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

Study Overview
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Brief Summary
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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.

Official Title
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Feasibility Study: Accuracy and Sensitivity of Deep-learning Artificial Intelligence (AI) Algorithm for Detection and Risk Stratification of Lung Nodules in Osteogenic Sarcoma Patients

Conditions
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Osteogenic Sarcoma

Intervention / Treatment
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* Radiation: computed tomography


Participation Criteria
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Eligibility Criteria
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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.

Ages Eligible for Study
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Maximum Age: 18 Years

Sexes Eligible for Study
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All

Study Plan
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How is the study designed?
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Arms and Interventions

| Intervention/Treatment |
| --- |
|Radiation: computed tomography|thoracic CT examinations for pre-treatment staging and/or subsequent post-treatment follow-up.|

What is the study measuring?
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Primary Outcome Measures

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

Secondary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| average number of false positives per scan (FPs/scan) | FPs/scan in number (N) based on free-response receiver operating characteristic (FROC) analysis | 3 years | 
| competition performance metric (CPM) | 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. | 3 years | 

 Terms related to the study
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Keywords Provided by Centre Hospitalier Valida
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Lung nodules, Deep learning, Artificial intelligence, Computer-aided diagnosis