Search Results (1,127)

Oral mucositis (OM) is a severe inflammatory condition of the oral mucosa that is commonly associated with cancer therapies. Traditional treatments typically have limited efficacy and significant side effects, necessitating alternative approaches. Nanobased drug delivery systems (DDSs) present promising solutions, enhancing therapeutic outcomes while minimizing side effects. This review aims to evaluate the use of nanobased DDSs to treat OM. To reach these aims, an extensive literature review was conducted using the following databases: BVS, PubMed, Scopus, and Web of Science. The search strategy included the keywords “microparticles,” “nanoparticles,” “drug delivery system,” “oral mucositis,” “therapy,” and “treatment,” combined with the Boolean operators “AND” and “OR.” After applying filters for language, relevance, full-text availability, exclusion of review articles, and removal of duplicates, a total of 32 articles were selected for analysis. Of the 32 studies included in this review, 25 employed polymeric micro- or nanosystems for the treatment of OM. Regarding the stage of investigation, 10 studies were conducted in vitro, 16 were conducted in vivo, and 6 corresponded to clinical trials. Compared with conventional drug delivery approaches, most of these studies reported improved therapeutic outcomes. These findings highlight the potential of nanosystems as innovative strategies for enhancing OM treatment. Nonetheless, challenges in large-scale manufacturing, including reproducibility and safety, and the limited number of clinical trials warrant careful consideration. Future research with larger clinical trials is essential to validate these findings and effectively guide clinical practice. Full article

This study presents and discusses evidence on the value of biomarker testing and precision medicine in Latin America through a health equity lens. It is essential to explore how to harness the benefits of precision medicine to narrow the health equity gap, ensuring all patients have access to the best cancer treatment. The methodology employed to develop this document consists of a non-systematic literature review, followed by a process of validation and feedback with a group of experts in relevant fields. Precision medicine could help reduce health inequities in Latin America by providing better diagnosis and treatment for everyone with cancer. However, its success in achieving this depends on the implementation of policies that promote equitable access. Findings indicate that the current policy landscape in the Latin American region is not conducive to improving access, reach, quality, or outcome-related problems in cancer care, nor to realizing the full potential of precision medicine. The study explores how precision medicine can advance health equity, concluding with an analysis of the challenges and recommendations for overcoming them. Full article

Person-centered navigation (PCN) in healthcare refers to a proactive collaboration among professionals, researchers, patients, and their families to guide individuals toward timely access to screening, treatment, follow-up, and psychosocial support. PCN—which includes professional, peer, and virtual guidance, is particularly crucial for rare cancers, where affected individuals face uncertainty, limited support, financial strain, and difficulties accessing relevant information, testing, and other services. The Canadian Cholangiocarcinoma Collaborative (C3) prioritizes PCN implementation to address these challenges in the context of Biliary Tract Cancers (BTCs). C3 uses a virtual PCN model and staffs a “C3 Research Navigator” who provides clinical and research navigation such as personalized guidance and support, facilitating access to molecular testing, clinical trials, and case reviews through national multidisciplinary rounds. C3 also supports a national network of BTC experts, a patient research registry, and advocacy activities. C3’s implementation strategies include co-design, timely delivery of support, and optimal outcomes across its many initiatives. Future priorities include expanding the C3 network, enhancing user engagement, and further integrating its innovative approach into routine care. Full article

Background: Lung cancer ranks as the leading cause of cancer-related mortality worldwide. The complexity of tumor delineation, crucial for radiation therapy, requires expertise often unavailable in resource-limited settings. Artificial Intelligence (AI), particularly with advancements in deep learning (DL) and natural language processing (NLP), offers potential solutions yet is challenged by high false positive rates. Purpose: The Oncology Contouring Copilot (OCC) system is developed to leverage oncologist expertise for precise tumor contouring using textual descriptions, aiming to increase the efficiency of oncological workflows by combining the strengths of AI with human oversight. Methods: Our OCC system initially identifies nodule candidates from CT scans. Employing Language Vision Models (LVMs) like GPT-4V, OCC then effectively reduces false positives with clinical descriptive texts, merging textual and visual data to automate tumor delineation, designed to elevate the quality of oncology care by incorporating knowledge from experienced domain experts. Results: The deployment of the OCC system resulted in a 35.0% reduction in the false discovery rate, a 72.4% decrease in false positives per scan, and an F1-score of 0.652 across our dataset for unbiased evaluation. Conclusions: OCC represents a significant advance in oncology care, particularly through the use of the latest LVMs, improving contouring results by (1) streamlining oncology treatment workflows by optimizing tumor delineation and reducing manual processes; (2) offering a scalable and intuitive framework to reduce false positives in radiotherapy planning using LVMs; (3) introducing novel medical language vision prompt techniques to minimize LVM hallucinations with ablation study; and (4) conducting a comparative analysis of LVMs, highlighting their potential in addressing medical language vision challenges. Full article

Lung cancer remains one of the most common and deadliest forms of cancer worldwide despite notable advancements in its management. Conventional treatments, such as chemotherapy, often have limitations in effectively targeting cancer cells, which frequently lead to off-target side effects. In this context, the pulmonary delivery of inhalable nanomaterials offers the advantages of being rapid, efficient, and target-specific, with minimal systemic side effects. This concise review summarizes the basic research and clinical translation of inhalable nanomaterials for the treatment of lung cancer. We also provide insights into the latest advances in pulmonary drug delivery systems, focusing on various types of pulmonary devices and nanomaterials. Furthermore, this paper discusses significant challenges in translating the discoveries of inhalable nanomaterials into clinical care for lung cancer and shares strategies to overcome these issues. Full article

Pneumonitis is characterized as inflammation of the lung parenchyma, and a potential adverse effect of several anti-cancer therapies. Diagnosing pneumonitis can be particularly challenging in lung cancer patients due to inherent similarities in symptoms and radiological presentation associated with pneumonitis, as well as other common conditions such as infection or disease progression. Furthermore, many lung cancer patients have underlying pulmonary conditions that might render them more susceptible to severe or fatal outcomes from pneumonitis. Novel anti-cancer agents, such as antibody–drug conjugates (ADCs) and bispecific antibodies (BsAbs), are being incorporated into the treatment of lung cancer; therefore, understanding the risk and mechanisms underlying the potential development of pneumonitis with these new therapies is important to ensure continuous improvements in patient care. This narrative review provides an overview of the incidence of pneumonitis observed with novel anti-cancer agents, characterizes potential pathophysiological mechanisms underlying pneumonitis risk and emerging predictive biomarkers, highlights management strategies, and explores future directions for minimizing the risk of pneumonitis for lung cancer patients. Full article

Prognostic markers such as overall survival (OS) and tertiary lymphoid structure (TLS) ratios, alongside diagnostic signatures like primary cancer-type classification, provide critical information for treatment selection, risk stratification, and longitudinal care planning across the oncology continuum. However, extracting these signals solely from sparse, high-dimensional multi-omics data remains a major challenge due to heterogeneity and frequent missingness in patient profiles. To address this challenge, we present SeNMo, a self-normalizing deep neural network trained on five heterogeneous omics layers—gene expression, DNA methylation, miRNA abundance, somatic mutations, and protein expression—along with the clinical variables, that learns a unified representation robust to missing modalities. Trained on more than 10,000 patient profiles across 32 tumor types from The Cancer Genome Atlas (TCGA), SeNMo provides a baseline that can be readily fine-tuned for diverse downstream tasks. On a held-out TCGA test set, the model achieved a concordance index of 0.758 for OS prediction, while external evaluation yielded 0.73 on the CPTAC lung squamous cell carcinoma cohort and 0.66 on an independent 108-patient Moffitt Cancer Center cohort. Furthermore, on Moffitt’s cohort, baseline SeNMo fine-tuned for TLS ratio prediction aligned with expert annotations (p < 0.05) and sharply separated high- versus low-TLS groups, reflecting distinct survival outcomes. Without altering the backbone, a single linear head classified primary cancer type with 99.8% accuracy across the 33 classes. By unifying diagnostic and prognostic predictions in a modality-robust architecture, SeNMo demonstrated strong performance across multiple clinically relevant tasks, including survival estimation, cancer classification, and TLS ratio prediction, highlighting its translational potential for multi-omics oncology applications. Full article

Breast cancer (BC) is the most prevalent malignancy in women worldwide. Despite most cases being diagnosed in the early stages, patients typically require a multimodal treatment approach. This typically involves a combination of surgery, radiotherapy, systemic treatments (including chemotherapy or immunotherapy), targeted therapy, and endocrine therapy, depending on the disease subtype and the risk of recurrence. Moreover, patients with BC and germline mutations in the breast cancer genes 1 or 2 (BRCA1/BRCA2), (gBRCAm), who are typically young women, often require more aggressive therapeutic interventions. These mutations present unique characteristics that necessitate a distinct treatment approach, potentially influencing the side effect profiles of patients with BC. Regardless of the clear benefit observed with these treatments in terms of reduced recurrence and mortality rates, long-term, treatment-related adverse events occur that negatively affect the health-related quality of life (HRQoL) of BC survivors. Thus, long-term adverse events need to be factored into the treatment decision algorithm of patients with early BC (eBC). Physical, functional, emotional, and psychosocial adverse events can occur and represent a significant concern and a challenge for clinicians, patients, and their families. This review article provides an overview of the various long-term adverse events that patients with eBC may experience, including their associated risk factors, as well as management and prevention strategies. We also explore the evidence of the long-term impact of treatment on the HRQoL of patients with gBRCAm. By providing a comprehensive overview of current evidence and recommendations regarding patients’ HRQoL, we aim to equip clinicians with scientific and clinical knowledge and provide guidance to optimize care and improve long-term outcomes. Full article

Purpose: Cancer immunotherapy has transformed oncology, but underserved populations face persistent disparities in access and outcomes. This review explores how artificial intelligence (AI) can help mitigate these barriers. Methods: We conducted a narrative review based on peer-reviewed literature selected for relevance to artificial intelligence, cancer immunotherapy, and healthcare challenges, without restrictions on publication date. We searched three major electronic databases: PubMed, IEEE Xplore, and arXiv, covering both biomedical and computational literature. The search included publications from January 2015 through April 2024 to capture contemporary developments in AI and cancer immunotherapy. Results: AI tools such as machine learning, natural language processing, and predictive analytics can enhance early detection, personalize treatment, and improve clinical trial representation for historically underrepresented populations. Additionally, AI-driven solutions can aid in managing side effects, expanding telehealth, and addressing social determinants of health (SDOH). However, algorithmic bias, privacy concerns, and data diversity remain major challenges. Conclusions: With intentional design and implementation, AI holds the potential to reduce disparities in cancer immunotherapy and promote more inclusive oncology care. Future efforts must focus on ethical deployment, inclusive data collection, and interdisciplinary collaboration. Full article

Prostate cancer diagnostics are rapidly advancing through innovations in nanotechnology, biosensing strategies, and molecular recognition. This review analyzes studies focusing on quantum dot (QD)-based biosensors for detecting prostate cancer biomarkers with high sensitivity and specificity. It covers diverse sensing platforms and signal transduction mechanisms, emphasizing the influence of the QD composition, surface functionalization, and bio interface engineering on analytical performance. Key metrics such as detection limits, dynamic range, and compatibility with biological samples, including serum, urine, and tissue, are critically assessed. Recent advances in green-synthesized QDs and smartphone-integrated diagnostic platforms are highlighted, including lateral flow assays, paper-based devices, and pH-responsive hydrogels for real-time, low-cost, and decentralized cancer screening. These innovations enable multiplexed biomarker detection and tumor microenvironment monitoring in point-of-care settings. This review concludes by addressing the current limitations, scalability challenges, and future research directions for translating QD-enabled biosensors into clinically viable diagnostic tools. Full article

Background/Objectives: The RTK-RAS signaling cascade is a central axis in colorectal cancer (CRC) pathogenesis, governing cellular proliferation, survival, and therapeutic resistance. Somatic alterations in key pathway genes—including KRAS, NRAS, BRAF, and EGFR—are pivotal to clinical decision-making in precision oncology. However, the integration of these genomic events with clinical and demographic data remains hindered by fragmented resources and a lack of accessible analytical frameworks. To address this challenge, we developed AI-HOPE-RTK-RAS, a domain-specialized conversational artificial intelligence (AI) system designed to enable natural language-based, integrative analysis of RTK-RAS pathway alterations in CRC. Methods: AI-HOPE-RTK-RAS employs a modular architecture combining large language models (LLMs), a natural language-to-code translation engine, and a backend analytics pipeline operating on harmonized multi-dimensional datasets from cBioPortal. Unlike general-purpose AI platforms, this system is purpose-built for real-time exploration of RTK-RAS biology within CRC cohorts. The platform supports mutation frequency profiling, odds ratio testing, survival modeling, and stratified analyses across clinical, genomic, and demographic parameters. Validation included reproduction of known mutation trends and exploratory evaluation of co-alterations, therapy response, and ancestry-specific mutation patterns. Results: AI-HOPE-RTK-RAS enabled rapid, dialogue-driven interrogation of CRC datasets, confirming established patterns and revealing novel associations with translational relevance. Among early-onset CRC (EOCRC) patients, the prevalence of RTK-RAS alterations was significantly lower compared to late-onset disease (67.97% vs. 79.9%; OR = 0.534, p = 0.014), suggesting the involvement of alternative oncogenic drivers. In KRAS-mutant patients receiving Bevacizumab, early-stage disease (Stages I–III) was associated with superior overall survival relative to Stage IV (p = 0.0004). In contrast, BRAF-mutant tumors with microsatellite-stable (MSS) status displayed poorer prognosis despite higher chemotherapy exposure (OR = 7.226, p < 0.001; p = 0.0000). Among EOCRC patients treated with FOLFOX, RTK-RAS alterations were linked to worse outcomes (p = 0.0262). The system also identified ancestry-enriched noncanonical mutations—including CBL, MAPK3, and NF1—with NF1 mutations significantly associated with improved prognosis (p = 1 × 10⁻⁵). Conclusions: AI-HOPE-RTK-RAS exemplifies a new class of conversational AI platforms tailored to precision oncology, enabling integrative, real-time analysis of clinically and biologically complex questions. Its ability to uncover both canonical and ancestry-specific patterns in RTK-RAS dysregulation—especially in EOCRC and populations with disproportionate health burdens—underscores its utility in advancing equitable, personalized cancer care. This work demonstrates the translational potential of domain-optimized AI tools to accelerate biomarker discovery, support therapeutic stratification, and democratize access to multi-omic analysis. Full article

The concept of “more is better” has long dominated cancer treatment, emphasizing aggressive therapies despite their toxicity. However, the rise of personalized medicine has fostered treatment de-escalation strategies aimed at minimizing toxicity, improving quality of life, and reducing costs. This position paper highlights key applications of de-escalation in medical oncology, with a primary focus on breast cancer and notable examples in colorectal, head and neck, ovarian, lung, and prostate cancers. Various approaches, including dose reduction, treatment duration shortening, and regimen optimization, have demonstrated efficacy without compromising clinical outcomes. Advances in molecular diagnostics, such as Oncotype Dx in breast cancer and circulating tumor DNA (ctDNA) analysis in colorectal cancer, have facilitated patient selection for de-escalation. While these strategies present promising results, challenges remain, particularly in balancing treatment intensity with oncologic control. The review underscores the need for further prospective trials to refine de-escalation approaches and ensure their safe integration into standard oncologic care. Full article

Background/Objectives: The increasing population of childhood cancer survivors presents new challenges for healthcare systems worldwide. While advances in oncological treatments have dramatically improved survival rates, survivors face a broad spectrum of late effects that extend beyond the biological to encompass profound psychological and social dimensions. Methods: This quasi-systematic review synthesizes data from recent studies on adolescent survivors, revealing significant disruptions in cognitive function, mental health, social integration, education, romantic relationships, and vocational outcomes. Results: This review highlights the inadequacy of a solely biomedical model and advocates for a biopsychosocial approach to long-term follow-up care. An emphasis is placed on the necessity of personalized, interdisciplinary, and developmentally informed interventions, especially in countries like Poland, where structured survivorship care models remain underdeveloped. Conclusions: The findings underscore the importance of integrating medical, psychological, and social services to ensure adolescent cancer survivors achieve not only physical recovery but also meaningful life participation and emotional well-being. Full article

Background: Gastrointestinal (GI) cancer patients face a four-fold higher suicide risk than the general US population. This study explores psychosocial aspects of GI cancer patient experiences, assessing suicidal ideation and behavior, mental distress during treatment phases, and psychosocial factors on mental health. Methods: A two-phase mixed-methods approach involved a web-based survey and follow-up interviews. Quantitative data analysis validated mental health and suicidal ideation constructs, and correlation analyses were performed. The patient journey was charted from diagnosis to treatment. Results: Two hundred and two individuals participated, with 76 from the rural Appalachian region and 78 undergoing treatments. Quantitative analysis showed a higher prevalence of passive suicidal ideation than active planning. The post-treatment recovery period was the most emotionally challenging. Qualitative data emphasized emotional support and vulnerability to isolation. Care quality concerns included individualized treatment plans and better communication. Patients also needed clear, comprehensive information about treatment and side effects. The in-depth interview with four GI cancer patients revealed a healthcare system prioritizing expedient treatment over comprehensive care, lacking formal psychological support. AI emerged as a promising avenue for enhancing patient understanding and treatment options. Conclusions: Our research advocates for a patient-centric model of care, enhanced by technology and empathetic communication. Full article

Background/Objectives: Next-generation-sequencing-based genomic profiling (GP) of advanced breast cancer (BC) has been increasingly integrated into clinical practice. The growing number of biomarker-based therapies in BC increasingly complicates treatment decisions. As a result, molecular tumor boards (MTBs) have become pivotal. However, real-world data on the utility of MTBs in advanced BC remain limited. This study evaluates the translation of molecular findings in BC patients into MTB recommendations and examines their implementation and outcomes in real-world clinical practice. Methods: This retrospective, single-center study included 103 BC patients who received GP between January 2018 and December 2023. Patients were discussed at the weekly multidisciplinary MTB of our institution. Data retrieved included patient characteristics, GP results, and MTB recommendations, which were consecutively matched with treatment outcomes, namely the proportion of patients receiving an MTB treatment recommendation, proportion of patients receiving molecularly matched targeted therapy (MTT), and best treatment response. Results: The MTB reviewed 94 patients and provided 155 recommendations to 68 patients (72.3%), including systemic anti-cancer treatment (n = 123), clinical study participation (n = 4), genetic counseling (n = 12), and additional molecular testing (n = 16) recommendations. Treatment recommendations were provided to 63 patients (67%), of whom 38 (60.3%) received MTT. Of the 35 patients eligible for response assessment, 16 (45.7%) demonstrated clinical benefit: three achieved a complete response, six a partial response, and ten a stable disease > 6 months. Conclusions: GP and MTBs expand biomarker-matched treatment options to BC patients beyond the standard of care. Around half of the patients who receive MTT experience a clinical benefit. The standardization of procedures, the development of multi-biomarker-based prediction, and the enhancement in MTT delivery to patients are key challenges, which should be addressed in future initiatives. Full article