Post-Operative Prediction of PulmonarY Function (POPPY)

Post-Operative Prediction of PulmonarY Function A Pilot Study to Assess the Benefit of Incorporating Regional Ventilation Information in the Prediction of Post-operative Lung Function for Lung Cancer Surgery

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. 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.

The primary aim is to quantify the difference between predicted postoperative values of pulmonary function metrics derived from CT ventilation imaging and standard anatomical segment counting method.

Study Overview

• Status: Recruiting
• Conditions: Lung Cancer

Detailed Description

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.

• Study Type: Observational
• Enrollment (Estimated): 15

Contacts and Locations

• Study Contact: Name: Clinical Trial Coordinator; Phone Number: +61 2 8627 1185; Email: shona.silvester@sydney.edu.au
• Study Contact Backup: Name: Study Manager; Phone Number: +61 2 8627 1185; Email: hunor.kertesz@sydney.edu.au

Study Locations

• Australia
  • New South Wales
    • Saint Leonards, New South Wales, Australia, 2065
      • Recruiting
      • Royal North Shore Hospital
      • Contact: Shona Silvester; Phone Number: 0413681368; Email: shona.silvester@sydney.edu.au

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Adult; Older Adult
• Accepts Healthy Volunteers: No

• Sampling Method: Probability Sample

Study Population

A pilot study is not a hypothesis testing study. Although this design has patients being their own control, the small sample size of 15 patients is unlikely to capture the full diversity of the population. Therefore, the statistical tests are for hypothesis generation and not definitive.

Description

Inclusion Criteria:

• Aged 18 years or older.
• ECOG performance status 0-2.
• Lung cancer surgical candidates.
• Undergo SPECT V/Q scans within 8 weeks of registration.
• Undergo BHCT scans within 8 weeks of registration.
• Pulmonary function tests within 8 weeks of registration.
• Willingness to give written informed consent.
• Willingness and ability to comply with the study procedures and visit requirements.

Exclusion Criteria:

• Interstitial lung disease.
• Pregnant women.

Study Plan

How is the study designed?

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
The accuracy of CT ventilation imaging compared to ASC in predicting postoperative values of the pulmonary function metric FEV1	The accuracy of CT ventilation imaging compared to ASC in predicting postoperative values of the pulmonary function metric FEV1 will be assessed via a comparison with actual postoperative values measured from PFTs. CT ventilation images will be used to produce regionally-informed ppoFEV1 by proportionally reducing the pre-surgery FEV1 by the proportion of ventilation lost by removal of the surgical target section of lung. As this is an investigative study, it is not powered for a statistical analysis. Bland-Altman analysis will be used to quantify the ability of ASC and CT ventilation to predict ppoFEV1	1 week

Secondary Outcome Measures

Outcome Measure	Measure Description	Time Frame
The accuracy of CT ventilation imaging in predicting ppoFEV1	The accuracy of CT ventilation imaging in predicting ppoFEV1will be compared to that of SPECT ventilation imaging. Bland-Altman analysis will be used to quantify the ability of SPECT and CT ventilation to predict ppoFEV1	1 week
The regional pattern of ventilation from CT ventilation imaging will be compared to ventilation from SPECT using Spearman correlation.	The average Spearman correlation across all patients will be > 0.6.	1 week
The regional pattern of perfusion from CT perfusion imaging will be compared to perfusion from SPECT using Spearman correlation.	The average Spearman correlation across all patients will be > 0.6.	1 week

Collaborators and Investigators

• Sponsor: University of Sydney
• Collaborators: Royal North Shore Hospital
• Investigators: Principal Investigator: Dasantha Jayamanne, Dr, University of Sydney

Study record dates

Study Major Dates

• Study Start (Actual): June 25, 2025
• Primary Completion (Estimated): December 1, 2026
• Study Completion (Estimated): December 1, 2026

Study Registration Dates

• First Submitted: July 2, 2024
• First Submitted That Met QC Criteria: July 2, 2024
• First Posted (Actual): July 10, 2024

Study Record Updates

• Last Update Posted (Actual): May 20, 2026
• Last Update Submitted That Met QC Criteria: May 17, 2026
• Last Verified: May 1, 2026

More Information

Terms related to this study

• Keywords: lung cancer; lung imaging; ventilation imaging; CT ventilation imaging
• Additional Relevant MeSH Terms: Neoplasms by Site; Neoplasms; Respiratory Tract Diseases; Lung Diseases; Respiratory Tract Neoplasms; Thoracic Neoplasms; Lung Neoplasms
• Other Study ID Numbers: IX-2023-OV-POPPY

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: YES
• IPD Plan Description: De-identified patient images and demographic data will be shared to a public repository for future research.
• IPD Sharing Time Frame: Following publication of final analysis
• IPD Sharing Access Criteria: To be determined
• IPD Sharing Supporting Information Type: STUDY_PROTOCOL; CSR

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No