Browsing by Subject "Prognostic model"

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    Diffusion tensor analysis of white matter tracts is prognostic of persisting post-concussion symptoms in collegiate athletes
    (Elsevier, 2024) Bertò, Giulia; Rooks, Lauren T.; Broglio, Steven P.; McAllister, Thomas A.; McCrea, Michael A.; Pasquina, Paul F.; Giza, Christopher; Brooks, Alison; Mihalik, Jason; Guskiewicz, Kevin; Goldman, Josh; Duma, Stefan; Rowson, Steven; Port, Nicholas L.; Pestilli, Franco; Psychiatry, School of Medicine
    Background and objectives: After a concussion diagnosis, the most important issue for patients and loved ones is how long it will take them to recover. The main objective of this study is to develop a prognostic model of concussion recovery. This model would benefit many patients worldwide, allowing for early treatment intervention. Methods: The Concussion Assessment, Research and Education (CARE) consortium study enrolled collegiate athletes from 30 sites (NCAA athletic departments and US Department of Defense service academies), 4 of which participated in the Advanced Research Core, which included diffusion-weighted MRI (dMRI) data collection. We analyzed the dMRI data of 51 injuries of concussed athletes scanned within 48 h of injury. All athletes were cleared to return-to-play by the local medical staff following a standardized, graduated protocol. The primary outcome measure is days to clearance of unrestricted return-to-play. Injuries were divided into early (return-to-play < 28 days) and late (return-to-play >= 28 days) recovery based on the return-to-play clinical records. The late recovery group meets the standard definition of Persisting Post-Concussion Symptoms (PPCS). Data were processed using automated, state-of-the-art, rigorous methods for reproducible data processing using brainlife.io. All processed data derivatives are made available at https://brainlife.io/project/63b2ecb0daffe2c2407ee3c5/dataset. The microstructural properties of 47 major white matter tracts, 5 callosal, 15 subcortical, and 148 cortical structures were mapped. Fractional Anisotropy (FA) and Mean Diffusivity (MD) were estimated for each tract and structure. Correlation analysis and Receiver Operator Characteristic (ROC) analysis were then performed to assess the association between the microstructural properties and return-to-play. Finally, a Logistic Regression binary classifier (LR-BC) was used to classify the injuries between the two recovery groups. Results: The mean FA across all white matter volume was negatively correlated with return-to-play (r = -0.38, p = 0.00001). No significant association between mean MD and return-to-play was found, neither for FA nor MD for any other structure. The mean FA of 47 white matter tracts was negatively correlated with return-to-play (rμ = -0.27; rσ = 0.08; rmin = -0.1; rmax = -0.43). Across all tracts, a large mean ROC Area Under the Curve (AUCFA) of 0.71 ± 0.09 SD was found. The top classification performance of the LR-BC was AUC = 0.90 obtained using the 16 statistically significant white matter tracts. Discussion: Utilizing a free, open-source, and automated cloud-based neuroimaging pipeline and app (https://brainlife.io/docs/tutorial/using-clairvoy/), a prognostic model has been developed, which predicts athletes at risk for slow recovery (PPCS) with an AUC=0.90, balanced accuracy = 0.89, sensitivity = 1.0, and specificity = 0.79. The small number of participants in this study (51 injuries) is a significant limitation and supports the need for future large concussion dMRI studies and focused on recovery.
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    Highly robust model of transcription regulator activity predicts breast cancer overall survival
    (BMC, 2020) Dong, Chuanpeng; Liu, Jiannan; Chen, Steven X.; Dong, Tianhan; Jiang, Guanglong; Wang, Yue; Wu, Huanmei; Reiter, Jill L.; Liu, Yunlong; Medical and Molecular Genetics, School of Medicine
    Background: While several multigene signatures are available for predicting breast cancer prognosis, particularly in early stage disease, effective molecular indicators are needed, especially for triple-negative carcinomas, to improve treatments and predict diagnostic outcomes. The objective of this study was to identify transcriptional regulatory networks to better understand mechanisms giving rise to breast cancer development and to incorporate this information into a model for predicting clinical outcomes. Methods: Gene expression profiles from 1097 breast cancer patients were retrieved from The Cancer Genome Atlas (TCGA). Breast cancer-specific transcription regulatory information was identified by considering the binding site information from ENCODE and the top co-expressed targets in TCGA using a nonlinear approach. We then used this information to predict breast cancer patient survival outcome. Result: We built a multiple regulator-based prediction model for breast cancer. This model was validated in more than 5000 breast cancer patients from the Gene Expression Omnibus (GEO) databases. We demonstrated our regulator model was significantly associated with clinical stage and that cell cycle and DNA replication related pathways were significantly enriched in high regulator risk patients. Conclusion: Our findings demonstrate that transcriptional regulator activities can predict patient survival. This finding provides additional biological insights into the mechanisms of breast cancer progression.
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    A New Prognostic Model Covering All Stages of Intrahepatic Cholangiocarcinoma
    (Xia & He Publishing, 2022) Zhou, Shuang-Nan; Lu, Shan-Shan; Ju, Da-Wei; Yu, Ling-Xiang; Liang, Xiao-Xiao; Xiang, Xiao; Liangpunsakul, Suthat; Roberts, Lewis R.; Lu, Yin-Ying; Zhang, Ning; Medicine, School of Medicine
    Background and aims: Intrahepatic cholangiocarcinoma (ICC) is the second most common primary hepatic malignancy that causes a poor survival. We aimed to identify its prognostic factors and to develop a nomogram that will predict survival of ICC patients among all stages. Methods: A total of 442 patients with pathology-proven ICC registered at the Fifth Medical Center of PLA General Hospital between July 2007 and December 2019 were enrolled. Subjects were followed for survival status until June 30, 2020. A prognostic model visualized as a nomogram was constructed in the training cohort using multivariate cox model, and was then validated in the validation cohort. Results: The median age was 55 years. With a median follow-up of 50.4 months, 337 patients died. The median survival was 11.6 months, with 1-, 3- and 5-year survival rates of 48.3%, 22.7% and 16.2%, respectively. Factors associated with overall survival were multiple tumors, lymph node involvement, vascular invasion, distant metastasis, decreased albumin, elevated lactate dehydrogenase (LDH), decreased iron, elevated fibrinogen, elevated CA125 and elevated CA19-9. A nomogram predicting survival of ICC patients at the time of diagnosis achieved a Harrel's c-statistic of 0.758, significantly higher than the 0.582 of the TNM stage alone. Predicted median survivals of those within the low, mid and high-risk subgroups were 35.6, 12.1 and 6.2 months, respectively. Conclusions: A nomogram based on imaging data and serum biomarkers at diagnosis showed good ability to predict survival in patients with all stages of ICC. Further studies are needed to validate the prognostic capability of our new model.