Examined lymph node counts affected the staging and survival in cervical cancer: a retrospective study using the SEER and Chinese cohort

BACKGROUND

The impact of examined lymph node (LN) count on survival in cervical cancer remains understudied, with no consensus on the optimal number required.

OBJECTIVES

We aimed to investigate the role of LN count in postoperative pathological evaluation for cervical cancer patients undergoing surgery.

METHODS

Data from the US SEER database and a retrospective Chinese cohort were analyzed. Multivariable logistic and Cox regression models assessed LN positivity detection and overall survival (OS), respectively. Smoothing spline curves and Chow tests identified structural change points in hazard ratios and LN-positive ratios.

RESULTS

Among 14,133 SEER and 2,811 Chinese cases, higher LN counts correlated with increased detection of positive LNs (OR: 1.040, 95% CI: 1.019-1.062) and improved OS (HR: 0.990, 95% CI: 0.984-0.996). Structural change points occurred at 20 LNs for LN-negative patients and 13 LNs for LN-positive patients (both P < 0.001). An elevated LN-positive ratio (positive/examined LNs) strongly predicted worse OS (HR: 4.208; 95% CI: 1.454-12.181).

CONCLUSIONS

An increased number of examined LNs significantly contributes to the detection of positive LNs and improved patient survival. For patients with claimed LN-negative and LN-positive disease, a minimum examination of 20 and 13 LNs, respectively, is recommended to ensure adequate LN evaluation. The LN positive ratio is a strong prognostic indicator in patients with LN-positive disease.

The role of RNA binding proteins in cancer biology: A focus on FMRP

RNA-binding proteins (RBPs) act as crucial regulators of gene expression within cells, exerting precise control over processes such as RNA splicing, transport, localization, stability, and translation through their specific binding to RNA molecules. The diversity and complexity of RBPs are particularly significant in cancer biology, as they directly impact a multitude of RNA metabolic events closely associated with tumor initiation and progression. The fragile X mental retardation protein (FMRP), as a member of the RBP family, is central to the neurodevelopmental disorder fragile X syndrome and increasingly recognized in the modulation of cancer biology through its influence on RNA metabolism. The protein's versatility, stemming from its diverse RNA-binding domains, enables it to govern a wide array of transcript processing events. Modifications in FMRP's expression or localization have been associated with the regulation of mRNAs linked to various processes pertinent to cancer, including tumor proliferation, metastasis, epithelial-mesenchymal transition, cellular senescence, chemotherapy/radiotherapy resistance, and immunotherapy evasion. In this review, we emphasize recent findings and analyses that suggest contrasting functions of this protein family in tumorigenesis. Our knowledge of the proteins that are regulated by FMRP is rapidly growing, and this has led to the identification of multiple targets for therapeutic intervention of cancer, some of which have already moved into clinical trials or clinical practice.

Generating research hypotheses to overcome key challenges in the early diagnosis of colorectal cancer - Future application of AI

We intend to explore the capability of ChatGPT 4.0 in generating innovative research hypotheses to address key challenges in the early diagnosis of colorectal cancer (CRC). We asked ChatGPT to generate hypotheses focusing on three main challenges: improving screening accuracy, overcoming technological limitations, and identifying reliable biomarkers. The hypotheses were evaluated for novelty. The experimental plans provided by ChatGPT for selected hypotheses were assessed for completion and feasibility. As a result, ChatGPT generated a total of 65 hypotheses. ChatGPT rated all 65 hypotheses, with 25 hypotheses receiving the highest rating (5) and 40 hypotheses receiving a rating of 4 or lower. The research team evaluated a total of 65 hypotheses, assigning them the following grades: hypotheses were rated as excellent (Grade 5), 16 were deemed suitable (Grade 4), 31 were classified as satisfactory (Grade 3), 12 were identified as needing Improvement (Grade 2), and one was considered poor (Grade 1). Additionally, the study determined that 17 of the generated hypotheses had corresponding publications. Out of the three experimental plans assessed, one was rated excellent (5) for feasibility, while the others received good (4) and moderate (3) ratings. Predicted outcomes and alternative approaches were rated as good, with some areas requiring further improvement. Our data demonstrate that AI has the potential to revolutionize hypothesis generation in medical research, though further validation through experimental and clinical studies is needed. This study suggests that while AI can generate novel hypotheses, human expertise is essential for evaluating their practicality and relevance in scientific research.

Inhalable DNase I@Au hybrid nanoparticles for radiation sensitization and metastasis inhibition by elimination of neutrophil extracellular traps

High-dose radiation therapy is a widely used clinical strategy to inhibit tumor growth. However, the rapid generation of excessive reactive oxygen species (ROS) triggers the formation of neutrophil extracellular traps (NETs), which capture free tumor cells in the bloodstream, promoting metastasis. In this study, we developed a hybrid nanoparticle composed of DNase I and gold (DNase I@Au) to enhance radiotherapy efficacy while mitigating metastasis by precisely eliminating NETs. The DNase I@Au nanoparticles, administered via aerosol inhalation, are efficiently delivered to lung tumor tissue, improving radiosensitization and reducing tumor size. Crucially, the nanoparticles could gradually release DNase I, effectively degrading ROS-induced NETs and preventing the interaction of free malignant cells with tumor sites or vasculature, thereby inhibiting metastasis. Therefore, we provide an enzyme and sensitizer co-loaded strategy that offers a promising approach to improve the therapeutic outcome of radiotherapy and reduce the risk of lung cancer metastasis under ROS stimulation.

The multifaceted role of agents counteracting metabolic syndrome: A new hope for gastrointestinal cancer therapy

Metabolic syndrome (MetS) is defined by the presence of at least three of five clinical parameters including abdominal obesity, insulin resistance, elevated triglycerides, reduced high-density lipoprotein (HDL) and hypertension. Major features describing MetS have been recognized risk factors for cancer onset, with an alarming impact on gastrointestinal (GI) tumors. Intriguingly, therapeutic administration of drugs to improve glycemic control and dyslipidemia (including metformin, statins) has been shown to have a preventive role in the development and in prognosis improvement of several cancer types. Overall, these observations highlight the key role of altered metabolism prevalently in cancer risk development and unveil anti-MetS agent repurposing potential beyond their conventional pharmacological action. The objective of this review is to summarize the current knowledge about the antitumor activity of anti-diabetic and anti-lipemic agents in GI cancer onset and progression. Here, pre-clinical evidence of their therapeutic potential and of their integration in novel compelling therapeutic strategies will be discussed. Possible clinical outcomes of these novel therapeutic combined protocols specifically dedicated to GI cancer patients will be put under the spotlight. In the future, these novel therapeutic options should be considered to improve conventional chemotherapy response and prognosis of this group of patients.

Solasodine inhibits the Th2 immune response and airway remodeling in asthmatic mice through the Runx3/NLRP3 pathway

PURPOSE

To explore the therapeutic effects of Solasodine on Th2 immune responses and airway remodeling, and to assess whether its mechanism involves NLRP3 inflammasome inactivation mediated by Runx3.

METHODS

We created an asthma model with wild-type and Runx3 knockout mice using ovalbumin (OVA). After oral administration of Solasodine, we assessed inflammatory and Th2 immune responses using HE staining, ELISA, and flow cytometry. Airway remodeling was assessed with Masson's trichrome staining and α-SMA and TGF-β immunohistochemistry. Mucus secretion was analyzed through MUC5AC immunohistochemistry, and expectoration assays. We studied NLRP3 inflammasome activation using immunohistochemistry and Western blot. We used western blotting and flow cytometry to evaluate how Solasodine regulates Runx3 protein levels.

RESULTS

Solasodine effectively inhibited the inflammatory response in OVA-induced asthmatic mice, evidenced by reducing inflammatory cell infiltration and lower IL-4, IL-5, and IL-13 levels, decreasing airway remodeling and mucus secretion. Solasodine reduced airway hyperresponsiveness, shown by a lower Penh value. Solasodine boosts Runx3 expression and suppresses NLRP3 inflammasome activation in asthmatic mice. We created an asthma model in Runx3 knockout mice and administered Solasodine at a consistent dose. Following OVA induction, Runx3 knockout mice showed greater inflammation, a Th2 immune response, airway remodeling, and mucus secretion than wild-type mice. Solasodine is less effective in Runx3 knockout asthmatic mice than in wild-type mice.

CONCLUSION

The anti-asthmatic effects of Solasodine are shown through the inhibition in the Th2 immune response, airway remodeling, hyperresponsiveness, and mucus secretion. The effectiveness may be linked to Runx3-mediated the NLRP3 inflammasomes.

Construction and Validation of a Risk Prediction Model for Acute Gastrointestinal Injury in Non-ICU Elderly Critically Ill Patients

BACKGROUND

Acute gastrointestinal injury (AGI) has a relatively high prevalence among elderly critically ill patients in non-intensive care units (non-ICUs), and significantly influences their clinical outcomes. Therefore, it is important to identify people at risk for AGI and take preventive measures as early as possible.

OBJECTIVE

We aimed to construct and validate a risk prediction model for AGI in non-ICU elderly critically ill patients.

DESIGN

Case-control study.

PARTICIPANTS

In total, 538 elderly critically ill patients admitted to the general medical department of a tertiary hospital in Shanxi from April 2021 to May 2024.

MAIN MEASURES

Influential factors for AGI were determined using univariate and multifactorial logistic regression analyses. We constructed a risk prediction model and created a nomogram. The bootstrap resampling method was utilized for internal validation. A total of 151 patients from different time periods were selected for the external validation.

KEY RESULTS

The multifactorial logistic regression analysis revealed that the independent predictors for AGI were the duration of antibiotic use, number of vasoactive drugs, delayed enteral nutrition, age-corrected Charlson comorbidity index, and white blood cell count, all of which were included in the model and created a nomogram. The Omnibus test showed that the overall efficacy of the model was good (P < 0.001). The area under the receiver operating characteristic curve (AUC) was 0.807, the corrected AUC was 0.806, and the AUC was 0.796 for external validation, indicating good model discrimination. The calibration curves and Hosmer-Lemeshow tests revealed that the model was well calibrated (P = 0.627, Brier = 0.172 in internal validation; and P = 0.366, Brier = 0.182 in external validation). The clinical decision curves showed that the model had good clinical utility.

CONCLUSIONS

AGI is common in non-ICU elderly critically ill patients. This AGI risk prediction model can be used as a screening tool to identify high-risk patients for AGI and assist clinical decision making.

Cancer-nervous system crosstalk: from biological mechanism to therapeutic opportunities

A growing body of research suggests a bidirectional interaction between cancer and the nervous system. Neural cells exert their effects on tumors by secreting neurotransmitters and cell adhesion molecules, which interact with specific receptors on tumor cells to modulate their behavior. Conversely, tumor-secreted factors, particularly including inflammatory factors, can alter neural activity and increase neuronal excitability, potentially contributing to neurological manifestations such as epilepsy. The immune system also serves as a crucial intermediary in the indirect communication between cancer and the nervous system. These insights have opened promising avenues for novel therapeutic strategies targeting both tumors and their associated neurological complications. In this review, we have synthesized the key biological mechanisms underlying cancer-nervous system interactions that have emerged over the past decade. We outline the molecular and cellular pathways mediating this cross-talk and explore the clinical implications of targeting the nervous system to suppress tumor growth and metastasis, mitigate neurological complications arising from cancer progression, and modulate the immune response through neural regulation in the context of cancer therapy.

Critical Review: Real-Time Dose-Guided Radiation Therapy

Dramatic strides have been made in real-time adaptive radiation therapy, where treating single tumors as dynamic but rigid bodies has demonstrated a halving of toxicities for prostate cancer. However, the human body is much more complex than a rigid body. This review explores the ongoing development and future potential of dose-guided radiation therapy, where the three core process steps of volumetric imaging of the patient, dose accumulation, and dose-guided treatment adaptation occur quasi-continuously during treatment, fully accounting for the complexity of the dynamic human body. The clinical evidence supporting real-time adaptive radiation therapy was reviewed. The foundational studies, status, and potential of real-time volumetric imaging using both x-ray and magnetic resonance imaging technology were described. The development of real-time dose accumulation to the dynamic patient was evaluated, and a method to measure real-time dose delivery was assessed. The growth of real-time treatment adaptation was examined. Literature demonstrates continued improvements in patient outcomes because the treatment becomes more conformal to the dynamic patient. Real-time volumetric imaging using both x-ray and magnetic resonance imaging technology is poised for broader implementation. Real-time dose accumulation has demonstrated clinical feasibility, with approximations made to achieve real-time operation. Real-time treatment adaptation to deforming targets and multiple targets has been experimentally demonstrated. Tying together the inputs of the real-time volumetric anatomy and dose accumulation is real-time treatment adaptation that uses the available degrees of freedom to optimize the dose delivered to the patient, maximizing the treatment intent. Opportunities exist for artificial intelligence to accelerate the application of dose-guided radiation therapy to broader patient use. In summary, the emerging field of real-time dose-guided radiation therapy has the potential to significantly improve patient outcomes. The advances are primarily software-driven and therefore could be widely available and cost-effective upgrades to improve imaging and targeting cancer.

Prediction of real-time cine-MR images during MRI-guided radiotherapy of liver cancer using a GAN-ConvLSTM network

BACKGROUND

Respiratory motion during radiotherapy (RT) may reduce the therapeutic effect and increase the dose received by organs at risk. This can be addressed by real-time tracking, where respiration motion prediction is currently required to compensate for system latency in RT systems. Notably, for the prediction of future images in image-guided adaptive RT systems, the use of deep learning has been considered.

PURPOSE

This study proposed a modified generative adversarial network (GAN) for predicting cine-MR images in real time.

METHODS

Sagittal cine magnetic resonance (cine-MR) images of 15 patients with liver cancer who received RT were collected. The image series length of each patient was 300, and each series was divided into training, validation, and test sets. The datasets were further divided using a sliding window size of 10 and a stride of 1. A pix2pix GAN with the generator replaced by convolutional long short-term memory (ConvLSTM) was proposed herein. A five-frame cine-MR image series was inputted into the network, which predicted the next five frames. The proposed network was compared with three advanced networks: ConvLSTM, Eidetic 3D LSTM (E3D-LSTM), and SwinLSTM. Personalized models were trained for each patient. The peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), visual information fidelity (VIF), Pearson correlation coefficient (Pearson corr), and respiratory motion accuracy of the predicted images were used to evaluate the methods.

RESULTS

The proposed network demonstrated optimal performance in the four networks across various indicators. The proposed method provided better SSIM values than ConvLSTM at time steps 1, 2, 3, and 4, and outperformed E3DLSTM at all time steps. In terms of the VIF, the proposed method outperformed E3D-LSTM at all time steps and SwinLSTM at time steps 2, 3, 4, and 5. The proposed method was not significantly different from other methods in terms of Pearson correlation values except that it outperformed E3DLSTM at time step 1. In terms of the Pearson corr, the proposed method consistently achieves better values, especially in the high-frequency components. Low average landmark tracking errors were provided by the proposed method at time steps 4 and 5 (2.42 ± 0.91 and 2.44 ± 0.96 mm, respectively).

CONCLUSIONS

The GAN-ConvLSTM network can generate high-acutance real-time cine-MR images and predict respiratory motion with better accuracy.

Learning Curve on Lymph Nodes Retrieval and Postoperative Length of Hospital Stay in Robotic Rectal Cancer: A Retrospective, Observational Study

Background and Aims

Robotic rectal cancer surgery has been introduced to reduce the difficulty and complications of the procedure. This study aims to assess the learning curve for robotic rectal cancer of individual surgeons with extensive laparoscopic rectal cancer surgery through multidimensional analyses.

Methods

Data were retrospectively collected on 156 patients who underwent robotic rectal cancer surgery by a single surgeon between January 2018 and December 2023.

Results

The operative time required for LAR can be divided into three distinct phases: an early or learning phase (1-24 cases), an intermediate or proficient phase (25-55 cases), and a late or mastery phase (56-72 cases). The study found that the learning curve for LAR and protective operative time can be divided into three distinct phases: an early or learning phase (1 to 15 cases), an intermediate or proficient phase (16 to 40 cases), and a late or mastery stage (41 to 63 cases). Following the completion of 46 cases of surgery, the next stage of the learning curve for lymph nodes retrieval has been reached. The discrepancy between the mean number of lymph nodes retrieved in each of the three stages was marginal, with an difference of 0.5 between the lowest and highest values observed (14.1 vs. 13.6 vs. 13.7). The length of hospital stay for patients decreases as the surgeon gains more experience, reaching a mean of 10.3 days in 2023.

Conclusion

This study shows that robotic surgery for rectal cancer has a significant learning curve with multiple stages. Robotic surgery can remove more lymph nodes than recommended by guidelines even during the learning phase (recommend that at least 12 lymph nodes should be retrieved). As the learning curve progresses to the mastery phase, the length of postoperative hospital stays gradually decreases to a lower level.

The autocrine motility factor receptor delays the pathological progression of Alzheimer's disease via regulating the ubiquitination-mediated degradation of APP

BACKGROUND

The ubiquitin-proteasome system (UPS) is responsible for most protein degradation and its malfunction is normally observed in neurodegenerative diseases, including Alzheimer's disease (AD). The autocrine motility factor receptor (AMFR) is an E3 ubiquitin ligase that resides on the endoplasmic reticulum membrane and is involved in various essential biological processes. However, the role of AMFR in AD is still unidentified.

METHODS

Behavioral experiments, including open-field test (OFT), novel object recognition test (NORT) and morris water maze test (MWMT) were conducted after adeno-associated virus (AAV) microinjection into AD model mice. Western blot, co-immunoprecipitation (Co-IP), qPCR and ubiquitination assay were used to analyze AMFR mediated ubiquitination degradation of amyloid precursor protein (APP). ELISA was employed to evaluate changes in amyloidogenic cleavage products of APP following upregulation or downregulation of AMFR in neural cells and analyze AMFR levels in serum and cerebrospinal fluid (CSF) of AD patients.

RESULTS

The progressive decline in AMFR levels was found not only in the hippocampus of APPswe/PSEN1dE9 (APP/PS1) mice but also in the CSF and serum of patients with AD. Moreover, the interaction of AMFR and APP was observed both in hippocampal tissues and brain neurons. In addition, AMFR promoted the K11-linked polyubiquitination of APP to speed up its proteasomal degradation, resulting in decreased Aβ production. Importantly, AMFR overexpression largely rescued the cognitive and synaptic deficits in APP/PS1 mice.

CONCLUSIONS

Taken together, our results demonstrated that AMFR reduced Aβ production and alleviated cognitive impairment by promoting the ubiquitination-mediated degradation of APP. This study indicated that AMFR could have the potential to be a therapeutic target of early-stage AD.

Gut microbiome links obesity to type 2 diabetes: insights from Mendelian randomization

BACKGROUND

Research has established links between the gut microbiome (GM) and both obesity and type 2 diabetes (T2D), which is much discussed, but underexplored. This study employed body mass index (BMI) as the measurement of obesity to delve deeper into the correlations from a genetic perspective.

METHODS

We performed the Mendelian randomization (MR) analysis to examine the causal effects of GM on T2D and BMI, and vice versa. Genome-wide association study (GWAS) summary datasets were utilized for the analysis, including T2D (N = 933,970), BMI (N = 806,834), and two GM datasets from the international consortium MiBioGen (211 taxa, N = 18,340) and the Dutch Microbiome Project (DMP) (207 taxa, N = 7,738). These datasets mainly cover European populations, with additional cohorts from Asia and other regions. To further explore the potential mediating role of GM in the connections between BMI and T2D, their interaction patterns were summarized into a network.

RESULTS

MR analysis identified 9 taxa that showed protective properties against T2D. Seven species were within the Firmicutes and Bacteroidales phyla in the DMP, and two were from the MiBioGen (Odds Ratio (OR): 0.94-0.95). Conversely, genetic components contributing to the abundance of 12 taxa were associated with increased risks of T2D (OR: 1.04-1.12). Furthermore, T2D may elevate the abundance of seven taxa (OR: 1.03-1.08) and reduce the abundance of six taxa (OR: 0.93-0.97). In the analysis of the influence of the genetic component of BMI on GM composition, BMI affected 52 bacterial taxa, with 28 decreasing (OR: 0.75-0.92) and 24 increasing (OR: 1.08-1.27). Besides, abundances of 25 taxa were negatively correlated with BMI (OR: 0.95-0.99), while positive correlations were detected for 14 taxa (OR: 1.01-1.05). Notably, we uncovered 11 taxa genetically associated with both BMI and T2D, which formed an interactive network.

CONCLUSIONS

Our findings provide evidence for the GM-mediated links between obesity and T2D. The identification of relevant GM taxa offers valuable insights into the potential role of the microbiome in these diseases.

Predictive factors and prognostic models for Hepatic arterial infusion chemotherapy in Hepatocellular carcinoma: a comprehensive review

Hepatocellular carcinoma (HCC) is a prevalent and lethal cancer, often diagnosed at advanced stages where traditional treatments such as surgical resection, liver transplantation, and locoregional therapies provide limited benefits. Hepatic arterial infusion chemotherapy (HAIC) has emerged as a promising treatment modality for advanced HCC, enhancing anti-tumor efficacy through targeted drug delivery while minimizing systemic side effects. However, the heterogeneous nature of HCC leads to variable responses to HAIC, highlighting the necessity for reliable predictive indicators to tailor personalized treatment strategies. This review explores the factors influencing HAIC success, including patient demographics, tumor characteristics, biomarkers, genomic profiles, and advanced imaging techniques such as radiomics and deep learning models. Additionally, the synergistic potential of HAIC combined with immunotherapy and molecular targeted therapies is examined, demonstrating improved survival outcomes. Prognostic scoring systems and nomograms that integrate clinical, molecular, and imaging data are discussed as superior tools for individualized prognostication compared to traditional staging systems. Understanding these predictors is essential for optimizing HAIC efficacy and enhancing survival and quality of life for patients with advanced HCC. Future research directions include large-scale prospective studies, integration of multi-omics data, and advancements in artificial intelligence to refine predictive models and further personalize treatment approaches.

PKM2 modulates chemotherapy sensitivity by regulating autophagy and predicts the prognosis and immunity in pancancer

One of the main characteristics of tumor metabolite reprogramming is enhanced glycolysis, and Pyruvate Kinase M2(PKM2) is a crucial enzyme that limits the pace of glycometabolism. Although PKM2 has been proven to affect the development of some cancers, a pan-cancer analysis of PKM2 has not yet been performed. We analyzed the expression and prognosis of PKM2 in pan-cancer using multiple databases. We performed epigenetic, functional enrichment, immune cell infiltration, immune checkpoint, and drug sensitivity analyses of PKM2. PKM2 was found to be significantly upregulated in most malignancies and associated with a bad prognosis. In some cancers, the PKM2 DNA promoter was hypomethylated. The expression of PKM2 was positively linked with most m6A-methylation-related genes in pan-cancer. The functions of PKM2 were primarily associated with the regulation of the immune system, glycolysis, hypoxia, angiogenesis, and epithelial-mesenchymal transition. PKM2 was favorably associated with neutrophils and cancer-associated fibroblasts in the tumor microenvironment of most cancers. Importantly, PKM2 showed a strikingly high correlation with CD274 (PD-L1), CD276, TGF-β1, VEGFA, and HAVCR2 in most cancers. Finally, using experiments, it was confirmed that silencing PKM2 could increase the sensitivity of esophageal squamous cell carcinoma to cisplatin by regulating autophagy. PKM2 affects autophagy - regulated tumor cell tolerance to chemotherapy, providing future research directions for solving tumor chemotherapy resistance.

Assessing awareness of colorectal cancer symptoms, risk factors and screening barriers among eligible adults in Jordan: a cross-sectional study

BACKGROUND

Colorectal cancer (CRC) is the second most prevalent malignancy in Jordan. Because early detection can greatly improve treatment outcomes, it is crucial to increase awareness of signs and symptoms, risk factors, and the significance of routine CRC screenings. In this study, we aimed to assess awareness levels regarding CRC symptoms and risk factors among adults in Jordan and to identify barriers to CRC screening.

METHODS

This web-based cross-sectional study was conducted in Jordan from March 5, 2024 to July 9, 2024, and targeted people aged 50-75 years who had no history of CRC. The sample size was calculated via a convenience sampling method. Data were collected via a validated, culturally adapted survey. Descriptive analysis was used when appropriate. Analytic statistics were performed to predict participants' awareness of CRC symptoms and risk factors.

RESULTS

The study included 400 participants, with a mean age of 58.42 years (SD = 6.511). More than half of the respondents were females (56.5%). The mean awareness score of CRC symptoms among the study participants was 4.97/9 (SD = 1.18), whereas that of risk factors was 5.21/10 (SD = 1.53). The overall mean awareness score was 10.18/19 (SD = 2.65). The top three reported barriers to CRC screening were: not at risk due to absence of symptoms (61.8%), not at risk due to adopting a healthy lifestyle (56.8%), not at risk due to absence of family history (51.8%).

CONCLUSION

Colorectal cancer awareness among the population was relatively low, with significant symptoms and risk factors being overlooked by the participants. In addition to that, notable barriers to screening, especially fear and embarrassment of the screening test, have surfaced. This prompts the need for more cancer education and healthcare provider involvement to overcome screening barriers and promote participation in screening programs to enable early detection.

RNA-binding proteins and autophagy in lung cancer: mechanistic insights and therapeutic perspectives

BACKGROUND

Lung cancer remains a leading cause of cancer-related mortality worldwide. Its progression is intricately associated with the dynamic regulation of autophagy and RNA-binding proteins (RBPs), which play crucial roles in mRNA stability, alternative splicing, and cellular stress responses.

OBJECTIVES

This review aims to systematically analyze the mechanisms through which RBPs and autophagy contribute to lung cancer progression and explore potential therapeutic strategies targeting these pathways.

METHODS

We reviewed recent studies on the molecular mechanisms by which RBPs regulate tumor proliferation, metabolic adaptation, and their interaction with autophagy. The review also examines the dual roles of autophagy in lung cancer, highlighting its context-dependent effects on cell survival and death.

RESULTS

The interactions and regulatory networks between RBPs and autophagy involve multiple levels of regulation. RBPs can directly influence autophagy processes and act as microRNA (miRNA) sponges to regulate mRNA stability. The modulation of RBPs affects the expression of autophagy-related genes (ATGs) and autophagosome formation. Additionally, RBPs participate in complex regulatory interactions with non-coding RNAs (ncRNAs), including long non-coding RNAs (lncRNAs), circular RNAs (circRNAs), and other proteins.

CONCLUSIONS

This review proposes innovative therapeutic strategies that combine RBP-targeting approaches (e.g., small molecule inhibitors, CRISPR gene editing) with autophagy modulators (e.g., mTOR inhibitors, chloroquine) to enhance treatment efficacy. Nanoparticle drug delivery systems and epigenetic regulation offer further opportunities for targeted interventions. This review lays a theoretical foundation for advancing lung cancer research and provides novel insights into synergistic therapies that target both RBPs and autophagy to improve treatment outcomes for lung cancer.

A machine learning toolkit assisted approach for IMRT fluence map optimization: feasibility and advantages

Purpose. Traditional machine learning (ML) and deep learning (DL) applications in treatment planning rely on complex model architectures and large, high-quality training datasets. However, they cannot fully replace the conventional optimization process. This study presents a novel application of ML in treatment planning where established ML/DL toolkits are directly applied to treatment plan optimization.Materials and Methods. A one-layer network was designed based on the dose deposition matrix and implemented in PyTorch's L-BFGS optimizer with GPU acceleration. The classical steepest descent optimizer was selected as a reference for comparison. Both optimizers utilized identical inputs and objective functions to ensure a fair comparison. DVH- and gEUD-based objectives were implemented in standard quadratic forms. Standard uniform and 1,000 random initializations were used to test optimizer's search ability under different starting conditions for prostate and head-and-neck cases.Results. The MLT-assisted framework demonstrated comparable or superior plan quality to classical optimization by achieving lower objective values, improved DVHs and capturing finer modulation details in fluence maps. For gEUD-based optimization, it effectively explored beam weight elevations that classical optimization could only reach with stricter convergence criteria and many more iterations. The quality differences primarily stemmed from convergence speed. The MLT-assisted framework required significantly fewer evaluations and iterations to achieve similar or better results. Optimization on random initial maps further demonstrated that it was more robust and less likely to be trapped. It does not require stricter convergence criteria or extended runs to reach high-quality optima, making it more efficient and reliable.Conclusion. This framework leverages ML toolkits in a novel way, enabling faster convergence, greater robustness and handling of complex constraints. As the first study of its kind, it establishes MLT-assisted optimization as a viable and effective alternative to classical methods.

Cross-cultural Adaptation and Validation of the Italian Version of the Revised Perceived Perioperative Competence Scale

PURPOSE

The nurse's role in the operating room requires essential competencies throughout the care process, such as theoretical knowledge, technical skills, and problem-solving abilities assessed by the Revised Perceived Perioperative Competence Scale (PPCS-R). The study aims to translate, cross-culturally adapt, and validate the Italian version of the scale.

DESIGN

A cross-cultural adaptation and validation of the Italian version of PPCS-R.

METHODS

The revised English version of the 40-item scale PPCS-R was translated into Italian according to Beaton's guidelines. The scale's internal consistency was assessed using Cronbach's α coefficient, ensuring the instrument's reliability. An exploratory factor analysis was conducted, preceded by the Kaiser-Meyer-Olkin test and Bartlett's sphericity test. Statistical analyses were performed using SPSS version 29.1 (IBM Corp.).

FINDINGS

The exploratory factor analysis of the Revised Perceived Perioperative Competence Scale-Italian Version (PPCS-R ITA) (N = 509) confirmed the 6 factors served in the original version. The final Italian version of PPCS-R comprises 40 items grouped into 6 latent factors. Each latent component showed a high consistency per Cronbach's α. The statistical analyses of the study confirm the reliability and good consistency of the tool.

CONCLUSIONS

The statistical analyses of the study confirm the reliability and consistency of the tool (Revised Perceived Perioperative Competence Scale-Italian Version). By investigating the perception of Italian professionals working in the operating room through a translated and validated scale, specific training and professional development needs of a specific context can be better understood to identify appropriate learning and management strategies.

IPEM topical report: guidance for the use of linac manufacturer integrated quality control

This report provides guidance for users of linear accelerator (linac) manufacturer integrated quality control (MIQC) tools. MIQC tools have been developed and introduced by radiotherapy linac vendors, and have the potential to improve both the quality and efficiency of linac quality control (QC). They usually utilise the Electronic Portal Imaging Device (EPID), but may acquire data from other sources, and automatically perform and analyse tests of various treatment machine QC parameters. The currently available systems meeting this definition are Varian machine performance check, CyberKnife automated quality assurance /end-to-end, TomoTherapy Quality Assurance, and Elekta machine QA (also known as AQUA). This guidance report covers the commissioning and implementation of MIQC. The guidance has been developed by a radiotherapy special interest group working party on behalf of the Institute of Physics and Engineering in Medicine. Recommendations within the report are derived from the experience of the working party members, existing guidance, literature, and a United Kingdom survey conducted in 2022 (Pearsonet al2023Phys. Med. Biol.68245018). Topics covered include developing an understanding of the QC system, independence review of MIQC, commissioning, implementation, ongoing QC and calibration, software upgrades and periodic review. The commissioning section covers detector commissioning, repeatability and reproducibility, baseline and tolerance setting, concordance with existing QC, sensitivity testing, cost-benefits analysis, and risk assessment methods. In order to offer practical guidance, case studies covering each aspect of commissioning are included. They are real-world examples or experiences from early adopters, each applied to a different example MIQC system. The examples will be directly applicable to users of that specific MIQC system, but also provide practical guidance on clinical implementation to users of the other systems.

Optimizing prediction of metastasis among colorectal cancer patients using machine learning technology

BACKGROUND AND AIM

Colorectal cancer is among the most prevalent and deadliest cancers. Early prediction of metastasis in patients with colorectal cancer is crucial in preventing it from the advanced stages and enhancing the prognosis among these patients. So far, previous studies have been conducted to predict metastasis in colorectal cancer patients using clinical data. The current research attempts to leverage a combination of demographic, lifestyle, nutritional, and clinical factors, such as diagnostic and therapeutical factors, to construct an ML model with more predictive insights and generalizability than previous ones.

MATERIALS AND METHODS

In this retrospective study, we used 1156 CRC patients referred to the Masoud internal clinic in Tehran City from January 2017 to December 2023. The chosen machine learning algorithms, including LightGBM, XG-Boost, random forest, artificial neural network, support vector machine, decision tree, K-Nearest Neighbor and logistic regression, were utilized to establish prediction models for predicting metastasis among colorectal cancer patients. We also assessed features based on the best-performing model to improve clinical usability. To show the generalizability of the established prediction model for predicting CRC metastasis, we leveraged the data of 115 CRC patients from Imam Khomeini Hospital in Sari City. We assessed the predictive ability of LightGBM as the best-performing model based on external data.

RESULTS

The LightGBM model with a PPV of 97.32%, NPV of 84.67%, sensitivity of 83.14%, specificity of 93.14%, accuracy of 88.14%, F1-score of 87.51%, and an AU-ROC of 0.9 [Formula: see text]0.01 obtained satisfactory performance for prediction purposes on this topic. Factors including the history of IBD, family history of CRC, number of lymph nodes involved, fruit intake, and tumor size were considered as more strengthful predictors for metastasis in colorectal cancer and clinical usability. The external validation cohort showed a PPV of 0.8, NPV of 0.85, sensitivity of 0.78, specificity of 0.86, accuracy of 0.834, F1-score of 0.795, and AU-ROC of 0.77[Formula: see text]0.03, demonstrating satisfactory generalizability when leveraging external data from other clinical settings.

CONCLUSION

The current empirical results indicated that LighGBM has predictive competency that can be leveraged by physicians in clinical environments for early prediction of metastasis and enhanced prognosis in patients with colorectal cancer.

CLINICAL TRIAL NUMBER

Not applicable.

Neutrophil extracellular traps mediate pathophysiology of hepatic cells during liver injury

Neutrophil extracellular traps (NETs) are web-like, bactericidal structures produced by neutrophils and are composed principally of extracellular DNA, histones, elastase, and myeloperoxidase, among other components. NET formation is an innate immune response that is beneficial for pathogen killing and clearance. However, excessive NET formation and clearance defects can lead to inflammation and induce damage to host organs. NETs are also implicated in the development of noninfectious inflammatory disorders, such as liver injury in chronic liver diseases. The liver parenchyma contains hepatocytes, liver sinusoidal endothelial cells, Kupffer cells, and hepatic stellate cells. Each of these cells possesses unique structures and functions, and their interactions with NETs result in pathophysiological changes contributing to liver injury. This review updates the findings related to the modes of action and molecular mechanisms by which NETs modulate the pathophysiology of various hepatic cells and potentiate liver injury. The article also reviews the roles of NETs in hepatic ischemia reperfusion injury, hepatocellular carcinoma pathogenesis, and cancer metastasis. Last, we examine data to determine whether NETs induce crosstalk among various hepatic cells during liver injury and to identify future research directions.

Development and validation of nomograms for predicting overall survival and cancer-specific survival in unresected colorectal cancer patients undergoing chemotherapy

This study aims to develop nomograms for predicting overall survival (OS) and cancer-specific survival (CSS) in colorectal cancer (CRC) patients who did not receive primary site surgery but underwent chemotherapy. We analyzed data from 3,050 patients treated with chemotherapy without primary site surgery from 2010 to 2015, sourced from the Surveillance, Epidemiology, and End Results (SEER) database. The data were randomly divided into training and validation sets. Initial variable selection was performed using the least absolute shrinkage and selection operator (LASSO), and multivariate Cox regression analysis was used to identify independent prognostic factors. Two nomograms were subsequently constructed based on these factors. Survival analysis was conducted using Kaplan-Meier plots and the log-rank test. We identified nine significant predictors of OS and CSS: age, marital status, primary site, grade, histology, T stage, M stage, tumor size, and CEA levels. The models for OS and CSS exhibited excellent predictability, with time-dependent area under the receiver operating characteristic curves (AUCs) exceeding 0.7. Calibration curves confirmed the accuracy of these predictions in the training and validation sets. Additionally, decision curve analysis (DCA) indicated that our models provide greater clinical benefit than traditional TNM staging. Notably, survival outcomes varied significantly across risk categories, affirming the models' effective discrimination. For CRC patients who did not receive primary site surgery but underwent chemotherapy, this validated nomogram enables precision prognostication fundamentally shifting the paradigm from population-level TNM estimates to individualized risk-adaptive management.

CCL26 as a prognostic biomarker in hepatocellular carcinoma: integrating bioinformatics analysis, clinical validation, and radiomics score

BACKGROUND

CCL26 has been identified as a potential prognostic biomarker in hepatocellular carcinoma (HCC). This study aimed to assess the prognostic significance of CCL26 and develop a radiomics score (Rad-score) for predicting outcomes in HCC patients.

METHODS

Data from 316 HCC patients, including genomic information, computed tomography (CT) images, and clinicopathological data, were analyzed. The prognostic value of CCL26 was evaluated in 295 TCGA patients using Kaplan-Meier and Cox regression analyses, and validated in 21 patients from Jiujiang No. 1 People's Hospital. Gene set variation and immune cell infiltration analyses were conducted to elucidate the biological functions of CCL26. Radiomic models for predicting CCL26 expression were constructed using CT images and genomic data from 34 TCGA patients. Radiomic features were extracted from tumor regions and screened using maximum relevance minimum redundancy (mRMR) and recursive feature elimination (RFE). Two Rad-scores were generated via logistic regression and validated using internal fivefold cross-validation. A prognostic nomogram incorporating the optimal Rad-score, gender, and hepatic inflammation was developed using Cox proportional hazards regression.

RESULTS

Elevated CCL26 levels correlated with poor prognosis, as confirmed by immunohistochemistry. The optimal Rad-score, combined with gender and hepatic inflammation, accurately predicted overall survival (OS), with areas under the receiver operating characteristic curve (AUCs) of 0.819, 0.902, and 0.982 for 24-, 36-, and 48 month survival, respectively. Calibration curves and decision curve analysis (DCA) demonstrated the accuracy and clinical utility of the model.

CONCLUSIONS

CCL26 serves as a significant prognostic biomarker in HCC. The developed Rad-score provides an effective, non-invasive tool for predicting patient outcomes and enhancing clinical decision-making. This study not only highlights the prognostic role of CCL26 but also offers a novel approach for evaluating HCC patient prognosis through radiomics.

ICG fluorescence-guided sentinel lymph node biopsy for decision-making in lateral lymph node dissection in local advanced rectal cancer: a retrospective study

Local advanced rectal cancer (LARC) carries high recurrence risks, especially with lateral lymph node (LLN) involvement. This study aims to evaluate the role of ICG-guided sentinel lymph node biopsy (SLNB) in patients with clinical negative LLNs (maximum diameter < 7 mm), potentially reducing unnecessary surgeries and associated complications in patients with LARC. A retrospective analysis of 301 consecutive patients with lower LARC who underwent fluorescent lateral pelvic sentinel lymph node biopsy (FL-SLNB) or conventional LLND at the Cancer Hospital, Chinese Academy of Medical Sciences between 2018 and 2022 was conducted. Clinical and pathological data were collected, and the patients were grouped into FL-SLNB and non-SLNB groups. Postoperative complications, recurrence rates, and survival outcomes were assessed. Statistical analysis was performed using χ2 tests, Mann-Whitney U tests, Kaplan-Meier survival curves, and Cox proportional hazards models. FL-SLNB (173 patients) showed better perioperative outcomes than non-SLNB (128 patients), with shorter hospital stays (7 vs. 10 days, P = 0.027), less blood loss (150 vs. 180 mL, P = 0.032), and fewer complications: intraoperative bleeding (2.9% vs. 6.3%, P = 0.041), anastomotic leakage (1.7% vs. 3.9%, P = 0.045), and urinary dysfunction (3.5% vs. 7.0%, P = 0.039). No significant differences were observed in survival or recurrence rates (P > 0.05). pN stage was a significant predictor of distant metastasis (HR 1.953, P = 0.037). ICG-guided SLNB enhanced surgical precision and reduced unnecessary LLND in lower LARC with clinically negative LLNs, and improved surgical decision-making and minimizes postoperative complications.

Hepatocellular carcinoma: pathogenesis, molecular mechanisms, and treatment advances

Hepatocellular Carcinoma (HCC), a highly prevalent malignancy, poses a significant global health challenge. Its pathogenesis is intricate and multifactorial, involving a complex interplay of environmental and genetic factors. Viral hepatitis, excessive alcohol consumption, and cirrhosis are known to significantly elevate the risk of developing HCC. The underlying biological processes driving HCC are equally complex, encompassing aberrant activation of molecular signaling pathways, dysregulation of hepatocellular differentiation and angiogenesis, and immune dysfunction. This review delves into the multifaceted nature of HCC, exploring its etiology and the intricate molecular signaling pathways involved in its development. We examine the role of immune dysregulation in HCC progression and discuss the potential of emerging therapeutic strategies, including immune-targeted therapy and tumor-associated macrophage interventions. Additionally, we explore the potential of traditional Chinese medicine (TCM) monomers in inhibiting tumor growth. By elucidating the complex interplay of factors contributing to HCC, this review aims to provide a comprehensive understanding of the disease and highlight promising avenues for future research and therapeutic development.

Epidermal Growth Factor Receptor (EGFR) Amplification May Lead to Invalid Cobas EGFR Mutation Test v2 Results

Background/Objectives: The cobas EGFR test v2, used for detecting EGFR mutations, can yield invalid results due to internal control (IC) issues, such as "IC not detected", "IC out of range: high Ct value", or "IC out of range: low Ct value". This study aimed to examine the incidence of invalid cobas results and explored the mechanism behind low IC Ct values. Methods: We retrospectively reviewed invalid cases, linking undetectable or high IC Ct values to inadequate DNA from small biopsies, as determined by conducting a pathological review. Cases with low IC Ct values were further tested, with the hypothesis of EGFR amplification confirmed using Sanger sequencing, the Idylla assay, next-generation sequencing (NGS), and fluorescence in situ hybridization (FISH). Results: Among 4148 cases, the incidence of invalid results was 0.99% (41/4148). In four cases with low IC Ct values, EGFR amplification was confirmed using alternative methods with successful cobas testing on diluted DNA. Conclusions: These findings suggest that EGFR amplification, rather than specimen inadequacy, is the cause of low IC Ct results, making rebiopsy unnecessary. Alternative assays or diluted DNA allow for successful EGFR testing.

Transanal total mesorectal excision for locally advanced rectal cancer following neoadjuvant chemoradiotherapy

PURPOSE

To examine the clinical impact of transanal total mesorectal excision (TaTME) for locally advanced rectal cancer after neoadjuvant chemoradiotherapy (NACRT).

METHODS

This retrospective study included 91 patients undergoing surgery for rectal cancer after NACRT between 2011 and 2022. Among them, 24, 22, and 45 patients underwent open (Open), conventional laparoscopic (Lap), and TaTME surgeries, respectively. We compared their clinical outcomes.

RESULTS

Operative time, blood loss, transfusion, morbidity, and hospital stay were significantly lower in the TaTME group than in the Open or Lap groups. The multivariate regression analyses identified only the TaTME approach as a significant factor for reducing morbidity. Both 3 yrear relapse-free survival (RFS) and local recurrence-free survival (LRFS) were significantly better in the TaTME group than in the Open or Lap groups (3 yr RFS: 94.7%, 80.4%, and 66.7%, and 3 yr LRFS: 100%, 90.5%, and 82.2% for the TaTME, Lap, and Open groups, respectively). Multivariate analyses of potential risk factors for recurrence identified body mass index, combined resection, and pathological stage, but not the TaTME approach, as significant predictors of recurrence.

CONCLUSION

TaTME reduced morbidity significantly in patients with locally advanced rectal cancer undergoing NACRT, compared with open or laparoscopic surgery.

Extracellular Traps in Inflammation: Pathways and Therapeutic Targets

New roles for immune cells, overcoming the classical cytotoxic response, have been highlighted by growing evidence. The immune cells, such as neutrophils, monocytes/macrophages, and eosinophils, are versatile cells involved in the release of web-like DNA structures called extracellular traps (ETs) which represent a relevant mechanism by which these cells prevent microbes' dissemination. In this process, many enzymes, such as elastase, myeloperoxidase (MPO), and microbicidal nuclear and granule proteins, which contribute to the clearance of entrapped microorganisms after DNA binding, are involved. However, an overproduction and release of ETs can cause unwanted and dangerous effects in the host, resulting in several pathological manifestations, among which are chronic inflammatory disorders, autoimmune diseases, cancer, and diabetes. In this review, we discuss the release mechanisms and the double-edged sword role of ETs both in physiological and in pathological contexts. In addition, we evaluated some possible strategies to target ETs aimed at either preventing their formation or degrading existing ones.

Molecule subtypes play important roles for second primary malignancies development based on 324,661 breast cancer survivors

The incidence trend of breast molecule subtypes was unclear. There was not quantified risk by subtype with the second primary malignancies (SPMs) and limited evidence about the risk factors for developing SPMs in first primary breast cancer(FPBC). Data from 18 SEER registries were used to identify FPBC, which were randomly selected for training and validation sets. The SPMs information of breast cancer survivors in Hebei were also collected to compare the distribution with SEER. Univariate and multivariate analysis were performed to explore the risk factors and integrated to the establishment of nomogram and risk stratification model. There was a decreased trend for TNBC, but an increased trend for Luminal A. The median survival months were 46, 46, 46 and 44 for Luminal A, Luminal B, HER2 enriched and TNBC, with the median latency time were 39, 39, 40 and 41.0 months, respecitvely, The cumulative incidence rates(CIR) of SPMs were 2.61%, 2.30%, 2.21% and 2.84%. Age at diagnosis, clinical lymph node status, radiotherapy and subtypes were independent risk factors for SPMs. A predict nomogram was established with the AUC of 0.682 and 0.679 for three- and five- year incidence risk in training set. Patients were divided into the low-risk (31.94%), intermediate-risk (51.83%) and high-risk (16.23%) groups by risk stratification model. The first common SPMs was second breast cancer in both SEER and Hebei cohort, the second and third rank SPMs were lung and gynecological cancer in SEER, but presented the opposite result in Hebei. The incidence rates and SPMs of subtypes were difference. The high risk individuals could be identified by risk stratification model, who need more closely followed up by Clinicians.

Next‐Generation Flexible Embolic Systems: Targeted Transarterial Chemoembolization Strategies for Hepatocellular Carcinoma

Transarterial chemoembolization (TACE) remains the gold standard for treating intermediate‐stage hepatocellular carcinoma (HCC), yet faces great challenges in overcoming tumor heterogeneity, hypoxia‐induced angiogenesis, and metastatic progression. The development of advanced flexible embolization materials marks a revolutionary leap in interventional therapy, offering opportunities to revolutionize embolization precision, drug delivery kinetics, and tumor microenvironment modulation. This comprehensive review systematically examines the paradigm shift toward next‐generation TACE technology, emphasizing the limitations of conventional approaches and innovations in flexible embolic agents. A detailed discussion of next‐generation nano‐flexible embolic systems is presented, emphasizing their unique coagulation dynamics, real‐time imaging capabilities, and therapeutic precision. The review delves into groundbreaking TACE strategies integrating hypoxia modulation, energy conversion therapeutics, and sophisticated tumor microenvironment engineering. Clinical translation aspects are thoroughly explored, including large‐scale trial outcomes, vascular recanalization dynamics, and patient‐specific treatment optimization. Looking forward, key frontiers in the field is identified: intelligent nanocomposite systems, synergistic combination therapies, and precision medicine approaches tailored to individual tumor biology. This work not only objectively evaluates current progress but also charts future research priorities, aiming to transform TACE from a palliative intervention to a precision medicine platform and ultimately reshaping the landscape of HCC treatment and patient care.

Scatter and beam hardening effect corrections in pelvic region cone beam CT images using a convolutional neural network

The aim of this study is to remove scattered photons and beam hardening effect in cone beam CT (CBCT) images and make an image available for treatment planning. To remove scattered photons and beam hardening effect, a convolutional neural network (CNN) was used, and trained with distorted projection data including scattered photons and beam hardening effect and supervised projection data calculated with monochromatic X-rays. The number of training projection data was 17,280 with data augmentation and that of test projection data was 540. The performance of the CNN was investigated in terms of the number of photons in the projection data used in the training of the network. Projection data of pelvic CBCT images (32 cases) were calculated with a Monte Carlo simulation with six different count levels ranging from 0.5 to 3 million counts/pixel. For the evaluation of corrected images, the peak signal-to-noise ratio (PSNR), the structural similarity index measure (SSIM), and the sum of absolute difference (SAD) were used. The results of simulations showed that the CNN could effectively remove scattered photons and beam hardening effect, and the PSNR, the SSIM, and the SAD significantly improved. It was also found that the number of photons in the training projection data was important in correction accuracy. Furthermore, a CNN model trained with projection data with a sufficient number of photons could yield good performance even though a small number of photons were used in the input projection data.

Roles of the gut microbiota in hepatocellular carcinoma: from the gut dysbiosis to the intratumoral microbiota

Hepatocellular carcinoma (HCC) is closely linked to alterations in the gut microbiota. This dysbiosis is characterized by significant changes in the microbial population, which correlate with the progression of HCC. Gut dysbiosis ultimately promotes HCC development in several ways: it damages the integrity of the gut-vascular barrier (GVB), alters the tumor microenvironment (TME), and even affects the intratumoral microbiota. Subsequently, intratumoral microbiota present a characteristic profile and play an essential role in HCC progression mainly by causing DNA damage, mediating tumor-related signaling pathways, altering the TME, promoting HCC metastasis, or through other mechanisms. Both gut microbiota and intratumoral microbiota have dual effects on HCC progression; a comprehensive understanding of their complex biological roles will provide a theoretical foundation for potential clinical applications in HCC treatment.

T1-contrast enhanced MRI generation from multi-parametric MRI for glioma patients with latent tumor conditioning

BACKGROUND

Gadolinium-based contrast agents (GBCAs) are commonly used in MRI scans of patients with gliomas to enhance brain tumor characterization using T1-weighted (T1W) MRI. However, there is growing concern about GBCA toxicity. This study develops a deep-learning framework to generate T1-postcontrast (T1C) from pre-contrast multiparametric MRI.

PURPOSE

We propose the tumor-aware vision transformer (TA-ViT) model that predicts high-quality T1C images. The predicted tumor region is significantly improved (p < 0.001) by conditioning the transformer layers from predicted segmentation maps through the adaptive layer norm zero mechanism. The predicted segmentation maps were generated with the multi-parametric residual (MPR) ViT model and transformed into a latent space to produce compressed, feature-rich representations. The TA-ViT model was applied to T1w and T2-FLAIR to predict T1C MRI images of 501 glioma cases from an open-source dataset. Selected patients were split into training (N = 400), validation (N = 50), and test (N = 51) sets. Model performance was evaluated with the peak-signal-to-noise ratio (PSNR), normalized cross-correlation (NCC), and normalized mean squared error (NMSE).

RESULTS

Both qualitative and quantitative results demonstrate that the TA-ViT model performs superior against the benchmark MPR-ViT model. Our method produces synthetic T1C MRI with high soft tissue contrast and more accurately synthesizes both the tumor and whole brain volumes. The synthesized T1C images achieved remarkable improvements in both tumor and healthy tissue regions compared to the MPR-ViT model. For healthy tissue and tumor regions, the results were as follows: NMSE: 8.53 ± 4.61E-4; PSNR: 31.2 ± 2.2; NCC: 0.908 ± 0.041 and NMSE: 1.22 ± 1.27E-4, PSNR: 41.3 ± 4.7, and NCC: 0.879 ± 0.042, respectively.

CONCLUSION

The proposed method generates synthetic T1C images that closely resemble real T1C images. Future development and application of this approach may enable contrast-agent-free MRI for brain tumor patients, eliminating the risk of GBCA toxicity and simplifying the MRI scan protocol.

Identification and Validation of Gastric Adenocarcinoma Prognosis Features Based on Neutrophil-Related Genes

PURPOSE

The aim of this study was to investigate the effect of neutrophil-related genes (NRGs) on prognosis and tumor microenvironment (TME) of patients with gastric adenocarcinoma (GA), to provide a new reference for prognosis evaluation and related mechanism research of GA.

METHODS

The gene expression data and clinical information of patients with GA were collected from The Cancer Genome Atlas database. NRG data are from the literature. Differential NRGs were obtained by difference analysis and regression analysis for the construction of the prognostic model, which was validated using the GSE84426 data set. The independent prognostic effect of risk score was analyzed by constructing a nomogram. The single-sample gene set enrichment analysis and CIBERSORT methods were used to evaluate differences in TME between a high-risk group (HRG) and a low-risk group (LRG) and to evaluate the differences in response to immunotherapy and sensitivity to different drugs in high and low risk score groups.

RESULTS

We established a prognostic model on the basis of seven NRGs (NHLRC3, PTPRJ, RTEL1, ST6GALNAC2, HRNR, HP, and MCEMP1) and validated its predictive value. Multivariable Cox regression analysis further demonstrated that the model remained an independent prognostic factor for overall survival, and a nomogram was constructed for clinical practice. Differential analysis of immune cell infiltration levels showed that macrophages, mast cells, and neutrophils were highly infiltrated in HRG compared with LRG. Compared with HRG, LRG was more sensitive to immunotherapy and more sensitive to candidates such as axitinib, cisplatin, and ulixertinib.

CONCLUSION

In summary, on the basis of expression levels of NRGs, a new prognostic model was established. NHLRC3, PTPRJ, RTEL1, ST6GALNAC2, HRNR, HP, and MCEMP1 were valid candidate biomarkers that may help personalize prognostic predictions and serve as references for clinical studies.

Neural Network and Logistic Regression Models Based on Ultrasound Radiomics and Clinical-Pathological Features to Predict Occult Level II Lymph Node Metastasis in Papillary Thyroid Carcinoma

RATIONALE AND OBJECTIVES

Papillary thyroid carcinoma (PTC) often metastasizes to lateral cervical lymph nodes, especially in level II. This study aims to develop predictive models to identify level II lymph node metastasis (LNM), guiding selective neck dissection (SND) to minimize unnecessary surgery and morbidity in low-risk patients.

METHODS

A retrospective cohort of 313 PTC patients who underwent modified radical neck dissection (MRND) between October 2020 and January 2023 was analyzed. The patients were randomly assigned to a training cohort (70%) and a validation cohort (30%). Five predictive models were developed using neural networks (NNET) and logistic regression (LR) based on ultrasound radiomic features, clinical-pathological data, or a combination of both. Each model's performance was evaluated based on accuracy, area under the receiver operating characteristic curve (AUC), sensitivity, and specificity in predicting occult level II LNM. SHapley Additive exPlanations and nomogram were used to interpret the most important features in the models.

RESULTS

The occurrence rate of level II LNM was 28% in the cohort. Among the five predictive models developed, the LR-radiomics signature model demonstrated the highest performance, achieving an accuracy of 96.8% and an AUC of 0.989 in the validation set. In comparison, the NNET-radiomic + clinical feature model achieved an AUC of 0.935, while other models exhibited moderate to low accuracy and AUCs ranging from 0.699 to 0.785. The decision curve analysis demonstrated that the LR-radiomics signature model provided the greatest clinical utility, offering the highest net benefit across a range of decision thresholds for identifying occult level II LNM.

CONCLUSION

Our study developed predictive models using ultrasound-derived radiomic features and clinical-pathological data to assess the risk of occult level II LNM in PTC. The LR-radiomics signature model demonstrated high accuracy, making it a valuable tool for guiding personalized treatment decisions, by informing MRND for high-risk patients and supporting SND for low-risk patients to minimize unnecessary surgical interventions and optimize clinical outcomes.

Unsupervised and Self-supervised Learning in Low-Dose Computed Tomography Denoising: Insights from Training Strategies

In recent years, X-ray low-dose computed tomography (LDCT) has garnered widespread attention due to its significant reduction in the risk of patient radiation exposure. However, LDCT images often contain a substantial amount of noises, adversely affecting diagnostic quality. To mitigate this, a plethora of LDCT denoising methods have been proposed. Among them, deep learning (DL) approaches have emerged as the most effective, due to their robust feature extraction capabilities. Yet, the prevalent use of supervised training paradigms is often impractical due to the challenges in acquiring low-dose and normal-dose CT pairs in clinical settings. Consequently, unsupervised and self-supervised deep learning methods have been introduced for LDCT denoising, showing considerable potential for clinical applications. These methods' efficacy hinges on training strategies. Notably, there appears to be no comprehensive reviews of these strategies. Our review aims to address this gap, offering insights and guidance for researchers and practitioners. Based on training strategies, we categorize the LDCT methods into six groups: (i) cycle consistency-based, (ii) score matching-based, (iii) statistical characteristics of noise-based, (iv) similarity-based, (v) LDCT synthesis model-based, and (vi) hybrid methods. For each category, we delve into the theoretical underpinnings, training strategies, strengths, and limitations. In addition, we also summarize the open source codes of the reviewed methods. Finally, the review concludes with a discussion on open issues and future research directions.

Evaluation of a novel ensemble model for preoperative ovarian cancer diagnosis: Clinical factors, O-RADS, and deep learning radiomics

BACKGROUND

Accurate early diagnosis of ovarian cancer is crucial. The objective of this research is to create a comprehensive model that merges clinical variables, O-RADS, and deep learning radiomics to support preoperative diagnosis and assess its efficacy for sonographers.

MATERIALS AND METHODS

Data from two centers were used: Center 1 for training and internal validation, and Center 2 for external validation. DL and radiomics features were extracted from transvaginal ultrasound images to create a DL radiomics model using the LASSO method. A machine learning model ensemble was created by merging clinical variables, O-RADS scores, and DL radiomics model predictions. The model's effectiveness was evaluated by measuring the area under the receiver operating characteristic curve (AUC) and analyzing its impact on improving the diagnostic skills of sonographers. Moreover, the model's additional usefulness was assessed through integrated discrimination improvement (IDI), net reclassification improvement (NRI), and subgroup analysis.

RESULTS

The ensemble model demonstrated superior diagnostic performance for ovarian cancer compared to standalone clinical models and clinical O-RADS models. Notably, there were significant improvements in the NRI and IDI across all three datasets, with p-values < 0.05. The ensemble model exhibited exceptional diagnostic performance, achieving AUCs of 0.97 in both the internal and external validation sets. Moreover, the implementation of this ensemble model substantially improved the diagnostic precision and reliability of sonographers. The sonographers' average AUC improved by 11 % in the internal validation set and by 7.7 % in the external validation set.

CONCLUSIONS

The ensemble model significantly enhances preoperative ovarian cancer diagnosis accuracy and improves sonographers' diagnostic capabilities and consistency.

Phase-contrast magnetic resonance imaging-based predictive modelling for surgical outcomes in patients with Chiari malformation type 1 with syringomyelia: a machine learning study

AIM

Prospective outcome prediction plays a crucial role in guiding preoperative decision-making in patients with Chiari malformation type I (CM-Ⅰ) with syringomyelia. Here, we aimed to develop a predictive model for postoperative outcomes in patients with CM-Ⅰ with syringomyelia by integrating clinical and radiological parameters.

MATERIALS AND METHODS

We retrospectively analysed the data of 151 adult patients diagnosed with CM-I with syringomyelia who underwent posterior fossa decompression surgery. Clinical outcomes were assessed using the Chicago Chiari Outcome Scale (CCOS). Predictors were investigated using bivariate and multiple linear regression analyses. Five factors were used to build seven independent machine learning (ML) models: Cat Boost classifier (CatBoost), random forest, light gradient boosting machine, decision tree classifier, logistic regression, K neighbours classifier, and support vector machine. The dataset was randomly divided into training (n = 121, 80%) and test (n = 30, 20%) sets. Model performance was evaluated using precision, recall, F-1 score, and area under the curve (AUC). Shapley additive explanations (SHAP) was used to interpret the feature significance.

RESULTS

The best independent model was the CatBoost model, with an AUC of 0.9583 and an accuracy of 0.9097. The cross-validation results indicated that the accuracy of the CatBoost model was 0.8667. The SHAP plot revealed the important ranking of the features affecting the CCOS score as syrinx diameter, preoperative symptom duration, gait instability, peak diastolic velocity at the foramen magnum, and age.

CONCLUSION

We successfully developed a model to predict the prognosis of patients with CM-Ⅰ with syringomyelia after posterior fossa decompression.

Optimal lymph node yield for long-term survival in elderly patients with right-sided colon cancer: a large population-based cohort study

BACKGROUND

Although the recommended minimal lymph node yield (LNY) in colon cancer is 12, this standard remains controversial in elderly patients with right-sided colon cancer (RSCC) due to insufficient evidence. This study aims to clarify this issue by assessing the relationship between LNY and long-term survival in elderly patients with RSCC.

METHODS

Data from the SEER database (split into 7:3 training and testing sets) and patients from the colorectal surgery departments of two tertiary hospitals in China (validation set) were analyzed. Elderly patients with stages I-III RSCC undergoing resection were included. The correlation between LNY and overall survival (OS) was evaluated by a multivariate model and the application of the restricted cubic spline curve (RCS). The odds ratios (ORs) for stage migration and the hazard ratios (HRs) for OS with increased LNY were estimated using Locally Weighted Scatterplot Smoothing (LOWESS), with structural breakpoints identified using the Chow test.

RESULTS

The distribution of LNY was similar across the training (median: 18, IQR [14, 23]), testing (median: 18, IQR [14, 23]), and validation (median: 17, IQR [14, 20]) sets. Increasing LNY was associated with significantly improved OS in all datasets (Training set: HR = 0.983; Testing set: HR = 0.981; Validation set: HR = 0.944, all P < 0.001) after adjusting for confounders. Cut-point analysis identified an optimal LNY threshold of 18, validated across datasets, effectively discriminating survival probabilities.

CONCLUSIONS

A higher LNY is associated with improved survival. Our findings robustly support 18 LNYs as the optimal threshold for assessing the quality of lymph node dissection and prognosis stratification in elderly patients with RSCC.

The truncated AXIN1 isoform promotes hepatocellular carcinoma metastasis through SRSF9-mediated exon 9 skipping

Axis inhibitor protein 1 (AXIN1) is a protein recognized for inhibiting tumor growth and is commonly involved in cancer development. In this study, we explored the potential molecular mechanisms that connect alternative splicing of AXIN1 to the metastasis of hepatocellular carcinoma (HCC). Transcriptome sequencing, RT‒PCR, qPCR and Western blotting were utilized to determine the expression levels of AXIN1 in human HCC tissues and HCC cells. The effects of the AXIN1 exon 9 alternative splice isoform and SRSF9 on the migration and invasion of HCC cells were assessed through wound healing and Transwell assays, respectively. The interaction between SRSF9 and AXIN1 was investigated using UV crosslink RNA immunoprecipitation, RNA pulldown, and RNA immunoprecipitation assays. Furthermore, the involvement of the AXIN1 isoform and SRSF9 in HCC metastasis was validated in a nude mouse model. AXIN1-L (exon 9 including) expression was downregulated, while AXIN1-S (exon 9 skipping) was upregulated in HCC. SRSF9 promotes the production of AXIN1-S by interacting with the sequence of exons 8 and 10 of AXIN1. AXIN1-S significantly promoted HCC cells migration and invasion by activating the Wnt pathway, while the opposite effects were observed for AXIN1-L. In vivo experiments demonstrated that AXIN1-L inhibited HCC metastasis, whereas SRSF9 promoted HCC metastasis in part by regulating the level of AXIN1-S. AXIN1, a tumor suppressor protein that targets the AXIN1/Wnt/β-catenin signaling axis, may be a promising prognostic factor and a valuable therapeutic target for HCC.

Saturated fatty acid-induced neutrophil extracellular traps contribute to exacerbation and biologic therapy resistance in obesity-related psoriasis

Psoriasis patients who are obese tend to have serious clinical manifestations and poor responses to various biological agents in most cases. However, the mechanisms by which obesity exacerbates psoriasis remain enigmatic. In this study, we found that the abundance of systemic and localized cutaneous neutrophil extracellular traps (NETs) associated with the obesity-induced aggravation of psoriasis was positively correlated with disease severity and that the inhibition of NETs alleviated psoriatic dermatitis in obese mice. Mechanistically, we found that changes in fatty acid composition in obese subjects resulted in the deposition of saturated fatty acids (SFAs), which promoted the release of NETs via the TLR4-MD2/ROS signaling pathway. We further revealed that NETs potentiate IL-17 inflammation, especially γδT17-mediated immune responses, in obesity-exacerbated psoriasis patients. Moreover, SFAs induced a decreased response to anti-IL17A treatment in psoriasis-like mice, whereas the inhibition of NETs improved the beneficial effects of anti-IL17A in psoriasis-like mice with lipid metabolism disorders. Our findings collectively suggest that SFA-induced NETs play a critical role in the exacerbation of obesity-related psoriasis and provide potential new strategies for the clinical treatment of refractory psoriasis patients with lipid metabolism disorders.

Lenvatinib-activated NDUFA4L2/IL33/PADI4 pathway induces neutrophil extracellular traps that inhibit cuproptosis in hepatocellular carcinoma

BACKGROUND

Lenvatinib is a potent first-line therapy for patients with hepatocellular carcinoma (HCC), but it also increased the number of neutrophils in HCC tumor microenvironment.

METHODS

CitH3, MPO-DNA, elastase and MPO activity were measured for assessing neutrophil extracellular traps (NETs) in vivo and in vitro. Cell cuproptosis was assessed by measurement of copper content, FDX1, and pyruvate. The functions of lenvatinib, DNase I, interleukin 33 (IL33) neutralizing antibody and GPX4 in tumor growth were explored in mice.

RESULTS

Lenvatinib induced NETs in the HCC tumor microenvironment via HCC cells, but not through the direct stimulation of neutrophils. In addition, NET clearance by DNase I improves the efficacy of lenvatinib therapy in HCC mouse models. Mechanistically, lenvatinib promoted the expression and secretion of IL33 by HCC cells that triggered NET formation. Moreover, IL33 knockdown in Hepa1-6 cells improved lenvatinib efficacy in Hepa1-6-bearing HCC model mice and reduced NET formation in the tumor microenvironment. Subsequently, lenvatinib increased IL33 production by increasing the NDUFA4L2 expression in HCC cells. Furthermore, we found that IL33 triggered NET formation in neutrophils by increasing the protein expression of PADI4 via the Akt/mTOR signaling pathway. Rapamycin inhibition of mTOR reduced PADI4 expression and NET formation. Consistently, PADI4 inhibition by the selective PAD4 inhibitor GSK484 hydrochloride (GSK484) improved lenvatinib response to HCC therapy. Importantly, NETs contribute to lenvatinib resistance by inhibiting cuproptosis, but not apoptosis, pyroptosis, or ferroptosis in HCC cells. Treatment with GSK484 reversed the inhibitory effects of NETs on cuproptosis and sensitized the HCC cells to lenvatinib.

CONCLUSIONS

Our study revealed that lenvatinib-induced NETs inhibited the cuproptosis of HCC cells, suggesting that targeting the IL33/PADI4/NET axis represents a promising therapeutic strategy for ameliorating lenvatinib resistance in HCC.

Revolutionizing prostate cancer therapy: Artificial intelligence - Based nanocarriers for precision diagnosis and treatment

Prostate cancer is one of the major health challenges in the world and needs novel therapeutic approaches to overcome the limitations of conventional treatment. This review delineates the transformative potential of artificial intelligence (AL) in enhancing nanocarrier-based drug delivery systems for prostate cancer therapy. With its ability to optimize nanocarrier design and predict drug delivery kinetics, AI has revolutionized personalized treatment planning in oncology. We discuss how AI can be integrated with nanotechnology to address challenges related to tumor heterogeneity, drug resistance, and systemic toxicity. Emphasis is placed on strong AI-driven advancements in the design of nanocarriers, structural optimization, targeting of ligands, and pharmacokinetics. We also give an overview of how AI can better predict toxicity, reduce costs, and enable personalized medicine. While challenges persist in the way of data accessibility, regulatory hurdles, and interactions with the immune system, future directions based on explainable AI (XAI) models, integration of multimodal data, and green nanocarrier designs promise to move the field forward. Convergence between AI and nanotechnology has been one key step toward safer, more effective, and patient-tailored cancer therapy.

ENTPD1 (CD39) and NT5E (CD73) expression in human medulloblastoma: an in silico analysis

Medulloblastoma is the most common malignant tumor in the pediatric population. Its classification has incorporated key molecular variations alongside histological characterization. CD39 (also known as ENTPD1) and CD73 (also known as NT5E), enzymes of the purinergic signaling pathway, act in synergy to generate extracellular adenosine, creating an immunosuppressive tumor microenvironment. Our study examined the expression of mRNA of these genes in previously described transcriptome data sets of medulloblastoma patient samples from the Cavalli Cohort (n = 763). Survival distribution was estimated according to the Kaplan-Meier method using a median cut-off and log-rank statistics (p ≤ 0.05). In non-WNT and non-SHH medulloblastoma Group 4 (n = 264), the high expression of ENTPD1 and NT5E was significantly related to a lower overall survival (p = 2.7e-04; p = 2.6e-03). In the SHH-activated group (n = 172), the high expression of ENTPD1 was significantly related to lower overall survival (p = 7.8e-03), while the high expression of NT5E was significantly related to greater overall survival (p = 0.017). In the WNT group (n = 63), the expressions of ENTPD1 and NT5E were not significantly correlated with overall survival (p = 0.212; p = 0.101). In non-WNT and non-SHH medulloblastoma Group 3 (n = 113), the high expression of ENTPD1 was significantly related to greater survival (p = 0.034), while expression of NT5E was not significantly related to survival of patients (p = 0.124). This in silico analysis indicates that ENTPD1 (CD39) and NT5E (CD73) can be seen as potential prognostic markers and therapeutic targets for primary medulloblastomas in non-WNT and non-SHH Group 4.

Pulsatilla saponin D inhibited the growth of osteosarcoma by regulating the JNK/ATF3 signaling pathway

Osteosarcoma (OS) is a highly malignant and aggressive bone tumor associated with early lung metastasis and high mortality. Traditional chemotherapy does not effectively improve the efficacy and survival rate of patients with OS. Thus, it is vital to search for alternative therapies. Pulsatilla saponin D (PSD) is a potent bioactive compound that has been widely employed in cancer therapy due to its diverse bioactivities and minimal adverse effects. However, any effect on OS remains unclear. We found that PSD induced apoptosis of OS cells and investigated the mechanisms thereof. In vitro, PSD dose-dependently induced apoptosis and inhibited the viability of HOS and K7M2 cells. Furthermore, PSD significantly suppressed cell migration and invasion, and caused cell cycle arrest at the G0/G1 phase. Mechanistically, PSD upregulated ATF3 and JUN transcription by controlling JNK expression. Compared to cells treated with PSD alone, cells pre-treated with SP600125 (a JNK inhibitor), or in which ATF3 had been knocked down ATF3 with siRNA, did not exhibit PSD-mediated cell apoptosis. In a murine OS model, PSD exhibited a powerful anti-cancer effect and an excellent safety profile. Our data imply that PSD could effectively prevent OS occurrence and progression.

Mismatch Repair Status and Lymph Node Ratio in Survival Prediction of Stage II/III Rectal Cancer Patients: A Comprehensive Analysis of a Multi-Center Retrospective Study

BACKGROUND

The microsatellite status (dMMR vs. pMMR) in colorectal cancer can serve as a guiding factor for patient prognosis and treatment, where dMMR status indicates a better prognosis and often obviates the need for adjuvant chemotherapy (ACT). Conversely, a higher lymph node ratio (LNR) is associated with a poorer prognosis. This study aims to elucidate the prognostic significance of LNR and MMR status in relation to ACT in stages II and III colorectal cancer.

METHODS

A total of 1946 patients who underwent radical resection for colorectal cancer and were pathologically staged as II and III from three medical centers between 2012 and 2019 were selected. Among them, 1104 patients were included after MMR status was tested and postoperative chemotherapy was administered, along with other clinical information. MMR (mismatch repair) status was determined via pathological immunohistochemistry (IHC), and LNR was calculated. Patients were divided into three groups based on the LNR value and subjected to Kaplan-Meier and Cox regression analysis to assess the impact of MMR, LNR, and ACT on overall survival (OS) and disease-free survival (DFS).

RESULTS

A total of 6.47% of stage II and III colorectal cancers were detected as dMMR. Significant differences in OS and DFS between dMMR and pMMR patients were observed when the LNR ranged from 0.03 to 0.31, with pMMR patients showing a better prognosis. Stratified analysis with ACT revealed that postoperative chemotherapy did not affect the prognosis within the dMMR patient group. However, compared to the pMMR group, dMMR patients experienced significantly adverse effects on prognosis after receiving postoperative chemotherapy (p < 0.05). This result was more pronounced in the stratified analysis based on LNR (0.03-0.31) (p < 0.01).

CONCLUSIONS

Integrating LNR based on the microsatellite status of colorectal tumors provides comprehensive prognostic predictions, enhancing postoperative prognostic considerations for tumor patients. Additionally, our study suggests that patients with stage II and III colorectal cancer with dMMR status do not require any adjuvant chemotherapy postoperatively.

Exploring the impact of RPN1 on tumorigenesis and immune response in cancer

The ribophorin family, including RPN1, has been associated with tumor progression, but its specific role in pan-cancer dynamics remains unclear. Using data from TCGA, GTEx, and Ualcan databases, we investigated the relationship of RPN1 with prognosis, genomic alterations, and epigenetic modifications across various cancers. Differential analysis revealed elevated RPN1 expression in multiple cancer types, indicating a potential prognostic value. Amplification was the predominant mutation type of RPN1 in pan-cancer, with notable correlations with DNA methylation and copy number variation. Gene set variation analysis identified RPN1's involvement in cancer development, immunity, and metabolism. Additionally, RPN1 expression correlated with the tumor microenvironment, immune response factors, and response to anti-tumor therapies. Functional validation in triple-negative breast cancer, glioblastoma, and bladder cancer cell lines demonstrated the role of RPN1 in tumor cell proliferation and migration. Our findings highlight RPN1 as a potential biomarker for cancer diagnosis and treatment response in pan-cancer therapy.

Radiomics Results for Adrenal Mass Characterization Are Stable and Reproducible Under Different Software

Background: This study aims to investigate stability and reproducibility of radiomics biomarkers for adrenal lesion characterization across different software packages. Methods: Unenhanced CT images from patients with adrenal tumors were analyzed. Radiomic features were extracted using SOPHIA Radiomics and SIBEX software. The datasets underwent Z-score normalization. Statistical comparisons were made using two-sample t-tests and Spearman correlation coefficients. Three classification models-Logistic Regression, Linear Discriminant Analysis, and Linear Support Vector Machine-were trained on the datasets. Model performance was evaluated using accuracy, precision, recall, F1 score, and ROC curves. Feature importance and the statistical significance of model performance differences were also analyzed. Results: The t-test results showed no significant differences in the radiomic features extracted by SOPHIA and SIBEX (p-values all equal to 1.0). Spearman correlation coefficients were high for most features, suggesting a strong similarity between the two software tools. Classification models generally performed better on the SOPHIA dataset, with higher accuracy and precision. Feature importance analysis identified "Quadratic mean" and "Strength" as consistently influential features. Paired t-tests indicated significant differences in accuracy and precision, while Wilcoxon signed-rank tests did not find significant differences across all performance metrics. Conclusions: Radiomic features extracted by SOPHIA and SIBEX are comparable, but slight variations in model performance highlight the need for standardized extraction protocols and fine-tuning of predictive features. The study underscores the importance of ensuring the stability and reproducibility of radiomics features for reliable clinical application in adrenal lesion characterization.