We use cookies to understand how you use our site and to improve your experience. This includes personalizing content and advertising. To learn more, click here. By continuing to use our site, you accept our use of cookies. Cookie Policy.

Features Partner Sites Information LinkXpress
Sign In
Advertise with Us

Download Mobile App

AI System Combines CT Imaging with Clinical and Genetic Data for Early Lung Cancer Detection

By MedImaging International staff writers
Posted on 20 Feb 2024
Print article
Image: A new study suggests CT imaging with automated AI system can predict EGFR genotype (Photo courtesy of 123RF)
Image: A new study suggests CT imaging with automated AI system can predict EGFR genotype (Photo courtesy of 123RF)

Lung carcinoma prognosis has evolved significantly with the discovery of molecular targets and their corresponding treatments. Specifically, mutations in the Epidermal Growth Factor Receptor (EGFR) gene, found in lung carcinoma, serve as key targets for specialized therapies. However, in countries with limited resources like India, advanced testing methods such as next-generation sequencing remain inaccessible for widespread use. Challenges also include obtaining sufficient tissue from lung core biopsies and dealing with the inherent intratumoral heterogeneity that complicates the identification of suitable tumor tissues. Now, researchers have demonstrated that an AI-based system can automatically detect and analyze lung nodule features from CT images, predicting the likelihood of EGFR mutations. This innovation aids oncologists and patients in resource-limited settings by providing near-optimal care and guiding appropriate treatment decisions.

Previous studies leveraging AI with CT imaging have shown promise in categorizing and analyzing lung nodules without incurring additional costs. However, most of these methods have focused solely on nodule detection in CT images. Moreover, while AI has been used to extract comprehensive lung information for predicting EGFR genotype and evaluating responses to targeted lung cancer therapy, such efforts have predominantly been centered on White and Chinese populations. With a primary focus on the Indian population, researchers led by the Rajiv Gandhi Cancer Institute and Research Centre (New Delhi, India) set out to develop an AI-based strategy that could not only detect but also characterize lung nodules, indicating the EGFR mutational status in lung carcinoma patients. This would help triage patients requiring extensive molecular profiling of the EGFR-driver gene.

The team created a fully automated AI-based Predictive System (AIPS) using machine learning (ML) and deep learning (DL) algorithms. This system can detect lung nodule features from CT images and assess the probability of an EGFR mutation, thus eliminating the need for time-consuming image annotation by radiologists and complex feature engineering. In addition to incorporating EGFR gene sequencing and CT imaging data from 2277 lung carcinoma patients across three cohorts in India and a White population cohort from TCIA, the researchers used the LIDC-IDRI cohort to train the AIPS-Nodule (AIPS-N) model. This model automatically detects and characterizes lung nodules. The AIPS-N model's combination with clinical factors in the AIPS-Mutation (AIPS-M) model was evaluated for its effectiveness in predicting the EGFR genotype, achieving area under the curve (AUC) values ranging from 0.587 to 0.910. The AIPS-N successfully detected nodules with an average AP50 of 70.19% and predicted scores for five lung nodule properties. This research suggests that CT imaging combined with an automated lung-nodule analysis AI system can non-invasively and cost-effectively predict EGFR genotype, identifying patients with EGFR mutations.

Related Links:
Rajiv Gandhi Cancer Institute and Research Centre

Gold Member
Solid State Kv/Dose Multi-Sensor
Pre-Op Planning Solution
Sectra 3D Trauma
Computed Tomography (CT) Scanner
Aquilion Serve SP
Silver Member
Mobile X-Ray Barrier
Lead Acrylic Mobile X-Ray Barriers

Print article



view channel
Image: The emerging role of MRI alongside PSA testing is redefining prostate cancer diagnostics (Photo courtesy of 123RF)

Combining MRI with PSA Testing Improves Clinical Outcomes for Prostate Cancer Patients

Prostate cancer is a leading health concern globally, consistently being one of the most common types of cancer among men and a major cause of cancer-related deaths. In the United States, it is the most... Read more

Nuclear Medicine

view channel
Image: The new SPECT/CT technique demonstrated impressive biomarker identification (Journal of Nuclear Medicine: doi.org/10.2967/jnumed.123.267189)

New SPECT/CT Technique Could Change Imaging Practices and Increase Patient Access

The development of lead-212 (212Pb)-PSMA–based targeted alpha therapy (TAT) is garnering significant interest in treating patients with metastatic castration-resistant prostate cancer. The imaging of 212Pb,... Read more

Imaging IT

view channel
Image: The new Medical Imaging Suite makes healthcare imaging data more accessible, interoperable and useful (Photo courtesy of Google Cloud)

New Google Cloud Medical Imaging Suite Makes Imaging Healthcare Data More Accessible

Medical imaging is a critical tool used to diagnose patients, and there are billions of medical images scanned globally each year. Imaging data accounts for about 90% of all healthcare data1 and, until... Read more
Copyright © 2000-2024 Globetech Media. All rights reserved.