Journal of Shanghai Jiao Tong University (Medical Science) >

2022 , Vol. 42 >Issue 8: 1062 - 1069

DOI: https://doi.org/10.3969/j.issn.1674-8115.2022.08.011

Clinical research

Feasibility of ultra-low-dose noncontrast CT based on deep learning image reconstruction to evaluate chest lesions

  • Keke ZHAO ,
  • Beibei JIANG ,
  • Lu ZHANG ,
  • Lingyun WANG ,
  • Yaping ZHANG ,
  • Xueqian XIE
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  • Department of Radiology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200080, China
XIE Xueqian, E-mail: xiexueqian@hotmail.com.

Received date: 2022-04-17

  Accepted date: 2022-07-25

  Online published: 2022-10-08

Supported by

National Natural Science Foundation of China(81971612);International Scientific Alliance Program of Ministry of Science and Technology of China(2016YFE0103000);Shanghai Municipal Education Commission—Gaofeng Clinical Medicine Grant Support(20181814)

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Abstract

Objective ·To explore the feasibility of using noncontrast ultra-low-dose CT (ULDCT) to evaluate chest target lesions based on response evaluation criteria in solid tumors (RECIST) and ground glass nodules (GGNs). Methods ·From April to June 2020, patients who underwent noncontrast chest ULDCT (0.07?0.14 mSv) and low-dose enhanced CT (2.38 mSv) and had measurable target lesions defined by RECIST and GGNs ≤1 cm in diameter were included. Four sets of CT images were reconstructed for each patient, including 3 sets of ULDCT images, i.e., adaptive statistical iterative reconstruction-V with an 80% strength level (ASIR-V-80%), deep learning image reconstruction of moderate strength (DLIR-M) and deep learning image reconstruction of high strength (DLIR-H), and one set of enhanced CT images as the reference. Results ·Eighty patients who had 80 target lesions and 27 GGNs met the inclusion criteria, and the average age was (62±11) years old. Between the ULDCT images (3 sets of image reconstruction) and enhanced CT, the measured values of target lesions (r=0.988, 0.987 and 0.990, respectively), GGNs≤1 cm in diameter (r=0.905, 0.906 and 0.969, respectively), mediastinal lymph node target lesions (r=0.969, 0.957 and 0.977, respectively), and hilar lymph node target lesions (r=0.972, 0.994 and 0.994, respectively) were highly correlated. Bland-Altman analysis showed that the difference between the measured size of lung target lesion and reference value in DLIR-H reconstruction image was 4.3% (95% limits of agreement: -5.7%?14.3%), and the difference between the measured size of mediastinal lymph node target lesion and reference value was 5.1% (-9.1%?19.3%), which was better than that of ASIR-V-80% [8.5% (-3.3%?20.3%), 9.7% (-6.0%?25.3%)] and DLIR-M [8.5% (-4.2%?21.3%), 8.8% (-9.9%?27.5%)]. The difference between the measured size of lymph node target lesions in DLIR-H images and the reference value was 18.3% (8.8%?27.9%), better than ASIR-V-80% [20.2% (-1.2%?41.5%)] and DLIR-M [23.4% (13.5%?33.2%)]. The difference between the measured size of GGNs in DLIR-H images and the reference value was 7.0% (-5.7%?19.7%), better than ASIR-V-80% [14.4% (-4.4%?33.2%)] and DLIR-M [16.3% (-4.1%?36.7%)]. Conclusion ·Noncontrast ULDCT based on DLIR-H is highly correlated and consistent with the traditional enhanced CT in evaluating chest target lesions and GGNs (≤1 cm in diameter), which is conducive to repeated scanning of tumors and GGNs at a significantly reduced radiation dose.

Key words： lung cancer; RECIST; deep learning; ultra-low-dose CT

Cite this article

Keke ZHAO , Beibei JIANG , Lu ZHANG , Lingyun WANG , Yaping ZHANG , Xueqian XIE . Feasibility of ultra-low-dose noncontrast CT based on deep learning image reconstruction to evaluate chest lesions[J]. Journal of Shanghai Jiao Tong University (Medical Science), 2022 , 42(8) : 1062 -1069 . DOI: 10.3969/j.issn.1674-8115.2022.08.011

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