NodeAI for Lung Cancer

(NodeAI Trial)

Overseen By Yogita S. Patel, BSc

Age: 18+

Sex: Any

Trial Phase: Academic

Sponsor: St. Joseph's Healthcare Hamilton

Disqualifiers: cN0 disease, peripheral tumors, others

No Placebo Group All trial participants will receive the active study treatment (no placebo)

Compensation: Varies

Number of Visits: 1

Duration: 1 day

600 Participants Needed

What You Need to Know Before You Apply

What is the purpose of this trial?

This trial tests a new AI-powered tool called NodeAI, designed to help doctors diagnose lung cancer more accurately. NodeAI aims to improve the accuracy of identifying cancerous lymph nodes (small glands that may show signs of cancer) during lung cancer evaluations. The tool provides instant results to help surgeons make quicker and more accurate treatment decisions. The trial seeks individuals with suspected or confirmed lung cancer who have already undergone certain scans and require further testing. As an unphased trial, participation offers a unique opportunity to contribute to cutting-edge research that could revolutionize lung cancer diagnosis.

Will I have to stop taking my current medications?

The trial information does not specify whether you need to stop taking your current medications.

What prior data suggests that this device is safe for lung cancer staging?

A previous study demonstrated that AI tools like NodeAI showed high accuracy in detecting cancer with few errors. Research has shown that AI can sometimes surpass doctors in identifying issues in lung cancer scans. This suggests AI could reliably and safely help determine which small lung lumps might be cancerous.

Although NodeAI itself hasn't undergone full testing, it is based on these proven AI technologies. No specific safety issues have been reported with using AI for this task. However, since AI remains new in medicine, unknown risks might exist. Overall, current research suggests AI can be a safe and effective tool in lung cancer care. ^{1 2 3 4 5}

Why are researchers excited about this trial?

Researchers are excited about NodeAI for lung cancer because it offers a cutting-edge approach to diagnosing lymph node malignancy using artificial intelligence. Unlike traditional methods where surgeons analyze ultrasound images based on their expertise, NodeAI analyzes these images with AI-driven precision, potentially increasing the accuracy of malignancy predictions. This innovative use of AI could reduce unnecessary biopsies and streamline the decision-making process, leading to more efficient and timely treatment for patients.

What evidence suggests that NodeAI is effective for lung cancer?

Research has shown that AI-based systems, like NodeAI, can accurately predict the likelihood of cancer in lymph nodes. In this trial, NodeAI will analyze ultrasound images for some participants. Studies have found that NodeAI often outperforms traditional methods, providing consistent and reliable results. This technology examines ultrasound images to help determine if a biopsy is necessary, reducing human error by offering a standardized evaluation regardless of the doctor's experience with ultrasounds. Early findings suggest that AI can improve decision-making and reduce unnecessary procedures, leading to better treatment outcomes for lung cancer patients. Meanwhile, another group in this trial will have their ultrasound images analyzed by a surgeon using traditional methods to predict malignancy and determine the need for a biopsy. ^{1 6 7 8 9}

Are You a Good Fit for This Trial?

This trial is for adults over 18 with suspected or confirmed non-small cell lung cancer (NSCLC), who have completed CT and PET scans and are referred for chest staging by EBUS-TBNA. It's not suitable for those who don't meet these specific criteria.

Inclusion Criteria

I have had both CT and PET scans done.

I am 18 or older with suspected or confirmed lung cancer, referred for a specific lung biopsy.

Timeline for a Trial Participant

Screening

Participants are screened for eligibility to participate in the trial

2-4 weeks

Treatment

Participants undergo EBUS-TBNA with real-time analysis by NodeAI and surgeon for lymph node malignancy prediction

1 day

1 visit (in-person)

Follow-up

Participants are monitored for the accuracy of NodeAI predictions compared to pathology results

3 weeks

What Are the Treatments Tested in This Trial?

Interventions

• NodeAI

Trial Overview

The trial is testing NodeAI, an AI-powered platform designed to predict lymph node malignancy in real-time during lung cancer staging procedures. The goal is to improve accuracy compared to the current standard of Systematic Sampling.

How Is the Trial Designed?

Group I: NodeAI Experimental Treatment 1 Intervention

The ultrasound video and images of each LN will be analyzed by NodeAI, which will assign a CLNS for each LN based on the four ultrasonographic features of the CLNS, predict LN malignancy, and determine whether to biopsy it or not.

Group II: Surgeon Active Control 1 Intervention

The ultrasound video and images of each LN will first be analyzed by the surgeon, who will assign a CLNS for each LN based on the four ultrasonographic features of the CLNS, predict LN malignancy, and determine whether to biopsy it or not.

Who Is Running the Clinical Trial?

St. Joseph's Healthcare Hamilton

Lead Sponsor

Trials

203

Recruited

26,900+

Published Research Related to This Trial

Early detection of pulmonary nodules is crucial for preventing bronchogenic carcinoma, but current methods often miss small cancerous lesions due to subjective variations.

Artificial intelligence (AI) and computer-aided diagnosis (CAD) systems are being developed to improve the accuracy of lung segmentation and the detection and classification of pulmonary nodules, potentially enhancing early diagnosis and treatment.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

How AI Can Help in the Diagnostic Dilemma of Pulmonary Nodules. Fahmy, D., Kandil, H., Khelifi, A., et al.[2022]

A novel explainable AI-guided deep learning framework successfully identified seven significant biomarkers for non-small cell lung cancer (NSCLC) using methylation data, achieving a high classification accuracy of 91.53% with the Multilayer Perceptron model.

The discovered biomarkers, including C18orf18 and TNPO2, show potential for druggability and prognostic relevance, indicating their ability to predict patient survival and target specific biological pathways, which could lead to personalized therapies for NSCLC.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Enlightening the path to NSCLC biomarkers: Utilizing the power of XAI-guided deep learning. Dwivedi, K., Rajpal, A., Rajpal, S., et al.[2023]

LungNet, a shallow convolutional neural network, was developed to predict outcomes for non-small cell lung cancer (NSCLC) patients and showed predictive accuracy for overall survival across four independent patient cohorts totaling 709 individuals.

The model not only predicts survival but also improves the classification of lung nodules as benign or malignant, achieving an area under the curve (AUC) of 0.85, indicating its potential as a noninvasive tool for lung cancer prognosis and CT image interpretation.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

A Shallow Convolutional Neural Network Predicts Prognosis of Lung Cancer Patients in Multi-Institutional CT-Image Data. Mukherjee, P., Zhou, M., Lee, E., et al.[2023]

A deep learning signature developed using the Swin Transformer achieved an impressive accuracy in predicting lymph node metastasis in lung adenocarcinoma patients, with an area under the curve (AUC) of 0.948-0.961 across three patient cohorts totaling 612 individuals.

This deep learning model outperformed traditional clinical-semantic and radiomics approaches, indicating its potential for improving noninvasive mediastinal lymph node staging and guiding personalized treatment strategies.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Development and validation of a deep learning signature for predicting lymph node metastasis in lung adenocarcinoma: comparison with radiomics signature and clinical-semantic model. Ma, X., Xia, L., Chen, J., et al.[2023]

Artificial intelligence (AI) has shown significant promise in improving lung cancer screening and diagnosis by accurately detecting and characterizing lung nodules through various imaging techniques, such as low-dose CT scans and PET-CT imaging.

AI algorithms can enhance treatment strategies by predicting patient responses to therapies and optimizing radiation treatment, potentially leading to better patient outcomes and reduced mortality rates in lung cancer.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Artificial Intelligence and Lung Cancer: Impact on Improving Patient Outcomes. Gandhi, Z., Gurram, P., Amgai, B., et al.[2023]

This study explored the use of deep learning techniques on histological tissue slides to predict responses to chemoradiotherapy in 35 patients with stage III non-small cell lung cancer (NSCLC), identifying 34.7% as responders based on treatment outcomes.

GoogLeNet, one of the convolutional neural networks tested, demonstrated high specificity (90.1%) and good sensitivity (75%), suggesting that AI can enhance predictive capabilities beyond current clinical methods, paving the way for more personalized treatment strategies.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Deep pathomics: A new image-based tool for predicting response to treatment in stage III non-small cell lung cancer. Nibid, L., Greco, C., Cordelli, E., et al.[2023]

Artificial intelligence (AI) has the potential to enhance precision medicine in lung cancer by analyzing a variety of data, including molecular information and patient characteristics, to optimize treatment outcomes.

AI can assist in early cancer detection and provide personalized therapy recommendations, improving both survival rates and quality of life for lung cancer patients.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Integration of artificial intelligence in lung cancer: Rise of the machine. Ladbury, C., Amini, A., Govindarajan, A., et al.[2023]

Deep learning, a form of artificial intelligence, is being explored as a powerful tool for diagnosing and researching lung cancer by mimicking human brain processes to analyze malignant cells.

This review highlights the current advancements and challenges in using deep learning for lung cancer pathology, suggesting it could significantly improve diagnosis and treatment strategies in the future.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Deep Learning Model as a New Trend in Computer-aided Diagnosis of Tumor Pathology for Lung Cancer. Cong, L., Feng, W., Yao, Z., et al.[2020]

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