Carcinosarcoma of the Uterine Corpus with Alpha-Fetoprotein-Producing Hepatoid Adenocarcinoma: A Report of Two Cases

We report two cases of uterine carcinosarcoma associated with alpha-fetoprotein (AFP)-producing hepatoid adenocarcinoma. Samples were obtained from two women aged 63 and 82 years. Serum AFP levels of the two samples were 10,131 and 401 ng/ml, respectively. Histologically, in both cases the tumor cells were composed of hepatoid adenocarcinoma component and sarcoma component including rhabdomyosarcoma. Immunohistochemical analyses revealed that AFP was expressed in the cytoplasm of the carcinomatous component. After surgery, the patients received six courses of carboplatin/paclitaxel chemotherapy, and the serum levels of AFP decreased to normal range. The first patient is alive and well at the 2-year follow-up, while the second patient died of disease 1 year after initial operative treatment. This is, to our knowledge, the second report of carcinosarcoma of the uterine corpus with AFP-producing hepatoid adenocarcinoma, as proven by immunohistochemical analyses

卵巣明細胞癌と類内膜癌の鑑別に関するMRIについての知見

BACKGROUND: Common cancerous histological types associated with endometriosis are clear cell carcinoma (CCC) and endometrioid carcinoma (EC). CCC is regarded as an aggressive, chemoresistant histological subtype. Magnetic resonance imaging (MRI) offers some potential advantages to diagnose ovarian tumors compared with ultrasonography or computed tomography. This study aimed to identify MRI features that can be used to differentiate between CCC and EC. METHODS: We searched medical records of patients with ovarian cancers who underwent surgical treatment at Nara Medical University Hospital between January 2008 and September 2018; we identified 98 patients with CCC or EC who had undergone preoperative MRI. Contrasted MRI scans were performed less than 2 months before surgery. Patients were excluded from the study if they had no pathology, other pathological subtype of epithelial ovarian cancer, and/or salvage treatment for recurrence and metastatic ovarian cancer at the time of study initiation. Clinically relevant variables that were statistically significant by univariate analysis were selected for subsequent multivariate regression analysis to identify independent factors to distinguish CCC from EC. RESULTS: MRI of CCC and EC showed a large cystic heterogeneous mixed mass with mural nodules protruding into the cystic space. Univariate logistic regression analysis revealed that the growth pattern (broad-based nodular structures [multifocal/concentric sign] or polypoid structures [focal/eccentric sign]), surface irregularity (a smooth/regular surface or a rough/irregular/lobulated surface), "Width" of mural nodule, "Height-to-Width" ratio (HWR), and presence of preoperative ascites were factors that significantly differed between CCC and EC. In the multivariate logistic regression analysis, the growth pattern of the mural nodule (odds ratio [OR] = 0.69, 95% confidence interval [CI]: 0.013-0.273, p = 0.0004) and the HWR (OR = 3.71, 95% CI: 1.128-13.438, p = 0.036) were independent predictors to distinguish CCC from EC. CONCLUSIONS: In conclusion, MRI data showed that the growth pattern of mural nodules and the HWR were independent factors that could allow differentiation between CCC and EC. This finding may be helpful to predict patient prognosis before operation.博士（医学）・乙第1433号・令和元年9月27日© The Author(s). 2019 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated

Long-Term Follow-Up after Surgical Management for Atypical Endometriosis: A Series of Nine Cases

Background and Objective: Atypical endometriosis is reported to possess a precancerous potential attributed to premalignant changes characterized by cytological atypia and architecture proliferation. Although the coexistence of atypical endometriosis and neoplasms has been reported, cases of atypical endometriosis transformation to carcinoma are rarely reported. The purpose of this case series was to evaluate the prognosis of atypical endometriosis. Subjects and Methods: Data from nine women who underwent surgical treatment including cystectomy and salpingo-oophorectomy with or without hysterectomy and diagnosed with atypical endometriosis was analyzed. Between January 2006 and January 2018, the clinical characteristics and prognosis of atypical endometriosis were evaluated. Results: During the follow-up period, eight of nine patients with atypical endometriosis did not develop malignant epithelial tumors, although one patient developed endometrioid carcinoma, grade 1, 48 months after her right laparoscopic cystectomy. The median overall survival period for all patients was 68 (range 13–131) months. Conclusion: When we encounter the cases of atypical endometriosis, it is necessary to consider the possibility of ovarian cancer and carefully follow those cases for long periods

Evidence for Activation of Toll-Like Receptor and Receptor for Advanced Glycation End Products in Preterm Birth

Objective. Individuals with inflammation have a myriad of pregnancy aberrations including increasing their preterm birth risk. Toll-like receptors (TLRs) and receptor for advanced glycation end products (RAGE) and their ligands were all found to play a key role in inflammation. In the present study, we reviewed TLR and RAGE expression, their ligands, and signaling in preterm birth. Research Design and Methods. A systematic search was performed in the electronic databases PubMed and ScienceDirect up to July 2010, combining the keywords “preterm birth,” “TLR”, “RAGE”, “danger signal”, “alarmin”, “genomewide,” “microarray,” and “proteomics” with specific expression profiles of genes and proteins. Results. This paper provides data on TLR and RAGE levels and critical downstream signaling events including NF-kappaB-dependent proinflammatory cytokine expression in preterm birth. About half of the genes and proteins specifically present in preterm birth have the properties of endogenous ligands “alarmin” for receptor activation. The interactions between the TLR-mediated acute inflammation and RAGE-mediated chronic inflammation have clear implications for preterm birth via the TLR and RAGE system, which may be acting collectively. Conclusions. TLR and RAGE expression and their ligands, signaling, and functional activation are increased in preterm birth and may contribute to the proinflammatory state

ミスマッチ修復遺伝子発現欠損を伴う子宮体癌のMRI所見と臨床像

Purpose: The purpose of this study was to identify the magnetic resonance imaging (MRI) features of uterine endometrial carcinoma (EC) with DNA mismatch repair (MMR) deficiency. Materials and methods: This was a retrospective study approved by our institutional review board. The study included 118 patients pathologically diagnosed as having EC in our institution from April 2014 to December 2016. Of 118 patients, 8 were excluded because of insufficient data. Immunohistochemical analysis of MMR was performed retrospectively to observe the expressions of MLH1, MSH2, MSH6, and PMS2. A tumor with MMR deficiency was detected in 17 of 110 cases (15%). Clinical background characteristics and MRI findings were reviewed. These findings were compared between MMR deficiency group and the other group as a control group. Statistical significance was determined using the Fisher's exact test and the Mann-Whitney U test, as appropriate. Results: The clinical background characteristics of patients with EC with MMR deficiency were not significantly different from those of other patients. On MRI, the tumor was significantly more often located in the lower uterine site (MMR(-) vs. MMR(+): 29.4 vs. 8.9% [p = 0.0366]). Conclusion: EC with MMR deficiency tends to be located lower in the uterus, though most other findings were not significantly different from those of EC without MMR deficiency.博士（医学）・甲第749号・令和2年6月30日© Japan Radiological Society 2018© 2018 Springer Nature Switzerland AG. Part of Springer Nature.This is a post-peer-review, pre-copyedit version of an article published in Japanese journal of radiology. The final authenticated version is available online at: http://doi.org/10.1007/s11604-018-0741-4

Assessing Versatile Machine Learning Models for Glioma Radiogenomic Studies across Hospitals

Radiogenomics use non-invasively obtained imaging data, such as magnetic resonance imaging (MRI), to predict critical biomarkers of patients. Developing an accurate machine learning (ML) technique for MRI requires data from hundreds of patients, which cannot be gathered from any single local hospital. Hence, a model universally applicable to multiple cohorts/hospitals is required. We applied various ML and image pre-processing procedures on a glioma dataset from The Cancer Image Archive (TCIA, n = 159). The models that showed a high level of accuracy in predicting glioblastoma or WHO Grade II and III glioma using the TCIA dataset, were then tested for the data from the National Cancer Center Hospital, Japan (NCC, n = 166) whether they could maintain similar levels of high accuracy. Results: we confirmed that our ML procedure achieved a level of accuracy (AUROC = 0.904) comparable to that shown previously by the deep-learning methods using TCIA. However, when we directly applied the model to the NCC dataset, its AUROC dropped to 0.383. Introduction of standardization and dimension reduction procedures before classification without re-training improved the prediction accuracy obtained using NCC (0.804) without a loss in prediction accuracy for the TCIA dataset. Furthermore, we confirmed the same tendency in a model for IDH1/2 mutation prediction with standardization and application of dimension reduction that was also applicable to multiple hospitals. Our results demonstrated that overfitting may occur when an ML method providing the highest accuracy in a small training dataset is used for different heterogeneous data sets, and suggested a promising process for developing an ML method applicable to multiple cohort