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Sun H, Gao Q, Zhu G, Han C, Yan H, Wang T. Identification of influential observations in high-dimensional survival data through robust penalized Cox regression based on trimming. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2023; 20:5352-5378. [PMID: 36896549 DOI: 10.3934/mbe.2023248] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/18/2023]
Abstract
Penalized Cox regression can efficiently be used for the determination of biomarkers in high-dimensional genomic data related to disease prognosis. However, results of Penalized Cox regression is influenced by the heterogeneity of the samples who have different dependent structure between survival time and covariates from most individuals. These observations are called influential observations or outliers. A robust penalized Cox model (Reweighted Elastic Net-type maximum trimmed partial likelihood estimator, Rwt MTPL-EN) is proposed to improve the prediction accuracy and identify influential observations. A new algorithm AR-Cstep to solve Rwt MTPL-EN model is also proposed. This method has been validated by simulation study and application to glioma microarray expression data. When there were no outliers, the results of Rwt MTPL-EN were close to the Elastic Net (EN). When outliers existed, the results of EN were impacted by outliers. And whenever the censored rate was large or low, the robust Rwt MTPL-EN performed better than EN. and could resist the outliers in both predictors and response. In terms of outliers detection accuracy, Rwt MTPL-EN was much higher than EN. The outliers who "lived too long" made EN perform worse, but were accurately detected by Rwt MTPL-EN. Through the analysis of glioma gene expression data, most of the outliers identified by EN were those "failed too early", but most of them were not obvious outliers according to risk estimated from omics data or clinical variables. Most of the outliers identified by Rwt MTPL-EN were those who "lived too long", and most of them were obvious outliers according to risk estimated from omics data or clinical variables. Rwt MTPL-EN can be adopted to detect influential observations in high-dimensional survival data.
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Affiliation(s)
- Hongwei Sun
- Department of Health Statistics, School of Public Health and Management, Binzhou Medical University, Yantai City, Shandong 264003, China
- Department of Health Statistics, School of Public Health, Shanxi Medical University, Taiyuan City, Shanxi 030001, China
| | - Qian Gao
- Department of Health Statistics, School of Public Health, Shanxi Medical University, Taiyuan City, Shanxi 030001, China
| | - Guiming Zhu
- Department of Health Statistics, School of Public Health and Management, Binzhou Medical University, Yantai City, Shandong 264003, China
| | - Chunlei Han
- Department of Health Statistics, School of Public Health and Management, Binzhou Medical University, Yantai City, Shandong 264003, China
| | - Haosen Yan
- Department of Health Statistics, School of Public Health and Management, Binzhou Medical University, Yantai City, Shandong 264003, China
| | - Tong Wang
- Department of Health Statistics, School of Public Health, Shanxi Medical University, Taiyuan City, Shanxi 030001, China
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Hu LS, Wang L, Hawkins-Daarud A, Eschbacher JM, Singleton KW, Jackson PR, Clark-Swanson K, Sereduk CP, Peng S, Wang P, Wang J, Baxter LC, Smith KA, Mazza GL, Stokes AM, Bendok BR, Zimmerman RS, Krishna C, Porter AB, Mrugala MM, Hoxworth JM, Wu T, Tran NL, Swanson KR, Li J. Uncertainty quantification in the radiogenomics modeling of EGFR amplification in glioblastoma. Sci Rep 2021; 11:3932. [PMID: 33594116 PMCID: PMC7886858 DOI: 10.1038/s41598-021-83141-z] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2020] [Accepted: 01/18/2021] [Indexed: 12/13/2022] Open
Abstract
Radiogenomics uses machine-learning (ML) to directly connect the morphologic and physiological appearance of tumors on clinical imaging with underlying genomic features. Despite extensive growth in the area of radiogenomics across many cancers, and its potential role in advancing clinical decision making, no published studies have directly addressed uncertainty in these model predictions. We developed a radiogenomics ML model to quantify uncertainty using transductive Gaussian Processes (GP) and a unique dataset of 95 image-localized biopsies with spatially matched MRI from 25 untreated Glioblastoma (GBM) patients. The model generated predictions for regional EGFR amplification status (a common and important target in GBM) to resolve the intratumoral genetic heterogeneity across each individual tumor-a key factor for future personalized therapeutic paradigms. The model used probability distributions for each sample prediction to quantify uncertainty, and used transductive learning to reduce the overall uncertainty. We compared predictive accuracy and uncertainty of the transductive learning GP model against a standard GP model using leave-one-patient-out cross validation. Additionally, we used a separate dataset containing 24 image-localized biopsies from 7 high-grade glioma patients to validate the model. Predictive uncertainty informed the likelihood of achieving an accurate sample prediction. When stratifying predictions based on uncertainty, we observed substantially higher performance in the group cohort (75% accuracy, n = 95) and amongst sample predictions with the lowest uncertainty (83% accuracy, n = 72) compared to predictions with higher uncertainty (48% accuracy, n = 23), due largely to data interpolation (rather than extrapolation). On the separate validation set, our model achieved 78% accuracy amongst the sample predictions with lowest uncertainty. We present a novel approach to quantify radiogenomics uncertainty to enhance model performance and clinical interpretability. This should help integrate more reliable radiogenomics models for improved medical decision-making.
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Affiliation(s)
- Leland S Hu
- Department of Radiology, Mayo Clinic Arizona, 5777 E. Mayo Blvd, Phoenix, AZ, 85054, USA. .,School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, 699 S Mill Ave, Tempe, AZ, 85281, USA. .,Mathematical NeuroOncology Lab, Precision Neurotherapeutics Innovation Program, Mayo Clinic Arizona, 5777 East Mayo Blvd, Support Services Building Suite 2-700, Phoenix, AZ, 85054, USA.
| | - Lujia Wang
- Department of Radiology, Mayo Clinic Arizona, 5777 E. Mayo Blvd, Phoenix, AZ, 85054, USA.,School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, 699 S Mill Ave, Tempe, AZ, 85281, USA.,Mathematical NeuroOncology Lab, Precision Neurotherapeutics Innovation Program, Mayo Clinic Arizona, 5777 East Mayo Blvd, Support Services Building Suite 2-700, Phoenix, AZ, 85054, USA
| | - Andrea Hawkins-Daarud
- Mathematical NeuroOncology Lab, Precision Neurotherapeutics Innovation Program, Mayo Clinic Arizona, 5777 East Mayo Blvd, Support Services Building Suite 2-700, Phoenix, AZ, 85054, USA
| | - Jennifer M Eschbacher
- Department of Pathology, Barrow Neurological Institute-St. Joseph's Hospital and Medical Center, Phoenix, AZ, 85013, USA
| | - Kyle W Singleton
- Mathematical NeuroOncology Lab, Precision Neurotherapeutics Innovation Program, Mayo Clinic Arizona, 5777 East Mayo Blvd, Support Services Building Suite 2-700, Phoenix, AZ, 85054, USA
| | - Pamela R Jackson
- Mathematical NeuroOncology Lab, Precision Neurotherapeutics Innovation Program, Mayo Clinic Arizona, 5777 East Mayo Blvd, Support Services Building Suite 2-700, Phoenix, AZ, 85054, USA
| | - Kamala Clark-Swanson
- Mathematical NeuroOncology Lab, Precision Neurotherapeutics Innovation Program, Mayo Clinic Arizona, 5777 East Mayo Blvd, Support Services Building Suite 2-700, Phoenix, AZ, 85054, USA
| | - Christopher P Sereduk
- Department of Neurosurgery, Mayo Clinic Arizona, 5777 E. Mayo Blvd, Phoenix, AZ, 85054, USA.,Department of Cancer Biology, Mayo Clinic Arizona, 5777 E. Mayo Blvd, Phoenix, AZ, 85054, USA
| | - Sen Peng
- Cancer and Cell Biology Division, Translational Genomics Research Institute, Phoenix, AZ, 85004, USA
| | - Panwen Wang
- Department of Quantitative Health Sciences, Center for Individualized Medicine, Mayo Clinic Arizona, Scottsdale, AZ, 85259, USA
| | - Junwen Wang
- Department of Quantitative Health Sciences, Center for Individualized Medicine, Mayo Clinic Arizona, Scottsdale, AZ, 85259, USA
| | - Leslie C Baxter
- Department of Neuropsychology, Mayo Clinic Arizona, 5777 E. Mayo Blvd, Phoenix, AZ, 85054, USA
| | - Kris A Smith
- Department of Neurosurgery, Barrow Neurological Institute-St. Joseph's Hospital and Medical Center, Phoenix, AZ, 85013, USA
| | - Gina L Mazza
- Department of Quantitative Health Sciences, Mayo Clinic Arizona, Scottsdale, AZ, 85259, USA
| | - Ashley M Stokes
- Department of Imaging Research, Barrow Neurological Institute-St. Joseph's Hospital and Medical Center, Phoenix, AZ, 85013, USA
| | - Bernard R Bendok
- Department of Neurosurgery, Mayo Clinic Arizona, 5777 E. Mayo Blvd, Phoenix, AZ, 85054, USA
| | - Richard S Zimmerman
- Department of Neurosurgery, Mayo Clinic Arizona, 5777 E. Mayo Blvd, Phoenix, AZ, 85054, USA
| | - Chandan Krishna
- Department of Neurosurgery, Mayo Clinic Arizona, 5777 E. Mayo Blvd, Phoenix, AZ, 85054, USA
| | - Alyx B Porter
- Department of Neuro-Oncology, Mayo Clinic Arizona, 5777 E. Mayo Blvd, Phoenix, AZ, 85054, USA
| | - Maciej M Mrugala
- Department of Neuro-Oncology, Mayo Clinic Arizona, 5777 E. Mayo Blvd, Phoenix, AZ, 85054, USA
| | - Joseph M Hoxworth
- Department of Radiology, Mayo Clinic Arizona, 5777 E. Mayo Blvd, Phoenix, AZ, 85054, USA
| | - Teresa Wu
- Department of Radiology, Mayo Clinic Arizona, 5777 E. Mayo Blvd, Phoenix, AZ, 85054, USA.,School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, 699 S Mill Ave, Tempe, AZ, 85281, USA
| | - Nhan L Tran
- Department of Neurosurgery, Mayo Clinic Arizona, 5777 E. Mayo Blvd, Phoenix, AZ, 85054, USA.,Department of Cancer Biology, Mayo Clinic Arizona, 5777 E. Mayo Blvd, Phoenix, AZ, 85054, USA
| | - Kristin R Swanson
- Mathematical NeuroOncology Lab, Precision Neurotherapeutics Innovation Program, Mayo Clinic Arizona, 5777 East Mayo Blvd, Support Services Building Suite 2-700, Phoenix, AZ, 85054, USA.,Department of Neurosurgery, Mayo Clinic Arizona, 5777 E. Mayo Blvd, Phoenix, AZ, 85054, USA
| | - Jing Li
- Department of Radiology, Mayo Clinic Arizona, 5777 E. Mayo Blvd, Phoenix, AZ, 85054, USA.,School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, 699 S Mill Ave, Tempe, AZ, 85281, USA.,Mathematical NeuroOncology Lab, Precision Neurotherapeutics Innovation Program, Mayo Clinic Arizona, 5777 East Mayo Blvd, Support Services Building Suite 2-700, Phoenix, AZ, 85054, USA
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Whitmire P, Rickertsen CR, Hawkins-Daarud A, Carrasco E, Lorence J, De Leon G, Curtin L, Bayless S, Clark-Swanson K, Peeri NC, Corpuz C, Lewis-de Los Angeles CP, Bendok BR, Gonzalez-Cuyar L, Vora S, Mrugala MM, Hu LS, Wang L, Porter A, Kumthekar P, Johnston SK, Egan KM, Gatenby R, Canoll P, Rubin JB, Swanson KR. Sex-specific impact of patterns of imageable tumor growth on survival of primary glioblastoma patients. BMC Cancer 2020; 20:447. [PMID: 32429869 PMCID: PMC7238585 DOI: 10.1186/s12885-020-06816-2] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2019] [Accepted: 04/01/2020] [Indexed: 11/19/2022] Open
Abstract
Background Sex is recognized as a significant determinant of outcome among glioblastoma patients, but the relative prognostic importance of glioblastoma features has not been thoroughly explored for sex differences. Methods Combining multi-modal MR images, biomathematical models, and patient clinical information, this investigation assesses which pretreatment variables have a sex-specific impact on the survival of glioblastoma patients (299 males and 195 females). Results Among males, tumor (T1Gd) radius was a predictor of overall survival (HR = 1.027, p = 0.044). Among females, higher tumor cell net invasion rate was a significant detriment to overall survival (HR = 1.011, p < 0.001). Female extreme survivors had significantly smaller tumors (T1Gd) (p = 0.010 t-test), but tumor size was not correlated with female overall survival (p = 0.955 CPH). Both male and female extreme survivors had significantly lower tumor cell net proliferation rates than other patients (M p = 0.004, F p = 0.001, t-test). Conclusion Despite similar distributions of the MR imaging parameters between males and females, there was a sex-specific difference in how these parameters related to outcomes.
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Affiliation(s)
- Paula Whitmire
- Precision Neurotherapeutics Innovation Program, Mayo Clinic, 5777 East Mayo Blvd , SSB 02-700, Phoenix, AZ, 85054, USA.
| | - Cassandra R Rickertsen
- Precision Neurotherapeutics Innovation Program, Mayo Clinic, 5777 East Mayo Blvd , SSB 02-700, Phoenix, AZ, 85054, USA
| | - Andrea Hawkins-Daarud
- Precision Neurotherapeutics Innovation Program, Mayo Clinic, 5777 East Mayo Blvd , SSB 02-700, Phoenix, AZ, 85054, USA
| | - Eduardo Carrasco
- Precision Neurotherapeutics Innovation Program, Mayo Clinic, 5777 East Mayo Blvd , SSB 02-700, Phoenix, AZ, 85054, USA
| | - Julia Lorence
- Precision Neurotherapeutics Innovation Program, Mayo Clinic, 5777 East Mayo Blvd , SSB 02-700, Phoenix, AZ, 85054, USA.,School of Life Sciences, Arizona State University, Tempe, AZ, USA
| | - Gustavo De Leon
- Precision Neurotherapeutics Innovation Program, Mayo Clinic, 5777 East Mayo Blvd , SSB 02-700, Phoenix, AZ, 85054, USA
| | - Lee Curtin
- Precision Neurotherapeutics Innovation Program, Mayo Clinic, 5777 East Mayo Blvd , SSB 02-700, Phoenix, AZ, 85054, USA.,Centre for Mathematical Medicine and Biology, University of Nottingham, Nottingham, UK
| | - Spencer Bayless
- Precision Neurotherapeutics Innovation Program, Mayo Clinic, 5777 East Mayo Blvd , SSB 02-700, Phoenix, AZ, 85054, USA
| | - Kamala Clark-Swanson
- Precision Neurotherapeutics Innovation Program, Mayo Clinic, 5777 East Mayo Blvd , SSB 02-700, Phoenix, AZ, 85054, USA
| | - Noah C Peeri
- Cancer Epidemiology Program, Moffitt Cancer Center, Tampa, FL, USA
| | - Christina Corpuz
- Department of Neurology, Columbia University Medical Center, New York, NY, USA
| | | | - Bernard R Bendok
- Precision Neurotherapeutics Innovation Program, Mayo Clinic, 5777 East Mayo Blvd , SSB 02-700, Phoenix, AZ, 85054, USA.,Department of Neurologic Surgery, Mayo Clinic, Phoenix, AZ, USA
| | - Luis Gonzalez-Cuyar
- Department of Pathology, Division of Neuropathology, University of Washington, Seattle, WA, USA
| | - Sujay Vora
- Department of Radiation Oncology, Mayo Clinic, Phoenix, AZ, USA
| | | | - Leland S Hu
- Department of Radiology, Mayo Clinic, Phoenix, AZ, USA
| | - Lei Wang
- Department of Radiology, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | - Alyx Porter
- Department of Neurology, Mayo Clinic, Phoenix, AZ, USA
| | - Priya Kumthekar
- Department of Neurology, Robert H Lurie Comprehensive Cancer Center, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Sandra K Johnston
- Precision Neurotherapeutics Innovation Program, Mayo Clinic, 5777 East Mayo Blvd , SSB 02-700, Phoenix, AZ, 85054, USA.,Department of Radiology, University of Washington, Seattle, WA, USA
| | - Kathleen M Egan
- Cancer Epidemiology Program, Moffitt Cancer Center, Tampa, FL, USA
| | - Robert Gatenby
- Cancer Biology and Evolution Program, Moffitt Cancer Center, Tampa, FL, USA
| | - Peter Canoll
- Division of Neuropathology, Department of Pathology and Cell Biology, Columbia University Medical Center, New York, NY, USA
| | - Joshua B Rubin
- Department of Pediatrics, Washington University School of Medicine, St Louis, MO, USA
| | - Kristin R Swanson
- Precision Neurotherapeutics Innovation Program, Mayo Clinic, 5777 East Mayo Blvd , SSB 02-700, Phoenix, AZ, 85054, USA
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