Introduction: The incidence of venous thromboembolism is estimated to be 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 the 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 (CINA-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 were 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 report 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. In total, 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 h. Conclusions: 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. Clinical relevance statement: In the oncology field, iPEs are common, with an increased risk of morbidity when missed with a delayed diagnosis. With the assistance of a reliable AI tool, the radiologist can focus on the challenging analysis of oncology results while dealing with urgent diagnosis such as PE by sending the patient straight to the ER (Emergency Room) for prompt treatment.

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.

Background/Objectives: To evaluate and optimize the reconstruction parameters of images acquired with a photon-counting CT scanner to achieve a stable radiomics signal. Methods: Radiomics is a quantitative imaging biomarker correlated to survival in oncology patients. Implementing radiomics in clinical routine remains challenging due to the feature’s instability. Photon-counting CT scans use innovative technology directly converting photons into electrical signals resulting in higher-resolution images with reduced artifacts. This study used two organic phantoms: a natural wet sponge and a dry sausage. UHR images were acquired using a NAEOTOM Alpha photon-counting CT scan (Siemens) with a 0.4 mm slice thickness and 0.3 × 0.3 mm pixel size. Tube current and voltage were fixed at 112 mA and 120 KvP. A total of 24 reconstruction parameter sets were obtained by combining different values of kernel (Br), quantitative iterative reconstruction (QIR), spectral reconstruction (keV), and matrix size. Ten successive acquisitions were obtained on both phantoms. In total, 93 radiomic features were extracted on an ROI using the default parameters of Pyradiomic 3.0.1. Each feature’s stability was evaluated using the coefficient of variation (CV) within each parameter set. Results: Of the 24 reconstruction parameter sets, 5 were selected based on best image quality by seven radiologists and three radiology technologists. Radiomics features were considered stable on a set when CV was less than 15%. Feature stability was impacted by reconstruction parameters and the phantom used. The most stable combination included 90 and 65 stable features of the 93 tested on the sausage and sponge respectively. It was configured with Br36, QIR 4, 60 keV, and a 1024 × 1024 matrix size. Conclusions: Images obtained on photon-counting CT scans offer promising radiomic feature stability with optimal parameter configurations that could be applied in a clinical setting.