Can photon-counting detector computed tomography (PCD-CT) and automated Lung Texture Analysis enhance the detection of interstitial lung disease (ILD) at reduced dosing in patients with systemic sclerosis (SSc)?
For a new prospective study, recently published in the European Journal of Radiology, researchers compared the combination PCD-CT) and Lung Texture Analysis (LTA) (Imbio/4DMedical) with the combination of energy-integrating detector CT (EID-CT) and LTA. The propensity-matched SSc cohort was comprised of 93 patients assessed with PCD-CT and 93 patients evaluated with EID-CT, according to the study.1
In comparison to EID-CT and LTA, the study authors found that PCD-CT and LTA offered a higher AUC for detecting ILD (84.6 percent vs. 77.2 percent) with a 70 percent lower mean CT dose index (0.6981 mGy vs. 2.2971 mGy).1
The researchers also noted that PCD-CT and LTA achieved higher AUCs than the combination of EID-CT and LTA for detecting ground-glass opacities (83 percent vs. 81.8 percent), reticulation (84.6 percent vs. 74.3 percent) and honeycombing (88.2 percent vs. 73.3 percent).1
“In this post-hoc analysis of prospectively collected data of SSc patients, LTA on PCD-CT scans demonstrated consistently excellent AUC results for the presence and extent of ILD, as well as for all investigated ILD features,” wrote lead study author Jasmin Happe, M.D., who is affiliated with the Diagnostic and Interventional Radiology Department at University Hospital Zurich in Zurich, Switzerland, and colleagues.
Three Key Takeaways
• PCD-CT plus Lung Texture Analysis improves ILD detection at much lower dose. The combination of PCD-CT and LTA achieved a higher AUC for detecting ILD than EID-CT with LTA (84.6 percent vs. 77.2 percent) while using approximately 70 percent less radiation dose.
• PCD-CT and LTA show stronger performance for key ILD features. Compared to EID-CT with LTA, the PCD-CT combination yielded higher AUCs for ground-glass opacities, reticulation, and honeycombing, reflecting enhanced sensitivity for hallmark ILD patterns in systemic sclerosis.
• EID-CT with LTA may better identify extensive ILD. Despite the overall advantages of PCD-CT, the study found that EID-CT with LTA performed better in detecting extensive ILD (>20 percent lung involvement). The authors note that this may relate to differences in cohort characteristics and the fact that LTA tools were originally trained on EID-CT data.
However, the researchers also found that the combination of EID-CT and LTA offered better detection of extensive ILD (> 20 percent lung involvement) in comparison to PCD-CT and LTA (97 percent vs. 84.2 percent), a finding which contrasted with results from a similar study published earlier this year.1,2
“This might be due to differences in study populations as the present cohort consisted exclusively of patients with more homogeneous disease characteristics and ILD features. … In addition, it has to be considered that the differing distributions of the study cohorts, along with the potential influence of LTA originally trained on EID-CT data, may have contributed to the discrepancies observed in the present study,” posited Happe and colleagues.
(Editor’s note: For related content, see “Emerging Advances in Photon-Counting Computed Tomography,” “Photon-Counting Computed Tomography: Eleven Takeaways from a New Literature Review” and “Study Shows Merits of CT Vascular Sign for Differentiating Solid Pulmonary Nodules.”)
In regard to study limitations, the authors acknowledged the indirect comparison of PCD-CT and EID-CT cohorts, the use of visual assessment by experienced thoracic radiologists in the reference standard for LTA-derived parameters and the use of low-dose imaging protocols.
References
- Happe J, Bruni C, Jungblut L, et al. Automated lung texture analysis for assessing interstitial lung disease in systemic sclerosis: diagnostic accuracy in photon-counting-detector and conventional energy-integrating-detector CT. Available at: https://www.ejradiology.com/article/S0720-048X(25)00691-6/fulltext . Published December 9, 2025. Accessed December 11, 2025.
2. Koo CW, Huis SJ, Baffour F, et al. Impact of photon-counting detector computed tomography on a quantitative interstitial lung disease machine learning model. J Thorac Imaging. 2025;40(3):e0807. doi: 10.1097/RTI.0000000000000807.
