2024 World Conference on Lung Cancer (WCLC) - Posters-P4.04D.08 Natural History Model for Second Primary Lung Cancer Among Lung Cancer Survivors: Utilizing the U.S. SEER Cancer Registries

P4.04D.08 Natural History Model for Second Primary Lung Cancer Among Lung Cancer Survivors: Utilizing the U.S. SEER Cancer Registries

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The enduring survival rates of lung cancer (LC) patients heighten the risk of developing second primary lung cancer (SPLC), necessitating customized surveillance approaches. To address this, a natural history model (NHM) can project tumor growth over time, guiding effective CT surveillance strategies. Current NHMs don't differentiate between initial primary lung cancer (IPLC) and multiple primary cancers, even though growth patterns might differ.<br /><br />The study aimed to refine Stanford's existing LC NHM to focus on IPLC-specific tumor growth and to create a new NHM for SPLC. This would facilitate comparisons between SPLC and IPLC tumor characteristics. The hypothesis is that while the growth rate of SPLC might mirror that of IPLC, SPLC tends to be detected earlier, often due to surveillance following an initial diagnosis.<br /><br />This research leverages survival data from the SEER cancer registries to develop distinct models: one for patients with a single lifetime LC (IPLC NHM) and another for those who developed SPLC five years post-IPLC diagnosis, aiming to attribute mortality appropriately to each type.<br /><br />Results indicate that the tumor volume doubling time (TVDT) of tumors in both IPLC and SPLC was comparable, suggesting similar tumor growth dynamics. However, SPLCs are often detected at earlier stages in clinical settings. The NHM models were validated against SEER data, showing a good fit, thereby supporting their utility in future studies assessing surveillance strategies for SPLC.<br /><br />The findings suggest that NHMs can effectively capture the natural history of both IPLC and SPLC, providing a crucial foundation for future microsimulation studies to evaluate and optimize surveillance strategies tailored to patients at risk of or suffering from SPLC. This work was supported by NIH Award R37CA226081, highlighting its significance for public health research and policy formulation.

Asset Subtitle

Eunji Choi

Meta Tag

Speaker Eunji Choi

Topic Screening & Early Detection

Keywords

lung cancer

second primary lung cancer

natural history model

tumor growth

CT surveillance

SEER cancer registries

tumor volume doubling time

surveillance strategies

microsimulation studies

NIH Award R37CA226081