Clinical Prognostic Variables Research Articles

Clinical features, case presentation and prognostic variables in horses with partial and full thickness oesophageal perforation

Clinical features, case presentation and prognostic variables in horses with partial and full thickness oesophageal perforation

Clinical features, case presentation and prognostic variables in horses with partial and full thickness oesophageal perforation

Clinical features, case presentation and prognostic variables in horses with partial and full thickness oesophageal perforation

Prognostic Analysis Combining Histopathological Features and Clinical Information to Predict Colorectal Cancer Survival from Whole-Slide Images.

Colorectal cancer (CRC) is a malignant tumor within the digestive tract with both a high incidence rate and mortality. Early detection and intervention could improve patient clinical outcomes and survival. This study computationally investigates a set of prognostic tissue and cell features from diagnostic tissue slides. With the combination of clinical prognostic variables, the pathological image features could predict the prognosis in CRC patients. Our CRC prognosis prediction pipeline sequentially consisted of three modules: (1) A MultiTissue Net to delineate outlines of different tissue types within the WSI of CRC for further ROI selection by pathologists. (2) Development of three-level quantitative image metrics related to tissue compositions, cell shape, and hidden features from a deep network. (3) Fusion of multi-level features to build a prognostic CRC model for predicting survival for CRC. Experimental results suggest that each group of features has a particular relationship with the prognosis of patients in the independent test set. In the fusion features combination experiment, the accuracy rate of predicting patients' prognosis and survival status is 81.52%, and the AUC value is 0.77. This paper constructs a model that can predict the postoperative survival of patients by using image features and clinical information. Some features were found to be associated with the prognosis and survival of patients.

Multimodal artificial intelligence models from baseline histopathology to predict prognosis in HR+ HER2- early breast cancer: Subgroup analysis.

101 Background: Prognostic assessment in HR+ HER2- early breast cancer (EBC) remains challenging given relatively low rates of disease progression. Modern artificial intelligence (AI)-based techniques have already provided substantial medical progress, particularly in prostate cancer. We have leveraged ArteraAI’s multimodal artificial intelligence (MMAI) platform to develop a research-level prognostic model in HR+ HER2- EBC, based on the WSG PlanB and ADAPT trials. Here, we quantify the value added by MMAI within clinically relevant subgroups. Methods: Histopathology image data was generated from pre-treatment breast biopsy and surgical hematoxylin and eosin (H&E) slides from the WSG PlanB and ADAPT trials. Patients with available images and complete data (n=5259) were allocated (stratified by trial, randomization arm and distant recurrence (DR)) to development (60%) and validation (40%) cohorts. An MMAI-based model using image data combined with clinical prognostic variables (age, T and N stage, tumor size) was developed to predict risk of DR. Univariable and multivariable Fine-Gray models were used to assess performance in the validation cohorts; subdistribution hazard ratios (sHR) refer to validation cohorts and are reported per standard deviation increase of the model scores (image-alone or combined). Pre-specified prognostic subgroups for analysis were defined by nodal status, menopausal status, and central tumor grade. All statistical tests were 2-sided at .05 significance. Results: The trained MMAI score was significantly associated with risk of DR in the validation cohort (sHR [95% CI] = 2.3 [2.0-2.8]) as a whole and in all considered subgroups. The score remained significant (sHR [95%CI] = 2.2 [1.7-2.8]) after adjusting for clinical prognostic factors. Moreover, the MMAI image component alone had significant prognostic value (sHR [95%CI] = 1.6 [1.3 - 1.9]) in the validation cohort. Remarkably, the MMAI image component alone had significant prognostic value separately within the G2 and G3 sub-groups, with sHR of about 1.5 per standard deviation increase, and also in most of the other predefined clinical subgroups. Conclusions: Preliminary results from the current MMAI breast model provide evidence that ArteraAI MMAI technology can be leveraged for outcome prediction in HR+ HER2- EBC using H&E-stained images to further personalize breast cancer management. The ability of image-only AI models to provide significant prognostic value within grade subgroups suggests that self-supervised AI has identified some novel image features with prognostic value beyond grade. To put the results into the clinical context, comprehensive validation analyses will be presented at the meeting.

Peripheral Immune Profiles Predict ALS Progression in an Age- and Sex-Dependent Manner.

Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease whose pathobiology associates with peripheral blood immune cell levels and activation patterns in an age and sex-dependent manner. This study's objective was to identify immune profile associations with ALS progression, whether the associations are age and sex-specific, and whether immune profiles can predict a future disease course. Flow cytometry immune profiles (a combination of 22 peripheral blood immune markers) were generated for 241 participants with ALS and linked to ALS progression, using progression-free survival, which is a composite combining the revised ALS Functional Rating Scale and survival. Participants were first grouped by immune profiles using unsupervised hierarchical clustering, and clusters were associated with subsequent progression-free survival. Next, individual immune markers were associated with progression-free survival using least absolute shrinkage and selection operator-Cox regression. Analyses were stratified by age and sex to identify demographic-specific immune mechanisms. Finally, random forest determined the predictive power of immune profiles on ALS progression in the whole population and again stratified by age and sex. Progression-free survival differed between clusters of participants with similar immune profiles, particularly reduced natural killer (NK)-cell activation associated with slower progression. Individual markers such as neutrophil levels and NK-cell NKp46 expression associated with faster ALS progression while overall NK-cell levels and NK-cell subpopulations associated with slower progression; the strength of these associations varied by age and sex. Adding these immune markers to prediction models dramatically increased short-term prediction compared with routine clinical prognostic variables alone, and the addition of NK-cell markers further improved the prediction accuracy in female participants. Specific immune profiles likely contribute to ALS progression in an age and sex-dependent manner, and peripheral immune markers enhance the prediction of short-term clinical outcomes. These findings suggest a complex milieu of immune profiles associated with ALS progression, and more detailed immunophenotyping in ALS will facilitate personalized immunotherapeutics in ALS.

Radiotherapy for patients with esophageal cancer aged 80 years or older: A 16-year experience.

Radiotherapy (RT) plays an important role in esophageal cancer (EC) patients aged ≥80 years. However, the survival modality and prognostic factors remain poorly understood. Thus, this study aimed to evaluate the tolerance and long-term overall survival (OS) of patients aged ≥80 years who were diagnosed with EC and underwent definitive RT. A total of 213 consecutive patients with EC over 80 years old who were treated with curative intent RT between February 1999 and December 2015 at our institution were retrospectively reviewed. The clinical prognostic variables were analyzed against OS in univariate analyses using log-rank tests and in a multivariate model using Cox regression proportional hazards analysis. The median patient age was 82 (range: 80-94) years. Atotal of 192 patients (90.1%) completed the definitive RT (median: 60 Gy, range: 50-72 Gy), and 11 patients had grade 4 or higher acute toxicity, including esophagitis, a cardiac event, infections, and sudden death. Atotal of 168 deaths (78.9%) were observed with a median follow up of 47 months (range: 0-153 months). The OS rates were 50.3%, 17.6%, and 13.2% at 1, 3, and 5 years, respectively. Multivariable analysis identified that tumors located in the cervical and upper thorax, a shorter tumor lesion, RT treatment of 50-60Gy, and a better response to treatment were the factors associated with longer OS. Definitive RT could be considered as an effective treatment for patients with EC who are older than 80 years, and 50-60 Gy seems to be a reasonable dose for these patients.

The Role of Immune Check Point Gene Expression of Pd-1 and Tim-3 in Patients with Acute Myeloid Leukemia

Acute myeloid leukemia (AML) is a hematopoietic cell cancer that spreads quickly to the blood and rapidly developing in the bone marrow. The prognosis for patients with acute myeloid leukemia (AML) is still poor, despite recent improvements in the therapeutic landscape. In hematological malignancies, immune checkpoint inhibitors have been studied, such as AML; however, the role of program cell death -1(PD-1) and T-cell immunoglobulin and mucin domain 3 (TIM3) in AML has not been thoroughly elucidated yet. Thus, the current study conducted to investigate the PD-1 and TIM‑3 gene expression in the AML patients and determine its associations with clinical outcomes and prognostic variables. The study collected 80 blood samples from acute myeloid leukemia (AML) patients and 40 blood samples from volunteer healthy individual were evaluated as control and real time quantitative (qRT-PCR) analysis was detect to performed PD-1 and TIM‑3 expression. The result showed there was non-significant (P>0.0001) in expression of TIM-3 in patients with AML, while expression of PD-1 statistically has high significant difference (P ≤ 0.0001). A cutoff value of PD-1 for patients vs. control was (0.853) with high sensitivity than cutoff value of TIM-3 for patients vs. control that can be diagnostically significant in distinguishing between patients and controls. Our data result showed that high expression of PD-1 in T cell is extremely correlated with progression of disease and down regulated gene expression of TIM-3 in AML patients.

Prognosis in meningoencephalitis of unknown origin in dogs: Risk factors associated with survival, clinical relapse, and long-term disability.

Meningoencephalitis of unknown origin (MUO) comprises a group of noninfectious inflammatory diseases affecting the central nervous system of dogs. Previous studies have reported individual risk factors for survival but prognostication for MUO remains challenging. Identify clinical prognostic variables in dogs with MUO. A retrospective study of 447 dogs presented to 2 UK referral hospitals and diagnosed with MUO. Medical records of dogs diagnosed with MUO were retrospectively reviewed. Multivariable logistic regression was used for the identification of risk factors for survival and Cox proportional hazards analysis for the identification of risk factors for clinical relapse. Eighty-two percent (366/447) of dogs with presumptive MUO survived to discharge and 63.5% (284/447) were alive at 6 months; 36% of the latter (103/284) had persistent neurological deficits. Breed (pugs; P = .03), epileptic seizures (P < .001), paresis (P < .001), and higher neurodisability scale (NDS) score (P < .001) at presentation were negatively associated with survival to 6 months. Dogs with persistent deficits had higher NDS scores on presentation (P = .001). Median follow-up time was 11 months (interquartile range [IQR], 1-24) and 50.6% (160/316) relapsed during treatment (median time to relapse, 7 months; IQR, 2-15). Incomplete resolution of the clinical signs during the 6 months after diagnosis (P < .001), higher NDS score (P < .001), and longer duration of the clinical signs (P < .001) were associated with relapse. Knowledge of risk factors associated with survival, incomplete recovery and clinical relapse in MUO can help guide monitoring and treatment and improve owner communications regarding prognosis for this debilitating disease.

Prognostic factors and clinical survival outcome in patients with primary mediastinal diffuse large B-cell lymphoma in rituximab era: A population-based study.

The goal of this study was to investigate the clinical characteristics, prognostic variables, and survival of patients with primary mediastinal diffuse large B cell lymphoma (PMBCL) in the rituximab era. The Surveillance, Epidemiology, and End Results (SEER) database was used to identify PMBCL patients diagnosed between 2000 and 2019. The Kaplan-Meier (K-M) technique and log-rank test were used to assess overall survival (OS) and disease-specific survival (DSS). The independent prognostic variables for OS and DSS were identified using univariate and multivariate Cox regression analysis. Nomograms were created to predict survival prospects according to identified prognostic indicators. Totally, 841 patients were enrolled with PMBCL. One-year, 5-year, and 10-year OS rates were 93.99%, 85.04%, and 81.76%, and the corresponding DSS rates were 95.27%, 87.37%, and 85.98%. The results of multivariate Cox regression analysis demonstrated that age, years of diagnosis, Ann arbor staging, and chemotherapy were independent prognostic factors for survival. Nomograms designed exclusively for PMBCL were created to forecast the likelihood of 1-year, 5-year, and 10-year OS and DSS, respectively. The Harrell concordance index (C-index) for the nomograms predictions of OS and DSS were 0.704 and 0.733, respectively, which showed the established model harboring powerful and accurate performance. The present study revealed that incidence of PMBCL has been consistently rising over the last 20 years. Simultaneously, survival rates have improved tremendously. Rituximab based immunochemotherapy has emerged as an effective treatment option, leading to enhanced OS and DSS outcomes. Furthermore, the nomograms specifically developed for PMBCL have demonstrated robustness and accuracy in forecasting OS and DSS rates at 1, 5, and 10 years. These predictive tools can be valuable for clinicians in accurately estimating prognosis and establishing personalized treatment plans and follow-up protocols.

Machine-learning prediction of treatment response to stereotactic body radiation therapy in oligometastatic gynecological cancer: A multi-institutional study

Background and purposeWe aimed to develop and validate different machine-learning (ML) prediction models for the complete response of oligometastatic gynecological cancer after SBRT. Material and methodsOne hundred fifty-seven patients with 272 lesions from 14 different institutions and treated with SBRT with radical intent were included. Thirteen datasets including 222 lesions were combined for model training and internal validation purposes, with an 80:20 ratio. The external testing dataset was selected as the fourteenth Institution with 50 lesions. Lesions that achieved complete response (CR) were defined as responders. Prognostic clinical and dosimetric variables were selected using the LASSO algorithm. Six supervised ML models, including logistic regression (LR), classification and regression tree analysis (CART) and support vector machine (SVM) using four different kernels, were trained and tested to predict the complete response of uterine lesions after SBRT. The performance of models was assessed by receiver operating characteristic curves (ROC), area under the curve (AUC) and calibration curves. An explainable approach based on SHapley Additive exPlanations (SHAP) method was deployed to generate individual explanations of the model's decisions. Results63.6% of lesions had a complete response and were used as ground truth for the supervised models. LASSO strongly associated complete response with three variables, namely the lesion volume (PTV), the type of lesions (lymph-nodal versus parenchymal), and the biological effective dose (BED10), that were used as input for ML modeling. In the training set, the AUCs for complete response were 0.751 (95% CI: 0.716–0.786), 0.766 (95% CI: 0.729–0.802) and 0.800 (95% CI: 0.742–0.857) for the LR, CART and SVM with a radial basis function kernel, respectively. These models achieve AUC values of 0.727 (95% CI: 0.669–0.795), 0.734 (95% CI: 0.649–0.815) and 0.771 (95% CI: 0.717–0.824) in the external testing set, demonstrating excellent generalizability. ConclusionML models enable a reliable prediction of the treatment response of oligometastatic lesions receiving SBRT. This approach may assist radiation oncologists to tailor more individualized treatment plans for oligometastatic patients.

Clinical manifestation, staging and prognosis of hepatocellular carcinoma in Gambian patients

BackgroundAs a result of the lack of screening programs and the difficulty in making a proper diagnosis, the majority of hepatocellular carcinoma (HHC) patients present late in low-resource countries. The study therefore assesses the clinical features, stage and prognostic variables of patients with HCC in The Gambia.MethodsFrom December 2015 to January 2019, patients with a confirmed diagnosis of HCC were enrolled. All patients’ medical history, ultrasound scan, FibroScan and laboratory details were collected.ResultsTwo hundred and sixty (260) patients were enrolled. The mean age of HCC patients was 40 years, and 210 (80.7%) of them were male. The most common gastrointestinal symptoms were early satiety 229 (88.1%) and abdominal pain 288 (87.7%), while the most common constitutional symptoms were weight loss 237 (91.2%) and easy fatiguability 237 (91.2%). Hepatomegaly 205 (78.8%) was the most common sign. On ultrasound scan, lesions were mostly multifocal 175 (67.3%), and the median FibroScan score was 75 kPa. The median fibrosis 4 and aspartate transferase platelet ratio index were 4.6 and 2.2, respectively. Hepatitis B surface antigen (HBsAg) was positive in 170 (65.4%) patients, and the median AFP level was 3263 ng/ml. HCC patients with positive HBsAg were more likely to be male 145 (85.3%) vs 62 (72.1%) (p = 0.011), much younger 39.9 vs 51.4 yrs (p = < 0.0001), more likely to have abdominal pain 156 (91.8%) vs 68 (79.1%) (p = 0.002), jaundice 78 (45.9%) vs 29 (33.7%) (p = 0.042), dark urine 117 (68.8%) vs 46 (53.5%) (p = 0.018), raised transaminases (Aspartate transaminases 224.5 (32–7886) vs 153 (18–610), p = < 0.01, Alanine transferases 71 (5–937) vs 47 (8–271), p = < 0.001) and decreased platelet count 207 (33–941) vs 252 (52- 641) (p = 0.021) compared to patients with HCC who were HBsAg-negative.ConclusionsThe prognosis of patients with HCC is poor in developing countries such as The Gambia, where screening programs and treatment modalities are scarce. Young males are disproportionately affected, and HBV is a major cause of HCC in The Gambia.

Early On-treatment Circulating Tumor DNA Measurements and Response to Immune Checkpoint Inhibitors in Advanced Urothelial Cancer

BackgroundImmune checkpoint inhibitors (ICIs) can induce durable disease control in metastatic urothelial cancer (mUC), but only 20–25% of patients respond. Early identification of a nondurable response will improve management strategies. ObjectiveTo investigate whether on-treatment circulating tumor DNA (ctDNA) measurements can predict ICI responsiveness in mUC patients. Design, setting, and participantsThis study consists of a discovery cohort of 40 mUC patients and a prospective multicenter validation cohort of 16 mUC patients. Plasma cell-free DNA was collected at baseline and after 3 and 6 wk on ICIs. The ctDNA levels were calculated from targeted sequencing. Outcome measurements and statistical analysisOutcome measurements were progression-free survival (PFS), overall survival (OS), and nondurable response (PFS ≤6 mo). Relationships with ctDNA were assessed using Cox regression. Changes in ctDNA level at 3 and 6 wk were categorized by an increase or decrease relative to baseline. Results and limitationsIn the discovery cohort, ctDNA was detected in 37/40 (93%) of patients at baseline. A ctDNA increase was observed in 12/15 (80%) and ten of 12 (83%) patients with a nondurable response at 3 and 6 wk, respectively. Of patients with a durable response (PFS >6 mo), 94% showed a decrease. A ctDNA increase at 3 wk was associated with shorter PFS (hazard ratio [HR] 7.8, 95% confidence interval [CI] 3.1–19.5) and OS (HR 8.0, 95% CI 3.0–21.0), independent of clinical prognostic variables. Similar results were observed at 6 wk. The 3-wk association with PFS was validated in a prospective cohort (HR 7.5, 95% CI 1.3–42.6). Limitations include the limited number of patients. ConclusionsEarly changes in ctDNA levels are strongly linked to the duration of ICI benefit in mUC and may contribute to timely therapy modifications. Patient summaryBenefit from immunotherapy can be predicted after only 3 wk of treatment by investigating cancer DNA in blood. This could help in timely therapy changes for urothelial cancer patients with limited benefit from immunotherapy.

Prediction of clinicopathological features, multi-omics events and prognosis based on digital pathology and deep learning in HR+/HER2- breast cancer.

Breast cancer has the highest incidence and mortality rates among women worldwide. Hormone receptor (HR)+/human epidermal growth factor receptor 2 (HER2)- breast cancer is the most common molecular subtype, accounting for 50-79% of breast cancers. Deep learning has been widely used in cancer image analysis, especially for predicting targets related to precise treatment and patient prognosis. However, studies focusing on therapeutic target and prognosis predicting in HR+/HER2- breast cancer are lacking. This study retrospectively collected hematoxylin and eosin (H&E)-stained slides of HR+/HER2- breast cancer patients between January 2013 and December 2014 at Fudan University Shanghai Cancer Center (FUSCC) and scanned to generate whole-slide images (WSIs). Then, we built a deep-learning-based workflow to train and validate model to predict clinicopathological features, multi-omics molecular features and prognosis; the area under the curve (AUC) of the receiver operating characteristic (ROC) and the concordance index (C-index) of the test set were used to assess model effectiveness. A total of 421 HR+/HER2- breast cancer patients were included in our study. Regarding clinicopathological features, grade III could be predicted with an AUC of 0.90 [95% confidence interval (CI): 0.84-0.97]. Regarding somatic mutations, TP53 and GATA3 mutation could be predicted with AUCs of 0.68 (95% CI: 0.56-0.81) and 0.68 (95% CI: 0.47-0.89), respectively. Regarding gene set enrichment analysis (GSEA) pathways, the G2-M checkpoint pathway was predicted with an AUC of 0.79 (95% CI: 0.69-0.90). Regarding markers of immunotherapy response, intratumoral tumor-infiltrating lymphocytes (iTILs), stromal tumor-infiltrating lymphocytes (sTILs), CD8A, and PDCD1 were predicted with AUCs of 0.78 (95% CI: 0.55-1.00), 0.76 (95% CI: 0.65-0.87), 0.71 (95% CI: 0.60-0.82), and 0.74 (95% CI: 0.63-0.85), respectively. In addition, we found that the integration of clinical prognostic variables and deep features of images can improve the stratification of patient prognosis. Using a deep-learning-based workflow, we developed models to predict the clinicopathological features, multi-omics features and prognosis of patients with HR+/HER2- breast cancer using pathological WSIs. This work may contribute to efficient patient stratification to promote the personalized management of HR+/HER2- breast cancer.

Development and validation of a novel risk model in newly diagnosed de novo bone metastatic prostate cancer (M1b): a retrospective study.

Previous studies suggested that bone metastasis has a significant effect on the time of progression to metastatic castration-resistant prostate cancer (CRPC) for newly diagnosed de novo bone metastatic hormone-sensitive prostate cancer (mHSPC). Nevertheless, the effect of different bone metastasis sites was not fully evaluated. This study aimed to develop and validate a novel bone metastatic risk model. We enrolled 122 patients who were newly diagnosed with de novo bone metastatic prostate cancer following primary androgen deprivation based therapy at our institution from January 2008 to June 2021. The metastatic bone sites were classified into six sites: skull; cervical, thoracic, and lumbar vertebrae; chest (ribs and sternum); pelvis; upper limbs; and lower limbs. We calculated the bone metastatic score (BMS) for each site: 0 points were assigned for non-metastasis and 1 point was assigned for metastasis. The X-tile was adopted to acquire optimal cutoff points of BMS. We defined high-risk group (HRG) as BMS ≥ 3 and low-risk group (LRG) as BMS < 3. The new bone risk stratification was validated by calculating the area under the receiver operating characteristic curve (AUC). Subsequently, the relevant clinical prognostic variables were added to construct a predictive nomogram for predicting CRPC. The median patient age was 73 years. Most patients had Gleason score ≤8 (93 cases, 76.2%). The median follow-up duration was 11.5 months (range: 2-92 months). Eighty-six patients progressed to CRPC during the follow-up. The most common bone metastatic site was the pelvis (90.2%). The median BMS was 4. Seventy-six patients had HRG, while forty-six had LRG. The 1-, 2-, and 3-year AUCs for H/LRG were 0.620, 0.754, and 0.793, respectively. The HRG was associated with earlier time to CRPC. A nomogram based on four parameters (Gleason score, H/LRG, prostate-specific antigen [PSA] nadir, and time to PSA nadir) was developed to predict CRPC. Internal validation using bootstrapping demonstrated good accuracy for predicting the CRPC (C-index: 0.727). The calibration analysis demonstrated that the model performed well. We established a novel H/LRG risk model for newly diagnosed de novo bone metastatic prostate cancer, which provided evidence to support clinical decision-making.

High GLI-1 Expression is a Reliable Indicator of Bad Prognosis in Newly Diagnosed Acute Leukemia Patients

Purpose: To explore the expression and prognostic significance of Hedgehog signaling transcription factor GLI-1 in newly diagnosed acute myeloid leukemia (AML) patients. Methods: Clinical specimens were obtained from 46 recently diagnosed AML patients. Real-time qPCR was used to measure the GLI-1 mRNA expression in bone marrow mononuclear cells.Also, the relationship between GLI-1 mRNA levels and clinical variables and prognostic variables was assessed. Results: GLI-1 was overexpressed in the bone marrow samples of our patients. GLI-1mRNA expression did not differ significantly across different age groups, between both sexes, or between different FAB subtypes (P = 0.882, P = 0.246, and P = 0.890, respectively). GLI-1 expression varied significantly in different risk categories, with the greatest levels observed in 11 patients with poor risk (24.6 versus 22.7) compared to intermediate risk (5.2 versus 3.9; P = 0.006) and favorable risk (4.2 versus 3; P = 0.001). Comparing patients with the wild FLT3 allele to those with the mutant one, GLI-1 gene levels were considerably greater in those with the mutant allele of FLT3.Following induction chemotherapy, the levels of GLI-1 mRNA were significantly higher in 22 patients who did not experience complete remission (CR) diagnosed with de novo non-acute promyelocytic leukemia (APL) compared to 17 patients who did (P = 0.017). Significantly greater levels of expression were observed in each category of the patients with favorable risk; wild FLT3 allele (P = 0.033) and CR failure P = 0.005). Conclusion: GLI-1 overexpression is a risk factor for poor prognosis and could be a novel therapeutic target for AML.

MRI Radiomics for Predicting Survival in Patients with Locally Advanced Hypopharyngeal Cancer Treated with Concurrent Chemoradiotherapy.

A reliable prognostic stratification of patients with locally advanced hypopharyngeal cancer who had been treated with concurrent chemoradiotherapy (CCRT) is crucial for informing tailored management strategies. The purpose of this retrospective study was to develop robust and objective magnetic resonance imaging (MRI) radiomics-based models for predicting overall survival (OS) and progression-free survival (PFS) in this patient population. The study participants included 198 patients (median age: 52.25 years (interquartile range = 46.88-59.53 years); 95.96% men) who were randomly divided into a training cohort (n = 132) and a testing cohort (n = 66). Radiomic parameters were extracted from post-contrast T1-weighted MR images. Radiomic features for model construction were selected from the training cohort using least absolute shrinkage and selection operator-Cox regression models. Prognostic performances were assessed by calculating the integrated area under the receiver operating characteristic curve (iAUC). The ability of radiomic models to predict OS (iAUC = 0.580, 95% confidence interval (CI): 0.558-0.591) and PFS (iAUC = 0.625, 95% CI = 0.600-0.633) was validated in the testing cohort. The combination of radiomic signatures with traditional clinical parameters outperformed clinical variables alone in the prediction of survival outcomes (observed iAUC increments = 0.279 [95% CI = 0.225-0.334] and 0.293 [95% CI = 0.232-0.351] for OS and PFS, respectively). In summary, MRI radiomics has value for predicting survival outcomes in patients with hypopharyngeal cancer treated with CCRT, especially when combined with clinical prognostic variables.

Identification and validation of transferrin receptor protein 1 for predicting prognosis and immune infiltration in lower grade glioma.

Transferrin receptor protein 1 (TFRC), an ananda molecule associated with ferroptosis, has been identified as affecting a wide spectrum of pathological processes in various cancers, but the prognostic value correlates with the tumor microenvironment of TFRC in lower-grade glioma (LGG) is still unclear. Clinical pathological information and gene expression data of patients with LGG come from The Cancer Genome Atlas (TCGA), Chinese Glioma Genome Atlas (CGGA), GTEx, Oncomine, UCSC Xena, and GEO databases. We then used various bioinformatics methods and mathematical models to analyze those data, aiming to investigate the clinical significance of TFRC in LGG and illustrate its association with tumor immunity. In addition, the molecular function and mechanisms of TFRC were revealed by gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and gene set enrichment analysis (GSEA). Immunohistochemical experiments and single-cell analysis have been performed. TFRC expression was highly expressed in many tumors and showed a poor prognosis. Including gliomas, it was significantly associated with several poor clinical prognostic variables, tumor immune microenvironment, tumor mutational burden (TMB), m6a modification, and ferroptosis in LGG. TFRC as a key factor was further used to build a prediction nomogram. The C-index, calibration curve, and decision curve analysis showed the nomogram was clinically useful and calibration was accurate. At the same time, we also demonstrated that promoter hypomethylation of DNA upstream of TFRC could lead to high TFRC expression and poor overall survival. There is a significant correlation between TFRC and CD8 + T cell, macrophage cell infiltration, and several immune checkpoints, such as PD-L1(cd274), CTLA4, and PD1, suggesting a novel direction for future clinical application. Functional and molecular mechanism analysis showed an association of TFRC expression with immune-related pathways through GSEA, GO, and KEGG analysis. Finally, immunohistochemical experiments and single-cell analysis confirmed the expression of TFRC in glioma. TFRC may be a potential prognostic biomarker and an immunotherapeutic target for glioma.

Prognostic value of quantitative assessment of macrophage content (CD68+) in the peritumoral zone of clear cell renal cell carcinoma

AIM: To quantitatively analyze the macrophage distribution density (MF) of the peritumoral zone (PZ) of clear cell renal cell carcinoma (CCRCC) and determine the relationship of MF distribution density in PZ with clinical and morphological prognostic variables and postoperative survival of patients.&#x0D; MATERIALS AND METHODS: The study included 52 patients with CCRCC, who had an average age of 56.70.8 years. Using monoclonal mouse antibodies for CD68 (clone PG-M1, DAKO), using an immunohistochemical method, macrophages were identified in the paraffin sections. The average amount of MF in the PZ was determined in three fields of view at 400 magnification. For the PZ, the tissue was taken directly adjacent to the tumor to the conditionally unchanged kidney tissue.&#x0D; RESULTS: The correlation analysis revealed that the density of the MF distribution in the CCRCC was interrelated with the stage of the disease (r=0.66; p=0.0001), size of the tumor node (r=0.52; p=0.0001), degree of anaplasia according to Fuhrman (r=0.80; p=0.0001), presence of metastases (r=0.62; 0.0001), and postoperative survival of patients (r=0.53; p=0.0001). However, there were no correlations with the gender (r=0.007; p=0.62) and age of the patients (r=0.03; p=0.83). With the number of MF in the PP 8.8, the cumulative proportion of survivors by the five-year term was 0.70 (70%). Moreover, when the number of MF in the PZ was 8.8, none of the patients lived to the five-year term, the cumulative proportion of survivors was 0 (log-rank-test, p=0.000001).&#x0D; CONCLUSION: The density of MF distribution in PZ is interrelated with significant clinical and morphological parameters of CCRCC and is a predictor of the prognosis of five-year postoperative survival of patients. Thus, quantitative estimation of the number of MF in the PZ can be used in predicting the course of CCRCC along with the traditional classical forecasting factors.

Prognostic Signature Development on the Basis of Macrophage Phagocytosis-Mediated Oxidative Phosphorylation in Bladder Cancer.

Background Macrophages are correlated with the occurrence and progression of bladder cancer (BCa). However, few research has focused on the predictive relevance of macrophage phagocytosis-mediated oxidative phosphorylation (MPOP) with BCa overall survival. Herein, we aimed to propose the targeted macrophage control based on MPOP as a treatment method for BCa immunotherapy. Methods The mRNA expression data sets and clinical data of bladder cancer originated from Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) data set. A systematic study of several GEO data sets found differentially expressed macrophage phagocytosis regulators (DE-MPR) between BCa and normal tissues. To discover overall survival-associated DE-MPR and develop prognostic gene signature with performance validated based on receiver operating curves and Kaplan-Meier curves, researchers used univariate and Lasso Cox regression analysis (ROC). External validation was done with GSE13057 and GSE69795. To clarify its molecular mechanism and immune relevance, GO/KEGG enrichment analysis and tumor immune analysis were used. To find independent bladder cancer prognostic variables, researchers employed multivariate Cox regression analysis. Finally, using TCGA data set, a predictive nomogram was built. Results In BCa, a four-gene signature of oxidative phosphorylation composed of PTPN6, IKZF3, HDLBP, and EMC1 was found to predict overall survival. With the MPOP feature, the ROC curve showed that TCGA data set and the external validation data set performed better in predicting overall survival than the traditional AJCC stage. The four-gene signature can identify cancers from normal tissue and separate patients into the high-risk and low-risk groups with different overall survival rates. The four MPOP-gene signature was an independent predictive factor for BCa. In predicting overall survival, a nomogram integrating genetic and clinical prognostic variables outperformed AJCC staging. Multiple oncological features and invasion-associated pathways were identified in the high-risk group, which were also correlated with significantly lower levels of immune cell infiltration. Conclusion This paper found the MPOP-feature gene and developed a predictive nomogram capable of accurately predicting bladder cancer overall survival. The above discoveries can contribute to the development of personalized treatments and medical decisions.

Prognostic significance of postoperative loss of skeletal muscle mass in patients underwent coronary artery bypass grafting.

BackgroundIncreasing life expectancy of coronary artery bypass grafting (CABG) remains to be the major concern of cardiac surgeons. However, few studies have investigated the effect of postoperative skeletal muscle index (SMI) loss on prognosis. This study aims to evaluate the prognostic role of postoperative SMI loss ≥ 5% after CABG, in order to develop a novel nomogram to predict overall survival (OS).MethodsPatients underwent CABG via midline sternotomy from December 2015 to March 2021 were recruited in this study. Preoperative and postoperative 3 months chest computed tomography (CT) images were compared to assess changes in SMI at T12 level. Based on this, patients were classified into the presence or absence of SMI loss ≥ 5%. The association between postoperative SMI loss ≥ 5% and OS was then analyzed by the Kaplan-Meier curves and Cox model. A novel nomogram incorporating independent clinical prognostic variables was also developed.ResultsThe study enrolled 506 patients receiving CABG, of whom 98 patients experienced T12 SMI loss ≥ 5% and had a significantly worse OS (P < 0.0001). Multivariate regression analysis showed that T12 SMI per cent change (%T12 SMI-change) was an independent prognostic factor for OS (HR = 0.809, 95% CI = 0.749–0.874). The nomogram incorporating %T12 SMI-change with other variables was accurate for predicting OS. Besides, we also found that postoperative oral nutritional supplement (ONS) can rescue T12 SMI loss.ConclusionPostoperative SMI loss can predict survival outcome after CABG. The nomogram incorporating changes in SMI provides a superior performance than existing systems.