Efficacy of zero echo time pulmonary MRI for RECIST 1.1 response classification in lung cancer: a prospective study

Introduction

Stage III and IV lung cancer patients often require multimodal comprehensive therapy (including radiochemotherapy, targeted therapy, and immunotherapy), with treatment cycles often extending for years (1,2). Regular imaging follow-up after treatment serves as the core basis for monitoring tumor response, assessing disease progression, and adjusting treatment strategies, directly influencing patient survival and prognosis (3-5). In current clinical practice, thin-slice computed tomography (CT), with its millimeter-level spatial resolution and clear visualization of fine pulmonary structures, remains the primary modality for evaluating lung lesion size, density changes, and detecting new metastatic foci (6). However, CT relies on ionizing radiation, and cumulative radiation doses—especially for patients with long-term survival or those receiving multiple lines of therapy—may increase the risk of hematopoietic system damage, immune impairment, and even secondary cancer (7,8). Therefore, radiation-free and efficient lung imaging techniques have become an urgent need in the field of precision oncology for lung cancer.

In recent years, the emergence of ultrashort echo time (UTE) and zero echo time (ZTE) magnetic resonance imaging (MRI) techniques has offered innovative solutions to these challenges. These technologies effectively capture signals from short T2 tissues, which are difficult to visualize with conventional sequences. Solid pulmonary tumors typically exhibit high cellular density, a high nucleus-to-cytoplasm ratio, and restricted extracellular space. These alterations in the microenvironment exacerbate spin-spin dephasing among protons, leading to a significant shortening of their T2 relaxation time. ZTE sequence effectively captures signals from such short-T2 tissues by utilizing an echo time (TE) that approaches zero (9-11). Additionally, ZTE’s unique gradient echo sequence design—combined with motion correction algorithms or breath-hold scanning—suppresses respiratory and cardiovascular pulsation artifacts, enabling high-contrast imaging of lung parenchyma and airway structures (11,12). Preliminary studies have shown that UTE and ZTE techniques perform comparably to thin-slice CT in Lung CT Screening Reporting and Data System (Lung-RADS) classification of pulmonary nodules (13,14). ZTE also demonstrates equivalent performance to CT in depicting morphological features of solid lung lesions larger than 1 cm. However, for small nodules dominated by pure ground-glass opacity, ZTE’s sensitivity may still be inferior to high-resolution CT due to their sparse tissue and extremely low proton density (15). The Response Evaluation Criteria in Solid Tumors (RECIST) 1.1 criteria, one of the most commonly used standards for lung cancer follow-up, provide clear and standardized evaluation frameworks (16,17). However, existing UTE/ZTE research has predominantly focused on pulmonary nodule screening (e.g., Lung-RADS classification), with no systematic assessment of ZTE’s value in lung cancer response monitoring based on RECIST 1.1 criteria. This study is the first to incorporate free-breathing ZTE and breath-hold ZTE into the evaluation system for treatment response in lung cancer. Although low-dose CT is widely used for patient follow-up and significantly reduces cumulative radiation risk, ZTE MRI completely avoids ionizing radiation, and for lung cancer patients who may become long-term survivors or receive multiple lines of therapy, the cumulative radiation risk remains a concern. Moreover, ZTE-MRI offers the potential for multiparametric imaging. By incorporating additional MRI sequences such as diffusion-weighted imaging (DWI), intravoxel incoherent motion (IVIM), and amide proton transfer (APT), it can provide functional information beyond morphology, aiding in the assessment of treatment response, prediction of EGFR expression, prognosis, and other aspects (5,18).

Therefore, this study aims to evaluate the clinical efficacy of free-breathing ZTE and breath-hold ZTE techniques in post-treatment follow-up of stage III/IV lung cancer based on RECIST 1.1 criteria. Through comparative analysis with thin-slice CT, this study seeks to clarify their value in measuring the maximum diameter of pulmonary target lesions, the sum of maximum diameters, new lesion detection, and response classification. We present this article in accordance with the STARD reporting checklist (available at https://qims.amegroups.com/article/view/10.21037/qims-2025-1982/rc).

Methods

Patients

The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. The study was approved by the Ethics Committee of The First Affiliated Hospital of Xinxiang Medical University (No. EC-024-222), and all participants have provided written informed consent. Between March 2024 and February 2025, 60 participants were recruited at The First Affiliated Hospital of Xinxiang Medical University. Inclusion criteria included: (I) patients with pathologically confirmed stage III/IV lung cancer who received non-surgical treatment; (II) patients who underwent at least one thoracic CT scan at our institution 2–18 months prior, with the most recent CT showing pulmonary target lesions ≥10 mm; (III) all consenting patients who underwent follow-up thin-slice thoracic CT within ≤48 h of free-breathing ZTE and breath-hold ZTE examinations. Exclusion criteria were as follows: (I) age <18 years; (II) pregnancy or lactation; (III) MRI contraindications (e.g., pacemakers, ferromagnetic implants) (n=3); (IV) concomitant atelectasis/acute inflammation affecting lesion measurement (n=4); (V) incomplete ZTE sequences (scan interruption due to inability to continue lying supine or difficulty in breath-holding during breath-hold ZTE acquisition) or image artifacts (motion artifacts) covering >50% of the lesion area (n=6, including 2 cases for free-breathing ZTE and 4 cases for breath-hold ZTE). After applying these criteria, 47 patients participated in the study (Figure 1).

Figure 1 Flow diagram of the patient selection process. CT, computed tomography; MRI, magnetic resonance imaging; ZTE, zero echo time.

Radiologic examination

Chest CT scans were performed using a 256-slice CT system (Revolution, GE Healthcare), with the scanning range extending from the apex of the lung to the level of the diaphragm. The CT scanning parameters were as follows: tube voltage of 120 kV; automatic tube current; pitch of 1.388; three-dimensional (3D) field of view (FOV) of 512×512×350 mm³; matrix size of 512×512×280; reconstruction slice thickness of 1.25 mm; voxel size of 1.00×1.00×1.25 mm³; using the adaptive statistical iterative reconstruction-veo (ASiR-V) algorithm; and a scan time of approximately 1 s.

MRI examinations were performed using a 3.0T scanner (SIGNA Pioneer, GE Healthcare) with a 30-channel body coil. ZTE MRI sequence based on fast gradient echo adopted a 3D slice excitation and radial centripetal acquisition mode. Free-breathing ZTE parameters were as follows: 3D FOV 400×400×224 mm³; matrix size 512×512×280; slice thickness 1.6 mm; voxel size 1.6×1.6×1.6 mm³; bandwidth 31.25 kHz; flip angle 3°; repetition time (TR) 0.9 ms; TE 0.02 ms; dead time gap 0.02 ms; k-space center filling was performed using a model-based iterative algorithm for data completion; image reconstruction employed a gridding algorithm and excitation profile correction. The number of radial spokes per segment was 280; number of excitations 3.00; scan time 455 s; respiratory triggering was used. Breath-hold ZTE parameters were as follows: FOV 400×400×224 mm³; matrix size 512×512×74; slice thickness 3.1 mm; voxel size 3.1×3.1×3.1 mm³; bandwidth 62.50 kHz; flip angle 3°; TR 1.25 ms; TE 0.02 ms; dead time gap 0.02 ms; the number of radial spokes per segment was 74; number of excitations 1.50; scan time 18 s. Image reconstruction employed a gridding algorithm and excitation profile correction.

Image analysis

All images were reviewed and analyzed in our institution’s picture archiving and communication system (PACS). Two radiologists with 9 and 10 years of experience in thoracic radiodiagnosis, respectively, independently reviewed all CT and ZTE images.

The maximum diameter of target lesions was measured as the long-axis diameter on the largest axial slice. For multiple pulmonary lesions, the two lesions with the maximum diameters were selected as target lesions (16,19). If a target lesion was not visualized, its maximum diameter was recorded as 0 mm. The average maximum diameter measured by the two radiologists was used as the final value, and the sum of maximum diameters was calculated as the aggregate of the final maximum diameter values of target lesions. Detection of new lesions was independently assessed by the two radiologists; in case of disagreement, a third radiologist with 20 years of experience in thoracic radiodiagnosis was consulted for adjudication.

Efficacy classification was performed according to the RECIST 1.1 as follows: complete response (CR) = disappearance of all target lesions; partial response (PR) = ≥30% reduction in the sum of maximum diameters; progressive disease (PD) = ≥20% increase in the sum of maximum diameters (with an absolute increase ≥5 mm) or appearance of new lesions; stable disease (SD) = changes not meeting the above criteria (16,17,19).

Statistical analysis

Statistical analysis was performed using SPSS software (IBM Corporation, Version 23.0), with statistical significance defined as P<0.05. The percentage of concordant cases was used to evaluate the efficacy of ZTE in detecting new lesions, while the weighted kappa coefficient was applied for intermodality pairwise agreement analysis of response classification. Intermodality and interobserver agreements for the maximum diameters of target lesions were assessed using intraclass correlation coefficients (ICC). Friedman test, Spearman’s correlation, median absolute deviation (MAD), median percentage error (MPE) and Bland-Altman analysis were used to assess the agreement between modalities, calculating the mean difference and the 95% limits of agreement (mean difference ±1.96 times the standard deviation of the differences). The interpretation of ICC or weighted kappa values was as follows: ≤0.20 = poor agreement; 0.21–0.40 = fair agreement; 0.41–0.60 = moderate agreement; 0.61–0.80 = substantial agreement; ≥0.81 = excellent agreement.

Results

Agreement in maximum diameters and sum of maximum diameters of target lesions

Forty-seven patients were included (63.7±9.2 years old, 26 males), with 57 target lesions evaluated at baseline. The maximum diameter of lesions on thin-slice CT was 32.8±20.6 mm. At follow-up, 56 target lesions were evaluated (two target lesions fused into one at follow-up), with the maximum diameter on thin-slice CT being 31.4±22.7 mm. The detection rate of target lesions on free-breathing ZTE-MRI was 100%, with a maximum diameter of 31.3±22.3 mm. On breath-hold ZTE-MRI, the detection rate was 98.2%, with a maximum diameter of 31.3±21.8 mm; one target lesion (4.9 mm in diameter) was undetected. Both readers demonstrated excellent agreement in evaluating the maximum diameters of target lesions on thin-slice CT, free-breathing ZTE, and breath-hold ZTE (Table 1).

No significant differences were found in the maximum diameters of target lesions measured by the three modalities (Chi-squared =0.081, P=0.960) (Table 2). Scatter plots (Figure 2) showed strong correlations in maximum diameters of target lesions among free-breathing ZTE-MRI, breath-hold ZTE-MRI, and CT (thin-slice CT vs. free-breathing ZTE: r=0.988, slope =0.982, intercept =0.531 mm, P<0.001; thin-slice CT vs. breath-hold ZTE: r=0.982, slope =0.955, intercept =1.298 mm, P<0.001; free-breathing ZTE vs. breath-hold ZTE: r=0.991, slope =0.974, intercept =0.757 mm, P<0.001). The ICC was 0.995 [95% confidence interval (CI): 0.992, 0.997]. Bland-Altman analysis (Figure 3) demonstrated intermodality agreement, with the mean and limits of agreement being 0.05 mm (−3.08, 3.18) for thin-slice CT vs. free-breathing ZTE; 0.10 mm (−5.70, 5.90) for thin-slice CT vs. breath-hold ZTE; and 0.05 mm (−4.37, 4.47) for free-breathing ZTE vs. breath-hold ZTE. The median absolute difference between free-breathing ZTE and CT was 0.8 mm (MPE 3.15%), between breath-hold ZTE and CT was 1.7 mm (7.30%), and between breath-hold ZTE and free-breathing ZTE was 1.0 mm (5.05%).

Figure 2 Scatter plots demonstrating inter-modality correlation of target lesion maximum diameters among the three imaging techniques. (A) Correlation between free-breathing ZTE and CT for maximum diameters; (B) Correlation between breath-hold ZTE and CT for maximum diameters; (C) Correlation between breath-hold ZTE and free-breathing ZTE for maximum diameters. CT, computed tomography; ZTE, zero echo time.

Figure 3 Bland-Altman plots demonstrating inter-modality agreement of target lesion maximum diameters among the three imaging techniques. CT, computed tomography; ZTE, zero echo time.

At follow-up, among 47 patients, the sum of maximum diameters of target lesions was 37.4±24.7 mm on thin-slice CT, 37.1±24.2 mm on free-breathing ZTE, and 37.3±23.7 mm on breath-hold ZTE. No significant differences were observed in the sum of maximum diameters of target lesions measured by the three modalities (Chi-squared =0.096, P=0.953) (Table 2). Strong correlations were found in the sum of maximum diameters of target lesions among free-breathing ZTE-MRI, breath-hold ZTE-MRI, and CT (CT vs. free-breathing ZTE: r=0.988, P<0.001; thin-slice CT vs. breath-hold ZTE: r=0.986, P<0.001; free-breathing ZTE vs. breath-hold ZTE: r=0.981, P<0.001). The intermodality ICC was 0.993 (95% CI: 0.989, 0.996). Bland-Altman analysis of intermodality agreement showed the mean and limits of agreement were 0.30 mm (−3.67, 4.27) for thin-slice CT vs. free-breathing ZTE; 0.14 mm (−6.13, 6.41) for thin-slice CT vs. breath-hold ZTE; and −0.15 mm (−6.46, 6.15) for free-breathing ZTE vs. breath-hold ZTE. Additionally, the median absolute difference for the sum of diameters between free-breathing ZTE and CT was 0.9 mm (MPE 2.90%), between breath-hold ZTE and CT was 2.1 mm (7.80%), and between breath-hold ZTE and free-breathing ZTE was 1.4 mm (5.10%).

Evaluation of new lesions

Four new lesions developed in four patients, with the maximum diameters on thin-slice CT being 24.9±18.6 mm. All new lesions were detectable on both free-breathing ZTE and breath-hold ZTE, with maximum diameters of 25.1±19.0 mm and 25.7±19.4 mm, respectively.

Evaluation of RECIST 1.1 response classification

For intermodality agreement in RECIST 1.1 evaluation, thin-slice CT and free-breathing ZTE showed excellent agreement (kappa =0.959), while thin-slice CT and breath-hold ZTE demonstrated substantial agreement (kappa =0.832) (Table 3). Free-breathing ZTE and breath-hold ZTE also showed excellent agreement (kappa =0.877). Figures 4 and 5 provide representative cases from CT, free-breathing ZTE, and breath-hold ZTE.

Figure 4 Lung cancer patient with disease progression (A-D), male, 64 years old. On baseline CT, there were no obvious nodules or masses in the left upper lobe of the lung (A). Follow-up CT, free-breathing ZTE, and breath-hold ZTE all showed new lesions in the left upper lobe with maximum diameters of 7.7, 7.1, and 6.9 mm, respectively, with the response classification for all three methods being disease progression (B-D). Another lung cancer patient with disease progression (E-H), female, 71 years old. The target lesion had a maximum diameter of 50.5 mm on baseline CT (E). At follow-up, the maximum diameters on CT, free-breathing ZTE, and breath-hold ZTE were 63.5, 65.9, and 66.5 mm, respectively, with the response classification for all three methods being disease progression (F-H). Red arrows indicate the lesions. CT, computed tomography; ZTE, zero echo time.

Figure 5 Lung cancer patient with stable disease (A-D), female, 52 years old. (A) The maximum diameter of the target lesion on baseline CT is 26.6 mm. (B-D) The maximum diameters of the target lesion on follow-up CT, free-breathing ZTE, and breath-hold ZTE are 29.1, 29.8, and 31.6 mm, respectively, with the response classification for all three examination methods being stable disease. (E-H) Lung cancer patient with partial response, female, 43 years old. (E) The maximum diameter of the target lesion on baseline CT is 29.6 mm. (F-H) The maximum diameters on follow-up CT, free-breathing ZTE, and breath-hold ZTE are 14.6, 18.2, and 17.7 mm, respectively, with the response classification for all three methods being partial response. Red arrows indicate the lesions. CT, computed tomography; ZTE, zero echo time.

Discussion

In this study, we compared thin-slice CT with free-breathing ZTE and breath-hold ZTE to evaluate the value of ZTE in lung cancer follow-up, including detection of target lesions and new lesions, measurement of maximum diameters and their sums, and response classification based on RECIST 1.1 criteria. The results showed that detection rates for target lesions and new lesions were very high, with free-breathing ZTE demonstrating perfect agreement with CT, and breath-hold ZTE nearly comparable to thin-slice CT. In terms of maximum diameter and sum of maximum diameters measurements, both free-breathing ZTE and breath-hold ZTE showed excellent agreement with CT. For response classification based on RECIST 1.1 criteria, free-breathing ZTE exhibited excellent agreement with thin-slice CT, while breath-hold ZTE showed substantial agreement with CT.

Based on thin-slice CT analysis, the detection rates of target lesions and new lesions with free-breathing ZTE were 100%. For breath-hold ZTE, the detection rate was 98.2% for target lesions and 100% for new lesions, with no false-positive cases observed. The detection rates in this study were significantly higher than those of UTE or ZTE for pulmonary nodules (13,14).

This may be attributed to the inclusion of stage III/IV lung cancer patients, whose target lesions are larger in diameter and have a higher proportion of solid components, making them more easily detectable by ZTE. The undetected lesion in breath-hold ZTE (4.9 mm in diameter) was likely due to the slightly thicker slice thickness (3.1 mm) used in this sequence. An 18 s single breath-hold represents the practical limit for most patients, and thinner slices would further prolong breath-hold duration or require multiple attempts, which is often challenging for lung cancer patients. Additionally, suboptimal breath-hold compliance in some patients may increase respiratory motion artifacts, making smaller lesions less visible (20). Although the lower resolution of the breath-hold ZTE sequence poses a challenge in detecting sub-centimeter nodules, its excellent agreement with CT in measuring target lesions (>10 mm) supports its clinical utility for RECIST-based assessment. Future technical improvements in breath-hold ZTE may enhance resolution without prolonging scan time.

Measurement of maximum diameters of pulmonary lesions is commonly used in clinical practice for dynamic monitoring of pulmonary nodules or efficacy evaluation of lung cancer (5,16,21,22). In this study, both free-breathing ZTE and breath-hold ZTE showed excellent intermodality and interobserver agreement with thin-slice CT. However, previous studies have shown that UTE tends to underestimate lesion diameters by 1–2 mm compared to CT, which may be due to lesion margin smoothing from different imaging modalities or end-expiratory acquisition in free-breathing UTE (23).

In our study, the maximum diameters of free-breathing ZTE (31.3±22.3 mm) and breath-hold ZTE (31.3±21.8 mm) were slightly smaller than those of thin-slice CT (31.4±22.7 mm), but without statistical significance, similar to the findings of Liu et al. (15). The discrepancy in results may be attributed to differences in inclusion criteria: the former study enrolled patients with pulmonary nodules, including pure ground-glass nodules or subsolid nodules, while the latter (this study) included larger lesions with more solid components. Therefore, regardless of end-expiratory free-breathing or post-inspiratory breath-hold acquisition, the impact on lesion size measurement was minimal, which also explains why no significant difference was observed in maximum diameter measurements between free-breathing and breath-hold ZTE in this study. Nevertheless, both ZTE techniques demonstrated excellent intermodality agreement with CT in maximum diameter measurement, with errors small enough for clinical use. This provides patients with reliable examination options under different respiratory patterns.

RECIST 1.1 response classification holds significant clinical value in developing subsequent treatment plans and prognosis for lung cancer (1,17). Our study demonstrated that both free-breathing ZTE and breath-hold ZTE showed excellent intermodality agreement with thin-slice CT in RECIST 1.1 response classification, which corroborates the findings of Liu et al. (15) and further expands the clinical application prospects of ZTE. However, free-breathing UTE or ZTE has only demonstrated moderate (14) or substantial (13) intermodality agreement with standard CT in Lung-RADS classification of pulmonary nodules, suggesting that free-breathing ZTE may be closer to clinical utility in lung cancer response classification than in pulmonary nodule classification.

This study focuses on the efficacy of ZTE in RECIST classification, but future work should explore the advantages of ZTE when combined with multi-parametric MRI imaging. Although the study population is relatively older, in the era of personalized therapy, where some patients exhibit extended survival, the reduction of radiation exposure may still hold clinical significance. Certainly, ZTE still faces challenges such as susceptibility to respiratory motion artifacts, long scan duration, requirement for good breathing coordination, and inadequate visualization of fine pulmonary structures (24,25). Future development of technologies like artificial intelligence will be needed to address or mitigate these limitations (26).

There are several limitations in this study. First, the sample size in this study was limited. We plan to expand the cohort and collaborate with other medical centers in future research to include a more diverse range of stage III/IV lung cancer cases. Second, patients with concomitant atelectasis or acute pulmonary inflammation were excluded, and the impact of these conditions on lesion visualization and measurement was not evaluated. Third, the slice thickness of breath-hold ZTE in this study was slightly thicker than that of CT and free-breathing ZTE, a choice made to balance patient compliance but one that is more applicable to clinical settings. Furthermore, six patients in this study were excluded due to incomplete ZTE sequences or artifacts. This suggests that ZTE may be more susceptible to motion artifacts, and breath-hold ZTE imposes stricter requirements on the patient’s breath-holding duration. This could represent a potential limitation for the clinical application of ZTE. Future efforts should focus on optimizing ZTE sequence design or incorporating artificial intelligence-based motion correction to reduce the risk of failure. Although this study covered the main clinical conditions for efficacy evaluation of pulmonary lesions in lung cancer patients as outlined in guidelines (1,2,16,17), these limitations may still introduce bias.

Conclusions

In conclusion, these results indicate that free-breathing ZTE is equivalent to thin-slice CT for efficacy evaluation in lung cancer and can serve as a radiation-free alternative; breath-hold ZTE is suitable for patients who can comply with breathing instructions and cannot tolerate prolonged MRI examinations. Both modalities meet the clinical requirements of RECIST 1.1 criteria.

Acknowledgments

None.

Footnote

Reporting Checklist: The authors have completed the STARD reporting checklist. Available at https://qims.amegroups.com/article/view/10.21037/qims-2025-1982/rc

Data Sharing Statement: Available at https://qims.amegroups.com/article/view/10.21037/qims-2025-1982/dss

Funding: This study was funded by the Key Scientific Research Project Plan of Henan Provincial Higher Education Institutions (No. 24B320017) and the Joint Construction Project of Henan Provincial Medical Science and Technology Research Project (No. LHGJ20230505).

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://qims.amegroups.com/article/view/10.21037/qims-2025-1982/coif). K.W. is an employee of GE Healthcare. Z.Y. is an employee of GE Medical Systems Trade & Development (Shanghai) Co. Ltd. The other authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. The study was approved by the Ethics Committee of The First Affiliated Hospital of Xinxiang Medical University (No. EC-024-222), and all participants have provided written informed consent.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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