PHILADELPHIA – A machine learning-based tool was able to predict the risk of malignancy among patients presenting with multiple pulmonary nodules and outperformed human experts, previously validated mathematical models, and a previously established artificial intelligence tool, according to results published in Clinical Cancer Research, a journal of the American Association for Cancer Research.
Tools currently available can predict malignancy in patients with single nodules; predictive tools for patients presenting with multiple nodules are limited.
“With the adoption of widespread use of thoracic computed tomography (CT) for lung cancer screening, the detection of multiple pulmonary nodules has become increasingly common,” said study author Kezhong Chen, MD, vice professor in the Department of Thoracic Surgery at Peking University People’s Hospital in China. Among patients presenting with a pulmonary nodule on a CT scan in a previous lung cancer screening trial, roughly 50 percent presented with multiple nodules, Chen said.
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“Current guidelines recommend the use of clinical models that incorporate nodule and sociodemographic features to estimate the probability of cancer prior to surgical treatment, and while there are several tools for patients that present with a single nodule, no such tool currently exists for patients with multiple nodules, representing an urgent medical need,” Chen added.
To address this unmet need, the researchers set out to develop a machine learning-based model to predict the probability of lung malignancy among patients presenting with multiple pulmonary nodules. First, the study authors used data from a training cohort of 520 patients (comprising 1,739 nodules) who were treated at Peking University People’s Hospital between January 2007 and December 2018. Using both radiographical nodule characteristics and sociodemographic variables, the authors developed a model, termed PKU-M, to predict the probability of cancer. The performance of the model was evaluated by calculating the area under the curve (AUC), where a score of 1 corresponds to a perfect prediction. In the training cohort, the model achieved an AUC of 0.91. Some of the top predictive features of the model included nodule size, nodule count, nodule distribution, and age of the patient.
The model was then validated using data from a cohort of 220 patients (comprising 583 nodules) who underwent surgical treatment in six independent hospitals in China and Korea between January 2016 and December 2018. The performance of the PKU-M model in this cohort was similar to its performance in the training cohort, with an AUC of 0.89. The researchers also compared the performance of their model with four prior logistic regression-based models that were developed for the prediction of lung cancer. The PKU-M model outperformed all four of the prior models, whose AUC values ranged from 0.68 to 0.81.
