How I will treat my next patient

New tools could help predict complication risks in lung and breast cancer


In this edition of “How I Will Treat My Next Patient,” I highlight the potential role of new models for predicting risks of common, clinically important situations in general oncology practice: severe neutropenia in lung cancer patients and locoregional recurrence of breast cancer.

Predicting neutropenia

Dr. Alan P. Lyss has been a community-based medical oncologist and clinical researcher for more than 35 years, practicing in St. Louis.

Dr. Alan P. Lyss

Accurate, lung cancer–specific prediction models would be useful to estimate risk of chemotherapy-induced neutropenia (CIN), especially febrile neutropenia (FN), since that particular toxicity is linked to infection, dose delays and dose reductions that can compromise treatment efficacy, and poor health-related quality of life. Lung cancer patients are often older adults, with advanced disease and comorbid conditions, so they are a particularly vulnerable population for CIN.

Xiaowen Cao of Duke University, Durham, N.C., and coinvestigators published a model for predicting risk of severe CIN in advanced lung cancer patients, based on 10 pretreatment variables (Lung Cancer. 2020 Jan 5. doi: 10.1016/j.lungcan.2020.01.004). They developed their model to overcome limitations of the previously published work of Gary H. Lyman, MD, and colleagues that is not specific to lung cancer and incorporated relative dose intensity as a predictor (Cancer. 2011;117:1917-27). Relative dose intensity is not determined until after a treatment course is completed.

The new prediction model was based on a lung cancer data set encompassing 11,352 patients from 67 phase 2-3 cooperative group studies conducted between 1991 and 2010. In this data set, the Lyman model had an area under the curve of 0.8772 in patients with small cell lung cancer, but an area under the curve of just 0.6787 in non–small cell lung cancer.

The derivation model was derived from about two-thirds of the patients, randomly selected. The validation set was conducted using the remaining third. The variables included were readily clinically available: age, gender, weight, body mass index, insurance status, disease stage, number of metastatic sites, chemotherapy agents used, number of chemotherapy agents, planned growth factor use, duration of planned therapy, pleural effusion, presence of symptoms, and performance status. Their model had an area under the curve of 0.8348 in the training set and 0.8234 in the testing set.

How these results influence practice

The risk of an initial episode of FN is highest during a patient’s initial cycle of chemotherapy, when most patients are receiving full-dose treatment, often without prophylactic measures. Guidelines from the National Comprehensive Cancer Network suggest the use of prophylactic growth factors in patients with more than a 20% risk of FN, and considering using prophylaxis in patients with 10%-20% risk of FN. Underestimating those risks and failure to take adequate precautions may be particularly important for patients with lung cancer who are generally older adults, with comorbid conditions.

The comprehensive risk model for neutropenic complications that was developed by Dr. Lyman and colleagues was based on a large, prospective cohort including nearly 3,800 patients. The model had a 90% sensitivity and 96% predictive value, but was not lung cancer specific and, in this latest study, did not perform as well in the 85% of lung cancer patients with non–small cell lung cancer. The Lyman data, however, was obtained in cancer patients treated with investigator-choice chemotherapy in community practices. It remains the National Comprehensive Cancer Network standard for evaluating FN risk in patients embarking on chemotherapy for advanced malignancies. That should remain the case, pending the additional validation testing of the new lung cancer–specific model at independent institutions, treating heterogeneous patients in real-world settings.

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