Investigation of the clinical implications of anterior cervical invasion in locally advanced cervical squamous cell carcinoma.

PURPOSES: This study investigates the clinical significance of the anterior parametrical invasion in surgically treated patients with cervical squamous cell carcinoma (SCC). METHODS: We included patients diagnosed with cervical SCC with local lesions classified as T2b, who were treated at our department between January 2006 and December 2020. We evaluated the degree of anterior invasion using pretreatment magnetic resonance imaging and divided patients into three groups: partial, equivocal, and full invasion. The frequency of recurrence within 3 years (early recurrence) and overall prognosis were assessed. RESULTS: There were 12, 24, and 46 cases in the partial equivocal, and full invasion groups, respectively. Neoadjuvant chemotherapy followed by surgery and adjuvant chemotherapy was the mainstay of treatment across all groups (7, 17, and 27 cases, respectively). Although the frequency of early recurrence tended to be worse in the full group (partial; 2/7 cases, equivocal; 3/17 cases and full; 9/27 cases), all early local recurrence cases in the full group (four cases) responded well to the subsequent treatment. As for overall survival, the full invasion group had the best prognosis among the three groups. CONCLUSIONS: In surgical treatment, although full anterior invasion may increase the risk of early local recurrence, it was considered to have little prognostic impact.

Preoperative Imaging Evaluation of Endometrial Cancer in FIGO 2023.

The staging of endometrial cancer is based on the International Federation of Gynecology and Obstetrics (FIGO) staging system according to the examination of surgical specimens, and has revised in 2023, 14 years after its last revision in 2009. Molecular and histological classification has incorporated to new FIGO system reflecting the biological behavior and prognosis of endometrial cancer. Nonetheless, the basic role of imaging modalities including ultrasound, computed tomography (CT), magnetic resonance imaging (MRI), and positron emission tomography, as a preoperative assessment of the tumor extension and also the evaluation points in CT and MRI imaging are not changed, other than several point of local tumor extension. In the field of radiology, it has also undergone remarkable advancement through the rapid progress of computational technology. The application of deep learning reconstruction techniques contributes the benefits of shorter acquisition time or higher quality. Radiomics, which extract various quantitative features from the images, is also expected to have the potential for the quantitative prediction of risk factors such as histological types and lymphovascular space invasion, which is newly included in the new FIGO system. This article reviews the preoperative imaging diagnosis in new FIGO system and recent advances in imaging analysis and their clinical contributions in endometrial cancer. EVIDENCE LEVEL: 4 TECHNICAL EFFICACY: Stage 3.

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.

A case of paraovarian tumor of borderline malignancy with decrease of apparent diffusion coefficient value and marked 18F-fluorodeoxyglucose accumulation.

Para-ovarian cysts are occasionally encountered in clinical practice; however, malignant tumors derived from them are rare. Due to its rarity, the characteristic imaging findings of para-ovarian tumors with borderline malignancy (PTBM) are largely unknown. Herein, we report a case of PTBM, along with imaging findings. A 37-year-old woman came to our department with a suspected malignant adnexal tumor. Pelvic contrast-enhanced magnetic resonance imaging (MRI) revealed a solid part within the cystic tumor with a decrease in the apparent diffusion coefficient (ADC) value (1.16 × 10-3 mm2/s). We also performed Positron Emission Tomography-MRI and showed a strong accumulation of 18F-fluorodeoxyglucose (FDG) in the solid part (SUVmax = 14.8). In addition, the tumor appeared to develop independently of the ovary. Because tumor was derived from para-ovarian cyst, we suspected PTBM preoperatively and planned fertility sparing treatment. Pathological examination revealed a serous borderline tumor and PTBM was confirmed. PTBM can have unique imaging characteristics, including a low ADC value and high FDG accumulation. When a tumor appears to develop from para-ovarian cysts, borderline malignancy can be suspected, even if imaging findings suggest malignant potential.

Label Distribution Learning for Automatic Cancer Grading of Histopathological Images of Prostate Cancer.

We aimed to develop and evaluate an automatic prediction system for grading histopathological images of prostate cancer. A total of 10,616 whole slide images (WSIs) of prostate tissue were used in this study. The WSIs from one institution (5160 WSIs) were used as the development set, while those from the other institution (5456 WSIs) were used as the unseen test set. Label distribution learning (LDL) was used to address a difference in label characteristics between the development and test sets. A combination of EfficientNet (a deep learning model) and LDL was utilized to develop an automatic prediction system. Quadratic weighted kappa (QWK) and accuracy in the test set were used as the evaluation metrics. The QWK and accuracy were compared between systems with and without LDL to evaluate the usefulness of LDL in system development. The QWK and accuracy were 0.364 and 0.407 in the systems with LDL and 0.240 and 0.247 in those without LDL, respectively. Thus, LDL improved the diagnostic performance of the automatic prediction system for the grading of histopathological images for cancer. By handling the difference in label characteristics using LDL, the diagnostic performance of the automatic prediction system could be improved for prostate cancer grading.

Automatic segmentation of bladder cancer on MRI using a convolutional neural network and reproducibility of radiomics features: a two-center study.

This study aimed to develop a versatile automatic segmentation model of bladder cancer (BC) on MRI using a convolutional neural network and investigate the robustness of radiomics features automatically extracted from apparent diffusion coefficient (ADC) maps. This two-center retrospective study used multi-vendor MR units and included 170 patients with BC, of whom 140 were assigned to training datasets for the modified U-net model with five-fold cross-validation and 30 to test datasets for assessment of segmentation performance and reproducibility of automatically extracted radiomics features. For model input data, diffusion-weighted images with b = 0 and 1000 s/mm2, ADC maps, and multi-sequence images (b0-b1000-ADC maps) were used. Segmentation accuracy was compared between ours and existing models. The reproducibility of radiomics features on ADC maps was evaluated using intraclass correlation coefficient. The model with multi-sequence images achieved the highest Dice similarity coefficient (DSC) with five-fold cross-validation (mean DSC = 0.83 and 0.79 for the training and validation datasets, respectively). The median (interquartile range) DSC of the test dataset model was 0.81 (0.70-0.88). Radiomics features extracted from manually and automatically segmented BC exhibited good reproducibility. Thus, our U-net model performed highly accurate segmentation of BC, and radiomics features extracted from the automatic segmentation results exhibited high reproducibility.

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.

MR findings of polypoid endometriosis of female genital organs: report of three cases.

Polypoid endometriosis is a benign, rare variant of endometriosis that forms polypoid nodules mimicking malignant tumors. For three cases of polypoid endometriosis of female genital organs, this report presents characteristic MR imaging features reflecting the histopathological findings. The solid and microcystic pattern or the multilocular pattern both reflecting dilated endometrial glands, and characteristic morphology of the nodules, multilobulated or polypoid-shaped, were helpful diagnostic clues present in these three cases. Earlier reported MR findings were also recognized, including signal intensity similar to that of the endometrium on T2-weighted image and contrast enhanced T1-weighted image, hypointense rim on T2-weighted image, lack of diffusion restriction, and hyperintense foci on T1-weighted image. Two cases were diagnosed preoperatively based on MR imaging findings as polypoid endometriosis. Fertility-preserving treatment was administered for one patient. Preoperative inference of polypoid endometriosis from MR imaging can avoid overtreatment and lead to fertility preservation.

MRI in the Diagnosis of Endometriosis and Related Diseases.

Endometriosis, a common chronic inflammatory disease in female of reproductive age, is closely related to patient symptoms and fertility. Because of its high contrast resolution and objectivity, MRI can contribute to the early and accurate diagnosis of ovarian endometriotic cysts and deeply infiltrating endometriosis without the need for any invasive procedure or radiation exposure. The ovaries, which are the most frequent site of endometriosis, can be afflicted by multiple related conditions and diseases. For the diagnosis of deeply infiltrating endometriosis and secondary adhesions among pelvic organs, fibrosis around the ectopic endometrial gland is usually found as a T2 hypointense lesion. This review summarizes the MRI findings obtained for ovarian endometriotic cysts and their physiologically and pathologically related conditions. This article also includes the key imaging findings of deeply infiltrating endometriosis.

Advantage of a higher position of the tracheostoma with glottic closure for preventing complications related to tracheostomy tube: a retrospective cohort study.

BACKGROUND: Surgery to prevent aspiration has complications related to tracheostomy tube, such as the trachea-brachiocephalic artery fistula. Glottic closure procedure makes tracheostoma at a position higher than the first ring of the trachea and theoretically has a potential to prevent such complications owing to a longer distance between the tip of tracheostomy tube and the tracheal membrane adjacent to the brachiocephalic artery. Our aim is to evaluate the safety of glottic closure in neurologically impaired patients by comparing outcomes with laryngotracheal separation. METHODS: This study is a single-center retrospective study from 2004 to 2019, using data of 15 and 12 patients who underwent glottic closure (GC) and laryngotracheal separation (LTS). The primary outcome was the incidence of postoperative complications induced by tracheostomy tube placement and adjustment of the tracheostomy tube position to prevent these complications, such as by converting to a length-adjustable tube and/or placing gauze between the skin and tube flange. Additionally, we analyzed the anatomical relationship between the tracheostomy tube tip and brachiocephalic artery and measured the distance between them using postoperative CT images. RESULTS: No patients in either group had trachea-brachiocephalic artery fistula. Erosion or granuloma formation occurred in 1 patient (7%) and 4 patients (33%) in the GC and LTS groups, respectively. Adjustment of the tracheostomy tube was needed in 2 patients (13%) and 6 patients (50%) in the GC and LTS groups. CT revealed a higher proportion of patients with the tracheostomy tube tip superior to the brachiocephalic artery in GC than LTS group. The mean tracheostoma-brachiocephalic artery distance was 40.8 and 32.4 mm in the GC and LTS groups. CONCLUSIONS: Glottic closure reduces the risk of postoperative complications related to a tracheostomy tube. This may be due to the higher position of the tracheostoma at the level of the cricoid cartilage, increasing the distance between the tracheostoma and brachiocephalic artery.

Chronic abruption-oligohydramnios sequence (CAOS) revisited: possible implication of premature rupture of membranes.

AIM: The pathogenic mechanism of chronic abruption-oligohydramnios sequence (CAOS) remains unknown, and there are no objective standards for diagnosis on imaging or using pathological evidence. We aimed to reconsider and clarify the true pathology of CAOS by integrating clinical, magnetic resonance imaging (MRI) and histopathological findings of the placenta. MATERIAL AND METHODS: This is a case series of patients with CAOS managed at our hospital between 2010 and 2020. The clinical data of the patients, including MRI findings and placental pathology, were reviewed retrospectively. RESULTS: A total of 18 patients were eligible. Preterm birth occurred in 17 (94%) cases; the median gestational age at delivery was 25. Three neonates (17%) died within two years, and 10 neonates (56%) developed chronic lung disease. MRI was performed in 13 cases and clearly showed intrauterine hematoma and hemorrhagic amniotic fluid. Pathologically, in all cases, retroplacental hematoma was not detected, and fetal membranes were extremely fragile and ragged. Shedding and necrosis of the amniotic epithelium was a characteristic finding, which was confirmed in 17 cases (94%). Diffuse chorionic hemosiderosis (DCH) was detected in all cases. CONCLUSIONS: The fundamental cause of CAOS is repeated intrauterine hemorrhage and subsequent subchorionic hematoma, which induces hemorrhagic amniotic fluid and DCH. Consequently, these factors result in the necrosis and weakening of the amnion. Therefore, the true pathology of CAOS is believed to be premature rupture of membranes rather than chronic abruption.

Radiomic machine learning for pretreatment assessment of prognostic risk factors for endometrial cancer and its effects on radiologists' decisions of deep myometrial invasion.

PURPOSE: To evaluate radiomic machine learning (ML) classifiers based on multiparametric magnetic resonance images (MRI) in pretreatment assessment of endometrial cancer (EC) risk factors and to examine effects on radiologists' interpretation of deep myometrial invasion (dMI). METHODS: This retrospective study examined 200 consecutive patients with EC during January 2004 -March 2017, divided randomly to Discovery (n = 150) and Test (n = 50) datasets. Radiomic features of tumors were extracted from T2-weighted images, apparent diffusion coefficient map, and contrast enhanced T1-weighed images. Using the Discovery dataset, feature selection and hyperparameter tuning for XGBoost were performed. Ten classifiers were built to predict dMI, histological grade, lymphovascular invasion (LVI), and pelvic/paraaortic lymph node metastasis (PLNM/PALNM), respectively. Using the Test dataset, the diagnostic performances of ten classifiers were assessed by the area under the receiver operator characteristic curve (AUC). Next, four radiologists assessed dMI independently using MRI with a Likert scale before and after referring to inference of the ML classifier for the Test dataset. Then, AUCs obtained before and after reference were compared. RESULTS: In the Test dataset, mean AUC of ML classifiers for dMI, histological grade, LVI, PLNM, and PALNM were 0.83, 0.77, 0.81, 0.72, and 0.82. AUCs of all radiologists for dMI (0.83-0.88) were better than or equal to mean AUC of the ML classifier, which showed no statistically significant difference before and after the reference. CONCLUSION: Radiomic classifiers showed promise for pretreatment assessment of EC risk factors. Radiologists' inferences outperformed the ML classifier for dMI and showed no improvement by review.

Diagnostic Value of DCE-MRI for Differentiating Malignant Adnexal Masses Compared with Contrast-enhanced-T1WI.

PURPOSE: To compare the diagnostic performance of dynamic contrast-enhanced-MR (DCE-MR) and delayed contrast-enhanced (CE)-MRI added to unenhanced MRI, including diffusion weighted image (DWI) for differentiating malignant adnexal tumors, conducting a retrospective blinded image interpretation study. METHODS: Data of 80 patients suspected of having adnexal tumors by ultrasonography between April 2008 and August 2018 were used for the study. All patients had undergone preoperative MRI and surgical resection at our institution. Four radiologists (two specialized in gynecological radiology and two non-specialized) were enrolled for blinded review of the MR images. A 3-point scale was used: 0 = benign, 1 = indeterminate, and 2 = malignant. Three imaging sets were reviewed: Set A, unenhanced MRI including DWI; Set B, Set A and delayed CE-T1WI; and Set C, Set A and DCE-MRI. Imaging criteria for benign and malignant tumors were given in earlier reports. The diagnostic performance of the three imaging sets of the four readers was calculated. Their areas under the curve (AUCs) were compared using the DeLong method. RESULTS: Accuracies of Set B were 81%-88%. Those of Set C were 81%-85%. The AUCs of Set B were 0.83 and 0.89. Those of Set C were 0.81-0.86. For two readers, Set A showed lower accuracy and AUC than Set B/Set C (less than 0.80), although those were equivalent in other readers. No significant difference in AUCs was found among the three sequence sets. Intrareader agreement was moderate to almost perfect in Sets A and B, and substantial to almost perfect in Set C. CONCLUSION: DCE-MR showed no superiority for differentiating malignant adnexal tumors from benign tumors compared to delayed CE-T1WI with conventional MR and DWI.

Differentiation of uterine low-grade endometrial stromal sarcoma from rare leiomyoma variants by magnetic resonance imaging.

The purpose of this study is to evaluate utility of MRI in differentiation of uterine low-grade endometrial stromal sarcoma (LGESS) from rare leiomyoma variants. This multi-center retrospective study included consecutive 25 patients with uterine LGESS and 42 patients with rare leiomyoma variants who had pretreatment MRI. Two radiologists (R1/R2) independently evaluated MRI features, which were analyzed statistically using Fisher's exact test or Student's t-test. Subsequently, using a five-point Likert scale, the two radiologists evaluated the diagnostic performance of a pre-defined MRI system using features reported as characteristics of LGESS in previous case series: uterine tumor with high signal intensity (SI) on diffusion-weighted images and with either worm-like nodular extension, intra-tumoral low SI bands, or low SI rim on T2-weighted images. Area under the receiver operating characteristic curve (AUC), sensitivity, and specificity of the two readers' Likert scales were analyzed. Intra-tumoral low SI bands (p < 0.001), cystic/necrotic change (p ≤ 0.02), absence of speckled appearance (p < 0.001) on T2-weighted images, and a low apparent diffusion coefficient value (p ≤ 0.02) were significantly associated with LGESS. The pre-defined MRI system showed very good diagnostic performance: AUC 0.86/0.89, sensitivity 0.95/0.95, and specificity 0.67/0.69 for R1/R2. MRI can be useful to differentiate uterine LGESS from rare leiomyoma variants.

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.

Automatic segmentation of uterine endometrial cancer on multi-sequence MRI using a convolutional neural network.

Endometrial cancer (EC) is the most common gynecological tumor in developed countries, and preoperative risk stratification is essential for personalized medicine. There have been several radiomics studies for noninvasive risk stratification of EC using MRI. Although tumor segmentation is usually necessary for these studies, manual segmentation is not only labor-intensive but may also be subjective. Therefore, our study aimed to perform the automatic segmentation of EC on MRI with a convolutional neural network. The effect of the input image sequence and batch size on the segmentation performance was also investigated. Of 200 patients with EC, 180 patients were used for training the modified U-net model; 20 patients for testing the segmentation performance and the robustness of automatically extracted radiomics features. Using multi-sequence images and larger batch size was effective for improving segmentation accuracy. The mean Dice similarity coefficient, sensitivity, and positive predictive value of our model for the test set were 0.806, 0.816, and 0.834, respectively. The robustness of automatically extracted first-order and shape-based features was high (median ICC = 0.86 and 0.96, respectively). Other high-order features presented moderate-high robustness (median ICC = 0.57-0.93). Our model could automatically segment EC on MRI and extract radiomics features with high reliability.

Diagnostic performance of preoperative MR imaging findings for differentiation of uterine leiomyoma with intraligamentous growth from subserosal leiomyoma.

PURPOSE: To evaluate the diagnostic performance of MRI findings for differentiating uterine leiomyoma with intraligamentous growth, or broad ligament fibroid, from subserosal leiomyoma. METHODS: This study included 37 patients with surgically confirmed uterine smooth muscle tumors (36 leiomyomas and one smooth muscle tumor of uncertain malignant potential) with intraligamentous growth (IL) and size-matched control of 37 patients with subserosal leiomyoma (SS). Two radiologists independently evaluated eight preoperative MRI findings: tumor shape, degeneration, attachment to uterus, ovary elevation, ureter displacement, bladder deformation, rectal displacement, and separation of round ligament (RL) and uterine artery (UA). The diagnostic values of these findings and interobserver agreement were assessed. Receiver-operating characteristic (ROC) analysis of the number of positive MRI findings for diagnosing IL was performed. Clinical outcomes including surgical method, operation time, intraoperative blood loss, perioperative complications, and postoperative hospital stay of the two groups were compared. RESULTS: Significant differences in tumor shape, attachment to uterus, ovary elevation, ureter displacement, and separation of RL and UA were found between IL and SS. Four of these findings, excluding ureter displacement, showed moderate to substantial interobserver agreement. When two or more of these four findings were positive, sensitivity, specificity, and area under the ROC curve were 91%, 77%, 0.90 in reader 1 and 82%, 89%, 0.91 in reader 2. The operation time was significantly longer for IL than for SS. CONCLUSION: Tumor shape, attachment to uterus, ovary elevation, and separation of RL and UA are useful MRI findings for differentiating intraligamentous leiomyoma from subserosal leiomyoma.