(AUC) of receiver operating characteristic (ROC). Data were exported into SPSS Statistics, version 25 (IBM Corp) for analysis and statistical significance was set a priori at P <.05. Results Sample Characteristics Mean age at diagnosis was 63 years (SD
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Linda Watson, Siwei Qi, Andrea DeIure, Eclair Photitai, Lindsi Chmielewski, and Louise Smith
Mostafa R. Mohamed, Kah Poh Loh, Supriya G. Mohile, Michael Sohn, Tracy Webb, Megan Wells, Sule Yilmaz, Rachael Tylock, Eva Culakova, Allison Magnuson, Can-Lan Sun, James Bearden, Judith O. Hopkins, Bryan A. Faller, and Heidi D. Klepin
hospitalization: (1) demographic variables including age, gender, race, education, and income; (2) clinical characteristics including cancer stage, treatment regimen (standard vs nonstandard), palliative treatment line (first- vs second-line or greater); and (3
Alex J. Ball, Imran Aziz, Sophie Parker, Ravishankar B. Sargur, Jonathan Aldis, and Matthew Kurien
. Receiver operating characteristics (ROC) are plotted for FIT against CRC diagnosis. Results FIT Returns and Patient Characteristics Between October 1, 2019, and December 31, 2019, a total of 4,219 FIT requests were received from primary care
Nicholas G. Zaorsky, Ying Zhang, Vonn Walter, Leila T. Tchelebi, Vernon M. Chinchilli, and Niraj J. Gusani
diagnosed in the United States. The NCDB 7 records demographics, comorbidities, tumor characteristics, and overall survival (OS), and contains information regarding therapies delivered during the first course of treatment (ie, surgery, radiation therapy [RT
Claire de Oliveira, Joyce Cheng, Kelvin Chan, Craig C. Earle, Murray Krahn, and Nicole Mittmann
and identified patient and system characteristics associated with high system costs after cancer treatment. The most common trajectory consisted of patients who were low-cost in the year before cancer treatment and remained low-cost after completing
Nirmala K. Manjappachar, John A. Cuenca, Claudia M. Ramírez, Mike Hernandez, Peyton Martin, Maria P. Reyes, Alba J. Heatter, Cristina Gutierrez, Nisha Rathi, Charles L. Sprung, Kristen J. Price, and Joseph L. Nates
bias. We also excluded all postsurgical patients. Data Sources and Measurements Demographic and clinical information such as age, sex, comorbidities, Charlson comorbidity index (CCI), and characteristics of cancer, such as cancer diagnosis
David S. Ettinger, Michael Kuettel, Jennifer Malin, Joan S. McClure, Mary Lou Smith, Andrew D. Zelenetz, and F. Marc Stewart
Much has changed in the treatment of cancer since the first NCCN Clinical Practice Guidelines in Oncology (NCCN Guidelines) were rolled out for 8 different tumor types in November 1996. NCCN Guidelines now include involved algorithms often containing multiple treatment alternatives and detailed pathways of care that depend on more-specific patient characteristics and molecular tumor diagnostics. With 47 different individual NCCN panels, all members of the cancer care team are now better informed than ever to guide patients through the often complex decision-making required to improve the odds of successful outcomes. At the NCCN 20th Annual Conference, a distinguished panel assembled to take a closer look at these invaluable clinical practice guidelines, first glancing backward to how it all started and then forward to explore the key ingredients of trustworthy guidelines.
Yao Zhu, Yu Wei, Hao Zeng, Yonghong Li, Chi-Fai Ng, Fangjian Zhou, Caiyun He, Guangxi Sun, Yuchao Ni, Peter K.F. Chiu, Jeremy Y.C. Teoh, Beihe Wang, Jian Pan, Fangning Wan, Bo Dai, Xiaojian Qin, Guowen Lin, Hualei Gan, Junlong Wu, and Dingwei Ye
), but detailed clinical characteristics were not available for patients from the commercial laboratory. Patients from the laboratory were offered germline sequencing, according to the genetic testing recommendations in the NCCN Clinical Practice
Benjamin R. Roman, Snehal G. Patel, Marilene B. Wang, Anna M. Pou, F. Christopher Holsinger, David Myssiorek, David Goldenberg, Samuel Swisher-McClure, Alexander Lin, Jatin P. Shah, and Judy A. Shea
-level factors related to the decision to use surveillance imaging are not well understood. Several studies have found that physician demographic and practice characteristics correlate with surveillance testing use in breast and colon cancers. 7 , 10 , 11
Benjamin L. Franc, Timothy P. Copeland, Robert Thombley, Miran Park, Ben Marafino, Mitzi L. Dean, W. John Boscardin, Hope S. Rugo, David Seidenwurm, Bhupinder Sharma, Stephen R. Johnston, and R. Adams Dudley
included in the study when the claim included an indication of breast cancer (ICD-9 codes 174.0–174.9). Patient characteristics examined included age group, whether RT was received, whether hormonal therapy was received, payer class, and MSA. Patients
