Technology in Cancer Research & Treatment最新文献

IntroductionThis study sought to develop a predictive model using CT-based habitat radiomics to forecast pathological complete response (pCR) and progression-free survival (PFS) in esophageal squamous cell carcinoma (ESCC) patients receiving standardized neoadjuvant chemoradiotherapy (nCRT) followed by curative surgery.MethodsWe retrospectively analyzed baseline CT imaging data from 228 ESCC patients in a prospective cohort database. Patients were randomly divided into training and validation sets (7:3 ratio). Whole-tumor and habitat-derived radiomic features were extracted from pretreatment CT scans. For pCR prediction, habitat signatures were developed using Logistic Regression (LR), RandomForest (RF), and XGBoost models, optimized via grid search. PFS prediction employed Cox proportional hazards modeling with selected features. Model performance was assessed using the area under the receiver operating characteristic curve (AUC), Hosmer-Lemeshow calibration curves, and decision curve analysis.ResultsThe habitat models retained 10 features for pCR prediction and 12 for PFS prediction. For pCR, the habitat-derived RF model demonstrated superior performance (training AUC: 0.821; validation AUC: 0.826), outperforming both other habitat models and the whole-tumor radiomics model (training AUC: 0.645). Similarly, the habitat-based RF model for PFS achieved higher AUCs (training: 0.759, 95% CI: 0.627-0.889; validation: 0.810, 95% CI: 0.653-0.966) compared to whole-tumor radiomics (training: 0.623; validation: 0.519).ConclusionOur analyses indicated a trend where habitat radiomics might outperform whole-tumor radiomics in predicting pCR and PFS for resectable ESCC after nCRT. While this merits further investigation, current evidence is insufficient to confirm its clinical utility for personalized treatment guidance.

ObjectivesIrreversible Electroporation (IRE) is both open surgery and minimally invasive cancer therapy used in the treatment of liver tumors. The therapy demands precision and accuracy to ensure complete tumor ablation. Reliable simulation tools can help achieve this goal by predicting the tissue regions that will reach the required electric field threshold and by suggesting correcting actions when the predicted outcome is inadequate. This article retrospectively compares segmented ablations from intra-procedural computed tomography (CT) scans with computer simulations to check their validity in predicting the operation outcome and the required electric field threshold.Methods10 patient ablation procedures were retrospectively analyzed using a detailed computational model of electroporation, informed by the patient-specific geometry of each case. CT scans were analyzed by three physicians over two sessions to assess intra- and inter-observer variability. Same day postoperative images were used for accuracy. The resulting measured ablations from the patient's data were compared to simulation predictions, both in terms of ablated volumes and 3D similarity scores (Dice coefficient).ResultsSimulated ablation volumes were computed across electric field thresholds (465-750 V/cm), showing highest volumes at 465 V/cm and lowest at 750 V/cm. Comparison with physician segmented volumes showed best match for 500-600 V/cm ablation threshold: this result was consistent across different patients despite differences among patient's conditions and characteristics. 3D analysis revealed Dice scores between 0.63 and 0.77 (mean: 0.71), indicating moderate to good agreement. Visual and statistical comparisons further validated the reliability of the simulation model within this threshold range.ConclusionThis study highlighted the accuracy of IRE ablation volume predictions by comparing retrospective CT based ablation volume segmentations with electric field simulations. The best match occurred at 500 to 600 V/cm thresholds, with post-procedure measurements. Despite observer variability and modeling limitations, Dice scores showed moderate to good agreement, validating the simulation model and emphasizing timely imaging for accuracy.

IntroductionSarcomatoid renal cell carcinoma (sRCC) is rare but highly aggressive and is associated with poor prognosis and limited treatment responsiveness. Despite several studies investigating its clinicopathological features, existing research is often limited by small sample sizes and short follow-up periods, and currently, no prognostic risk model is specific to patients with non-metastatic sRCC. This study aimed to investigate the clinicopathological characteristics of patients with non-metastatic sRCC and develop a predictive model for postoperative mortality risk.MethodsIn this retrospective study, we analyzed the clinical data of 45 patients diagnosed with non-metastatic sRCC who underwent surgical treatment at our institution's Department of Urology, between January 2008 and June 2024. These patients were compared with 527 patients with non-sarcomatoid renal cell carcinoma (non-sRCC). The primary endpoint was death, and the exact cause of death was recorded. Routine postoperative examinations and treatment details were documented through outpatient and inpatient electronic medical record systems.ResultsThe results indicated significant differences in body mass index, hypertension, surgical approach, nephrectomy type, surgical duration, maximum tumor diameter, tumor necrosis, T stage, and Ki-67 expression between patients with sRCC and those with non-sRCC (P < 0.05). Survival analysis revealed that the cancer-specific survival (CSS) for patients with sRCC was significantly lower than that for patients with non-sRCC (P < 0.001). Cox univariate and multivariate analyses identified maximum pathological tumor diameter, T stage, and high Ki-67 expression as independent risk factors. Based on these factors, we developed a postoperative mortality risk prediction model for patients with sRCC, with the calibration curves demonstrating a good fit of the model.ConclusionsThe proposed model is designed for patients with non-metastatic sRCC. It has potential clinical application value, aiding in the identification of high-risk patients and providing guidance for individualized treatment and close follow-up.

Lymphoma is a highly heterogeneous malignancy, demanding accurate and precise diagnosis to guide the selection of the appropriate treatment for optimal outcome. Copy number aberration (CNA) has been suggested to play an important role in the occurrence and development of lymphoma and thus can be explored as biomarker to improve disease management. It is believed that CNAs in variable forms and complexities can be triggered by both exogenous (eg viral infection and ionizing radiation) and endogenous factors (eg genetic predisposition and evolutionary forces). However, conventional cytogenetic methods have limitations to detect all types of CNAs with accuracy and adequate details. The emergence of new technologies, including fluorescence in situ hybridization (FISH), chromosome microarray analysis (CMA), and especially next-generation sequencing (NGS) has made significant progress in the identification and characterization of CNAs or CNA-related genomic aberrations. Accumulating data addressing molecular insights and clinical implications have provided us more theoretical and experimental support for its clinical translation. Currently, while only limited number of CNAs or CNA-related genomic variation, such as deletion/amplification of DNA segments, have been documented in major guidelines or consensus for their clinical potential in lymphoma, more CNAs remain to be further characterized and/or discovered for their clinical relevance. Taking together, with available and upcoming evidence, CNA should play an important role as a diagnostic and prognostic biomarker while integrated with the current settings in lymphoma.

The battle against cancer remains a top priority for society, with an urgent need to develop therapies capable of targeting challenging tumours while preserving patient's quality of life. Hadron Therapy (HT), which employs accelerated beams of protons, carbon ions, and other charged particles, represents a significant frontier in cancer treatment. This modality offers superior precision and efficacy compared to conventional methods, delivering therapeutic the dose directly to tumours while sparing healthy tissue. Even though 350,000 patients have already been treated worldwide with protons and 50,000 with carbon ions, HT is still a relatively young field and more research as well as novel, cost-effective and compact accelerator technologies are needed to make this treatment more readily available globally. Interestingly the very first patient was irradiated with protons in September 1954, the same month and year CERN was founded. Both of these endeavours are embedded in cutting edge technologies and multidisciplinary collaboration. HT is finally gaining ground and, even after 70 years, the particle therapy field continues innovating and improving for the benefits of patients globally. Developing technologies that are both affordable and easy to use is key and would allow access to more patients. Advances in accelerator-driven Boron Neutron Capture Therapy (BNCT), image-guided hadron beams delivery, clinical trials and immunotherapy, together with the recent interest and advances in FLASH therapy, which is currently an experimental treatment modality that involves ultrahigh-dose rate delivery, are just a few examples of innovation that may eventually help to provide access to a larger number of patients.

Introduction: Exosomes play significant roles in transferring cargo materials like proteins, RNAs (including miRNAs), and DNA. However, the role of serum exosome shuttled RNAs and miRNAs in head and neck cancer (HNC) remains unclear. This study assessed the diagnostic and prognostic significance of exosomal miR-17, miR-20a, and TGFBR2 in HNC patients. Methods: Exosomes were isolated, from 400 confirmed HNC patients and 400 healthy controls, and characterized by NTA, TEM, Immunolabelling, and ELISA. Quantitative PCR was used to check the expressions of exosomal molecules. Oxidative stress was also measured through ELISA in cancer patients and healthy controls. Results: Data analysis revealed significant dysregulation in the expressional levels of miR-17 (p < .0001), miR-20a (p = .0003), and TGFBR2 (p = .0005), which were found associated with aggressiveness and poor survival of HNC patients. Spearman correlation revealed a positive statistically significant association between miR-20a versus miR-17 (r = 0.534; p < .01), while a negative correlation was found between TGFBR2 versus miR-17 (r = -0.240; p = .015). Significantly decreased levels of peroxidase (POD) (p < .0001) and an increased level of 8-Oxoguanine (p < .0001) were observed. Conclusion: The results showed that these exosomal miRNAs and target gene may serve as potential and noninvasive diagnostic and prognostic markers for head and neck cancer patients.

IntroductionBreast radiotherapy is associated with a higher risk of cardiac diseases. Although deep inspiration breath-hold (DIBH) reduces the heart dose, it is underutilized. The selection of proper candidates for DIBH remains an unresolved issue. This study compared dosimetric parameters between free breathing (FB) and DIBH, monitored myocardial enzymes, and aimed to identify factors that can predict cardiac injury thus developing a method to identify proper patients for DIBH.MethodsThis is a prospective cohort study, enrolling 58 patients with left-sided breast cancer following breast-conserving surgery. All patients underwent computed tomography scans in both FB and DIBH states. A comparative analysis of dosimetric features between DIBH and FB was conducted. Myocardial enzyme was monitored until six months post-radiation therapy. T-tests were used to assess differences between the DIBH and the FB. Pearson correlation and receiver operating characteristic (ROC) analysis was conducted to identify factors associated with the subclinical acute cardiac injury.ResultsThe mean heart dose (MHD) of the DIBH group significantly dropped as compared to the FB group (3.81 Gy vs 1.65 Gy p = 0.001). Cardiac V40, V30, V25, V10, and V5 volumes also significantly reduced. 9(15.51%) patients exhibited increased myocardial enzyme, with cTnI being the most sensitive indicator. The heart dose was a predictor for the cardiac enzyme's elevation. The ROC curve analysis revealed an area under the curve of 0.6. With an MHD threshold of 2 Gy, both sensitivity and specificity exceeded 0.7.ConclusionDIBH significantly diminishes radiation exposure to the heart and LAD compared with FB. Cardiac enzyme analysis facilitates the early detection of cardiac injury following radiation therapy. An MHD threshold of less than 2 Gy is associated with a reduced risk of subclinical cardiac injury, potentially obviating the need for DIBH, which optimizes clinical efficiency and economic viability.

Cancer remains a major global health burden, with incidence rates rising globally. The Arab world, which is often regarded as an underrepresented population in literature, shows distinct patterns in cancer incidences, genetics, and outcomes in comparison with Western populations. This review aims to highlight key genomic studies conducted in the Arab world. We describe the epidemiological and genetic landscape of cancer in the Arab populations, focusing on lung, breast, and colorectal cancers, given their prominence and distinctive patterns in the region. We utilised data from GLOBOCAN 2022 and published genomic studies to assess subregional incidence trends, identify significant mutations, and explore hereditary and early-onset cancers profiles. Breast, lung, and colorectal cancers dominate the cancer profile in the region, with disparities in genetic alterations when compared to global trends. Variation in EGFR mutation frequencies in lung cancer across diverse ethnicities in the MENA region is representative of the extreme heterogeneity in the Arab region. Variations in BRCA1/2 mutation frequency, and unique founder mutations highlight breast cancer's particular regional genetic traits. Similarly, colorectal cancer studies show variations in mutational profiles, such as a low incidence of BRAF mutations and distinct epigenetic characteristics that represent region-specific disease pathways. Early-onset cancers, particularly breast and colorectal cancers, occur at higher rates than in Western populations and often diverge from the typical germline mutation patterns reported globally. The review emphasises the importance of conducting localised genetic studies in improving personalised medicine and public health strategies. Despite these efforts, significant gaps remain, particularly in understanding early-onset cancers and hereditary cancer genetic disorders, which are overrepresented in the region. Further research on the genetic basis of cancer in Arab populations is essential for advancing personalised treatment and improving cancer outcomes in these under-researched groups.

IntroductionThe study aims to evaluate the performance of an interpretable machine learning model in predicting preoperative axillary lymph node metastasis using primary breast cancer and lymph node features derived from contrast-enhanced mammography (CEM) and ultrasound (US) breast imaging reporting and data systems (BI-RADS).MethodsThis retrospective study included patients diagnosed with primary breast cancer. Two experienced radiologists extracted the BI-RADS features from the largest cross-section of the lesions and axillary lymph nodes based on CEM and US images, creating three datasets. Each dataset will train six base models to predict axillary lymph nodes, with pathological results serving as the gold standard. The top three models were used to train the five ensemble models. Additionally, SHapley Additive exPlanations (SHAP) was used to interpret the optimal model. The receiver-operating characteristic curve (ROC) and AUC were used to evaluate model performance.ResultsThis study involved 292 female patients, of whom 99 had axillary lymph node metastasis and 193 did not. The combination of CEM and ultrasound BI-RADS demonstrated the best performance in predicting axillary lymph node metastasis. Among these, the LightGBM achieved the highest AUC (0.762) and specificity (86.67%, while the ensemble model using RF as the meta-model had an AUC (0.754) and specificity (83.33%. The most important variables identified by SHAP were the long diameters of the lymph nodes in the CEM recombined image, along with their complete morphology in the low-energy image.ConclusionThe machine learning model using CEM and US BI-RADS features accurately predicted axillary lymph node metastasis before surgery, thereby serving as a valuable tool for clinical decision-making in patients with breast cancer.