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

NCT04022512 | Completed

• 1 other identifier: 2019.421
• Study Type: observational
• Target: 100
• Locations: 1 country
• Sites: 1

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.

Conditions

Osteogenic Sarcoma

Keywords

Lung nodules; Deep learning; Artificial intelligence; Computer-aided diagnosis

Outcome Measures

Primary Outcomes (4)

• 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 Outcomes (2)

• average number of false positives per scan (FPs/scan)

  3 years

• competition performance metric (CPM)

  3 years

Interventions

computed tomography RADIATION

thoracic CT examinations for pre-treatment staging and/or subsequent post-treatment follow-up.

Eligibility Criteria

• Age: Up to 18 Years
• Sex: all
• Age Groups: Child (0-17), Adult (18-64)
• Sampling Method: Probability Sample

Study Population

This is a single institutional retrospective cohort study of patients diagnosed with osteogenic sarcoma between the year 2000 and 2018. All patients' data will be retrieved via the electronic patient database of our institution. Patient demographics, imaging and histological data, disease and treatment history will be recorded, including age at onset, details of chemotherapy, time interval of pulmonary metastasis from diagnosis, surgery for the primary bony tumor, subsequent pulmonary metastatectomy if performed, the length of survival, clinical outcome and so on.

You may qualify if:

• 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.

You may not qualify if:

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

Sponsors & Collaborators

• Chinese University of Hong Kong: lead
• IBM China/Hong Kong Limited: collaborator

Study Sites (1)

The Chinese University of Hong Kong, Prince of Wale Hospital

Hong Kong, Shatin, Hong Kong

Location

MeSH Terms

Conditions

Osteosarcoma

Condition Hierarchy (Ancestors)

Neoplasms, Bone Tissue; Neoplasms, Connective Tissue; Neoplasms, Connective and Soft Tissue; Neoplasms by Histologic Type; Neoplasms; Sarcoma

Study Design

• Study Type: observational
• Observational Model: CASE ONLY
• Time Perspective: RETROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Professor

Study Record Dates

• First Submitted: July 15, 2019
• First Posted: July 17, 2019
• Study Start: November 6, 2019
• Primary Completion: August 1, 2023
• Study Completion: January 31, 2024
• Last Updated: February 7, 2024

Record last verified: 2024-02

Data Sharing

• IPD Sharing: Will not share

Locations

HK (1)