ArticleJournal of thoracic disease2022
Native T1-mapping and diffusion-weighted imaging (DWI) can be used to identify lung cancer pathological types and their correlation with Ki-67 expression.
Article in Journal of thoracic disease, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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17 citing papers in PubMed.
- Differentiating pulmonary sarcoidosis from mediastinal lymph node metastasis via conventional computed tomography features and quantitative imaging parameters: a retrospective study.Quantitative imaging in medicine and surgery · 2026Article
- T1 mapping-based multi-parametric MRI for subtyping and differentiation grading of non-small cell lung cancer.Quantitative imaging in medicine and surgery · 2026Article
- Development and validation of an MRI-based clinical-radiomics nomogram for predicting lymph node metastasis in non-small cell lung cancer.BMC medical imaging · 2026Article
- The value of T1 mapping and intravoxel incoherent motion parameters in predicting PD-L1 expression and dynamically monitoring immunotherapy in advanced lung cancer.Frontiers in oncology · 2026Article
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- B1 corrected T1 mapping in the differentiation and prediction of breast cancer.Scientific reports · 2025Article
- The value of multisequence MR in the morphological evaluation of peripheral lung cancer.BMC medical imaging · 2025Article
- Prediction of non-small cell lung cancer subtypes is possible through restricted spectrum imaging.Frontiers in oncology · 2025Article
- Amide proton transfer-weighted imaging and T1 mapping as diagnostic tools to evaluate high-risk histopathologic phenotypes before surgery in rectal adenocarcinoma and their correlation with Ki-67 expression:a two-center study.Frontiers in oncology · 2025Article
- T1 mapping-based radiomics in the identification of histological types of lung cancer: a reproducibility and feasibility study.BMC medical imaging · 2024Article
- Effectiveness of Apparent Diffusion Coefficient Values in Predicting Pathologic Subtypes and Grade in Non-Small-Cell Lung Cancer.Diagnostics (Basel, Switzerland) · 2024Article
- Article
- Histogram analysis of multiple diffusion models for predicting advanced non-small cell lung cancer response to chemoimmunotherapy.Cancer imaging : the official publication of the International Cancer Imaging Society · 2024Article
- Differentiating inflammatory and malignant pulmonary lesions on 3T lung MRI with radiomics of apparent diffusion coefficient maps and T2w derived radiomic feature maps.Journal of thoracic disease · 2024Article
- T1 mapping and multimodel diffusion-weighted imaging in the assessment of cervical cancer: a preliminary study.The British journal of radiology · 2023Article
- Observational Study of the Natural Growth History of Peripheral Small-Cell Lung Cancer on CT Imaging.Diagnostics (Basel, Switzerland) · 2023Article
- The Role of Native T1 and T2 Mapping Times in Identifying PD-L1 Expression and the Histological Subtype of NSCLCs.Cancers · 2023Article
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Abstract
Background: This study aimed to explore the value of native T1-mapping and diffusion-weighted imaging (DWI) in differentiating the pathological types and degree of tumor differentiation of lung cancer and their correlation with Ki-67 protein expression. Methods: A total of 78 consecutive lung cancer patients who received chest magnetic resonance imaging (MRI) scans between May 2020 and June 2021 were enrolled in this study. Two radiologists independently analyzed the apparent diffusion coefficient (ADC) and T1 values for each lesion. The intraclass correlation coefficient (ICC) and Bland-Altman plots were generated to assess interobserver agreement of the T1 and ADC mean values in lesions. The difference in ADC and T1 values among different pathological types, as well as between high- and low-differentiated lung cancers were analyzed, and diagnostic efficacy was evaluated by receiver operating characteristic (ROC) curve analysis. The correlation between ADC value, T1 value, and Ki-67 protein expression index was determined. Results: The ADC and T1 values showed excellent interobserver agreement (ICC 0.820, 0.942, respectively). There was a significant difference in ADC values between small cell carcinoma and squamous carcinoma (P<0.05), and between small cell carcinoma and adenocarcinoma (P<0.05), but not between squamous carcinoma and adenocarcinoma (P>0.05). A significant difference in T1 values was observed between small cell carcinoma (P<0.05) and adenocarcinoma, and between squamous carcinoma (P<0.05) and adenocarcinoma, but not between squamous carcinoma and small cell carcinoma (P>0.05). There were statistically significant differences in ADC and T1 values between the moderately and highly differentiated group and the poorly differentiated group (P<0.05). ROC curve analysis showed that the T1 combined with ADC value had high diagnostic value for the degree of differentiation of the tumor [area under the curve (AUC) =0.912]. Pearson correlation analysis showed a significant positive correlation between T1 value and Ki-67 index (r=0.66, P<0.001) and a significant negative correlation between ADC value and Ki-67 index (r=-0.45, P<0.01). Conclusions: T1 and ADC values can be used to distinguish the pathological type and differentiation degree of lung cancer.
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