Chan Kyo Kim

Pathological characteristics and risk stratification in patients with stage I endometrial cancer: utility of apparent diffusion coefficient histogram analysis

Objectives: Accurate pre-operative prediction of risk stratification using a non-invasive imaging tool is clinically important for planning optimal treatment strategies, particularly in early-stage endometrial cancer (EC). This study aimed to investigate the utility of apparent diffusion coefficient (ADC) histogram analysis in evaluating the pathological characteristics and risk stratification in patients with Stage I EC. Methods: Between October 2009 and December 2014, a total of 108 patients with surgically proven Stage I EC (endometrioid type = 91; non-endometrioid type = 17) excluding stage ≥II that underwent preoperative 3T-diffusion-weighted imaging without administration of contrast medium were enrolled in this retrospective study. Risk stratification was divided into four risk categories based on the ESMO-ESGO-ESTRO Guidelines: low, intermediate, high-intermediate, and high risk. The ADC histogram parameters (minimum, mean [ADCmean], 10th–90th percentile, and maximum [ADCmax]) of the tumor were generated using an in-house software. The ADC histogram parameters were compared between patients with endometrioid type and non-endometrioid type, between Stage IA and IB, between histological grades, and evaluated for differentiating non-high risk group from high risk group. Inter-reader agreement for tumor ADC measurements was also evaluated. Statistical analyses were performed using the Student’s t-test, Mann–Whitney U test, receiver operating characteristics (ROC) analysis, or intraclass correlation coefficient (ICC). Results: In differentiating endometrioid type from non-endometrioid type EC, all ADC histogram parameters were statistically significant (p < 0.05). In differentiating histological grades, 90th percentile ADC and ADCmax showed significantly higher values in tumor Grade III than in tumor Grade I-II (p < 0.05). In differentiating superficial myometrial invasion from deep myometrial invasion, all ADC histogram parameters were statistically significant (p < 0.05), except ADCmax. In differentiating non-high risk group from high risk group, ADCmean, 75th–90th percentile ADC, and ADCmax were statistically significant (p < 0.05). For predicting the high risk group, the area under the ROC curve of ADCmax was 0.628 and the highest among other histogram parameters. All histogram parameters revealed moderate to good inter-reader reliability (ICC = 0.581‒0.769). Conclusion: The ADC histogram analysis as reproducible tool may be useful for evaluating the pathological characteristics and risk stratification in patients with early-stage EC. Advances in knowledge: ADC histogram analysis may be useful for evaluating risk stratification in early-stage endometrial cancer patients.

Utility of diffusion‐weighted imaging in association with pathologic upgrading in biopsy‐proven grade I endometrial cancer

BackgroundPrediction of pathologic upgrading is clinically meaningful to identify the optimal candidate of fertility‐preserving hormonal treatment in the young patients with biopsy‐proven grade I endometrial cancer.PurposeTo investigate the utility of diffusion‐weighted imaging (DWI) in association with pathologic upgrading in endometrial cancer.Study TypeRetrospective.SubjectsPreoperative MRI datasets of 221 patients with grade I endometrial cancer on endometrial biopsy (n = 146), dilatation and curettage (n = 66), or either (n = 9).Field Strength/Sequence3.0T, including T2‐weighted imaging, DWI with a b‐value of 1000 s/mm2, and dynamic contrast enhanced imaging.AssessmentThe tumor size was determined as the longest diameter of the lesion. The minimum apparent diffusion coefficient (ADCmin) was calculated using histogram analysis of the entire tumor.Statistical TestsMann–Whitney U‐test, Pearson's chi‐square test, Fisher's exact test, intraclass correlation coefficient (ICC) analysis, receiver operating characteristic (ROC) curve analysis, univariate and multivariate logistic regression analysis.ResultsPathologic upgrading was identified in 42 patients (19.0%). Patients with pathologic upgrading had larger tumors and showed lower ADCmin values than those without pathologic upgrading (both P < 0.001). The area under the ROC curve of ADCmin and tumor size was 0.812 and 0.758, respectively. On multivariate analysis, tumor ADCmin ≤0.600 × 10‐3 mm2/s (odds ratio [OR], 11.8; P < 0.001) and tumor size on MRI >3 cm (OR, 3.24; P = 0.009) were independently associated with pathologic upgrading. Upgrading occurred in 23 of 31 patients (74.2%) with ADCmin ≤0.600 × 10‐3 mm2/s and tumor size >3 cm, and in 7 of 114 patients (6.1%) with ADCmin >0.600 × 10‐3 mm2/s and tumor size ≤3 cm.Data ConclusionTumor ADC and tumor size on MRI may be useful parameters in association with pathologic upgrading in biopsy‐proven grade I endometrial cancer.Level of Evidence: 4Technical Efficacy: Stage 3 J. Magn. Reson. Imaging 2020;51:117–123.

Histogram analysis of apparent diffusion coefficients for predicting pelvic lymph node metastasis in patients with uterine cervical cancer

To investigate the value of apparent diffusion coefficient (ADC) histogram analysis in predicting pelvic lymph node (LN) metastasis in patients with cervical cancer undergoing surgery. A total of 162 cervical cancer patients who underwent radical abdominal hysterectomy with pelvic LN dissection performed with pelvic 3 T-MRI including diffusion-weighted imaging were enrolled in this study. The ADC histogram variables (minimum, mean, median, 97.5th percentile [ADC Pelvic LN metastasis was identified histopathologically in 50 patients (30.9%). In patients with LN metastasis, all ADC histogram variables were significantly different from those without LN metastasis (all p < 0.01). Univariate analysis demonstrated that long- and short-axis diameter of LN, MRI T-stage, squamous cell carcinoma antigen, tumor size, and the ADC The ADC

Magnetic resonance imaging-based texture analysis for the prediction of postoperative clinical outcome in uterine cervical cancer

Magnetic resonance imaging (MRI)-based texture analysis (MRTA) is a novel image analysis tool that offers objective information about the spatial arrangement of MRI signal intensity. We aimed to investigate the value of MRTA in predicting the postoperative clinical outcome of patients with uterine cervical cancer. This retrospective study included 115 patients with surgically proven cervical cancer who underwent preoperative pelvic 3T-MRI, and MRTA was performed on T2-weighted images (T2), apparent diffusion coefficient (ADC) maps, and contrast-enhanced T1-weighted images (CE-T1). Filtration histogram-based texture analysis was used to generate six first-order statistical parameters [mean intensity, standard deviation (SD), mean of positive pixels (MPP), entropy, skewness, and kurtosis] at five spatial scaling factors (SSFs, 2-6 mm) as well as from unfiltered images. Cox proportional hazard models and time-dependent receiver operating characteristic analyses were used to evaluate the associations between parameters and recurrence-free survival (RFS). During a median follow-up of 36 months, tumor recurrence was found in 26 patients (22.6%). Multivariate analysis demonstrated that CE-T1 MPP and T2 kurtosis at SSF3-5, CE-T1 MPP at SSF6, and CE-T1 SD at unfiltered images were independent predictors of RFS (p  optimal cutoff values demonstrated significantly worse survival than those with ≤ optimal cutoff values (p < 0.05). Preoperative MRTA may be useful for predicting postoperative outcome in patients with cervical cancer.