Clinical trial NCT04022512: Accuracy of Deep-learning Algorithm for Detection and Risk Stratification of Lung Nodules
Official title: Feasibility Study: Accuracy and Sensitivity of Deep-learning Artificial Intelligence (AI) Algorithm for Detection and Risk Stratification of Lung Nodules in Osteogenic Sarcoma Patients
Study type: OBSERVATIONAL
Overall status: COMPLETED
Conditions: Osteogenic Sarcoma
Keywords: Lung nodules
Deep learning
Artificial intelligence
Computer-aided diagnosis
Lead sponsor: Chinese University of Hong Kong
Sponsor class: OTHER
Collaborators: name: IBM China/Hong Kong Limited
First posted: 2019-07-17
Enrollment: 100
Sex: ALL
Maximum age (years): 18
Standard ages: CHILD
ADULT
Observational model: CASE_ONLY
Time perspective: RETROSPECTIVE
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.

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.


Eligibility criteria: 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.

Primary outcomes:
measure: accuracy
timeFrame: 3 years
measure: sensitivity
timeFrame: 3 years
measure: specificity
timeFrame: 3 years
measure: area under curve (AUC)
timeFrame: 3 years
Secondary outcomes:
measure: average number of false positives per scan (FPs/scan)
timeFrame: 3 years
measure: competition performance metric (CPM)
timeFrame: 3 years
Other outcomes:
Locations:
city: Hong Kong
country: Hong Kong
facility: The Chinese University of Hong Kong, Prince of Wale Hospital
state: Shatin
Study officials:
References: