Machine Learning Model Unveils Stunning Lung Cancer Risk Predictions

Dublin, Oct. 05, 2023 (GLOBE NEWSWIRE) -- In a recent groundbreaking study published on October 3rd in the open access journal PLOS Medicine, Thomas Callender of University College London and his team revealed that a machine learning model can predict lung cancer risk with startling accuracy using only three predictors: age, smoking duration, and pack-years.

Such a development underscores the significance of the latest product offering on, the "Large-Cell and Other Lung Cancer in the United States, 2022-2042: Cancer Populations USA Report and Data Dashboard", as the future of cancer research and treatment pivots towards data-driven approaches.

The report provides a comprehensive overview of the lung cancer landscape, forecasting trends up to 20 years, and is a vital tool in aiding clinical trial design, enrolment, and post-marketing authorization activities. This is especially pertinent given the urgency to address lung cancer, the leading cause of cancer death worldwide.

Unique to the Cancer Populations USA report series is its routine stratification of key cancer populations by race and ethnicity. This approach not only helps assess the representativeness of clinical trial populations but also identifies health disparities across different race-ethnicity groups, ensuring a holistic approach to cancer treatment and research.

Derived from nationally representative datasets, including population-based cancer registries and large epidemiological studies, the report offers estimates for 2022 and forecasts to 2032 and 2042. Accompanying the report, is a data dashboard containing supplementary tables, patient journey diagrams, and interactive heat maps, offering a comprehensive view of the cancer landscape.

In light of the recent study by University College London, the stratification of incident cases provided in the report becomes invaluable. As research leans heavily into early detection and targeted treatment, understanding the distribution of cases by stage, age, gender, race-ethnicity, anatomical subsite, and even PD-1/PD-L1 status is paramount.

Moreover, the forecast model utilises a proprietary algorithm that accounts for historical trends, long-term evolutions, and trends in exposure to known and unknown risk factors. This ensures a detailed, nuanced, and most importantly, actionable insight into the evolving cancer landscape. For those seeking state-specific insights, estimates for the year 2022 for almost all US states, including Puerto Rico, come with accompanying heat maps and tabulated results.

The findings of the PLOS Medicine study only further reinforce the importance of tools in addressing the challenges posed by lung cancer. With data-driven approaches gaining traction in medical research, products like this will be indispensable.

For those looking to leverage the power of data to make strides in cancer research and treatment, "Large-Cell and Other Lung Cancer in the United States, 2022-2042: Cancer Populations USA Report and Data Dashboard" is an essential acquisition.

For more information about this report visit

Article source: News-Medical

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Large-Cell and Other Lung Cancer in the United States: Patient Journey Infographic for All Race/Ethnicity Groups in 2022

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