A study on diffusion and kurtosis features of cervical cancer based on non-Gaussian diffusion weighted model

Value of diffusion kurtosis MR imaging and conventional diffusion weighed imaging for evaluating response to first-line chemotherapy in unresectable pancreatic cancer

OBJECTIVE

To investigate the diagnostic value of diffusion kurtosis magnetic resonance imaging (DKI) and conventional diffusion-weighted imaging (DWI) for evaluating the response to first-line chemotherapy in unresectable pancreatic cancer.

MATERIALS AND METHODS

We retrospectively analyzed 21 patients with clinically and pathologically confirmed unresected pancreatic cancer who received palliative chemotherapy. Three-tesla MRI examinations containing DWI sequences with b values of 0, 100, 700, 1400, and 2100 s/mm2 were performed before and after chemotherapy. Parameters included the apparent diffusion coefficient (ADC), mean diffusion coefficient (MD), and mean diffusional kurtosis (MK). The performances of the DWI and DKI parameters in distinguishing the response to chemotherapy were evaluated by the area under the curve (AUC) of the receiver operating characteristic (ROC) curve. Overall survival (OS) was calculated from the date of first treatment to the date of death or the latest follow-up date.

RESULTS

The ADCchange and MDchange were significantly higher in the responding group (PR group) than in the nonresponding group (non-PR group) (ADCchange: 0.21 ± 0.05 vs. 0.11 ± 0.09, P = 0.02; MDchange: 0.37 ± 0.24 vs. 0.10 ± 0.12, P = 0.002). No statistical significance was shown when comparing ADCpre, ADCpost, MKpre, MKpost, MKchange, MDpre, and MDpost between the PR and non-PR groups. The ROC curve analysis indicated that MDchange (AUC = 0.898, cutoff value = 0.7143) performed better than ADCchange (AUC = 0.806, cutoff value = 0.1369) in predicting the response to chemotherapy.

CONCLUSION

The ADCchange and MDchange demonstrated strong potential for evaluating the response to chemotherapy in unresectable pancreatic cancer. The MDchange showed higher specificity in the classification of PR and non-PR than the ADCchange. Other parameters, including ADCpre, ADCpost, MKpre, MKpost, MKchange, MDpre, and MDpost, are not suitable for response evaluation. The combined model SUMchange demonstrated superior performance compared to the individual DWI and DKI models. Further experiments are needed to evaluate the potential of DWI and DKI parameters in predicting the prognosis of patients with unresectable pancreatic cancer.

Prediction of Recurrent Cervical Cancer in 2-Year Follow-Up After Treatment Based on Quantitative and Qualitative Magnetic Resonance Imaging Parameters: A Preliminary Study

PURPOSE

This study investigated predictors of cervical cancer (CC) recurrence from native T1 mapping, conventional imaging, and clinicopathologic metrics.

PATIENTS AND METHODS

In total, 144 patients with histopathologically confirmed CC (90 with and 54 without surgical treatment) were enrolled in this prospective study. Native T1 relaxation time, conventional imaging, and clinicopathologic characteristics were acquired. The association of quantitative and qualitative parameters with post-treatment tumor recurrence was assessed using univariate and multivariate Cox proportional hazard regression analyses. Independent risk factors were combined into a model and individual prognostic index equation for predicting recurrence risk. The receiver operating characteristic (ROC) curve determined the optimal cutoff point.

RESULTS

In total, 12 of 90 (13.3%) surgically treated patients experienced tumor recurrence. Native T1 values (X1) [hazard ratio (HR) 1.008; 95% confidence interval (CI) 1.001-1.016], maximum tumor diameter (X2) (HR 1.065; 95% CI 1.020-1.113), and parametrial invasion (X3) (HR 3.930; 95% CI 1.013-15.251) were independent tumor recurrence risk factors. The individual prognostic index (PI) of the established recurrence risk model was PI = 0.008X1 + 0.063X2 + 1.369X3. The area under the ROC curve (AUC) of the Cox regression model was 0.923. A total of 20 of 54 (37.0%) non-surgical patients experienced tumor recurrence. Native T1 values (X1) (HR 1.012; 95% CI 1.007-1.016) and lymph node metastasis (X2) (HR 4.064; 95% CI 1.378-11.990) were independent tumor recurrence risk factors. The corresponding PI was calculated as follows: PI = 0.011X1 + 1.402X2; the Cox regression model AUC was 0.921.

CONCLUSIONS

Native T1 values combined with conventional imaging and clinicopathologic variables could facilitate the pretreatment prediction of CC recurrence.

Intravoxel incoherent motion and diffusion kurtosis imaging at 3T MRI: Application to ischemic stroke

BACKGROUND AND PURPOSE

The DKI-IVIM model that incorporates DKI (diffusional kurtosis imaging) into the IVIM (Intravoxel Incoherent Motion) concept was investigated to assess its utility for both enhanced diffusion characterization and perfusion measurements in ischemic stroke at 3 T.

METHODS

Fifteen stroke patients (71 ± 11 years old) were enrolled and DKI-IVIM analysis was performed using 9 b-values from 0 to 1500 s/mm² chosen with the Cramer-Rao-Lower-Bound optimization approach. Pseudo-diffusion coefficient D*, perfusion fraction f, blood flow-related parameter fD*, the diffusion coefficient D and an additional parameter, the kurtosis, K were determined in the ischemic lesion and controlateral normal tissue based on a region of interest approach. The apparent diffusion coefficient (ADC) and arterial spin labelling (ASL) cerebral blood flow (CBF) parameters were also assessed and parametric maps were obtained for all parameters.

RESULTS

Significant differences were observed for all diffusion parameters with a significant decrease for D (p < 0.0001), ADC (p < 0.0001), and a significant increase for K (p < 0.0001) in the ischemic lesions of all patients. f decreased significantly in these regions (p = 0.0002). The fD* increase was not significant (p = 0.56). The same significant differences were found with a motion correction except for fD* (p = 0.47). CBF significantly decreased in the lesions. ADC was significantly positively correlated with D (p < 0.0001) and negatively with K (p = 0.0002); K was also negatively significantly correlated with D (p = 0.01).

CONCLUSIONS

DKI-IVIM model enables for simultaneous cerebral perfusion and enhanced diffusion characterization in an acceptable clinically acquisition time for the ischemic stroke diagnosis with the additional kurtosis factor estimation, that may better reflect the microstructure heterogeneity.

Risk factors for the recurrence of cervical cancer using MR-based T1 mapping: A pilot study

Objectives

This study aimed to identify risk factors for recurrence in patients with cervical cancer (CC) through quantitative T1 mapping.

Methods

A cohort of 107 patients histopathologically diagnosed with CC at our institution between May 2018 and April 2021 was categorized into surgical and non-surgical groups. Patients in each group were further divided into recurrence and non-recurrence subgroups depending on whether they showed recurrence or metastasis within 3 years of treatment. The longitudinal relaxation time (native T1) and apparent diffusion coefficient (ADC) value of the tumor were calculated. The differences between native T1 and ADC values of the recurrence and non-recurrence subgroups were analyzed, and receiver operating characteristic (ROC) curves were drawn for parameters with statistical differences. Logistic regression was performed for analysis of significant factors affecting CC recurrence. Recurrence-free survival rates were estimated by Kaplan–Meier analysis and compared using the log-rank test.

Results

Thirteen and 10 patients in the surgical and non-surgical groups, respectively, showed recurrence after treatment. There were significant differences in native T1 values between the recurrence and non-recurrence subgroups in the surgical and non-surgical groups (P&lt;0.05); however, there was no difference in ADC values (P&gt;0.05). The areas under the ROC curve of native T1 values for discriminating recurrence of CC after surgical and non-surgical treatment were 0.742 and 0.780, respectively. Logistic regression analysis indicated that native T1 values were risk factors for tumor recurrence in the surgical and non-surgical groups (P=0.004 and 0.040, respectively). Compared with cut-offs, recurrence-free survival curves of patients with higher native T1 values of the two groups were significantly different from those with lower ones (P=0.000 and 0.016, respectively).

Conclusion

Quantitative T1 mapping could help identify CC patients with a high risk of recurrence, supplementing information on tumor prognosis other than clinicopathological features and providing the basis for individualized treatment and follow-up schemes.

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.

Current Update on PET/MRI in Gynecological Malignancies-A Review of the Literature

Early detection of gynecological malignancies is vital for patient management and prolonging the patient's survival. Molecular imaging, such as positron emission tomography (PET)/computed tomography, has been increasingly utilized in gynecological malignancies. PET/magnetic resonance imaging (MRI) enables the assessment of gynecological malignancies by combining the metabolic information of PET with the anatomical and functional information from MRI. This article will review the updated applications of PET/MRI in gynecological malignancies.

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.

Comparative analysis of the value of amide proton transfer-weighted imaging and diffusion kurtosis imaging in evaluating the histological grade of cervical squamous carcinoma

Background

Uterine cervical cancer (UCC) was the fourth leading cause of cancer death among women worldwide. The conventional MRI hardly revealing the microstructure information. This study aimed to compare the value of amide proton transfer-weighted imaging (APTWI) and diffusion kurtosis imaging (DKI) in evaluating the histological grade of cervical squamous carcinoma (CSC) in addition to routine diffusion-weighted imaging (DWI).

Methods

Forty-six patients with CSC underwent pelvic DKI and APTWI. The magnetization transfer ratio asymmetry (MTRasym), apparent diffusion coefficient (ADC), mean diffusivity (MD) and mean kurtosis (MK) were calculated and compared based on the histological grade. Correlation coefficients between each parameter and histological grade were calculated.

Results

The MTRasym and MK values of grade 1 (G1) were significantly lower than those of grade 2 (G2), and those parameters of G2 were significantly lower than those of grade 3 (G3). The MD and ADC values of G1 were significantly higher than those of G2, and those of G2 were significantly higher than those of G3. MTRasym and MK were both positively correlated with histological grade (r = 0.789 and 0.743, P <  0.001), while MD and ADC were both negatively correlated with histological grade (r = − 0.732 and - 0.644, P <  0.001). For the diagnosis of G1 and G2 CSCs, AUC (APTWI+DKI + DWI) > AUC (DKI + DWI) > AUC (APTWI+DKI) > AUC (APTWI+DWI) > AUC (MTRasym) > AUC (MK) > AUC (MD) > AUC (ADC), where the differences between AUC (APTWI+DKI + DWI), AUC (DKI + DWI) and AUC (ADC) were significant. For the diagnosis of G2 and G3 CSCs, AUC (APTWI+DKI + DWI) > AUC (APTWI+DWI) > AUC (APTWI+DKI) > AUC (DKI + DWI) > AUC (MTRasym) > AUC (MK) > AUC (MD > AUC (ADC), where the differences between AUC (APTWI+DKI + DWI), AUC (APTWI+DWI) and AUC (ADC) were significant.

Conclusion

Compared with DWI and DKI, APTWI is more effective in identifying the histological grades of CSC. APTWI is recommended as a supplementary scan to routine DWI in CSCs.

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.

The feasibility of a radial turbo-spin-echo T2 mapping for preoperative prediction of the histological grade and lymphovascular space invasion of cervical squamous cell carcinoma

PURPOSE

The study aimed to analyze the feasibility of a radial turbo-spin-echo (TSE) T2 mapping to differentiate the histological grades and lymphovascular space invasion (LVSI) of cervical squamous cell carcinoma (CSCC) in comparison with diffusion-weighted imaging (DWI).

METHODS

A total of 58 patients with CSCC and 40 healthy volunteers underwent T2 mapping and DWI before therapy. The T2 and apparent diffusion coefficient (ADC) values were calculated using different tumor characteristics. The differences, efficacies and correlations between parameters were determined.

RESULTS

The T2 and ADC values were significantly different between CSCC and normal cervical stroma (both p < 0.05). Poorly differentiated (G3) tumor showed lower T2 and ADC values than well differentiated (G1) and moderately differentiated (G2) tumor (all p < 0.05). The T2 values were significantly lower in LVSI-positive CSCC than LVSI-negative CSCC (p < 0.05). No significant difference was found in ADC values for LVSI status (p = 0.561). The area under the ROC (AUC) for T2 and ADC values to distinguish G1/G2 and G3 tumor were 0.741 and 0.763, respectively. The AUC for T2 and ADC values to distinguish LVSI-positive and LVSI-negative CSCC were 0.877 and 0.537, respectively. The T2 and ADC values were negatively correlated with the tumor grades (r = -0.402 and r = -0.339, respectively).

CONCLUSIONS

Radial TSE T2 mapping is feasible for CSCC. Similar to ADC values, quantitative T2 values could serve as a noninvasive biomarker to predict histological grades preoperatively. Moreover, T2 values could determine the presence of LVSI better than ADC values.

Uterine Cervical Carcinoma: Evaluation Using Non-Gaussian Diffusion Kurtosis Imaging and Its Correlation With Histopathological Findings

OBJECTIVE

The aim of the study was to assess non-Gaussian diffusion kurtosis imaging (DKI)'s usefulness as a noninvasive method to evaluate tumor invasion depth, histological grade, and lymph node metastasis in cervical carcinoma (CC) patients.

METHODS

Twenty-two consecutive patients with histologically confirmed CC were examined by 1.5-T MRI and non-Gaussian DKI with 4 b values of 0, 500, 1000, and 2000 s/mm2. Kurtosis (K), diffusivity (D), and apparent diffusion coefficient (ADC) maps were compared with histopathological findings.

RESULTS

Kurtosis maps revealed the fibrous stroma as a distinct high K zone (1.442 ± 0.373) that was significantly different from values of the cervical mucosa, outer stroma, and parametrium (0.648 ± 0.083, 0.715 ± 0.113, and 0.504 ± 0.060, respectively, P < 0.0001). Kurtosis (1.189 ± 0.228) and D (0.961 ± 0.198 × 10-3 mm2/s) values of all CCs were significantly different from those of all uterine cervical wall layers. Kurtosis and D values were significantly correlated with histological grades of CCs (r = 0.934, P < 0.0001, and r = -0.925, P < 0.0001, respectively), whereas no significant differences were found in ADC values between grades 2 and 3 CCs (P = 0.787). Metastatic and nonmetastatic lymph nodes showed significantly different K (P < 0.0001) and D (P < 0.0001) values; however, their ADC values did not show significant differences (P = 0.437). For differentiating grade 3 CCs from grade 1 or 2 CCs, the areas under the curve for K (0.991, P = 0.0375) and D (0.982, P = 0.0337) values were significantly higher than those for ADC values (0.759). For differentiating metastatic and nonmetastatic lymph nodes, the areas under the curve for K (0.974, P = 0.0028) and D (0.968, P = 0.0018) values were significantly higher than those for ADC (0.596).

CONCLUSIONS

Non-Gaussian DKI may be clinically useful for noninvasive evaluation of tumor invasion depth, histological grade, and lymph node metastasis in CC patients.

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.

Diffusion Kurtosis Imaging in the Assessment of Cervical Carcinoma

RATIONALE AND OBJECTIVES

To evaluate the additional value of diffusion kurtosis imaging (DKI) in the characterization of cervical carcinoma.

MATERIALS AND METHODS

Seventy-five patients (56.9 ± 13.4 years) with histologic-confirmed cervical carcinoma were included. Diffusion-weighted imaging (DWI) was acquired on a 3T MRI with five b values (0, 500, 800, 1000, and 1500 s/mm²). Data were analyzed based on DKI model (5 b values) and conventional DWI (0 and 1000 s/mm²). Largest single-slice region of interest (ROI) and volume of interest (VOI) were drawn around the tumor. Mean diffusivity (MD), mean kurtosis (MK), and apparent diffusion coefficient (ADC) of cervical carcinoma and normal myometrium were measured and compared. MD, MK, and ADC of cervical carcinoma were compared among histologic subtypes, tumor grades, and FIGO stages.

RESULTS

ROI- and VOI-derived DKI parameters and ADC were all in excellent consistency (intraclass correlation coefficient, ICC > 0.90, respectively). Cervical carcinoma had significantly lower MD, ADC, and higher MK than normal myometrium (p < 0.001). MD and ADC showed significant differences between histologic subtypes and FIGO stages, lower in squamous cell carcinoma than adenocarcinoma and higher in FIGO I-II than FIGO III-IV (p < 0.050), but not with tumor grade. No difference was observed in MK for different clinicopathologic features tested.

CONCLUSION

ROI and VOI analyses were in excellent consistency. MD and ADC were able to distinguish histologic subtypes and separating FIGO stages, MK could not. DKI showed no clear added value over conventional DWI in the characterization of cervical carcinoma.

A histogram analysis of diffusion and perfusion features of cervical cancer based on intravoxel incoherent motion magnetic resonance imaging

OBJECTIVE

To evaluate the diagnostic potential based on histogram analysis of IVIM parameters between uterine cervical cancers (CC) - normal myometrium (Myo) versus CC - gluteus maximus muscle (GM) and to study the feasibility of histogram analysis of IVIM parameters to differentiate the early from locally advanced stage CCs.

METHODS

64 patients with pathologically confirmed CC were enrolled. Histogram indices mean, median, 25th, and ð 75th percentile of apparent diffusion coefficient (ADC), true diffusion coefficient (D), pseudo-diffusion coefficient (D*), and perfusion fraction (f) value of entire tumor were statistically analyzed and compared between CC - GM versus CC - Myo, as well as between early and locally advanced stage CCs. A multivariate analysis was performed to identify indices that could best distinguish early from locally advanced stage CC. Receiver operating characteristic curves (ROC) were used to evaluate the diagnostic efficiency of every histogram parameter.

RESULTS

All the tested histogram indices significantly differed between the patients with CC - GM vs. CC - Myo, nonetheless, CC - GM yielded higher range area under the curve (AUC) value of 0.8-0.99 vs. 0.6-0.99. The additional significant difference was found among all the tested histogram indices of D*, mean, median, and 75th percentile of f, mean and 75th percentile of ADC, and 75th percentile of D discriminating early from locally advanced CCs. ROC curves indicated that the 75th percentile of D* value 28.17 × 10^-3 mm²/s could best differentiate early from locally advanced stage CCs, with AUC of 0.776. In the multivariate analysis, ROC indicated the 50th percentile of D* and f was the most significant with AUCs of 0.856.

CONCLUSIONS

The histogram analysis of IVIM parameters depicted that gluteus maximus served better reference tissue in comparison to myometrium. The histogram index 75th percentile of ADC, D, D*, and f may serve a diagnostic biomarker to differentiate the early from locally advanced stage CCs.