer-related indications, that had been approved within the accelerated route and subsequently withdrawn.

[Comparative pathology in oncology-Best practice]. / Vergleichende Pathologie in der onkologischen Forschung.

Comparative experimental pathology is a research field at the interface of human and veterinary medicine. It is focused on the comparative study of similarities and differences between spontaneous and experimentally induced diseases in animals (animal models) compared to human diseases. The use of animal models for studying human diseases is an essential component of biomedical research. Interdisciplinary teams with species-specific expertise should collaborate wherever possible and maintain close communication. Mutual openness, cooperation, and willingness to learn form the basis for a fruitful collaboration. Research projects jointly led by or involving both animal and human pathologists make a significant contribution to high-quality biomedical research. Such approaches are promising not only in oncological research, as outlined in this article, but also in other research areas where animal models are regularly used, such as infectiology, neurology, and developmental biology.

[Personalized medicine in oncology]. / Personalisierte Medizin in der Onkologie.

Due to the considerable technological progress in molecular and genetic diagnostics as well as increasing insights into the molecular pathogenesis of diseases, there has been a fundamental paradigm shift in the past two decades from a "one-size-fits-all approach" to personalized, molecularly informed treatment strategies. Personalized medicine or precision medicine focuses on the genetic, physiological, molecular, and biochemical differences between individuals and considers their effects on the development, prevention, and treatment of diseases. As a pioneer of personalized medicine, the field of oncology is particularly noteworthy, where personalized diagnostics and treatment have led to lasting change in the treatment of cancer patients in recent years. In this article, the significant change towards personalized treatment concepts, especially in the field of personalized oncology, will be discussed and examined in more detail.

Concordance in Molecular Tumor Board Case Reviews in the ASCO TAPUR Study.

PURPOSE: With the advent of precision medicine, molecular tumor boards (MTBs) were established to interpret genomic results and guide decision making for targeted therapy in oncology patients. There are currently no universal guidelines for how MTBs should operate and thus variance can be seen depending on which MTB is reviewing the case. This study assesses the concordance of MTB recommendations when a participant case is reviewed by two different MTBs, establishes potential reasons for discordance, and advocates for the establishment of standard MTB operating guidelines. PATIENTS AND METHODS: Participants with advanced cancer, who had exhausted all standard treatment options were screened for the Targeted Agent and Profiling Utilization Registry (TAPUR) Study. Cases were submitted for MTB review if the treatment proposal was outside the protocol genomic matching rules, or if multiple treatment options were identified. Of the 306 cases submitted for review by the TAPUR MTB from 2016 to 2018, 107 were randomly selected for secondary review by a different MTB group. Recommendations from the original review were not disclosed. Concordance between MTB group recommendations was assessed. Concordance was defined as agreement between MTB reviews on the genomic alteration and study drug match proposed by the clinical site. Thematic qualitative analysis was conducted for the discordant cases to assess reasons for discordance. RESULTS: Complete or partial concordance was observed in 79% of cases (95% CI, 70 to 86; one-sided P = .25). Most discordant analyses were due to disagreements on the strength of evidence regarding efficacy of the proposed treatment (32%). CONCLUSION: When presented with identical participant cases, different MTB review groups make the same or similar treatment recommendations approximately 80% of the time.

Development and validation of impact of early integration of palliative care and oncology(IEI PCO) questionnaire: a survey for medical oncologists and nurses.

OBJECTIVES: Many associations have recently recommended early integration of oncology and palliative care for more standard cancer care and better quality of life. We aimed to create a questionnaire to assess the opinion of medical oncologists and nurses about the clinical impact of the integrated palliative care and oncology (PCO) program. METHODS: A novel semi-structured questionnaire called Impact of Early Integration of Palliative Care Oncology (IEI PCO) questionnaire was developed and tested for validity and reliability then distributed to the oncologists and nurses working in Kuwait Cancer Control Center. RESULTS: After the pilot stage, testing the final questionnaire for validity and reliability was done with satisfactory results. Finally, the complete questionnaires were 170 out of 256 (response rate 66.41%). More awareness about the available palliative care services and the new available PCO services (p-value < 0.001 for all). Most of the oncologists and nurses agreed with the currently available structure of PCO, appreciated the patients' discharge plan and continuity of care of palliative medicine, admitted less work burden, a better attitude, and higher satisfaction (p-value for all < 0.001) toward palliative care. Significant improvements in symptoms were appreciated by oncologists and nurses after the integration of palliative care (p-value for all < 0.001. Oncologists and nurses valued repeated honest communication, discussion of the goals of care, dealing more effectively with ending active treatment, and higher acceptance of patients and families of PC policy of transfer, and significant progress in the care of end-of-life symptoms (p-value for all < 0.001). CONCLUSIONS: The IEI PCO questionnaire demonstrated the psychometric criteria for content, face, and construct validity and reliability. It provides a valuable tool to assess the impact of PCO integration. The opinion of medical oncologists and nurses was significantly positive toward the early integration of PCO in Kuwait in most aspects of care. This integration led to improved symptom control, end-of-life care, communication, and planned discharge and follow-up plans. Moreover, decreases the work burden, improves attitude, higher satisfaction of the oncology staff, and continuity of care.

Improving Care Continuity in Oncology Settings: A Lean Management Approach to Minimize Discharges Without Follow-Up Appointments.

OBJECTIVE: This study aimed to reduce the number of patients discharged without scheduled follow-up appointments by implementing lean management principles. METHODS: Conducted at the Sultan Qaboos Comprehensive Cancer Center in Muscat, Oman, the research utilized a one-group pretest-posttest quasi-experimental design to evaluate the impact of lean management interventions on the rate of patient discharges without follow-up appointments. Strategies such as the Kaizen principle, Gemba Walks, cross-functional collaboration, standard work procedures, and waste reduction were employed to enhance operational efficiency. RESULTS: Spanning from Quarter 3 of 2022 to Quarter 2 of 2023, the study demonstrated a significant decrease in the percentage of patients discharged without planned follow-up appointments. The rate dropped from 9% in September 2022 to 0% in March 2023, with statistically significant differences observed (X2= 65.05, p value=<.0001). CONCLUSION: By effectively implementing lean management principles, this research successfully enhanced care continuity for oncology patients after being discharged.

Levels of evidence and grades of recommendation supporting European society for medical oncology clinical practice guidelines.

Background: The European Society for Medical Oncology (ESMO) guidelines are among the most comprehensive and widely used clinical practice guidelines (CPGs) globally. However, the level of scientific evidence supporting ESMO CPG recommendations has not been systematically investigated. This study assessed ESMO CPG levels of evidence (LOE) and grades of recommendations (GOR), as well as their trends over time across various cancer settings. Methods: We manually extracted every recommendation with the Infectious Diseases Society of America (IDSA) classification from each CPG. We examined the distribution of LOE and GOR in all available ESMO CPG guidelines across different topics and cancer types. Results: Among the 1,823 recommendations in the current CPG, 30% were classified as LOE I, and 43% were classified as GOR A. Overall, there was a slight decrease in LOE I (-2%) and an increase in the proportion of GOR A (+1%) in the current CPG compared to previous versions. The proportion of GOR A recommendations based on higher levels of evidence such as randomized trials (LOE I-II) shows a decrease (71% vs. 63%, p = 0.009) while recommendations based on lower levels of evidence (LOE III-V) show an increase (29% vs. 37%, p = 0.01) between previous and current version. In the current versions, the highest proportion of LOE I (42%) was found in recommendations related to pharmacotherapy, while the highest proportion of GOR A recommendations was found in the areas of pathology (50%) and diagnostic (50%) recommendations. Significant variability in LOE I and GOR A recommendations and their changes over time was observed across different cancer types. Conclusion: One-third of the current ESMO CPG recommendations are supported by the highest level of evidence. More well-designed randomized clinical trials are needed to increase the proportion of LOE I and GOR A recommendations, ultimately leading to improved outcomes for cancer patients.

[Precision oncology and molecular tumor boards]. / Präzisionsonkologie und molekulare Tumorboards.

Precision oncology is a field of personalized medicine in which tumor biology forms the basis for tailored treatments. The preferred approach currently applied in clinical practice is based on the concept of malignant tumors as genetic diseases that are caused by mutations in oncogenes and tumor suppressors. On the one hand, these can be targeted by molecular drugs, while on the other hand, next-generation sequencing allows for comprehensive analysis of all relevant aberrations, thus enabling the matching of appropriate treatments across entities based on molecular information. Rational molecular therapies are developed and annotated with supporting evidence by molecular tumor boards, which have been established at various academic centers in recent years. Advancing precision oncology to a new standard of care requires improved applicability of personalized molecular therapies and thorough scientific evaluation of precision oncology programs.

A Review of Practice-Changing Therapies in Oncology in the Era of Personalized Medicine.

In the past decade, a lot of insight was gathered into the composition of the host and tumor factors that promote oncogenesis and treatment resistance. This in turn has led to the ingenious design of multiple new classes of drugs, which have now become the new standards of care in cancer therapy. These include novel antibody-drug conjugates, chimeric antigen receptor T cell therapies (CAR-T), and bispecific T cell engagers (BitTE). Certain host factors, such as the microbiome composition, are also emerging not only as biomarkers for the response and toxicity to anti-cancer therapies but also as potentially useful tools to modulate anti-tumor responses. The field is slowly moving away from one-size-fits-all treatment options to personalized treatments tailored to the host and tumor. This commentary aims to cover the basic concepts associated with these emerging therapies and the promises and challenges to fight cancer.

Large Language Models in Oncology: Revolution or Cause for Concern?

The technological capability of artificial intelligence (AI) continues to advance with great strength. Recently, the release of large language models has taken the world by storm with concurrent excitement and concern. As a consequence of their impressive ability and versatility, their provide a potential opportunity for implementation in oncology. Areas of possible application include supporting clinical decision making, education, and contributing to cancer research. Despite the promises that these novel systems can offer, several limitations and barriers challenge their implementation. It is imperative that concerns, such as accountability, data inaccuracy, and data protection, are addressed prior to their integration in oncology. As the progression of artificial intelligence systems continues, new ethical and practical dilemmas will also be approached; thus, the evaluation of these limitations and concerns will be dynamic in nature. This review offers a comprehensive overview of the potential application of large language models in oncology, as well as concerns surrounding their implementation in cancer care.