scholarly journals Validation of a rectal cancer outcome prediction model with a cohort of Chinese patients

Oncotarget ◽  
2015 ◽  
Vol 6 (35) ◽  
pp. 38327-38335 ◽  
Author(s):  
Lijun Shen ◽  
Johan van Soest ◽  
Jiazhou Wang ◽  
Jialu Yu ◽  
Weigang Hu ◽  
...  
Keyword(s):  
Rectal Cancer ◽  
Prediction Model ◽  
Outcome Prediction ◽  
Chinese Patients ◽  
Cancer Outcome

Validation of a Rectal Cancer Outcome Prediction Model in Routine Chinese Patients

2014 ◽  
Vol 90 (1) ◽  
pp. S387-S388
Author(s):  
L. Shen ◽  
J. Van Soest ◽  
J. Yu ◽  
J. Wang ◽  
W. Hu ◽  
...  
Keyword(s):  
Rectal Cancer ◽  
Prediction Model ◽  
Outcome Prediction ◽  
Chinese Patients ◽  
Cancer Outcome

scholarly journals EP-1200: Validation of a rectal cancer outcome prediction model in routine Chinese patients

2015 ◽  
Vol 115 ◽  
pp. S650-S651
Author(s):  
L. Shen ◽  
J. Van Soest ◽  
J. Wang ◽  
J. Yu ◽  
W. Hu ◽  
...  
Keyword(s):  
Rectal Cancer ◽  
Prediction Model ◽  
Outcome Prediction ◽  
Chinese Patients ◽  
Cancer Outcome

1575: External Validation of Outcome Prediction Model for Ureteral/Renal Calculi

2005 ◽  
Vol 173 (4S) ◽  
pp. 427-427
Author(s):  
Sijo J. Parekattil ◽  
Udaya Kumar ◽  
Nicholas J. Hegarty ◽  
Clay Williams ◽  
Tara Allen ◽  
...  
Keyword(s):  
Prediction Model ◽  
Outcome Prediction ◽  
External Validation ◽  
Renal Calculi

scholarly journals Ensemble classifier based on context specific miRNA regulation modules: a new method for cancer outcome prediction

2013 ◽  
Vol 14 (Suppl 12) ◽  
pp. S6 ◽  
Author(s):  
Xionghui Zhou ◽  
Juan Liu ◽  
Xinghuo Ye ◽  
Wei Wang ◽  
Jianghui Xiong
Keyword(s):  
Outcome Prediction ◽  
Ensemble Classifier ◽  
New Method ◽  
Mirna Regulation ◽  
Cancer Outcome ◽  
Context Specific

scholarly journals S147. FUNCTIONAL BRAIN CONNECTIVITY DATA IMPROVE CLINICAL OUTCOME PREDICTION IN YOUTH AT RISK FOR PSYCHOSIS

2020 ◽  
Vol 46 (Supplement_1) ◽  
pp. S92-S92
Author(s):  
Guusje Collin ◽  
Alfonso Nieto-Castanon ◽  
Martha Shenton ◽  
Ofer Pasternak ◽  
Sinead Kelly ◽  
...  
Keyword(s):  
Prediction Model ◽  
Resting State ◽  
Outcome Prediction ◽  
Brain Connectivity ◽  
Network Connectivity ◽  
Resting State Functional Connectivity ◽  
Clinical Predictors ◽  
Functional Brain ◽  
Improved Outcome ◽  
Better Than

Abstract Background Improved outcome prediction in individuals at high risk for psychosis may facilitate targeted early intervention. Studies suggest that improved outcome prediction may be achieved through the use of neurocognitive or neuroimaging data, on their own or in addition to clinical data. This study examines whether adding resting-state functional connectivity data to validated clinical predictors of psychosis improve outcome prediction in the prodromal stage. Methods This study involves 137 adolescents and young adults at Clinical High Risk (CHR) for psychosis from the Shanghai At Risk for Psychosis (SHARP) program. Based on outcome after one-year follow-up, participants were separated into three outcome categories: good outcome (symptom remission, N = 71), intermediate outcome (ongoing CHR symptoms, N = 30), and poor outcome (conversion to psychosis or treatment-refractory, N = 36). Resting-state fMRI data were acquired for each participant and processed using the Conn toolbox, including rigorous motion correction. Multinomial logistic regression analysis and leave-one-out cross-validation were used to assess the performance of three prediction models: 1) a clinical-only model using validated clinical predictors from the NAPLS-2 psychosis-risk calculator, 2) an fMRI-only model using measures of functional connectome organization and within/between-network connectivity among established resting-state networks, and 3) a combined clinical and fMRI prediction model. Model performance was assessed using the harmonic mean of the positive predictive value and sensitivity for each outcome category. This F1 measure was compared to expected chance-levels using a permutation test with 1,000 sampled permutations in order to evaluate the statistical significance of the model’s prediction. Results The clinical-only prediction model failed to achieve a significant level of outcome prediction (F1 = 0.32, F1-chance = 0.26 □ 0.06, p = .154). The fMRI-only model did predict clinical outcome to a significant degree (F1 = 0.41, F1-chance = 0.29 □ 0.06, p = .016), but the combined clinical and fMRI prediction model showed the best performance (F1 = 0.46, F1-chance = 0.29 □ 0.06, p < .001). On average, positive predictive values (reflecting the probability that an outcome label predicted by the model was correct) were 39% better than chance-level and 32% better than the clinical-only model. Analyzing the contribution of individual predictor variables showed that GAF functional decline, a family history of psychosis, and performance on the Hopkins Verbal Learning Test were the most influential clinical predictors, whereas modular connectome organization, default-mode and fronto-parietal within-network connectivity, and between-network connectivity among language, salience, dorsal attention, cerebellum, and sensorimotor networks were the leading fMRI predictors. Discussion This study’s findings suggest that functional brain abnormalities reflected by alterations in resting-state functional connectivity precede and may drive subsequent changes in clinical functioning. Moreover, the findings show that markers of functional brain connectivity may be useful for improving early identification and clinical decision-making in prodromal psychosis.

scholarly journals A data-driven interactome of synergistic genes improves network-based cancer outcome prediction

2019 ◽  
Vol 15 (2) ◽  
pp. e1006657 ◽  
Author(s):  
Amin Allahyar ◽  
Joske Ubels ◽  
Jeroen de Ridder
Keyword(s):  
Outcome Prediction ◽  
Data Driven ◽  
Cancer Outcome
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