Post-Operative Prediction of PulmonarY Function

Lung cancer is the leading cause of cancer mortality worldwide with non-small cell lung cancers (NSCLC) accounting for approximately 85% of all lung cancers diagnosed. Surgical resection is the primary treatment for stage I and II non-small cell lung cancer (NSCLC) in patients with good or low surgical risk. However, in high-risk or medically inoperable patients, radiation therapy may be recommended for primary treatment. Risk stratification of lung cancer patients for surgery is an important preoperative physiological assessment, taking into account cardiovascular health, pulmonary function, comorbidities and predicted postoperative lung function.

Patients with predicted postoperative forced expiratory volume in 1 second (ppoFEV1) or predicted postoperative diffusing capacity for carbon monoxide (ppoDLCO) of less than 40% of predicted normal values have significantly increased risk of perioperative complications or death. Due to the possibility of postoperative respiratory failure, these patients and are often excluded from surgical resection.

Prediction of postoperative lung function is currently based on anatomical segment counting (ASC), which incorporates pulmonary function test (PFT) results. Standard PFTs such as spirometry can only measure pulmonary capacity as an average over the entire lung and do not take regional function differences into account. The predictive validity of the ASC method is less accurate for patients with physiologically compromised lungs such as those with chronic obstructive pulmonary disease (COPD), which is highly prevalent in the NSCLC population. Moreover, as pulmonary function deficit is most likely to be concentrated in the region of the tumour, the ASC method may underestimate post-operative lung function, leading to some patients being wrongly ruled out from receiving surgical treatment.

Nuclear medicine is recommended where regional functional imaging is required to inform surgical decisions. However, nuclear medicine scans are expensive, time consuming and not available in all institutions. CT-ventilation imaging is a cheaper and more accessible alternative to nuclear medicine for informing lung cancer patient treatment choices.

Introduction to CT ventilation imaging CT Ventilation imaging is a novel software-based solution for generating lung function (ventilation) maps from respiratory correlated CT scans, such as breath hold CT (BHCT), where the patient holds their breath for the duration of the scan.

The key steps in CT ventilation imaging are:

1. Acquire CT images of the lung at exhale and inhale states, using breath hold CT (BHCT). In BHCT, static end-inspiration and end-expiration images of the lung are acquired as the patient holds their breath for around 10 seconds.
2. Deformable image registration is used to determine a spatial mapping (deformation map) between the different CT images (from peak inhale to peak exhale).
3. Application of a ventilation metric to quantify high and low functioning lungs which involve quantitative analysis based on the information from the deformable image registration.

The resulting ventilation image is superimposed directly onto the anatomic image, providing an added dimension of functional information which is easy to understand and can be of direct benefit in surgery interventions.

Use of CT Ventilation imaging in assessing lung function for surgery CT Ventilation imaging has been proposed to improve predicted estimates of post-operative lung function by providing regional information on lung function. A preliminary study carried out at Royal North Shore Hospital testing the feasibility of CT Ventilation imaging as a decision tool for marginally resectable patients concluded that lung function derived by CT ventilation imaging correlates strongly with the gold standard PET ventilation on a lobar level.

CT perfusion imaging Lung perfusion imaging is commonly performed together with SPECT ventilation imaging by injecting 99mTc labelled macroaggregated albumen. Following the success of CT based ventilation imaging technique, a new emerging research area is focusing on the development of novel algorithms to assess the blood flow information from the acquired CT images. These modalities will enable us to derive both ventilation and perfusion information.