T1 mapping-based multi-parametric MRI for subtyping and differentiation grading of non-small cell lung cancer

Introduction

Lung cancer remains the leading cause of cancer-related deaths globally, with both incidence and mortality continuing to rise in China (1). Among all histological types, non-small cell lung cancer (NSCLC) accounts for approximately 80–85% of cases (2). Accurate identification of histologic subtypes and assessment of tumor differentiation are essential for guiding treatment decisions and predicting patient outcomes. For instance, targeted therapies and immunotherapies yield superior responses in adenocarcinoma (AD) compared with squamous cell carcinoma (SCC), whereas tumor differentiation status is closely associated with aggressiveness, treatment response, and prognosis (3,4). Currently, histopathologic examination of biopsy or surgical specimens remains the reference standard for tumor characterization. However, this approach is invasive and carries procedural risks. These limitations underscore the need for reliable, non-invasive, and reproducible imaging biomarkers for NSCLC phenotyping.

Current clinical practice in pulmonary imaging primarily relies on positron emission tomography (PET), computed tomography (CT) density analysis, and, more recently, radiomics-based approaches (5,6). PET/CT provides information on tumor metabolism; however, it involves exposure to ionizing radiation and depends heavily on tumor fluorodeoxyglucose (FDG) avidity, which may limit specificity in the presence of inflammatory or infectious processes. Conventional CT likewise relies on ionizing radiation and predominantly offers morphological information, with limited ability to characterize tissue microstructure and functional properties, particularly in early-stage or microscopic disease. Although radiomics enables high-dimensional feature extraction from medical images, the biological interpretability of many derived features remains unclear, posing challenges for establishing robust imaging-biology correlations and potentially limiting clinical translation. Recent advances in magnetic resonance imaging (MRI) have facilitated its application in thoracic oncology. MRI provides excellent soft-tissue contrast and enables multi-parametric assessment of tumor biology (7-9). Among these techniques, T1 mapping has emerged as a quantitative method capable of measuring the intrinsic longitudinal relaxation time (T1 value) of tissues, thereby reflecting microscopic structural and compositional changes in the tumor microenvironment. This technique has been extensively validated in other organ systems, including the quantification of myocardial fibrosis (10,11), assessment of liver (12) and pancreatic diseases (13), and functional evaluation in chronic obstructive pulmonary disease (COPD) (14,15). These applications demonstrate the versatility and robustness of T1 mapping as a non-invasive biomarker and highlight its potential value in the evaluation of lung cancer (16,17).

Despite these advances, the application of T1 mapping in NSCLC remains limited, with existing studies often constrained by small sample sizes or lacking systematic analyses of histologic subtypes and differentiation grades. Moreover, the diagnostic performance of combining T1 mapping with other quantitative MRI parameters, such as diffusion-weighted imaging (DWI) and dynamic contrast-enhanced MRI (DCE-MRI), has not been comprehensively investigated.

Therefore, this study aimed to evaluate the utility of pre- and post-contrast T1 mapping in differentiating histologic subtypes and assessing tumor differentiation in NSCLC, both individually and in combination with other quantitative MRI metrics. By establishing non-invasive, quantitative imaging biomarkers, our goal was to enhance diagnostic precision and provide clinically meaningful information to support personalized treatment strategies for patients with NSCLC. We present this article in accordance with the STARD reporting checklist (available at https://qims.amegroups.com/article/view/10.21037/qims-2025-2001/rc).

Methods

Patient population

This study prospectively collected consecutive patients with a clinical or radiologic suspicion of lung cancer who were admitted to The First Affiliated Hospital of Soochow University from November 2023 to January 2025. The inclusion criteria were as follows: (I) a primary pulmonary lesion identified on conventional imaging (e.g., X-ray or CT); (II) maximum lesion diameter ≥1 cm; (III) no prior antitumor therapy before MRI, including radiotherapy, chemotherapy, biopsy, targeted therapy, or surgery; and (IV) completion of lung MRI within 7 days before histopathologic sampling (biopsy or surgical resection). The exclusion criteria were as follows: (I) incomplete clinical data (n=4); (II) contraindications to MRI (e.g., metallic implants, severe claustrophobia); (III) poor general condition or inability to cooperate with MRI (n=3); and (IV) non-NSCLC pathology on histology, including benign lesions, small-cell lung cancer, granulomas, large-cell carcinoma, or adenosquamous carcinoma (n=7). Ultimately, 76 patients with histopathologically confirmed NSCLC were included in the final analysis. The patient selection process is summarized in Figure 1.

Figure 1 Flow chart of patient selection. MRI, magnetic resonance imaging; NSCLC, non-small cell lung cancer.

The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. The study was approved by the Institutional Review Board of The First Affiliated Hospital of Soochow University (No. 2025008). Written informed consent was provided by all participants prior to MRI and subsequent pathological confirmation. This trial was registered in the Chinese Clinical Trial Registry (ChiCTR2100045624).

MRI acquisitions

All examinations were performed on a 3.0T whole-body MRI scanner (Signa Premier, GE Healthcare, Milwaukee, WI, USA) with anterior and posterior array coils. Patients were placed in the supine position with arms raised and underwent breathing training prior to scanning to minimize respiratory motion artifacts. Conventional sequences were performed using (I) coronal single-shot fast spin echo T2-weighted imaging (SSFSE T2WI): repetition time/echo time (TR/TE), 2,000/87 ms; voxel size, 1.0×1.5×4.0 mm³; field of view (FOV), 400 mm; 24 slices; (II) axial 3D gradient-echo T1-weighted imaging (3D-T1WI): TR/TE, 4.11/1.24 ms; voxel size, 1.3×1.3×3.5 mm³; FOV, 420 mm; 80 slices; (III) axial PROPELLER T2WI: TR/TE, 3,000/87 ms; voxel size, 1.3×1.3×4.0 mm³; FOV, 400 mm; 24 slices. DWI was performed using a respiratory-triggered Focus-MUSE DWI sequence with the following parameters: TR/TE, 5,600/72 ms; voxel size, 2.4×2.4×4.0 mm³; FOV, 320 mm; 24 slices; b-values, 50 and 800 s/mm². Dynamic contrast imaging was acquired using the DISCO-Star technique during free breathing. A gadolinium-based contrast agent (Gadopentetate acid dimeglumine, Bayer-Schering, Berlin, Germany; 0.2 mmol/kg) was injected via the antecubital vein at 3.0 mL/s, followed by a 20-mL saline flush. The acquisition parameters were as follows: TR/TE, 2.9/1.3 ms; voxel size, 1.8×1.8×4.0 mm³; FOV, 400 mm; 56 slices; temporal resolution, 8.9 s per phase; total phases, 30; total acquisition time, approximately 294 seconds.

Native and post-contrast T1 mapping were performed using an axial steady-state free precession single-shot modified Look-Locker inversion recovery (MOLLI) sequence. Data were acquired at end-diastole during a single breath-hold with a 5(3)3 acquisition scheme. The acquisition parameters were as follows: TR/TE, 2.7/1.1 ms; voxel size, 2.4×2.4×5.0 mm³; FOV, 360 mm; 3 slices. Post-contrast imaging was performed 5 minutes after contrast injection. Parametric T1 maps were automatically reconstructed, with voxel intensity representing the quantitative T1 value (18). During T1 map reconstruction, motion correction was applied to align source images acquired at different inversion times. MOLLI source images and reconstructed T1 maps were visually inspected to ensure acceptable image quality and fitting reliability.

Image analysis

All imaging data were transferred to a dedicated post-processing workstation (AW 4.7, GE Healthcare) for quantitative analysis. Two chest radiologists with 8 and 10 years of experience, blinded to clinical and histopathological information, independently evaluated the images. For each tumor, regions of interest (ROIs) were manually delineated on the slice with the maximum tumor diameter. The ROIs were carefully drawn to encompass as much solid tumor tissue as possible while avoiding necrotic, cystic, hemorrhagic, or adjacent normal lung regions by referencing CT and conventional MRI images for anatomical correlation. Apparent diffusion coefficient (ADC) maps were generated using a mono-exponential model with b-values of 50 and 800 s/mm². The mean ADC value within the ROI was recorded for each lesion. Quantitative analysis of DCE-MRI data was performed using a pharmacokinetic two-compartment extended Tofts model. The following parameters were extracted: (I) Ktrans (min⁻¹): volume transfer constant from plasma to the extravascular extracellular space (EES); (II) Kep (min⁻¹): reflux rate constant from EES to plasma; (III) Ve: fractional volume of the EES. For native (T1_pre) and post-contrast (T1_post) T1 maps, ROIs were drawn on the pseudocolored parametric maps at the same slice level and with consistent size and placement. The mean of the two readers’ measurements was used as the final quantitative value for subsequent statistical analyses. The absolute decrease in T1 (T1_d) was calculated as: T1_d = T1_pre−T1_post. The percentage decrease (T1_d%) was calculated as: T1_d% = (T1_d/T1_pre) × 100%.

Statistical analysis

All statistical analyses were performed using the software SPSS 25.0 (IBM Corp., Armonk, NY, USA). The consistency of T1 values and ADC values measured by two radiologists was assessed using the intraclass correlation coefficient (ICC). An ICC >0.75 was considered indicative of high consistency. The normality of continuous variables was evaluated using the Shapiro-Wilk test. Normally distributed data were expressed as mean ± standard deviation (SD), whereas non-normally distributed data were reported as median (interquartile range). Categorical variables were expressed as counts and percentages. Independent-samples t-tests or chi-square tests were used based on different data distributions. For parameters showing statistically significant differences between groups, receiver operating characteristic (ROC) curve analysis was performed to evaluate diagnostic performance. The area under the curve (AUC), optimal cutoff values, sensitivity, specificity, and Youden index were calculated. All tests were two-sided, and P<0.05 was considered statistically significant.

Results

Clinical characteristics

A total of 76 patients with histopathologically confirmed NSCLC were included, consisting of 48 AD cases and 28 SCC cases, with tumor sizes ranging from 1.6 to 11 cm. Among these, 32 were classified as the poorly differentiated group (11 AD and 21 SCC), whereas 44 were categorized as the moderately/highly differentiated group (37 AD and 7 SCC). No statistically significant differences were observed between the SCC group and AD group in terms of age, tumor size, or clinical stage (P>0.05). However, significant differences were identified in gender, smoking history, and differentiation degree (P=0.001, 0.004, and <0.001, respectively; Table 1). Representative MRI images from a SCC and an AD patient are presented in Figures 2,3.

Figure 2 A 68-year-old man diagnosed with squamous cell carcinoma of the right lower lobe of the lung by surgery. (A) T2-weighted image, the lesion shows high signal intensity. (B) T1-weighted enhanced image, the lesion shows significant enhancement. (C) T1 mapping pre-enhancement image, average T1 value of the lesion is 1,582 ms. (D) T1 mapping post-enhancement image, average T1 value of the lesion is 714 ms. (E) DWI image, the lesion shows high signal intensity. (F) ADC image, average ADC value of the lesion is 1,108×10^-6 mm²/s. The location of the lesion is indicated by the arrow (A,B). ADC, apparent diffusion coefficient; DWI, diffusion-weighted imaging.

Figure 3 A 54-year-old man diagnosed with adenocarcinoma cancer of the left upper lobe of the lung by surgery. (A) T2-weighted image, the lesion shows high signal intensity. (B) T1-weighted enhanced image, the lesion shows uneven enhancement. (C) T1 mapping pre-enhancement image, average T1 value of the lesion is 1,511 ms. (D) T1 mapping post-enhancement image, average T1 value of the lesion is 419 ms. (E) DWI image, the lesion shows high signal intensity. (F) ADC map, average ADC value of the lesion is 977×10⁻⁶ mm²/s. The location of the lesion is indicated by the arrow (A,B). ADC, apparent diffusion coefficient; DWI, diffusion-weighted imaging.

Inter-observer reproducibility

There was no statistically significant difference in T1_pre, T1_post, or ADC values between the two observers (P>0.05). The reproducibility of measurements was high, with ICC values of 0.938 for T1_pre, 0.922 for T1_post, and 0.814 for ADC (Table 2), supporting the use of the mean values for subsequent analyses.

Comparison of quantitative parameters between histologic subtypes

The AD group demonstrated significantly higher ADC values (P=0.031) and lower T1_pre (P<0.001), T1_post (P=0.039), and T1_d (P=0.041) values compared with the SCC group (Figure 4). In contrast, Ktrans, Kep, Ve, and T1_d% showed no significant differences between the two histologic subtypes (P>0.05) (Table 3). ROC analysis indicated that the combined diagnostic model incorporating ADC, T1_pre, T1_post, and T1_d achieved the best performance for differentiating AD from SCC, yielding an AUC of 0.805, sensitivity of 75%, and specificity of 79.2% (Table 4 and Figure 5A).

Figure 4 Box plots of the distribution in AD and SCC. The quantitative parameter values ADC (A), T1_pre (B), T1_post (C), and T1_d (D) showed statistically significant differences between patients with AD and SCC (P<0.05). AD, adenocarcinoma; ADC, apparent diffusion coefficient; SCC, squamous cell carcinoma; T1_pre, T1 native maps; T1_post, T1 post-contrast maps; T1_d, the absolute decrease in T1.

Figure 5 ROC curve analysis was performed to evaluate diagnostic performance. (A) ROC curves for distinguishing adenocarcinoma based on ADC value, T1_pre, T1_post, T1_d value, and their combination, with AUC values of 0.679, 0.767, 0.653, 0.621, and 0.805, respectively. (B) ROC curves for distinguishing poorly differentiated carcinoma based on ADC value, T1_pre value, and their combination, with AUC values of 0.801, 0.727, and 0.866, respectively. ADC, apparent diffusion coefficient; AUC, area under the curve; ROC, receiver operating characteristic; T1_pre, T1 native maps; T1_post, T1 post-contrast maps; T1_d, the absolute decrease in T1.

Diagnostic performance of quantitative parameters across differentiation grades

The poorly differentiated group exhibited significantly lower ADC values (P<0.001) and higher T1_pre values (P=0.001) compared with the moderately/highly differentiated group. No significant differences were observed for Ktrans, Kep, Ve, T1_post, T1_d, or T1_d% (P>0.05) (Table 5). ROC analysis demonstrated that the combination of ADC and T1_pre provided the best diagnostic performance for identifying poorly differentiated tumors, yielding an AUC of 0.866, with 77.3% sensitivity and 84.4% specificity (Table 6; Figures 5B,6).

Figure 6 Box plots of the distribution of quantitative parameter values in different degrees of differentiation. The quantitative parameter values ADC (A) and T1_pre (B) have statistically significant differences between patients with poorly-differentiated and moderately-/highly-differentiated groups (P<0.05). ADC, apparent diffusion coefficient; T1_pre, T1 native maps.

Discussion

This study demonstrated that pre- and post-contrast T1 mapping, particularly when combined with ADC values, could differentiate histologic subtypes and assess the degree of differentiation in NSCLC. For subtype classification (AD vs. SCC), the multivariate model combining ADC, native T1 (T1_pre), post-contrast T1 (T1_post), and the T1_d achieved the best diagnostic performance (AUC 0.805 with 75% sensitivity and 79.2% specificity). For grading tumor differentiation, the bivariate combination of ADC + T1_pre performed best (AUC 0.866 with 77.3% sensitivity and 84.4% specificity). These findings suggest that T1 mapping in tandem with ADC may serve as reliable, minimally invasive, and reproducible imaging biomarkers for tumor characterization in NSCLC.

T1 mapping enables quantitative assessment of intrinsic tissue properties by measuring voxel-wise T1 and detecting changes before and after contrast administration, thereby objectively reflecting alterations in the tumor microenvironment (19). This technique has gained increasing attention in oncologic imaging and has shown preliminary promise in lung cancer assessment. In our previous study (16), native T1 mapping demonstrated diagnostic value for characterizing histologic subtypes and tumor differentiation, with AD showing significantly lower T1_pre values compared with SCC. In the current study, performed on imaging systems from different vendors, we confirmed this finding, again observing significantly lower T1_pre values in AD than in SCC. However, these results are not entirely consistent with those of Jiang et al. (17) and Bortolotto et al. (20), likely due to differences in patient populations and acquisition parameters, which can lead to variability in measured T1 values. It should be noted that differences in T1 mapping acquisition protocols directly affect the ranges of normal and abnormal T1 values obtained under different technical conditions. This implies that absolute T1 values can only be directly compared when using identical acquisition protocols, at the same magnetic field strength, and with consistent post-processing methods (21).

Consistent with our prior work, this study employed an improved MOLLI sequence. Compared with the dual flip-angle fast gradient-echo 3D breath-hold technique, MOLLI provides near-perfect ex vivo accuracy and comparable in vivo T1 values, as validated by Tirkes et al. (22). Beyond native T1 values, our study also demonstrated that T1_post and T1_d differed significantly between histologic subtypes, suggesting additional diagnostic potential. Tissue T1 values are influenced by several biological factors, including macromolecular concentration, water content, and water-binding state (23). Post-contrast T1 values are primarily influenced by the concentration of extracellular contrast agents, which is closely related to tissue perfusion, vascular permeability, and the fractional volume of the EES (13). In the present study, ADs demonstrated lower post-contrast T1 values than SCCs, which may indicate greater contrast agent retention in AD tissue during the delayed phase. This phenomenon may be attributable to histopathological differences between these subtypes. Lung AD typically exhibits a lepidic or replacement growth pattern, with relatively abundant blood supply and a lower propensity for extensive necrosis. In addition, ADs often show prominent stromal collagen deposition, forming fibrous septa within the interstitium. The increased fibrotic content may impede lymphatic drainage in the extravascular space, thereby prolonging contrast agent retention and resulting in lower T1_post values compared with SCC (24). In contrast, SCC more commonly demonstrates a solid growth pattern and is more prone to central ischemia, necrosis, and expansion of intercellular spaces due to insufficient blood supply (24). Necrotic regions lack intact vasculature and cellular architecture, leading to reduced contrast inflow and accelerated washout, which may partially explain the relatively higher T1_post values observed in SCC. Furthermore, the greater T1_d observed in AD may be associated with increased expression of tissue factors and higher tumor microvessel density—a phenomenon reported in various malignant tumors. These processes not only promote microvascular formation but also upregulate vascular endothelial growth factor (VEGF) expression, which in turn facilitates angiogenesis and extracellular matrix remodeling (25). Collectively, inherent differences in cellular architecture, growth patterns, vascularity, and necrotic extent between tumor subtypes are likely reflected in post-contrast T1-derived parameters, including T1_post and T1_d. Nevertheless, definitive confirmation of these mechanistic interpretations requires future studies directly correlating imaging-derived parameters with histopathologic metrics obtained from biopsy or surgical specimens, such as cellular density, necrotic fraction, microvessel density markers (e.g., CD31 and VEGF), and proliferation indices (e.g., Ki-67), although related associations have been explored in our previous work (16).

In recent years, DCE-MRI has gained considerable attention for its potential to characterize tumor biology. DCE-MRI provides information on the microvascular properties of tumors, including perfusion and permeability. The principal quantitative parameters derived from the extended Tofts model include Ktrans, Kep, Ve, and Vp (26). However, the intrinsic characteristics of the lung, such as low proton density and respiratory motion, have limited the clinical application of DCE imaging in thoracic oncology. In this study, we applied a free-breathing ultrafast DCE protocol (DISCO-Star) and found that none of the quantitative DCE parameters showed statistically significant differences in distinguishing histologic subtypes or tumor differentiation grades. This result may be related to our acquisition protocol. First, although free-breathing acquisition improves clinical feasibility, it inevitably introduces respiratory motion, which may lead to inaccuracies in pharmacokinetic modeling and result in underestimation of perfusion- and permeability-related parameters. Second, the temporal resolution of DCE-MRI in this study was 8.9 seconds. Such temporal resolution may be insufficient for accurate quantification of tissue perfusion and permeability, particularly in lesions with relatively rapid blood flow (e.g., highly differentiated tumors) or high vascular permeability (e.g., certain adenocarcinomas). Lower temporal resolution tends to smooth the arterial input function and the initial upslope of tissue enhancement, potentially leading to underestimation of high perfusion values and reduced accuracy in the measurement of parameters such as Ktrans. Consequently, subtle hemodynamic differences between histologic subtypes may not have been adequately captured. Therefore, these technical factors may have obscured underlying microcirculatory differences. Prior studies have suggested that DCE parameters can facilitate NSCLC subtyping; for example, one report demonstrated significantly higher Ktrans in AD compared with SCC (0.252±0.087 vs. 0.160±0.049, P<0.01) (27). Although the differences were not statistically significant in our cohort, we observed a trend toward lower Ktrans values in SCC relative to AD. This trend is biologically plausible, as AD generally demonstrates higher microvascular density and VEGF expression compared with SCC, indicating greater angiogenic potential and vascular permeability (28). Nonetheless, it should be emphasized that thoracic DCE-MRI was performed under free-breathing conditions in this study; the combined effects of cardiac and respiratory motion likely limited both image quality and the consistency of parameter estimation. Future applications incorporating motion correction techniques and sequences with higher temporal resolution may help unveil the value of DCE-MRI in characterizing lung cancer. DWI reflects the degree of water molecule diffusion restriction within tissues and has been widely applied in both the differentiation of lung tumor histologic subtypes (29) and the assessment of therapeutic response in lung cancer (30,31). The cellular density and integrity of cell membranes in tumors directly influence water diffusion (32). Increased cellular density and a more compact internal structure restrict water molecule movement, leading to higher DW signal intensity and lower ADC values. Histopathologically, highly-differentiated AD typically demonstrates a replacement growth pattern, where cylindrical tumor cells grow along the walls of preexisting alveoli, preserving the underlying lung architecture. In contrast, SCC often exhibits a solid growth pattern, characterized by compressive and expansive proliferation of malignant cells without replacement of the existing structure (33). Consequently, the cellularity of AD—particularly in highly-differentiated tumors—may be lower than that of poorly-differentiated AD and SCC. This histologic distinction explains why the ADC values of moderately-/highly-differentiated AD are generally higher compared with those of poorly-differentiated AD and SCC.

The limitations of this study should be acknowledged. First, this was a single-center study with a modest sample size. Second, although the ROI was carefully placed in areas of relatively homogeneous tumor signal, the inherent heterogeneity of malignant tumors means that small necrotic regions may have been inadvertently included, potentially influencing the measured T1 values. Future applications incorporating thinner slice acquisition or 3D acquisition techniques may help to mitigate measurement bias induced by this effect. Third, the present study employed an axial steady-state free precession single-shot MOLLI sequence for T1 mapping, without direct comparison to alternative T1 mapping techniques, such as a B1 field-corrected 3D variable flip angle (VFA) VIBE sequences. Although MOLLI acquisition was performed with electrocardiogram (ECG) gating during end-expiratory breath-hold and incorporated motion correction, conventional MOLLI implementations remain susceptible to heart rate-dependent timing variability and residual motion effects, which may introduce uncertainty in T1 estimation. Therefore, although the diagnostic performance observed in this study is encouraging, further validation using alternative T1 mapping sequences is warranted to confirm the robustness of these findings. Fourth, this study relied on manually delineated ROIs for quantitative analysis. Due to intratumoral heterogeneity, partial volume effects, and motion-related artifacts at lesion boundaries, ROI placement may influence the accuracy and reproducibility of T1 measurements. Although inter-observer agreement was assessed and demonstrated good consistency, manual ROI-based analysis cannot fully capture the 3D spatial heterogeneity of tumors. Whole-tumor volume segmentation or multiple ROI measurements may allow more comprehensive characterization of intratumoral features and potentially improve differentiation and predictive performance. Future work will focus on validating the added value of volumetric segmentation methods for the quantitative parameters identified in this study. Fifth, detailed histopathological characteristics—including tumor cellularity, microvessel density, Ki-67 proliferation index, and gene mutation status—were not incorporated into the current analysis. Integration of these biologic markers in future studies may enable more comprehensive radiologic–pathologic correlation and facilitate the development of more robust prognostic models. In addition, further investigations are warranted to evaluate the clinical utility of quantitative T1 mapping and ADC metrics for differentiating benign and malignant lung lesions, prognostic stratification in patients with inoperable lung cancer, and longitudinal assessment of treatment response, thereby supporting more personalized therapeutic decision-making.

Conclusions

Magnetic resonance T1 mapping combined with ADC measurements offers a reproducible, non-invasive approach for subtyping and grading NSCLC. The four-parameter model (ADC + T1_pre + T1_post + T1_d) showed the best performance for distinguishing AD from SCC, whereas the two-parameter model (ADC + T1_pre) was optimal for grading tumor differentiation. These findings underscore the potential of multi-parametric MRI as a practical imaging biomarker to support precision diagnosis and individualized treatment planning in clinical practice.

Acknowledgments

None.

Footnote

Reporting Checklist: The authors have completed the STARD reporting checklist. Available at https://qims.amegroups.com/article/view/10.21037/qims-2025-2001/rc

Data Sharing Statement: Available at https://qims.amegroups.com/article/view/10.21037/qims-2025-2001/dss

Funding: This work was mainly supported by the Key Program of Jiangsu Commission of Health (No. K2023027), the Medicine plus X Project from Suzhou Medical School of Soochow University (No. ML12203423), a grant from Infectious and Inflammatory Radiology Committee of Jiangsu Research Hospital Association (No. GY202301), Jiangsu Province Capability Improvement Project through Science, Technology and Education (Jiangsu Provincial Medical Key Discipline Cultivation Unit) (No. JSDW202242), Suzhou Key Laboratory of Medical Imaging (No. SZS2024032), and Lianyungang Municipal Health and Science Technology Project (No. 202532).

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://qims.amegroups.com/article/view/10.21037/qims-2025-2001/coif). J.S. is an employee of GE HealthCare and an MR Collaboration Scientist who provided technical support for this study under the GE collaboration regulations. No payment or personal conflicts of interest were involved in this study. 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 institutional review board of The First Affiliated Hospital of Soochow University (No. 2025008). Written informed consent was obtained from all participants prior to MRI and subsequent pathological confirmation.

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/.

References

1. Bray F, Laversanne M, Sung H, Ferlay J, Siegel RL, Soerjomataram I, Jemal A. Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin 2024;74:229-63. [Crossref] [PubMed]
2. Detterbeck FC, Boffa DJ, Kim AW, Tanoue LT. The Eighth Edition Lung Cancer Stage Classification. Chest 2017;151:193-203.
3. Hill A, Gupta R, Zhao D, Vankina R, Amanam I, Salgia R. Targeted Therapies in Non-small-Cell Lung Cancer. Cancer Treat Res 2019;178:3-43. [Crossref] [PubMed]
4. Rao S, Sigl V, Wimmer RA, Novatchkova M, Jais A, Wagner G, et al. RANK rewires energy homeostasis in lung cancer cells and drives primary lung cancer. Genes Dev 2017;31:2099-112. [Crossref] [PubMed]
5. Ohno Y, Seo JB, Parraga G, Lee KS, Gefter WB, Fain SB, Schiebler ML, Hatabu H. Pulmonary Functional Imaging: Part 1-State-of-the-Art Technical and Physiologic Underpinnings. Radiology 2021;299:508-23. [Crossref] [PubMed]
6. Gillies RJ, Kinahan PE, Hricak H. Radiomics: Images Are More than Pictures, They Are Data. Radiology 2016;278:563-77. [Crossref] [PubMed]
7. Meier-Schroers M, Homsi R, Gieseke J, Schild HH, Thomas D. Lung cancer screening with MRI: Evaluation of MRI for lung cancer screening by comparison of LDCT- and MRI-derived Lung-RADS categories in the first two screening rounds. Eur Radiol 2019;29:898-905. [Crossref] [PubMed]
8. Meier-Schroers M, Homsi R, Schild HH, Thomas D. Lung cancer screening with MRI: characterization of nodules with different non-enhanced MRI sequences. Acta Radiol 2019;60:168-76. [Crossref] [PubMed]
9. Usuda K, Sagawa M, Motono N, Ueno M, Tanaka M, Machida Y, Matoba M, Kuginuki Y, Taniguchi M, Ueda Y, Sakuma T. Advantages of diffusion-weighted imaging over positron emission tomography-computed tomography in assessment of hilar and mediastinal lymph node in lung cancer. Ann Surg Oncol 2013;20:1676-83. [Crossref] [PubMed]
10. Germain P, El Ghannudi S, Jeung MY, Ohlmann P, Epailly E, Roy C, Gangi A. Native T1 mapping of the heart - a pictorial review. Clin Med Insights Cardiol 2014;8:1-11. [Crossref] [PubMed]
11. Jellis CL, Kwon DH. Myocardial T1 mapping: modalities and clinical applications. Cardiovasc Diagn Ther 2014;4:126-37. [PubMed]
12. Yoon JH, Lee JM, Kang HJ, Ahn SJ, Yang H, Kim E, Okuaki T, Han JK. Quantitative Assessment of Liver Function by Using Gadoxetic Acid-enhanced MRI: Hepatocyte Uptake Ratio. Radiology 2019;290:125-33. [Crossref] [PubMed]
13. Tirkes T, Lin C, Cui E, Deng Y, Territo PR, Sandrasegaran K, Akisik F, Quantitative MR. Evaluation of Chronic Pancreatitis: Extracellular Volume Fraction and MR Relaxometry. AJR Am J Roentgenol 2018;210:533-42. [Crossref] [PubMed]
14. Alamidi DF, Morgan AR, Hubbard Cristinacce PL, Nordenmark LH, Hockings PD, Lagerstrand KM, Young SS, Naish JH, Waterton JC, Maguire NC, Olsson LE, Parker GJ. COPD Patients Have Short Lung Magnetic Resonance T1 Relaxation Time. COPD 2016;13:153-9. [Crossref] [PubMed]
15. Morgan AR, Parker GJ, Roberts C, Buonaccorsi GA, Maguire NC, Hubbard Cristinacce PL, Singh D, Vestbo J, Bjermer L, Jögi J, Taib Z, Sarv J, Bruijnzeel PL, Olsson LE, Bondesson E, Nihlén U, McGrath DM, Young SS, Waterton JC, Nordenmark LH. Feasibility assessment of using oxygen-enhanced magnetic resonance imaging for evaluating the effect of pharmacological treatment in COPD. Eur J Radiol 2014;83:2093-101. [Crossref] [PubMed]
16. Li G, Huang R, Zhu M, Du M, Zhu J, Sun Z, Liu K, Li Y. Native T1-mapping and diffusion-weighted imaging (DWI) can be used to identify lung cancer pathological types and their correlation with Ki-67 expression. J Thorac Dis 2022;14:443-54. [Crossref] [PubMed]
17. Jiang J, Cui L, Xiao Y, Zhou X, Fu Y, Xu G, Shao W, Chen W, Hu S, Hu C, Hao S B. (1) -Corrected T1 Mapping in Lung Cancer: Repeatability, Reproducibility, and Identification of Histological Types. J Magn Reson Imaging 2021;54:1529-40. [Crossref] [PubMed]
18. Messroghli DR, Radjenovic A, Kozerke S, Higgins DM, Sivananthan MU, Ridgway JP. Modified Look-Locker inversion recovery (MOLLI) for high-resolution T1 mapping of the heart. Magn Reson Med 2004;52:141-6. [Crossref] [PubMed]
19. Puntmann VO, Peker E, Chandrashekhar Y, Nagel E. T1 Mapping in Characterizing Myocardial Disease: A Comprehensive Review. Circ Res 2016;119:277-99. [Crossref] [PubMed]
20. Bortolotto C, Messana G, Lo Tito A, Stella GM, Pinto A, Podrecca C, Bellazzi R, Gerbasi A, Agustoni F, Han F, Nickel MD, Zacà D, Filippi AR, Bottinelli OM, Preda L. The Role of Native T1 and T2 Mapping Times in Identifying PD-L1 Expression and the Histological Subtype of NSCLCs. Cancers (Basel) 2023;15:3252. [Crossref] [PubMed]
21. Taylor AJ, Salerno M, Dharmakumar R, Jerosch-Herold M. T1 Mapping: Basic Techniques and Clinical Applications. JACC Cardiovasc Imaging 2016;9:67-81. [Crossref] [PubMed]
22. Tirkes T, Zhao X, Lin C, Stuckey AJ, Li L, Giri S, Nickel D. Evaluation of variable flip angle, MOLLI, SASHA, and IR-SNAPSHOT pulse sequences for T(1) relaxometry and extracellular volume imaging of the pancreas and liver. MAGMA 2019;32:559-66. [Crossref] [PubMed]
23. Nakamori S, Dohi K, Ishida M, Goto Y, Imanaka-Yoshida K, Omori T, Goto I, Kumagai N, Fujimoto N, Ichikawa Y, Kitagawa K, Yamada N, Sakuma H, Ito M. Native T1 Mapping and Extracellular Volume Mapping for the Assessment of Diffuse Myocardial Fibrosis in Dilated Cardiomyopathy. JACC Cardiovasc Imaging 2018;11:48-59. [Crossref] [PubMed]
24. Travis WD, Brambilla E, Nicholson AG, Yatabe Y, Austin JHM, Beasley MB, Chirieac LR, Dacic S, Duhig E, Flieder DB, Geisinger K, Hirsch FR, Ishikawa Y, Kerr KM, Noguchi M, Pelosi G, Powell CA, Tsao MS, Wistuba I. The 2015 World Health Organization Classification of Lung Tumors: Impact of Genetic, Clinical and Radiologic Advances Since the 2004 Classification. J Thorac Oncol 2015;10:1243-60. [Crossref] [PubMed]
25. Mineo TC, Ambrogi V, Baldi A, Rabitti C, Bollero P, Vincenzi B, Tonini G. Prognostic impact of VEGF, CD31, CD34, and CD105 expression and tumour vessel invasion after radical surgery for IB-IIA non-small cell lung cancer. J Clin Pathol 2004;57:591-7. [Crossref] [PubMed]
26. Sourbron SP, Buckley DL. On the scope and interpretation of the Tofts models for DCE-MRI. Magn Reson Med 2011;66:735-45. [Crossref] [PubMed]
27. Guo W, Lv B, Yang T, Tian M, Liu M, Lin X, Zhao P. Role of Dynamic Contrast-Enhanced Magnetic Resonance Imaging Parameters and Extracellular Volume Fraction as Predictors of Lung Cancer Subtypes and Lymph Node Status in Non-Small-Cell Lung Cancer Patients. J Cancer 2023;14:3108-16. [Crossref] [PubMed]
28. Zhang J, Chen L, Chen Y, Wang W, Cheng L, Zhou X, Wang J. Tumor vascularity and glucose metabolism correlated in adenocarcinoma, but not in squamous cell carcinoma of the lung. PLoS One 2014;9:e91649. [Crossref] [PubMed]
29. Matoba M, Tonami H, Kondou T, Yokota H, Higashi K, Toga H, Sakuma T. Lung carcinoma: diffusion-weighted mr imaging--preliminary evaluation with apparent diffusion coefficient. Radiology 2007;243:570-7. [Crossref] [PubMed]
30. Ohno Y, Yui M, Takenaka D, Yoshikawa T, Koyama H, Kassai Y, Yamamoto K, Oshima Y, Hamabuchi N, Hanamatsu S, Obama Y, Ueda T, Ikeda H, Hattori H, Murayama K, Toyama H. Computed DWI MRI Results in Superior Capability for N-Stage Assessment of Non-Small Cell Lung Cancer Than That of Actual DWI, STIR Imaging, and FDG-PET/CT. J Magn Reson Imaging 2023;57:259-72. [Crossref] [PubMed]
31. Grundberg O, Skribek M, Swerkersson S, Skorpil M, Kölbeck K, Grozman V, Nyren S, Tsakonas G. Diffusion weighted MRI and apparent diffusion coefficient as a prognostic biomarker in evaluating chemotherapy-antiangiogenic treated stage IV non-small cell lung cancer: A prospective, single-arm, open-label, clinical trial (BevMar). Eur J Radiol 2024;177:111557. [Crossref] [PubMed]
32. Surov A, Meyer HJ, Wienke A. Correlation Between Minimum Apparent Diffusion Coefficient (ADC(min)) and Tumor Cellularity: A Meta-analysis. Anticancer Res 2017;37:3807-10. [PubMed]
33. Brambilla E, Travis WD, Colby TV, Corrin B, Shimosato Y. The new World Health Organization classification of lung tumours. Eur Respir J 2001;18:1059-68. [Crossref] [PubMed]