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qTrack

This study aimed to investigate the benefit of artificial intelligence (AI) chest X-rays in detecting missed lung cancer diagnoses in community-based cancer centres where most of the chest X-ray interpretation is done by general practitioners.

Methods: We retrospectively reviewed the chest X-ray image database of newly diagnosed lung cancer patients from 1 January to 31 December 2022 at Phrapokklao Cancer Center of Excellence. A total of 197 patients were screened, the eligible 77 patients who obtained more than six months before lung cancer (LC) diagnosis. These images were evaluated by CXR&nbsp;Qure.AI&nbsp;software to identify abnormal nodules or perihilar thickening for more than six months before a definite diagnosis of lung cancer, defined as missed lung cancer (MLC) diagnoses. We assessed the median time of MLC diagnosis.

Results: Among the 77 patients analysed, fourteen patients (18.18%) were found to have MLC diagnoses. The mean duration of MLC diagnosis was 32.3 months with a 95% CI from 20.6 to 44.08 months, with a maximum of 101 months and a minimum of 8.2 months. Of 14 MLC patients, six patients (42.9%) were diagnosed with stage IV lung cancer, while three patients (21.1%) were diagnosed with stage III lung cancer.Conclusions: This study's findings show the potential of AI technology to facilitate the detection of lung cancer from chest X-ray images.

The poster presentation of this took place at International Association for the Study of Lung Cancer (IASLC) 2024 /&nbsp;World Conference on Lung Cancer in San Diego 2024.&nbsp;

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Evidence at 2024 World Conference on Lung Cancer cites chest X-ray AI’s potential to detect lung nodules early

Radiologists are currently ill equipped to precisely estimate disease burden and track the progression of idiopathic pulmonary fibrosis (IPF). Development of an automated method for IPF segmentation is challenging, due to the complexity of the fibrosis pattern and degree of variation between patients. Deep neural networks are machine learning algorithms that overcome these challenges. We describe the development and validation of a novel deep learning method to quantify the IPF pattern.

We used high-resolution chest CT scans from 23 patients with IPF as training data. The fibrosis pattern was marked out on 60 slices per scan. Annotated scans, with 6 additional normal scans were used to train a convolutional neural network to outline the IPF disease pattern. Segmentation accuracy was measured using Dice score. For each patient, percentage of lungs affected by IPF was calculated. An independent set of 50 scans was used for clinical validation. Disease volume was independently estimated by 2 thoracic radiologists blinded to the algorithm estimate. Algorithm-derived estimates were correlated with radiologist estimates of disease volume.

A 3-dimensional neural network architecture coupled with 2-dimensional post-processing of each slice produced the most accurate segmentation, with a Dice score of 0.77. The correlation between algorithm-derived disease volume estimate and average radiologist estimates was 0.92. Inter-radiologist correlation was 0.89. Radiologist estimates of disease volume varied by 5.5% (range 0-15%).

We demonstrate that a deep neural network, trained using expert-annotated images, can accurately quantify the percentage of lung volume affected by IPF.

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Clinical validation of a deep learning algorithm for quantification of the idiopathic pulmonary fibrosis pattern

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