Abstract
The coronavirus disease 2019 (COVID-19) pandemic has led to treatment delays for many patients with cancer. While published guidelines provide suggestions on which cases are appropriate for treatment delay, there are no good quantitative estimates on the association of delays with tumor control or risk of new metastases. To develop a simplified mathematical model of tumor growth, control, and new metastases for cancers with varying doubling times and metastatic potential and to estimate tumor control probability (TCP) and metastases risk as a function of treatment delay interval. This decision analytical model describes a quantitative model for 3 tumors (ie, head and neck, colorectal, and non-small cell lung cancers). Using accepted ranges of tumor doubling times and metastatic development from the clinical literature from 2001 to 2020, estimates of tumor growth, TCP, and new metastases were analyzed for various treatment delay intervals. Risk estimates for potential decreases in local TCP and increases in new metastases with each interval of treatment delay. For fast-growing head and neck tumors with a 2-month treatment delay, there was an estimated 4.8% (95% CI, 3.4%-6.4%) increase in local tumor control risk and a 0.49% (0.47%-0.51%) increase in new distal metastases risk. A 6-month delay was associated with an estimated 21.3% (13.4-30.4) increase in local tumor control risk and a 6.0% (5.2-6.8) increase in distal metastases risk. For intermediate-growing colorectal tumors, there was a 2.1% (0.7%-3.5%) increase in local tumor control risk and a 2.7% (2.6%-2.8%) increase in distal metastases risk at 2 months and a 7.6% (2.2%-14.2%) increase in local tumor control risk and a 24.7% (21.9%-27.8%) increase in distal metastases risk at 6 months. For slower-growing lung tumors, there was a 1.2% (0.0%-2.8%) increase in local tumor control risk and a 0.19% (0.18%-0.20%) increase in distal metastases risk at 2 months, and a 4.3% (0.0%-10.6%) increase in local tumor control risk and a 1.9% (1.6%-2.2%) increase in distal metastases risk at 6 months. This study proposed a model to quantify the association of treatment delays with local tumor control and risk of new metastases. The detrimental associations were greatest for tumors with faster rates of proliferation and metastasis. The associations were smaller, but still substantial, for slower-growing tumors.
Highlights
The effects of the coronavirus disease 2019 (COVID-19) pandemic have altered medical care globally
This study proposed a model to quantify the association of treatment delays with local tumor control and risk of new metastases
We propose a mathematical model, built on previously published tumor growth and control models and informed by published literature on the association of delayed treatment with cancer upstaging, to determine the potential association of cancer treatment delays with local tumor control probability (TCP) and the formation of new metastases.[14,15]
Summary
The effects of the coronavirus disease 2019 (COVID-19) pandemic have altered medical care globally. From March 2020 to the present, hospital systems across the United States and globally have altered their cancer care practices and functions to provide the necessary resources to handle the large number of patients with COVID-19.1,2 Routine procedures, including cancer screening, diagnosis, and treatments (ie, surgery, chemotherapy, and radiotherapy), have been postponed or delayed to accommodate the current situation.[3,4,5,6] In addition, even after the peak of the pandemic, patients have continued to avoid or postpone medical center visits and treatments because of concerns about the risk of COVID-19 exposure.[4] In parallel, sharp increases in the unemployment rate nationally will likely lead to a significant loss in access to medical care.[7,8] It is not clear when established standards of cancer care will routinely resume, and the effects of these delays on patients with cancer remain understudied. In response to the rapidly changing medical landscape, guidelines have been published and distributed to help oncologists determine which patients are appropriate for treatment delays.[2,6] These global measures to contain the spread of COVID-19 have in turn focused recent attention on measuring the indirect effect of the pandemic on cancer treatment.[1,3,9,10,11,12] In particular, there have been several recent modeling studies and expert commentaries reporting on the potential impact of cancer treatment delays due to the COVID-19 pandemic and associated mortality risks.[1,2,3,4,5,8,9,10,11,12,13] There is general consensus on the critical importance of more high-quality data and quantitative analysis on the potential impact of treatment delays on cancer outcomes
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