The role of volumetric ADC histogram analysis in preoperatively evaluating the tumour subtype and grade of endometrial cancer

Intratumoral and peritumoral habitat imaging based on multiparametric MRI to predict cervical stromal invasion in early-stage endometrial carcinoma

RATIONALE AND OBJECTIVES

To evaluate the validity of multiparametric MRI-based intratumoral and peritumoral habitat imaging for predicting cervical stromal invasion (CSI) in patients with early-stage endometrial carcinoma (EC) and to compare the performance of structural and functional habitats.

MATERIALS AND METHODS

The preoperative MRI and clinical data of 680 patients with early-stage EC from three centers were retrospectively analyzed. Based on cohort-level, gaussian mixture model (GMM) algorithm was used for habitat clustering of MRI images. Structural habitats were clustered using T2-weighted imaging (T2WI) and contrast-enhanced T1-weighted imaging (CE-T1WI), and functional habitats were clustered using apparent diffusion coefficient (ADC) mapping and CE-T1WI. Habitat parameters were extracted from four volumes of interest (VOIs): intratumoral regions (ROI), peritumoral loops of 3 mm dilation (L3), intratumoral regions + peritumoral loops of 3 mm dilation (R3), and peritumoral loops of 3 mm dilation + peritumoral loops of 3 mm erosion (DE3). Clinical-habitat models were constructed by combining clinical independent predictors and optimal habitat models. The model performance was evaluated by the area under the curve (AUC).

RESULTS

Deep myometrial invasion (DMI) was an independent predictor. L3 models showed the best performance for both structural and functional habitats, and the L3 functional habitat model had the highest average AUC (0.807) in external test groups, and the average AUC increased to 0.815 when combing with the clinical independent predictor.

CONCLUSION

Multiparametric MRI-based intratumoral and peritumoral habitat imaging provides a noninvasive approach to predict CSI in EC patients. The combination of the clinical predictor with the L3 functional habitat model improved predictive performance.

Updated endometrial cancer FIGO staging: the role of MRI in determining newly included histopathological criteria

PURPOSE

Endometrial cancer (EC) is among the prevalent malignancies in gynecology, showing an increasing occurrence and mortality rate. The updated 2023 FIGO staging integrates both histopathological and molecular analyses, which significantly impact the prognosis and treatment approaches. This research aims to examine the effectiveness of MRI in identifying essential histopathological tumor features, including histological subtype, grade, and lymphovascular space invasion.

METHODS

A total of 106 patients diagnosed with EC from February 2018 to December 2023 underwent preoperative pelvic MRI. Surgical procedures followed ESMO guidelines, with histopathological assessments using FIGO 2009 criteria. Two radiologists independently evaluated MRI images, measuring maximum tumor size, minimum tumor ADC value (using a free-hand ROI technique), and ADC tumor/myometrium ratio. MRI findings were compared with histopathological data.

RESULTS

Peritoneal implant presence and tumor size exhibited significant differences between endometrioid adenocarcinoma (EAC) and non-endometrioid endometrial carcinoma (NEEC), with p values of < 0.001 and 0.003, respectively. Significant differences in age, tumor size, ADC tumor, and ADC tumor/myometrium between low-grade and high-grade tumors were observed, with p values of < 0.001, 0.004, 0.006, and 0.011, respectively. Increased tumor size, reduced ADC tumor, ADC tumor/myometrium, and pelvic peritoneal implant presence were significantly associated with LVSI, with p values of < 0.001, 0.001, 0.002, and 0.001, respectively. The AUC values for tumor size, ADC tumor, and ADC tumor/myometrium were 0.842, 0.781 and 0.747, respectively, in distinguishing between low and high-grade endometrial tumors. Similarly, obtained AUC values for predicting LVSI were 0.836, 0.719, and 0.696, respectively.

CONCLUSION

Our study emphasizes MRI's role in predicting tumor characteristics such as histological subtype, grade, and LVSI based on updated FIGO criteria. By highlighting the potential of MRI, this research contributes to our comprehension of improving diagnostic and clinical management for EC. Further multicenter studies are warranted to validate these findings and establish MRI's role in EC management.

Multiparametric MRI radiomics improves preoperative diagnostic performance for local staging in patients with endometrial cancer

PURPOSE

To determine whether multiparametric magnetic resonance imaging (MRI) radiomics-based machine learning methods can improve preoperative local staging in patients with endometrial cancer (EC).

METHODS

Data of patients with histologically confirmed EC who underwent preoperative MRI were retrospectively analyzed and divided into a training or test set. Radiomic features extracted from multiparametric MR images were used to train and test the prediction of deep myometrial invasion (DMI) and cervical stromal invasion (CSI). Two radiologists assessed the presence of DMI and CSI on conventional MR images. A combined model incorporating a radiomic signature and conventional MR images was constructed and presented as a nomogram. Performance of the predictive models was assessed using the area under curve (AUC) in the receiver operating curve analysis and pairwise comparison using DeLong's test with Bonferroni correction.

RESULTS

This study included 198 women (training set = 138, test set = 60). Conventional MRI achieved AUCs of 0.837 and 0.799 for detecting DMI and 0.825 and 0.858 for detecting CSI in the training and test sets, respectively. The nomogram achieved AUCs of 0.928 and 0.869 for detecting DMI and 0.913 and 0.937 for detecting CSI in the training and test sets, respectively. The ability of the nomogram to detect DMI and CSI in the two sets was superior to that of conventional MRI (adjusted p < 0.05), except for the ability to detect CSI in the test set (adjusted p > 0.05).

CONCLUSION

A nomogram incorporating radiomics signature into conventional MRI improved the efficacy of preoperative local staging of EC.

Whole-tumor apparent diffusion coefficient histogram analysis for preoperative risk stratification in endometrial endometrioid adenocarcinoma

OBJECTIVE

To investigate the application of whole-tumor apparent diffusion coefficient (ADC) histogram metrics for preoperative risk stratification in endometrial endometrioid adenocarcinoma (EEA).

METHODS

Preoperative MRI of 502 EEA patients were retrospectively analyzed. Whole tumor ADC histogram analysis was performed with regions of interest drawn on all tumor slices of diffusion-weighted imaging scans. Risk stratification was based on ESMO-ESTRO-ESP guidelines: low-, intermediate-, high-intermediate-, and high-risk. Univariable analysis was used to compare ADC histogram metrics (tumor volume, minADC, maxADC, and meanADC; 10th, 25th, 50th, 75th, and 90th percentiles of ADC [recorded as P10, P25, P50, P75, and P90 ADC, respectively]; skewness; and kurtosis) between different risk EEAs, and multivariable logistic regression analysis to determine the optimal metric or combined model for risk stratifications. Receiver operating characteristic curve analysis with the area under the curve (AUC) was used for diagnostic performance evaluation.

RESULTS

A decreasing tendency in multiple ADC values was observed from the low- to high-intermediate-risk EEAs. The (low + intermediate)-risk EEAs and low-risk EEAs had significantly smaller tumor volumes and higher minADCs, meanADCs, P10, P25, P50, P75, and P90 ADCs than the (high-intermediate + high)-risk EEAs and non-low-risk EEAs (all P < 0.05), respectively. The combined models of the (meanADC + volume) and the (P75 ADC + volume) yielded the largest AUCs of 0.775 and 0.780 in identifying the (low + intermediate)- and the low-risk EEAs from the other EEAs, respectively.

CONCLUSION

Whole-tumor ADC histogram metrics might be helpful for preoperatively identifying low- and (low + intermediate)-risk EEAs, facilitating personalized therapeutic planning.

Can the ADC Value Be Used as an Imaging "Biopsy" in Endometrial Cancer?

BACKGROUND

The tumor histological grade is closely related to the prognosis of patients with endometrial cancer (EC). Multiparametric MRI, including diffusion-weighted imaging (DWI), provides information about the cellular density that may be useful to differentiate between benign and malignant uterine lesions. However, correlations between apparent diffusion coefficient (ADC) values and histopathological grading in endometrial cancer remain controversial.

MATERIAL AND METHODS

We retrospectively evaluated 92 patients with endometrial cancers, including both endometrioid adenocarcinomas (64) and non-endometrioid adenocarcinomas (28). All patients underwent DWI procedures, and mean ADC values were calculated in a region of interest. These values were then correlated with the tumor grading offered by the histopathological examination, which was considered the gold standard. In this way, the patients were divided into three groups (G1, G2, and G3). The ADC values were then compared to the results offered by the biopsy to see if the DWI sequence and ADC map could replace this procedure. We also compared the mean ADC values to the myometrial invasion (50%) and lymphovascular space invasion.

RESULTS

We have divided the ADC values into three categories corresponding to three grades: >0.850 × 10-3 mm2/s (ADC1), 0.730-0.849 × 10-3 mm2/s (ADC2) and <0.730 × 10-3 mm2/s (ADC3). The diagnostic accuracy of the ADC value was 85.71% for ADC1, 75.76% for ADC2, and 91.66% for ADC3. In 77 cases out of 92, the category in which they were placed using the ADC value corresponded to the result offered by the histopathological exam with an accuracy of 83.69%. For only 56.52% of patients, the biopsy result included the grading system. For each grading category, the mean ADC value showed better results than the biopsy; for G1 patients, the mean ADC value had an accuracy of 85.71% compared to 66.66% in the biopsy, G2 had 75.76% compared to 68.42%, and G3 had 91.66 compared to 75%. For both deep myometrial invasion and lymphovascular space invasion, there is a close, inversely proportional correlation with the mean ADC value.

CONCLUSIONS

Mean endometrial tumor ADC on MR-DWI is inversely related to the histological grade, deep myometrial invasion and lymphovascular space invasion. Using this method, the patients could be better divided into risk categories for personalized treatment.

Analysis of Pancreatobiliary and Intestinal Type Periampullary Carcinomas Using Volumetric Apparent Diffusion Coefficient Histograms

RATIONALE AND OBJECTIVES

Magnetic resonance imaging plays an important role in the evaluation of patients with known or suspected periampullary masses. The utilization of volumetric apparent diffusion coefficient (ADC) histogram evaluation for the entire lesion eradicates the potential for subjectivity in the region of interest placement, thus guaranteeing the accuracy of computation and repeatability.

PURPOSE

To investigate the value of volumetric ADC histogram analysis in the differentiation of intestinal-type (IPAC) and pancreatobiliary-type periampullary adenocarcinomas (PPAC).

MATERIALS AND METHODS

This retrospective study included 69 patients with histopathologically confirmed periampullary adenocarcinoma (54 PPAC and 15 IPAC). Diffusion-weighted imaging was obtained at b values of 1000 mm²/s. The histogram parameters of ADC values, comprising the mean, minimum, maximum, 5th, 10th, 25th, 50th, 75th, 90th, and 95th percentiles, as well as skewness, kurtosis, and variance, were calculated independently by two radiologists. Using the interclass correlation coefficient, the interobserver agreement was evaluated.

RESULTS

The ADC parameters for the PPAC group were all lower than those of the IPAC group. The PPAC group had higher variance, skewness, and kurtosis than the IPAC group. However, the difference between the kurtosis (P = .003), the 5th (P = .032), 10th (P = .043), and 25th (P = .037) percentiles of ADC values was statistically significant. The area under the curve (AUC) of the kurtosis was the highest (AUC=0.752; cut-off value=-0.235; sensitivity=61.1%; specificity=80.0%).

CONCLUSION

Volumetric ADC histogram analysis with b values of 1000 mm²/s can discriminate subtypes noninvasively before surgery.

Endometrial Cancer Staging: Is There Value in ADC?

PURPOSE

To assess the ability of apparent diffusion coefficient (ADC) measurements in predicting the histological grade of endometrial cancer. A secondary goal was to assess the agreement between MRI and surgical staging as an accurate measurement.

METHODS

Patients with endometrial cancers diagnosed between 2018-2020 and having received both MRI and surgical staging were retrospectively enrolled. Patients were characterized according to histology, tumor size, FIGO stage (MRI and surgical stage), and functional MRI parameters (DCE and DWI/ADC). Statistical analysis was performed to determine if an association could be identified between ADC variables and histology grade. Secondarily, we assessed the degree of agreement between the MRI and surgical stages according to the FIGO classification.

RESULTS

The cohort included 45 women with endometrial cancer. Quantitative analysis of ADC variables did not find a statistically significant association with histological tumor grades. DCE showed higher sensitivity than DWI/ADC in the assessment of myometrial invasion (85.00% versus 65.00%) with the same specificity (80.00%). A good agreement between MRI and histopathology for the FIGO stage was found (kappa of 0.72, p < 0.01). Differences in staging between MRI and surgery were detected in eight cases, which could not be justified by the interval between MRI and surgery.

CONCLUSIONS

ADC values were not useful for predicting endometrial cancer grade, despite the good agreement between MRI interpretation and histopathology of endometrial cancer staging at our center.

Tissue Characteristics of Endometrial Carcinoma Analyzed by Quantitative Synthetic MRI and Diffusion-Weighted Imaging

BACKGROUND

This study investigates the association of T1, T2, proton density (PD) and the apparent diffusion coefficient (ADC) with histopathologic features of endometrial carcinoma (EC).

METHODS

One hundred and nine EC patients were prospectively enrolled from August 2019 to December 2020. Synthetic magnetic resonance imaging (MRI) was acquired through one acquisition, in addition to diffusion-weighted imaging (DWI) and other conventional sequences using 1.5T MRI. T1, T2, PD derived from synthetic MRI and ADC derived from DWI were compared among different histopathologic features, namely the depth of myometrial invasion (MI), tumor grade, cervical stromal invasion (CSI) and lymphovascular invasion (LVSI) of EC by the Mann-Whitney U test. Classification models based on the significant MRI metrics were constructed with their respective receiver operating characteristic (ROC) curves, and their micro-averaged ROC was used to evaluate the overall performance of these significant MRI metrics in determining aggressive histopathologic features of EC.

RESULTS

EC with MI had significantly lower T2, PD and ADC than those without MI (p = 0.007, 0.006 and 0.043, respectively). Grade 2-3 EC and EC with LVSI had significantly lower ADC than grade 1 EC and EC without LVSI, respectively (p = 0.005, p = 0.020). There were no differences in the MRI metrics in EC with or without CSI. Micro-averaged ROC of the three models had an area under the curve of 0.83.

CONCLUSIONS

Synthetic MRI provided quantitative metrics to characterize EC with one single acquisition. Low T2, PD and ADC were associated with aggressive histopathologic features of EC, offering excellent performance in determining aggressive histopathologic features of EC.

Volumetric apparent diffusion coefficient histogram analysis of the testes in nonobstructive azoospermia: a noninvasive fingerprint of impaired spermatogenesis?

OBJECTIVES

To explore the association between testicular volumetric apparent diffusion coefficient (ADC) histogram analysis metrics and histologic categories in nonobstructive azoospermia (NOA). The role of ADC histogram analysis in predicting the presence of spermatozoa, prior to testicular sperm extraction (TESE), was also investigated.

METHODS

Forty-one NOA men and 17 age-matched controls underwent scrotal MRI with diffusion-weighted imaging. Histogram analysis of ADC data of the whole testis was performed. Metrics including mean, standard deviation, median, mode, 25th percentile, 75th percentile, skewness, kurtosis, and entropy of volumetric ADC histograms were calculated. Nonparametric statistical tests were used to assess differences in ADC histogram parameters between NOA histologic categories (hypospermatogenesis, severe hypospermatogenesis, early maturation arrest, and Sertoli cell-only syndrome) and normal testes and, between NOA with positive and negative sperm retrieval.

RESULTS

Normal testes had a lower mean, median, mode, 25th percentile (p < 0.001), and 75th percentile of ADC (p = 0.001), compared to NOA histologic phenotypes. NOA with hypospermatogenesis had a lower 25th percentile of ADC compared to NOA with severe hypospermatogenesis. Regression analysis revealed that the 25th percentile of ADC had a moderately negative correlation with NOA histologic phenotype. The median ADC proved the most significant metric (p = 0.007) to predict the presence of sperm.

CONCLUSIONS

Testicular volumetric ADC histogram parameters may contribute in the identification of the subpopulation of NOA men with a specific type of spermatogenic arrest.

KEY POINTS

• Volumetric ADC histogram analysis metrics may be used as noninvasive markers of impaired spermatogenesis in nonobstructive azoospermia. • The 25th percentile of ADC proved useful in discriminating between NOA testes with hypospermatogenesis and severe hypospermatogenesis. • The median ADC proved the most significant parameter to predict the presence of viable spermatozoa prior to TESE.

Whole-lesion apparent diffusion coefficient (ADC) histogram as a quantitative biomarker to preoperatively differentiate stage IA endometrial carcinoma from benign endometrial lesions

BACKGROUND

To assess the value of whole-lesion apparent diffusion coefficient (ADC) histogram analysis in differentiating stage IA endometrial carcinoma (EC) from benign endometrial lesions (BELs) and characterizing histopathologic features of stage IA EC preoperatively.

METHODS

One hundred and six BEL and 126 stage IA EC patients were retrospectively enrolled. Eighteen volumetric histogram parameters were extracted from the ADC map of each lesion. The Mann-Whitney U or Student's t-test was used to compare the differences between the two groups. Models based on clinical parameters and histogram features were established using multivariate logistic regression. Receiver operating characteristic (ROC) analysis and calibration curves were used to assess the models.

RESULTS

Stage IA EC showed lower ADC_10th, ADC_90th, ADC_min, ADC_max, ADC_mean, ADC_median, interquartile range, mean absolute deviation, robust mean absolute deviation (rMAD), root mean squared, energy, total energy, entropy, variance, and higher skewness, kurtosis and uniformity than BELs (all p < 0.05). ADC_median yielded the highest area under the ROC curve (AUC) of 0.928 (95% confidence interval [CI] 0.895-0.960; cut-off value = 1.161 × 10^-3 mm²/s) for differentiating stage IA EC from BELs. Moreover, multivariate analysis demonstrated that ADC-score (ADC_10th + skewness + rMAD + total energy) was the only significant independent predictor (OR = 2.641, 95% CI 2.045-3.411; p < 0.001) for stage IA EC when considering clinical parameters. This ADC histogram model (ADC-score) achieved an AUC of 0.941 and a bias-corrected AUC of 0.937 after bootstrap resampling. The model performed well for both premenopausal (accuracy = 0.871) and postmenopausal (accuracy = 0.905) patients. Besides, ADC_min and ADC_10th were significantly lower in Grade 3 than in Grade 1/2 stage IA EC (p = 0.022 and 0.047). At the same time, no correlation was found between ADC histogram parameters and the expression of Ki-67 in stage IA EC (all p > 0.05).

CONCLUSIONS

Whole-lesion ADC histogram analysis could serve as an imaging biomarker for differentiating stage IA EC from BELs and assisting in tumor grading of stage IA EC, thus facilitating personalized clinical management for premenopausal and postmenopausal patients.