Ms. O? Background Adjuvant! Online (AO) and PREDICT, using clinicopathological characteristics to estimate prognosis, 1 , 2 help oncologists make adjuvant chemotherapy decisions. Over the past decade, however, evidence has shown that gene
Search Results
Incorporating Tumor Characteristics to Maximize 21-Gene Assay Utility: A Cost-Effectiveness Analysis
Shi-Yi Wang, Tiange Chen, Weixiong Dang, Sarah S. Mougalian, Suzanne B. Evans, and Cary P. Gross
Radiosurgery for Brain Metastases: Changing Practice Patterns and Disparities in the United States
Benjamin H. Kann, Henry S. Park, Skyler B. Johnson, Veronica L. Chiang, and James B. Yu
-beam RT to the brain who met the inclusion criteria. Statistical Methodology Patient-level characteristics included age, year of diagnosis, sex, malignancy type, Charlson-Deyo comorbidity score (CDS), race/ethnicity, insurance status, distance from
Real-World Impact of Prophylactic Growth Factor Use on Timing of Febrile Neutropenia and Infection After High-Risk Chemotherapy
Douglas W. Blayney, Nicole M. Kuderer, Alice Kate Cummings Joyner, John Jarvis, Dominic Nunag, Jasmine Wells, Lan Huang, Ramon Monhanlal, and Gary H. Lyman
our analysis. Rates of PP G-CSF use, defined as ≥1 claim for a G-CSF on or up to 3 days after the date of chemotherapy initiation (index date), were assessed overall and by regimen. Baseline characteristics were evaluated during the 6-month period
Prevalent Pseudoprogression and Pseudoresidue in Patients With Rectal Cancer Treated With Neoadjuvant Immune Checkpoint Inhibitors
Yumo Xie, Jinxin Lin, Ning Zhang, Xiaolin Wang, Puning Wang, Shaoyong Peng, Juan Li, Yuanhui Wu, Yaoyi Huang, Zhuokai Zhuang, Dingcheng Shen, Mingxuan Zhu, Xiaoxia Liu, Guangjian Liu, Xiaochun Meng, Meijin Huang, Huichuan Yu, and Yanxin Luo
study to evaluate the response pattern of nICIs compared with conventional neoadjuvant chemoradiotherapy (nCRT) in patients with MSI-H/dMMR rectal cancer and explored the underlying pathologic characteristics. Based on these findings, we proposed a
SOURCE-PANC: A Prediction Model for Patients With Metastatic Pancreatic Ductal Adenocarcinoma Based on Nationwide Population-Based Data
Héctor G. van den Boorn, Willemieke P.M. Dijksterhuis, Lydia G.M. van der Geest, Judith de Vos-Geelen, Marc G. Besselink, Johanna W. Wilmink, Martijn G.H. van Oijen, and Hanneke W.M. van Laarhoven
treatment of metastatic disease. Tools that can accurately predict survival while taking individual characteristics and treatments into account can be helpful for clinicians and patients when making treatment decisions. Within the past decade, the
Effective Translation of Research to Practice: Hospital-Based Rehabilitation Program Improves Health-Related Physical Fitness and Quality of Life of Cancer Survivors
Amy A. Kirkham, Riggs J. Klika, Tara Ballard, Paul Downey, and Kristin L. Campbell
with a goniometer and with the sit and reach test following standardized instructions, 15 respectively. Statistics Baseline characteristics were compared between the total group that enrolled in the program and the group of participants with a
Nationwide Trends and Determinants of Germline BRCA1/2 Testing in Patients With Breast and Ovarian Cancer
Kelsey S. Lau-Min, Anne Marie McCarthy, Katherine L. Nathanson, and Susan M. Domchek
other high-penetrance cancer susceptibility genes for a subset of patients with breast cancer (eg, based on tumor characteristics, age at diagnosis, ancestry, and family history) and all patients diagnosed with ovarian cancer. 2 Despite these
The Impact of Insurance Status on Tumor Characteristics and Treatment Selection in Contemporary Patients With Prostate Cancer
Nicola Fossati, Daniel P. Nguyen, Quoc-Dien Trinh, Jesse Sammon, Akshay Sood, Alessandro Larcher, Giorgio Guazzoni, Francesco Montorsi, Alberto Briganti, Mani Menon, and Firas Abdollah
characteristics, 5 , 6 and none of these examined the interplay between insurance status, cancer characteristics, and treatment selection. This is of utmost importance, because tumor characteristics represent established predictors of cancer prognosis and play a
Machine Learning–Based Early Warning Systems for Acute Care Utilization During Systemic Therapy for Cancer
Robert C. Grant, Jiang Chen He, Ferhana Khan, Ning Liu, Sho Podolsky, Yosuf Kaliwal, Melanie Powis, Faiyaz Notta, Kelvin K.W. Chan, Marzyeh Ghassemi, Steven Gallinger, and Monika K. Krzyzanowska
. We also considered a soft-voting ensemble model of the 5 models. We selected the best model based on the area under the receiver operating characteristic curve (AUROC) in the tuning cohort. We also developed a simple baseline model that used the
Reaching Populations to Address Disparities in Cancer Care Delivery: Results From a Six-Site Initiative
Noël Arring, Christopher R. Friese, Bidisha Ghosh, Marita Titler, Heidi Hamann, Sanja Percac-Lima, Adrian Sandra Dobs, Michelle J. Naughton, Pooja Mishra, Melissa A. Simon, Bingxin Chen, Electra D. Paskett, Robert J. Ploutz-Snyder, Martha Quinn, and Debra L. Barton
evaluates the representativeness of the population that participates in an intervention/program by assessing the number, characteristics, and proportions of those who participated. 23 The purpose of this publication is to report the collective results of
