NCT07256457

Postoperative Pulmonary Function Assessment Based on Deep Learning Study

Not yet recruiting NA Last updated 1 December 2025

What this trial tests

NA trial testing Segmentectomy in Lung Cancer (NSCLC) in 192 participants. Not yet recruiting.

Timeline

1 July 2026

Primary endpoint
30 June 2029

31 December 2029

Quick facts

Lead sponsor	Chinese University of Hong Kong
Phase	NA
Status	Not yet recruiting
Study type	INTERVENTIONAL
Allocation	non randomized
Design	parallel
Masking	none
Primary purpose	other
Enrollment	192
Start date	1 July 2026
Primary completion	30 June 2029
Estimated completion	31 December 2029
Sites	1 location across Hong Kong

Drugs / interventions tested

• Segmentectomy
• Lobectomy
• Bronchoscopic Ablation

Conditions studied

• Lung Cancer (NSCLC) — all drugs for Lung Cancer (NSCLC) →

Sponsor

Chinese University of Hong Kong

Who can join

18 and older, any sex, with Lung Cancer (NSCLC). Patients with the condition only — healthy volunteers not accepted.

Sponsor's own description

Improvements in low-dose CT screening have led to an increase in early-stage NSCLC diagnoses, with surgical resection-usually lobectomy or segmentectomy-remaining the primary curative option. In Hong Kong, however, patients frequently present with comorbidities such as chronic respiratory disease or cardiovascular issues, making the preservation of healthy lung tissue crucial for their long-term quality of life. Traditional surgical resection, such as lobectomy or segmentectomy, has certain limitations in terms of functional preservation. In contrast, robotic/navigational bronchoscopic ablation has emerged in recent years as a novel minimally invasive endoscopic treatment strategy. This approach has been implemented in select centers and demonstrates potential advantages, including faster postoperative recovery, reduced trauma, and improved preservation of pulmonary function. By leveraging advanced navigation systems, bronchoscopic ablation enables precise localization and ablation of pulmonary nodules, avoiding the extensive resection of healthy lung tissue required in traditional surgery. These benefits hold promise for enhancing patients' long-term quality of life and survival rates.

Moreover, conventional pulmonary function tests like FEV₁ and diffusing capacity of the lung for carbon monoxide provide only a global assessment of respiratory capacity, which may not fully capture the regional changes in pulmonary function that occur following segmentectomy or lobectomy. Likewise, basic CT volumetry overlooks finer anatomical details such as segmental airway distribution, microvascular networks, and local alveolar compliance. Furthermore, there is currently a paucity of direct comparative studies between robotic/navigational bronchoscopic ablation and traditional surgical resection regarding postoperative pulmonary function and long-term outcomes. Supported by Research Grants Council, our work since 2019 has validated the feasibility and safety of this technique, leading to widespread recognition and numerous publications. However, most existing research is retrospective or derived from single-center data, with a primary focus on short-term safety and technical feasibility.

To address these limitations, an integrative approach leveraging 3D-CT imaging and ML is proposed. Machine learning is a technology that uses algorithms to automatically learn from data and make predictions or decisions. Deep learning, a subfield of ML, utilizes multi-layer neural networks to effectively extract features and recognize patterns in complex, high-dimensional data. ML, and particularly DL, has demonstrated remarkable potential in various medical imaging applications, including lesion detection, tissue segmentation, and outcome prediction. By automatically learning complex patterns in high-dimensional data, ML and DL models can interpret subtle radiologic characteristics that may be missed by conventional analyses. In the context of 3D-CT imaging for NSCLC, DL architectures-such as convolutional neural networks-can extract detailed features from volumetric scans, enabling robust quantification of tumor size, shape, and location as well as refined assessment of lung parenchyma. When integrated with pulmonary function parameters and clinical data, these algorithms provide a powerful means to generate predictive models, identify at-risk patients earlier, and guide individualized treatment planning. Moreover, ML-driven approaches can adapt to evolving datasets over time, continuously refining and improving their performance. This scalability and adaptability are especially valuable in prospective studies, where large, multimodal datasets are collected to evaluate the long-term impact of different treatment strategies. Consequently, incorporating ML and DL in this research not only enhances the precision of outcome prediction but also contributes to a standardized framework for dynamic, personalized assessment of pulmonary function, guiding more informed clinical decision-making.

The primary aim of this study is to determine whether segmentectomy truly offers better functional preservation than lobectomy, whether robotic/navigation-guided bronchoscopic ablation indeed achieves superior pulmonary function preservation compared to traditional surgical resection, and under which specific patient conditions each approach may yield the greatest benefit. By undertaking a prospective, well-designed investigation, the research will fill a critical gap in evidence regarding long-term functional outcomes, providing clearer criteria for selecting the most appropriate resection type. Moreover, the introduction of a standardized, integrative assessment tool has the potential to optimize surgical decision-making and postoperative care, ultimately improving survival and quality of life for early-stage NSCLC patients in Hong Kong and potentially informing best practices in other healthcare contexts.

Publications & conference data

No peer-reviewed publications indexed yet for this trial.