Clinical & Translational Oncology最新文献

Background: The primary aim of this research was to create and rigorously assess a deep learning radiomics (DLR) framework utilizing magnetic resonance imaging (MRI) to forecast the histological differentiation grades of oropharyngeal cancer.

Methods: This retrospective analysis encompassed 122 patients diagnosed with oropharyngeal cancer across three medical institutions in China. The participants were divided at random into two groups: a training cohort comprising 85 individuals and a test cohort of 37. Radiomics features derived from MRI scans, along with deep learning (DL) features, were meticulously extracted and carefully refined. These two sets of features were then integrated to build the DLR model, designed to assess the histological differentiation of oropharyngeal cancer. The model's predictive efficacy was gaged through the area under the receiver operating characteristic curve (AUC) and decision curve analysis (DCA).

Results: The DLR model demonstrated impressive performance, achieving strong AUC scores of 0.871 on the training cohort and 0.803 on the test cohort, outperforming both the standalone radiomics and DL models. Additionally, the DCA curve highlighted the significance of the DLR model in forecasting the histological differentiation of oropharyngeal cancer.

Conclusions: The MRI-based DLR model demonstrated high predictive ability for histological differentiation of oropharyngeal cancer, which might be important for accurate preoperative diagnosis and clinical decision-making.

Purpose: Small-molecule inhibitors have transformed oncology in recent years. This study compared regulatory approvals of these agents across the United States Food and Drug Administration (FDA), the European Medicines Agency (EMA) and the Japanese Pharmaceuticals and Medical Devices Agency (PMDA), focusing on timelines, dosing recommendations, and pediatric labelling.

Methods: A review of regulatory databases was conducted to identify small-molecule inhibitors approved for adult malignancies. Drug labels were compared to determine dosing concordance (Fully, Partially, or Non-Concordant), approval dates, and pediatric indications. Data extraction involved two independent reviewers.

Results: Fifty-five inhibitors were approved by all three agencies. Adult dosing was Fully Concordant in 49 (89%), partially in 4 (7%), and Non-Concordant in 2 (4%). The median approval gap was 25 months (range: 1-88). FDA granted first approval for 85.5% of agents, followed by PMDA (12.7%) and EMA (1.8%). Among these 55 drugs, only 15 had pediatric indications (27%), 7 of them (46.7%) approved across all three regions. No complete divergence in pediatric dosing was observed, although minimum age thresholds varied.

Discussion: Despite strong alignment in adult dosing, regulatory disparities in approval timelines and pediatric labelling persist, risking delays in therapy availability. More harmonized multinational trials and regulatory alignment could facilitate timely approvals while allowing for population-specific considerations in dosing and safety.

Purpose: To compare the long-term outcomes of accelerated partial breast irradiation (APBI) using multicatheter interstitial brachytherapy versus whole breast irradiation (WBI), in terms of late toxicity, cosmetic results, quality of life, and survival, in a real-world clinical setting.

Methods: Updated analysis of two prospectively collected cohorts comprising 76 patients with stage I-II breast cancer treated with breast-conserving surgery followed by adjuvant radiotherapy. Patients who underwent APBI met the GEC-ESTRO eligibility criteria. We assessed follow-up and additional analyses of late toxicity, quality of life (assessed using the validated QLQ-BR23 and S-BIS questionnaires), cosmetic outcomes (via Visual Analog Scale), overall survival (OS), and disease-free survival (DFS), providing a more comprehensive evaluation of long-term outcomes.

Results: At a median follow-up of 11 years, patient-reported quality of life remained significantly better in the APBI group, particularly in both physical and psychological domains, consistent with previous findings. No significant differences were observed in late clinical toxicity or cosmetic outcomes between groups. However, late mammographic findings showed a higher incidence of architectural distortion and tissue retraction in the APBI group, confirming earlier observations. The estimated 5- and 10-year OS rates were 94.7 and 81.1%, respectively. Corresponding DFS rates were 92.1% and 79.7%, with no statistically significant differences between treatment groups.

Conclusion: With extended follow-up, our results reinforce that APBI using multicatheter interstitial brachytherapy is a safe and effective alternative to WBI in selected patients, providing long-term tumor control and survival comparable to WBI, while offering improved quality of life.

Objectives: To determine the most effective diagnostic imaging modality including computed tomography (CT), magnetic resonance imaging (MRI), positron emission tomography-computed tomography (PET/CT), and ultrasound (US) for the identification of regional lymph node involvement in patients with oral squamous cell carcinoma (OSCC).

Methods: A search was performed to identify qualified studies using PubMed, Scopus, and Web of Science electronic databases from January 2013 to December 2023. The outcome assessed was the diagnostic performance of different imaging techniques in the detection of cervical lymph node metastasis in OSCC, with lymph node yield in neck dissection specimens as the gold standard for validation. Summary estimates for diagnostic performance were sensitivity, specificity, and diagnostic odds ratio (DOR) with 95% confidence intervals (CIs). Differences in sensitivities, specificities, and DOR were tested using a bivariate random effects model.

Results: Twelve observational studies including PET/CT, MRI, and CT alone or combined in OSCC patients were included in the meta-analysis. Data from a single study based on US were insufficient to assess the value of this imaging modality. The pooled estimates of sensitivity were 0.87 for PET/CT, 0.70 for MRI, and 0.66 for CT. The corresponding pooled specificities were 0.79, 0.79, and 0.81, respectively. The DOR was 24.85 for PET/CT, 8.60 for MRI, and 8.59 for CT.

Conclusion: PET/CT may be recommended in clinical practice for detecting cervical metastatic disease in OSCC patients as this technique showed the most favorable diagnostic performance as compared with MRI and CT.

Cancer is one of the main causes of morbidity and mortality in Spain and has a significant impact in the workplace, where exposure to carcinogens in the work environment can increase the risk of developing this disease. The lack of communication between oncologists and occupational physicians limits the accurate assessment of cancer as an occupational disease and as a cause of disability. In 2020, the Spanish Society of Medical Oncology (SEOM) and the Spanish Association of Occupational Medicine Specialists (AEEMT) launched a joint initiative to strengthen prevention, reporting, and management of occupational cancer. This consensus provides a structured framework for assessing the occupational origin of cancer, facilitating case notification and classification as occupational contingencies, and supporting informed evaluations of disability and work reintegration in patients with cancer. The collaboration between both societies aims not only to advance occupational cancer prevention but also to promote evidence-based strategies for return-to-work planning.

Endometrial cancer (EC) presents a major global health challenge due to its heterogeneity and complex pathophysiology. The tumor microenvironment (TME), comprising stromal cells, immune cells, endothelial cells, and non-cellular components, critically influences EC progression. Single-cell RNA sequencing (scRNA-seq) has revolutionized our understanding of EC by revealing TME cellular heterogeneity and interactions. This review synthesizes recent scRNA-seq applications in EC, focusing on two key areas: ① Mapping transcriptional heterogeneity across major cellular components (epithelial, stromal, endothelial, and immune cells); ② Elucidating the factors driving tumor evolution, immune evasion, and differential immunotherapy response. Collectively, these scRNA-seq-derived insights into the dynamic TME ecosystem provide the foundation for translating basic discoveries toward clinical applications. Such advances could thereby promote TME-based personalized therapies and more accurate prognostic predictions.

Oral potentially malignant disorders (OPMDs) refer to oral mucosal disorders with an increased risk of malignancy, primarily oral squamous cell carcinoma (OSCC), especially in South and Southeast Asia. Since not all patients with OPMDs develop oral cancer, accurate early detection and diagnosis of malignant transformation are critically important for clinicians to determine the optimal therapeutic approach. Therefore, distinguishing OPMDs from early-stage OSCC is an increasing challenge in the clinic. Artificial intelligence (AI) technology has recently been shown to quickly identify high-risk conditions/lesions for screening oral cancer early. Moreover, the AI algorithm can also be used to determine the prognosis of OPMDs. In this review, we systematically summarize the medical records, oral images, pathological examinations, biomarkers, omics data and other aspects of the main outcomes of AI applied to address OPMDs-related issues. Furthermore, we discuss automated diagnostic systems and risk prediction tools for malignant transformation with pleasant outcomes and the potential to ultimately assist clinicians. Finally, we introduce the current challenges and barriers to AI in OPMDs on the premise that more advanced AI models and larger datasets will lead to the use of AI models in OPMDs.

Background: Breast cancer remains a major global health concern due to its heterogeneous nature influenced by genetic, biological, and environmental factors. Serum biomarkers offer promises for improving diagnostic precision and monitoring treatment response.

Objective: To evaluate the diagnostic and prognostic significance of serum biomarkers including CA-125, CA-19-9, and CA-15-3 in breast cancer patients and to explore associations with clinical and biochemical parameters.

Methods: A retrospective, single-center study was conducted in the Hail region, involving 187 breast cancer patients. Data were extracted from electronic health records. Statistical analyses, including ANOVA and regression models, assessed the relationships between serum biomarkers and clinical variables, such as age, cancer stage, obesity, and laboratory parameters. Serum biomarkers CA-125, CA-19-9, and CA-15-3 were quantified using electro-chemi-luminescence immunoassay (ECLIA) assay with Elecsys kits Roche Diagnostics, with detection limits of ~ 1.0 U/ml and inter-assay variability < 7%.

Results: The highest incidence was observed in women aged 46-55 (26.7%) with obesity present in 50% of cases. Advanced stages (2 and 3) comprised 82.6% of diagnoses. CA-125 levels were elevated in middle-aged patients, while CA-19-9 was higher in younger individuals. CA-15-3 showed increased levels in early-stage cancer, suggesting its utility for early detection. Obesity was linked to increased CA-125 and decreased CA-19-9 levels. Laboratory findings revealed hypocalcemia, elevated bilirubin, high GGT, and increased HbA1c, indicating potential risks of bone metastases, hepatic dysfunction, and poor glycemic control.

Conclusion: Serum biomarkers demonstrate significant diagnostic and prognostic potential in breast cancer management. Findings support the importance of early detection, obesity management, and integrated monitoring to enhance outcomes and reduce relapse risk.

Esophageal cancer (EC) is one of the most serious health issues around the world, ranking seventh among the most lethal types of cancer and eleventh among the most common types of cancer worldwide. Traditional therapies-such as surgery, chemotherapy, and radiation therapy-often yield limited success, especially in the advanced stages of EC, prompting the pursuit of novel and more effective treatment strategies. Immunotherapy has emerged as a promising option; nonetheless, its clinical success is hindered by variable patient responses. This underscores the urgent need for predictive biomarkers that can identify patients most likely to benefit from immunotherapeutic interventions. Biomarker-based patient stratification can improve treatment outcomes, prevent unnecessary exposures, and conserve healthcare resources. This review explores established and emerging biomarkers for predicting response to immunotherapy in EC. We discuss these biomarkers by categorizing them into four major groups: (i) tumor-related biomarkers (PD-L1 expression, tumor mutational burden, and microsatellite instability), (ii) tumor-immune microenvironment-related biomarkers (tumor-infiltrating lymphocytes and immune cell subtypes and ratios), (iii) blood-based biomarkers (circulating tumor DNA, exosomes, and soluble proteins), and (iv) microbiomes (oral, esophageal, and gut microbiomes). In addition, Advancements in biomarker discovery technologies such as high-throughput sequencing, multi-omics approaches, artificial intelligence and machine learning, single-cell analysis, and liquid biopsy are also discussed for their potential to refine biomarker identification and clinical application.