Predictor Of Survival Research Articles

Targeting ferroptosis for improved radiotherapy outcomes in HPV‐negative head and neck squamous cell carcinoma

To enhance the efficacy of radiotherapy (RT) in human papillomavirus (HPV)‐negative head and neck squamous cell carcinoma (HNSCC), we explored targeting ferroptosis, a regulated cell death process. We developed a gene signature associated with ferroptosis using Cox proportional hazard modeling in HPV‐negative HNSCC patients who underwent RT. This ferroptosis‐related gene signature (FRGS) was a significant predictor of overall survival and recurrence‐free survival in HPV‐negative HNSCC patients who received RT. Subtype B of the FRGS, characterized by decreased expression of ferroptosis inducers [nuclear receptor coactivator 4 (NCOA4) and natural resistance‐associated macrophage protein 2 homolog/divalent metal transporter 1 (NRAMP2/DMT1)] and increased expression of suppressors [phospholipid hydroperoxide glutathione peroxidase (GPX4) and ferritin heavy chain (FTH1)], was associated with poorer prognosis, potentially indicating the inhibition of ferroptosis. Furthermore, our in vitro and in vivo studies demonstrated that treatment with statins, such as atorvastatin and simvastatin, induced ferroptosis and sensitized radioresistant HNSCC cells to irradiation, improving radiosensitivity and potentially enhancing the response to RT. Additionally, in xenograft models, the combination of statins and RT led to a significant reduction in tumor initiation. These findings provide valuable insights for enhancing treatment and improving prognosis in HPV‐negative HNSCC by targeting ferroptosis and utilizing statins to sensitize tumors to RT‐induced cell death.

Deep PSA response and extended time‐to‐nadir as robust predictors of survival in Asian patients with de novo metastatic hormone‐sensitive prostate cancer receiving upfront intensified treatment

AbstractIntroductionIn de novo metastatic hormone‐sensitive prostate cancer (mHSPC) treated with upfront intensification using androgen receptor signaling inhibitor or chemotherapy (Docetaxel), achieving a PSA nadir less than 0.2 ng/mL, indicative of superior survival in trials, may often be unattainable in real‐world settings. We explored the predictive value of the degree of PSA decline and time to PSA nadir (TTPN) on oncological outcomes.MethodsA prospectively maintained database of consecutive prostate cancer cases in Hong Kong was accessed. Patients diagnosed with de novo mHSPC from 2016 to 2022 and treated with upfront intensification were included in this analysis. Landmark analysis on PSA kinetics at 6‐months following treatment intensification was performed. They were classified based on 1) TTPN (≥6 months vs. <6 months), and 2) a combined response (deep responders achieving both ≥95% PSA decline and TTPN ≥ 6 months vs. shallow responders). Multivariable regression analysis was employed to identify the effects of confounders.FindingsA total of 131 patients were included in this analysis. Classifying patients by combined response best predicted survival outcomes. Deep responders had better progression‐free survival (HR = 0.56; 95%CI = 0.34–0.91; p = 0.019), overall survival (HR = 0.50; 95%CI = 0.26–0.97; p = 0.036), and cancer‐specific survival (HR = 0.43; 95%CI = 0.19–0.99; p = 0.042). Difference in overall survival remained significant after adjustment in multivariable regression analysis.ConclusionOur analysis demonstrates that alternative PSA targets can predict treatment response and survival outcomes in de novo mHSPC patients in a real‐world setting, providing valuable information for patient counselling and potentially guiding future trial design.

Circulating tumor DNA (ctDNA) as a biomarker of response to therapy in advanced Hepatocellular carcinoma treated with Nivolumab.

Circulating tumor DNA (ctDNA) is a promising non-invasive marker for detection, diagnosis, treatment selection, and prognosis of hepatocellular carcinoma (HCC). This study aimed to examine the utility of ctDNA as a prognostic and predictive tool in HCC patients treated with nivolumab. We analyzed pre-treatment ctDNA from 44 HCC patients using comprehensive genomic testing on a commercially available platform. We utilized log rank test and univariate Cox models to correlate overall survival (OS) and progression-free survival (PFS) with ctDNA expressions. Of 44patients, 77.3% were men with median age of 67years. All but 3patients had at least one alteration identified, and TP53 was the most commonly altered gene (52.3%). Median OS was 17.5months (95% CI: 12.7, NA). Mutations involving PIK3CA, BRCA1, and CCND1 amplification were associated with shorter OS (P 0.0001, 0.0001 and 0.01, respectively). Median PFS time was 4.01months (95% CI: 3.06, 9.33). Mutations involving KIT and PIK3CA were associated with shorter PFS (P 0.0001 and 0.0004, respectively), while mutation involving CTNNB1 were associated with longer PFS (p= 0.04). ctDNA profiling may provide a benefit for prediction of survival and progression of HCC patients treated with nivolumab. Future studies are needed for confirmation.

Machine learning to predict distant metastasis and prognostic analysis of moderately differentiated gastric adenocarcinoma patients: a novel focus on lymph node indicators.

Moderately differentiated gastric adenocarcinoma (MDGA) has a high risk of metastasis and individual variation, which strongly affects patient prognosis. Using large-scale datasets and machine learning algorithms for prediction can improve individualized treatment. The specific efficacy of several lymph node indicators in predicting distant metastasis (DM) and patient prognosis in MDGA remains obscure. We collected data from MDGA patients from the SEER database from 2010 to 2019. Additionally, we collected data from MDGA patients in China. We used nine machine learning algorithms to predict DM. Subsequently, we used Cox regression analysis to determine the risk factors affecting overall survival (OS) and cancer-specific survival (CSS) in DM patients and constructed nomograms. Furthermore, we used logistic regression and Cox regression analyses to assess the specific impact of six lymph node indicators on DM incidence and patient prognosis. We collected data from 5,377 MDGA patients from the SEER database and 109 MDGC patients from hospitals. T stage, N stage, tumor size, primary site, number of positive lymph nodes, and chemotherapy were identified as independent risk factors for DM. The random forest prediction model had the best overall predictive performance (AUC = 0.919). T stage, primary site, chemotherapy, and the number of regional lymph nodes were identified as prognostic factors for OS. Moreover, T stage, number of regional lymph nodes, primary site, and chemotherapy were also influential factors for CSS. The nomograms showed good predictive value and stability in predicting the 1-, 3-, and 5-year OS and CSS in DM patients. Additionally, the log odds of a metastatic lymph node and the number of negative lymph nodes may be risk factors for DM, while the regional lymph node ratio and the number of regional lymph nodes are prognostic factors for OS. The random forest prediction model accurately identified high-risk populations, and we established OS and CSS survival prediction models for MDGA patients with DM. Our hospital samples demonstrated different characteristics of lymph node indicators in terms of distant metastasis and prognosis.

The role of KRT7 in metastasis and prognosis of pancreatic cancer

PurposeThe aim of this study is to delve into the value of N6-Methyladenosine (m6A)-associated genes (MAGs) in pancreatic cancer (PC) prognosis.MethodsPC sequencing data and corresponding clinicopathological information were retrieved from GEO and TCGA databases. We filtered 19 MAGs in PC specimens and implemented functional annotation in biology. Later, the m6A modification pattern was stratified into m6Acluster A-B according to MAG expression levels, and further categorized into genecluster A-C based on differentially expressed genes between m6Acluster A and B. Next, a MAG-based prognostic prediction model was established by the least absolute shrinkage and selection operator (LASSO) regression analysis and multivariate Cox regression analysis. At last, the role of KRT7 in PC were explored.ResultsWe found m6Acluster A pattern presented enrichment pathways associated with cell apoptosis, proliferation, migration, and cancer pathways. Additionally, high-risk group displayed more dismal prognosis and a higher programmed death-ligand 1 expression. The survival prediction ability of the model was verified in three independent PC GEO datasets. KRT7 is the most momentous risk gene in the established prognostic model. Among 18 clinical samples, the KRT7 protein in the surviving patient samples is lower than that in the deceased patient samples. We also identified elevated expression of KRT7 in PC tumor tissues compared to normal tissues using GEPIA 2. Then, the metastasis of PC cells was promoted by overexpressed KRT7 in vitro and in vivo. And IGF2BP3 upregulated KRT7 by increasing the mRNA stability of KRT7.ConclusionsThe PPM built based on CXCL5, LY6K and KRT7 is an encouraging biomarker to define the prognosis. Additionally, IGF2BP3 promoted KRT7 by stabilizing mRNA of KRT7. And KRT7 promoted the metastasis of PC cells by promoting EMT.

Predicting post-lung transplant survival in systemic sclerosis using CT-derived features from preoperative chest CT scans.

The current understanding of survival prediction of lung transplant (LTx) patients with systemic sclerosis (SSc) is limited. This study aims to identify novel image features from preoperative chest CT scans associated with post-LTx survival in SSc patients and integrate them into comprehensive prediction models. We conducted a retrospective study based on a cohort of SSc patients with demographic information, clinical data, and preoperative chest CT scans who underwent LTx between 2004 and 2020. This cohort consists of 102 patients (mean age, 50 years ± 10, 61% (62/102) females). Five CT-derived body composition features (bone, skeletal muscle, visceral, subcutaneous, and intramuscular adipose tissues) and three CT-derived cardiopulmonary features (heart, arteries, and veins) were automatically computed using 3-D convolutional neural networks. Cox regression was used to identify post-LTx survival factors, generate composite prediction models, and stratify patients based on mortality risk. Model performance was assessed using the area under the receiver operating characteristics curve (ROC-AUC). Muscle mass ratio, bone density, artery-vein volume ratio, muscle volume, and heart volume ratio computed from CT images were significantly associated with post-LTx survival. Models using only CT-derived features outperformed all state-of-the-art clinical models in predicting post-LTx survival. The addition of CT-derived features improved the performance of traditional models at 1-year, 3-year, and 5-year survival prediction with maximum AUC scores of 0.77 (0.67-0.86), 0.85 (0.77-0.93), and 0.90 (95% CI: 0.83-0.97), respectively. The integration of CT-derived features with demographic and clinical features can significantly improve t post-LTx survival prediction and identify high-risk SSc patients. Question What CT features can predict post-lung-transplant survival for SSc patients? Finding CT body composition features such as muscle mass, bone density, and cardiopulmonary volumes significantly predict survival. Clinical relevance Our individualized risk assessment tool can better guide clinicians in choosing and managing patients requiring lung transplant for systemic sclerosis.

Hispanic/Latino Ethnicity is an Independent Predictor of Worse Survival for Gastric Cancer in a Multicenter Safety-Net Patient Population.

Various population-based studies have shown Hispanic/Latino ethnicity is a risk factor for worse survival in patients with gastric cancer linked to disparate access to care. We aimed to address whether Hispanic patients treated within safety-net hospital systems continue to experience this survival deficit compared to non-Hispanic patients. We performed a retrospective cohort study comparing survival between Hispanic and non-Hispanic patients diagnosed with gastric adenocarcinoma between January 1, 2016 to December 31, 2020 within Los Angeles County's safety-net hospital system. Gastric cancer-specific survival was compared between the two cohorts using the Kaplan-Meier estimate and Cox proportional-hazards regression model. 448 patients who received care from five medical centers were included. 348 (77.7%) patients self-identified as Hispanic and 100 (22.3%) as non-Hispanic. Mean follow-up time was 2.0 years (median 0.91 years, IQR, 0.34-2.5 years). Hispanic patients were found to be diagnosed at a younger age (55.6 vs 60.7 years, p <0.01), demonstrate higher state area deprivation index (6.4 vs 5.0, p <0.01), and present with metastatic disease (59.8% vs 45%, p =0.04). After adjusting social and oncologic variables, Hispanic ethnicity remained an independent risk factor for worse survival (HR 1.56, [95% CI 1.06-2.28], p = 0.02). Hispanic patients treated within a large, multi-center safety-net hospital system experience worse survival compared to non-Hispanic patients. This suggests ethnic disparities exist within safety-net hospital systems, independent of known clinicopathologic factors. Improving outcomes for Hispanic patients with gastric cancer requires future efforts aimed at defining and addressing these unidentified barriers to care.

An Update on the Survival of the First 50 Face Transplants Worldwide.

Since 2005, a total of 50 face transplants have been reported from 18 centers in 11 countries. The overall survival of the grafts has not yet been established. To assess the survival of the face transplant grafts and evaluate factors potentially influencing it. Data on all the transplants included in this multicenter cohort study were collected at participating transplant centers for updated nonpublished data, supplemented with literature review for nonparticipating centers. Data from 2005 until September 2023, were included. Data were analyzed from November 11, 2005, through September 18, 2023. Patients included the first 50 patients in the world to have received a face transplant. Face transplant graft. The primary outcome was the overall survival of the face transplant graft, defined as either transplant loss or patient death. The secondary outcome was the number of acute rejection episodes per year. The 50 transplants were performed on 39 men (81%) and 9 women (19%) with a median age of 35 (range, 19-68) years at the time of the transplant. The median follow-up time was 8.9 (range, 0.2-16.7) years. During the follow-up, 6 transplants were lost with 2 patients retransplanted. There were 10 patients who died, 2 of whom had lost a transplant. The 5- and 10-year survival of the transplants was 85% (SD, 5%) and 74% (SD, 7%), respectively. The sequential number of the transplant in the world was a significant predictor of survival (hazard ratio, 95; 95% CI, 90-100; P < 05). The median number of acute rejection episodes per year was 1.2 (range, 0-5.3) for the transplants that were lost and 0.7 (range, 0-4.6) for the transplants that survived. No correlation with patient and transplant variables was detected for either the transplant survival or the number of rejection episodes. In this study, the overall survival of the face transplants is encouraging. These data suggest that the acceptable long-term survival of face transplants makes them a reconstructive option for extensive facial defects.

Conditional survival estimates for ependymomas reveal the dynamic nature of prognostication

BackgroundTraditional survival analysis is frequently used to assess the prognosis of ependymomas (EPNs); however, it may not provide additional survival insights for patients who have survived for several years. Thus, the conditional survival (CS) pattern of this disease is yet to be further investigated. This study aimed to evaluate the improvement of survival over time using CS analysis and develop a CS-based nomogram model for real-time dynamic survival estimation for EPN patients.MethodsData on patients with EPN were collected from the Surveillance, Epidemiology, and End Results (SEER) database. In order to construct and validate the model effectively, the selected patients were randomly divided at 7:3 ratio. CS is defined as the probability of surviving for a specified time period (y years) after initial diagnosis, given that the patient has survived x years. The CS pattern of EPN patients were explored. Then, the least absolute shrinkage and selection operator (LASSO) regression method with tenfold cross-validation was employed to identify prognostic predictors. Multivariate Cox regression was employed to develop a CS-based nomogram model, and we used this model to quantify EPN patient risk. Finally, the performance of the prediction model was also evaluated and verified.ResultsIn total, 1829 patients diagnosed with EPN were included in the study, with 1280 and 549 patients in the training and validation cohorts, respectively. The CS analysis demonstrated that patients' OS saw gradual improvements over time. With each additional year of survival post-diagnosis, the 10-year survival rate of EPN patients saw an increase, updating from 74% initially to 79%, 82%, 85%, 87%, 89%, 91%, 93%, 96%, and 98% (after surviving for 1–9 years, respectively). The LASSO regression model, which implements tenfold cross-validation, identified 7 significant predictors (age, tumor grade, tumor site, tumor extension, tumor size, surgery and radiotherapy) to develop a CS-based nomogram model. And further risk stratification was conducted based on nomogram model for these patients. Furthermore, this survival prediction model was successfully validated.ConclusionThis study described the CS pattern of EPN patients and highlighted the gradual improvement of survival observed over time for long-term survivors. We also developed the first novel CS-nomogram model that enabled individualized and real-time prognosis prediction. But patients must be counselled that individual circumstances may not always accurately reflect the findings of the nomogram.

Identification and validation of a novel five-gene signature in high-risk MYCN-not-amplified neuroblastoma

ObjectiveHigh-risk neuroblastoma patients often have poor outcomes despite multi-treatment options. The risk stratification of high-risk MYCN-not-amplified (HR-MYCN-NA) patients remains difficult. This study aims to identify a gene set signature that can help further stratify HR-MYCN-NA patients for a potential personalized therapeutic strategy.MethodsThree microarrays and one single-cell RNA sequence dataset were acquired and analyzed. Firstly, the prognostic-related genes (PRGs) in HR-MYCN-NA tumor cells were identified using TARGET-NB and GSE137804 datasets. Then, the prognostic model was established by LASSO-Cox regression, and verified in external cohort (GSE49710, GSE45547). Moreover, a time-dependent receiver operating characteristic curve (ROC) and area under the ROC (AUC) was used to assess survival prediction. A nomogram was established to predict the 1-, 3- and 5-year overall survival (OS) of HR-MYCN-NA patients.ResultsIn the training set, a five-PRGs signature, which include GAL, GFRA3, MARCKS, PSMD13, and ZNHIT3 genes, was identified and successfully stratified HR-MYCN-NA patients into ultra-high risk (UHR) and high-risk (HR) subtypes (HR = 4.29, P < 0.001). ROC curve analysis confirmed its predictive power (AUC = 0.74–0.82), suggesting a good predictive efficacy. Consistently, high-risk scores also predicted worse OS (HR = 2, P = 0.033) in the external validation dataset (AUC = 0.67–0.71). Moreover, the overall C-index of the nomogram was 0.75 (P < 0.001), which indicated good agreement between the observed and predicted survival rates. Further integrating the five PRGs signature with clinical factors, these 5 gene signature (HR = 4.45, P < 0.001) and tumor grade (HR = 4.15, P = 0.02) were found to be independent prognostic factors for HR-MYCN-NA patients.ConclusionThe novel five PRGs signature could well predict the survival of HR-MYCN-NA patients, which may provide constructive information for these subsets.

Poor Prognostic Factors in Long-Term Survivors of Resected Pancreatic Ductal Adenocarcinoma: An International, Multicenter Cohort Study.

To measure the rate of LTS in resected PDAC and determine the association between predictors of OS and LTS. Long-term survival (>5y, LTS) remains rare in pancreatic ductal adenocarcinoma (PDAC). Multiple predictors of overall survival (OS) are known but their association with LTS remains unclear. An international, multicenter retrospective study was conducted. Included were patients from 2012-2019 with resected PDAC. Excluded were those with metastases at diagnosis or resection, R2 resections, and 90-day mortality. Predictors of OS were identified using multivariable Cox regression and their prevalence in patients with LTS assessed. LTS was calculated by excluding patients with shorter follow-up and predictors of LTS were identified using multivariable logistic regression. 3,003 patients were included (27.4% received neoadjuvant chemotherapy). Elevated baseline CA19-9, high tumor grade, nodal disease, and perineural and lymphovascular invasion were negative independent predictors of OS, while receipt of adjuvant chemotherapy predicted improved OS (all P<0.05). LTS was observed in 220/2,436 patients (9.0%), of whom 198 (90%) harbored poor prognostic factors: elevated baseline CA19-9 (58.1%), poor tumor differentiation (51.0%), nodal disease (46.8%), and perineural invasion (76.0%). Of those without any of these four features, 50.0% achieved LTS as compared to 21.3%, 13.3%, 5.2%, and 3.5% in those with 1, 2, 3, or 4 features. This bi-national cohort demonstrates a true LTS rate of 9.0% in resected PDAC. Clinicians should remain aware that presence of poor prognostic factors does not preclude LTS.

Real-World Primary Resistance to First-Line Immune-Based Combinations in Patients with Advanced Renal Cell Carcinoma (ARON-1).

Therapeutic advancements based on immuno-oncology combinations have revolutionized the management of patients with renal cell carcinoma. However, patients who have progressive disease as the best response, "primary refractory" (Pref), face dismal outcomes. Our multicenter retrospective real-world study aims to assess the prevalence and clinicopathological characteristics of Pref patients. This study collected data from 72 centers across 22 countries (1709 patients), involving patients aged ≥18 years with metastatic clear cell renal cell carcinoma. All patients were treated with first-line immune-oncology combinations. Data included patient demographics, histology, metastatic sites, and treatment responses. Radiological assessments followed Response Evaluation Criteria in Solid Tumors version 1.1. Statistical analyses employed Kaplan-Meier method, Cox proportional hazard models, logistic regression, and the receiver operating characteristic curve. In our study, the Pref rate was 19%. Nivolumab/ipilimumab showed the highest Pref rate (27%), while pembrolizumab/lenvatinib exhibited the lowest (10%). Primary refactory patients demonstrated significantly lower median overall survival (7.6 months) compared with non-Pref patients (55.7 months), p < 0.001. At the multivariate analysis, nephrectomy, sarcomatoid de-differentiation, intermediate/poor International Metastatic RCC Database Consortium risk, and bone and brain metastases emerged as significant predictors of overall survival for Pref patients with renal cell carcinoma. Logistic regression showed a significant relationship between liver metastases, intermediate/poor International Metastatic RCC Database Consortium risk, and no surgery and an increased risk of Pref. This study presents limitations, mainly because of its retrospective design. The ARON-1 study provides valuable insights into Pref patients, emphasizing the challenges of this precociously resistant subgroup. Identified predictors could guide risk stratification, aiding clinicians in tailored therapeutic approaches.

Risk Factors for Mortality in Critically Ill Patients with Coagulation Abnormalities: A Retrospective Cohort Study.

Coagulation abnormalities are common and prognostically significant in intensive care units (ICUs) and are associated with increased mortality. This study aimed to explore the association between the levels of coagulation markers and the risk of mortality among ICU patients with coagulation abnormalities. This retrospective study investigated patients with coagulation abnormalities in the ICU between January 2021 and December 2022. The initial point for detecting hemostatic biomarkers due to clinical assessment of coagulation abnormalities was designated day 0. Patients were followed up for 28 days, and multivariate logistic regression analysis was utilized to identify risk factors for mortality. Of the 451 patients analyzed, 115 died, and 336 were alive at the end of the 28-day period. Multivariate analysis revealed that elevated thrombin-antithrombin complex (TAT), tissue plasminogen activator inhibitor complex (tPAIC), prolonged prothrombin time, and thrombocytopenia were independent risk factors for mortality. For nonovert disseminated intravascular coagulation (DIC) patients, older age and thrombocytopenia were associated with increased risks of mortality, whereas elevated levels of plasmin α2-plasmin inhibitor complex (PIC) were found to be independent predictors of survival. In patients with overt DIC, elevated levels of tPAIC were independently associated with increased risks of mortality. Nevertheless, thrombocytopenia was independently associated with increased risks of mortality in patients with pre-DIC. Coagulation markers such as the TAT, tPAIC, PIC, and platelet count were significantly associated with mortality, underscoring the importance of maintaining a balance between coagulation and fibrinolysis. These findings highlight the potential for targeted therapeutic interventions based on specific coagulation markers to improve patient outcomes.

Only FLT3-ITD co-mutation did not have a deleterious effect on acute myeloid leukemia patients with NPM1 mutation, but concomitant with DNMT3A co-mutation or a < 3log reduction of MRD2 predicted poor survival.

Co-occurring mutations are frequently observed in acute myeloid leukemia (AML) with NPM1 mutation, and NPM1 measurable residual disease (MRD) is an effective prognostic biomarker. This retrospective study investigated the impact of gene co-mutations and NPM1 MRD on outcomes in these patients. Among 234 patients, 11.5% carried the rare type NPM1 mutation (NPM1RT). The median age was 49years (IQR 36-58), with a median follow-up of 30.4months (IQR 12.1-55.7). Nine genes were mutated in > 10%, with DNMT3A (53.8%) and FLT3-ITD (44.4%) being most prevalent. Univariable analysis in 137 patients showed FLT3-ITD, DNMT3A co-mutations, and MRD2 < 3 log reduction predicted poorer survival. FLT3-ITD and DNMT3A co-mutations correlated with the lowest event-free (EFS) and overall survival (OS) (3-year EFS 30.0%; 3-year OS 34.4%; both p < 0.001). FLT3-ITD alone did not worsen survival compared to patients without FLT3-ITD. Multivariable analysis identified DNMT3A co-mutation [EFS, HR = 1.9, p = 0.021; OS, HR = 2.2, p = 0.023] and MRD2 ≥ 3 log reduction (EFS, HR = 0.2; OS, HR = 0.1, both p < 0.001) as independent survival predictors. Patients with FLT3-ITD and DNMT3A co-mutations or a MRD2 < 3 log reduction were identified as high risk, but allogeneic hematopoietic stem cell transplantation (allo-HSCT) improved survival significantly compared to chemotherapy only (3-year EFS, 57.9% vs. 30.0%, p = 0.012; 3-year OS, 72.9% vs. 34.4%, p = 0.001). In AML patients with NPM1 mutation, the detrimental impact of FLT3-ITD mutation was exacerbated by DNMT3A co-mutation. Poor-risk younger patients identified by FLT3-ITD and DNMT3A co-mutations or MRD2 < 3 log reduction benefit from allo-HSCT.

Semi-supervised ViT knowledge distillation network with style transfer normalization for colorectal liver metastases survival prediction

Colorectal liver metastases (CLM) affect almost half of all colon cancer patients and the response to systemic chemotherapy plays a crucial role in patient survival. While oncologists typically use tumor grading scores, such as tumor regression grade (TRG), to establish an accurate prognosis on patient outcomes, including overall survival (OS) and time-to-recurrence (TTR), these traditional methods have several limitations. They are subjective, time-consuming, and require extensive expertise, which limits their scalability and reliability. Additionally, existing approaches for prognosis prediction using machine learning mostly rely on radiological imaging data, but recently histological images have been shown to be relevant for survival predictions by allowing to fully capture the complex microenvironmental and cellular characteristics of the tumor. To address these limitations, we propose an end-to-end approach for automated prognosis prediction using histology slides stained with Hematoxylin and Eosin (H&E) and Hematoxylin Phloxine Saffron (HPS). We first employ a Generative Adversarial Network (GAN) for slide normalization to reduce staining variations and improve the overall quality of the images that are used as input to our prediction pipeline. We propose a semi-supervised model to perform tissue classification from sparse annotations, producing segmentation and feature maps. Specifically, we use an attention-based approach that weighs the importance of different slide regions in producing the final classification results. Finally, we exploit the extracted features for the metastatic nodules and surrounding tissue to train a prognosis model. In parallel, we train a vision Transformer model in a knowledge distillation framework to replicate and enhance the performance of the prognosis prediction. We evaluate our approach on an in-house clinical dataset of 258 CLM patients, achieving superior performance compared to other comparative models with a c-index of 0.804 (0.014) for OS and 0.735 (0.016) for TTR, as well as on two public datasets. The proposed approach achieves an accuracy of 86.9% to 90.3% in predicting TRG dichotomization. For the 3-class TRG classification task, the proposed approach yields an accuracy of 78.5% to 82.1%, outperforming the comparative methods. Our proposed pipeline can provide automated prognosis for pathologists and oncologists, and can greatly promote precision medicine progress in managing CLM patients.

Clinicopathological characteristics and survival analysis of different molecular subtypes of breast invasive ductal carcinoma achieving pathological complete response through neoadjuvant chemotherapy

BackgroundTo investigate the prognostic differences following the achievement of a pathological complete response (pCR) through neoadjuvant chemotherapy across different molecular subtypes of breast invasive ductal carcinoma.MethodsData from the Surveillance, Epidemiology, and End Results (SEER) were identified for patients undergoing neoadjuvant chemotherapy who achieved pathological complete response for invasive ductal carcinoma of the breast between 2010 and 2019.Comparing the clinicopathological characteristics of patients across different molecular subtypes. Univariate and Cox multivariate analyses were utilized to identify independent predictors of overall survival (OS) and cancer-specific survival (CSS). The Kaplan–Meier method is used to compare OS and CSS among different molecular subtypes. After propensity score matching, subgroup analysis results were presented through forest plots.ResultsThis study included 9,380 patients diagnosed with invasive ductal carcinoma, who were categorized into four molecular subtypes: 2,721 (29.01%) HR + /HER-2 + , 1,661 (17.71%) HR + /HER2-, 2,082 (22.20%) HR-/HER2 + , and 2,916 (31.08%) HR-/HER-2-. HR + /HER-2- subgroup exhibited a significantly higher proportion of patients under 50 years old than the other subtype groups (54.67% vs 40.2%, 50.35% and 51.82%, p < 0.01), and had a higher N2 + N3 stage (11.2% vs 7.24%, 8.69% and 7.48%, p < 0.01). Univariate and multivariate analysis revealed that molecular subtype was the independent risk factor for OS and CSS in patients(p < 0.05). The Kaplan–Meier curves indicated that the HR + /HER-2 + subtype had the highest OS and CSS(p < 0.05). Next, were the HR-/HER-2 + and HR-/HER-2- subtypes, with the HR + /HER-2- group having the lowest OS and CSS(p < 0.05). After propensity score matching, the OS and CSS of patients in the HR + /HER-2 + group remained higher compared to HR + /HER-2- group(p < 0.05).ConclusionsPatients with invasive ductal carcinoma of different molecular subtypes exhibit varying prognoses after achieving pCR to neoadjuvant chemotherapy. Those in the HR + /HER-2- group are younger, have a higher lymph node stage, and the lowest OS and CSS, whereas patients in the HR + /HER-2 + group have the highest OS and CSS.

Soluble PD-L1 shows no association to relapse and overall survival in early stage non-small cell lung cancer (NSCLC)

BackgroundCancer immune evasion is critical in non-small cell lung cancer (NSCLC) and has been targeted by immunotherapy. High soluble (s)PD-L1 is associated with reduced survival and treatment failure in advanced stages. Here we evaluated the effects of sPD-L1 on T cells, relapse free survival, and overall survival in early stage NSCLC. MethodsIn vitro T cell stimulation was performed in the presence of sPD-L1 to evaluate its immunomodulatory activity. Data from The Cancer Genome Atlas (TCGA) were investigated for PD-L1 splice variants and enzymes involved in proteolytic cleavage (i.e. ADAM10). Plasma from 74 NSCLC (stage IA-IIIB), as well as an additional 73 (control cohort) patients was collected prior to curative surgery. Thereafter sPD-L1 levels from an immunosorbent assay were correlated with patient outcome. ResultsIn vitro sPD-L1 inhibited IFN-γ production and proliferation of T cells and induced a terminal effector CD4 T cell subtype expressing CD27. Data from the TCGA demonstrated that elevated mRNA levels of ADAM10 is a negative predictor of outcome in NSCLC patients. To investigate the clinical relevance of these in vitro and TCGA findings, we quantified sPD-L1 in the plasma of early-stage NSCLC patients. In the first cohort we found significantly higher sPD-L1 levels in relapsing NSCLC patients, with a multivariate analysis revealing high sPD-L1 (>1000 pg/mL) as an independent predictor of survival. However, these findings could not be validated in two independent control cohorts. DiscussionAlthough in vitro and TCGA data support the suppressive effect of sPD-L1 we were unable to translate this in our clinical setting. These results may be due to the small patient number and their heterogeneity as well as the lack of a standardized sPD-L1 ELISA. Our inconclusive results regarding the value of sPD-L1 in early stage NSCLC warrant assay validation and further investigation in larger (neo-)adjuvant trials.

Association between pre-arrest left ventricular ejection fraction and survival in nontraumatic out-of-hospital cardiac arrest.

Out-of-hospital cardiac arrest (OHCA) poses major public health issues. Pre-arrest heart function is a prognostic factor, but the specific contribution of pre-arrest echocardiographic evaluation in predicting OHCA outcome remains limited. The primary objective was to investigate the association between left ventricular ejection fraction (LVEF) measured in echocardiography prior to OHCA and survival to hospital discharge. This multicenter retrospective cohort study analyzed data from the National Taiwan University Hospital and its affiliated hospitals. We included adult nontraumatic OHCA patients who were treated by the emergency medical services (EMS) and underwent echocardiography within 6 months prior to the OHCA event from January 2016 to December 2022. Data included demographics, preexisting diseases, resuscitation events, and echocardiographic reports. The primary outcome was the survival to hospital discharge after post-arrest care. Statistical analysis involved multivariable logistic regression to modify potential confounders, reported as adjusted odds ratio (aOR) and 95% confidence interval (CI), and evaluate the association between echocardiographic findings and survival to hospital discharge. This study analyzed 950 patients, with 33.6% surviving to discharge. A higher pre-arrest LVEF was independently associated with increased survival. Compared to patients with LVEF < 40%, those with LVEF between 40% and 60% had significantly higher odds of survival (aOR = 3.68, 95% CI = 2.14-6.35, P < 0.001), and those with LVEF > 60% had even greater odds of survival (aOR = 5.46, 95% CI = 3.09-9.66, P < 0.001). There was also an association between lower tricuspid regurgitation pressure gradient and survival (aOR = 0.98, 95% CI = 0.97-1.00, P = 0.015). Younger age, male gender, dyslipidemia, stroke, cancer, witnessed arrest, initial shockable rhythm, and shorter low-flow time are other significant predictors of survival. In adult, nontraumatic, EMS-treated OHCA patients, a higher LVEF 6 months prior to OHCA was associated with improved survival at hospital discharge.

Clinical significance of the modified Naples prognostic score in patients with stage II-III colon cancer undergoing curative resection: a retrospective study from the real world.

The Naples prognostic score (NPS) determined by the nutritional and inflammatory condition of an individual is attracting growing attention for predicting postoperative outcomes in a variety of malignancies. The study aimed to assess the clinical significance of a modified NPS (M-NPS) and establish and validate nomograms incorporating M-NPS in curative stage II-III colon cancer patients. We retrospectively analyzed 328 stage II-III colon cancer patients receiving radical surgical resection at our hospital from January 2011 to December 2016. Kaplan-Meier (KM) survival analysis and Cox regression analysis were executed for overall survival (OS) and cancer-specific survival (CSS). Independent predictive indicators were applied to develop nomograms. The model's performance was evaluated using many different methods. Of a total of 328 cases, 153 cases were in group 0, 145 in group 1, and 30 in group 2. In terms of OS or CSS, there were obvious differences between groups 0 and 1, and between groups 0 and 2. Age, obstruction, N stage, gross tumor type, and M-NPS group were independent prognostic indicators for OS, while obstruction, gross tumor type, M-NPS group, and N stage were independent predictive parameters for CSS. Furthermore, the training and validation sets were randomly allocated among a cohort of 328 patients. OS and CSS prediction nomograms were developed. In the training and validation cohort, the C-index and ROC analysis showed good discrimination, calibration curves exhibited an excellent level of consistency between model-predicted survival and actual survival outcomes, and DCA curves demonstrated good clinical performance. M-NPS is a reliable survival predictor in patients with curative stage II-III colon cancer. Nomograms incorporating M-NPS for OS and CSS have good predictive performance and clinical utility.

New prognostic model for liver transplantation outcomes in hepatocellular carcinoma

Background. Liver transplantation remains a priority treatment option for hepatocellular carcinoma in the presence of liver cirrhosis; yet precise outcome prediction post-operation continues to be a complex challenge. Existing prognostic model often overlook patient age and donor type. Enhanced models that incorporate these parameters can improve prediction accuracy and treatment efficacy, which is critically important in the dynamically evolving field of transplantation.Objective. The aim of this study is to develop a prognostic model for liver transplantation outcomes in patients with hepatocellular carcinoma and liver cirrhosis.Material and methods. This retrospective study included 69 patients with hepatocellular carcinoma on the background of liver cirrhosis who underwent liver transplantation between May 2010 and December 2022. Of these, 42 patients (61%) received organs from living donors, and 27 (39%) from deceased donors. The study involved analysis of alpha-fetoprotein levels in blood, as well as assessment of radiological (maximum tumor nodule size, number of nodules) and histological parameters (maximum tumor nodule size, number of nodules, presence of vascular invasion). Cox regression model was used to predict recurrence-free survival, and the results for five-year recurrence-free survival, recipient age, and donor type were reused in the Cox model to predict overall survival.Results. Four models for predicting recurrence-free survival and overall survival based on histological and radiological data were developed, demonstrating high prognostic value with C-indexes on training/test data of 0.76/1; 0.73/1; 0.78/0.8; 0.6/0.8 respectively. All models showed recurrence-free survival prediction accuracy comparable to the Milan criteria. The model outcomes are available as a calculator on the website https://nadit.ru/calculate_HCC.Conclusion. The developed prognostic models are vital tools for personalized outcome prediction after liver transplantation for hepatocellular carcinoma. To enhance the accuracy of these models, further amalgamation and validation of data from various medical centers, as well as open scientific collaboration, are necessary.