Digital illustration comparing a traditional x-ray lung image with a digitally-enhanced similar subtraction lung image.

Second Opinion, Same Image: How 'Similar Subtraction' Could Change Lung Nodule Detection

Nico Varela in Health & Wellness July 2025 • 4 min read.

"When a past X-ray isn't available, a new technique using images from other patients shows promise in spotting lung nodules."

Catching respiratory diseases early is crucial, and digital chest X-rays (CXRs) are a key tool. They're quick, cost-effective, and expose patients to less radiation than CT scans. But spotting subtle abnormalities on a CXR can be challenging. That's where techniques like temporal subtraction (TS) come in, which compares current and past images to highlight changes.

However, TS requires previous CXRs, which aren't always available. To address this, researchers have developed 'similar subtraction' (SS). Instead of relying on a patient's past images, SS uses CXRs from other patients with similar lung shapes. The question is: how well does it work compared to traditional methods?

A new study published in Radiological Physics and Technology dives into this question. Researchers compared SS images with TS images to see how well each technique could depict simulated lung nodules. This article breaks down their findings and what it could mean for the future of lung disease detection.

Similar Subtraction vs. Temporal Subtraction: A Head-to-Head Comparison

Digital illustration comparing a traditional x-ray lung image with a digitally-enhanced similar subtraction lung image.

The study involved 100 patients. The most recent CXR for each patient was selected and a simulated nodule was added to mimic the presence of a potential lung abnormality. The goal was to see how well the SS technique could reveal this nodule, especially when compared to the TS method.

For the SS technique, researchers used a database of over 24,000 anonymized CXRs to find the most similar images to each patient's target image. Similarity was determined using a template-matching technique focused on the shape of the ribs. The most similar image (Top 1) and the ten most similar images (Top 10) were then used to create SS images.

Temporal Subtraction (TS): This traditional method subtracted a previous CXR from the current one. Two intervals were used: 2-year and 7-year gaps between the images.
Similar Subtraction (SS): This newer technique subtracted a 'similar' CXR (from a different patient) from the current one. The 'Top 1' most similar image and the 'Top 10' most similar images were tested.
Image Processing: A non-linear image-warping technique was applied in both TS and SS to enhance alignment and reduce artifacts.

The effectiveness of each technique was measured using the contrast-to-noise ratio (CNR). CNR assesses how well the nodule stands out from the background 'noise' in the image. A higher CNR means the nodule is easier to see.

The Verdict: Can 'Similar Subtraction' Replace Past X-Rays?

The study revealed promising results for similar subtraction. In a portion of cases, the Top 1 SS images showed a higher CNR than the TS images. Specifically, 28% of Top 1 SS images outperformed TS images with a 2-year interval, and 33% outperformed TS images with a 7-year interval. Furthermore, when considering the Top 10 SS images, these percentages jumped to 56% and 72%, respectively.

This suggests that in a significant number of cases, SS can provide comparable or even better nodule depiction than TS, especially when long intervals exist between a patient’s current and previous CXRs.

While the study indicates the potential of SS, the researchers emphasize that it shouldn't replace traditional diagnostic methods. Instead, SS could serve as a valuable supplementary tool, increasing confidence in diagnoses and guiding decisions about further examinations, such as CT scans. Further research is needed to explore different types of nodules, and varied image qualities to fine-tune the SS technique and fully understand its capabilities.

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: 10.1007/s12194-018-0489-7, Alternate LINK

Title: Evaluation Of The Depiction Ability Of Similar Subtraction Images Using Digital Chest Radiographs Of Different Patients

Subject: Radiology, Nuclear Medicine and imaging

Journal: Radiological Physics and Technology

Publisher: Springer Science and Business Media LLC

Authors: Yoichiro Shimizu, Junji Morishita, Yusuke Matsunobu, Yongsu Yoon, Yasuo Sasaki, Shigehiko Katsuragawa, Hidetake Yabuuchi

Published: 2018-11-20

Everything You Need To Know

What are the primary methods used for detecting lung nodules in the context?

Digital chest X-rays (CXRs) are a key tool for early detection of respiratory diseases. They are quick, cost-effective, and expose patients to less radiation than CT scans. However, spotting subtle abnormalities on a CXR can be challenging. Temporal subtraction (TS) is a technique that compares current and past images to highlight changes, but it requires previous CXRs, which aren't always available.

How does Temporal Subtraction work, and what are its limitations?

Temporal Subtraction (TS) is a traditional method for detecting lung nodules. It works by subtracting a previous CXR from the current one. This highlights any changes that may indicate a lung nodule. The study used 2-year and 7-year intervals between the images. The main limitation of TS is its reliance on previous CXRs; it cannot be used if prior images are unavailable.

How does Similar Subtraction work, and what data does it use?

Similar Subtraction (SS) is a newer technique that addresses the limitations of Temporal Subtraction (TS). Instead of relying on a patient's past images, SS uses CXRs from other patients with similar lung shapes. The researchers used a database of over 24,000 anonymized CXRs to find the most similar images to each patient's target image. The similarity was determined using a template-matching technique focused on the shape of the ribs. The 'Top 1' most similar image and the 'Top 10' most similar images were then used to create SS images.

How did the study compare Similar Subtraction and Temporal Subtraction?

The study compared the effectiveness of Similar Subtraction (SS) and Temporal Subtraction (TS) techniques in detecting simulated lung nodules. The effectiveness was measured using the contrast-to-noise ratio (CNR). A higher CNR means the nodule is easier to see. The results showed that in a portion of cases, the Top 1 SS images showed a higher CNR than the TS images. Specifically, 28% of Top 1 SS images outperformed TS images with a 2-year interval, and 33% outperformed TS images with a 7-year interval. Furthermore, when considering the Top 10 SS images, these percentages jumped to 56% and 72%, respectively.

What is the significance of Similar Subtraction for the future of lung disease detection?

The implications of Similar Subtraction (SS) are significant. SS could improve early detection of lung nodules, especially when a patient's previous images aren't available. This could lead to earlier diagnoses and potentially better outcomes for patients with lung diseases. The study's findings are promising and suggest that SS could be a valuable tool in the future of lung disease detection. Non-linear image-warping technique was applied in both TS and SS to enhance alignment and reduce artifacts.