Yuki Himoto

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 Level4Technical EfficacyStage 3

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

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. 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. 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 β 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. 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. 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. •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.

Multiparametric magnetic resonance imaging facilitates the selection of patients prior to fertility-sparing management of endometrial cancer

To compare the diagnostic performance of biparametric magnetic resonance imaging (bpMRI) versus multiparametric MRI (mpMRI) for the staging of well-differentiated endometrioid endometrial cancer (EC) in potential candidates for fertility-sparing management. This multi-center retrospective study included 48 potential candidates for fertility-sparing management (age <46 years, grade 1 endometroid EC) who did not wish to undergo fertility-sparing management and thus underwent definitive surgery. Two readers (R1, R2) independently reviewed bpMRI (T1, T2, and diffusion-weighted imaging) and mpMRI (bpMRI and dynamic contrast-enhanced imaging, DCE) during two separate sessions spaced one month apart for the presence of myometrial invasion (MI), cervical stromal involvement (CSI), malignant adnexal disease (mAD), and pelvic lymphadenopathy (pLNM). Each reader also recorded maximum tumor diameter, tumor volume, and tumor-to-uterine volume ratio (TVR) on T2-weighted imaging. The diagnostic performance of bpMRI and mpMRI was determined for each reader with surgical pathology serving as a gold standard. The area under the receiver operating curve (AUC) for bpMRI versus mpMRI was 0.76/0.78 (R1/R2) versus 0.84/0.83 for MI, 0.79/0.76 versus 0.99/0.80 for CSI, 0.84/0.84 versus 0.84/0.80 for mAD, and 0.82/0.82 for pLMN. The sensitivity and specificity of MRI for detecting tumor spread beyond the endometrium were 71%/77% and 71%/65% for bpMRI (R1/R2) vs. 84%/90% and 71%/65% for mpMRI (R1/R2), respectively. The AUC of maximum tumor diameter, tumor volume, and TVR for MI was 0.71/0.61, 0.73/0.75, and 0.75/0.77 for R1/R2, respectively. MRI had moderate diagnostic performance across potential candidates for fertility-sparing treatment of EC. mpMRI outperformed bpMRI for detecting EC spreading beyond the endometrium.

Limited diagnostic performance of imaging evaluation for staging in gastric-type endocervical adenocarcinoma: a multi-center study

Abstract Purpose The purposes of the study are to assess the diagnostic performance of preoperative imaging for staging factors in gastric-type endocervical adenocarcinoma (GEA) and to compare the performance for GEA with that of usual-type endocervical adenocarcinoma (UEA) among patients preoperatively deemed locally early stage (DLES) (&lt; T2b without distant metastasis). Materials and methods For this multi-center retrospective study, 58 patients were enrolled. All had undergone MRI with or without CT and FDG PET-CT preoperatively and had been pathologically diagnosed with GEA at five institutions. Based on the medical charts and radiological reports, the diagnostic performances of preoperative imaging for the International Federation of Gynecology and Obstetrics staging factors were assessed retrospectively. Next, the imaging performance was assessed in preoperatively DLES-GEA (n = 36) and DLES-UEA (n = 136, with the same inclusion criteria). The proportions of underestimation of GEA and UEA were compared using Fisher’s exact test. Results Imaging diagnostic performance for GEA was limited, especially for sensitivity: parametrial invasion, 0.49; vaginal invasion, 0.54; pelvic lymph node metastasis (PELNM), 0.48; para-aortic lymph node metastasis, 0.00; and peritoneal dissemination, 0.25. Among preoperatively DLES patients, the proportions of underestimation were significantly higher in GEA than in UEA; parametrial invasion, 35% vs. 5% (p &lt; 0.01); vaginal invasion, 28% vs. 6% (p &lt; 0.01); PELNM, 24% vs. 6% (p &lt; 0.05); peritoneal dissemination, 6% vs. 0% (p &lt; 0.05). Conclusion At present, preoperative imaging diagnostic performance for staging factors in GEA does not meet clinical expectations, especially for sensitivity. Among patients preoperatively DLES, the proportions of underestimation in GEA were significantly higher than in UEA. Future incorporation of approaches specifically emphasizing GEA is desirable to improve imaging performance.

Survival impact of the degree of parametrial invasion on MRI in locally advanced cervical cancer

Differentiation of uterine fibroids and sarcomas by MRI and serum LDH levels: a multicenter study of the KAMOGAWA study

In the differential diagnosis between uterine fibroids and uterine sarcomas, real-world magnetic resonance imaging (MRI) diagnostic information is scarce; furthermore, high diagnostic sensitivity is important in clinical practice. We previously developed a diagnostic algorithm to detect uterine sarcoma with high sensitivity using simple MRI images and serum lactate dehydrogenase (LDH) levels. In this multicenter study, we investigated the preoperative diagnosis of sarcoma in the real world and further validated the usefulness of our diagnostic algorithm. Of 154 uterine sarcomas and 154 uterine fibroids treated at 15 centers between January 2006 and December 2020, 139 sarcomas (16 smooth muscle tumors of uncertain malignant potential) and 141 fibroids with diffusion-weighted imaging information were included in the analysis. The diagnostic algorithm was validated by 3 radiologists who were blinded to the clinical information and pathologic diagnoses and who read the MRIs. The sensitivity/specificity of preoperative diagnosis was 77.7%/92.9% for the preoperative report; 92.1%/72.3% for algorithm A; and 82.0%/85.8% for algorithm B (McNemar's test p<0.05). Comparison of overall survival rates among 3 groups (Group 1: negative A, Group 2: positive A and negative B; Group 3: positive B) using algorithms A and B showed p=0.012. On multivariate analysis, stage, and serum LDH level were independent prognostic factors. MRI is useful for preoperative diagnosis of uterine sarcoma, and the sarcoma diagnostic algorithm presented in this study is an option for diagnosing sarcoma with greater sensitivity. This information should be shared with patients.