Version 1
: Received: 15 September 2024 / Approved: 16 September 2024 / Online: 16 September 2024 (10:35:56 CEST)
How to cite:
AMMARI, S.; Camez, A. O.; Ayobi, A.; Quenet, S.; Zemmouri, A.; Mniai, E. M.; Chaibi, Y.; Franciosini, A.; Clavel, L.; Bidault, F.; Muller, S.; Lassau, N.; Balleyguier, C.; Assi, T. Contribution of an Artificial Intelligence Tool in the Detection of Incidental Pulmonary Embolism on Oncology Assessment Scans. Preprints2024, 2024091207. https://doi.org/10.20944/preprints202409.1207.v1
AMMARI, S.; Camez, A. O.; Ayobi, A.; Quenet, S.; Zemmouri, A.; Mniai, E. M.; Chaibi, Y.; Franciosini, A.; Clavel, L.; Bidault, F.; Muller, S.; Lassau, N.; Balleyguier, C.; Assi, T. Contribution of an Artificial Intelligence Tool in the Detection of Incidental Pulmonary Embolism on Oncology Assessment Scans. Preprints 2024, 2024091207. https://doi.org/10.20944/preprints202409.1207.v1
AMMARI, S.; Camez, A. O.; Ayobi, A.; Quenet, S.; Zemmouri, A.; Mniai, E. M.; Chaibi, Y.; Franciosini, A.; Clavel, L.; Bidault, F.; Muller, S.; Lassau, N.; Balleyguier, C.; Assi, T. Contribution of an Artificial Intelligence Tool in the Detection of Incidental Pulmonary Embolism on Oncology Assessment Scans. Preprints2024, 2024091207. https://doi.org/10.20944/preprints202409.1207.v1
APA Style
AMMARI, S., Camez, A. O., Ayobi, A., Quenet, S., Zemmouri, A., Mniai, E. M., Chaibi, Y., Franciosini, A., Clavel, L., Bidault, F., Muller, S., Lassau, N., Balleyguier, C., & Assi, T. (2024). Contribution of an Artificial Intelligence Tool in the Detection of Incidental Pulmonary Embolism on Oncology Assessment Scans. Preprints. https://doi.org/10.20944/preprints202409.1207.v1
Chicago/Turabian Style
AMMARI, S., Corinne Balleyguier and Tarek Assi. 2024 "Contribution of an Artificial Intelligence Tool in the Detection of Incidental Pulmonary Embolism on Oncology Assessment Scans" Preprints. https://doi.org/10.20944/preprints202409.1207.v1
Abstract
INTRODUCTION
The incidence of venous thromboembolism is estimated at around 3% of cancer patients. However, a majority of incidental pulmonary embolism (iPE) can be overlooked by radiologists in asymptomatic patients, performing CT scans for disease surveillance, which may significantly impact the patient's health and management. Routine imaging in oncology is usually reviewed with delayed hours after the acquisition of images. Nevertheless, the advent of AI in radiology could reduce the risk of diagnostic delay of iPE by an optimal triage immediately at the acquisition console. This study aimed to determine the accuracy rate of an AI algorithm (CIN A-iPE) in detecting iPE and the duration until the management of cancer patients in our center in addition to describing the characteristics of patients with a confirmed pulmonary embolism (PE).
MATERIALS AND METHODS
This is a retrospective analysis of the role of Avicenna's CE-certified and FDA-cleared CINA-iPE algorithm in oncology patients treated at Gustave Roussy Cancer Campus. The results obtained from the AI algorithm were compared with the attending radiologist's report and analyzed by both a radiology resident and a senior radiologist. In case of any discordant results, the reason for this discrepancy was further investigated. The duration between the exact time of the CT scan and analysis was assessed as well as the duration from the result’s reporting and the start of active management.
RESULTS
Out of 3047 patients, 104 alerts were detected for iPE (prevalence of 1.3%) while 2942 had negative findings. 36 of the 104 patients had confirmed PE while 68 alerts were false positives. Only one patient reported as negative by the AI tool was deemed to have a PE by the radiologist. The sensitivity and specificity of the AI model were 97.3% and 97.74% while the PPV and NPV were 34.62% and 99.97% respectively. Most causes of FP were artifacts (22 cases, 32.3%) and lymph nodes (11 cases, 16.2%). Seven patients experienced delayed diagnosis, requiring them to return to the ER for treatment after being sent home following their scan. The remaining patients received prompt care immediately after their testing with a mean delay time of 8.13 hours.
CONCLUSION
The addition of an AI system for the detection of unsuspected PEs on chest CT scans in routine oncology care demonstrated a promising efficacy in comparison to human performance. Despite a low prevalence, the sensitivity and specificity of the AI tool reached 97.3% and 97.7% respectively with detection of all the reported clinical PEs except one single case. This study describes the potential synergy between AI and radiologists for an optimal diagnosis of iPE in routine clinical cancer care.
Medicine and Pharmacology, Oncology and Oncogenics
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
