Accuracy of Deep-learning Algorithm for Detection and Risk Stratification of Lung Nodules

Completed

• Conditions: Osteogenic Sarcoma

• Registration Number: NCT04022512
• Lead Sponsor: Chinese University of Hong Kong

Brief Summary

Osteosarcoma is regarded as most common malignant bone tumor in children and adolescents. Approximately 15% to 20% of patients with osteosarcoma present with detectable metastatic disease, and the majority of whom (85%) have pulmonary lesions as the sole site of metastasis. Previous studies have shown that the overall survival rate among patients with localized osteosarcoma without metastatic disease is approximately 60% to 70% whereas survival rate reduces to 10% to 30% in patients with metastatic disease. Though lately, neoadjuvant and adjuvant chemotherapeutic regimens can decline the mortality rate, 30% to 50% of patients still die of pulmonary metastases. Number, distribution and timing of lung metastases are of prognostic value for survival and hence computed tomography (CT) thorax imaging still plays a vital role in disease surveillance. In the last decade, the technology of multidetector CT scanner has enhanced the detection of numerous smaller lung lesions, which on one hand can increase the diagnostic sensitivity for lung metastasis, however, the specificity may be reduced. In recent years, deep-learning artificial intelligence (AI) algorithm in a wide variety of imaging examinations is a hot topic. Currently, an increasing number of Computer-Aided Diagnosis (CAD) systems based on deep learning technologies aiming for faster screening and correct interpretation of pulmonary nodules have been rapidly developed and introduced into the market. So far, the researches concentrating on the improving the accuracy of benign/malignant nodule classification have made substantial progress, inspired by tremendous advancement of deep learning techniques. Consequently, the majority of the existing CAD systems can perform pulmonary nodule classification with accuracy of 90% above. In clinical practice, not only the malignancy determination for pulmonary nodule, but also the distinction between primary carcinoma and intrapulmonary metastasis is crucial for patient management. However, most existing classification of pulmonary nodule applied in CAD system remains to be binary pattern (benign Vs malignant), in the lack of more thorough nodule classification characterized with splitting of primary and metastatic nodule. To the best of our knowledge, only a few studies have focuses on the performance of deep learning-based CAD system for identifying metastatic pulmonary nodule till now. In this proposed study, the investigators sought to determine the accuracy and sensitivity of one computer-aided system based on deep-learning artificial intelligence algorithm for detection and risk stratification of lung nodules in osteogenic sarcoma patients.

Detailed Description

Not available

Study Timeline

• Study First Submitted: 2019-07-15
• Study First Submitted QC: 2019-07-15
• Study First Posted: 2019-07-17
• Study Start: 2019-11-06
• Primary Completion: 2023-08-01
• Study Completion: 2024-01-31
• Status Verified: 2024-02
• Last Update Submitted: 2024-02-06
• Last Update Posted: 2024-02-07

Recruitment & Eligibility

• Status: COMPLETED
• Sex: All
• Target Recruitment: 100

Inclusion Criteria

• Patients with histologically confirmed osteogenic sarcoma
• With an age younger than 18 years old.
• Patients who underwent thin-section thoracic CT examinations for pre-treatment staging and/or subsequent post-treatment follow-up.
• With suspicious lung nodules detected on thoracic CT images.

Exclusion Criteria

• Patients with concurring lesions that may influence analysis of lung nodules.

Study & Design

• Study Type: OBSERVATIONAL
• Study Design: Not specified

Primary Outcome Measures

Name	Time	Method
specificity	3 years	true negative rate in percentage (%) derived by ROC analysis
sensitivity	3 years	true positive rate in percentage(%) derived by ROC analysis
area under curve (AUC)	3 years	area under ROC curve in percentage (%)
accuracy	3 years	proportion of true results(both true positives and true negatives) among whole instances

Secondary Outcome Measures

Name	Time	Method
competition performance metric (CPM)	3 years	Competitive performance metric (CPM) is a criterion used for CAD system evaluation. Based on FROC paradigm, CPM score is computed as an average sensitivity at seven predefined average false positive rates. CPM score ranges from 0 to 1, with higher CPM score indicating better CAD performance.
average number of false positives per scan (FPs/scan)	3 years	FPs/scan in number (N) based on free-response receiver operating characteristic (FROC) analysis

Trial Locations

Locations (1)

The Chinese University of Hong Kong, Prince of Wale Hospital; 🇭🇰 Hong Kong, Shatin, Hong Kong