Current oncology最新文献

Outpatient neurosurgical oncology has expanded with advances in anesthesia, imaging, and minimally invasive techniques, enabling safe same-day discharge for selected patients undergoing procedures such as stereotactic biopsy and craniotomy. In this review, we find that across multiple international series, same-day discharge rates in several studies ranging from 85 to 95%, with low complication (3-6%) and readmission rates when structured pathways, including standardized selection criteria, enhanced recovery protocols, and routine 4-h postoperative CT imaging, are used. Studies on economic analyses demonstrate substantial cost savings driven by reduced inpatient bed utilization, with no increase in adverse events. Key challenges identified include medicolegal concerns amongst physicians, patient education, and limitations in organization adoption. Telemedicine and remote monitoring are increasingly incorporated to streamline preoperative evaluation and postoperative follow-up, improving access and continuity of care. Emerging technologies such as laser interstitial thermal therapy and focused ultrasound may further expand the outpatient neuro-oncology repertoire. Overall, current evidence supports outpatient neurosurgical oncology as a safe, efficient, and patient-centered model when applied with structured clinical pathways and patient selection.

Background: Despite the availability of contouring guidelines and advanced imaging modalities, interobserver variability (IOV) in the delineation of the planning target volume and organs at risk remains a critical factor influencing treatment quality in radiotherapy. The aim of this study was to examine variations in contour delineation with respect to anatomical landmarks, as well as differences in the inclusion of lymph node levels within the PTV. Methods: Ten senior radiation oncologists from six different institutions participated in the study and contoured PTV1, PTV2 and 16 OARs in a patient with oropharyngeal carcinoma. Interobserver variation was quantified by volume statistics such as mean, standard deviation (SD) and ranges, as well as using coefficient of variance (CoV) and conformity index (CI). Results: High agreement was observed in the inclusion of the ipsilateral lymph node levels Ib-IVa and VIIa+b, whereas notable discrepancies were identified in the delineation inclusion of the cervical triangle group and lateral supraclavicular nodes. Regarding OARs, the greatest variability was observed in the delineation of the left and right inner ear, with volume ranges of 0.12-2.84 cm³ and 0.11-2.38 cm³, respectively. Conclusions: This study reaffirms the presence of significant interobserver variability in the delineation of PTVs and OARs in patients with oropharyngeal carcinoma. Especially inclusion of elective lymph node levels and definition of margins around the gross tumor volume are substantial factors for IOV. By emphasizing structured anatomical assessment as a standard approach, variability can be minimized, treatment consistency enhanced, and ultimately, patient outcomes improved.

This paper explores recent advancements in end-of-life (EOL) care in Canada, focusing on palliative care (PC) in oncology, advance care planning (ACP), and medical assistance in dying (MAiD). Despite improvements in cancer treatment, cancer remains a leading cause of death in Canada, with patients facing significant physical, psychosocial, and emotional challenges throughout the illness trajectory. Over the past few decades, PC has evolved to address serious illness from diagnosis onward, enhancing symptom management, quality of life, and patient satisfaction, while reducing hospital admissions and unnecessary treatments. However, barriers such as misconceptions about PC, late PC referrals, and limited access to PC, particularly in rural and remote areas, still exist. This perspective paper draws on the authors' collective clinical and research experience in oncology and PC, complemented by a focused review of key literature. Ongoing education for oncology nurses on EOL care, including on PC, ACP, and MAiD, as well as continued efforts to expand access to PC for all Canadians, are imperative in order to improve the EOL experience for people affected by cancer nationwide.

Background: As a newly recognized type of cell death implicated in cancer, ferroptosis plays multiple roles in tumor biology. Here, we sought to construct a prognostic framework for EC on the basis of ferroptosis-related long non-coding RNAs (FerlncRNAs), microRNAs (FermiRNAs), and mRNAs (FRGs) for endometrial cancer (EC). Methods: Transcriptomic profiles of tumors and matched clinical data for 544 EC patients were retrieved from TCGA-UCEC. A prognostic framework was generated through Cox regression, integrating ferroptosis-linked lncRNAs, miRNAs, and mRNAs. EC cases were stratified into groups with high or low predicted risk based on ferroptosis-related gene expression. The model's prognostic utility was examined through Kaplan-Meier (K-M) analysis and receiver operating characteristic curves. Results: A prognostic model based on 16 RNAs, including 10 FerlncRNAs, 2 FermiRNAs, and 4 FRGs, was developed. Analysis using K-M plots showed that high-risk patients experienced shorter overall survival than their low-risk counterparts (p < 0.001). The model's area under curve (AUC) values were 0.731, 0.749, and 0.768 at 1-, 3-, and 5-year time points, surpassing those of standard clinical parameters. Furthermore, in an external validation cohort, these signature RNAs were associated with EC prognosis. Conclusions: The novel ferroptosis-related lncRNA-miRNA-mRNA prognostic model provides a basis to assess clinical prognosis in EC patients.

Oesophageal cancer remains a significant global health burden with poor survival outcomes despite multimodal treatment. Recent advances in magnetic resonance imaging (MRI) have opened opportunities to improve radiotherapy delivery. This review examines the role of MRI and MR-guided radiotherapy (MRgRT) in oesophageal cancer, focusing on applications in staging, treatment planning, and response assessment, with particular emphasis on magnetic resonance linear accelerator (MR-Linac)-based delivery. Compared to computed tomography (CT), MRI offers superior soft-tissue contrast, enabling more accurate tumour delineation and the potential for reduced treatment margins. Real-time MR imaging during treatment can facilitate motion management, while daily adaptive planning can accommodate anatomical changes throughout the treatment course. Functional MRI sequences, including diffusion-weighted and dynamic contrast-enhanced imaging, offer quantitative data for treatment response monitoring. Early clinical and dosimetric studies demonstrate that MRgRT can significantly reduce radiation dose to critical organs while maintaining target coverage. However, clinical evidence for MRgRT in oesophageal cancer is limited to small early-phase studies, with no phase II/III trials demonstrating improvements in survival, toxicity, or patient-reported outcomes. Long-term clinical benefits and cost-effectiveness remain unproven, highlighting the need for prospective outcome-focused studies to define the role for MRgRT within multimodality treatment pathways.

The use of deep learning radiomics to predict whether advanced prostate cancer (PCa) will progress within two years after treatment has been validated, yet there remains a lack of research on estimating time to progression. Patients were enrolled from October 2017 to March 2024. One hundred and eighty-two patients with advanced PCa diagnosed through ultrasound-guided systematic prostate biopsy were enrolled. A deep learning-based radiomics model for predicting progression was firstly developed using pretreatment MR apparent diffusion coefficient (ADC) maps, and the performance of manual (ROIref) versus AI-derived (ROIai) tumor segmentations was compared. Then, survival analysis was performed to compare ROIref-based and ROIai-based radiomics-predicted probabilities in the risk stratification. The area under the receiver operating characteristics curve (AUC) was used to estimate the model efficacy. The model achieved high AUC values for progression prediction in test sets (ROIref: 0.840, ROIai: 0.852). No significant difference was observed between ROIai-based and ROIref-based approaches (ΔAUC = 0.012, p = 0.870) in the test set. Both ROIref-predicted and ROIai-predicted probabilities independently predicted progression in multivariate Cox proportional hazard regression models (p < 0.001) and stratified patients into distinct survival groups (log-rank p < 0.001). Decision curve analysis confirmed equivalent clinical utility across thresholds (0.1-0.6), with net benefit exceeding the "treat all" and "treat none" strategies. In conclusion, deep learning-based radiomics models could effectively predict advanced PCa progression, with AI-derived tumor annotations performing equally to manual expert ones.

Background: Pancreatic cancer is the least survivable malignancy, with five-year survival below 10%. Its vague, non-specific symptoms contribute to late diagnosis and poor outcomes. Targeted education for healthcare professionals, students, patients, carers, and the public may improve awareness, confidence, and early help-seeking. This scoping review aimed to map and synthesize peer-reviewed evidence on pancreatic cancer education, identifying intervention types, outcomes, and gaps in knowledge. Methods: A scoping review was undertaken using the Joanna Briggs Institute (JBI) framework and the Arksey and O'Malley framework and reported in accordance with PRISMA-ScR guidelines. The protocol was registered on the Open Science Framework. Four databases (MEDLINE, Embase, CINAHL, PsycINFO) were searched for English-language, peer-reviewed studies evaluating educational interventions on pancreatic cancer for healthcare students, professionals, patients, carers, or the public. Grey literature was excluded to maintain a consistent methodological standard. Data were charted and synthesised narratively. Results: Nine studies (2018-2024) met inclusion criteria, predominantly from high-income countries. Interventions targeted students and professionals (n = 3), patients (n = 2), the public (n = 2), or mixed groups (n = 2), using modalities such as team-based learning, workshops, virtual reality, serious games, and digital animations. Four interrelated themes were identified, encompassing (1) Self-efficacy; (2) Knowledge; (3) Behavior; and (4) Acceptability. Digital and interactive approaches demonstrated particularly strong engagement and learning gains. Conclusions: Pancreatic cancer education shows clear potential to enhance knowledge, confidence, and engagement across diverse audiences. Digital platforms offer scalable opportunities but require quality assurance and long-term evaluation to sustain impact. The evidence base remains limited and fragmented, highlighting the need for validated outcome measures, longitudinal research, and greater international representation to support the integration of education into a global pancreatic cancer control strategy. Future studies should also evaluate how educational interventions influence clinical practice and real-world help-seeking behaviour.

Evaluation of pituitary neuroendocrine tumors remains complex depending on the exact growth pattern, involvement of critical neurovascular structures, pituitary function and endocrinological activity of the tumor. A predominant growth into the sphenoid sinus and clivus poses specific challenges. We reviewed 557 surgeries for pituitary neuroendocrine tumors in an endonasal endoscopic technique performed between 1 January 2015 and 31 August 2025 to identify 13 cases (2.3%). Clinical, radiological and surgical data were selected by chart review. Thirteen cases aged from 31 to 68 years with almost exclusively non-functioning or clinically silent tumors (92%) were identified. Clival infiltration was restricted to the dorsum sellae in 2/13 (15%), spread to the floor of the sphenoid in 6/13 (46%) and extended inferior to the sphenoid in 5/13 (38%) cases with a high rate of cavernous sinus (62%) and sphenoid sinus infiltration (69%). Complete resection was achieved in 31%, and the residual tumor was clival/sphenoidal in 5/13 cases or within the cavernous sinus in 6/13 cases. The diaphragma sellae was reported to be intact in 92% of cases, and postoperative transient arginine vasopressin deficiency did not occur. Pituitary neuroendocrine tumors predominantly growing below the sella and infiltrating the clivus and sphenoid present specific challenges with a high rate of preoperative pituitary insufficiency, frequent cavernous sinus infiltration and postoperative tumor residuals in the cavernous sinus, sphenoid bone and clivus which are sometimes difficult to delineate. The surgical approach must be tailored specifically to treat the clival infiltration zone to reduce the risk of recurrence.

The dramatic shift in human behavior from hunter-gatherer to modern lifestyles has led to a systematic disruption of the human circadian cycle. Contributors include night-shift work, jet lag, and less intuitive but widespread factors, such as exposure to artificial light at night and irregular eating schedules. Circadian disruption is classified as a Group 2A carcinogen by the International Agency for Research on Cancer (IARC). Hepatocellular carcinoma (HCC) is the third most deadly cancer worldwide, with a rising prevalence in Western countries, largely driven by increasing rates of obesity and steatotic liver disease-associated hepatocarcinogenesis. Emerging evidence suggests that circadian disruption plays a significant role in HCC pathogenesis. Several genes involved in metabolism, cell survival, and immunosurveillance are under the control of circadian rhythms. Experimental preclinical data and epidemiological studies have indicated a strong association between circadian disruption and HCC development. Moreover, molecular signatures related to circadian regulation appear to accurately predict the prognosis of patients with HCC. The concept of chronotherapy is also gaining interest, with studies suggesting improved immunotherapy effectiveness when immune checkpoint inhibitors are administered in the morning. This review summarizes the current literature on the impact of circadian disruption on HCC pathogenesis, prognosis, and treatment.

Immunohistochemistry (IHC) is essential for diagnostic, prognostic, and predictive biomarker assessment in oncology, but manual interpretation is limited by subjectivity and inter-observer variability. Machine learning (ML), a computational subset of AI that allows algorithms to recognise patterns and learn from annotated datasets to make predictions or decisions, has led to advancements in digital pathology by supporting automated quantification of biomarker expression on whole-slide images (WSIs). This review evaluates the role of ML-assisted IHC scoring in the transition from validated biomarkers to the discovery of emerging prognostic and predictive IHC biomarkers for genitourinary (GU) tumours. Current applications include ML-based scoring of routinely used biomarkers such as ER/PR, HER2, mismatch repair (MMR) proteins, PD-L1, and Ki-67, demonstrating improved consistency and scalability. Emerging studies in GU cancers show that algorithms can quantify markers including androgen receptor (AR), PTEN, cytokeratins, Uroplakin II, Nectin-4 and immune checkpoint proteins, with early evidence indicating associations between ML-derived metrics and clinical outcomes. Important limitations remain, including limited availability of training datasets, variability in staining protocols, and regulatory challenges. Overall, ML-assisted IHC scoring is a reproducible and evolving approach that may support biomarker discovery and enhance precision GU oncology.