Background: This study investigates the association of T1, T2, proton density (PD) and the apparent diffusion coefficient (ADC) with histopathologic features of endometrial carcinoma (EC). Methods: One hundred and nine EC patients were prospectively enrolled from August 2019 to December 2020. Synthetic magnetic resonance imaging (MRI) was acquired through one acquisition, in addition to diffusion-weighted imaging (DWI) and other conventional sequences using 1.5T MRI. T1, T2, PD derived from synthetic MRI and ADC derived from DWI were compared among different histopathologic features, namely the depth of myometrial invasion (MI), tumor grade, cervical stromal invasion (CSI) and lymphovascular invasion (LVSI) of EC by the Mann–Whitney U test. Classification models based on the significant MRI metrics were constructed with their respective receiver operating characteristic (ROC) curves, and their micro-averaged ROC was used to evaluate the overall performance of these significant MRI metrics in determining aggressive histopathologic features of EC. Results: EC with MI had significantly lower T2, PD and ADC than those without MI (p = 0.007, 0.006 and 0.043, respectively). Grade 2–3 EC and EC with LVSI had significantly lower ADC than grade 1 EC and EC without LVSI, respectively (p = 0.005, p = 0.020). There were no differences in the MRI metrics in EC with or without CSI. Micro-averaged ROC of the three models had an area under the curve of 0.83. Conclusions: Synthetic MRI provided quantitative metrics to characterize EC with one single acquisition. Low T2, PD and ADC were associated with aggressive histopathologic features of EC, offering excellent performance in determining aggressive histopathologic features of EC.

https://www.mdpi.com/2075-4418/12/12/2956/pdf?version=1669628267

Tissue Characteristics of Endometrial Carcinoma Analyzed by Quantitative Synthetic MRI and Diffusion-Weighted Imaging

Simple Summary T1 and T2 mapping are MRI techniques that are routinely used in the evaluation of both benign and malignant lesions in different organs and tissues. Immunohistochemical analysis is the gold standard method to assess the programmed death-ligand 1 protein (PD-L1) expression status in patients diagnosed with non-small cell lung cancer (NSCLC) to guide immunotherapy. There have been no studies on the correlation between T1 and T2 mapping values and PD-L1 expression of NSCLCs nor on the correlation between T2 mapping values and histological subtypes of lung tumors. Therefore, we present our preliminary results on the evaluation of the possible association of T1 and T2 mapping values with PD-L1 TPS and of their potential in distinguishing between the different histological subtypes of NSCLCs. In the future, T1 values could offer the possibility to help in the diagnosis and pathological classification of NSCLC through a non-invasive MRI exam. Abstract We investigated the association of T1/T2 mapping values with programmed death-ligand 1 protein (PD-L1) expression in lung cancer and their potential in distinguishing between different histological subtypes of non-small cell lung cancers (NSCLCs). Thirty-five patients diagnosed with stage III NSCLC from April 2021 to December 2022 were included. Conventional MRI sequences were acquired with a 1.5 T system. Mean T1 and T2 mapping values were computed for six manually traced ROIs on different areas of the tumor. Data were analyzed through RStudio. Correlation between T1/T2 mapping values and PD-L1 expression was studied with a Wilcoxon–Mann–Whitney test. A Kruskal–Wallis test with a post-hoc Dunn test was used to study the correlation between T1/T2 mapping values and the histological subtypes: squamocellular carcinoma (SCC), adenocarcinoma (ADK), and poorly differentiated NSCLC (PD). There was no statistically significant correlation between T1/T2 mapping values and PD-L1 expression in NSCLC. We found statistically significant differences in T1 mapping values between ADK and SCC for the periphery ROI (p-value 0.004), the core ROI (p-value 0.01), and the whole tumor ROI (p-value 0.02). No differences were found concerning the PD NSCLCs.

/pdf/the-role-of-native-t1-and-t2-mapping-times-in-identifying-pd-2n9zvrby.pdf

The Role of Native T1 and T2 Mapping Times in Identifying PD-L1 Expression and the Histological Subtype of NSCLCs

Abstract Objective To determine the performance of intravoxel incoherent motion (IVIM) parameters and the extracellular volume fraction (ECV) in distinguishing between different subtypes of lung cancer and predicting lymph node metastasis (LNM) status in patients with non-small-cell lung cancer (NSCLC). Methods One hundred sixteen patients with lung cancer were prospectively recruited. IVIM, native, and postcontrast T1 mapping examinations were performed, and the T1 values were measured to calculate the ECV. The differences in IVIM parameters and ECV were compared between NSCLC and small-cell lung cancer (SCLC), adenocarcinoma (Adeno-Ca) and squamous cell carcinoma (SCC), and NSCLC without and with LNM. The assessment of each parameter’s diagnostic performance was based on the area under the receiver operating characteristic curve (AUC). Results The apparent diffusion coefficient (ADC), true diffusion coefficient ( D ), and ECV values in SCLC were considerably lower compared with NSCLC (all p &lt; 0.001, AUC &gt; 0.887). The D value in SCC was substantially lower compared with Adeno-Ca ( p &lt; 0.001, AUC = 0.735). The perfusion fraction ( f ) and ECV values in LNM patients were markedly higher compared with those without LNM patients ( p &lt; 0.01, &lt; 0.001, AUC &gt; 0.708). Conclusion IVIM parameters and ECV can serve as non-invasive biomarkers for assisting in the pathological classification and LNM status assessment of lung cancer patients. Critical relevance statement IVIM parameters and ECV demonstrated remarkable potential in distinguishing pulmonary carcinoma subtypes and predicting LNM status in NSCLC. Key Points Lung cancer is prevalent and differentiating subtype and invasiveness determine the treatment course. True diffusion coefficient and ECV showed promise for subtyping and determining lymph node status. These parameters could serve as non-invasive biomarkers to help determine personalized treatment strategies. Graphical Abstract 

Predictors of lung cancer subtypes and lymph node status in non-small-cell lung cancer: intravoxel incoherent motion parameters and extracellular volume fraction

Additional file 1 of T1 and T2 mapping for identifying malignant lymph nodes in head and neck squamous cell carcinoma

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.

Native T1-mapping and Diffusion-Weighted Imaging (DWI) Can Be Used to Identify Lung Cancer Pathological Types and Their Correlation with Ki-67 Expression.

BACKGROUND T1 mapping can potentially quantitatively assess the intrinsic properties of tumors. B1 correction can reduce the magnetic field inhomogeneity. PURPOSE To assess the repeatability and reproducibility of B1 -corrected T1 mapping for lung cancer and the ability to identify pathological types. STUDY TYPE Prospective reproducibility study. POPULATION Sixty lung cancer patients (22 with emphysema) with a total of 60 lesions (adenocarcinoma [n = 23], squamous cell carcinoma [n = 19], and small-cell lung cancer [SCLC] [n = 18]). FIELD STRENGTH/SEQUENCE A 3 T/B1 -corrected 3D variable flip angle T1 mapping and free-breathing diffusion-weighted imaging. ASSESSMENT Intraobserver, interobserver, and test-retest reproducibility of minimum, maximum, mean, and SD of lung tumor T1 values were assessed. The correlation between mean T1 and apparent diffusion coefficient (ADC) and differences between different histological types of lung cancer were evaluated. STATISTICAL TESTS Intraclass correlation coefficients (ICCs), within-subject coefficients of variation (WCVs), Bland-Altman plots, Pearson's correlation coefficient (r), and analysis of variance (ANOVA). A P value <0.05 was considered to be statistically significant. RESULTS No significant differences were found in minimum, maximum, mean, and SD T1 values for repeated measurements (intraobserver and interobserver) and repeated examinations (P = 0.103-0.979). All parameters showed good intraobserver, interobserver and test-retest reproducibility (ICC, 0.780-0.978), except the maximum T1 value (ICC, 0.645-0.922). The mean T1 exhibited the best reproducibility and repeatability, with an average difference <6% for repeated measurements, <8% for repeated scans in lung cancer patients, and<10% for repeated scans in those with emphysema. The mean T1 correlated moderately with ADC (r = -0.580, -0.516, and -0.511 for observers A, B, and C). Both mean T1 and mean ADC were significantly different in SCLC patients compared with those in adenocarcinoma and squamous cell carcinoma patients. DATA CONCLUSION The mean T1 from B1 -corrected T1 mapping is a repeatable parameter with the potential to identify histological types of lung cancer and thus may be a promising imaging biomarker for characterizing lung cancer. EVIDENCE LEVEL 1 TECHNICAL EFFICACY: Stage 2.

B1 -Corrected T1 Mapping in Lung Cancer: Repeatability, Reproducibility, and Identification of Histological Types.

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B1 -Corrected T1 Mapping in Lung Cancer: Repeatability, Reproducibility, and Identification of Histological Types.