Predict Survival Time Research Articles

Artificial Intelligence in Pancreatic Imaging: A Systematic Review.

The rising incidence of pancreatic diseases, including acute and chronic pancreatitis and various pancreatic neoplasms, poses a significant global health challenge. Pancreatic ductal adenocarcinoma (PDAC) for example, has a high mortality rate due to late-stage diagnosis and its inaccessible location. Advances in imaging technologies, though improving diagnostic capabilities, still necessitate biopsy confirmation. Artificial intelligence, particularly machine learning and deep learning, has emerged as a revolutionary force in healthcare, enhancing diagnostic precision and personalizing treatment. This narrative review explores Artificial intelligence's role in pancreatic imaging, its technological advancements, clinical applications, and associated challenges. Following the PRISMA-DTA guidelines, a comprehensive search of databases including PubMed, Scopus, and Cochrane Library was conducted, focusing on Artificial intelligence, machine learning, deep learning, and radiomics in pancreatic imaging. Articles involving human subjects, written in English, and published up to March 31, 2024, were included. The review process involved title and abstract screening, followed by full-text review and refinement based on relevance and novelty. Recent Artificial intelligence advancements have shown promise in detecting and diagnosing pancreatic diseases. Deep learning techniques, particularly convolutional neural networks (CNNs), have been effective in detecting and segmenting pancreatic tissues as well as differentiating between benign and malignant lesions. Deep learning algorithms have also been used to predict survival time, recurrence risk, and therapy response in pancreatic cancer patients. Radiomics approaches, extracting quantitative features from imaging modalities such as CT, MRI, and endoscopic ultrasound, have enhanced the accuracy of these deep learning models. Despite the potential of Artificial intelligence in pancreatic imaging, challenges such as legal and ethical considerations, algorithm transparency, and data security remain. This review underscores the transformative potential of Artificial intelligence in enhancing the diagnosis and treatment of pancreatic diseases, ultimately aiming to improve patient outcomes and survival rates.

The impact of body mass index on mortality according to age in hemodialysis patients: an analysis of the Korean Renal Data System.

The impact of age on the relationship between body mass index (BMI) and all-cause mortality in hemodialysis (HD) patients is not clearly understood. We analyzed the association between BMI and all-cause mortality, stratified by age, in patients undergoing HD using data from the Korean Renal Data System (KORDS). We analyzed 66,129 HD patients from the 2023 KORDS database, with data collected between 2001 and 2022. Patients were grouped by age: <65 years ("young" group, n = 24,589), 65-74 years ("younger-old" group, n = 17,732), and ≥75 years ("older-old" group, n = 23,808). Patients were further stratified into BMI quartiles. Kaplan-Meier curves and event time ratio for the relative change in the survival time were calculated. During the follow-up period, 14,360 (21.7%) of the patients died, with a median follow-up of 3.4 years. Kaplan-Meier curves revealed poorer outcomes in lower BMI quartiles across all age groups. The lowest BMI quartile was significantly associated with a shorter survival time compared to the highest BMI quartile, with a 15% reduction in the young group (p = 0.001) and a 12% reduction in the older-old group (p = 0.002). Predicted survival time increases with rising BMI in the young group, but the rate of increase slows in the younger-old group and plateaus in the older-old group after around a BMI of 25 kg/m2. The decline in survival time with age was more pronounced in the 7-year survival than the 2-year survival. Lower BMI is associated with higher all-cause mortality in HD patients, with a more pronounced impact in younger patients.

Classification-augmented survival estimation (CASE): A novel method for individualized long-term survival prediction with application to liver transplantation.

Survival analysis is critical in many fields, particularly in healthcare where it can guide medical decisions. Conventional survival analysis methods like Kaplan-Meier and Cox proportional hazards models to generate survival curves indicating probability of survival v. time have limitations, especially for long-term prediction, due to assumptions that all instances follow a general population-level survival curve. Machine learning classification models, even those designed for survival predictions like random survival forest (RSF), also struggle to provide accurate long-term predictions due to class imbalance. We improve upon traditional survival machine learning approaches through a novel framework called classification-augmented survival estimation (CASE), which treats survival as a classification task that ultimately yields survival curves, beginning with dataset augmentation to improve class imbalance for use with any classification model. Unlike other approaches, CASE additionally provides an exact survival time prediction. We demonstrate CASE on a liver transplant case study to predict >20 years survival post-transplant, finding that CASE dataset augmentation improved AUCs from 0.69 to 0.88 and F1 scores from 0.32 to 0.73. Compared to Kaplan-Meier, Cox, and RSF survival models, the CASE framework demonstrated better performance across various existing survival metrics, as well as our novel metric, mean of individual areas under the survival curve (mAUSC). Further, we develop novel temporal feature importance methods to understand how different features may vary in survival importance over time, potentially providing actionable insights in real-world survival problems.

A predictive model for advanced esophageal cancer involving the lower third of the esophagus.

Esophageal cancer (EC) is one of the most common malignant tumors worldwide, which has severely threatened human health. This study aims to evaluate the prognostic factors and predictors of survival in patients diagnosed with advanced lower third esophageal carcinoma (aLEC). Based on the Surveillance, Epidemiology, and End Results (SEER) database, we developed a model (nomogram) to provide accurate and individualized survival prediction for the patients who have lost the opportunity to undergo radical surgery. Using SEER database, the basic information and Medicare data of patients diagnosed with aLEC from 2010 to 2015 were collected. The patients were randomly divided into the training and validation set according to an 8:2 ratio. Univariate and multivariate Cox analyses were used to investigate variables significantly correlated with disease-specific survival (DSS). A nomogram was constructed to predict the prognosis of EC patients. We used the area under the curve (AUC) of the receiver operating characteristic (ROC) curve for the evaluation of performance. Furthermore, calibration curves were used to evaluate the accuracy of the model. The clinical utility was also assessed via decision curve analysis (DCA). Differences in clinicopathological characteristics between T1NanyM1 and T2-4NanyM1 stages were evaluated using the Chi-squared test. Cox regression analysis was performed and Kaplan-Meier curves were plotted to evaluate the impact of T-stage, chemotherapy, and radiotherapy on the survival time of EC patients. Results of multivariate regression analysis demonstrated that histology type, T stage, and chemotherapy were independent prognostic factors for predicting survival time in patients with aLEC. Notably, the constructed nomogram suggested that patients with stage T2 or T3 had a higher survival rate at 6 months, 1 year, and 2 years compared with those with stage T1. DCAs showed that the predictive nomogram was clinically useful. There were fewer patients with stage T1NanyM1 receiving chemotherapy (P=0.004) or radiotherapy (P<0.001) than patients with stage T2-4NanyM1. Moreover, patients with stage T1NanyM1 who underwent chemotherapy had a better prognosis than those who did not [hazard ratio (HR) 3.15, 95% confidence interval (CI): 2.58-3.83; P<0.001]. For patients with stage T1NanyM1, radiotherapy did not improve outcomes (HR 0.98, 95% CI: 0.82-1.17; P=0.80). A prognostic nomogram integrating three clinicopathological factors was constructed to predict survival in aLEC patients. Chemotherapy improves outcomes of patients with stage T1NanyM1 aLEC.

A next generation probabilistic approach to analyze cancer patients data with inference and applications

This study addresses the critical challenges faced in cancer care, particularly in predicting survival times for patients with lung cancer and acute myeloid leukemia. Despite recent advancements in medical science, existing models often fall short in accurately capturing disease progression, leading to less effective clinical decision-making, and compromised patient outcomes. The need for advanced predictive models is urgent to improve survival time forecasts and enhance treatment strategies. In response to this, we introduce a novel probabilistic approach, the New Weibull (NEWE) model, which is part of a newly generated class designed to model cancer patient data more effectively. Our methodology includes using seven well-known estimation methods, each rigorously evaluated for consistency through Monte Carlo simulation studies focused on key metrics such as absolute bias, mean square error, and mean relative error. The datasets analyzed include survival times for twenty acute myeloid leukemia patients, 121 breast cancer patients from 1929 to 1938, 33 patients with acute myelogenous leukemia, data from eighteen individuals who died from causes unrelated to cancer, and survival times of advanced lung cancer patients undergoing standard chemotherapy. The NEWE model outperformed competing models, particularly in Anderson-Darling, Cramer-von Mises, and Kolmogorov-Smirnov tests, with significantly higher p-values. These findings highlight the NEWE model’s potential to transform predictive oncology by offering more precise survival time predictions, improving the quality of care and decision-making in cancer treatment.

Multiple Instance Bagging and Risk Histogram for Survival Time Analysis Based on Whole Slide Images of Brain Cancer Patients

This study tackles the challenges in computer-aided prognosis for glioblastoma multiforme, a highly aggressive brain cancer, using only whole slide images (WSIs) as input. Unlike traditional methods that rely on random selection or region-of-interest (ROI) extraction to choose meaningful subsets of patches representing the whole slide, we propose a multiple instance bagging approach. This method utilizes all patches extracted from the whole slide, employing different subsets in each training epoch, thereby leveraging information from the entire slide while keeping the training computationally feasible. Additionally, we developed a two-stage framework based on the ResNet-CBAM model which estimates not just the usual survival risk, but also predicts the actual survival time. Using risk scores of patches estimated from the risk estimation stage, a risk histogram can be constructed and used as input to train a survival time prediction model. A censor hinge loss based on root mean square error was also developed to handle censored data when training the regression model. Tests using the Cancer Genome Atlas Program’s glioblastoma public database yielded a concordance index of 73.16±2.15%, surpassing existing models. Log-rank testing on predicted high- and low-risk groups using the Kaplan–Meier method revealed a p-value of 3.88×10−9, well below the usual threshold of 0.005, indicating the model’s ability to significantly differentiate between the two groups. We also implemented a heatmap visualization method that provides interpretable risk assessments at the patch level, potentially aiding clinicians in identifying high-risk regions within WSIs. Notably, these results were achieved using 98% fewer parameters compared to state-of-the-art models.

Association Between Body Mass Index and Survival in Patients with De Novo Metastatic Non-Small Cell Lung Cancer.

BACKGROUND This retrospective study from a single center included 289 patients diagnosed with advanced non-small cell lung cancer (NSCLC) between 2010 to 2017 and aimed to evaluate the effects of body mass index (BMI) on overall survival. MATERIAL AND METHODS This retrospective study involved 289 patients diagnosed with metastatic-stage NSCLC at a single institution between January 2010 and December 2017. Patients were categorized into 2 groups based on their BMI at diagnosis: those with a BMI <25 kg/m² and those with a BMI ≥25 kg/m². Univariate and multivariate Cox regression analyses were conducted to identify factors associated with overall survival. RESULTS A total of 289 patients (241 men, 48 women) were included in the study, with a mean age of 60.1±11.1 years. Among them, 175 patients (60.6%) had a BMI less than 25 kg/m². Multivariate analysis revealed that BMI, pathological diagnosis, and complete response after first-line treatment were independently associated with survival in patients with lung cancer. Predicted survival time was significantly shorter in the BMI <25 group than in the BMI ≥25 group (9.3 months vs 13.0 months, P<0.05). CONCLUSIONS The study demonstrated that a higher BMI at the time of diagnosis is associated with improved overall survival in patients with de novo metastatic NSCLC. BMI may serve as an important prognostic factor in this patient population. Future prospective, multi-center studies are necessary to further validate the role of BMI in predicting survival outcomes in NSCLC patients across different treatment modalities.

Container Plasty in Advanced Painful Osteolytic Vertebral Metastases With Posterior Wall Defect: A Retrospective Observational Study.

Review of mesh-container plasty (MCP) in osteolytic vertebral metastases. This study aims to assess the efficacy and safety of MCP in treating advanced vertebral metastases with posterior wall defects. Diagnosis of vertebral metastases typically relies on the patient's tumor history, bone scans, or vertebral MRI. Surgical intervention often involves sampling vertebral body tissue for pathologic diagnosis. The revised Tokuhashi score is commonly used to predict survival time in patients with bone metastases. Outcome evaluation frequently employs the visual analog scale (VAS) and the Oswestry disability index (ODI) in assessing spinal surgery outcomes. A retrospective analysis included 111 patients treated between January 2014 and January 2018 in our hospital. Patients were categorized into 2 groups: MCP group (n=51) and PVP group (n=60). Grades based on the percentage of posterior wall defect area were established: grade I (≤25%), grade II (26% to 50%), grade III (51% to 75%), and grade IV (76% to 100%). Efficacy was assessed using VAS and ODI. Both MCP and PVP groups exhibited significant pain relief and improved motor function. No significant differences were observed in VAS and ODI scores at any follow-up point (P>0.05). In the MCP group, bone cement leakage occurred in 13 cases, with a leakage rate of 25.49%. However, none of the patients experienced clinical or neurological symptoms. In the PVP group, bone cement leakage occurred in 50% of patients, with 6 patients developing neurological symptoms. Significant differences between the groups were observed in major complications related to bone cement leakage (P=0.03). MCP demonstrates efficacy in pain relief and safety in treating vertebral metastases with deficient posterior walls. It represents a promising option for spinal surgeons managing vertebral metastases with posterior wall deficiencies.

Pairwise analysis of gene expression for oral squamous cell carcinoma via a large-scale transcriptome integration.

Among all cancers occurring in the head and neck region, oral squamous cell carcinoma (OSCC) is the most common oral malignant tumours characterized by its aggressiveness and metastasis. The development of transcriptomics technology has greatly facilitated the diagnosis of various cancers. However, identifying genetic biomarkers is limited by data from a single batch of OSCC samples, and integrating analysis across different platforms remains a great challenge. In this study, we integrated five OSCC transcriptome datasets using an innovative strategy capable of mitigating batch effect, and extracting information from different datasets based on changes in the relative expression of gene pairs. By leveraging a machine learning method, we developed a prediction model including 27 differential gene pairs (DGPs) to discriminate OSCC from control samples, achieving an area under the receiver operating characteristic curve (AUC) of 0.8987 for the training set. Moreover, the model demonstrated commendable performance in four external validation sets, with AUCs of 0.9926, 0.9688, 0.8052 and 0.8565, respectively. Subsequently, a prognostic model was constructed based on six key gene pairs through univariate and multivariate Cox regression analysis. The AUCs of the model at 1-year and 3-year overall survival time prediction were 0.717 and 0.779 in an independent dataset. Our result demonstrates the effectiveness of this new method of integrating data and identifying DGPs. Using DGPs can significantly improve the performance of both diagnostic and prognostic models.

Ayurveda management in terminally sick patients of hepatocellular carcinoma: A case series

Hepatocellular Carcinoma (HCC) is the third leading cause of cancer-related deaths worldwide. The traditional system of medicines is gaining momentum with increasing acceptance in the management of various pathologies; HCC is also one among such ones. Still, there is no consensus on the Ayurveda treatment algorithm for hepatocellular cancer. This case series is intended to provide evidence of the efficacy of Ayurveda treatment protocol for patients diagnosed with HCC. The case series presents four diagnosed cases of HCC, who were evaluated through pretreatment and during treatment assessment through laboratory data, patient performance status, Child–Pugh Scale, and the Barcelona clinic’s Liver cancer staging system. The survival outcome, i.e., the time from initiation of Ayurveda medication until death was noted for all the cases. An Ayurveda treatment regimen was prepared from conventional practices and administrated to four cases of advanced HCC. The Ayurveda intervention increased the survival time more than the predicted survival time irrespective of etiology, without the contemporary chemotherapy medications, and patient performance status in all four cases. However, hepatitis B virus-infected patients had less survival time than HCC manifested from alcoholic cirrhosis. These preliminary observations have brought an idea about the positive effects of Ayurveda interventions in terminal HCC patients. Further studies are recommended to prepare the Ayurveda treatment algorithm for hepatocellular cancer.

Predicting Survival Time of Women with Cervical Cancer in Osun State

Predicting Survival Time of Women with Cervical Cancer in Osun State

Analysis of metastasis‑related risk factors and clinical relevance in adult soft‑tissue sarcoma.

Metastasis occurs in nearly 50% of cases of adult soft-tissue sarcoma (ASTS), leading to a dismal prognosis, with a 2-year survival rate of ~30%. Consequently, a prognostic model that incorporates metastatic characteristics may be instrumental in predicting survival time and in crafting optimal personalized therapeutic strategies for patients with ASTS. In the present study, a prognostic prediction model for ASTS was developed by examining genes that are differentially expressed between non-metastatic and metastatic patients in the Gene Expression Omnibus dataset. The prognostic model, which includes five featured genes [actin γ2 (ACTG2), apolipoprotein D, coatomer protein complex subunit γ2 imprinted transcript 1, collagen type VI α6 chain and osteomodulin], was further validated in patients with ASTS from the Cancer Genome Atlas dataset. Based on these five-gene signatures, patients were categorized into high- and low-risk groups. Functional and pathway analyses revealed disparities in stemness, extracellular matrix and cell adhesion-related pathways between the two risk groups, particularly noting the activation of the PI3K-Akt pathway in high-risk cases. Analysis of immune infiltration also revealed variations in immune microenvironment changes between the two risk groups. Immunohistochemical staining substantiated the prognostic significance of these gene signatures in a specific sarcoma subtype. Additionally, wound-healing and Transwell assays demonstrated that inhibition of ACTG2 by shRNAs curbed cell migration and invasion in a sarcoma HOS cell line, underscoring its role in sarcoma metastasis. In conclusion, the present study successfully developed and validated a metastasis-based prognosis prediction model. This model not only reliably forecasts the survival of patients with ASTS, but also may pave the way for further investigation into the processes underlying sarcoma metastasis, ultimately aiding in the design of tailored therapeutic regimens.

A Research on Machine Learning for Predicting Survival Time in Cancer Patients

A Research on Machine Learning for Predicting Survival Time in Cancer Patients

Identification and model construction of survival-associated proteins for pancreatic cancer based on deep learning

Pancreatic cancer (PC) is a malignancy typified by its insidious onset, rapid progression, limited resectability, poor treatment response, and exceedingly dismal prognosis. The transition from precursor lesions to infiltrating malignant tumors in pancreatic cancer is concomitant with the accrual of genetic mutations. The elucidation of proteins linked to the prognosis of pancreatic cancer holds paramount importance in the realm of pancreatic cancer management. Herein, we introduce DeepPCSA, a model tailored for the screening of target proteins and the prediction of survival time, employing both conventional methodologies and deep learning techniques for patient survival analysis. This framework leverages the LASSOCOX regression approach on differentially expressed genes (DEGs) to discern pivotal genes, succeeded by the development of a prognostic model employing convolutional and fully connected layers to prognosticate patient survival duration. Furthermore, we account for covariates such as age, gender, and other pertinent factors for independent prognostic scrutiny, affirming the autonomy of our model as a prognostic determinant. Employing unsupervised clustering methodologies, we delineated two molecular subtypes delineated by disparate biological processes. Ultimately, via drug sensitivity analysis, we delineated the interrelation between survival duration and pharmaceuticals, substantiating the necessity for tailored drug interventions catering to patients of varying risk strata.

Skeletal Muscle Index-Based Cachexia Index as a Predictor of Prognosis in Patients With Cancer: A Meta-Analysis and Systematic Review.

Cachexia is associated with poor survival rates. In the clinical setting, the diagnosis of cancer cachexia is challenging. The cachexia index (CXI), a new index for predicting survival time, is a promising tool for diagnosing cancer cachexia; however, its efficacy in predicting patient survival has not been validated. This meta-analysis and systematic review aimed to explore the CXI's prognostic value in patients with cancer. The PubMed, Embase, MEDLINE, and Cochrane Library databases were searched for relevant studies to determine the association between CXI findings and prognosis. The outcomes were overall survival (OS), progression-, disease-, and recurrence-free survival (PFS/DFS/RFS) rates, and the rate of complete response. The QUality In Prognostic Studies (QUIPS) tool was used to evaluate the quality of the included trials. This meta-analysis comprised 14 studies involving 2777 patients. A low CXI was associated with decreased OS (hazard ratio [HR] 2.34, 95% confidence interval [CI] 2.01-2.72; P < .001), PFS/DFS/RFS (HR 1.93, 95% CI 1.68-2.22; P < .001), and complete response (odds ratio [OR] 0.49, 95% CI 0.36-0.66; P < .001). Patients with a low CXI had a lower body mass index (mean difference [MD] -0.75, 95% CI -1.00 to 0.50; P < .001), skeletal muscle index (standardized MD -0.80, 95% CI -0.98 to -0.61; P < .001), and serum albumin level (MD -0.23, 95% CI -0.26 to -0.20; P < .001); and a higher neutrophil-lymphocyte ratio (MD 1.88, 95% CI 1.29-2.47; P < .001) and more advanced disease stages (OR 0.80, 95% CI 0.71-0.91; P = .001). A low CXI was found to be associated with poor survival in patients with cancer. While the CXI is a promising marker for predicting cancer cachexia, further studies are required to verify its usefulness.

Outcomes of minimally invasive surgery for pulmonary metastasis: who benefits the most?

Metastatic disease is one of the main causes of death and factors affecting overall survival. It is known that selected patients with pulmonary oligometastases whose primary tumor is under control and who have adequate respiratory capacity may benefit from metastasectomy by resecting all detected lesions. To report our findings on the use of video-assisted thoracoscopic surgery (VATS) for pulmonary metastasectomy, with a focus on identifying suitable candidates. Between August 2010 and 2023 a total of 532 pulmonary metastasectomy procedures were performed in our institution. Metastasectomy was performed with VATS for 281 of those patients. VATS metastasectomy was performed in 131 patients with a single lesion on preoperative imaging, while 110 patients underwent metastasectomy for multiple lesions. The rate was significantly (p < 0.05) lower in the group with multiple lesions removed during surgery (38 months) than in the group with only one lesion removed during surgery (60 months). The predicted survival time in the group with other tumor histology (79 months) was significantly (p < 0.05) higher than in the groups with tumor histology carcinoma (41.4 months) and sarcoma (55.5 months). The best prognosis after metastasectomy is provided in cases with a single nodule. Grade is also an important prognostic factor affecting survival, particularly for grade 1 tumor. The histopathological type of the primary tumor is also a significant prognostic factor affecting survival after pulmonary metastasectomy in secondary pulmonary neoplasms, particularly for sarcoma and carcinoma.

SurvNet: A low-complexity convolutional neural network for survival time classification of patients with glioblastoma

Background and objectiveMalignant primary brain tumors cause the greatest number of years of life lost than any other cancer. Grade 4 glioma is particularly devastating: The median survival without any treatment is less than six months and with standard-of-care treatment is only 14.6 months. Accurate identification of the overall survival time of patients with brain tumors is of profound importance in many clinical applications. Automated image analytics with magnetic resonance imaging (MRI) can provide insights into the prognosis of patients with brain tumors. MethodsIn this paper, We propose SurvNet, a low-complexity deep learning architecture based on the convolutional neural network to classify the overall survival time of patients with brain tumors into long-time and short-time survival cohorts. Through the incorporation of diverse MRI modalities as inputs, we facilitate deep feature extraction at various anatomical sites, thereby augmenting the precision of predictive modeling. We compare SurvNet with the Inception V3, VGG 16 and ensemble CNN models on pre-operative magnetic resonance image datasets. We also analyzed the effect of segmented brain tumors and training data on the system performance. ResultsSeveral measures, such as accuracy, precision, and recall, are calculated to examine the perfor-mance of SurvNet on three-fold cross-validation. SurvNet with T1 MRI modality achieved a 62.7 % accuracy, compared with 52.9 % accuracy of the Inception V3 model, 58.5 % accuracy of the VGG 16 model, and 54.9 % of the ensemble CNN model. By increasing the MRI input modalities, SurvNet becomes more accurate and achieves 76.5 % accuracy with four MRI modalities. Combining the segmented data, SurvNet achieved the highest accuracy of 82.4 %. ConclusionsThe research results show that SurvNet achieves higher metrics such as accuracy and f1-score than the comparisons. Our research also proves that by using multiparametric MRI modalities, SurvNet is able to learn more image features and performs a better classification accuracy. We can conclude that SurvNet with the complete scenario, i.e., segmented data and four MRI modalities, achieved the best accuracy, showing the validity of segmentation information during the survival time prediction process.

Smart Biosensor for Breast Cancer Survival Prediction Based on Multi-View Multi-Way Graph Learning.

Biosensors play a crucial role in detecting cancer signals by orchestrating a series of intricate biological and physical transduction processes. Among various cancers, breast cancer stands out due to its genetic underpinnings, which trigger uncontrolled cell proliferation, predominantly impacting women, and resulting in significant mortality rates. The utilization of biosensors in predicting survival time becomes paramount in formulating an optimal treatment strategy. However, conventional biosensors employing traditional machine learning methods encounter challenges in preprocessing features for the learning task. Despite the potential of deep learning techniques to automatically extract useful features, they often struggle to effectively leverage the intricate relationships between features and instances. To address this challenge, our study proposes a novel smart biosensor architecture that integrates a multi-view multi-way graph learning (MVMWGL) approach for predicting breast cancer survival time. This innovative approach enables the assimilation of insights from gene interactions and biosensor similarities. By leveraging real-world data, we conducted comprehensive evaluations, and our experimental results unequivocally demonstrate the superiority of the MVMWGL approach over existing methods.

Validation of Modified Objective Prognostic Score in Patients with Advanced Cancer in Taiwan.

Modified versions of the Objective Prognostic Score (mOPS) needs to be validated to reflect practical palliative care circumstances in Taiwan. We compared the abilities of an mOPS score of 1.5 or higher versus a Karnofsky Performance Status (KPS) score of 30 or lower to predict 2-week mortality in patients with advanced cancer in Taiwan. Observational study. We performed a secondary analysis of an international multicenter cohort study of patients in East Asia. Participants were inpatients with advanced cancer in palliative care units (PCUs) in Taiwan. We compared the mOPS-B model, which does not require laboratory tests, with the KPS in a 2-week survival timeframe. We compared the accuracy of the prognostic models using sensitivity, specificity, and area under the receiver operating characteristic curve (AUROC). Calibration plots and net reclassification indices (NRI) for 2-week survival were compared between the two models. Differences in survival between the higher- and lower-scoring groups of each model were identified using the log-rank test. We included 317 patients, with a median survival of 14.0 days. The mOPS-B had a high sensitivity (0.82) and high AUROC value (0.69). By contrast, the KPS demonstrated good sensitivity (0.77) and an acceptable AUROC value (0.65) for predicting 2-week survival. The calibration plot did not demonstrate satisfactory agreement between the actual and predicted survival times in either the mOPS-B or the KPS groups. Our NRI was positive (absolute value: 22%), indicating that mOPS-B predicted 2-week survival better than KPS. The mOPS-B may serve better than the KPS as a screening tool for admission to PCUs in Taiwan because it was more accurate at predicting 2-week survival.

Serum tRF-33-RZYQHQ9M739P0J as a novel biomarker for auxiliary diagnosis and disease course monitoring of hepatocellular carcinoma

ObjectiveIn most cases, patients with hepatocellular carcinoma (HCC) develop advanced disease when diagnosed. Finding new molecules to combine with traditional biomarkers is crucial for HCC early diagnosis. In cancer development, tRNA-derived small RNAs (tsRNA) play a crucial role. Here, we aimed to identify a novel biomarker among tsRNAs that can facilitate HCC diagnosis and monitor its prognosis. MethodsWe screened candidate tsRNAs in 3 pairs of HCC and adjacent tissues through high-throughput sequencing. tRF-33-RZYQQ9M739P0J was screened in tissues, sera, and cells through quantitative real-time polymerase chain reaction (qRT-PCR) for further analysis. tRF-33-RZYQHQ9M739P0J was characterized using agarose gel electrophoresis, Sanger sequencing, and nuclear and cytoplasmic RNA isolation. Experiments at room temperature and repeated freeze-thaw cycles were conducted to evaluate the detection performance of tRF-33-RZYQHQ9M739P0J. We measured the levels of differential expression of tRF-33-RZYQHQ9M739P0J in sera using qRT-PCR. We applied the chi-square test to evaluate the correlation between tRF-33-RZYQHQ9M739P0J expression levels and clinicopathological features, and assessed its prognostic value by plotting Kaplan-Meier curves. The diagnostic efficacy of tRF-33-RZYQHQ9M739P0J was evaluated using the receiver operating characteristic (ROC) curve. Finally, the downstream genes related to tRF-33-RZYQHQ9M739P0J were explored through bioinformatics prediction. ResultstRF-33-RZYQHQ9M739P0J was highly expressed in HCC tissues and sera, and its expression was correlated with metastasis, TNM stage, BCLC stage, and vein invasion. Expression of tRF-33-RZYQHQ9M739P0J were decreased after surgery in patients with HCC. High serum tRF-33-RZYQHQ9M739P0J levels are associated with low survival rates, and they can predict survival times in patients with HCC according to the Kaplan-Meier analysis. Combining tRF-33-RZYQHQ9M739P0J with serum alpha-fetoprotein and prothrombin induced by vitamin K absence II can improve the diagnostic efficiency of HCC, suggesting its potential as a biomarker for HCC. ConclusiontRF-33-RZYQHQ9M739P0J may not only be a promising non-invasive marker for early diagnosis, but also a predictor of liver cancer progression.