Diffusion Kurtosis Imaging in the Assessment of Cervical Carcinoma

Whole-tumor histogram analysis of multiple non-Gaussian diffusion models at high b values for assessing cervical cancer

PURPOSE

To assess the diagnostic potential of whole-tumor histogram analysis of multiple non-Gaussian diffusion models for differentiating cervical cancer (CC) aggressive status regarding of pathological types, differentiation degree, stage, and p16 expression.

METHODS

Patients were enrolled in this prospective single-center study from March 2022 to July 2023. Diffusion-weighted images (DWI) were obtained including 15 b-values (0 ~ 4000 s/mm2). Diffusion parameters derived from four non-Gaussian diffusion models including continuous-time random-walk (CTRW), diffusion-kurtosis imaging (DKI), fractional order calculus (FROC), and intravoxel incoherent motion (IVIM) were calculated, and their histogram features were analyzed. To select the most significant features and establish predictive models, univariate analysis and multivariate logistic regression were performed. Finally, we evaluated the diagnostic performance of our models by using receiver operating characteristic (ROC) analyses.

RESULTS

89 women (mean age, 55 ± 11 years) with CC were enrolled in our study. The combined model, which incorporated the CTRW, DKI, FROC, and IVIM diffusion models, offered a significantly higher AUC than that from any individual models (0.836 vs. 0.664, 0.642, 0.651, 0.649, respectively; p < 0.05) in distinguishing cervical squamous cell cancer from cervical adenocarcinoma. To distinguish tumor differentiation degree, except the combined model showed a better predictive performance compared to the DKI model (AUC, 0.839 vs. 0.697, respectively; p < 0.05), no significant differences in AUCs were found among other individual models and combined model. To predict the International Federation of Gynecology and Obstetrics (FIGO) stage, only DKI and FROC model were established and there was no significant difference in predictive performance among different models. In terms of predicting p16 expression, the predictive ability of DKI model is significantly lower than that of FROC and combined model (AUC, 0.693 vs. 0.850, 0.859, respectively; p < 0.05).

CONCLUSION

Multiple non-Gaussian diffusion models with whole-tumor histogram analysis show great promise to assess the aggressive status of CC.

Integrated pretreatment diffusion kurtosis imaging and serum squamous cell carcinoma antigen levels: a biomarker strategy for early assessment of radiotherapy outcomes in cervical cancer

OBJECTIVE

This study aims to explore the utility of pretreatment DKI parameters and serum SCC-Ag in evaluating the early therapeutic response of cervical cancer to radiotherapy.

MATERIALS AND METHODS

A total of 33 patients diagnosed with cervical cancer, including 31 cases of cervical squamous cell carcinoma and two cases of adenosquamous carcinoma, participated in the study. All patients underwent conventional MRI and DKI scans on a 3T magnetic resonance scanner before radiotherapy and after ten sessions of radiotherapy. The therapeutic response was evaluated based on the Response Evaluation Criteria in Solid Tumors (RECIST) version 1.1. Patients were categorized into a response group (RG), comprising Complete Remission (CR) and Partial Remission (PR), and a non-response group (NRG), comprising Stable Disease (SD) and Progressive Disease (PD). LASSO was employed to select pretreatment DKI parameters, and ROC curves were generated for the selected parameters and serum SCC-Ag.

RESULTS

Significant differences were observed in pretreatment MD, Da, Dr, MK, Ka, Kr, and SCC-Ag between the RG and NRG groups (P < 0.01). However, no significant differences were noted for FA and FAK (P = 0.441&0.928). The two selected parameters (MD and MK) demonstrated area under the curve (AUC), sensitivity, and specificity of 0.810, 0.769, 0.850 and 0.827, 0.846, 0.750, respectively. The combination of MD and MK exhibited an improved AUC of 0.901, sensitivity of 0.692, and specificity of 1.000, with a higher Youden index compared to the individual parameters. Conversely, the AUC, sensitivity, and specificity of the combination of MD, MK, and SCC-Ag were 0.852, 0.615, and 1.000, with a Youden index of 0.615.

CONCLUSION

Pretreatment MD, MK, and SCC-Ag demonstrate potential clinical utility, with the combined application of MD and MK showing enhanced efficacy in assessing the early therapeutic response of cervical cancer to radiotherapy. The addition of SCC-Ag did not contribute further to the assessment efficacy.

T1 mapping as a quantitative imaging biomarker for diagnosing cervical cancer: a comparison with diffusion kurtosis imaging

BACKGROUND

T1 mapping can potentially quantitatively assess the intrinsic properties of tumors. This study was conducted to explore the ability of T1 mapping in distinguishing cervical cancer type, grade, and stage and compare the diagnostic performance of T1 mapping with diffusion kurtosis imaging (DKI).

METHODS

One hundred fifty-seven patients with pathologically confirmed cervical cancer were enrolled in this prospectively study. T1 mapping and DKI were performed. The native T1, difference between native and postcontrast T1 (T1diff), mean kurtosis (MK), mean diffusivity (MD), and apparent diffusion coefficient (ADC) were calculated. Cervical squamous cell carcinoma (CSCC) and adenocarcinoma (CAC), low- and high-grade carcinomas, and early- and advanced-stage groups were compared using area under the receiver operating characteristic (AUROC) curves.

RESULTS

The native T1 and MK were higher, and the MD and ADC were lower for CSCC than for CAC (all p < 0.05). Compared with low-grade CSCC, high-grade CSCC had decreased T1diff, MD, ADC, and increased MK (p < 0.05). Compared with low-grade CAC, high-grade CAC had decreased T1diff and increased MK (p < 0.05). Native T1 was significantly higher in the advanced-stage group than in the early-stage group (p < 0.05). The AUROC curves of native T1, MK, ADC and MD were 0,772, 0.731, 0.715, and 0.627, respectively, for distinguishing CSCC from CAC. The AUROC values were 0.762 between high- and low-grade CSCC and 0.835 between high- and low-grade CAC, with T1diff and MK showing the best discriminative values, respectively. For distinguishing between advanced-stage and early-stage cervical cancer, only the AUROC of native T1 was statistically significant (AUROC = 0.651, p = 0.002).

CONCLUSIONS

Compared with DKI-derived parameters, native T1 exhibits better efficacy for identifying cervical cancer subtype and stage, and T1diff exhibits comparable discriminative value for cervical cancer grade.

T1 mapping and multimodel diffusion-weighted imaging in the assessment of cervical cancer: a preliminary study

OBJECTIVE

To evaluate the clinical feasibility of T1 mapping and multimodel diffusion-weighted imaging (DWI) for assessing the histological type, grade, and lymphovascular space invasion (LVSI) of cervical cancer.

METHODS

Eighty patients with cervical cancer and 43 patients with a normal cervix underwent T1 mapping and DWI with 11 b-values (0-2000 s/mm²). Monoexponential, biexponential, and kurtosis models were fitted to calculate the apparent diffusion coefficient (ADC), pure molecular diffusion (D), pseudo-diffusion (D*), perfusion fraction (f), mean diffusivity (MD), and mean kurtosis (MK). Native T1 and DWI-derived parameters (ADC_mean, ADC_min, D_mean, D_min, D*, f, MD_mean, MD_min, MK_mean, and MK_max) were compared based on histological type, grade, and LVSI status.

RESULTS

Native T1 and DWI-derived parameters differed significantly between cervical cancer and normal cervix (all p < 0.05), except D* (p = 0.637). Native T1 and MK_mean varied significantly between squamous cell carcinoma (SCC) and adenocarcinoma (both p < 0.05). ADC_min, D_min, and MD_min were significantly lower while MK_max was significantly higher in the high-grade SCC group than in the low-grade SCC group (all p < 0.05). LVSI-positive SCC had a significantly higher MK_mean than LVSI-negative SCC (p < 0.05).

CONCLUSION

Both T1 mapping and multimodel DWI can effectively differentiate cervical cancer from a normal cervix and cervical adenocarcinoma from SCC. Furthermore, multimodel DWI may provide quantitative metrics for non-invasively predicting histological grade and LVSI status in SCC patients.

ADVANCES IN KNOWLEDGE

Combined use of T1 mapping and multimodel DWI may provide more comprehensive information for non-invasive pre-operative evaluation of cervical cancer.

T1 mapping and extracellular volume fraction measurement to evaluate the poor-prognosis factors in patients with cervical squamous cell carcinoma

PURPOSE

To evaluate the clinical feasibility of T₁ mapping and extracellular volume fraction (ECV) measurement in assessing prognostic factors in patients with cervical squamous cell carcinoma (CSCC).

MATERIALS AND METHODS

A total of 117 CSCC patients and 59 healthy volunteers underwent T₁ mapping and diffusion-weighted imaging (DWI) on a 3 T system. Native T₁ , contrast-enhanced T₁ , ECV, and apparent diffusion coefficient (ADC) were calculated and compared based on surgico-pathologically verified deep stromal infiltration, parametrial invasion (PMI), lymphovascular space invasion (LVSI), lymph node metastasis, stage, histologic grade, and the Ki-67 labeling index (LI).

RESULTS

Native T₁ , contrast-enhanced T₁ , ECV, and ADC values were significantly different between CSCC and the normal cervix (all p < 0.05). No significant differences were observed in any parameters of CSCC when the tumors were grouped by stromal infiltration or lymph node status, respectively (all p > 0.05). In subgroups of the tumor stage and PMI, native T₁ was significantly higher for advanced-stage (p = 0.032) and PMI-positive CSCC (p = 0.001). In subgroups of the grade and Ki-67 LI, contrast-enhanced T₁ was significantly higher for high-grade (p = 0.012) and Ki-67 LI ≥ 50% tumors (p = 0.027). ECV was significantly higher in LVSI-positive CSCC than in LVSI-negative CSCC (p < 0.001). ADC values showed a significant difference for the grade (p < 0.001) but none for the other subgroups.

CONCLUSION

Both T₁ mapping and DWI could stratify the CSCC histologic grade. In addition, T₁ mapping and ECV measurement might provide more quantitative metrics for noninvasively predicting poor prognostic factors and aiding in preoperative risk assessment in CSCC patients.

Cervical Carcinoma: Evaluation Using Diffusion MRI With a Fractional Order Calculus Model and its Correlation With Histopathologic Findings

Objective

The objective of the study is to investigate the feasibility of using the fractional order calculus (FROC) model to reflect tumor subtypes and histological grades of cervical carcinoma.

Methods

Sixty patients with untreated cervical carcinoma underwent multi-b-value diffusion-weighted imaging (DWI) at 3.0T magnetic resonance imaging (MRI). The mono-exponential and the FROC models were fitted. The differences in the histological subtypes and grades were evaluated by the Mann–Whitney U test. Receiver operating characteristic (ROC) analyses were performed to assess the diagnostic performance and to determine the best predictor for both univariate analysis and multivariate analysis. Differences between ROC curves were tested using the Hanley and McNeil test, while the sensitivity, specificity, and accuracy were compared using the McNemar test. P-value <0.05 was considered as significant difference. The Bonferroni corrections were applied to reduce problems associated with multiple comparisons.

Results

Only the parameter β, derived from the FROC model could differentiate cervical carcinoma subtypes (P = 0.03) and the squamous cell carcinoma (SCC) lesions exhibited significantly lower β than that in the adenocarcinoma (ACA) lesions. All the individual parameters, namely, ADC, β, D, and μ derived from the FROC model, could differentiate low-grade cervical carcinomas from high-grade ones (P = 0.022, 0.009, 0.004, and 0.015, respectively). The combination of all the FROC parameters showed the best overall performance, providing the highest sensitivity (81.2%) and AUC (0.829).

Conclusion

The parameters derived from the FROC model were able to differentiate the subtypes and grades of cervical carcinoma.

Prediction of lymphovascular space invasion in cervical carcinoma using diffusion kurtosis imaging

BACKGROUND

This study aimed to investigate the potential relationship between diffusion kurtosis imaging (DKI)- derived parameters and lymphovascular space invasion (LVSI) in patients with cervical carcinoma.

PATIENTS AND METHODS

This prospective study included 30 patients with cervical carcinoma. The patients underwent MRI, diffusion-weighted imaging (DWI), and DKI prior to surgery. The surgical pathology results were accepted as the reference standard for determining the LVSI status. The DKI-derived parameters, including mean diffusivity (MD) and mean kurtosis (MK), were measured. The apparent diffusion coefficient (ADC) value was also assessed.

RESULTS

The MD value of LVSI positive cervical carcinomas was significantly lower than LVSI negative carcinomas (p-value = 0.01). MK value was significantly higher in LVSI positive tumors (p-value = 0.01). However, the ADC value did not show a significant difference between LVSI positive and LVSI negative tumors (p-value = 0.2). MD and MK parameters showed similar diagnostic accuracy in identifying the LVSI status, with the area under the curve of 0.77 and 0.78, respectively.

CONCLUSION

In this study, DKI-derived parameters were associated with the LVSI status in cervical carcinomas. Further studies with larger sample size are required to confirm these results.

A comparative study of four diffusion-weighted imaging models in the diagnosis of cervical cancer

BACKGROUND

Most commonly used diffusion-weighted imaging (DWI) models include intravoxel incoherent motion (IVIM), diffusion kurtosis imaging (DKI), stretched exponential model (SEM), and mono-exponential model (MEM). Previous studies of the four models were inconsistent on which model was more effective in distinguishing cervical cancer from normal cervical tissue.

PURPOSE

To assess the performance of four DWI models in characterizing cervical cancer and normal cervical tissue.

MATERIAL AND METHODS

Forty-seven women with suspected cervical carcinoma underwent DWI using eight b-values before treatment. Imaging parameters, calculated using IVIM, SEM, DKI, and MEM, were compared between cervical cancer and normal cervical tissue. The diagnostic performance of the models was evaluated using independent t-test, Mann-Whitney U test, receiver operating characteristic (ROC) curve analysis, and multivariate logistic regression analysis.

RESULTS

All parameters except pseudo-diffusion coefficient (D*) differed significantly between cervical cancer and normal cervical tissue (P < 0.001). Through logistic regression analysis, all combined models showed a significant improvement in area under the ROC curve (AUC) compared to individual DWI parameters. The model with combined IVIM parameters had a larger AUC value compared to those of other combined models (P < 0.05).

CONCLUSION

All four DWI models are useful for differentiating cervical cancer from normal cervical tissue and IVIM may be the optimal model.

Radiomic Features of T2-weighted Imaging and Diffusion Kurtosis Imaging in Differentiating Clinicopathological Characteristics of Cervical Carcinoma

RATIONALE AND OBJECTIVES

Clinicopathological characteristics including histological subtypes, tumour grades and International Federation of Gynecology and Obstetrics (FIGO) stages are crucial factors in the clinical decision for cervical carcinoma (CC). The purpose of this study was to evaluate the ability of T2-weighted imaging (T2WI) and diffusion kurtosis imaging (DKI) radiomics in differentiating clinicopathological characteristics of CC.

MATERIALS AND METHODS

One hundred and seventeen histologically confirmed CC patients (mean age 56.5 ± 14.0 years) with pre-treatment magnetic resonance imaging were retrospectively reviewed. DKI was acquired with 4 b-values (0-1500 s/mm²). Volumes of interest were contoured around the tumours on T2WI and DKI. Radiomic features including shape, first-order and grey-level co-occurrence matrix with wavelet transforms were extracted. Intraclass correlation coeffient between 2 radiologists was used for features reduction. Feature selection was achieved by elastic net and minimum redundancy maximum relevance. Selected features were used to build random forest (RF) models. The performances for differentiating histological subtypes, tumour grades and FIGO stages were assessed by receiver operating characteristic analysis.

RESULTS

Area under the curves (AUCs) for T2WI-only RF models for discriminating histological subtypes, tumour grades and FIGO stages were 0.762, 0.686, and 0.719. AUCs for DWI-only models were 0.663, 0.645, and 0.868, respectively. AUCs of the combined T2WI and DKI models were 0.823, 0.790, and 0.850, respectively.

CONCLUSION

T2WI and DKI radiomic features could differentiate the clinicopathological characteristics of CC. A combined model showed excellent diagnostic discrimination for histological subtypes, while a DKI-only model presented the best performance in differentiating FIGO stages.

Whole-tumor texture model based on diffusion kurtosis imaging for assessing cervical cancer: a preliminary study

OBJECTIVES

To evaluate the diagnostic potential of diffusion kurtosis imaging (DKI) functional maps with whole-tumor texture analysis in differentiating cervical cancer (CC) subtype and grade.

METHODS

Seventy-six patients with CC were enrolled. First-order texture features of the whole tumor were extracted from DKI and DWI functional maps, including apparent kurtosis coefficient averaged over all directions (MK), kurtosis along the axial direction (Ka), kurtosis along the radial direction (Kr), mean diffusivity (MD), fractional anisotropy (FA), and ADC maps, respectively. The Mann-Whitney U test and ROC curve were used to select the most representative texture features. Models based on each individual and combined functional maps were established using multivariate logistic regression analysis. Conventional parameters-the average values of ADC and DKI parameters derived from the conventional ROI method-were also evaluated.

RESULTS

The combined model based on Ka, Kr, MD, and FA maps yielded the best diagnostic performance in discrimination of cervical squamous cell cancer (SCC) and cervical adenocarcinoma (CAC) with the highest AUC (0.932). Among individual functional map derived models, Kr map-derived model showed the best performance when differentiating tumor subtypes (AUC = 0.828). MK_90th percentile was useful for distinguishing high-grade and low-grade in SCC tumors with an AUC of 0.701. The average values of MD, FA, and ADC were significantly different between SCC and CAC, but no conventional parameters were useful for tumor grading.

CONCLUSIONS

The whole-tumor texture analysis applied to DKI functional maps can be used for differential diagnosis of cervical cancer subtypes and grading SCC.

KEY POINTS

• The whole-tumor texture analysis applied to DKI functional maps allows accurate differential diagnosis of CC subtype and grade. • The combined model derived from multiple functional maps performs significantly better than the single models when differentiating tumor subtypes. • MK_90th percentile was useful for distinguishing poorly and well-/moderately differentiated SCC tumors with an AUC of 0.701.

MRI texture features differentiate clinicopathological characteristics of cervical carcinoma

OBJECTIVES

To evaluate MRI texture analysis in differentiating clinicopathological characteristics of cervical carcinoma (CC).

METHODS

Patients with newly diagnosed CC who underwent pre-treatment MRI were retrospectively reviewed. Texture analysis was performed using commercial software (TexRAD). Largest single-slice ROIs were manually drawn around the tumour on T2-weighted (T2W) images, apparent diffusion coefficient (ADC) maps and contrast-enhanced T1-weighted (T1c) images. First-order texture features were calculated and compared among histological subtypes, tumour grades, FIGO stages and nodal status using the Mann-Whitney U test. Feature selection was achieved by elastic net. Selected features from different sequences were used to build the multivariable support vector machine (SVM) models and the performances were assessed by ROC curves and AUC.

RESULTS

Ninety-five patients with FIGO stage IB~IVB were evaluated. A number of texture features from multiple sequences were significantly different among all the clinicopathological subgroups (p < 0.05). Texture features from different sequences were selected to build the SVM models. The AUCs of SVM models for discriminating histological subtypes, tumour grades, FIGO stages and nodal status were 0.841, 0.850, 0.898 and 0.879, respectively.

CONCLUSIONS

Texture features derived from multiple sequences were helpful in differentiating the clinicopathological signatures of CC. The SVM models with selected features from different sequences offered excellent diagnostic discrimination of the tumour characteristics in CC.

KEY POINTS

• First-order texture features are able to differentiate clinicopathological signatures of cervical carcinoma. • Combined texture features from different sequences can offer excellent diagnostic discrimination of the tumour characteristics in cervical carcinoma.

Diffusion-weighted magnetic resonance imaging of primary cervical cancer in the detection of sub-centimetre metastatic lymph nodes

BACKGROUND

Magnetic resonance imaging (MRI) has limited accuracy in detecting pelvic lymph node (PLN) metastasis. This study aimed to examine the use of intravoxel incoherent motion (IVIM) in classifying pelvic lymph node (PLN) involvement in cervical cancer patients.

METHODS

Fifty cervical cancer patients with pre-treatment magnetic resonance imaging (MRI) were examined for PLN involvement by one subspecialist and one non-subspecialist radiologist. PLN status was confirmed by positron emission tomography or histology. The tumours were then segmented by both radiologists. Kruskal-Wallis tests were used to test for differences between diffusion tumour volume (DTV), apparent diffusion coefficient (ADC), pure diffusion coefficient (D), and perfusion fraction (f) in patients with no malignant PLN involvement, those with sub-centimetre and size-significant PLN metastases. These parameters were then considered as classifiers for PLN involvement, and were compared with the accuracies of radiologists.

RESULTS

Twenty-one patients had PLN involvement of which 10 had sub-centimetre metastatic PLNs. DTV increased (p = 0.013) while ADC (p = 0.015), and f (p = 0.006) decreased as the nodal status progressed from no malignant involvement to sub-centimetre and then size-significant PLN metastases. In determining PLN involvement, a classification model (DTV + f) had similar accuracies (80%) as the non-subspecialist (76%; p = 0.73) and subspecialist (90%; p = 0.31). However, in identifying patients with sub-centimetre PLN metastasis, the model had higher accuracy (90%) than the non-subspecialist (30%; p = 0.01) but had similar accuracy with the subspecialist (90%, p = 1.00). Interobserver variability in tumour delineation did not significantly affect the performance of the classification model.

CONCLUSION

IVIM is useful in determining PLN involvement but the added value decreases with reader experience.