Predictor Of Long-term Survival Research Articles

Novel Multiparametric Magnetic Resonance Imaging-Based Deep Learning and Clinical Parameter Integration for the Prediction of Long-Term Biochemical Recurrence-Free Survival in Prostate Cancer after Radical Prostatectomy

Radical prostatectomy (RP) is the main treatment of prostate cancer (PCa). Biochemical recurrence (BCR) following RP remains the first sign of aggressive disease; hence, better assessment of potential long-term post-RP BCR-free survival is crucial. Our study aimed to evaluate a combined clinical-deep learning (DL) model using multiparametric magnetic resonance imaging (mpMRI) for predicting long-term post-RP BCR-free survival in PCa. A total of 437 patients with PCa who underwent mpMRI followed by RP between 2008 and 2009 were enrolled; radiomics features were extracted from T2-weighted imaging, apparent diffusion coefficient maps, and contrast-enhanced sequences by manually delineating the index tumors. Deep features from the same set of imaging were extracted using a deep neural network based on pretrained EfficentNet-B0. Here, we present a clinical model (six clinical variables), radiomics model, DL model (DLM-Deep feature), combined clinical–radiomics model (CRM-Multi), and combined clinical–DL model (CDLM-Deep feature) that were built using Cox models regularized with the least absolute shrinkage and selection operator. We compared their prognostic performances using stratified fivefold cross-validation. In a median follow-up of 61 months, 110/437 patients experienced BCR. CDLM-Deep feature achieved the best performance (hazard ratio [HR] = 7.72), followed by DLM-Deep feature (HR = 4.37) or RM-Multi (HR = 2.67). CRM-Multi performed moderately. Our results confirm the superior performance of our mpMRI-derived DL algorithm over conventional radiomics.

Lymph Node Staging in Perihilar Cholangiocarcinoma: The Key to the Big Picture.

Klatskin tumors have a bad prognosis despite aggressive therapy. The role and extent of lymph node dissection during surgery is a matter of discussion. This retrospective study analyzes our current experience of surgical treatments in the last decade. Patients and Methods: A retrospective single-center analysis of patients (n = 317) who underwent surgical treatment for Klatskin tumors. Univariable and multivariable logistic regression and Cox proportional analysis were performed. The primary endpoint was to investigate the role of lymph node metastasis for patient survival after complete tumor resection. The secondary endpoint was the prediction of lymph node status and long-term survival from preoperatively available parameters. Results: In patients with negative resection margins, a negative lymph node status was the prognosis-determining factor with a 1-, 3-, and 5-year survival rate of 87.7%, 37%, and 26.4% compared with 69.5%, 13.9%, and 9.3% for lymph-node-positive patients, respectively. Multivariable logistic regression for complete resection and negative lymph node status demonstrated only Bismuth type 4 (p = 0.01) and tumor grading (p = 0.002) as independent predictors. In multivariate Cox regression analysis, independent predictors of survival after surgery were the preoperative bilirubin level (p = 0.03), intraoperative transfusion (p = 0.002), and tumor grading (G) (p = 0.001). Conclusion: Lymph node dissection is of utmost importance for adequate staging in patients undergoing surgery for perihilar cholangiocarcinoma. In spite of extensive surgery, long-term survival is clearly associated with the aggressiveness of the disease.

The peripheral TCR repertoire diversity indexes can be used as an independent indicator for long term survival rate of ovarian cancer patients.

e17558 Background: According to GLOBOCAN 2020 data, ovarian cancer (OC) ranks 19th in incidence and 15th in mortality with a rate of 1.6% and 2.1%, respectively, High-grade serous OC is the most common sub-type of malignant ovarian tumors, remains the leading cause of cancer-related death among all gynecological cancers in the developed parts of the world. In the United States in 2016, 5-year survival rate was only 10-25% in stage III or IV OC patients, and there was considerable variability in survival in an advanced stage. At present, there is no reliable molecular marker to identify patients with better or worse prognosis. As is known to all, the adaptive immune system defends the body against different foreign antigens, using two main types of antigen receptors: T-cell receptors (TCRs) and antibodies. Now we use high-throughput sequencing approach to analyze the expressed TCRβ repertoires in the peripheral blood of OC patients and healthy women donors. Methods: The experimental design and participant recruitment were approved by Medical Ethics Committee of West China Second University Hospital (Ethics Approval for Research: 2022). Total RNA was extracted from PBMC using TRIZOL reagent. High through put sequencing of the TCRβ gene, assignment of the Vβ, Dβ, and Jβ germline gene, and TCR diversity indexes analyses were conducted essentially following our previously published protocols. Statistical analysis and graph drawing were performed using R language (R 4.0.3). Results: Comparison of the TCR repertoires between the 232 OC patients (OC group) and the 250 healthy women donors (HD group) revealed significant differences. For examples, the VJ combinations of the OC group were distinct from those of the HD group. In addition, the TCR repertoire diversity indexes of the OC group were significantly lower than those of the HD group ( p < 0.001), including D50s, Simpson indexes, the number of highly expressed clones (HECs), and the frequencies of the largest TCR clones. As reported before, the TCR repertoire diversity indexes decreased gradually with increasing age in the HD group (all p < 0.001). Moreover, the TCR repertoire diversities in the survival group were significantly higher than those in the death group (D50: p = 0.0342; the number of HECs: p = 0.0414). Using as the cut off value, the OC patients with D50 < 0.032 had significantly higher mortality rates compared to the OC patients with D50 > 0.032 ( p = 0.0019). Similarly, using the numbers of HECs or the frequencies of the largest TCR clones can also distinguish OC patients in long term survival. Conclusions: Analyzing the peripheral TCR repertoires in the OC and HD revealed significant differences, and the peripheral TCR repertoire diversity indexes may be used as a potential predictor for long term survival for ovarian cancer.

Prediction of long-term survival among patients with cirrhosis using time-varying models.

Risk prediction among patients with cirrhosis has historically focused on short-term (ie, 90 days) mortality among patients waitlisted for a transplant. Although several models have been developed to predict intermediate and longer term survivals, they have important limitations, namely, including only baseline laboratory and clinical variables to predict survival over a time horizon of years. We developed prediction models using time-varying laboratory and clinical data among patients with cirrhosis in the OneFlorida Clinical Research Consortium. We fit extended Cox models and assessed model discrimination and calibration in complete-case analysis and imputation of missing laboratory data. Among 15,277 patients, 9922 (64.9%) were included in the complete-case analysis. Final models included demographic (age and sex), time-updating laboratory (albumin, alanine transaminase, alkaline phosphatase, bilirubin, platelet, and sodium), and time-updating clinical (ascites, hepatic encephalopathy, spontaneous bacterial peritonitis, and bleeding esophageal varices) variables. Model discrimination was excellent in the complete-case analysis [AUC and concordance-index (C-index) > 0.85] at 1-, 2-, 3-, 4-, and 5-year time points. Model performance was unchanged with the exclusion of race and ethnicity as model predictors. Model discrimination was excellent (C-index >0.8) when imputation was used for patients with 1 or 2 missing laboratory variables. Using data from a statewide sample of patients with cirrhosis, we developed and internally validated a time-updating model to predict survival with excellent discrimination. Based on its measures of discrimination (AUC and c-index), this model matched or exceeded the performance of other published risk models depending on the time horizon. If externally validated, this risk score could improve the care of patients with cirrhosis by improving counseling on intermediate and longer term outcomes to guide clinical decision-making and advanced care planning.

ASO Visual Abstract: Predictors of Long-Term Survival in Pancreatic Ductal Adenocarcinoma After Pancreatectomy-TP53 and SMAD4 Mutation Scoring in Combination with CA19-9.

ASO Visual Abstract: Predictors of Long-Term Survival in Pancreatic Ductal Adenocarcinoma After Pancreatectomy-TP53 and SMAD4 Mutation Scoring in Combination with CA19-9.

ASO Author Reflections: Prediction of Long-Term Cancer-Specific Survival of Lung Carcinoid Patients.

ASO Author Reflections: Prediction of Long-Term Cancer-Specific Survival of Lung Carcinoid Patients.

876 Significant Survival Improvements for Patients with Melanoma Brain Metastases: Can We Reach Cure in the Current Era?

INTRODUCTION: New therapies for melanoma have been associated with increasing survival expectations, as opposed to the dismal outcomes of only a decade ago. METHODS: We reviewed 171 melanoma patients with BM receiving stereotactic radiosurgery (SRS) who were followed with point-of-care data collection between 2012-2020. Clinical, molecular and imaging data were collected, including systemic treatment and radiosurgical parameters. RESULTS: Mean age was 63 ± 15 years, 39% were female and 29% had BRAF-mutated tumors. Median overall survival after radiosurgery was 15.7 months (95% Confidence Interval: 11.4-27.7) and 25 months in patients managed since 2015. Thirty-two patients survived 5 years from their initial SRS. BRAF mutation-targeted therapies showed a survival advantage in comparison to chemotherapy (p = 0.009), but not to immunotherapy (p = 0.09). In a multivariable analysis, both immunotherapy and the number of metastases at 1st SRS were predictors of long-term survival (5 years) from initial SRS (p = 0.023 and p = 0.018, respectively). Five patients (16%) of the long-term survivors required no active treatment for 5 years. CONCLUSIONS: Long-term survival in patients with melanoma BM is achievable in the current era of SRS combined with immunotherapies. For those alive 5 years after first SRS, 16% had been also off systemic or local brain therapy for over 5 years. Given late recurrences of melanoma, caution is warranted, however prolonged survival off active treatment in a subset of our patients raises the potential for cure.

Growth Differentiation Factor 15 and Risk of Death in Haemodialysis Patients.

Noninvasive identification of haemodialysis patients at high risk of cardiovascular events and death might improve their outcome. Growth differentiation factor 15 is a prognostic biomarker in multiple disease entities, including cardiovascular disease. The aim of this study was to assess the association between plasma GDF-15 and mortality in a cohort of haemodialysis patients. Circulating GDF-15 was measured in 30 patients after a regular haemodialysis session, followed by a clinical follow-up for all-cause death. Measurements were performed using the Proseek Multiplex Cardiovascular disease panels (Olink Proteomics AB) and validated using the Elecsys GDF-15 electrochemiluminescence immunoassay on a Cobas E801 analyzer (Roche Diagnostics). During a median of 38 months, 9 patients (30%) died. Seven deaths occurred in the group of patients with a circulating GDF-15 above the median and two in the group with lower GDF-15. Mortality was significantly higher in patients with circulating GDF-15 levels above the median, log-rankP = 0.044. The performance of circulating GDF-15 to predict long-term mortality has an area under the ROC curve of 0.76, P = 0.028. Prevalence of most relevant comorbidities and the Charlson comorbidity index were similar across the two groups. A high agreement with a correlation among both diagnostic methods was observed (Spearman's rho = 0.83, P < 0.001). Plasma GDF-15 displays promising prognostic properties for the prediction of long-term survival beyond clinical parameters in patients on maintenance haemodialysis.

Geriatric Nutritional Risk Index at Hospital Admission or Discharge in Patients with Acute Decompensated Heart Failure

Geriatric Nutritional Risk Index (GNRI) is known both as a reliable indicator of nutritional status and a predictor of long-term survival among patients with acute decompensated heart failure (ADHF). However, the optimal timing to evaluate GNRI during hospitalization remains unclear. In the present study, we retrospectively analyzed patients hospitalized with ADHF in the West Tokyo Heart Failure (WET-HF) registry. GNRI was assessed at hospital admission (a-GNRI) and discharge (d-GNRI). Out of 1474 patients included in the present study, 568 (40.1%) and 796 (57.2%) patients had lower GNRI (<92) at hospital admission and discharge, respectively. After the follow-up (median 616 days), 290 patients died. The multivariable analysis showed that all-cause mortality was independently associated with d-GNRI (per 1 unit decrease, adjusted hazard ratio [aHR]: 1.06, 95% confidence interval [CI]: 1.04-1.09, p < 0.001), but not with a-GNRI (aHR: 0.99, 95% CI: 0.97-1.01, p = 0.341). The predictability of GNRI for long-term survival was more pronounced when evaluated at hospital discharge than at hospital admission (area under the curve 0.699 vs. 0.629, DeLong's test p < 0.001). Our study suggested that GNRI should be evaluated at hospital discharge, regardless of the assessment at hospital admission, to predict the long-term prognosis for patients hospitalized with ADHF.

Disease-free survival as a predictor of overall survival in localized renal cell carcinoma following initial nephrectomy: A retrospective analysis of Surveillance, Epidemiology and End Results-Medicare datac.

This study aimed to assess whether disease-free survival (DFS) may serve as a predictor for long-term survival among patients with intermediate-high risk or high risk renal cell carcinoma (RCC) post-nephrectomy when overall survival (OS) is unavailable. The Surveillance, Epidemiology and End Results-Medicare database (2007-2016) was used to identify patients with non-metastatic intermediate-high risk and high risk RCC post-nephrectomy. Landmark analysis and Kendall's τ were used to evaluate the correlation between DFS and OS. Multivariable regression models were used to quantify the incremental OS post-nephrectomy associated with increased time to recurrence among patients with recurrence, adjusting for baseline covariates. A total of 643 patients were analyzed; mean age of 75 years; >95% of patients had intermediate-high risk RCC at diagnosis; 269 patients had recurrence post-nephrectomy. For patients with versus without recurrence at the landmark points of 1, 3, and 5 years post-nephrectomy, the 5-year OS were 37.0% versus 70.1%, 42.3% versus 72.8%, and 53.2% versus 78.6%, respectively. The Kendall's τ between DFS and OS post-nephrectomy was 0.70 (95% CI: 0.65, 0.74; p < 0.001). After adjusting for baseline covariates, patients with one additional year of time to recurrence were associated with 0.73 years longer OS post-nephrectomy (95% CI: 0.40, 1.05; p < 0.001). The significant positive association of DFS and OS among patients with intermediate-high risk and high risk RCC post-nephrectomy from this study supports the use of DFS as a potential predictor of OS for these patients when OS data are immature.

Long-term survival and second malignant tumor prediction in pediatric, adolescent, and young adult cancer survivors using Random Survival Forests: a SEER analysis

Survival and second malignancy prediction models can aid clinical decision making. Most commonly, survival analysis studies are performed using traditional proportional hazards models, which require strong assumptions and can lead to biased estimates if violated. Therefore, this study aims to implement an alternative, machine learning (ML) model for survival analysis: Random Survival Forest (RSF). In this study, RSFs were built using the U.S. Surveillance Epidemiology and End Results to (1) predict 30-year survival in pediatric, adolescent, and young adult cancer survivors; and (2) predict risk and site of a second tumor within 30 years of the first tumor diagnosis in these age groups. The final RSF model for pediatric, adolescent, and young adult survival has an average Concordance index (C-index) of 92.9%, 94.2%, and 94.4% and average time-dependent area under the receiver operating characteristic curve (AUC) at 30-years since first diagnosis of 90.8%, 93.6%, 96.1% respectively. The final RSF model for pediatric, adolescent, and young adult second malignancy has an average C-index of 86.8%, 85.2%, and 88.6% and average time-dependent AUC at 30-years since first diagnosis of 76.5%, 88.1%, and 99.0% respectively. This study suggests the robustness and potential clinical value of ML models to alleviate physician burden by quickly identifying highest risk individuals.

CT characteristics for predicting prognosis of gastric cancer with synchronous peritoneal metastasis.

To explore the CT characteristics for the prediction of long term survival in gastric cancer patients with synchronous peritoneal metastasis (PM). Sixty-six patients diagnosed as gastric cancer with synchronous peritoneum metastasis were enrolled in this retrospective study. Ten anatomic peritoneal regions were evaluated to check for the signs of PM on CT. One positive area equaled one score. The CT characteristic-based PM score (CT-PMS) was the sum of the total points assigned to all 10 regions, with a range of 0-10. The triple tract dilatation (TTD) sign caused by peritoneal metastasis, the presence of extensive lymph node metastasis (ELM), and the grade of ascites were recorded. The overall survival (OS) was used as the prognostic indicator. The performance of the CT characteristics was assessed by the Kaplan-Meier analysis and Cox proportional hazards model, while its reproducibility was evaluated by Kappa statistic and weighted Kappa statistic. Patients with a CT-PMS of 3-10 had significantly poorer OS (P = .02). Patients with either the presence of TTD sign, or ELM had a trend toward unfavorable OS (both P = .07), and when CT-PMS of 3-10 was detected simultaneously, the survival was further reduced (P = .00 for TTD sign; P = .01 for ELM). The grade of ascites failed to show a significant correlation with OS. The interobserver reproducibility for assessing the CT-PMS, the presence of TTD sign, the presence of ELM, and the grade of ascites had a substantial to almost perfect agreement. The prognosis of gastric cancer patients with PM has a correlation with the extent of metastasis dissemination on baseline CT. A CT-PMS of 3-10 is associated with a worse prognosis than that of 0-2. The presence of TTD sign and ELM may help further select patients with extraordinarily poor prognoses.

The AUGIS Survival Predictor: Prediction of Long-Term and Conditional Survival After Esophagectomy Using Random Survival Forests.

The aim of this study was to develop a predictive model for overall survival after esophagectomy using pre/postoperative clinical data and machine learning. For patients with esophageal cancer, accurately predicting long-term survival after esophagectomy is challenging. This study investigated survival prediction after esophagectomy using a RandomSurvival Forest (RSF) model derived from routine data from a large, well-curated, national dataset. Patients diagnosed with esophageal adenocarcinoma or squamous cell carcinoma between 2012 and 2018 in England and Wales who underwent an esophagectomy were included. Prediction models for overall survival were developed using the RSF method and Cox regression from 41 patient and disease characteristics. Calibration and discrimination (time-dependent area under the curve) were validated internally using bootstrap resampling. The study analyzed 6399 patients, with 2625 deaths during follow-up. Median follow-up was 41 months. Overall survival was 47.1% at 5 years. The final RSF model included 14 variables and had excellent discrimination with a 5-year time-dependent area under the receiver operator curve of 83.9% [95% confidence interval (CI) 82.6%-84.9%], compared to 82.3% (95% CI 81.1%-83.3%) for the Cox model. The most important variables were lymph node involvement, pT stage, circumferential resection margin involvement (tumor at < 1 mm from cut edge) and age. There was a wide range of survival estimates even within TNM staging groups, with quintiles of prediction within Stage 3b ranging from 12.2% to 44.7% survival at 5 years. An RSF model for long-term survival after esophagectomy exhibited excellent discrimination and well-calibrated predictions. At a patient level, it provides more accuracy than TNM staging alone and could help in the delivery of tailored treatment and follow-up.

CT radiomics-based long-term survival prediction for locally advanced non-small cell lung cancer patients treated with concurrent chemoradiotherapy using features from tumor and tumor organismal environment

BackgroundDefinitive concurrent chemoradiotherapy (CCRT) is the standard treatment for locally advanced non-small cell lung cancer (LANSCLC) patients, but the treatment response and survival outcomes varied among these patients. We aimed to identify pretreatment computed tomography-based radiomics features extracted from tumor and tumor organismal environment (TOE) for long-term survival prediction in these patients treated with CCRT.MethodsA total of 298 eligible patients were randomly assigned into the training cohort and validation cohort with a ratio 2:1. An integrated feature selection and model training approach using support vector machine combined with genetic algorithm was performed to predict 3-year overall survival (OS). Patients were stratified into the high-risk and low-risk group based on the predicted survival status. Pulmonary function test and blood gas analysis indicators were associated with radiomic features. Dynamic changes of peripheral blood lymphocytes counts before and after CCRT had been documented.ResultsNine features including 5 tumor-related features and 4 pulmonary features were selected in the predictive model. The areas under the receiver operating characteristic curve for the training and validation cohort were 0.965 and 0.869, and were reduced by 0.179 and 0.223 when all pulmonary features were excluded. Based on radiomics-derived stratification, the low-risk group yielded better 3-year OS (68.4% vs. 3.3%, p < 0.001) than the high-risk group. Patients in the low-risk group had better baseline FEV1/FVC% (96.3% vs. 85.9%, p = 0.046), less Grade ≥ 3 lymphopenia during CCRT (63.2% vs. 83.3%, p = 0.031), better recovery of lymphopenia from CCRT (71.4% vs. 27.8%, p < 0.001), lower incidence of Grade ≥ 2 radiation-induced pneumonitis (31.6% vs. 53.3%, p = 0.040), superior tumor remission (84.2% vs. 66.7%, p = 0.003).ConclusionPretreatment radiomics features from tumor and TOE could boost the long-term survival forecast accuracy in LANSCLC patients, and the predictive results could be utilized as an effective indicator for survival risk stratification. Low-risk patients might benefit more from radical CCRT and further adjuvant immunotherapy.Trial registration:retrospectively registered.

Prediction of Long-Term Survival after Coronary Artery Bypass with Bilateral Internal Thoracic Artery Grafting: External Validation of A Contemporary Nomogram.

This study aimed to verify the external validation of a contemporary nomogram in predicting long-term survival after an isolated coronary artery bypass with bilateral internal thoracic artery grafting (CABG-BITA). Consecutive patients who underwent CABG-BITA at a single center were included in the study. All the predictors of the original risk score (age, diabetes mellitus, chronic obstructive pulmonary disease, congestive heart failure, chronic renal failure, old myocardial infarction, ejection fraction, intra-aortic balloon pump and peripheral arterial disease) were available for analysis. Among the 2846 consecutive patients, a total of 1176 (41.3%) deaths were recorded during the 31,383 patient years of follow-up. The median EuroSCORE II was 2.35, and the median follow-up was 11.1 years. The risk score showed 72.7% overall discriminatory ability as measured by Harrell's concordance statistic. It showed satisfactory calibration at 10, 15 and 20 years of follow-up. The risk score showed a time-varying nonlinear effect, and accordingly, adjusted long-term survival predictions were calculated. There were subgroups (scores &lt; 50 points) with favorable 20-year survival rates ranging from 77% to 60%. Higher risk subgroups (scores &gt; 90 points) showed poor 20-year survival rates ranging from 22% to 4%. The validated risk score represents a useful algorithm for the detection of patients who could benefit after CABG-BITA with respect to long-term survival. Although further multi-center studies are required worldwide to reveal the usefulness of this score in the clinical setting, its wide adoption may act as a motivation for cardiac surgeons resulting in higher numbers of CABG-BITA procedures.

Value of the Preoperative D-Dimer to Albumin Ratio for Survival and Recurrence Patterns in Gastric Cancer.

D-dimer (DDI) and albumin are prognostic markers for numerous cancers; however, the predictive value of the preoperative DDI-to-albumin ratio (DAR) on the survival and recurrence patterns of gastric cancer (GC) remains unclear. The aim of this study was to explore the prognostic value of the DAR in GC. Our study included 1766 patients with GC, divided into training and testing cohorts at a ratio of 7:3. Patients were classified into either a high-DAR group (> 0.0145) or low-DAR group (≤ 0.0145) according to the cut-off value of receiver operating characteristic (ROC) curve analysis. The relationship between the DAR and recurrence pattern was analyzed in stage II/III patients. Eight preoperative hematological factors were included and 17 composite inflammatory markers were constructed. ROC and random forest analyses indicated that among 17 markers, DAR was the best predictor for overall survival (OS) in GC (p<0.01). High DAR was significantly associated with poor OS (hazard ratio [HR] 1.89, p<0.001) and recurrence-free survival (RFS; HR 1.85, p<0.001). Subgroup analysis showed no differences in OS and RFS between the high- and low-DAR groups in stage I or pT1/2 or pN0/1 patients; however, in stage II/III or pT3/4 or pN2/3 patients, the high-DAR group had shorter OS and RFS rates than the low-DAR group (p<0.001). Similar results were found in the testing cohort. According to the multivariate analysis based on the training cohort, five indices, including DAR, cT stage, cN stage, age and body mass index (BMI), were incorporated to establish a nomogram model to predict the long-term prognosis of GC. The model showed comparable forecast performance in predicting OS (C-index: 0.773 vs. 0.786) and RFS (C-index: 0.788 vs. 0.795) compared with pTNM. Recurrence pattern analysis in stage II/III patients showed that the high-DAR group had a higher incidence of peritoneal implantation and early recurrence (ER) than the low-DAR group, and the post-recurrence survival in the high-DAR group was significantly shorter than that in the low-DAR group (p=0.016). The preoperative DAR is a new biomarker for the long-term survival prediction of GC. In advanced GC, a preoperative DAR > 0.0145 aids the timely detection of ER and peritoneal recurrence after surgery, thus guiding individual follow-up strategies.

Can the Duration of In-Hospital Ventilation in Patients with Sepsis Help Predict Long-Term Survival?

Mechanical ventilation is a cornerstone in the treatment of critical illness, especially sepsis. Prolonged mechanical ventilation, for a duration exceeding 21 days, is associated with higher rates of in-hospital and post-discharge mortality. Our aim was to assess the association between in-hospital ventilation duration and long-term life expectancy in patients ventilated in intensive care units specifically due to sepsis of any origin. We conducted a population-based retrospective cohort study of adults hospitalized in a general intensive care unit for 24 h or more during 2007–2017, who were diagnosed with sepsis or septic shock, treated with invasive mechanical ventilation for a maximum of 60 days and survived hospitalization. The primary exposure was the length of invasive mechanical ventilation. In an adjusted multivariable regression model, survival rates at 1, 2, 3 and 4 years post-hospitalization did not differ significantly between patients who were ventilated for 3–8 days (n = 169), 9–21 days (n = 160) or 22–60 days (n = 170), and those who were ventilated for 1–2 days (n = 192). We concluded that the duration of in-hospital ventilation in patients with sepsis cannot serve as a predictor for long-term survival. Thus, the duration of ventilation in itself should not guide the level of care in ventilated patients with sepsis.

A Deep Neural Network for Gastric Cancer Prognosis Prediction Based on Biological Information Pathways.

Background Gastric cancer (GC) is one of the deadliest cancers in the world, with a 5-year overall survival rate of lower than 20% for patients with advanced GC. Genomic information is now frequently employed for precision cancer treatment due to the rapid advancements of high-throughput sequencing technologies. As a result, integrating multiomics data to construct predictive models for the GC patient prognosis is critical for tailored medical care. Results In this study, we integrated multiomics data to design a biological pathway-based gastric cancer sparse deep neural network (GCS-Net) by modifying the P-NET model for long-term survival prediction of GC. The GCS-Net showed higher accuracy (accuracy = 0.844), area under the curve (AUC = 0.807), and F1 score (F1 = 0.913) than traditional machine learning models. Furthermore, the GCS-Net not only enables accurate patient survival prognosis but also provides model interpretability capabilities lacking in most traditional deep neural networks to describe the complex biological process of prognosis. The GCS-Net suggested the importance of genes (UBE2C, JAK2, RAD21, CEP250, NUP210, PTPN1, CDC27, NINL, NUP188, and PLK4) and biological pathways (Mitotic Anaphase, Resolution of Sister Chromatid Cohesion, and SUMO E3 ligases) to GC, which is consistent with the results revealed in biological- and medical-related studies of GC. Conclusion The GCS-Net is an interpretable deep neural network built using biological pathway information whose structure represents a nonlinear hierarchical representation of genes and biological pathways. It can not only accurately predict the prognosis of GC patients but also suggest the importance of genes and biological pathways. The GCS-Net opens up new avenues for biological research and could be adapted for other cancer prediction and discovery activities as well.

Multi-lesion radiomics of PET/CT for non-invasive survival stratification and histologic tumor risk profiling in patients with lung adenocarcinoma.

This study investigates the ability of machine learning (ML) models trained on clinical data and 2-deoxy-2-[18F]fluoro-D-glucose(FDG) positron emission tomography/computed tomography (PET/CT) radiomics to predict overall survival (OS), tumor grade (TG), and histologic growth pattern risk (GPR) in lung adenocarcinoma (LUAD) patients. A total of 421 treatment-naive patients with histologically-proven LUAD and available FDG PET/CT imaging were retrospectively included. Four cohorts were assessed for predicting 4-year OS (n = 276), 3-year OS (n = 280), TG (n = 298), and GPR (n = 265). FDG-avid lesions were delineated, and 2082 radiomics features were extracted and combined with endpoint-specific clinical parameters. ML models were built for the prediction of 4-year OS (M4OS), 3-year OS (M3OS), tumor grading (MTG), and histologic growth pattern risk (MGPR). A 100-fold Monte Carlo cross-validation with 80:20 training to validation split was employed as a performance evaluation for all models. The association between the M4OS and M3OS predictions with OS was assessed by the Kaplan-Meier survival analysis. The area under the receiver operator characteristics curve (AUC) was the highest for M4OS (AUC 0.88, 95% confidence interval (CI) 86.7-88.7), followed by M3OS (AUC 0.84, CI 82.9-84.9), while MTG and MGPR performed equally well (AUC 0.76, CI 74.4-77.9, CI 74.6-78, respectively). Predictions of M4OS (hazard ratio (HR) -2.4, CI -2.47 to -1.64, p < 0.05) and M3OS (HR -2.36, CI -2.79 to -1.93, p < 0.05) were independently associated with OS. ML models are able to predict long-term survival outcomes in LUAD patients with high accuracy. Furthermore, histologic grade and predominant growth pattern risk can be predicted with satisfactory accuracy. • Machine learning models trained on pre-therapeutic PET/CT radiomics enable highly accurate long-term survival prediction of patients with lung adenocarcinoma. • Highly accurate survival predictions are achieved in lung adenocarcinoma patients despite heterogenous histologies and treatment regimens. • Radiomic machine learning models are able to predict lung adenocarcinoma tumor grade and histologic growth pattern risk with satisfactory accuracy.

Lung cancer survival prediction and biomarker identification with an ensemble machine learning analysis of tumor core biopsy metabolomic data.

While prediction of short versus long term survival from lung cancer is clinically relevant in the context of patient management and therapy selection, it has proven difficult to identify reliable biomarkers of survival. Metabolomic markers from tumor core biopsies have been shown to reflect cancer metabolic dysregulation and hold prognostic value. Implement and validate a novel ensemble machine learning approach to evaluate survival based on metabolomic biomarkers from tumor core biopsies. Data were obtained from tumor core biopsies evaluated with high-resolution 2DLC-MS/MS. Unlike biofluid samples, analysis of tumor tissue is expected to accurately reflect the cancer metabolism and its impact on patient survival. A comprehensive suite of machine learning algorithms were trained as base learners and then combined into a stacked-ensemble meta-learner for predicting "short" versus "long" survival on an external validation cohort. An ensemble method of feature selection was employed to find a reliable set of biomarkers with potential clinical utility. Overall survival (OS) is predicted in external validation cohort with AUROCTEST of 0.881 with support vector machine meta learner model, while progression-free survival (PFS) is predicted with AUROCTEST of 0.833 with boosted logistic regression meta learner model, outperforming a nomogram using covariate data (staging, age, sex, treatment vs. non-treatment) as predictors. Increased relative abundance of guanine, choline, and creatine corresponded with shorter OS, while increased leucine and tryptophan corresponded with shorter PFS. In patients that expired, N6,N6,N6-Trimethyl-L-lysine, L-pyrogluatmic acid, and benzoic acid were increased while cystine, methionine sulfoxide and histamine were decreased. In patients with progression, itaconic acid, pyruvate, and malonic acid were increased. This study demonstrates the feasibility of an ensemble machine learning approach to accurately predict patient survival from tumor core biopsy metabolomic data.