Prognostic Value of 18F-FDG PET/CT Radiomics in Extranodal Nasal-Type NK/T Cell Lymphoma.

OBJECTIVE: To investigate the prognostic utility of radiomics features extracted from 18F-fluorodeoxyglucose (FDG) PET/CT combined with clinical factors and metabolic parameters in predicting progression-free survival (PFS) and overall survival (OS) in individuals diagnosed with extranodal nasal-type NK/T cell lymphoma (ENKTCL). MATERIALS AND METHODS: A total of 126 adults with ENKTCL who underwent 18F-FDG PET/CT examination before treatment were retrospectively included and randomly divided into training (n = 88) and validation cohorts (n = 38) at a ratio of 7:3. Least absolute shrinkage and selection operation Cox regression analysis was used to select the best radiomics features and calculate each patient's radiomics scores (RadPFS and RadOS). Kaplan-Meier curve and Log-rank test were used to compare survival between patient groups risk-stratified by the radiomics scores. Various models to predict PFS and OS were constructed, including clinical, metabolic, clinical + metabolic, and clinical + metabolic + radiomics models. The discriminative ability of each model was evaluated using Harrell's C index. The performance of each model in predicting PFS and OS for 1-, 3-, and 5-years was evaluated using the time-dependent receiver operating characteristic (ROC) curve. RESULTS: Kaplan-Meier curve analysis demonstrated that the radiomics scores effectively identified high- and low-risk patients (all P < 0.05). Multivariable Cox analysis showed that the Ann Arbor stage, maximum standardized uptake value (SUVmax), and RadPFS were independent risk factors associated with PFS. Further, ß2-microglobulin, Eastern Cooperative Oncology Group performance status score, SUVmax, and RadOS were independent risk factors for OS. The clinical + metabolic + radiomics model exhibited the greatest discriminative ability for both PFS (Harrell's C-index: 0.805 in the validation cohort) and OS (Harrell's C-index: 0.833 in the validation cohort). The time-dependent ROC analysis indicated that the clinical + metabolic + radiomics model had the best predictive performance. CONCLUSION: The PET/CT-based clinical + metabolic + radiomics model can enhance prognostication among patients with ENKTCL and may be a non-invasive and efficient risk stratification tool for clinical practice.

Application of diffusion kurtosis imaging and 18F-FDG PET in evaluating the subtype, stage and proliferation status of non-small cell lung cancer.

Background: Lung cancer has become one of the deadliest tumors in the world. Non-small cell lung cancer (NSCLC) is the most common type of lung cancer, accounting for approximately 80%-85% of all lung cancer cases. This study aimed to investigate the value of diffusion kurtosis imaging (DKI), diffusion-weighted imaging (DWI) and 2-[18F]-fluoro-2-deoxy-D-glucose positron emission tomography (18F-FDG PET) in differentiating squamous cell carcinoma (SCC) and adenocarcinoma (AC) and to evaluate the correlation of each parameter with stage and proliferative status Ki-67. Methods: Seventy-seven patients with lung lesions were prospectively scanned by hybrid 3.0-T chest 18F-FDG PET/MR. Mean kurtosis (MK), mean diffusivity (MD), apparent diffusion coefficient (ADC), maximum standard uptake value (SUVmax), metabolic tumor volume (MTV) and total lesion glycolysis (TLG) were measured. The independent samples t test or Mann-Whitney U test was used to compare and analyze the differences in each parameter of SCC and AC. The diagnostic efficacy was evaluated by receiver operating characteristic (ROC) curve analysis and compared with the DeLong test. A logistic regression analysis was used for the evaluation of independent predictors. Bootstrapping (1000 samples) was performed to establish a control model, and calibration curves and ROC curves were used to validate its performance. Pearson's correlation coefficient and Spearman's correlation coefficient were calculated for correlation analysis. Results: The MK and ADC values of the AC group were significantly higher than those of the SCC group (all P< 0.05), and the SUVmax, MTV, and TLG values of the SCC group were significantly higher than those of the AC group (all P<0.05). There was no significant difference in the MD value between the two groups. Moreover, MK, SUVmax, TLG and MTV were independent predictors of the NSCLC subtype, and the combination of these parameters had an optimal diagnostic efficacy (AUC, 0.876; sensitivity, 86.27%; specificity, 80.77%), which was significantly better than that of MK (AUC = 0.758, z = 2.554, P = 0.011), ADC (AUC = 0.679, z = 2.322, P = 0.020), SUVmax (AUC = 0.740, z = 2.584, P = 0.010), MTV (AUC = 0.715, z = 2.530, P = 0.011) or TLG (AUC = 0.716, z = 2.799, P = 0.005). The ROC curve showed that the validation model had high accuracy in identifying AC and SCC (AUC, 0.844; 95% CI, 0.785-0.885);. The SUVmax value was weakly positively correlated with the Ki-67 index (r = 0.340, P< 0.05), the ADC and MD values were weakly negatively correlated with the Ki-67 index (r = -0.256, -0.282, P< 0.05), and the MTV and TLG values were weakly positively correlated with NSCLC stage (r = 0.342, 0.337, P< 0.05). Conclusion: DKI, DWI and 18F-FDG PET are all effective methods for assessing the NSCLC subtype, and some parameters are correlated with stage and proliferation status.

A comparison study of monoexponential and fractional order calculus diffusion models and 18F-FDG PET in differentiating benign and malignant solitary pulmonary lesions and their pathological types.

Objective: To evaluate the application value of monoexponential, fractional order calculus (FROC) diffusion models and PET imaging to distinguish between benign and malignant solitary pulmonary lesions (SPLs) and malignant SPLs with different pathological types and explore the correlation between each parameter and Ki67 expression. Methods: A total of 112 patients were enrolled in this study. Prior to treatment, all patients underwent a dedicated thoracic 18F-FDG PET/MR examination. Five parameters [including apparent diffusion coefficient (ADC) derived from the monoexponential model; diffusion coefficient (D), a microstructural quantity (µ), and fractional order parameter (ß) derived from the FROC model and maximum standardized uptake value (SUVmax) derived from PET] were compared between benign and malignant SPLs and different pathological types of malignant SPLs. Independent sample t test, Mann-Whitney U test, DeLong test and receiver operating characteristic (ROC) curve analysis were used for statistical evaluation. Pearson correlation analysis was used to calculate the correlations between Ki-67 and ADC, D, µ, ß, and SUVmax. Results: The ADC and D values were significantly higher and the µ and SUVmax values were significantly lower in the benign group [1.57 (1.37, 2.05) µm2/ms, 1.59 (1.52, 1.72) µm2/ms, 5.06 (3.76, 5.66) µm, 5.15 ± 2.60] than in the malignant group [1.32 (1.03, 1.51) µm2/ms, 1.43 (1.29, 1.52) µm2/ms, 7.06 (5.87, 9.45) µm, 9.85 ± 4.95]. The ADC, D and ß values were significantly lower and the µ and SUVmax values were significantly higher in the squamous cell carcinoma (SCC) group [1.29 (0.66, 1.42) µm2/ms, 1.32 (1.02, 1.42) µm2/ms, 0.63 ± 0.10, 9.40 (7.76, 15.38) µm, 11.70 ± 5.98] than in the adenocarcinoma (AC) group [1.40 (1.28, 1.67) µm2/ms, 1.52 (1.44, 1.64) µm2/ms, 0.70 ± 0.10, 5.99 (4.54, 6.87) µm, 8.76 ± 4.18]. ROC curve analysis showed that for a single parameter, µ exhibited the best AUC value in discriminating between benign and malignant SPLs groups and AC and SCC groups (AUC = 0.824 and 0.911, respectively). Importantly, the combination of monoexponential, FROC models and PET imaging can further improve diagnostic performance (AUC = 0.872 and 0.922, respectively). The Pearson correlation analysis showed that Ki67 was positively correlated with µ value and negatively correlated with ADC and D values (r = 0.402, -0.346, -0.450, respectively). Conclusion: The parameters D and µ derived from the FROC model were superior to ADC and SUVmax in distinguishing benign from malignant SPLs and adenocarcinoma from squamous cell carcinoma, in addition, the combination of multiple parameters can further improve diagnostic performance. The non-Gaussian FROC diffusion model is expected to become a noninvasive quantitative imaging technique for identifying SPLs.

The value of diffusion kurtosis imaging, diffusion weighted imaging and 18F-FDG PET for differentiating benign and malignant solitary pulmonary lesions and predicting pathological grading.

Objective: To explore the value of PET/MRI, including diffusion kurtosis imaging (DKI), diffusion weighted imaging (DWI) and positron emission tomography (PET), for distinguishing between benign and malignant solitary pulmonary lesions (SPLs) and predicting the histopathological grading of malignant SPLs. Material and methods: Chest PET, DKI and DWI scans of 73 patients with SPL were performed by PET/MRI. The apparent diffusion coefficient (ADC), mean diffusivity (MD), mean kurtosis (MK), maximum standard uptake value (SUVmax), metabolic total volume (MTV) and total lesion glycolysis (TLG) were calculated. Student's t test or the Mann-Whitney U test was used to analyze the differences in parameters between groups. Receiver operating characteristic (ROC) curves were used to evaluate the diagnostic efficacy. Logistic regression analysis was used to evaluate independent predictors. Results: The MK and SUVmax were significantly higher, and the MD and ADC were significantly lower in the malignant group (0.59 ± 0.13, 10.25 ± 4.20, 2.27 ± 0.51[×10-3 mm2/s] and 1.35 ± 0.33 [×10-3 mm2/s]) compared to the benign group (0.47 ± 0.08, 5.49 ± 4.05, 2.85 ± 0.60 [×10-3 mm2/s] and 1.67 ± 0.33 [×10-3 mm2/s]). The MD and ADC were significantly lower, and the MTV and TLG were significantly higher in the high-grade malignant SPLs group (2.11 ± 0.51 [×10-3 mm2/s], 1.35 ± 0.33 [×10-3 mm2/s], 35.87 ± 42.24 and 119.58 ± 163.65) than in the non-high-grade malignant SPLs group (2.46 ± 0.46 [×10-3 mm2/s], 1.67 ± 0.33[×10-3 mm2/s], 20.17 ± 32.34 and 114.20 ± 178.68). In the identification of benign and malignant SPLs, the SUVmax and MK were independent predictors, the AUCs of the combination of SUVmax and MK, SUVmax, MK, MD, and ADC were 0.875, 0.787, 0.848, 0.769, and 0.822, respectively. In the identification of high-grade and non-high-grade malignant SPLs, the AUCs of MD, ADC, MTV, and TLG were 0.729, 0.680, 0.693, and 0.711, respectively. Conclusion: DWI, DKI, and PET in PET/MRI are all effective methods to distinguish benign from malignant SPLs, and are also helpful in evaluating the pathological grading of malignant SPLs.

Identification of histological features of endometrioid adenocarcinoma based on amide proton transfer-weighted imaging and multimodel diffusion-weighted imaging.

BACKGROUND: Noninvasive identification of the histological features of endometrioid adenocarcinoma is necessary. This study aimed to investigate whether amide proton transfer-weighted imaging (APTWI) and multimodel (monoexponential, biexponential, and stretched exponential) diffusion-weighted imaging (DWI) could predict the histological grade of endometrial adenocarcinoma (EA). In addition, we analyzed the correlation between each parameter and the Ki-67 index. METHODS: A total of 90 EA patients who received pelvic magnetic resonance imaging (MRI) were enrolled. The magnetization transfer ratio asymmetry [MTRasym (3.5 ppm)], apparent diffusion coefficient (ADC), diffusion coefficient (D), pseudo-diffusion coefficient (D*), perfusion fraction (f), distributed diffusion coefficient (DDC), and water molecular diffusion heterogeneity index (α) were measured and compared. Correlation coefficients between each parameter and histological grade and the Ki-67 index were calculated. Statistical methods included the independent samples t test, Spearman's correlation, and logistic regression. RESULTS: MTRasym (3.5 ppm) [(3.72%±0.31%) vs. (3.27%±0.48%)], f [(3.15%±0.36%) vs. (2.69%±0.83%)], and α [(0.89±0.05) vs. (0.81±0.09)] were higher and ADC [(0.82±0.08) vs. (0.89±0.10) ×10-3 mm2/s], D [(0.67±0.09) vs. (0.81±0.11) ×10-3 mm2/s], and DDC [(1.04±0.09) vs. (1.13±0.13) ×10-3 mm2/s] were lower in high-grade EA than in low-grade EA (P<0.05). MTRasym (3.5 ppm) and D were independent predictors for the histological grade of EA. The combination of MTRasym (3.5 ppm) and D were better able to identify high- and low-grade EA than was each parameter. MTRasym (3.5 ppm) and α were moderately and weakly positively correlated, respectively, with histological grade and the Ki-67 index (r=0.528, r=0.514, r=0.395, and r=0.367; P<0.05). D was moderately negatively correlated with histological grade and the Ki-67 index (r=-0.540 and r=-0.529; P<0.05). DDC was weakly and moderately negatively correlated with histological grade and the Ki-67 index, respectively (r=-0.473 and r=-0.515; P<0.05). ADC was weakly negatively correlated with histological grade and the Ki-67 index (r=-0.417 and r=-0.427; P<0.05). f was weakly positively correlated with histological grade and the Ki-67 index (r=0.294 and r=0.355; P<0.05). CONCLUSIONS: Our study found that both multimodel DWI and APTWI could be used to estimate the histological grade and Ki-67 index of EA, and the combination of high MTRasym (3.5 ppm) and low D may be an effective imaging marker for predicting the grade of EA.

A Comparative Study of Amide Proton Transfer Weighted Imaging and Intravoxel Incoherent Motion MRI Techniques Versus (18) F-FDG PET to Distinguish Solitary Pulmonary Lesions and Their Subtypes.

BACKGROUND: Amide proton transfer weighted imaging (APTw), intravoxel incoherent motion (IVIM), and positron emission tomography (PET) imaging all have the potential to characterize solitary pulmonary lesions (SPLs). PURPOSE: To compare APTw and IVIM with PET imaging for distinguishing between benign and malignant SPLs and their subtypes. STUDY TYPE: Prospective. POPULATION: Ninety-five patients, 78 with malignant SPLs (including 48 with adenocarcinoma [AC] and 17 with squamous cell carcinoma [SCC]), and 17 with benign SPLs. FIELD STRENGTH/SEQUENCE: Fast spin-echo (FSE) T2WI, FSE APTw and echo-planar imaging IVIM, MR-base attenuation correction (MRAC), and PET imaging on a 3-T whole-body PET/MR system. ASSESSMENT: The magnetization transfer ratio asymmetry (MTRasym) at 3.5 ppm, diffusion coefficient (D), pseudo diffusion coefficient (D*), perfusion fraction (f), and the maximum standardized uptake value (SUVmax) were analyzed. STATISTICAL TESTS: Individual sample t-test, Delong test, Pearson's correlation analysis, and area under the receiver operating characteristic curve (AUC). P < 0.05 indicated statistical significance. RESULTS: The MTRasym and SUVmax were significantly higher, and D was significantly lower in the malignant group (3.3 ± 2.6 [%], 7.8 ± 5, and 1.2 ± 0.3 [×10-3 mm2 /second]) compared to the benign group (-0.3 ± 1.6 [%], 3.1 ± 3.8, and 1.6 ± 0.3 [×10-3 mm2 /second]). The MTRasym and D were significantly lower, and SUVmax was significantly higher in the SCC group (0.8 ± 1.0 [%], 1.0 ± 0.2 [×10-3 mm2 /second] than in the AC group (4.1 ± 2.6 [%], 1.3 ± 0.3 [×10-3 mm2 /second], 6.7 ± 4.6). Besides, the combination (AUC = 0.964) of these three methods showed higher diagnostic efficacy than any individual method (AUC = 0.917, 0.851, 0.82, respectively) in identifying malignant and benign SPLs. However, APTw showed better diagnostic efficacy than the combination of three methods or PET imaging alone in distinguishing SCC and AC groups (AUC = 0.934, 0.781, 0.725, respectively). DATA CONCLUSION: APTw, IVIM, and PET imaging are all effective methods to distinguish benign and malignant SPLs and their subtypes. APTw is potentially more capable than PET imaging of distinguishing lung SCC from AC. LEVEL OF EVIDENCE: 2 TECHNICAL EFFICACY: Stage 2.

Whole-Body Parametric Imaging of 18F-FDG PET Using uEXPLORER with Reduced Scanning Time.

Parametric imaging of the net influx rate (Ki ) in 18F-FDG PET has been shown to provide improved quantification and specificity for cancer detection compared with SUV imaging. Current methods of generating parametric images usually require a long dynamic scanning time. With the recently developed uEXPLORER scanner, a dramatic increase in sensitivity has reduced the noise in dynamic imaging, making it more robust to use a nonlinear estimation method and flexible protocols. In this work, we explored 2 new possible protocols besides the standard 60-min one for the possibility of reducing scanning time for Ki imaging. Methods: The gold standard protocol (protocol 1) was conventional dynamic scanning with a 60-min scanning time. The first proposed protocol (protocol 2) included 2 scanning periods: 0-4 min and 54-60 min after injection. The second proposed protocol (protocol 3) consisted of a single scanning period from 50 to 60 min after injection, with a second injection applied at 56 min. The 2 new protocols were simulated from the 60-min standard scans. A hybrid input function combining the population-based input function and the image-derived input function (IDIF) was used. The results were also compared with the IDIF acquired from protocol 1. A previously developed maximum-likelihood approach was used to estimate the Ki images. In total, 7 cancer patients imaged using the uEXPLORER scanner were enrolled in this study. Lesions were identified from the patient data, and the lesion Ki values were compared among the different protocols. Results: The acquired hybrid input function was comparable in shape to the IDIF for each patient. The average difference in area under the curve was about 3%, suggesting good quantitative accuracy. The visual difference between the Ki images generated using IDIF and those generated using the hybrid input function was also minimal. The acquired Ki images using different protocols were visually comparable. The average Ki difference in the lesions was 2.8% ± 2.1% for protocol 2 and 1% ± 2.2% for protocol 3. Conclusion: The results suggest that it is possible to acquire Ki images using the nonlinear estimation approach with a much-reduced scanning time. Between the 2 new protocols, the protocol with dual injection shows the greatest promise in terms of practicality.

Application of Simultaneous 18 F-FDG PET With Monoexponential, Biexponential, and Stretched Exponential Model-Based Diffusion-Weighted MR Imaging in Assessing the Proliferation Status of Lung Adenocarcinoma.

BACKGROUND: Ki-67 proliferation index (PI) is important for providing information on tumor behavior, treatment response, and prognosis. Integrated positron emission tomography/magnetic resonance (PET/MR) may have the potential to assess Ki-67 PI in patients with lung adenocarcinoma. PURPOSE: To explore the value of simultaneous 18 F-fluorodeoxyglucose (18 F-FDG) PET/MR-derived parameters in assessing the proliferation status of lung adenocarcinoma and to determine the best combination of parameters. STUDY TYPE: Prospective. POPULATION: Seventy-eight patients with lung adenocarcinoma and with Ki-67 PI. FIELD STRENGTH/SEQUENCE: 3.0 T, simultaneous PET/MRI including diffusion-weighted imaging (DWI) and 18 F-FDG PET. ASSESSMENT: DWI-derived parameters, namely, apparent diffusion coefficient (ADC), true diffusion coefficient (D), pseudo diffusion coefficient (D*), perfusion fraction (f), diffusion heterogeneity index (α), and distributed diffusion coefficient (DDC); and PET-derived parameters, namely, maximum standardized uptake value (SUVmax ), metabolic tumor volume (MTV), and total lesion glycolytic volume (TLG), were calculated and compared between the high (>25%) and low (≤25%) Ki-67 PI groups. The correlations between PET-derived parameters and DWI-derived parameters were analyzed. STATISTICAL TESTS: Student's t-test, Mann-Whitney U test, chi-square test, and receiver operating characteristic (ROC) curves. A P-value <0.05 was considered statistically significant. RESULTS: The SUVmax , MTV, TLG, ADC, D, and DDC values were significantly different between the high (N = 35) and low Ki-67 PI groups (N = 43). D, SUVmax , and MTV independently predicted the Ki-67 PI status. The combination of D, SUVmax , and MTV had the largest area under the ROC curve (AUC = 0.900), which was significantly larger than the AUC alone of DDC (AUC = 0.725), SUVmax (AUC = 0.815), MTV (AUC = 0.774), or TLG (AUC = 0.783). The perfusion fraction did not correlate with SUVmax , MTV, or TLG (r = -0.03, -0.11, and -0.04, respectively; P = 0.786, 0.348, and 0.733). DATA CONCLUSION: The combination of D, SUVmax , and MTV may predict Ki-67 PI status. No correlation was observed between perfusion parameters and metabolic parameters. LEVEL OF EVIDENCE: 1 TECHNICAL EFFICACY: Stage 2.

Whole-lesion histogram analysis of multiple diffusion metrics for differentiating lung cancer from inflammatory lesions.

Background: Whole-lesion histogram analysis can provide comprehensive assessment of tissues by calculating additional quantitative metrics such as skewness and kurtosis; however, few studies have evaluated its value in the differential diagnosis of lung lesions. Purpose: To compare the diagnostic performance of conventional diffusion-weighted imaging (DWI), intravoxel incoherent motion (IVIM) magnetic resonance imaging (MRI) and diffusion kurtosis imaging (DKI) in differentiating lung cancer from focal inflammatory lesions, based on whole-lesion volume histogram analysis. Methods: Fifty-nine patients with solitary pulmonary lesions underwent multiple b-values DWIs, which were then postprocessed using mono-exponential, bi-exponential and DKI models. Histogram parameters of the apparent diffusion coefficient (ADC), true diffusivity (D), pseudo-diffusion coefficient (D*), and perfusion fraction (f), apparent diffusional kurtosis (Kapp) and kurtosis-corrected diffusion coefficient (Dapp) were calculated and compared between the lung cancer and inflammatory lesion groups. Receiver operating characteristic (ROC) curves were constructed to evaluate the diagnostic performance. Results: The ADCmean, ADCmedian, D mean and D median values of lung cancer were significantly lower than those of inflammatory lesions, while the ADCskewness, Kapp mean, Kapp median, Kapp SD, Kapp kurtosis and Dapp skewness values of lung cancer were significantly higher than those of inflammatory lesions (all p < 0.05). ADCskewness (p = 0.019) and D median (p = 0.031) were identified as independent predictors of lung cancer. D median showed the best performance for differentiating lung cancer from inflammatory lesions, with an area under the ROC curve of 0.777. Using a D median of 1.091 × 10-3 mm2/s as the optimal cut-off value, the sensitivity, specificity, positive predictive value and negative predictive value were 69.23%, 85.00%, 90.00% and 58.62%, respectively. Conclusions: Whole-lesion histogram analysis of DWI, IVIM and DKI parameters is a promising approach for differentiating lung cancer from inflammatory lesions, and D median shows the best performance in the differential diagnosis of solitary pulmonary lesions.

Amide Proton Transfer-Weighted Imaging and Multiple Models Diffusion-Weighted Imaging Facilitates Preoperative Risk Stratification of Early-Stage Endometrial Carcinoma.

BACKGROUND: Endometrial carcinoma (EC) risk stratification is generally based on histological assessment. It would be beneficial to perform risk stratification noninvasively by MRI. PURPOSE: To investigate the application of amide proton transfer-weighted imaging (APTWI), monoexponential, biexponential, and stretched exponential intravoxel incoherent motion (IVIM), and diffusion kurtosis imaging (DKI) for the evaluation of risk stratification in early-stage EC. STUDY TYPE: Prospective. POPULATION: Eighty patients with early-stage EC (47 classified as low risk, 20 as medium risk, and 13 as high risk by histological grade and International Federation of Gynecology and Obstetrics stage). FIELD STRENGTH/SEQUENCE: T1-weighted imaging, T2-weighted imaging, IVIM, APTWI, and DKI MRI at 3 T. ASSESSMENT: The magnetization transfer ratio asymmetry (MTRasym [3.5 ppm]), apparent diffusion coefficient (ADC), diffusion coefficient (D), pseudo diffusion coefficient (D*), perfusion fraction (f), distributed diffusion coefficient (DDC), water molecular diffusion heterogeneity index (α), mean kurtosis (MK), and mean diffusivity (MD) were calculated and compared between low-risk and non-low-risk groups. STATISTICAL TESTS: Individual sample t test, analysis of variance, and logistic regression. A P-value <0.05 was considered statistically significant. RESULTS: The α, ADC, D, DDC, and MD were significantly higher and the f, MK, and MTRasym (3.5 ppm) were significantly lower in the low-risk group than in the non-low-risk group. The difference in D* between the two groups was not significant (P = 0.289). MTRasym (3.5 ppm), D, and MK were independent predictors of risk stratification. The combination of these three parameters was better able to identify low- and non-low-risk groups than each individual parameter. DATA CONCLUSION: The IVIM, DKI, and APTWI parameters have potential as imaging markers for risk stratification in early-stage EC. LEVEL OF EVIDENCE: 2 TECHNICAL EFFICACY: Stage 3.

Evaluation of amide proton transfer-weighted imaging for endometrial carcinoma histological features: a comparative study with diffusion kurtosis imaging.

OBJECTIVES: To investigate whether amide proton transfer-weighted imaging (APTWI) and diffusion kurtosis imaging (DKI) can be used to evaluate endometrial carcinoma (EC) in terms of clinical type, histological grade, subtype, and Ki-67 index. METHODS: Eighty-eight patients with EC underwent pelvic DKI and APTWI. The non-Gaussian diffusion coefficient (Dapp), apparent kurtosis coefficient (Kapp), and magnetization transfer ratio asymmetry (MTRasym (3.5 ppm)) were calculated and compared based on the clinical type (type I, II), histological grade (high- and low-grade), and subtype (endometrioid adenocarcinoma (EA) and non-EA). Correlation coefficients were calculated for each parameter with histological grades and the Ki-67 index. RESULTS: The MTRasym (3.5 ppm) and Kapp values were higher in the type II group and high-grade group than in the type I and low-grade groups, respectively, while the Dapp values were lower in the type I and low-grade groups, respectively (all p < 0.05). The Kapp value was higher in the EA group than in the non-EA group (p = 0.022). The Kapp value was the only independent predictor for the histological grade of EA and the clinical type of EC. The AUC (DKI) was higher than the AUC (APTWI) in the identification of type I and II EC and high- and low-grade EA (Z = 2.042, 2.013, p = 0.041, 0.044), while in the identification of EA and non-EA, only the difference in Kapp was statistically significant. Moreover, the Kapp and MTRasym (3.5 ppm) values and Dapp values correlated positively and negatively, respectively, with histological grade (r = 0.759, 0.555, 0.624, and 0.462, all p < 0.05) and Ki-67 index (r = -0.704, -0.507, all p < 0.05). CONCLUSION: Both DKI- and APTWI-related parameters have potential as imaging markers in estimating the histological features of EC, while DKI shows better performance than APTWI in this study. KEY POINTS: • DKI and APTWI can be used to preliminarily evaluate the histological characteristics of endometrial carcinoma (EC). • The Kapp was the only independent predictor for the histological grade of EA and the clinical type of EC. • The Kapp, MTRasym (3.5 ppm), and Dapp correlated positively and negatively, respectively, with histological grade and Ki-67 index.