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

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

FIGO進行期分類IB-IIIB期子宮頸癌の予後予測因子としての治療前ADC値の有用性の検討

This is a non-final version of an article published in final form in International Journal of Gynecological Cancer. Final publication is available at http://journals.lww.com/ijgc/Pages/default.aspx京都大学0048新制・課程博士博士(医学)甲第19557号医博第4064号新制||医||1013(附属図書館)32593京都大学大学院医学研究科医学専攻(主査)教授 武藤 学, 教授 平岡 眞寛, 教授 古川 壽亮学位規則第4条第1項該当Doctor of Medical ScienceKyoto UniversityDFA

CT and MR imaging findings of systemic complications occurring during pregnancy and puerperal period, adversely affected by natural changes

Dynamic physiological and anatomical changes for delivery may adversely induce various specific non-obstetric complications during pregnancy and puerperal period. These complications can be fatal to both the mother and the fetus, thus a precise and early diagnosis ensued by an early treatment is essential. Along with ultrasonography, computed tomography (CT) and magnetic resonance imaging (MRI) have assumed an increasing role in the diagnosis. This article aims to discuss the pathophysiology of these complications, the indications for CT and MRI, and the imaging findings

Synthesis, Crystal Structure, and Electroconducting Properties of a 1D Mixed-Valence Cu(I)–Cu(II) Coordination Polymer with a Dicyclohexyl Dithiocarbamate Ligand

A new mixed-valence Cu(I)–Cu(II) 1D coordination polymer, [CuI4CuIIBr4(Cy2dtc)2]n, with an infinite chain structure is synthesized by the reaction of Cu(Cy2dtc)2 (Cy2dtc− = dicyclohexyl dithiocarbamate, C13H22NS2) with CuBr·S(CH3)2. The as-synthesized polymer consists of mononuclear copper(II) units of CuII(Cy2dtc)2 and tetranuclear copper(I) cluster units, CuI4Br4. In the cluster unit, all the CuI ions have distorted trigonal pyramidal coordination geometries, and the CuI–CuI or CuI–CuII distances between the nearest copper ions are shorter than the sum of van der Waals radii for Cu–Cu

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

Abstract 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

Machine learning-based prediction of microsatellite instability and high tumor mutation burden from contrast-enhanced computed tomography in endometrial cancers

Abstract To evaluate whether radiomic features from contrast-enhanced computed tomography (CE-CT) can identify DNA mismatch repair deficient (MMR-D) and/or tumor mutational burden-high (TMB-H) endometrial cancers (ECs). Patients who underwent targeted massively parallel sequencing of primary ECs between 2014 and 2018 and preoperative CE-CT were included (n = 150). Molecular subtypes of EC were assigned using DNA polymerase epsilon (POLE) hotspot mutations and immunohistochemistry-based p53 and MMR protein expression. TMB was derived from sequencing, with > 15.5 mutations-per-megabase as a cut-point to define TMB-H tumors. After radiomic feature extraction and selection, radiomic features and clinical variables were processed with the recursive feature elimination random forest classifier. Classification models constructed using the training dataset (n = 105) were then validated on the holdout test dataset (n = 45). Integrated radiomic-clinical classification distinguished MMR-D from copy number (CN)-low-like and CN-high-like ECs with an area under the receiver operating characteristic curve (AUROC) of 0.78 (95% CI 0.58–0.91). The model further differentiated TMB-H from TMB-low (TMB-L) tumors with an AUROC of 0.87 (95% CI 0.73–0.95). Peritumoral-rim radiomic features were most relevant to both classifications (p ≤ 0.044). Radiomic analysis achieved moderate accuracy in identifying MMR-D and TMB-H ECs directly from CE-CT. Radiomics may provide an adjunct tool to molecular profiling, especially given its potential advantage in the setting of intratumor heterogeneity