Evidence map›Paper›PMID 42878810›Full record

ArticleEuropean radiology2026

Low-dose vs standard-dose photon-counting CT for myopathy-associated interstitial lung disease: a prospective feasibility study.

Zhenchen Zhu, Kepei Xu, Yiyun Pang, Ge Hu, Luc Nicodème, Johannes Hofmanninger, Wei Han, Sebastian Röhrich, Georg Langs, Jeanny Pan and 9 more

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Article in European radiology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

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0 citing papers in PubMed.

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4 · The record

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PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

19 authors.

Zhenchen Zhu *Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Kepei Xu *Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Yiyun Pang *State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Beijing, China.
Ge HuDepartment of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Luc NicodèmeDepartment of Medical AI Solutions, Contextflow GmbH, Vienna, Austria.
Johannes HofmanningerDepartment of Medical AI Solutions, Contextflow GmbH, Vienna, Austria.
Wei HanDepartment of Evidence-based Medicine, Xuanwu Hospital, Capital Medical University, Beijing, China.
Sebastian RöhrichDepartment of Medical AI Solutions, Contextflow GmbH, Vienna, Austria.
Georg LangsDepartment of Medical AI Solutions, Contextflow GmbH, Vienna, Austria.
Jeanny PanComputational Imaging Research Lab, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria.
Mengzhe LyuSiemens Healthineers Digital Technology (Shanghai) Co. Ltd., Shanghai, China.
Xuemei ZhaoDepartment of Radiology, The First People's Hospital of Yinchuan, The Second Affiliated Hospital of Ningxia Medical School, Yinchuan, China.
Wei GuoDepartment of Radiology, The first Affiliated Hospital of Henan University of Traditional Chinese Medicine, Zhengzhou, China.
Ying-Qian GeSiemens Healthineers Digital Technology (Shanghai) Co. Ltd., Shanghai, China.
Mengtao LiState Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Beijing, China.
Zhengyu JinDepartment of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Lan SongDepartment of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China. songl@pumch.cn.ORCID http://orcid.org/0000-0001-6598-6964
Shuang ZhouState Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Beijing, China. doctorcat18@163.com.
Qian WangState Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Beijing, China.

Funding

Beijing Municipal Science and Technology Planning Project Z211100003521009CAMS Innovation Fund for Medical Sciences 2023-I2M-C&T-B-035, 2023-I2M-2-005Chinese National Key Technology R&D Program, Ministry of Science and Technology 2024YFC2510300Peking Union Medical College Hospital Talent Cultivation Program Category C UBJ10148the National Natural Science Foundation of China NSFC No. 82171934
6 · The paper itself

Abstract

objectivesTo evaluate the clinical utility of photon counting computed tomography (PCCT)-derived quantitative parameters for assessing interstitial lung disease (ILD) burden in patients with inflammatory idiopathic myopathy (IIM), comparing standard-dose (SD) and tin-filtered low-dose (LD) protocols, and to assess the added value of ultrathin reconstructions. MATERIALS AND

methodsIn this single-centre prospective study (September 2023-November 2024), outpatients with IIM-ILD underwent two consecutive PCCT scans. Quantitative ILD features were extracted using a commercial deep-learning software (ContextFlow ADVANCE Chest CT). Primary analyses compared LD 1.0 mm with SD 1.0 mm reconstructions using Bland-Altman analysis, with exploratory analyses of ultrathin (0.4 mm) reconstructions. Correlation analyses between deep learning-derived quantitative parameters, visual assessment, and pulmonary function tests were performed.

resultsForty-eight adults with IIM-ILD (40 females; mean age, 53.88 ± 11.13 years) were included. The LD protocol achieved a substantial dose reduction (CTDIvol: 0.77 vs 5.40 mGy) while maintaining good agreement with SD (mean net differences [MNDs] < 1% for all patterns). LD-1.0 mm protocol yielded higher volume of ground-glass opacity (MND, 0.98%) and lower volume of fibrosis extent (MND, -0.64%). Reticular pattern showed strong association with FVC% (rho

conclusionLD PCCT serves as a radiation-sparing alternative for quantitative disease assessment. Ultrathin reconstructions provide limited additional value. Future longitudinal studies are warranted to validate the performance of LD PCCT for disease monitoring. KEY POINTS: Question Can LD photon-counting CT provide robust quantitative assessment of ILD burden in patients with inflammatory myopathy compared with SD imaging? Findings Deep learning segmentation yielded LD ILD patterns that closely matched SD results, with all MNDs below 1%. Clinical relevance LD PCCT maintains quantitative robustness and physiological validity for IIM-ILD assessment, with total disease burden showing good agreement with the SD protocol, offering a radiation-sparing alternative without compromising CT-physiology correlation.

Indexed as

Artificial intelligenceDeep learningLung diseases, InterstitialMyositisTomography (X-ray computed)

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.