Idiopathic pulmonary fibrosis (IPF) is a chronic lung disease which is caused by forming scar tissue within the lungs [spagnolo_idiopathic_2015]. IPF leads to a gradual, irreversible deterioration of lung function by replacing the healthy lung tissues with scar tissue over time. IPF can potentially lead to rapid deterioration from long-term stability, which results in complete pulmonary dysfunction [raghu_diagnosis_2018]. Due to the high variability in deterioration speed, management of pulmonary fibrosis relies on the decline in the lung function progression. Therefore,
an accurate estimation of the lung function progression decline would lead to better management of IPF.
The current guideline for IPF diagnosis follows several procedures, such as surgical or transbronchial lung biopsy [raghu_diagnosis_2018]. After the diagnosis, physicians often assess the decline of lung function by Force vital capacity (FVC) using spirometry tests to monitor the prognosis of IPF. FVC measures the total amount of air exhaled after breathing in as deeply as possible [zappala2010marginal]. To assess the lung function, observing the FVC at intervals of six to twelve months is recommended [raghu_diagnosis_2018]. While the FVC provides a general understanding of the prognosis of the IPF [flaherty_idiopathic_2006], there are no widely used techniques to estimate the IPF progression. As such, due to the heterogeneous course of IPF, imaging modalities may provide valuable information regarding IPF prognosis.
Computed tomography (CT) images of the chest can be effectively used to assess the lung function decline from pulmonary fibrosis as the CT scans contain several visual signs essential for assessment by radiologists. Shi et al. [shi_prediction_2019] developed a voxel-wise radio-logical model using high-resolution CT scans and achieved accuracy in predicting the progression of IPF. Furthermore, Salisbury et al. [salisbury_idiopathic_2016] utilized CT scans of IPF patients to predict the survival and FVC decline for 12 months with a significant correlation value of 0.6 between visual and predicted measurement. These studies have demonstrated the effectiveness of utilizing CT imaging as an important modality to predict the progression of pulmonary fibrosis. However, precisely predicting the progression of IPF from CT images remains challenging due to the high variability.
The recent advancements of artificial intelligence (e.g., convolutional neural networks (CNNs) [he2016deep]
) and the Kaggle: OSIC Pulmonary Fibrosis Progression Challenge [osic_kaggle]
have significantly inspired to develop CT image based machine learning systems to obtain computer-aided clinical decision for IPF prognosis.
In particular, Wong et al. [wong_fibrosis-net_2021]
recently proposed Fibrosis-Net based on deep CNNs for predicting pulmonary fibrosis progression from chest CT images. Fibrosis-Net utilized the chest CT scans of a patient along with spirometry measurement and clinical metadata to predict the FVC of a patient at a specific time-point in the future [wong_fibrosis-net_2021]
. While the existing CNNs based approaches have a higher capacity to predict pulmonary fibrosis progression from chest CT images, we strongly believe there is still room for improvement in terms of overall correctness. In this work, we argue that extracted convolutional features from chest CT scans along with patient’s clinical or demographic features are not discriminative enough to correctly predict the FVC of a patient in cases where the network requires to focus on a specific region of the lung. To address this issue, we proposed a simple and efficient end-to-end multi-modal network, termed as Fibro-CoSANet, which utilized both the chest CT scan images and demographic information, such as sex, age, smoking history to predict the FVC of a patient at a specific time-point. Our proposed Fibro-CoSANet used a convolutional self-attention network that extracted features from a randomly selected CT image which are merged with the normalized demographics features. The merged features were then passed through a one-layer perceptron to obtain the predicted FVC. While the Fibrosis-Net [wong_fibrosis-net_2021]
utilized the multiple CT slices to generate convolutional features, we introduced an efficient formulation of the IPF prognosis task where we randomly selected a single CT image from multiple scans to extract convolutional features. However, we used the approximated lung volume information from all the available scans as a shallow feature which was merged with the convolutional features. In addition, we predicted the slope
of FVC based on a linear prior assumption
to reduce the computational overhead, while Fibrosis-Net [wong_fibrosis-net_2021]
used an elastic net to obtain the local FVCs.