Whole-tumour diffusion kurtosis MR imaging histogram analysis of rectal adenocarcinoma: Correlation with clinical pathologic prognostic factors

Pathological prognostic factors of rectal cancer based on diffusion-weighted imaging, intravoxel incoherent motion, and diffusion kurtosis imaging

OBJECTIVES

To explore diffusion-weighted imaging (DWI), intravoxel incoherent motion (IVIM), and diffusion kurtosis imaging (DKI) for assessing pathological prognostic factors in patients with rectal cancer.

MATERIALS AND METHODS

A total of 162 patients (105 males; mean age of 61.8 ± 13.1 years old) scheduled to undergo radical surgery were enrolled in this prospective study. The pathological prognostic factors included histological differentiation, lymph node metastasis (LNM), and extramural vascular invasion (EMVI). The DWI, IVIM, and DKI parameters were obtained and correlated with prognostic factors using univariable and multivariable logistic regression. Their assessment value was evaluated using receiver operating characteristic (ROC) curve analysis.

RESULTS

Multivariable logistic regression analyses showed that higher mean kurtosis (MK) (odds ratio (OR) = 194.931, p < 0.001) and lower apparent diffusion coefficient (ADC) (OR = 0.077, p = 0.025) were independently associated with poorer differentiation tumors. Higher perfusion fraction (f) (OR = 575.707, p = 0.023) and higher MK (OR = 173.559, p < 0.001) were independently associated with LNMs. Higher f (OR = 1036.116, p = 0.024), higher MK (OR = 253.629, p < 0.001), lower mean diffusivity (MD) (OR = 0.125, p = 0.038), and lower ADC (OR = 0.094, p = 0.022) were independently associated with EMVI. The area under the ROC curve (AUC) of MK for histological differentiation was significantly higher than ADC (0.771 vs. 0.638, p = 0.035). The AUC of MK for LNM positivity was higher than f (0.770 vs. 0.656, p = 0.048). The AUC of MK combined with MD (0.790) was the highest among f (0.663), MK (0.779), MD (0.617), and ADC (0.610) in assessing EMVI.

CONCLUSION

The DKI parameters may be used as imaging biomarkers to assess pathological prognostic factors of rectal cancer before surgery.

CLINICAL RELEVANCE STATEMENT

Diffusion kurtosis imaging (DKI) parameters, particularly mean kurtosis (MK), are promising biomarkers for assessing histological differentiation, lymph node metastasis, and extramural vascular invasion of rectal cancer. These findings suggest DKI's potential in the preoperative assessment of rectal cancer.

KEY POINTS

Mean kurtosis outperformed the apparent diffusion coefficient in assessing histological differentiation in resectable rectal cancer. Perfusion fraction and mean kurtosis are independent indicators for assessing lymph node metastasis in rectal cancer. Mean kurtosis and mean diffusivity demonstrated superior accuracy in assessing extramural vascular invasion.

Standard diffusion-weighted, diffusion kurtosis and intravoxel incoherent motion in differentiating invasive placentas

PURPOSE

To investigate the diagnostic value of monoexponential, biexponential, and diffusion kurtosis MR imaging (MRI) in distinguishing invasive placentas.

METHODS

A total of 53 patients with invasive placentas and 47 patients with noninvasive placentas undergoing conventional diffusion-weighted imaging (DWI), intravoxel incoherent motion (IVIM), and diffusion kurtosis imaging (DKI) were retrospectively enrolled. The mean, minimum, and maximum parameters including the apparent diffusion coefficient (ADC) and exponential ADC (eADC) from standard DWI, diffusion kurtosis (MK), and diffusion coefficient (MD) from DKI and pure diffusion coefficient (D), pseudo-diffusion coefficient (D*), and perfusion fraction (f) from IVIM were measured and compared from the volumetric analysis. Receiver operating characteristics (ROC) curve and logistic regression analyses were conducted to evaluate the diagnostic efficiency of different diffusion parameters for distinguishing invasive placentas.

RESULTS

Comparisons between accreta lesions in patients with invasive placentas (AL) and lower 1/3 part of the placenta in patients with noninvasive placentas (LP) demonstrated that MD mean, D mean, and D* mean were significantly lower while ADC max and D max were significantly higher in invasive placentas (all p < 0.05). Multivariate analysis demonstrated that D mean, D max and D* mean differed significantly among all the studied parameters for invasive placentas. A combined use of these three parameters yielded an AUC of 0.86 with sensitivity, specificity, and accuracy of 84.91%, 76.60%, and 80%, respectively.

CONCLUSION

The combined use of different IVIM parameters is helpful in distinguishing invasive placentas.

Preoperative Grading of Rectal Cancer with Multiple DWI Models, DWI-Derived Biological Markers, and Machine Learning Classifiers

Background: this study aimed to utilize various diffusion-weighted imaging (DWI) techniques, including mono-exponential DWI, intravoxel incoherent motion (IVIM), and diffusion kurtosis imaging (DKI), for the preoperative grading of rectal cancer. Methods: 85 patients with rectal cancer were enrolled in this study. Mann-Whitney U tests or independent Student's t-tests were conducted to identify DWI-derived parameters that exhibited significant differences. Spearman or Pearson correlation tests were performed to assess the relationships among different DWI-derived biological markers. Subsequently, four machine learning classifier-based models were trained using various DWI-derived parameters as input features. Finally, diagnostic performance was evaluated using ROC analysis with 5-fold cross-validation. Results: With the exception of the pseudo-diffusion coefficient (Dp), IVIM-derived and DKI-derived parameters all demonstrated significant differences between low-grade and high-grade rectal cancer. The logistic regression-based machine learning classifier yielded the most favorable diagnostic efficacy (AUC: 0.902, 95% Confidence Interval: 0.754-1.000; Specificity: 0.856; Sensitivity: 0.925; Youden Index: 0.781). Conclusions: utilizing multiple DWI-derived biological markers in conjunction with a strategy employing multiple machine learning classifiers proves valuable for the noninvasive grading of rectal cancer.

Differentiation of placenta percreta through MRI features and diffusion-weighted magnetic resonance imaging

OBJECTIVES

To identify whether parameters measured from diffusion kurtosis and intravoxel incoherent motion help diagnose placenta percreta.

METHODS

We retrospectively enrolled 75 patients with PAS disorders including 13 patients with placenta percreta and 40 patients without PAS disorders. Each patients underwent diffusion-weighted imaging (DWI), intravoxel incoherent motion (IVIM), and diffusion kurtosis imaging (DKI). The apparent diffusion coefficient (ADC), perfusion fraction (f), pure diffusion coefficient (D), pseudo-diffusion coefficient (D*), mean diffusion kurtosis (MK) and mean diffusion coefficient (MD) were measured by the volumetric analysis and compared. MRI features were also analyzed and compared. The receiver operating characteristic (ROC) curve and logistic regression analysis were used to evaluate the diagnostic efficiency of different diffusion parameters and MRI features for distinguishing placental percreta.

RESULTS

D* was an independent risk factor from DWI for predicting placenta percreta with sensitivity of 73% and specificity of 76%. Focal exophytic mass remained as independent risk factor from MRI features for predicting placenta percreta with sensitivity of 72.7% and specificity of 88.1%. When the two risk factors were combined together, the AUC was the highest, 0.880 (95% CI 0.8-0.96).

CONCLUSION

D* and focal exophytic mass were associated with placenta percreta. A combination of the 2 risk factors can be used to predict placenta percreta.

CRITICAL RELEVANCE STATEMENT

A combination of D* and focal exophytic mass can be used to differentiate placenta percreta.

Diffusion kurtosis imaging and MRI-detected extramural venous invasion in rectal cancer: correlation with clinicopathological prognostic factors

OBJECTIVE

To investigate the prognostic value of the diffusion kurtosis imaging (DKI)-derived parameters D value, K value, diffusion-weighted imaging (DWI) parameter apparent diffusion coefficient (ADC) value, and magnetic resonance imaging (MRI)-detected extramural venous invasion (EMVI) (mrEMVI) in rectal cancer patients.

METHODS

Forty patients who underwent MRI for rectal cancer were retrospectively evaluated. DKI-derived parameters D and K were measured using the Medical Imaging Interaction Toolkit. Conventional ADC values were measured from the corresponding DWI images. An experienced radiologist evaluated the mrEMVI status on MR images using the mrEMVI scoring system. An independent sample t-test or analysis of variance was used to analyze and compare the measurement data. The x² test or Fisher exact test was used for categorical variables. Receiver operating characteristic curves were used to assess the diagnostic performance of these parameters.

RESULTS

Among the 40 patients, MRI showed positive EMVI in 15 patients and negative EMVI in 25 patients. Positive mrEMVI status was associated with age, positive circumferential resection margin, pT-stage, lymphovascular invasion (LVI), distant metastasis, and serum carcinoembryonic antigen (CEA) level (P = 0.004-0.036). The dispersion coefficient (D) values and ADC values were significantly higher in the mucinous adenocarcinoma (MC) group than in the common adenocarcinoma (AC) group (P = 0.001), while kurtosis coefficient (K) values were lower in the MC group than in the AC group (P = 0.022). D values were significantly higher in the KRAS-mutated group than in the wild-type group (P < 0.05), whereas K values were lower in the KRAS-mutated group than in the wild-type group (P < 0.05). All three parameters (D, K, and ADC values) showed good diagnostic performance for discriminating MC from AC. Both the D and K values showed certain diagnostic performance for discriminating KRAS mutation.

CONCLUSION

DKI-derived parameters, conventional ADC values, and mrEMVI are associated with different histopathological prognostic factors. All DKI-derived parameters and conventional ADC values may distinguish MC from AC. DKI-derived parameters may also be used to discriminate KRAS mutation.

Evaluating the renal mild tubulointerstitial damage and renal function in IgAN patients: a comparative study based on diffusion kurtosis imaging and diffusion tensor imaging

OBJECTIVE

To compare the performance of 3.0 T magnetic resonance diffusion kurtosis imaging (DKI) and diffusion tensor imaging (DTI) in evaluation of the degree of tubulointerstitial damage and renal function in Immunoglobulin A Nephropathy (IgAN) patients.

METHODS

Both DKI and DTI were performed in 40 IgAN patients and 17 healthy volunteers. IgAN patients were divided into two groups according to tubulointerstitial lesion score: Mild injury group, n = 24; Moderate-severe injury group, n = 16. DKI characteristic parameters [mean kurtosis (MK), axial kurtosis (Ka), radial kurtosis (Kr)] and DTI parameters [fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (Da), radial diffusivity (Dr)] of renal cortex and medulla were measured and compared among different groups. Correlations between DKI, DTI parameters and clinicopathological characteristics were assessed. Diagnostic performance of DKI and DTI to evaluate tubulointerstitial damage of IgAN was compared.

RESULTS

Cortical MK, Kr, Da and parenchymal Ka significantly differed among three groups (P < 0.05). Cortical MK, Kr, Ka were negatively correlated with estimated glomerular filtration rate (eGFR) (MK: r = - 0.613; Kr: r = - 0.539; Ka: r = - 0.664) and positively correlated with tubulointerstitial lesion score (MK: r = 0.655; Kr: r = 0.577; Ka: r = 0.661) (all P < 0.001). Lower correlation coefficient was found among cortical FA, MD, Dr and eGFR, tubulointerstitial lesion score (all|r|< 0.350). The AUCs of DKI and DTI parameters for differentiating Mild injury group from control group were (cortical MK 0.822, cortical Ka 0.816; cortical FA 0.515, cortical MD 0.714) and for differentiating Mild injury group from Moderate-severe injury group were (cortical MK 0.813, cortical Ka 0.831; medulla FA 0.784, medulla MD 0.586).

CONCLUSION

Compared with DTI, DKI was more sensitive and accurate to probe the renal function and the tubulointerstitial damage of IgAN, especially the mild tubulointerstitial damage.

Whole-tumor amide proton transfer-weighted imaging histogram analysis to predict pathological extramural venous invasion in rectal adenocarcinoma: a preliminary study

OBJECTIVES

To evaluate amide proton transfer-weighted (APTw)-derived whole-tumor histogram analysis parameters in predicting pathological extramural venous invasion (pEMVI) positive status of rectal adenocarcinoma (RA).

METHODS

Preoperative MR including APTw imaging of 125 patients with RA (mean 61.4 ± 11.6 years) were retrospectively analyzed. Two radiologists reviewed each case's EMVI status based on the MR-based modified 5-point scale system with conventional MR images. The APTw histogram parameters of primary tumors were obtained automatically using whole-tumor volume histogram analysis. The independent risk factors markedly correlated with pEMVI-positive status were assessed using univariate and multivariate logistic regression analyses. Diagnosis performance was assessed by receiver operating characteristic curve (ROC) analysis. The AUCs were compared using the Delong method.

RESULTS

Univariate analysis demonstrated that MR-tumor (T) stage, MR-lymph node (N) stage, APTw-10%, APTw-90%, interquartile range, APTw-minimum, APTw-maximum, APTw-mean, APTw-median, entropy, kurtosis, mean absolute deviation (MAD), and robust MAD were significantly related to pEMVI-positive status (all p < 0.05). Multivariate analysis demonstrated that MR-T stage (OR = 4.864, p = 0.018), MR-N stage (OR = 4.967, p = 0.029), interquartile range (OR = 0.892, p = 0.037), APT-minimum (OR = 1.046, p = 0.031), entropy (OR = 11.604, p = 0.006), and kurtosis (OR = 1.505, p = 0.007) were the independent risk factors enabling prediction of pEMVI-positive status. The AUCs for diagnostic ability of conventional MRI assessment, the APTw histogram model, and the combined model (including APTw histogram and clinical variables) were 0.785, 0.853, and 0.918, respectively. The combined model outperformed the APTw histogram model (p = 0.013) and the conventional MRI assessment (p = 0.006).

CONCLUSIONS

Whole-tumor histogram analysis of APTw images combined with clinical factors showed better diagnosis efficiency in predicting EMVI involvement in RA.

KEY POINTS

• Rectal adenocarcinomas with pEMVI-positive status are typically associated with higher APTw-SI values. • APTw-minimum, interquartile range, entropy, kurtosis, MR-T stage, and MR-N stage are the independent risk factors for EMVI involvement. • The best prediction for EMVI involvement was obtained with a combined model of APTw histogram and clinical variables (area under the curve, 0.918).

Diffusion and perfusion MRI parameters in the evaluation of placenta accreta spectrum disorders in patients with placenta previa

OBJECTIVES

To evaluate the placental function by monoexponential, biexponential, and diffusion kurtosis MR imaging (MRI) in patients with placenta previa.

METHODS

A total of 62 patients with placenta accreta spectrum (PAS) disorders and 11 patients with normal placentas were retrospectively enrolled, who underwent conventional diffusion-weighted imaging (DWI), intravoxel incoherent motion (IVIM), and diffusion kurtosis imaging (DKI). The apparent diffusion coefficient (ADC) and exponential ADC (eADC) from standard DWI, mean kurtosis (MK), and diffusion coefficient (MD) from DKI, and pure diffusion coefficient (D), pseudo-diffusion coefficient (D*), and perfusion fraction (f) from IVIM were measured and compared from the volumetric analysis.

RESULTS

Comparisons between patients with PAS disorders and patients with normal placentas demonstrated that MD mean, D mean, and D* mean values in patients with PAS disorders were significantly higher than those in patients with normal placentas (p < 0.05). Comparisons between patients with accreta, increta, and percreta, and patients with normal placentas showed that the D mean was significantly higher in patients with placenta increta and percreta than in patients with normal placentas (p < 0.05).

CONCLUSION

The accreta lesions in PAS disorders had deceased cellularity and increased blood movement. The alteration of placental cellularity was more prominent in placenta increta and percreta.

Predicting perineural invasion using histogram analysis of zoomed EPI diffusion-weighted imaging in rectal cancer

PURPOSE

To investigate the utility of histogram analysis of zoomed EPI diffusion-weighted imaging (DWI) for predicting the perineural invasion (PNI) status of rectal cancer (RC).

METHODS

This prospective study evaluated 94 patients diagnosed with histopathologically confirmed RC between July 2020 and July 2021. Patients underwent preoperative rectal magnetic resonance imaging (MRI) examinations, including the zoomed EPI DWI sequence. Ten whole-tumor histogram parameters of each patient were derived from zoomed EPI DWI. Reproducibility was evaluated according to the intra-class correlation coefficient (ICC). The association of the clinico-radiological and histogram features with PNI status was assessed using univariable analysis for trend and multivariable logistic regression analysis with β value calculation. Receiver operating characteristic (ROC) curve analysis was conducted to assess the diagnostic performance.

RESULTS

Forty-two patients exhibited positive PNI. The inter- and intraobserver agreements were excellent for the histogram parameters (all ICCs > 0.80). The maximum (p = 0.001), energy (p = 0.021), entropy (p = 0.021), kurtosis (p < 0.001), and skewness (p < 0.001) were significantly higher in the positive PNI group than in the negative PNI group. Multivariable analysis showed that higher MRI T stage [β = 2.154, 95% confidence interval (CI) 0.932-3.688; p = 0.002] and skewness (β = 0.779, 95% CI 0.255-1.382; p = 0.006) were associated with positive PNI. The model combining skewness and MRI T stage had an area under the ROC curve of 0.811 (95% CI 0.724-0.899) for predicting PNI status.

CONCLUSION

Histogram parameters in zoomed EPI DWI can help predict the PNI status in RC.

The Preoperative Diagnostic Performance of Multi-Parametric Quantitative Assessment in Rectal Carcinoma: A Preliminary Study Using Synthetic Magnetic Resonance Imaging

Objective

Synthetic MRI (SyMRI) can reconstruct different contrast-weighted images(T1, T2, PD) and has shorter scan time, easier post-processing and better reproducibility. Some studies have shown splendid correlation with conventional mapping techniques and no degradation in the quality of syMRI images compared with conventional MRI. It is crucial to select an individualized treatment plan based on the preoperative images of rectal carcinoma (RC). We tried to explore the feasibility of syMRI on T, N stage and extramural vascular invasion (EMVI) of rectal cancer.

Materials and Methods

A total of 100 patients (37 females and 63 males) diagnosed with rectal carcinoma were enrolled. All the patients underwent preoperative pelvic MR examinations including conventional MR sequence and synthetic MRI. Two radiologists evaluated the MRI findings of each rectal carcinoma and EMVI score in consensus. The values for T1, T2 relaxation times and PD value were measured in tumor(ROI-1) and pararectal fat space(ROI-2) and analyzed independently. A receiver operating characteristic (ROC) analysis was performed. Correlations between the T1, T2 and PD values and EMVI score were also evaluated.

Results

Compared with the normal rectal wall, the values of T1 and T2 relaxation times of the tumor were significantly higher (P <0.001). There was no statistically significant difference in the PD value (P >0.05). As for ROI, the ROI of pararectal fat space(ROI-2) had better significance than rectal cancer lesion (ROI-1). T2 value of ROI-1 and T1 value of ROI-2 were higher in the pEMVI positive group than in the negative group (P=0.002 and 0.001) and T1 value of ROI-2 had better performance with an AUC of 0.787, (95% CI:0.693- 0.882). T1 value, T2 value and PD value from ROI-2 were effective for both T and N stage of rectal cancer. High-grade pathological stage had showed higher T1 value (P_{T stage}=0.013,P_{N stage}=0.035), lower T2 value (P_{T stage}=0.025,P_{N stage}=0.034) and lower PD value (P_{T stage}=0.017). We also enrolled the characteristics with P < 0.05 in the combined model which had better diagnostic efficacy. A significant positive correlation was found between the T1 value of pararectal fat space(ROI-2) and EMVI score (r value = 0.519, P<0.001). The T2 value(r=0.213,P=0.049) and PD value(r=0.354,P=0.001) from ROI-1 was correlated with EMVI score. Correlation analysis did not show any significant associations between T2 value of tumor, T2, PD values of pararectal fat space and EMVI scores.

Conclusion

Synthetic MRI can provide multi-parameter quantitative image maps with a easier measurement and slightly shorter acquisition time compared with conventional MRI. The measurement of multi-parametric quantitative values contributes to diagnosing the tumor and evaluating T stage, N stage and EMVI. It has the potential to be used as a preoperative diagnostic and grading technique in rectal carcinoma.

Role of diffusion-weighted MRI in recurrent rectal cancer treated with carbon ion radiotherapy

Aim: To evaluate the association between pretreatment diffusion-weighted MRI (DW-MRI) and 12-month radiological response in locally recurrent rectal cancer treated with carbon ion radiotherapy. Methods: Histogram analysis was performed on pretreatment DW-MRI for patients re-irradiated with carbon ion radiotherapy for local recurrence of rectal cancer. Results: A total of 17 patients were enrolled in the study. Pretreatment DW-MRI b-value of 1000 s/mm² (b1000) and apparent diffusion coefficient (ADC) lesion median values for 1-year nonresponders (six patients) and responders (11 patients) demonstrated a median (interquartile of median values) of 62.5 (23.9) and 34.0 (13.0) and 953.0 (277.0) and 942.5 (339.0) μm²/s, respectively. All b1000 histogram features (h-features) and ADC h-kurtosis showed statistically significant differences, whereas only b1000 h-median, b1000 h-interquartile range and ADC h-kurtosis demonstrated remarkable diagnostic accuracy. Conclusion: DW-MRI showed promising results in predicting carbon ion radiotherapy outcome in local recurrence of rectal cancer, particularly with regard to b1000 h-median, b1000 h-interquartile range and ADC h-kurtosis.

Diffusion weighted imaging and diffusion kurtosis imaging in abdominal oncological setting: why and when

This article provides an overview of diffusion kurtosis (DKI) imaging in abdominal oncology. DKI allows for more data on tissue structures than the conventional diffusion model (DWI). However, DKI requires high quality images at b-values greater than 1000 s/mm² and high signal-to-noise ratio (SNR) that traditionally MRI systems are not able to acquire and therefore there are generally amplified anatomical distortions on the images due to less homogeneity of the field. Advances in both hardware and software on modern MRI scanners have currently enabled ultra-high b-value imaging and offered the ability to apply DKI to multiple extracranial sites. Previous studies have evaluated the ability of DKI to characterize and discriminate tumor grade compared to conventional DWI. Additionally, in several studies the DKI sequences used were based on planar echo (EPI) acquisition, which is susceptible to motion, metal and air artefacts and prone to low SNRs and distortions, leading to low quality images for some small lesions, which may affect the accuracy of the results. Another problem is the optimal b-value of DKI, which remains to be explored and not yet standardized, as well as the manual selection of the ROI, which could affect the accuracy of some parameters.

Lymph Nodes Evaluation in Rectal Cancer: Where Do We Stand and Future Perspective

The assessment of nodal involvement in patients with rectal cancer (RC) is fundamental in disease management. Magnetic Resonance Imaging (MRI) is routinely used for local and nodal staging of RC by using morphological criteria. The actual dimensional and morphological criteria for nodal assessment present several limitations in terms of sensitivity and specificity. For these reasons, several different techniques, such as Diffusion Weighted Imaging (DWI), Intravoxel Incoherent Motion (IVIM), Diffusion Kurtosis Imaging (DKI), and Dynamic Contrast Enhancement (DCE) in MRI have been introduced but still not fully validated. Positron Emission Tomography (PET)/CT plays a pivotal role in the assessment of LNs; more recently PET/MRI has been introduced. The advantages and limitations of these imaging modalities will be provided in this narrative review. The second part of the review includes experimental techniques, such as iron-oxide particles (SPIO), and dual-energy CT (DECT). Radiomics analysis is an active field of research, and the evidence about LNs in RC will be discussed. The review also discusses the different recommendations between the European and North American guidelines for the evaluation of LNs in RC, from anatomical considerations to structured reporting.

Monoexponential, biexponential and diffusion kurtosis MR imaging models: quantitative biomarkers in the diagnosis of placenta accreta spectrum disorders

BACKGROUND

To investigate the diagnostic value of monoexponential, biexponential, and diffusion kurtosis MR imaging (MRI) in differentiating placenta accreta spectrum (PAS) disorders.

METHODS

A total of 65 patients with PAS disorders and 27 patients with normal placentas undergoing conventional DWI, IVIM, and DKI were retrospectively reviewed. The mean, minimum, and maximum parameters including the apparent diffusion coefficient (ADC) and exponential ADC (eADC) from standard DWI, diffusion kurtosis (MK), and mean diffusion coefficient (MD) from DKI and pure diffusion coefficient (D), pseudo-diffusion coefficient (D*), and perfusion fraction (f) from IVIM were measured from the volumetric analysis and compared between patients with PAS disorders and patients with normal placentas. Univariate and multivariated logistic regression analyses were used to evaluate the value of the above parameters for differentiating PAS disorders. Receiver operating characteristics (ROC) curve analyses were used to evaluate the diagnostic efficiency of different diffusion parameters for predicting PAS disorders.

RESULTS

Multivariate analysis demonstrated that only D mean and D max differed significantly among all the studied parameters for differentiating PAS disorders when comparisons between accreta lesions in patients with PAS (AP) and whole placentas in patients with normal placentas (WP-normal) were performed (all p < 0.05). For discriminating PAS disorders, a combined use of these two parameters yielded an AUC of 0.93 with sensitivity, specificity, and accuracy of 83.08, 88.89, and 83.70%, respectively.

CONCLUSION

The diagnostic performance of the parameters from accreta lesions was better than that of the whole placenta. D mean and D max were associated with PAS disorders.

The Utility of ADC First-Order Histogram Features for the Prediction of Metachronous Metastases in Rectal Cancer: A Preliminary Study

Simple Summary

Metachronous metastases are the main factors affecting survival in rectal cancer, and 15–25% of patients will develop them at a 5-year follow-up. Early identification of patients with higher risk of developing distant metachronous metastases would help to improve therapeutic protocols and could allow for a more accurate, personalized management. Apparent diffusion coefficient (ADC) represents an MRI quantitative biomarker, which can assess the diffusion characteristics of tissues, depending on the microscopic mobility of water, showing information related to tissue cellularity. First-order histogram-based features statistics describe the frequency distribution of intensity values within a region of interest, revealing microstructural alterations. In our study, we demonstrated that whole-tumor ADC first-order features may provide useful information for the assessment of rectal cancer prognosis, regarding the occurrence of metachronous metastases.

Abstract

This study aims the ability of first-order histogram-based features, derived from ADC maps, to predict the occurrence of metachronous metastases (MM) in rectal cancer. A total of 52 patients with pathologically confirmed rectal adenocarcinoma were retrospectively enrolled and divided into two groups: patients who developed metachronous metastases (n = 15) and patients without metachronous metastases (n = 37). We extracted 17 first-order (FO) histogram-based features from the pretreatment ADC maps. Student’s t-test and Mann–Whitney U test were used for the association between each FO feature and presence of MM. Statistically significant features were combined into a model, using the binary regression logistic method. The receiver operating curve analysis was used to determine the diagnostic performance of the individual parameters and combined model. There were significant differences in ADC 90th percentile, interquartile range, entropy, uniformity, variance, mean absolute deviation, and robust mean absolute deviation in patients with MM, as compared to those without MM (p values between 0.002–0.01). The best diagnostic was achieved by the 90th percentile and uniformity, yielding an AUC of 0.74 [95% CI: 0.60–0.8]). The combined model reached an AUC of 0.8 [95% CI: 0.66–0.90]. Our observations point out that ADC first-order features may be useful for predicting metachronous metastases in rectal cancer.

T2*-weighted imaging and diffusion kurtosis imaging (DKI) of rectal cancer: correlation with clinical histopathologic prognostic factors

PURPOSE

Histopathologic prognostic factors of rectal cancer are closely associated with local recurrence and distant metastasis. We aim to investigate the feasibility of T2*WI in assessment of clinical prognostic factors of rectal cancer, and compare with DKI.

METHODS

This retrospective study enrolled 50 out of 205 patients with rectal cancer according to the inclusion criteria. The following parameters were obtained: R2* from T2*WI, mean diffusivity (MD_k), mean kurtosis (MK), and mean diffusivity (MD_t) from DKI using tensor method. Above parameters were compared by Mann-Whitney U-test or students' t test. Spearman correlations between different parameters and histopathological prognostic factors were determined. The diagnostic performances of R2* and DKI-derived parameters were analyzed by receiver operating characteristic curves (ROC), separately and jointly.

RESULTS

There were positive correlations between R2* and multiple prognostic factors of rectal cancer such as T category, N category, tumor grade, CEA level, and LVI (P < 0.004). MD_k and MD_t showed negative correlations with almost all the histopathological prognostic factors except CRM and TIL involvement (P < 0.003). MK correlated positively with the prognostic factors except CA19-9 level and CRM involvement (P < 0.006). The AUC ranges were 0.724-0.950 for R2* and 0.755-0.913 for DKI-derived parameters for differentiation of prognostic factors. However, no significant differences of diagnostic performance were found between T2*WI, DKI, or the combined imaging methods in characterizing rectal cancer.

CONCLUSION

R2* and DKI-derived parameters were associated with different histopathological prognostic factors, and might act as noninvasive biomarkers for histopathological characterization of rectal cancer.

Histogram analysis of diffusion-weighted magnetic resonance imaging as a biomarker to predict LNM in T3 stage rectal carcinoma

BACKGROUND

Preoperative identification of rectal cancer lymph node status is crucial for patient prognosis and treatment decisions. Rectal magnetic resonance imaging (MRI) plays an essential role in the preoperative staging of rectal cancer, but its ability to predict lymph node metastasis (LNM) is insufficient. This study explored the value of histogram features of primary lesions on multi-parametric MRI for predicting LNM of stage T3 rectal carcinoma.

METHODS

We retrospectively analyzed 175 patients with stage T3 rectal cancer who underwent preoperative MRI, including diffusion-weighted imaging (DWI) before surgery. 62 patients were included in the LNM group, and 113 patients were included in the non-LNM group. Texture features were calculated from histograms derived from T2 weighted imaging (T2WI), DWI, ADC, and T2 maps. Stepwise logistic regression analysis was used to screen independent predictors of LNM from clinical features, imaging features, and histogram features. Predictive performance was evaluated by receiver operating characteristic (ROC) curve analysis. Finally, a nomogram was established for predicting the risk of LNM.

RESULTS

The clinical, imaging and histogram features were analyzed by stepwise logistic regression. Preoperative carbohydrate antigen 199 level (p = 0.009), MRN stage (p < 0.001), _T2WIKurtosis (p = 0.010), _DWIMode (p = 0.038), _DWICV (p = 0.038), and _T2-mapP5 (p = 0.007) were independent predictors of LNM. These factors were combined to form the best predictive model. The model reached an area under the ROC curve (AUC) of 0.860, with a sensitivity of 72.8% and a specificity of 85.5%.

CONCLUSION

The histogram features on multi-parametric MRI of the primary tumor in rectal cancer were related to LN status, which is helpful for improving the ability to predict LNM of stage T3 rectal cancer.

Value of multiple models of diffusion-weighted imaging for improving the nodal staging of preoperatively node-negative rectal cancer

OBJECTIVE

To investigate the parameters of multiple diffusion-weighted imaging (DWI) models for improving nodal staging of preoperatively node-negative rectal cancer.

MATERIALS AND METHODS

A total of 74 rectal cancer patients without suspected metastatic lymph nodes on conventional MRI who underwent direct surgical resection between November 2018 and January 2020 were enrolled in this prospective study. DWI parameters of mono-exponential model (ADC), intravoxel incoherent motion (D, D* and f), stretched exponential model (DDC and α), and diffusion kurtosis imaging (MD and MK) within the whole tumor were measured to predict the nodal staging in rectal cancer patients.

RESULTS

The D*, DDC, and MK values were significantly different in patients with pN0 and pN1-2 (all P < 0.001). The D*, DDC, and MK showed good diagnostic performance with the area under the receiver operating characteristic (AUC) of 0.788, 0.827 and 0.799. Multivariate analysis indicated D* (odds ratio, OR = 1.163, P = 0.003) and DDC (OR = 0.007, P = 0.019) as significant predictors of nodal staging. The combination of DDC and D* demonstrated superior diagnostic performance with the AUC, sensitivity, specificity and accuracy of 0.872, 0.800, 0.932 and 0.878, respectively.

CONCLUSION

Multiple functional DWI parameters were potential to identify the rectal cancer patients with micro-nodal involvement for accurate treatment.

Diffusion kurtosis imaging-derived histogram metrics for prediction of resistance to neoadjuvant chemoradiotherapy in rectal adenocarcinoma: Preliminary findings

PURPOSE

This study aimed to evaluate the potential role of diffusion kurtosis imaging (DKI)-derived parameters for assessing resistance to CRT in patients with Locally advanced rectal cancer (LARC) by using histogram analysis derived from whole-tumor volumes.

METHOD

136 consecutive patients with histologically confirmed rectal adenocarcinoma who underwent MRI examination before and after chemoradiotherapy were enrolled in our retrospective study. The parameters D, K, and conventional apparent diffusion coefficient (ADC) were measured using whole-tumor volume histogram analysis. The AJCC tumor regression grading (TRG) system was the standard reference (resistance: TRG 3; non-resistance: TRG 0-2). Receiver operating characteristic (ROC) curves were used for evaluating the diagnostic performance.

RESULTS

Aside from the skew and kurtosis values, we found all the histogram metrics of D and ADC values significantly increased after CRT (all p < 0.001). In contrast, the histogram metrics of K values significantly decreased after CRT. The majority of percentiles metrics of D, K, and ADC values were correlated with tumor resistance before and after CRT (P < 0.05), except for the skew and kurtosis values. Regarding the comparison of the diagnostic performance of all the histogram metrics, the percentage D_mean change (ΔD_mean) showed the highest AUC value of 0.939, and the corresponding sensitivity, specificity, PPV, and NPV were 84.1% and 94.6%, 88.1% and 92.6%, respectively.

CONCLUSIONS

These preliminary results demonstrated that DKI-derived histogram metrics, especially the pre-treatment metrics and ΔD_mean, were useful to assess tumoral resistance to CRT and individual clinical management for patients with LARC.

Histogram models based on intravoxel incoherent motion diffusion-weighted imaging to predict nodal staging of rectal cancer

PURPOSE

To develop a model based on histogram parameters derived from intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) for predicting the nodal staging of rectal cancer (RC).

MATERIAL AND METHODS

A total of 95 RC patients who underwent direct surgical resection were enrolled in this prospective study. The nodal staging on conventional magnetic resonance imaging (MRI) was evaluated according to the short axis diameter and morphological characteristics. Histogram parameters were extracted from apparent diffusion coefficient (ADC), true diffusion coefficient (D), pseudo-diffusion coefficient (D*), and perfusion fraction (f) maps. Multivariate binary logistic regression analysis was conducted to establish models for predicting nodal staging among all patients and those underestimated on conventional MRI.

RESULTS

The combined model based on multiple maps demonstrated superior diagnostic performance to single map models, with an area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and accuracy of 0.959, 94.3%, 88.3%, and 90.5%, respectively. The AUC of the combined model was significantly higher than that of the conventional nodal staging (P < 0.001). Additionally, 85.0% of the underestimated patients had suspicious lymph nodes with 5-8 mm short-axis diameter. The histogram model for these subgroups of patients showed good diagnostic efficacy with an AUC, sensitivity, specificity, and accuracy of 0.890, 100%, 75%, and 80.5%.

CONCLUSION

The histogram model based on IVIM-DWI could improve the diagnostic performance of nodal staging of RC. In addition, histogram parameters of IVIM-DWI may help to reduce the uncertainty of nodal staging in underestimated patients on conventional MRI.

Preoperative volumetric synthetic magnetic resonance imaging of the primary tumor for a more accurate prediction of lymph node metastasis in rectal cancer

Background

An accurate assessment of lymph node (LN) status in patients with rectal cancer is important for treatment planning and an essential factor for predicting local recurrence and overall survival. In this study, we explored the potential value of histogram parameters of synthetic magnetic resonance imaging (SyMRI) in predicting LN metastasis in rectal cancer and compared their predictive performance with traditional morphological characteristics and chemical shift effect (CSE).

Methods

A total of 70 patients with pathologically proven rectal adenocarcinoma who received direct surgical resection were enrolled in this prospective study. Preoperative rectal MRI, including SyMRI, were performed, and morphological characteristics and CSE of LN were assessed. Histogram parameters were extracted on a T1 map, T2 map, and proton density (PD) map, including mean, variance, maximum, minimum, 10th percentile, median, 90th percentile, energy, kurtosis, entropy, and skewness. Receiver operating characteristic (ROC) curves were used to explore their predictive performance for assessing LN status.

Results

Significant differences in the energy of the T1, T2, and PD maps were observed between LN-negative and LN-positive groups [all P<0.001; the area under the ROC curve (AUC) was 0.838, 0.858, and 0.823, respectively]. The maximum and kurtosis of the T2 map, maximum, and variance of PD map could also predict LN metastasis with moderate diagnostic power (P=0.032, 0.045, 0.016, and 0.047, respectively). Energy of the T1 map [odds ratio (OR) =1.683, 95% confidence interval (CI): 1.207-2.346, P=0.002] and extramural venous invasion on MRI (mrEMVI) (OR =10.853, 95% CI: 2.339-50.364, P=0.002) were significant predictors of LN metastasis. Moreover, the T1 map energy significantly improved the predictive performance compared to morphological features and CSE (P=0.0002 and 0.0485).

Conclusions

The histogram parameters derived from SyMRI of the primary tumor were associated with LN metastasis in rectal cancer and could significantly improve the predictive performance compared with morphological features and CSE.

Prediction of pathological prognostic factors of rectal cancer by relaxation maps from synthetic magnetic resonance imaging

PURPOSE

To explore the feasibility of relaxation maps from synthetic MRI for predicting pathological prognostic factors of rectal cancer (RC) and to compare the predictive performance of quantitative values and conventional subjective evaluation.

MATERIAL AND METHODS

A total of 94 patients with pathologically proven RC who underwent direct surgical resection were enrolled in this prospective study. Preoperative rectal MRI including synthetic MRI was performed. The mean T₁, T₂, and PD value of the whole tumor was obtained to preoperatively assess the pathological T stage, N stage, extramural venous invasion (EMVI), differentiation, and perineural invasion. Receiver operating characteristic curves were used to explore the predictive performance for assessing the prognostic factors. The T stage, N stage and EMVI status on conventional T2WI were evaluated and compared with the quantitative values.

RESULTS

The T₂ value decreased significantly in patients with positive perineural invasion, lymph node metastasis (LNM), EMVI, and higher T stage RC (p =  0.007 and < 0.001). The T₁ value of LNM and EMVI positive groups was significantly lower than those of the negative groups (p =  0.034 and 0.011). For predicting N stage and EMVI, the T₂ value demonstrated good performance with an AUC of 0.883 (95 % confidence interval, CI, 0.801-0.940) and 0.821 (95 % CI, 0.729-0.893); the T₂ value was superior to the T₁ value and subjective evaluation of radiologists (all p < 0.05).

CONCLUSION

Synthetic MRI is a promising tool for noninvasive evaluation of prognostic factors of RC by generating relaxation maps.

The value of intravoxel incoherent motion and diffusion kurtosis imaging in the assessment of tumor regression grade and T stages after neoadjuvant chemoradiotherapy in patients with locally advanced rectal cancer

PURPOSE

To evaluate the role of IVIM and diffusion kurtosis imaging (DKI) in identifying pathologic complete response (pCR) and T stages after neoadjuvant chemoradiotherapy (nCRT) in locally advanced rectal cancer (LARC).

METHOD

Forty-two patients with biopsy-proven rectal adenocarcinoma, who underwent both pre-and post-CRT MRI with IVIM and DKI sequences on a 3 T scanner, were enrolled prospectively. According to the pathologic ypTNM stages and tumor regression grade (TRG), patients were grouped into pCR (TRG0) and non-pCR (TRG1-3) groups and low T stage (ypT0-2) and high T stage (ypT3-4) groups. IVIM parameters (the slow diffusion coefficient [D], fast diffusion coefficient [D*], perfusion fraction [f]), DKI parameters (mean diffusivity [MD] and mean kurtosis [MK]), and mono-exponential ADC were calculated and analyzed between groups.

RESULTS

The pCR group had significantly higher post-CRT ADC, D*, f, and MD values than non-pCR group, and higher percent changes in the ADC, f, and MD values (all P < 0.05). The post-CRT MD values yielded the highest AUC (0.788) with higher sensitivity than post-ADC values (82.9 % vs. 77.1 %, respectively). Post-CRT ADC and MD values and the percent changes in the ADC and MD values were also negatively correlated with TRG (all P < 0.05). Besides, negative correlations were found among the pre-CRT MD, post-CRT ADC, D, f, and MD values and the ypT stages (all P < 0.05).

CONCLUSIONS

Both IVIM and DKI parameters could provide more information when evaluating pCR and T stages after nCRT. In particular, the diagnostic performance of the MD values was more valuable than ADC values in being able to determine pCR.

Meningiomas: Preoperative predictive histopathological grading based on radiomics of MRI

PURPOSE

We aimed to develop a radiomics model to predict the histopathological grading of meningiomas by magnetic resonance imaging (MRI) before surgery.

METHODS

We recruited 131 patients with pathological diagnosis of meningiomas. All the patients had undergone MRI before surgery on a 3.0 T MRI scanner to obtain T1 fluid- attenuated inversion recovery (T1 FLAIR) images, T2-weighted images (T2WI) and T1 FLAIR with contrast enhancement (CE-T1 FLAIR) images covering the whole brain. The removing features with low variance, univariate feature selection, and least absolute shrinkage and selection operator (LASSO) were used to select radiomics features. Six classifiers were used to train the models (logistic regression (LR), k-nearest neighbor (KNN), decision tree (DT), support vector machine (SVM), random forests (RF), and XGBoost), and then 24 models were established using a random verification method to differentiate low-grade from high-grade meningiomas. The performance was assessed by receiver-operating characteristic (ROC) analysis, the f1-score, sensitivity, and specificity.

RESULTS

The radiomics features were significantly associated with the histopathological grading. Quantitative imaging features (n = 1409) were extracted, and nine features were selected to predict the grades of meningiomas. The best performance of the radiomics model for the degree of differentiation was obtained by SVM (area under the curve (AUC), 0.956; 95% confidence interval (CI), 0.83-1.00; sensitivity, 0.87; specificity, 0.92; f1-score, 0.90).

CONCLUSION

The radiomics models are of great value in predicting the histopathological grades of meningiomas, and have broad prospects in radiology and clinics.

Three-dimension amide proton transfer MRI of rectal adenocarcinoma: correlation with pathologic prognostic factors and comparison with diffusion kurtosis imaging

OBJECTIVES

To investigate the utility of 3D amide proton transfer (APT) MRI in predicting pathologic factors for rectal adenocarcinoma, in comparison with diffusion kurtosis imaging.

METHODS

Sixty-one patients with rectal adenocarcinoma were enrolled in this prospective study. 3D APT and diffusion kurtosis imaging (DKI) were performed. Mean APT-weighted signal intensity (APTw SI), mean kurtosis (MK), mean diffusivity (MD), and ADC values of tumors were calculated on these maps. Pathological analysis included WHO grades, pT stages, pN stages, and extramural venous invasion (EMVI) status. Student's t test, Spearman correlation, and receiver operating characteristics (ROC) analysis were used for statistical analysis.

RESULTS

High-grade rectal adenocarcinoma showed significantly higher mean APTw SI and MK values (2.771 ± 0.384 vs 2.108 ± 0.409, 1.167 ± 0.216 vs 1.045 ± 0.175, respectively; p < 0.05). T3 rectal adenocarcinoma demonstrated higher mean APTw SI and MK than T2 tumors (2.433 ± 0.467 vs 1.900 ± 0.302, p < 0.05). No kurtosis, diffusivity, and ADC differences were found between T2 and T3 tumors. Tumors with lymph node metastasis and EMVI involvement showed significantly higher mean APTw SI, MK. No difference was found in diffusivity and ADC between pN0 and pN1-2 groups, and EMVI-negative and EMVI-positive statuses. Mean APTw SI exhibited a significantly high positive correlation with WHO grades, demonstrating 92.31% sensitivity and 79.17% specificity for distinguishing low- from high-grade rectal adenocarcinoma, providing a better diagnostic capacity than MK, MD, and mean ADC values.

CONCLUSION

3D-APT could serve as a non-invasive biomarker for evaluating prognostic factors of rectal adenocarcinoma.

KEY POINTS

• Mean APTw SI was significantly higher in high-grade compared to low-grade rectal adenocarcinoma. • Mean APTw SI was significantly higher in T3 stage rectal adenocarcinoma, with lymph node metastasis, or in EMVI-positive status. • APTw SI exhibited greater diagnostic capability in discriminating low-grade from high-grade rectal adenocarcinoma, compared with kurtosis, diffusivity, and ADC.

Preoperatively Grading Rectal Cancer with the Combination of Intravoxel Incoherent Motions Imaging and Diffusion Kurtosis Imaging

Purpose

To combine Intravoxel Incoherent Motions (IVIM) imaging and diffusion kurtosis imaging (DKI) which can aid in the quantification of different biological inspirations including cellularity, vascularity, and microstructural heterogeneity to preoperatively grade rectal cancer.

Methods

A total of 58 rectal patients were included into this prospective study. MRI was performed with a 3T scanner. Different combinations of IVIM-derived and DKI-derived parameters were performed to grade rectal cancer. Pearson correlation coefficients were applied to evaluate the correlations. Binary logistic regression models were established via integrating different DWI parameters for screening the most sensitive parameter. Receiver operating characteristic analysis was performed for evaluating the diagnostic performance.

Results

For individual DWI-derived parameters, all parameters except the pseudodiffusion coefficient displayed the capability of grading rectal cancer (p < 0.05). The better discrimination between high- and low-grade rectal cancer was achieved with the combination of different DWI-derived parameters. Similarly, ROC analysis suggested the combination of D (true diffusion coefficient), f (perfusion fraction), and K_app (apparent kurtosis coefficient) yielded the best diagnostic performance (AUC = 0.953, p < 0.001). According to the result of binary logistic analysis, cellularity-related D was the most sensitive predictor (odds ratio: 9.350 ± 2.239) for grading rectal cancer.

Conclusion

The combination of IVIM and DKI holds great potential in accurately grading rectal cancer as IVIM and DKI can provide the quantification of different biological inspirations including cellularity, vascularity, and microstructural heterogeneity.

Histogram analysis in prostate cancer: a comparison of diffusion kurtosis imaging model versus monoexponential model

BACKGROUND

There is still little research about histogram analysis of diffusion kurtosis imaging (DKI) using in prostate cancer at present.

PURPOSE

To verify the utility of histogram analysis of DKI model in detection and assessment of aggressiveness of prostate cancer, compared with monoexponential model (MEM).

MATERIAL AND METHODS

Twenty-three patients were enrolled in this study. For DKI model and MEM, the D_app, K_app, and apparent diffusion coefficient (ADC) were obtained by using single-shot echo-planar imaging sequence. The pathologies were confirmed by in-bore magnetic resonance (MR)-guided biopsy. Regions of interest (ROI) were drawn manually in the position where biopsy needle was put. The mean values and histogram parameters in cancer and noncancerous foci were compared using independent-samples T test. Receiver operating characteristic curves were used to investigate the diagnostic efficiency. Spearman's test was used to evaluate the correlation of parameters and Gleason scores.

RESULTS

The mean, 10th, 25th, 50th, 75th, and 90th percentiles of ADC and D_app were significantly lower in prostate cancer than non-cancerous foci (P < 0.001). The mean, 50th, 75th, and 90th percentiles of K_app were significantly higher in prostate cancer (P < 0.05). There was no significant difference between the AUCs of two models (0.971 vs. 0.963, P > 0.05). With the increasing Gleason scores, the 10th ADC decreased (ρ = -0.583, P = 0.018), but the 90th K_app increased (ρ = 0.642, P = 0.007).

CONCLUSION

Histogram analysis of DKI model is feasible for diagnosing and grading prostate cancer, but it has no significant advantage over MEM.

Whole solid tumor volume histogram parameters for predicting the recurrence in patients with epithelial ovarian carcinoma: a feasibility study on quantitative DCE-MRI

BACKGROUND

Preoperative prediction of the recurrence of epithelial ovarian carcinoma (EOC) can guide the clinical treatment and improve the prognosis. However, there are still no reliable predictive biomarkers.

PURPOSE

To evaluate whether whole solid tumor volume histogram parameters measured from quantitative dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) can predict the recurrence in patients with EOC.

MATERIAL AND METHODS

We followed up 56 patients with surgical and histopathologically diagnosed EOC who underwent quantitative DCE-MRI scans. The differences of the histogram parameters between patients with and without recurrence were compared. Mann-Whitney U test, Pearson's Chi-squared test, or Fisher's exact test, and receiver operating characteristic (ROC) curves were used for statistical analysis.

RESULTS

All histogram parameters of K^trans, k_ep, and v_e were not significantly different between EOC patients with and without recurrence (P>0.05). For 30 patients with high-grade serous ovarian carcinoma (HGSOC), the histogram parameters of K^trans (mean and 5th, 10th, 25th, 50th, 75th percentiles) and k_ep (mean and 50th percentile) in 12 patients with recurrence were significantly lower than those in 18 patients without recurrence (all P<0.05). ROC curves showed that the 5th percentile of K^trans had the largest area under the curve (AUC) of 0.792 for predicting the recurrence in patients with HGSOC. When the threshold value was ≤0.0263/min, the sensitivity, specificity, and accuracy were 100%, 66.7%, and 80%, respectively.

CONCLUSION

Instead of predicting the recurrence of EOC, whole solid tumor volume quantitative DCE-MRI histogram parameters could predict the recurrence of HGSOC and may be potential biomarkers for the prediction of HGSOC recurrence.

Multiparametric MRI in rectal cancer

MRI has a pivotal role in both pretreatment staging and posttreatment evaluation of rectal cancer. The accuracy of MRI in pretreatment staging is higher compared with posttreatment evaluation. This occurs due to similar signal intensities of tumoral and posttreatment fibrotic, necrotic, and inflamed tissue. This limitation occurs with conventional MRI of the rectum with morphologic sequences. There is a need towards increasing the accuracy of MRI, especially for posttreatment evaluation. The term multiparametric MRI implies addition of functional sequences, namely, diffusion and perfusion to the routine protocol. This review summarizes the technique, potential implications and previously published studies about multiparametric MRI of rectal cancer.

Histological grades of rectal cancer: whole-volume histogram analysis of apparent diffusion coefficient based on reduced field-of-view diffusion-weighted imaging

Background

To explore the role of whole-lesion histogram analysis of apparent diffusion coefficient (ADC) derived from reduced field-of-view (rFOV) diffusion-weighted imaging (DWI) technique in discriminating histological grades of rectal carcinoma.

Methods

Altogether, 49 patients with rectal cancer were enrolled in this retrospective study. All patients received preoperative 3.0 T MR scan. Histogram parameters from rFOV DWI were calculated and correlated with histological differentiation of rectal cancer. The parameters were compared between different histological grades of rectal cancer by independent Student's t-test or Man-Whitney U-test. The Spearman correlation test analyzed correlations between histological grade and histogram parameters. The diagnostic performance of individual parameters for distinguishing poorly from well-/moderately differentiated tumors was assessed by receiver operating characteristic curve (ROC) analysis.

Results

There were significant differences for ADCmean, 25^th, 50^th, 75^th, 90^th, 95^th percentiles, skewness, and kurtosis of rFOV DWI sequence between well-, moderately, and poorly differentiated rectal cancers (P<0.05). Significant correlations were noted between histological grades and the above histogram parameters (r=0.679, 0.540, 0.701, 0.730, 0.669, 0.574, -0.730, and -0.760 respectively, P<0.001). Among the individual histogram parameter, kurtosis achieved the highest AUC of 0.882 with an optimal cutoff value of 1.934 in distinguishing poorly from well-/moderately differentiated rectal cancers. The combination of ADCmean, 75^th percentile, and kurtosis yielded the highest AUC of 0.927 with a sensitivity of 88.00% and a sensitivity of 91.7% using logistic regression.

Conclusions

Quantitative whole-lesion ADC histogram analysis based on the rFOV DWI technique could help differentiate histological grades of rectal cancer. The combination of ADCmean, 75^th percentile, and kurtosis may be the best choice.

Evaluation of diffusion kurtosis and diffusivity from baseline staging MRI as predictive biomarkers for response to neoadjuvant chemoradiation in locally advanced rectal cancer

PURPOSE

To evaluate the role of diffusion kurtosis and diffusivity as potential imaging biomarkers to predict response to neoadjuvant chemoradiation therapy (CRT) from baseline staging magnetic resonance imaging (MRI) in locally advanced rectal cancer (LARC).

MATERIALS AND METHODS

This retrospective study included 45 consecutive patients (31 male/14 female) who underwent baseline MRI with high b-value sequences (up to 1500 mm/s²) for LARC followed by neoadjuvant chemoradiation and surgical resection. The mean age was 57.4 years (range 34.2-72.9). An abdominal radiologist using open source software manually segmented T2-weighted images. Segmentations were used to derive diffusion kurtosis and diffusivity from diffusion-weighted images as well as volumetric data. These data were analyzed with regard to tumor regression grade (TRG) using the four-tier American Joint Committee on Cancer (AJCC) classification, TRG 0-3. Proportional odds regression was used to analyze the four-level ordinal outcome. A sensitivity analysis was performed using univariable logistic regression for binary TRG groups, TRG 0/1 (> 90% response), or TRG 2/3 (< 90% response). p < 0.05 was considered significant throughout.

RESULTS

In the univariable proportional odds regression analysis, higher diffusivity summary (D_sum) values were observed to be significantly associated with higher odds of being in one or more favorable TRG group (TRG 0 or 1). In other words, on average, patients with higher D_sum values were more likely to be in a more favorable TRG group. These results are mostly consistent with the sensitivity analysis, in which higher values for most D_sum values [all but region of interest (ROI)-max D median (p = 0.08)] were observed to be significantly associated with higher odds of being TRG 0 or 1. Tumor volume of interest (VOI) and ROI volume, ROI kurtosis mean and median, and VOI kurtosis mean and median were not significantly associated with TRG.

CONCLUSION

Diffusivity derived from the baseline staging MRI, but not diffusion kurtosis or volumetric data, is associated with TRG and therefore shows promise as a potential imaging biomarker to predict the response to neoadjuvant chemotherapy in LARC.

CLINICAL RELEVANCE STATEMENT

Diffusivity shows promise as a potential imaging biomarker to predict AJCC TRG following neoadjuvant CRT, which has implications for risk stratification. Patients with TRG 0/1 have 5-year disease-free survival (DFS) of 90-98%, as opposed to those who are TRG 2/3 with 5-year DFS of 68-73%.

Diffusion kurtosis imaging in patients with locally advanced rectal cancer: current status and future perspectives

Morphological magnetic resonance imaging is currently the best imaging technique for local staging in patients with rectal cancer. However, morphological sequences have some limitations, especially after preoperative chemoradiotherapy (pCRT). Diffusion-weighted imaging has been applied to rectal cancer for detection of lesions, characterization of tissue, and evaluation of the response to therapy. In 2005, a non-Gaussian diffusion model called diffusion kurtosis imaging (DKI) was suggested.

 Several electronic databases were evaluated in the present review. The search included articles published from January 2000 to May 2018. The references of all articles were also evaluated. All titles and abstracts were assessed, and only the studies of DKI in patients with rectal cancer were retained.

 We identified 35 potentially relevant references through the electronic search. According to the inclusion and exclusion criteria, we retained five clinical studies that met the inclusion criteria.

 DKI is a useful tool for assessment of tumor aggressiveness, the nodal status, and the risk of early metastases as well as prediction of the response to pCRT. The results of DKI should be considered in treatment decision-making during the work-up of patients with rectal cancer.

Diffusion kurtosis imaging (DKI) of hepatocellular carcinoma: correlation with microvascular invasion and histologic grade

BACKGROUND

The aim of this study was to prospectively evaluate the diagnostic efficacy of diffusion kurtosis imaging (DKI) in predicting microvascular invasion (MVI) and histologic grade of hepatocellular carcinoma (HCC) with comparison to the conventional diffusion-weighted imaging (DWI).

METHODS

This prospective study was approved by the Institutional Review Board, and written informed consent was obtained from all patients. From September 2015 to January 2017, 74 consecutive HCC patients were enrolled in this study. Preoperative magnetic resonance imaging including DKI protocol was performed, and patients were followed up for at least one year after surgery. Diffusion parameters including the mean corrected apparent diffusion coefficient (MD), mean apparent kurtosis coefficient (MK), and apparent diffusion coefficient (ADC) were calculated. Differences of diffusion parameters among different histopathological groups were compared. For parameters that were significantly different between pathological groups, receiver operating characteristics (ROC) curve analyses were performed to evaluate the diagnostic efficiency for identifying MVI and predicting high-grade HCC. Univariate and multivariate logistic regression analyses were used to evaluate the relative value of clinical and laboratory variables and diffusion parameters as risk factors for early recurrence (≤1 year).

RESULTS

Among all the studied diffusion parameters, only MK differed significantly between the MVI-positive and MVI-negative group (0.91±0.10 vs. 0.82±0.09, P<0.001), and showed moderate diagnostic efficacy (AUC =0.77) for identifying MVI. High-grade HCCs showed significantly higher MK values (0.93±0.10 vs. 0.82±0.09, P<0.001), along with MD (1.34±0.18 vs. 1.54±0.22, P<0.001) and ADC values (1.17±0.15 vs. 1.30±0.16, P=0.001) than low-grade HCCs. For differentiating high-grade from low-grade HCCs, MK demonstrated a higher area under the ROC curve (AUC) and significantly higher specificity than MD and ADC (AUC =0.81 vs. 0.76 and 0.74; specificity =82.2% vs. 60.0% and 60.0%, P=0.02). In addition, higher MK (OR =5.700, P=0.002) and Barcelona Clinic Liver Cancer (BCLC) stage C (OR =6.329, P=0.005) were independent risk factors for early HCC recurrence.

CONCLUSIONS

DKI-derived MK values outperformed conventional ADC values for predicting MVI and histologic grade of HCC, and are associated with increased risk of early tumor recurrence.

Renal cell carcinoma: preoperative evaluate the grade of histological malignancy using volumetric histogram analysis derived from magnetic resonance diffusion kurtosis imaging

Background

To investigate the value of histogram analysis of magnetic resonance (MR) diffusion kurtosis imaging (DKI) in the assessment of renal cell carcinoma (RCC) grading before surgery.

Methods

A total of 73 RCC patients who had undergone preoperative MR imaging and DKI were classified into either a low- grade group or a high-grade group. Parametric DKI maps of each tumor were obtained using in-house software, and histogram metrics between the two groups were analyzed. Receiver operating characteristic (ROC) curve analysis was used for obtaining the optimum diagnostic thresholds, the area under the ROC curve (AUC), sensitivity, specificity and accuracy of the parameters.

Results

Significant differences were observed in 3 metrics of ADC histogram parameters and 8 metrics of DKI histogram parameters (P<0.05). ROC curve analyses showed that K_app mean had the highest diagnostic efficacy in differentiating RCC grades. The AUC, sensitivity, and specificity of the K_app mean were 0.889, 87.9% and 80%, respectively.

Conclusions

DKI histogram parameters can effectively distinguish high- and low- grade RCC. K_app mean is the best parameter to distinguish RCC grades.

Diffusion-weighted imaging in rectal cancer: current applications and future perspectives

This review summarizes current applications and clinical utility of diffusion-weighted imaging (DWI) for rectal cancer and in addition provides a brief overview of more recent developments (including intravoxel incoherent motion imaging, diffusion kurtosis imaging, and novel postprocessing tools) that are still in more early stages of research. More than 140 papers have been published in the last decade, during which period the use of DWI have slowly moved from mainly qualitative (visual) image interpretation to increasingly advanced methods of quantitative analysis. So far, the largest body of evidence exists for assessment of tumour response to neoadjuvant treatment. In this setting, particularly the benefit of DWI for visual assessment of residual tumour in post-radiation fibrosis has been established and is now increasingly adopted in clinics. Quantitative DWI analysis (mainly the apparent diffusion coefficient) has potential, both for response prediction as well as for tumour prognostication, but protocols require standardization and results need to be prospectively confirmed on larger scale. The role of DWI for further clinical tumour and nodal staging is less well-defined, although there could be a benefit for DWI to help detect lymph nodes. Novel methods of DWI analysis and post-processing are still being developed and optimized; the clinical potential of these tools remains to be established in the upcoming years.

Diffusion Kurtosis at 3.0T as an in vivo Imaging Marker for Breast Cancer Characterization: Correlation With Prognostic Factors

BACKGROUND

Diffusion-kurtosis imaging (DKI) has preliminarily shown promise as a relatively new MRI technique to provide useful information regarding breast lesions, but the diagnostic performance of DKI has not been fully evaluated.

PURPOSE

To compare the diagnostic accuracy of DKI, diffusion-weighted imaging (DWI), dynamic contrast-enhanced (DCE)-MRI) and proton MR spectroscopy (¹ H-MRS) in differentiating malignant from benign breast lesions independently or jointly, and explore the correlation between DKI-derived parameters and prognostic factors.

STUDY TYPE

Prospective.

SUBJECTS

Seventy-one patients with breast lesions (50 malignant, 26 benign).

SEQUENCE

DKI, DWI, DCE-MRI, and ¹ H-MRS were performed at 3.0T.

ASSESSMENT

Mean kurtosis (MK), mean diffusivity (MD), apparent diffusion coefficient (ADC), BI-RADS category, and choline peaks were analyzed by two experienced radiologists.

STATISTICAL TESTS

Student's t-test was used for continuous variables; receiver operating characteristic (ROC) analysis for assessing the diagnostic accuracy of imaging parameters; Spearman or Pearson correlations for assessing the associations between imaging parameters and prognostic factors.

RESULTS

MK exhibited higher area under the curves (AUCs) for differentiating malignant from benign lesions than did MD, ADC, DCE, and tCho (0.979 vs. 0.928, 0.911, 0.777, and 0.833, respectively, P < 0.05). MK showed a positive association with Ki-67 expression (r = 0.508) and histologic grades (r = 0.551), whereas MD and ADC were negatively correlated with Ki-67 expression (r = -0.416 and r = -0.458) and histologic grades (r = -0.411 and r = -0.319). Moreover, MK showed relatively higher AUCs compared with MD and ADC in detecting breast cancers with lymph nodal involvement, histologic grades, and Ki-67 expression.

DATA CONCLUSION

MK has higher diagnostic accuracy compared with ADC, DCE, and tCho regarding detection of breast cancer. Moreover, DKI shows promise as a quantitative imaging technique for characterizing breast lesions, highlighting the potential utility of MK as a promising imaging marker for predicting tumor aggressiveness.

LEVEL OF EVIDENCE

2 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;49:845-856.

Diffusion kurtosis imaging-derived histogram metrics for prediction of KRAS mutation in rectal adenocarcinoma: Preliminary findings

BACKGROUND

Although histological examination is the standard method for assessing genetic status, the development of a noninvasive method, which can display the heterogeneity of the whole tumor to supplement genotype analysis, might be important for personalized treatment strategies.

PURPOSE

To evaluate the potential role of diffusion kurtosis imaging (DKI)-derived parameters using histogram analysis derived from whole-tumor volumes for prediction of the status of KRAS mutations in patients with rectal adenocarcinoma.

STUDY TYPE

Retrospective.

SUBJECTS

In all, 148 consecutive patients with histologically confirmed rectal adenocarcinoma who were treated at our institution.

SEQUENCE

DKI was performed with a 3.0 T MRI system using a single-shot echo-planar imaging sequence with b values of 0, 700, 1400, and 2100 sec/mm² .

ASSESSMENT

D, K, and apparent diffusion coefficient (ADC) values were measured using whole-tumor volume histogram analysis and were compared between different KRAS mutations status.

STATISTICAL TESTS

Student's t-test or Mann-Whitney U-test, and receiver operating characteristic (ROC) curves were used for statistical analysis.

RESULTS

All the percentile metrics of ADC and D values were significantly lower in the mutated group than those in the wildtype group (all P < 0.05), except for the minimum value of ADC and D (both P > 0.05), while K-related percentile metrics were higher in the mutated group compared with those in the wildtype group (all P < 0.05). Regarding the comparison of the diagnostic performance of all the histogram metrics, K_75th showed the highest AUC value of 0.871, and the corresponding values for sensitivity, specificity, positive predictive value, and negative predictive value were 81.43%, 78.21%, 77.03%, and 82.43%, respectively.

DATA CONCLUSION

DKI metrics with whole-tumor volume histogram analysis is associated with KRAS mutations, and thus may be useful for predicting the KRAS status of rectal cancers for guiding targeted therapy.

LEVEL OF EVIDENCE

3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;50:930-939.

ADC-histogram analysis in head and neck squamous cell carcinoma. Associations with different histopathological features including expression of EGFR, VEGF, HIF-1α, Her 2 and p53. A preliminary study

OBJECTIVE

Apparent diffusion coefficient (ADC) values derived from Diffusion-weighted images are able to reflect tumor microstructure, such as cellularity, extracellular matrix or proliferation potential. This present study sought to correlate prognostic relevant histopathologic parameters with ADC values derived from a whole lesion measurement in head and neck squamous cell carcinoma (HNSCC).

MATERIALS AND METHODS

Thirty-four patients with histological proven primary HNSCC were prospectively acquired. Histogram analysis was derived from ADC maps. In all cases, expression of Hif1-alpha, VEGF, EGFR, p53, p16, Her 2 were analyzed.

RESULTS

In the overall patient sample, ADCmax correlated with p53 expression (p = -0.446, p = 0.009) and ADCmode correlated with Her2-expression (p = -0.354, p = 0.047). In the p16 positive group there were several correlations. P25, P90 and entropy correlated with Hif1-alpha (p = -0.423, p = 0.05, p = -0.494, p = 0.019, p = 0.479, p = 0.024, respectively). Kurtosis correlated with P53 expression (p = -0.466, p = 0.029). For p16 negative carcinomas the following associations could be identified. Mode correlated with VEGF-expression (p = -0.657, p = 0.039). ADCmax, P75, P90, and Std correlated with p53-expression (p = -0.827, p = 0.002, p = -0.736, p = 0.01, p = -0.836, p = 0.001 and p = -0.70, p = 0.016, respectively). There were no statistically significant differences of ADC histogram parameters between p16 positive and p16 negative carcinomas.

CONCLUSION

ADC histogram values can reflect different histopathological features in HNSCC. Associations between ADC histogram analysis parameters and histopathology depend on p16 status.

ADC histogram analysis of muscle lymphoma-correlation with histopathology in a rare entity

OBJECTIVE:

Diffusion-weighted imaging is able to reflect histopathology architecture. A novel imaging approach, namely histogram analysis, is used to further characterize lesion on MRI. To correlate histogram parameters derived from apparent diffusion coefficient (ADC) maps with histopathology parameters in muscle lymphoma.

METHODS:

Eight patients (mean age 64.8 years, range 45-72 years) with histopathologically confirmed muscle lymphoma were retrospectively identified. Cell count, total nucleic and average nucleic areas were estimated using ImageJ. Additionally, Ki67-index was calculated. Diffusion-weightedimaging was obtained on a 1.5 T scanner by using the b-values of 0 and 1000 s mm^-2. Histogram analysis was performed as a whole lesion measurement by using a custom-made Matlab-based application.

RESULTS:

All ADC parameters showed a good to excellent interreader variability. Cell count correlated well with ADCmean (ρ = -0.76, p = 0.03) and ADCp75 (ρ =-0.79, p = 0.02). Kurtosis and entropy correlated with average nucleic area (ρ = -0.81, p = 0.02, ρ =0.88, p = 0.007, respectively). None of the analyzed ADC parameters correlated with total nucleic area and with Ki67-index.

CONCLUSION:

ADC histogram analysis parameters can reflect cellularity in muscle lymphoma.

ADVANCES IN KNOWLEDGE:

Histogram parameters derived from ADC maps can reflect several different cellularity parameters in muscle lymphoma.