Soft tissue sarcoma: IVIM and DKI parameters correlate with Ki-67 labeling index on direct comparison of MRI and histopathological slices

Differentiation of benign, intermediate, and malignant soft-tissue tumours by using multiple diffusion-weighted imaging models

AIM

The aim of this study was to determine whether intravoxel incoherent motion (IVIM) and diffusion kurtosis imaging (DKI) can differentiate benign, intermediate, and malignant soft-tissue tumours (STTs) of the extremities and trunk.

MATERIALS AND METHODS

We prospectively recruited 100 STT patients (32, 15, and 53 patients with benign, intermediate, and malignant tumours, respectively). The patients underwent IVIM and DKI, and the following parameters were measured: standard apparent diffusion coefficient (ADC), perfusion fraction (f), true diffusion coefficient (Dslow), pseudo-diffusion coefficient (Dfast), water diffusion heterogeneity index (α), distributed diffusion coefficient (DDC), mean diffusivity (MD), and mean kurtosis (MK). Statistical analyses were performed using receiver operating characteristic curves, the Kruskal-Wallis H test, and post hoc test with Bonferroni correction.

RESULTS

Standard ADC, Dslow, DDC, and MD values gradually decreased from benign to intermediate and malignant STTs. Intermediate STTs displayed a lower f value than benign tumours (P=0.029). The MK value was higher in malignant tumours than in intermediate and benign tumours (P=0.021 and <0.001, respectively). The DDC value best differentiated benign tumours from nonbenign (intermediate and malignant) tumours (area under the curve [AUC] = 0.884, 0853, and 0.892, respectively). The optimal MK cut-off value for differentiating intermediate and malignant tumours was 0.65 (sensitivity: 73.33%, specificity: 81.13%, accuracy: 79.41%).

CONCLUSION

IVIM and DKI parameters were helpful for differentiating benign, intermediate, and malignant STTs and can complement conventional MRI, with DDC and MK values showing high diagnostic efficacy.

Comparative Analysis of Amide Proton Transfer and Diffusionweighted Imaging for Assessing Ki-67, p53 and PD-L1 Expression in Bladder Cancer

RATIONALE AND OBJECTIVES

To evaluate amide proton transfer (APT) imaging for assessing Ki-67, p53 and PD-L1 status in bladder cancer (BC) and compare its diagnostic efficacy with that of diffusion-weighted imaging (DWI).

MATERIALS AND METHODS

Consecutive patients suspected of BC were recruited for preoperative multiparametric MRI. APT signal was quantified by asymmetric magnetization transfer ratio (MTRasym). MTRasym and apparent diffusion coefficient (ADC) were measured by two radiologists, with interobserver agreement assessed. Spearman's correlation analyzed MTRasym values and molecular markers. The Whitney U test evaluated MTRasym and ADC variation based on molecular marker status. Optimal cutoff points were determined using area under the curve (AUC) analysis.

RESULTS

88 patients (72 ± 10 years; 77 men) with BC were studied. MTRasym values were significantly correlated with Ki-67, p53 and PD-L1 levels (P < 0.05). Higher MTRasym values were found in high Ki-67 expression BCs (1.89% [0.73%] vs. 1.23% ± 0.26%; P < 0.001), high p53 expression BCs (1.63% [0.56%] vs. 1.24% [0.56%]; P < 0.001) and positive PD-L1 expression BCs (2.02% [0.81%] vs. 1.48% [0.38%]; P < 0.001). Lower ADCs were found in high Ki-67 expression BCs (1.06 ×10-3 mm2/s [0.32 ×10-3 mm2/s] vs. 1.38 ×10-3 mm2/s [0.39 ×10-3 mm2/s]; P < 0.001). For p53 status, an MTRasym threshold of 1.27% had 95% sensitivity, 60% specificity, and AUC of 0.781. For PD-L1 status, a 1.90% threshold had 88% sensitivity, 92% specificity, and AUC of 0.859.

CONCLUSION

APT may significantly enhance the preoperative assessment of BC aggressiveness and inform targeted immunotherapy decisions, with performance superior to DWI.

Nomogram based on the neutrophil-to-lymphocyte ratio and MR diffusion quantitative parameters for predicting Ki67 expression in hepatocellular carcinoma from a prospective study

This study aimed to establish and validate a multiparameter prediction model for Ki67 expression in hepatocellular carcinoma (HCC) patients while also exploring its potential to predict the one-year recurrence risk. The clinical, pathological, and imaging data of 83 patients with HCC confirmed by postoperative pathology were analyzed, and the patients were randomly divided into a training set (n = 58) and a validation set (n = 25) at a ratio of 7:3. All patients underwent a magnetic resonance imaging (MRI) scan that included multi-b value diffusion-weighted scanning before surgery, and quantitative parameters were obtained via intravoxel incoherent motion (IVIM) and diffusion kurtosis (DKI) models. Univariate and multivariate logistic regression analyses were conducted using the training set data to construct a model, which was internally validated. The area under the curve (AUC) of the receiver operating characteristics (ROC), a decision curve analysis (DCA), and a calibration analysis were used to evaluate the model's performance. Additionally, for patients with available follow-up data, the combined model was evaluated for its potential utility in predicting the one-year recurrence risk by analyzing the area under the curve (AUC) of the receiver operating characteristic (ROC) curve.The combined model outperformed the clinicaland parametric models in predicting high Ki67 expression. The nomograms based on the combined model included the neutrophil-to-lymphocyte ratio (NLR), ADCslow_Aver. The model showed strong discrimination in the training set, with an AUC of 0.836 (95% CI: 0.729-0.942) and acceptable calibration (Hosmer-Lemeshow p = 0.109). In the validation set, the model maintained moderate discrimination (AUC 0.806, 95% CI: 0.621-0.990) with good calibration (p = 0.663). DCA revealed that the combined model provided good clinical value and correction effects. Additionally, when used to predict the one-year recurrence risk, the combined model achieved moderate accuracy (AUC = 0.747), highlighting its potential utility in identifying patients at a higher risk of recurrence. A nomogram incorporating the NLR and quantitative MR diffusion parameters effectively predicts Ki67 expression in HCC patients before surgery. The model also shows promise in predicting recurrence risk, which may aid in postoperative risk stratification and patient management. Clinical Relevance Statement We established a model that incorporated the NLR and quantitative magnetic resonance diffusion parameters, which demonstrated robust performance in predicting both high Ki67 expression and the one-year recurrence risk in HCC patients. This model shows potential clinical value in guiding postoperative risk stratification and personalized treatment planning.

Differentiating low- and high-proliferative soft tissue sarcomas using conventional imaging features and radiomics on MRI

BACKGROUND

Soft-tissue sarcomas are rare tumors of the soft tissue. Recent diagnostic studies mainly dealt with conventional image analysis and included only a few cases. This study investigated whether low- and high-proliferative soft tissue sarcomas can be differentiated using conventional imaging and radiomics features on MRI.

METHODS

In this retrospective study, soft tissue sarcomas were separated into two groups according to their proliferative activity: high-proliferative (Ki-67 ≥ 20%) and low-proliferative soft tissue sarcomas (Ki-67 < 20%). Several radiomics features, and various conventional imaging features on MRI like tumor heterogeneity, peritumoral edema, peritumoral contrast-enhancement, percentage of ill-defined tumor margins, Apparent Diffusion Coefficient (ADC) values, and area under the curve (AUC) in contrast dynamics were collected. These imaging features were independently compared with the two mentioned groups.

RESULTS

118 sarcoma cases were included in this study. Metastases were more prevalent in high-proliferative soft tissue sarcomas (p < 0.001), and time till metastasis negatively correlated with the Ki-67 proliferation index (k -0.43, p = 0.021). Several radiomics features representing intratumoral heterogeneity differed significantly between both groups, especially in T2-weighted (T2w) and contrast-enhanced T1-weighted (CE-T1w) sequences. Peritumoral contrast enhancement and edema were significantly more common in soft tissue sarcomas with a high Ki-67 index (p < 0.001). Tumor configuration, heterogeneity, and ill-defined margins were commonly seen in high-proliferative soft tissue sarcomas (p = 0.001-0.008). Diffusion restriction (ADC values) and contrast dynamics (AUC values) did not present significant differences between low- and high-proliferative soft tissue sarcomas.

CONCLUSIONS

Several radiomics and conventional imaging features indicate a higher Ki-67 proliferation index in soft tissue sarcomas and can therefore be used to distinguish between low- and high-proliferative soft tissue sarcomas.

DKI and 1H-MRS in angiogenesis evaluation of soft tissue sarcomas: a prospective clinical study based on MRI-pathology control method

BACKGROUND

The vascular endothelial growth factor (VEGF) and microvessel density (MVD) have been widely employed as angiogenesis indicators in the diagnosis and treatment of soft tissue sarcomas. While diffusion kurtosis imaging (DKI) and proton magnetic resonance spectroscopy (1H-MRS) imaging hold potential in assessing angiogenesis in other tumors, their reliability in correlating with angiogenesis in soft tissue sarcomas remains uncertain, contingent upon accurately acquiring the region of interest (ROI).

METHODS

23 patients with soft tissue sarcomas (STSs) confirmed by pathology were selected, underwent DKI and 1H-MRS at 3.0T MRI. The DKI parameters mean diffusivity (MD), mean kurtosis (MK), kurtosis anisotropy (KA), and 1H-MRS parameters choline (Cho), lipid/lactate (LL) were measured by two radiologists. Two pathologists obtained pathological slices using a new sampling method called MRI-pathology control and evaluated VEGF and MVD in the selected regions. Correlations between MRI parameters and angiogenesis markers were assessed by Person or Spearman tests.

RESULTS

The DKI parameters MD and KA, and the 1H-MRS parameters Cho and LL, have varying degrees of correlation with the expression levels of VEGF and MVD. Among them, Cho exhibits the strongest correlation (r = 0.875, P < 0.001; r = 0.807, P < 0.001).

CONCLUSION

Based on this preliminary clinical studies, DKI and 1H-MRS parameters are correlated with angiogenesis markers obtained through the "MRI-pathology control" method.

An Exploratory Study: Can Native T1 Mapping Differentiate Sarcoma from Benign Soft Tissue Tumors at 1.5 T and 3 T?

Background/Objectives: T1 relaxation time has been shown to be valuable in detecting and characterizing tumors in various organs. This study aims to determine whether native T1 relaxation time can serve as a useful tool in distinguishing sarcomas from benign tumors. Methods: In this retrospective study, patients with histologically confirmed soft tissue sarcomas and benign tumors were included. Only patients who had not undergone prior treatment or surgery and whose magnetic resonance imaging (MRI) included native T1 mapping were considered. Images were acquired using both 1.5 T and 3 T MRI scanners. T1 histogram parameters were measured in regions of interest encompassing the entire tumor volume, as well as in healthy muscle tissue. Results: Out of 316 cases, 16 sarcoma cases and 9 benign tumor cases were eligible. The T1 values observed in sarcoma did not significantly differ from those in benign lesions in both 1.5 T and 3 T MRIs (p1.5T = 0.260 and p3T = 0.119). However, T1 values were found to be lower in healthy tissues compared to sarcoma at 3 T (p = 0.020), although this difference did not reach statistical significance at 1.5 T (p = 0.063). At both 1.5 T and 3 T, no significant difference between healthy muscle measured in sarcoma cases or benign tumor cases was observed (p1.5T = 0.472 and p3T = 0.226). Conclusions: T1 mapping has the potential to serve as a promising tool for differentiating sarcomas from benign tumors in baseline assessments. However, the standardization of imaging protocols and further improvements in T1 mapping techniques are necessary to fully realize its potential.

Qualitative and quantitative MRI analysis of alveolar soft part sarcoma: correlation with histological grade and Ki-67 expression

OBJECTIVE

To investigate the correlation between MRI findings and histological features for preoperative prediction of histological grading and Ki-67 expression level in alveolar soft part sarcoma (ASPS).

METHODS

A retrospective analysis was conducted on 63 ASPS patients (Jan 2017-May 2023). All patients underwent 3.0-T MRI examinations, including conventional sequences, dynamic contrast-enhanced scans with time-intensity curve analysis, and diffusion-weighted imaging with apparent diffusion coefficient (ADC) measurements. Patients were divided into low-grade (histological Grade I) and high-grade (histological Grade II/III) groups based on pathology. Immunohistochemistry was used to assess Ki-67 expression levels in ASPS. Statistical analysis included chi-square tests, Wilcoxon rank-sum test, binary logistic regression analysis, Spearman correlation analysis, and receiver operating characteristic curve analysis of various observational data.

RESULTS

There were 29 low-grade and 34 high-grade patients (26 males and 37 females) and a wide age range (5-68 years). Distant metastasis, tumor enhancement characteristics, and ADC values were independent predictors of high-grade ASPS. High-grade ASPS had lower ADC values (p = 0.002), with an area under the curve (AUC), sensitivity, and specificity of 0.723, 79.4%, and 58.6%, respectively, for high-grade prediction. There was a negative correlation between ADC values and Ki-67 expression (r = -0.526; p < 0.001). When the cut-off value of ADC was 0.997 × 10-3 mm²/s, the AUC, sensitivity, and specificity for predicting high Ki-67 expression were 0.805, 65.6%, and 83.9%, respectively.

CONCLUSION

Qualitative and quantitative MRI parameters are valuable for predicting histological grading and Ki-67 expression levels in ASPS.

CRITICAL RELEVANCE STATEMENT

This study will help provide a more nuanced understanding of ASPS and guide personalized treatment strategies.

KEY POINTS

There is limited research on assessing ASPS prognosis through MRI. Metastasis, enhancement, and ADC correlated with histological grade; ADC related to Ki-67 expression. MRI provides clinicians with valuable information on ASPS grading and proliferation activity.

Characterization of parotid gland tumors: Whole-tumor histogram analysis of diffusion weighted imaging, diffusion kurtosis imaging, and intravoxel incoherent motion - A pilot study

PURPOSE

To investigate the diagnostic performance of histogram features of diffusion parameters in characterizating parotid gland tumors.

METHOD

From December 2018 to January 2023, patients who underwent diffusion weighted imaging (DWI), diffusion kurtosis imaging (DKI), and intravoxel incoherent motion (IVIM) were consecutively enrolled in this retrospective study. The histogram features of diffusion parameters, including apparent diffusion coefficient (ADC), diffusion coefficient (Dk), diffusion kurtosis (K), pure diffusion coefficient (D), pseudo-diffusion coefficient (DP), and perfusion fraction (FP) were analyzed. The Mann-Whitney U test was used for comparison between benign parotid gland tumors (BPGTs) and malignant parotid gland tumors (MPGTs). Receiver operating characteristic curve and logistic regression analysis were used to identify the differential diagnostic performance. The Spearman's correlation coefficient was used to analyze the correlation between diffusion parameters and Ki-67 labeling index.

RESULTS

For diffusion MRI, twenty-three histogram features of diffusion parameters showed significant differences between BPGTs and MPGTs (all P < 0.05). Compared with the DWI model, the IVIM model and combined model had better diagnostic specificity (58 %, 94 %, and 88 %, respectively; both corrected P < 0.001) and accuracy (64 %, 89 %, and 86 %, respectively; both corrected P = 0.006). The combined model was superior to the single DWI model with improved IDI (IDI improvement 0.25). Significant correlations were found between Ki-67 and ADCmean, Dkmean, Kmean, and Dmean (r = -0.57 to 0.53; all P < 0.05).

CONCLUSIONS

Whole-tumor histogram analysis of IVIM and combined diffusion model could further improve the diagnostic performance for differentiating BPGTs from MPGTs.

Synovial Sarcoma in the Extremity: Diversity of Imaging Features for Diagnosis and Prognosis

Synovial sarcomas are rare and highly aggressive soft-tissue sarcomas, primarily affecting adolescents and young adults aged 15-40 years. These tumors typically arise in the deep soft tissues, often near the large joints of the extremities. While the radiological features of these tumors are not definitely indicative, the presence of calcification in a soft-tissue mass (occurring in 30% of cases), adjacent to a joint, strongly suggests the diagnosis. Cross-sectional imaging characteristics play a crucial role in diagnosing synovial sarcomas. They often reveal significant characteristics such as multilobulation and pronounced heterogeneity (forming the "triple sign"), in addition to features like hemorrhage and fluid-fluid levels with septa (resulting in the "bowl of grapes" appearance). Nevertheless, the existence of non-aggressive features, such as gradual growth (with an average time to diagnosis of 2-4 years) and small size (initially measuring < 5 cm) with well-defined margins, can lead to an initial misclassification as a benign lesion. Larger size, older age, and higher tumor grade have been established as adverse predictive indicators for both local disease recurrence and the occurrence of metastasis. Recently, the prognostic importance of CT and MRI characteristics for synovial sarcomas was elucidated. These include factors like the absence of calcification, the presence of cystic components, hemorrhage, the bowl of grape sign, the triple sign, and intercompartmental extension. Wide surgical excision remains the established approach for definitive treatment. Gaining insight into and identifying the diverse range of presentations of synovial sarcomas, which correlate with the prognosis, might be helpful in achieving the optimal patient management.

Advances in The Application and Research of Magnetic Resonance Diffusion Kurtosis Imaging in The Musculoskeletal System

Magnetic resonance diffusion kurtosis imaging (DKI) is an emerging magnetic resonance imaging (MRI) technique that can reflect microstructural changes in tissue through non-Gaussian diffusion of water molecules. Compared to traditional diffusion weighted imaging (DWI), the DKI model has shown greater sensitivity for diagnosis of musculoskeletal diseases and can help formulate more reasonable treatment plans. Moreover, DKI is an important auxiliary examination for evaluation of the motor function of the musculoskeletal system. This article briefly introduces the basic principles of DKI and reviews the application and research of DKI in the evaluation of disorders of the musculoskeletal system (including bone tumors, soft tissue tumors, spinal lesions, chronic musculoskeletal diseases, musculoskeletal trauma, and developmental disorders) as well as the normal musculoskeletal tissues. EVIDENCE LEVEL: 5 TECHNICAL EFFICACY: 1.

Methodological considerations on segmenting rhabdomyosarcoma with diffusion-weighted imaging-What can we do better?

PURPOSE

Diffusion-weighted MRI is a promising technique to monitor response to treatment in pediatric rhabdomyosarcoma. However, its validation in clinical practice remains challenging. This study aims to investigate how the tumor segmentation strategy can affect the apparent diffusion coefficient (ADC) measured in pediatric rhabdomyosarcoma.

MATERIALS AND METHODS

A literature review was performed in PubMed using search terms relating to MRI and sarcomas to identify commonly applied segmentation strategies. Seventy-six articles were included, and their presented segmentation methods were evaluated. Commonly reported segmentation strategies were then evaluated on diffusion-weighted imaging of five pediatric rhabdomyosarcoma patients to assess their impact on ADC.

RESULTS

We found that studies applied different segmentation strategies to define the shape of the region of interest (ROI)(outline 60%, circular ROI 27%), to define the segmentation volume (2D 44%, multislice 9%, 3D 21%), and to define the segmentation area (excludes edge 7%, excludes other region 19%, specific area 27%, whole tumor 48%). In addition, details of the segmentation strategy are often unreported. When implementing and comparing these strategies on in-house data, we found that excluding necrotic, cystic, and hemorrhagic areas from segmentations resulted in on average 5.6% lower mean ADC. Additionally, the slice location used in 2D segmentation methods could affect ADC by as much as 66%.

CONCLUSION

Diffusion-weighted MRI studies in pediatric sarcoma currently employ a variety of segmentation methods. Our study shows that different segmentation strategies can result in vastly different ADC measurements, highlighting the importance to further investigate and standardize segmentation.