Differentiation of carcinosarcoma from endometrial carcinoma on magnetic resonance imaging using deep learning

Purpose: To verify whether deep learning can be used to differentiate between carcinosarcomas (CSs) and endometrial carcinomas (ECs) using several magnetic resonance imaging (MRI) sequences. Material and methods: This retrospective study included 52 patients with CS and 279 patients with EC. A deep-learning model that uses convolutional neural networks (CNN) was trained with 572 T2-weighted images (T2WI) from 42 patients, 488 apparent diffusion coefficient of water maps from 33 patients, and 539 fat-saturated contrast-enhanced T1-weighted images from 40 patients with CS, as well as 1612 images from 223 patients with EC for each sequence. These were tested with 9-10 images of 9-10 patients with CS and 56 images of 56 patients with EC for each sequence, respectively. Three experienced radiologists independently interpreted these test images. The sensitivity, specificity, accuracy, and area under the receiver operating characteristic curve (AUC) for each sequence were compared between the CNN models and the radiologists. Results: The CNN model of each sequence had sensitivity 0.89-0.93, specificity 0.44-0.70, accuracy 0.83-0.89, and AUC 0.80-0.94. It also showed an equivalent or better diagnostic performance than the 3 readers (sensitivity 0.43-0.91, specificity 0.30-0.78, accuracy 0.45-0.88, and AUC 0.49-0.92). The CNN model displayed the highest diagnostic performance on T2WI (sensitivity 0.93, specificity 0.70, accuracy 0.89, and AUC 0.94). Conclusions: Deep learning provided diagnostic performance comparable to or better than experienced radiologists when distinguishing between CS and EC on MRI

Sorafenib-induced Prostate Volume Reduction, a New Adverse Effect Detected by Imaging: A Pilot Study

Background:  Sorafenib has been used in the treatment of advanced hepatocellular carcinoma (HCC) and renal cell carcinoma (RCC). Sorafenib-associated organ reduction have been reported on imaging, such as thyroid, pancreas and muscle, but there has been no research on prostate volume reduction (PVR). Methods:  We retrospectively analyzed 26 patients (twenty with HCC and six patients with RCC) who underwent sorafenib therapy for 31 to 1225 days (median, 100 days). PVR was estimated by two independent readers using CT volumetry. Results:  The sum of all prostate volumes measured by reader 1 was 24.2 ± 13.8 cm3 on the baseline CT and 20.4 ± 10.6 cm3 on the follow-up CT (p < 0.001), and that measured by reader 2 was 22.3 ± 13.9 cm3 on the baseline CT and 19.2 ± 10.6 cm3 on the follow-up CT (p < 0.001). The concordance correlation coefficient for the prostate volume measured by the two readers was 0.95 on the baseline CT scans and 0.94 on the follow-up CT scans. Sorafenib-associated PVR demonstrated slight dependence to the exposure time (r = –0.23). One patient with benign prostatic hyperplasia (BPH) showed PVR (from 80.4 to 61.5 cm3 [reader 1]; 83.4 to 61.6 cm3 [reader 2]) after sorafenib administration. Sorafenib-associated PVR occurred in patients both with and without underlying liver dysfunction with relative prostate volume changes of 86.7 ± 12.0% and 85.0 ± 9.0%, respectively. Conclusion: Our study demonstrated significant PVR with sorafenib treatment in patients regardless of the presence of BPH and underlying liver dysfunction

Sorafenib-induced Prostate Volume Reduction, a New Adverse Effect Detected by Imaging: A Pilot Study

Background: Sorafenib has been used in the treatment of advanced hepatocellular carcinoma (HCC) and renal cell carcinoma (RCC). Sorafenib-associated organ reduction have been reported on imaging, such as thyroid, pancreas and muscle, but there has been no research on prostate volume reduction (PVR).Methods: We retrospectively analyzed 26 patients (twenty with HCC and six patients with RCC) who underwent sorafenib therapy for 31 to 1225 days (median, 100 days). PVR was estimated by two independent readers using CT volumetry.Results: The sum of all prostate volumes measured by reader 1 was 24.2 ± 13.8 cm3 on the baseline CT and 20.4 ± 10.6 cm3 on the follow-up CT (p < 0.001), and that measured by reader 2 was 22.3 ± 13.9 cm3 on the baseline CT and 19.2 ± 10.6 cm3 on the follow-up CT (p < 0.001). The concordance correlation coefficient for the prostate volume measured by the two readers was 0.95 on the baseline CT scans and 0.94 on the follow-up CT scans. Sorafenib-associated PVR demonstrated slight dependence to the exposure time (r = –0.23). One patient with benign prostatic hyperplasia (BPH) showed PVR (from 80.4 to 61.5 cm3 [reader 1]; 83.4 to 61.6 cm3 [reader 2]) after sorafenib administration. Sorafenib-associated PVR occurred in patients both with and without underlying liver dysfunction with relative prostate volume changes of 86.7 ± 12.0% and 85.0 ± 9.0%, respectively.Conclusion: Our study demonstrated significant PVR with sorafenib treatment in patients regardless of the presence of BPH and underlying liver dysfunction

Clinical and MRI Characteristics of Uterine Cervical Adenocarcinoma: Its Variants and Mimics

Adenocarcinoma currently accounts for 10–25% of all uterine cervical carcinomas and has a variety of histopathological subtypes. Among them, mucinous carcinoma gastric type is not associated with high-risk human papillomavirus (HPV) infection and a poor prognosis, while villoglandular carcinoma has an association with high-risk HPV infection and a good prognosis. They show relatively characteristic imaging findings which can be suggested by magnetic resonance imaging (MRI), though the former is sometimes difficult to be distinguished from lobular endocervical glandular hyperplasia. Various kinds of other tumors including squamous cell carcinoma should be also differentiated on MRI, while it is currently difficult to distinguish them on MRI, and HPV screening and pathological confirmation are usually necessary for definite diagnosis and further patient management

The whole blood transcriptional regulation landscape in 465 COVID-19 infected samples from Japan COVID-19 Task Force

「コロナ制圧タスクフォース」COVID-19患者由来の血液細胞における遺伝子発現の網羅的解析 --重症度に応じた遺伝子発現の変化には、ヒトゲノム配列の個人差が影響する--. 京都大学プレスリリース. 2022-08-23.Coronavirus disease 2019 (COVID-19) is a recently-emerged infectious disease that has caused millions of deaths, where comprehensive understanding of disease mechanisms is still unestablished. In particular, studies of gene expression dynamics and regulation landscape in COVID-19 infected individuals are limited. Here, we report on a thorough analysis of whole blood RNA-seq data from 465 genotyped samples from the Japan COVID-19 Task Force, including 359 severe and 106 non-severe COVID-19 cases. We discover 1169 putative causal expression quantitative trait loci (eQTLs) including 34 possible colocalizations with biobank fine-mapping results of hematopoietic traits in a Japanese population, 1549 putative causal splice QTLs (sQTLs; e.g. two independent sQTLs at TOR1AIP1), as well as biologically interpretable trans-eQTL examples (e.g., REST and STING1), all fine-mapped at single variant resolution. We perform differential gene expression analysis to elucidate 198 genes with increased expression in severe COVID-19 cases and enriched for innate immune-related functions. Finally, we evaluate the limited but non-zero effect of COVID-19 phenotype on eQTL discovery, and highlight the presence of COVID-19 severity-interaction eQTLs (ieQTLs; e.g., CLEC4C and MYBL2). Our study provides a comprehensive catalog of whole blood regulatory variants in Japanese, as well as a reference for transcriptional landscapes in response to COVID-19 infection

DOCK2 is involved in the host genetics and biology of severe COVID-19

「コロナ制圧タスクフォース」COVID-19疾患感受性遺伝子DOCK2の重症化機序を解明 --アジア最大のバイオレポジトリーでCOVID-19の治療標的を発見--. 京都大学プレスリリース. 2022-08-10.Identifying the host genetic factors underlying severe COVID-19 is an emerging challenge. Here we conducted a genome-wide association study (GWAS) involving 2, 393 cases of COVID-19 in a cohort of Japanese individuals collected during the initial waves of the pandemic, with 3, 289 unaffected controls. We identified a variant on chromosome 5 at 5q35 (rs60200309-A), close to the dedicator of cytokinesis 2 gene (DOCK2), which was associated with severe COVID-19 in patients less than 65 years of age. This risk allele was prevalent in East Asian individuals but rare in Europeans, highlighting the value of genome-wide association studies in non-European populations. RNA-sequencing analysis of 473 bulk peripheral blood samples identified decreased expression of DOCK2 associated with the risk allele in these younger patients. DOCK2 expression was suppressed in patients with severe cases of COVID-19. Single-cell RNA-sequencing analysis (n = 61 individuals) identified cell-type-specific downregulation of DOCK2 and a COVID-19-specific decreasing effect of the risk allele on DOCK2 expression in non-classical monocytes. Immunohistochemistry of lung specimens from patients with severe COVID-19 pneumonia showed suppressed DOCK2 expression. Moreover, inhibition of DOCK2 function with CPYPP increased the severity of pneumonia in a Syrian hamster model of SARS-CoV-2 infection, characterized by weight loss, lung oedema, enhanced viral loads, impaired macrophage recruitment and dysregulated type I interferon responses. We conclude that DOCK2 has an important role in the host immune response to SARS-CoV-2 infection and the development of severe COVID-19, and could be further explored as a potential biomarker and/or therapeutic target

Differences in the position of endometriosis-associated and non-associated ovarian cancer relative to the uterus

Abstract Background Preoperative assessment of the histological type of ovarian cancer is essential to determine the appropriate treatment strategy. Tumor location may be helpful in this regard. The purpose of this study was to compare the position of endometriosis-associated (EAOCs) and non-associated (non-EAOCs) ovarian cancer relative to the uterus using MRI. Methods This retrospective study included patients with pathologically confirmed malignant epithelial ovarian tumors who underwent MRI at our hospital between January 2015 and January 2023. T2-weighted images of the sagittal and axial sections of the long axis of the uterine body were used for the analysis. Three blinded experienced radiologists independently interpreted the images and assessed whether the ovarian tumor was attached to the uterus, and the angle between the uterus and the tumor was measured. The presence of attachment and the measured angles were compared for each histology. In addition, the angles between EAOCs, including endometrioid carcinomas (ECs) and clear cell carcinomas (CCCs), were compared with non-EAOCs. Results In total, 184 women (mean age, 56 years; age range, 20–91 years) were evaluated. High-grade serous carcinomas (HGSCs) were significantly smaller than the others and had significantly less uterine attachment than CCCs (p < 0.01 for all readers). According to the mean of the measured angles, CCCs were positioned significantly more posteriorly than HGSCs and mucinous carcinomas (p < 0.02), and EAOCs were positioned significantly more posteriorly to the uterus than non-EAOCs (p < 0.01). Conclusion HGSCs are often not attached to the uterus, and EAOCs are positioned more posteriorly to the uterus than non-EAOCs. Critical relevance statement High-grade serous carcinomas were often not attached to the uterus, and endometriosis-associated ovarian cancers were positioned more posteriorly to the uterus than non-endometriosis-associated ovarian cancers. Key points • The position of the ovarian tumor can be determined using MRI. • High-grade serous carcinomas had less attachment to the uterus. • Endometriosis-associated cancers were positioned more posteriorly to the uterus. • The location of ovarian tumors is helpful in estimating histology. Graphical abstrac

Organ atrophy induced by sorafenib and sunitinib : quantitative computed tomography (CT) evaluation of the pancreas, thyroid gland and spleen

BACKGROUND: To evaluate organ atrophy induced by sorafenib and sunitinib, we retrospectively reviewed the CT scans of renal cell carcinoma (RCC) patients receiving molecular targeted therapy (MTT) using sorafenib or sunitinib, and performed volumetric analysis of the pancreas, thyroid gland, and spleen. MATERIAL AND METHODS: Thirteen RCC patients receiving MTT were assigned as the evaluation cases (MTT group), while thirteen additional RCC patients not receiving MTT were retrieved as the Control group. We evaluated the baseline and follow-up CT studies. The volume of the three organs estimated by CT volumetry was compared between the baseline and follow-up CTs. The atrophic ratio of the organ volume in the follow-up CT to that in the baseline CT was calculated, and compared between the MTT and Control groups. RESULTS: All measured organs in the MTT group showed statistically significant volume loss, while no significant change was observed in the Control group. Mean atrophic ratio in the MTT group was 0.74, 0.58, and 0.82 for the pancreas, thyroid and spleen, respectively. The differences in atrophic ratios between both groups were all statistically significant (P<0.05). CONCLUSIONS: Single-agent sorafenib or sunitinib therapy induced statistically significant atrophy in the pancreas, thyroid, and spleen

Diagnosing Ovarian Cancer on MRI: A Preliminary Study Comparing Deep Learning and Radiologist Assessments

Background: This study aimed to compare deep learning with radiologists&rsquo; assessments for diagnosing ovarian carcinoma using MRI. Methods: This retrospective study included 194 patients with pathologically confirmed ovarian carcinomas or borderline tumors and 271 patients with non-malignant lesions who underwent MRI between January 2015 and December 2020. T2WI, DWI, ADC map, and fat-saturated contrast-enhanced T1WI were used for the analysis. A deep learning model based on a convolutional neural network (CNN) was trained using 1798 images from 146 patients with malignant tumors and 1865 images from 219 patients with non-malignant lesions for each sequence, and we tested with 48 and 52 images of patients with malignant and non-malignant lesions, respectively. The sensitivity, specificity, accuracy, and AUC were compared between the CNN and interpretations of three experienced radiologists. Results: The CNN of each sequence had a sensitivity of 0.77&ndash;0.85, specificity of 0.77&ndash;0.92, accuracy of 0.81&ndash;0.87, and an AUC of 0.83&ndash;0.89, and it achieved a diagnostic performance equivalent to the radiologists. The CNN showed the highest diagnostic performance on the ADC map among all sequences (specificity = 0.85; sensitivity = 0.77; accuracy = 0.81; AUC = 0.89). Conclusion: The CNNs provided a diagnostic performance that was non-inferior to the radiologists for diagnosing ovarian carcinomas on MRI