Browsing by Author "Monje, Michelle"

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    Characteristics of patients ≥10 years of age with diffuse intrinsic pontine glioma: a report from the International DIPG/DMG Registry
    (Oxford University Press, 2022) Erker, Craig; Lane, Adam; Chaney, Brooklyn; Leary, Sarah; Minturn, Jane E.; Bartels, Ute; Packer, Roger J.; Dorris, Kathleen; Gottardo, Nicholas G.; Warren, Katherine E.; Broniscer, Alberto; Kieran, Mark W.; Zhu, Xiaoting; White, Peter; Dexheimer, Phillip J.; Black, Katie; Asher, Anthony; DeWire, Mariko; Hansford, Jordan R.; Gururangan, Sridharan; Nazarian, Javad; Ziegler, David S.; Sandler, Eric; Bartlett, Allison; Goldman, Stewart; Shih, Chie-Schin; Hassall, Tim; Dholaria, Hetal; Bandopadhayay, Pratiti; Samson, Yvan; Monje, Michelle; Fisher, Paul G.; Dodgshun, Andrew; Parkin, Sarah; Chintagumpala, Murali; Tsui, Karen; Gass, David; Larouche, Valerie; Broxson, Emmett; Garcia Lombardi, Mercedes; Shiqi Wang, Stacie; Ma, Jie; Hawkins, Cynthia; Hamideh, Dima; Wagner, Lars; Koschmann, Carl; Fuller, Christine; Drissi, Rachid; Jones, Blaise V.; Leach, James; Fouladi, Maryam; Pediatrics, School of Medicine
    Background: Diffuse intrinsic pontine gliomas (DIPG) generally occur in young school-age children, although can occur in adolescents and young adults. The purpose of this study was to describe clinical, radiological, pathologic, and molecular characteristics in patients ≥10 years of age with DIPG enrolled in the International DIPG Registry (IDIPGR). Methods: Patients ≥10 years of age at diagnosis enrolled in the IDIPGR with imaging confirmed DIPG diagnosis were included. The primary outcome was overall survival (OS) categorized as long-term survivors (LTS) (≥24 months) or short-term survivors (STS) (<24 months). Results: Among 1010 patients, 208 (21%) were ≥10 years of age at diagnosis; 152 were eligible with a median age of 12 years (range 10-26.8). Median OS was 13 (2-82) months. The 1-, 3-, and 5-year OS was 59.2%, 5.3%, and 3.3%, respectively. The 18/152 (11.8%) LTS were more likely to be older (P < .01) and present with longer symptom duration (P < .01). Biopsy and/or autopsy were performed in 50 (33%) patients; 77%, 61%, 33%, and 6% of patients tested had H3K27M (H3F3A or HIST1H3B), TP53, ATRX, and ACVR1 mutations/genome alterations, respectively. Two of 18 patients with IDH1 testing were IDH1-mutant and 1 was a LTS. The presence or absence of H3 alterations did not affect survival. Conclusion: Patients ≥10 years old with DIPG have a median survival of 13 months. LTS present with longer symptom duration and are likely to be older at presentation compared to STS. ATRX mutation rates were higher in this population than the general DIPG population.
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    MRI-based radiomics for prognosis of pediatric diffuse intrinsic pontine glioma: an international study
    (Oxford University Press, 2021-03-05) Tam, Lydia T.; Yeom, Kristen W.; Wright, Jason N.; Jaju, Alok; Radmanesh, Alireza; Han, Michelle; Toescu, Sebastian; Maleki, Maryam; Chen, Eric; Campion, Andrew; Lai, Hollie A.; Eghbal, Azam A.; Oztekin, Ozgur; Mankad, Kshitij; Hargrave, Darren; Jacques, Thomas S.; Goetti, Robert; Lober, Robert M.; Cheshier, Samuel H.; Napel, Sandy; Said, Mourad; Aquilina, Kristian; Ho, Chang Y.; Monje, Michelle; Vitanza, Nicholas A.; Mattonen, Sarah A.; Radiology and Imaging Sciences, School of Medicine
    Background: Diffuse intrinsic pontine gliomas (DIPGs) are lethal pediatric brain tumors. Presently, MRI is the mainstay of disease diagnosis and surveillance. We identify clinically significant computational features from MRI and create a prognostic machine learning model. Methods: We isolated tumor volumes of T1-post-contrast (T1) and T2-weighted (T2) MRIs from 177 treatment-naïve DIPG patients from an international cohort for model training and testing. The Quantitative Image Feature Pipeline and PyRadiomics was used for feature extraction. Ten-fold cross-validation of least absolute shrinkage and selection operator Cox regression selected optimal features to predict overall survival in the training dataset and tested in the independent testing dataset. We analyzed model performance using clinical variables (age at diagnosis and sex) only, radiomics only, and radiomics plus clinical variables. Results: All selected features were intensity and texture-based on the wavelet-filtered images (3 T1 gray-level co-occurrence matrix (GLCM) texture features, T2 GLCM texture feature, and T2 first-order mean). This multivariable Cox model demonstrated a concordance of 0.68 (95% CI: 0.61-0.74) in the training dataset, significantly outperforming the clinical-only model (C = 0.57 [95% CI: 0.49-0.64]). Adding clinical features to radiomics slightly improved performance (C = 0.70 [95% CI: 0.64-0.77]). The combined radiomics and clinical model was validated in the independent testing dataset (C = 0.59 [95% CI: 0.51-0.67], Noether's test P = .02). Conclusions: In this international study, we demonstrate the use of radiomic signatures to create a machine learning model for DIPG prognostication. Standardized, quantitative approaches that objectively measure DIPG changes, including computational MRI evaluation, could offer new approaches to assessing tumor phenotype and serve a future role for optimizing clinical trial eligibility and tumor surveillance.
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    The National Cancer Institute clinical trials planning meeting to address gaps in observational and intervention trials for cancer-related cognitive impairment
    (Oxford University Press, 2025) Janelsins, Michelle C.; Van Dyk, Kathleen; Hartman, Sheri J.; Koll, Thuy T.; Cramer, Christina K.; Lesser, Glenn J.; Barton, Debra L.; Mustian, Karen M.; Wagner, Lynne I.; Ganz, Patricia A.; Cole, Peter D.; Bakos, Alexis; Root, James C.; Hardy, Kristina; Magnuson, Allison; Ferguson, Robert J.; McDonald, Brenna C.; Saykin, Andrew J.; Gonzalez, Brian D.; Wefel, Jeffrey S.; Morilak, David A.; Dahiya, Saurabh; Heijnen, Cobi J.; Conley, Yvette P.; Morgans, Alicia K.; Mabbott, Donald; Monje, Michelle; Rapp, Stephen R.; Gondi, Vinai; Bender, Catherine; Embry, Leanne; McCaskill Stevens, Worta; Hopkins, Judith O.; St. Germain, Diane; Dorsey, Susan G.; Radiology and Imaging Sciences, School of Medicine
    Cancer-related cognitive impairment is a broad term encompassing subtle cognitive problems to more severe impairment. The severity of this impairment is influenced by host, disease, and treatment factors, and the impairment affects patients before, during, and following cancer treatment. The National Cancer Institute (NCI) Symptom Management and Health-Related Quality of Life Steering Committee (SxQoL SC) convened a clinical trial planning meeting to review the state of the science on cancer-related cognitive impairment and develop phase II/III intervention trials aimed at improving cognitive function in cancer survivors with non-central nervous system disease and longitudinal studies to understand the trajectory of cognitive impairment and contributing factors. Participants included experts in the field of cancer-related cognitive impairment, members of the SxQoL SC, patient advocates, representatives from all 7 NCI Community Oncology Research Program research bases, and the NCI. Presentations focused on the following topics: measurement, lessons learned from pediatric and geriatric oncology, biomarker and mechanism endpoints, longitudinal study designs, and pharmacological and behavioral intervention trials. Panel discussions provided guidance on priority cognitive assessments, considerations for remote assessments, inclusion of relevant biomarkers, and strategies for ensuring broad inclusion criteria. Three clinical trial planning meeting working groups (longitudinal studies as well as pharmacological and behavioral intervention trials) convened for 1 year to discuss and report on top priorities and to design studies. The meeting experts concluded that sufficient data exist to advance phase II/III trials using selected pharmacological and behavioral interventions for the treatment of cancer-related cognitive impairment in the non-central nervous system setting, with recommendations included herein.