Discovery and validation of a cross-platform 21-gene prognostic signature in neuroblastoma.

2021 ◽  
Vol 39 (15_suppl) ◽  
pp. 10035-10035
Author(s):  
Mehul Gupta ◽  
Sunand Nageswaran Kannappan ◽  
Aru Narendran ◽  
Pinaki Bose
Keyword(s):  
Machine Learning ◽  
Risk Stratification ◽  
Clinical Decision Making ◽  
Proportional Hazards ◽  
Risk Model ◽  
Learning Algorithm ◽  
Clinical Decision ◽  
Gene Signature ◽  
Cox Proportional Hazards ◽  
Transcriptomic Signature

10035 Background: Neuroblastoma (NB) is the most common extracranial solid tumor in children. Despite the development of risk stratification tools to improve prognostication, prediction of patient survival outcomes in NB remains poor. In this study we used an unbiased machine-learning algorithm to develop and validate a transcriptomic signature capable of predicting 5-year overall (OS) and event-free survival (EFS) in these patients. Methods: The TARGET-Neuroblastoma dataset (n = 243) was used as the training set. Two independent NB cohorts, E-MTAB-179 (n = 478) and GSE85047 (n = 266) were used as validation sets. Elastic net regression was employed to identify transcripts associated with EFS. Association of the developed signature with EFS and OS was evaluated using univariate Cox proportional hazards (CoxPH), Kaplan-Meier, and 5-year receiver-operator characteristic curves in validation cohorts. Further, the independent prognostic value of the signature was assessed using multivariate CoxPH models with relevant clinicopathologic variables including age, INSS stage, and N-Myc amplification status in both validation sets. Finally, a nomogram was developed to integrate the signature with prognostic clinicopathologic variables to evaluate their combined efficacy for prediction of 5-year EFS and OS. Results: We identified a 21-gene signature that demonstrates significant association with EFS and OS in both E-MTAB-178 and GSE49710 validation cohorts. Moreover, the signature is independent of clinicopathological variables and can be effectively incorporated into a risk model, improving the prognostic performance. Several genes within the signature have been previously implicated in NB, including ECEL1, HOXC9 and ARAF1. Conclusions: To the best of our knowledge, we are the first to use an unbiased machine learning approach to generate a transcriptomic gene signature for neuroblastoma prognosis externally validated in multiple cohorts across platforms. This 21-gene transcriptomic signature significantly associated with EFS and OS in this disease. Combining this signature with current prognostic clinicopathologic variables will improve risk stratification of affected patients and may inform effective clinical decision-making.[Table: see text]

scholarly journals Cancer Grade Model: a multi-gene machine learning-based risk classification for improving prognosis in breast cancer

2021 ◽  
Author(s):  
E. Amiri Souri ◽  
A. Chenoweth ◽  
A. Cheung ◽  
S. N. Karagiannis ◽  
S. Tsoka
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Clinical Decision Making ◽  
Histological Grade ◽  
Tumour Size ◽  
Clinical Decision ◽  
Gene Signature ◽  
Risk Classification ◽  
Breast Cancers ◽  
Grade 3

Abstract Background Prognostic stratification of breast cancers remains a challenge to improve clinical decision making. We employ machine learning on breast cancer transcriptomics from multiple studies to link the expression of specific genes to histological grade and classify tumours into a more or less aggressive prognostic type. Materials and methods Microarray data of 5031 untreated breast tumours spanning 33 published datasets and corresponding clinical data were integrated. A machine learning model based on gradient boosted trees was trained on histological grade-1 and grade-3 samples. The resulting predictive model (Cancer Grade Model, CGM) was applied on samples of grade-2 and unknown-grade (3029) for prognostic risk classification. Results A 70-gene signature for assessing clinical risk was identified and was shown to be 90% accurate when tested on known histological-grade samples. The predictive framework was validated through survival analysis and showed robust prognostic performance. CGM was cross-referenced with existing genomic tests and demonstrated the competitive predictive power of tumour risk. Conclusions CGM is able to classify tumours into better-defined prognostic categories without employing information on tumour size, stage, or subgroups. The model offers means to improve prognosis and support the clinical decision and precision treatments, thereby potentially contributing to preventing underdiagnosis of high-risk tumours and minimising over-treatment of low-risk disease.

Predictive Data Modeling Using sp-kNN for Risk Factor Evaluation in Urban Demographical Healthcare Data

2021 ◽  
Vol 11 (1) ◽  
pp. 7-14
Author(s):  
Uzair Aslam Bhatti ◽  
Linwang Yuan ◽  
Zhaoyuan Yu ◽  
Saqib Ali Nawaz ◽  
Anum Mehmood ◽  
...  
Keyword(s):  
Machine Learning ◽  
Clinical Decision Making ◽  
Learning Algorithm ◽  
Clinical Decision ◽  
Eye Diseases ◽  
Machine Learning Algorithms ◽  
Medical Resources ◽  
Individual Level ◽  
Healthcare Data ◽  
Increasing Demand

Healthcare diseases are spreading all around the globe day to day. Hospital datasets are full from the data with much information. It's an urgent requirement to use that data perfectly and efficiently. We propose a novel algorithm for predictive model for eye diseases using KNN with machine learning algorithms and artificial intelligence (AI). The aims are to evaluate the connection between the accumulated preoperative risk variables and different eye diseases and to manufacture a model that can anticipate the results on an individual level, thus giving relevance to impactful factors and geographic and demographic features. Risk factors of the desired diseases were calculated and machine learning algorithm applied to provide the prediction of the diseases. Health monitoring is an economic discipline that focuses on the effective allocation of medical resources, mainly to maximize the benefits of society to health through the available resources. With the increasing demand for medical services and the limited allocation of medical resources, the application of health economics in clinical practice has been paid more and more attention, and it has gradually played an important role in clinical decision-making.

scholarly journals Prognostic Factors and a Nomogram Predicting Overall Survival in Patients with Limb Chondrosarcomas: A Population-Based Study

2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Xinjie Wu ◽  
Yanlei Wang ◽  
Wei Sun ◽  
Mingsheng Tan
Keyword(s):  
Overall Survival ◽  
Prognostic Factors ◽  
Clinical Decision Making ◽  
Proportional Hazards ◽  
Cox Regression ◽  
Training Group ◽  
Clinical Decision ◽  
The United States ◽  
Cox Proportional Hazards ◽  
Predictive Values

Introduction. We aimed to develop and validate a nomogram for predicting the overall survival of patients with limb chondrosarcomas. Methods. The Surveillance, Epidemiology, and End Results (SEER) program database was used to identify patients diagnosed with chondrosarcomas, from which data was extracted from 18 registries in the United States between 1973 and 2016. A total of 813 patients were selected from the database. Univariate and multivariate analyses were performed using Cox proportional hazards regression models on the training group to identify independent prognostic factors and construct a nomogram to predict the 3- and 5-year survival probability of patients with limb chondrosarcomas. The predictive values were compared using concordance indexes ( C -indexes) and calibration plots. Results. All 813 patients were randomly divided into a training group ( n = 572 ) and a validation group ( n = 241 ). After univariate and multivariate Cox regression, a nomogram was constructed based on a new model containing the predictive variables of age, site, grade, tumor size, histology, stage, and use of surgery, radiotherapy, or chemotherapy. The prediction model provided excellent C -indexes (0.86 and 0.77 in the training and validation groups, respectively). The good discrimination and calibration of the nomograms were demonstrated for both the training and validation groups. Conclusions. The nomograms precisely and individually predict the overall survival of patients with limb chondrosarcomas and could assist personalized prognostic evaluation and individualized clinical decision-making.

scholarly journals Predicting Clustered Dental Implant Survival Using Frailty Methods

2006 ◽  
Vol 85 (12) ◽  
pp. 1147-1151 ◽  
Author(s):  
S.-K. Chuang ◽  
T. Cai
Keyword(s):  
Risk Factors ◽  
Clinical Decision Making ◽  
Proportional Hazards ◽  
Smoking Status ◽  
Joint Probability ◽  
Frailty Model ◽  
Clinical Decision ◽  
Cox Proportional Hazards ◽  
Implant Survival ◽  
Survival Status

The purpose of this study was to predict future implant survival using information on risk factors and on the survival status of an individual’s existing implant(s). We considered a retrospective cohort study with 677 individuals having 2349 implants placed. We proposed to predict the survival probabilities using the Cox proportional hazards frailty model, with three important risk factors: smoking status, timing of placement, and implant staging. For a non-smoking individual with 2 implants placed, an immediate implant and in one stage, the marginal probability that 1 implant would survive 12 months was 85.8% (95%CI: 77%, 91.7%), and the predicted joint probability of surviving for 12 months was 75.1% (95%CI: 62.1%, 84.7%). If 1 implant was placed earlier and had survived for 12 months, then the second implant had an 87.5% (95%CI: 80.3%, 92.4%) chance of surviving 12 months. Such conditional and joint predictions can assist in clinical decision-making for individuals.

scholarly journals Nomogram to Predict the Overall Survival of Colorectal Cancer Patients: A Multicenter National Study

2021 ◽  
Vol 18 (15) ◽  
pp. 7734
Author(s):  
Nasrin Borumandnia ◽  
Hassan Doosti ◽  
Amirhossein Jalali ◽  
Soheila Khodakarim ◽  
Jamshid Yazdani Charati ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Overall Survival ◽  
Family History ◽  
Clinical Decision Making ◽  
Proportional Hazards ◽  
Clinical Decision ◽  
Tumor Stage ◽  
Cox Proportional Hazards ◽  
National Study ◽  
Historical Cohort

Background: Colorectal cancer (CRC) is the third foremost cause of cancer-related death and the fourth most commonly diagnosed cancer globally. The study aimed to evaluate the survival predictors using the Cox Proportional Hazards (CPH) and established a novel nomogram to predict the Overall Survival (OS) of the CRC patients. Materials and methods: A historical cohort study, included 1868 patients with CRC, was performed using medical records gathered from Iran’s three tertiary colorectal referral centers from 2006 to 2019. Two datasets were considered as train set and one set as the test set. First, the most significant prognostic risk factors on survival were selected using univariable CPH. Then, independent prognostic factors were identified to construct a nomogram using the multivariable CPH regression model. The nomogram performance was assessed by the concordance index (C-index) and the time-dependent area under the ROC curve. Results: The age of patients, body mass index (BMI), family history, tumor grading, tumor stage, primary site, diabetes history, T stage, N stage, and type of treatment were considered as significant predictors of CRC patients in univariable CPH model (p < 0.2). The multivariable CPH model revealed that BMI, family history, grade and tumor stage were significant (p < 0.05). The C-index in the train data was 0.692 (95% CI, 0.650–0.734), as well as 0.627 (0.670, 0.686) in the test data. Conclusion: We improved a novel nomogram diagram according to factors for predicting OS in CRC patients, which could assist clinical decision-making and prognosis predictions in patients with CRC.

scholarly journals Perinatal Hypoxia Diagnostic System by using Scalable Machine Learning Algorithms

2019 ◽  
Vol 8 (12) ◽  
pp. 1954-1959
Keyword(s):  
Machine Learning ◽  
Clinical Decision Making ◽  
Learning Algorithm ◽  
Diagnostic System ◽  
Clinical Decision ◽  
Machine Learning Algorithms ◽  
Machine Learning Algorithm ◽  
Learning Approaches ◽  
Perinatal Hypoxia ◽  
Fast Computation

Collaborating big data and machine learning approaches in healthcare can help in improving clinical decision making and treatment by identifying and accumulating accurate features. Prenatal hypoxia can also be identified by cardiotocography (CTG) monitoring that helps in identifying the condition of the fetus. Imposing the data over distributed approaches can help in fast computation to rate the fetal and mother wellbeing before delivery. Our research aims to propose and implement a scalable Machine learning Algorithm based perinatal Hypoxia diagnostic system for larger datasets. This system was implemented on the CTG dataset using python and pyspark models like SVM, Random Forest, and Logistic regression. In the proposed method experiment results contributing to spark RF are more accurate than other techniques and achieved the precision of 0.97, recall of 0.99, f-1 score of 0. 98, AUC of 0.97 and gained 97% accuracy

scholarly journals An approach to rapidly assess sepsis through multi-biomarker host response using machine learning algorithm

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Abha Umesh Sardesai ◽  
Ambalika Sanjeev Tanak ◽  
Subramaniam Krishnan ◽  
Deborah A. Striegel ◽  
Kevin L. Schully ◽  
...  
Keyword(s):  
Machine Learning ◽  
Immune Response ◽  
Clinical Decision Making ◽  
Learning Algorithm ◽  
Clinical Decision ◽  
Host Immune Response ◽  
Supervised Machine Learning ◽  
Machine Learning Algorithm ◽  
Patient Stratification ◽  
Sepsis Management

AbstractSepsis is a life-threatening condition and understanding the disease pathophysiology through the use of host immune response biomarkers is critical for patient stratification. Lack of accurate sepsis endotyping impedes clinicians from making timely decisions alongside insufficiencies in appropriate sepsis management. This work aims to demonstrate the potential feasibility of a data-driven validation model for supporting clinical decisions to predict sepsis host-immune response. Herein, we used a machine learning approach to determine the predictive potential of identifying sepsis host immune response for patient stratification by combining multiple biomarker measurements from a single plasma sample. Results were obtained using the following cytokines and chemokines IL-6, IL-8, IL-10, IP-10 and TRAIL where the test dataset was 70%. Supervised machine learning algorithm naïve Bayes and decision tree algorithm showed good accuracy of 96.64% and 94.64%. These promising findings indicate the proposed AI approach could be a valuable testing resource for promoting clinical decision making.

Predictors of stopping chemotherapy early and short survival in patients with potentially platinum sensitive (PPS) recurrent ovarian cancer (ROC) who have had ≥3 lines of prior chemotherapy: The GCIG symptom benefit study (SBS).

2017 ◽  
Vol 35 (15_suppl) ◽  
pp. 5575-5575
Author(s):  
Felicia Roncolato ◽  
Florence Joly ◽  
Rachel O'Connell ◽  
Anne Lanceley ◽  
Florian Heitz ◽  
...  
Keyword(s):  
Clinical Trial ◽  
Clinical Decision Making ◽  
Proportional Hazards ◽  
Recurrent Ovarian Cancer ◽  
Clinical Decision ◽  
Cox Proportional Hazards ◽  
Variable Response ◽  
Platinum Sensitive ◽  
Response To Chemotherapy ◽  
Gi Symptoms

5575 Background: PPS ROC is defined by a platinum free interval > 6 months. Women starting ≥3 lines of chemotherapy for PPS ROC are however a heterogeneous group with variable response to chemotherapy and OS. We sought to identify baseline characteristics (health related quality of life [HRQL] and clinical features) that were associated with stopping chemotherapy early and shorter OS to improve patient selection for palliative chemotherapy. Methods: 378 women with PPS ROC starting ≥3 lines chemotherapy enrolled in GCIG SBS. HRQL was assessed with EORTC QLQ-C30/QLQ-OV28. Associations with stopping chemotherapy early (by 8 weeks) were assessed with logistic regression. Associations with OS were assessed with Cox proportional hazards regression. Variables significant in univariable analysis (p < 0.05) were included as candidates for multivariable analyses using backward elimination to select those independently significant at p < 0.05. Results: Median age was 64 years. The line of chemotherapy was third in 40%, fourth in 29%, and ≥ fifth in 31%. Chemotherapy was stopped early in 45/378 (12%) and their median OS was 3.4 months. Poor physical function (PF) and global health status (GHS) at baseline were significant univariable predictors of stopping chemotherapy early (p < 0.008); PF remained significant in a multivariable model adjusting for clinical factors (haemoglobin [Hb], ascites, abdominal cramps, neutrophil: lymphocyte≥5, platelets, log CA125); p = 0.03. Median OS in the whole group was 16.6 months. PF, role function, GHS and abdominal/GI symptoms were significant univariable predictors of OS (p < 0.001); PF and GHS remained significant predictors of OS in multivariable models including Hb, ascites, neutrophil: lymphocyte≥5, platelets, log CA125, ECOG and BMI (p < 0.007). Conclusions: In women with PPS ROC ≥3 lines chemotherapy, baseline PF and GHS are independent significant predictors of stopping chemotherapy early and short OS. HRQOL is easily measured, prognostic and may improve clinical trial stratification, patient-doctor communication and support clinical decision making. Clinical trial information: 12607000603415.

scholarly journals Nomogram to Predict the Benefit of Radiation for Older Patients With Breast Cancer Treated With Conservative Surgery

2012 ◽  
Vol 30 (23) ◽  
pp. 2837-2843 ◽  
Author(s):  
Jeffrey M. Albert ◽  
Diane D. Liu ◽  
Yu Shen ◽  
I-Wen Pan ◽  
Ya-Chen Tina Shih ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Older Women ◽  
Older Patients ◽  
Clinical Decision Making ◽  
Proportional Hazards ◽  
Estrogen Receptor Status ◽  
Clinical Decision ◽  
Conservative Surgery ◽  
Cox Proportional Hazards ◽  
Cox Proportional Hazards Models

Purpose The role of radiation therapy (RT) after conservative surgery (CS) remains controversial for older patients with breast cancer. Guidelines based on recent clinical trials have suggested that RT may be omitted in selected patients with favorable disease. However, it is not known whether this recommendation should extend to other older women. Accordingly, we developed a nomogram to predict the likelihood of long-term breast preservation with and without RT. Methods We used Surveillance, Epidemiology, and End Results–Medicare data to identify 16,092 women age 66 to 79 years treated with CS between 1992 and 2002, using claims to identify receipt of RT and subsequent mastectomy. Time to mastectomy was estimated using the Kaplan-Meier method. Cox proportional hazards models determined the effect of covariates on mastectomy-free survival (MFS). A nomogram was developed to predict 5- and 10-year MFS, given associated risk factors, and bootstrap validation was performed. Results With a median follow-up of 7.2 years, the overall 5- and 10-year MFS rates were 98.1% (95% CI, 97.8% to 98.3%) and 95.4% (95% CI, 94.9% to 95.8%), respectively. In multivariate analysis, age, race, tumor size, estrogen receptor status, and receipt of RT were predictive of time to mastectomy and were incorporated into the nomogram. Nodal status was also included given a significant interaction with RT. The resulting nomogram demonstrated good accuracy in predicting MFS, with a bootstrap-corrected concordance index of 0.66. Conclusion This clinically useful tool predicts 5- and 10-year MFS among older women with early breast cancer using readily available clinicopathologic factors and can aid individualized clinical decision making by estimating predicted benefit from RT.

The effect of the employment of consultants in the Emergency Department on quality of care and equity

2020 ◽  
Vol 30 (Supplement_5) ◽  
Author(s):  
M B Lindstroem ◽  
O Andersen ◽  
T Kallemose ◽  
L J H Rasmussen ◽  
S Rosthoej ◽  
...  
Keyword(s):  
Socioeconomic Status ◽  
Quality Of Care ◽  
Hospital Mortality ◽  
Clinical Decision Making ◽  
Proportional Hazards ◽  
Clinical Decision ◽  
Cox Proportional Hazards ◽  
Medical Patients ◽  
Negative Effect

Abstract Background Hospitals struggle with increasing acute admissions and crowding in Emergency Departments (EDs) negatively affect length of hospitalisation, in-hospital mortality, patient safety and flow. In response to this, the Danish Health Authorities have recommended the presence of consultants in the ED to expedite the clinical decision-making process. In 2016, consultant-led triage and continuous presence of consultants was introduced at the ED at Hvidovre Hospital, Denmark. However, little is known on the effect of consultants in the ED, and how it affects care for patients of varying socioeconomic status. This study investigated whether the employment of consultants in a Danish ED affected the quality of care for acutely admitted medical patients in terms of length of admission, readmission, and mortality, and how this effect was distributed across socioeconomic status in patients. Methods Admission data was collected during two 7-month periods, one prior to and one after the organisational intervention, with 9,869 adult medical patients admitted for up to 48 hours in the ED. Linear regression and Cox proportional hazards regression analyses adjusted for age, sex, comorbidities, level of education and employment status were applied. Results Following the employment of consultants, an overall 11% increase in index-admissions was observed, and 90% of patients were discharged by a consultant with a reduced mean length of admission by 1.4 hours (95% CI: 1.0 - 1.9). No significant change was found in in-hospital mortality, readmission, or mortality within 90 days after discharge. No difference was found in quality of care across socioeconomic status. Conclusions Consultants in the ED was found to reduce length of admission without a negative effect on the quality of care for ED admitted medical patients in general, or for patients with lower socioeconomic status. To reduce readmission and mortality among acutely admitted patients, other means must be initiated. Key messages Consultants in the ED may reduce length of admission without a negative effect on the quality of care. To reduce readmission and mortality among acutely admitted patients, other means must be initiated.

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