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It Just Keeps Getting Better!

Margaret Tempero

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Letter to the Editor: Inclusive and Adequate Care Overcoming All Health Care Gaps: The Need to Specifically Look to the LGBTQI+ Population

Massimo De Martinis and Lia Ginaldi

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Letter to the Editor Re: Influence of Food With Different Fat Concentrations on Alectinib Exposure: A Randomized Crossover Pharmacokinetic Trial

Andrew D. Frugé, Kristen S. Smith, Sylvia L. Crowder, and Wendy Demark-Wahnefried

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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

Background: Emergency department visits and hospitalizations frequently occur during systemic therapy for cancer. We developed and evaluated a longitudinal warning system for acute care use. Methods: Using a retrospective population-based cohort of patients who started intravenous systemic therapy for nonhematologic cancers between July 1, 2014, and June 30, 2020, we randomly separated patients into cohorts for model training, hyperparameter tuning and model selection, and system testing. Predictive features included static features, such as demographics, cancer type, and treatment regimens, and dynamic features, such as patient-reported symptoms and laboratory values. The longitudinal warning system predicted the probability of acute care utilization within 30 days after each treatment session. Machine learning systems were developed in the training and tuning cohorts and evaluated in the testing cohort. Sensitivity analyses considered feature importance, other acute care endpoints, and performance within subgroups. Results: The cohort included 105,129 patients who received 1,216,385 treatment sessions. Acute care followed 182,444 (15.0%) treatments within 30 days. The ensemble model achieved an area under the receiver operating characteristic curve of 0.742 (95% CI, 0.739–0.745) and was well calibrated in the test cohort. Important predictive features included prior acute care use, treatment regimen, and laboratory tests. If the system was set to alarm approximately once every 15 treatments, 25.5% of acute care events would be preceded by an alarm, and 47.4% of patients would experience acute care after an alarm. The system underestimated risk for some treatment regimens and potentially underserved populations such as females and non-English speakers. Conclusions: Machine learning warning systems can detect patients at risk for acute care utilization, which can aid in preventive intervention and facilitate tailored treatment. Future research should address potential biases and prospectively evaluate impact after system deployment.

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NCCN Guidelines® Insights: Genetic/Familial High-Risk Assessment: Breast, Ovarian, and Pancreatic, Version 2.2024

Featured Updates to the NCCN Guidelines

Mary B. Daly, Tuya Pal, Kara N. Maxwell, Jane Churpek, Wendy Kohlmann, Zahraa AlHilli, Banu Arun, Saundra S. Buys, Heather Cheng, Susan M. Domchek, Susan Friedman, Veda Giri, Michael Goggins, Andrea Hagemann, Ashley Hendrix, Mollie L. Hutton, Beth Y. Karlan, Nawal Kassem, Seema Khan, Katia Khoury, Allison W. Kurian, Christine Laronga, Julie S. Mak, John Mansour, Kevin McDonnell, Carolyn S. Menendez, Sofia D. Merajver, Barbara S. Norquist, Kenneth Offit, Dominique Rash, Gwen Reiser, Leigha Senter-Jamieson, Kristen Mahoney Shannon, Kala Visvanathan, Jeanna Welborn, Myra J. Wick, Marie Wood, Matthew B. Yurgelun, Mary A. Dwyer, and Susan D. Darlow

The NCCN Guidelines for Genetic/Familial High-Risk Assessment: Breast, Ovarian, and Pancreatic focus primarily on assessment of pathogenic/likely pathogenic (P/LP) variants associated with increased risk of breast, ovarian, pancreatic, and prostate cancer, including BRCA1, BRCA2, CDH1, PALB2, PTEN, and TP53, and recommended approaches to genetic counseling/testing and care strategies in individuals with these P/LP variants. These NCCN Guidelines Insights summarize important updates regarding: (1) a new section for transgender, nonbinary and gender diverse people who have a hereditary predisposition to cancer focused on risk reduction strategies for ovarian cancer, uterine cancer, prostate cancer, and breast cancer; and (2) testing criteria and management associated with TP53 P/LP variants and Li-Fraumeni syndrome.

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NCCN News

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Prostate Cancer, Version 4.2023, NCCN Clinical Practice Guidelines in Oncology

Edward M. Schaeffer, Sandy Srinivas, Nabil Adra, Yi An, Daniel Barocas, Rhonda Bitting, Alan Bryce, Brian Chapin, Heather H. Cheng, Anthony Victor D’Amico, Neil Desai, Tanya Dorff, James A. Eastham, Thomas A. Farrington, Xin Gao, Shilpa Gupta, Thomas Guzzo, Joseph E. Ippolito, Michael R. Kuettel, Joshua M. Lang, Tamara Lotan, Rana R. McKay, Todd Morgan, George Netto, Julio M. Pow-Sang, Robert Reiter, Mack Roach III, Tyler Robin, Stan Rosenfeld, Ahmad Shabsigh, Daniel Spratt, Benjamin A. Teply, Jonathan Tward, Richard Valicenti, Jessica Karen Wong, Dorothy A. Shead, Jenna Snedeker, and Deborah A. Freedman-Cass

The NCCN Guidelines for Prostate Cancer provide a framework on which to base decisions regarding the workup of patients with prostate cancer, risk stratification and management of localized disease, post-treatment monitoring, and treatment of recurrence and advanced disease. The Guidelines sections included in this article focus on the management of metastatic castration-sensitive disease, nonmetastatic castration-resistant prostate cancer (CRPC), and metastatic CRPC (mCRPC). Androgen deprivation therapy (ADT) with treatment intensification is strongly recommended for patients with metastatic castration-sensitive prostate cancer. For patients with nonmetastatic CRPC, ADT is continued with or without the addition of certain secondary hormone therapies depending on prostate-specific antigen doubling time. In the mCRPC setting, ADT is continued with the sequential addition of certain secondary hormone therapies, chemotherapies, immunotherapies, radiopharmaceuticals, and/or targeted therapies. The NCCN Prostate Cancer Panel emphasizes a shared decision-making approach in all disease settings based on patient preferences, prior treatment exposures, the presence or absence of visceral disease, symptoms, and potential side effects.

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Reply To the Letter to the Editor by Frugé et al

Daan A.C. Lanser, Anne-Marie C. Dingemans, Ron H.J. Mathijssen, and G.D. Marijn Veerman

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Validation of the PREDICT Prognostication Tool in US Patients With Breast Cancer

Nickolas Stabellini, Lifen Cao, Christopher W. Towe, Megan E. Miller, Artur H. Sousa-Santos, Amanda L. Amin, and Alberto J. Montero

Background: PREDICT is an online prognostication tool derived from breast cancer registry information on approximately 6,000 women treated in the United Kingdom that estimates the postsurgical treatment benefit of surgery alone, chemotherapy, trastuzumab, endocrine therapy, and/or adjuvant bisphosphonates in early-stage breast cancer. Our aim was to validate the PREDICT algorithm in predicting 5- and 10-year overall survival (OS) probabilities using real-world outcomes among US patients with breast cancer. Methods: A retrospective study was performed including women diagnosed with unilateral breast cancer in 2004 through 2012. Women with primary unilateral invasive breast cancer were included. Patients with bilateral or metastatic breast cancer, no breast surgery, or missing critical clinical information were excluded. Prognostic scores from PREDICT were calculated and external validity was approached by assessing statistical discrimination through area under time-dependent receiver-operator curves (AUC) and comparing the predicted survival to the observed OS in relevant subgroups. Results: We included 708,652 women, with a median age of 58 years. Most patients were White (85.4%), non-Hispanic (88.4%), and diagnosed with estrogen receptor–positive breast cancer (79.6%). Approximately 50% of patients received adjuvant chemotherapy, 67% received adjuvant endocrine therapy, 60% underwent a partial mastectomy, and 59% had 1 to 5 axillary sentinel nodes removed. Median follow-up time was 97.7 months. The population’s 5- and 10-year OS were 89.7% and 78.7%, respectively. Estimated 5- and 10-year median survival with PREDICT were 88.3% and 73.8%, and an AUC of 0.77 and 0.76, respectively. PREDICT performed most poorly in patients with high Charlson-Deyo comorbidity scores (2–3), where PREDICT overestimated OS. Sensitivity analysis by year of diagnosis and HER2 status showed similar results. Conclusions: In this prognostic study utilizing the National Cancer Database, the PREDICT tool accurately predicted 5- and 10-year OS in a contemporary and diverse population of US patients with nonmetastatic breast cancer.

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Volume 21 (2023): Issue 9 (Sep 2023)