Nodal infiltration in endometrial cancer: a prediction model using best subset regression.

OBJECTIVES: To build preoperative prediction models with and without MRI for regional lymph node metastasis (r-LNM, pelvic and/or para-aortic LNM (PENM/PANM)) and for PANM in endometrial cancer using established risk factors. METHODS: In this retrospective two-center study, 364 patients with endometrial cancer were included: 253 in the model development and 111 in the external validation. For r-LNM and PANM, respectively, best subset regression with ten-time fivefold cross validation was conducted using ten established risk factors (4 clinical and 6 imaging factors). Models with the top 10 percentile of area under the curve (AUC) and with the fewest variables in the model development were subjected to the external validation (11 and 4 candidates, respectively, for r-LNM and PANM). Then, the models with the highest AUC were selected as the final models. Models without MRI findings were developed similarly, assuming the cases where MRI was not available. RESULTS: The final r-LNM model consisted of pelvic lymph node (PEN) ≥ 6 mm, deep myometrial invasion (DMI) on MRI, CA125, para-aortic lymph node (PAN) ≥ 6 mm, and biopsy; PANM model consisted of DMI, PAN, PEN, and CA125 (in order of correlation coefficient ß values). The AUCs were 0.85 (95%CI: 0.77-0.92) and 0.86 (0.75-0.94) for the external validation, respectively. The model without MRI for r-LNM and PANM showed AUC of 0.79 (0.68-0.89) and 0.87 (0.76-0.96), respectively. CONCLUSIONS: The prediction models created by best subset regression with cross validation showed high diagnostic performance for predicting LNM in endometrial cancer, which may avoid unnecessary lymphadenectomies. CLINICAL RELEVANCE STATEMENT: The prediction risks of lymph node metastasis (LNM) and para-aortic LNM can be easily obtained for all patients with endometrial cancer by inputting the conventional clinical information into our models. They help in the decision-making for optimal lymphadenectomy and personalized treatment. KEY POINTS: •Diagnostic performance of lymph node metastases (LNM) in endometrial cancer is low based on size criteria and can be improved by combining with other clinical information. •The optimized logistic regression model for regional LNM consists of lymph node ≥ 6 mm, deep myometrial invasion, cancer antigen-125, and biopsy, showing high diagnostic performance. •Our model predicts the preoperative risk of LNM, which may avoid unnecessary lymphadenectomies.

Risk Stratification for Pregnancies Diagnosed With Fetal Growth Restriction Based on Placental MRI.

BACKGROUND: Diagnosis of fetal growth restriction (FGR) entails difficulties with differentiating fetuses not fulfilling their growth potential because of pathologic conditions, such as placental insufficiency, from constitutionally small fetuses. The feasibility of placental MRI for risk stratification among pregnancies diagnosed with FGR remains unexplored. PURPOSE: To explore quantitative MRI features useful to identify pregnancies with unfavorable outcomes and to assess the diagnostic performance of visual analysis of MRI to detect pregnancies with unfavorable outcomes, among pregnancies diagnosed with FGR. STUDY TYPE: Retrospective. POPULATION: Thirteen pregnancies with unfavorable outcomes (preterm emergency cesarean section or intrauterine fetal death) and 11 pregnancies with favorable outcomes performed MRI at gestational weeks 21-36. FIELD STRENGTH/SEQUENCE: A 5-T, half-Fourier-acquired single-shot turbo spin echo (HASTE), spin-echo echo-planar imaging (SE-EPI) and T2 map derived from SE-EPI. ASSESSMENT: Placental size on HASTE sequences and T2 mapping-based histogram features were extracted. Three radiologists qualitatively evaluated the visibility of maternal cotyledon on HASTE and SE-EPI sequences with echo times (TEs) = 60, 90, and 120 msec using 3-point Likert scales: 0, absent; 1, equivocal; and 2, present. STATISTICAL TESTS: Welch's t-test or Mann-Whitney U test for quantitative features between the favorable and unfavorable outcome groups. Areas under the receiver operating curves (AUCs) of the three readers' visual analyses to detect pregnancies with unfavorable outcomes. A P value of <0.05 was inferred as statistically significant. RESULTS: Placental size (major and minor axis, estimated area of placental bed, and volume of placenta) and T2 mapping-based histogram features (mean, skewness, and kurtosis) were statistically significantly different between the two groups. Visual analysis of HASTE and SE-EPI with TE = 60 msec showed AUCs of 0.80-0.86 to detect pregnancies with unfavorable outcomes. DATA CONCLUSION: Placental size, histogram features, and visual analysis of placental MRI may allow for risk stratification regarding outcomes among pregnancies diagnosed with FGR. EVIDENCE LEVEL: 3 TECHNICAL EFFICACY: Stage 5.

Diagnostic Accuracy of Magnetic Resonance Imaging for International Federation of Gynecology and Obstetrics 2018 IB to IIB Cervical Cancer Staging: Comparison Among Magnetic Resonance Sequences and Pathologies.

OBJECTIVE: This study aimed to investigate the most accurate magnetic resonance (MR) sequence for tumor detection, maximal tumor diameter, and parametrial invasion compared with histopathologic diagnoses. METHODS: Fifty-one patients with International Federation of Gynecology and Obstetrics 2018 IB1 to IIB cervical cancer underwent preoperative MR imaging and surgical resection. Two radiologists independently evaluated the tumor detection, parametrial invasion, and tumor size in each of T2-weighted image, diffusion-weighted image, and contrast-enhanced T1-weighted image. Results obtained for squamous cell carcinoma (SCC) and adenocarcinoma were also compared. RESULTS: Neither the tumor detection rate nor parametrial invasion was found to be significantly different among sequences. Tumor size assessment using MR imaging with pathology showed good correlation: r = 0.63-0.72. The adenocarcinoma size tended to be more underestimated than SCC in comparison with the pathologic specimen. CONCLUSIONS: Cervical cancer staging by MR images showed no significant difference among T2-weighted image, diffusion-weighted image, and contrast-enhanced T1-weighted image. Adenocarcinoma was prone to be measured as smaller than the pathologic specimen compared with SCC.