Radiomic lung analysis showing tumor aggressiveness prediction.

Decoding Lung Adenocarcinoma: How Imaging Radiomics Can Predict Tumor Aggressiveness

Mira Elwood in Health & Wellness November 2025 • 4 min read.

"Harnessing the power of dual-energy CT scans and radiomics to improve early-stage lung cancer treatment and patient outcomes."

Lung cancer remains a significant health challenge, with non-small cell lung cancer (NSCLC) accounting for approximately 85% of all lung cancer cases. Adenocarcinoma, a prominent subtype of NSCLC, presents a particularly complex challenge due to its varied behavior and prognosis, even in its early stages. While some patients experience favorable outcomes, others face unexpectedly low survival rates, highlighting the need for more precise methods of assessing and treating this disease.

Traditionally, assessing the aggressiveness of lung adenocarcinoma has relied on analyzing tumor samples obtained through invasive procedures. These methods, while valuable, have limitations. Biopsy samples may not fully represent the entire tumor, which can exhibit considerable heterogeneity. This is where radiomics comes in, offering a non-invasive way to gain a more comprehensive understanding of the tumor's characteristics.

Radiomics involves extracting a large number of quantitative features from medical images, such as CT scans. These features, often invisible to the naked eye, can provide valuable insights into the tumor's texture, shape, and overall composition. By analyzing these features, researchers aim to develop predictive models that can accurately assess tumor aggressiveness and guide treatment decisions.

Radiomics: A New Frontier in Lung Cancer Assessment

Radiomic lung analysis showing tumor aggressiveness prediction.

A recent study published in Oncotarget delved into the potential of radiomics in improving the stratification of operable lung adenocarcinoma. The researchers focused on radiomics features extracted from dual-energy computed tomography (DECT) images. DECT is an advanced imaging technique that provides additional information compared to conventional CT scans, allowing for a more detailed analysis of the tumor's composition.

The study involved 80 patients with clinically and radiologically suspected stage I or II lung adenocarcinoma. All patients underwent DECT and F-18-fluorodeoxyglucose (FDG) positron emission tomography (PET)/CT, followed by surgery. The researchers then used a radiomics approach to evaluate quantitative CT and PET imaging characteristics, aiming to identify features that could predict the aggressiveness of the tumors.

The study's findings revealed several key insights:

Pathologic grade, a measure of tumor aggressiveness, was divided into three grades: 1, 2, and 3.
Multinomial logistic regression analysis identified i-uniformity and the 97.5th percentile CT attenuation value as independent factors significantly stratifying grade 2 or 3 from grade 1.
The area under the curve (AUC) values, calculated from leave-one-out cross-validation, demonstrated the model's accuracy in discriminating between the grades: 0.9307 (95% CI: 0.8514–1) for grades 1, 0.8610 (95% CI: 0.7547-0.9672) for grades 2, and 0.8394 (95% CI: 0.7045-0.9743) for grades 3.

These results suggest that quantitative radiomics values derived from DECT imaging metrics can indeed help predict the pathologic aggressiveness of lung adenocarcinoma. This is a significant step forward in personalized medicine, offering the potential to tailor treatment strategies based on a more accurate assessment of the individual tumor's characteristics.

The Future of Lung Cancer Treatment

While this study offers promising results, the researchers acknowledge certain limitations. The relatively small sample size and the single-center design necessitate further validation with larger, multi-center studies. Additionally, future research should explore the incorporation of other factors, such as genetic and molecular markers, to further refine the predictive models. The potential of radiomics to revolutionize lung cancer treatment is undeniable. By harnessing the power of advanced imaging and sophisticated data analysis, we can move closer to a future where treatment is tailored to the individual patient, leading to improved outcomes and survival rates.

About this Article -

This article was crafted using a human-AI hybrid and collaborative approach. AI assisted our team with initial drafting, research insights, identifying key questions, and image generation. Our human editors guided topic selection, defined the angle, structured the content, ensured factual accuracy and relevance, refined the tone, and conducted thorough editing to deliver helpful, high-quality information.See our About page for more information.

This article is based on research published under:

DOI-LINK: 10.18632/oncotarget.13476, Alternate LINK

Title: Pathologic Stratification Of Operable Lung Adenocarcinoma Using Radiomics Features Extracted From Dual Energy Ct Images

Subject: Oncology

Journal: Oncotarget

Publisher: Impact Journals, LLC

Authors: Jung Min Bae, Ji Yun Jeong, Ho Yun Lee, Insuk Sohn, Hye Seung Kim, Ji Ye Son, O Jung Kwon, Joon Young Choi, Kyung Soo Lee, Young Mog Shim

Published: 2016-11-21

Everything You Need To Know

What is Radiomics and how does it apply to assessing Lung Adenocarcinoma?

Radiomics extracts quantitative features from medical images like CT scans to analyze a tumor's texture, shape, and composition. This non-invasive method provides insights that can help predict tumor aggressiveness. The advantage of radiomics is its capacity to offer a comprehensive understanding of a tumor's characteristics, potentially overcoming the limitations of traditional biopsy methods that may not fully represent the entire tumor due to heterogeneity.

How does dual-energy computed tomography (DECT) improve the process of identifying and evaluating lung tumors?

Dual-energy computed tomography (DECT) enhances conventional CT scans by providing additional information about a tumor's composition. By using DECT, doctors gain a more detailed analysis, which is critical for extracting meaningful radiomics features. When combined with F-18-fluorodeoxyglucose (FDG) positron emission tomography (PET)/CT, it provides a comprehensive imaging approach for evaluating lung adenocarcinoma.

What key factors help determine the aggressiveness of tumors based on the study?

In the study, pathologic grade, which indicates tumor aggressiveness, was categorized into three grades: 1, 2, and 3. Statistical analysis identified 'i-uniformity' and the '97.5th percentile CT attenuation value' as independent factors that could significantly differentiate between grade 2 or 3 tumors from grade 1 tumors. The area under the curve (AUC) values indicated the model's accuracy in distinguishing between these grades.

How accurate are the predictions of tumor aggressiveness using radiomics in the study?

The study achieved AUC values of 0.9307 (95% CI: 0.8514–1) for grade 1, 0.8610 (95% CI: 0.7547-0.9672) for grade 2, and 0.8394 (95% CI: 0.7045-0.9743) for grade 3. These AUC values suggest a high degree of accuracy in using radiomics, derived from dual-energy CT imaging, to predict the aggressiveness of lung adenocarcinomas. AUC measures the ability of a test to discriminate between two groups, with a higher AUC indicating better performance.

What are the limitations of using radiomics to predict the aggressiveness of lung adenocarcinoma, and what future research is needed?

While the study demonstrates the potential of radiomics in predicting lung adenocarcinoma aggressiveness, it's important to consider its limitations. The study had a relatively small sample size and was conducted at a single center, necessitating validation through larger, multi-center studies. Future research should incorporate genetic and molecular markers alongside radiomics data to refine predictive models further, potentially enhancing the precision of personalized treatment strategies.