Fetal abdominal cysts: antenatal course and postnatal outcomes

Prenatal Diagnosis and Intervention in a Fetal Enterogenous Cyst

The ultrasound diagnosis of a fetal intra-abdominal cyst is typically established during the second or third trimester in the majority of cases. They primarily arise from the gastrointestinal or genitourinary system during the development of intra-abdominal structure and if isolated may resolve spontaneously. Enteric or enterogenous or enteric duplication cysts, which are congenital developments from the intestine, are most common. This case of an enterogenous cyst is presented because of its extreme rarity, its large size, and the need for prenatal intervention. Early identification and definitive treatment are necessary for proper management of this condition.

A multi-class deep learning model for early lung cancer and chronic kidney disease detection using computed tomography images

Lung cancer is a fatal disease caused by an abnormal proliferation of cells in the lungs. Similarly, chronic kidney disorders affect people worldwide and can lead to renal failure and impaired kidney function. Cyst development, kidney stones, and tumors are frequent diseases impairing kidney function. Since these conditions are generally asymptomatic, early, and accurate identification of lung cancer and renal conditions is necessary to prevent serious complications. Artificial Intelligence plays a vital role in the early detection of lethal diseases. In this paper, we proposed a modified Xception deep neural network-based computer-aided diagnosis model, consisting of transfer learning based image net weights of Xception model and a fine-tuned network for automatic lung and kidney computed tomography multi-class image classification. The proposed model obtained 99.39% accuracy, 99.33% precision, 98% recall, and 98.67% F1-score for lung cancer multi-class classification. Whereas, it attained 100% accuracy, F1 score, recall and precision for kidney disease multi-class classification. Also, the proposed modified Xception model outperformed the original Xception model and the existing methods. Hence, it can serve as a support tool to the radiologists and nephrologists for early detection of lung cancer and chronic kidney disease, respectively.

Prenatal diagnosis at different gestational times and clinical features of choledochal cysts: a single tertiary center report

PURPOSE

This study aimed to evaluate the effect of prenatal diagnosis at different gestational times on the clinical features of patients with choledochal cysts (CDCs).

METHODS

Medical records of patients with prenatally diagnosed CDCs admitted to our hospital (April 2013-April 2018) were retrospectively reviewed. The clinical characteristics and pathological CDC features were analyzed.

RESULTS

Two hundred eighteen cases were included. Patients were divided into two groups. Group 1 and group 2 had a prenatal diagnosis at ≤ 27 weeks of gestation (second trimester of gestation, n = 157) and > 27 weeks (third trimester of gestation, n = 61), respectively. The incidence of jaundice and the TBIL, IBIL and GGT levels were higher in Group 1 (P = 0.021, P = 0.029, P = 0.042, P = 0.007, respectively). The maximum cyst diameter at the time of surgery was larger in Group 1 (P = 0.015). An association study showed that the time of prenatal diagnosis was negatively correlated with the maximum cyst diameter both postnatally (r = - 0.223, P = 0.001) and at the time of surgery (r = - 0.268, P < 0.001).

CONCLUSION

Unlike patients diagnosed at a late prenatal age, patients diagnosed at an early prenatal age tend to present clinical symptoms (jaundice, manifested as high indirect bilirubin), hepatic function damage, and large cysts at the time of surgery.

Kidney Diseases Classification using Hybrid Transfer-Learning DenseNet201-Based and Random Forest Classifier

There are several disease kinds in global populations that may be related to human lifestyles, social, genetic, economic, and other factors related to the nature of the country they live in. Most of the recent studies have focused on investigating prevalent diseases that spread in the population in order to minimize mortality risks, choose the best method for treatment, and improve community healthcare. Kidney disease is one of the most widespread health problems in modern society. This study focuses on kidney stones, cysts, and tumors, the three most common types of renal illness, using a dataset of 12,446 CT urogram and whole abdomen images, aiming to move toward an AI-based kidney disease diagnosis system while contributing to the wider field of artificial intelligence research. In this study, a hybrid technique is used by utilizing both pre-train models for feature extraction and classification using machine learning algorithms for the task of kidney disease image diagnosis. The pre-trained model used in this study is the Densenet-201 model. As well as using Random Forest for classification, the Densenet-201-Random-Forest approach has outperformed many of the previous models used in other studies, having an accuracy rate of 99.719 percent.

A Case of Multicystic Dysplastic Kidney Presenting as a Single Midline Pelvic Cyst

We present an unusual case of a female neonate presenting with a single midline pelvic cyst. Prenatal imaging was suggestive of multicystic dysplastic kidney (MCDK), but postnatal imaging was atypical for this diagnosis given the location and singular cyst noted. The patient ultimately underwent surgical exploration and was diagnosed with an ectopic MCDK. Ectopic MCDK should be considered in the differential diagnosis of unilocular cystic pelvic lesions identified in the perinatal period.

Predictive value of prenatal ultrasound in foetal intraabdominal cystic lesions and evaluation of perinatal outcomes: a single-centre study results

The prenatal diagnosis of intra-abdominal cystic lesions is relatively common and it can be due to a wide variety of clinical conditions. The aims of this study were to determine the accuracy of the prenatal ultrasound in identifying the aetiology of foetal intra-abdominal cysts and to describe the prenatal and postnatal outcomes. This study is a retrospective analysis of 137 foetuses diagnosed with intraabdominal cysts during the prenatal period, except those originating from the urinary system, conducted from April 2015 to August 2018. Among 137 intraabdominal cysts identified as antenatal, ovarian cysts had the highest rate of prenatal diagnosis. The most frequently misdiagnosed pathologies were gastrointestinal system obstructions with 6 cases. There were 129 (94.2%)cases of intraabdominal cysts born alive. Intrabdominal cystic lesions resolved spontaneously in 23(16.8%) cases in the intrauterine period and 28 cases within 12 (20.4%) months after birth during follow-up. Postpartum surgical treatment was performed in 44 (%32.1) cases. The overall neonatal mortality was 21/137 (15.3%),with no death in the intrauterine period; 8(5.8%) of these were death following termination of pregnancy, 6(4.4%) were postpartum, and 7(5.1%) were post-surgical death. Postnatal results of intraabdominal cysts are variable. Therefore, individual assessing and managing each case is of clear benefit due to cyst's variable course. IMPACT STATEMENTWhat is already known on this subject? The prenatal diagnosis of intra- abdominal cystic lesions is relatively common and prenatal ultrasound is the main screening tool. The diagnosis of a foetal intra-abdominal cyst might represent a diagnostic and management dilemma due to the wide variety of potential diagnosis.What the results of this study add? The accuracy of prenatal ultrasound in identifying the origin of a foetal intra-abdominal cyst seems to be high with detailed ultrasonographic examinations and the perinatal outcome of these foetuses is favourable in most of them.What the implications are of these findings for clinical practice and/or future research? Regardless of the cyst's origin, it should be kept in mind that prenatal and postnatal clinical courses can be variable in each case, and every pregnant woman with a foetus with an intraabdominal cyst must be managed individually in every aspect.

Vision transformer and explainable transfer learning models for auto detection of kidney cyst, stone and tumor from CT-radiography

Renal failure, a public health concern, and the scarcity of nephrologists around the globe have necessitated the development of an AI-based system to auto-diagnose kidney diseases. This research deals with the three major renal diseases categories: kidney stones, cysts, and tumors, and gathered and annotated a total of 12,446 CT whole abdomen and urogram images in order to construct an AI-based kidney diseases diagnostic system and contribute to the AI community's research scope e.g., modeling digital-twin of renal functions. The collected images were exposed to exploratory data analysis, which revealed that the images from all of the classes had the same type of mean color distribution. Furthermore, six machine learning models were built, three of which are based on the state-of-the-art variants of the Vision transformers EANet, CCT, and Swin transformers, while the other three are based on well-known deep learning models Resnet, VGG16, and Inception v3, which were adjusted in the last layers. While the VGG16 and CCT models performed admirably, the swin transformer outperformed all of them in terms of accuracy, with an accuracy of 99.30 percent. The F1 score and precision and recall comparison reveal that the Swin transformer outperforms all other models and that it is the quickest to train. The study also revealed the blackbox of the VGG16, Resnet50, and Inception models, demonstrating that VGG16 is superior than Resnet50 and Inceptionv3 in terms of monitoring the necessary anatomy abnormalities. We believe that the superior accuracy of our Swin transformer-based model and the VGG16-based model can both be useful in diagnosing kidney tumors, cysts, and stones.

Posterior urethral valves and urachal cyst: an unusual combination

Combination of posterior urethral valves and urachus remnants has been described as rare occurrence. We report a case of a baby boy with normal antenatal scans, in whom the presence of large urachal cyst and posterior urethral valves causing high pressure bladder and chronic kidney disease was found. The patient underwent ablation of posterior urethral valves at 23 days of life and urachal cyst removal at age of 4 and a half months.

Outcomes of sonographically-suspected fetal intra-abdominal cysts: Surgical intervention, conservative management and spontaneous regression

OBJECTIVE

The prenatal diagnosis of fetal intra-abdominal cysts is challenging. This study aimed to evaluate the diagnostic ability of prenatal ultrasound for fetal intra-abdominal cysts and to develop a predictive method for pre- and postnatal outcomes.

METHODS

We retrospectively reviewed fetuses with ultrasound-detected intra-abdominal cysts between January 2013 and January 2020. The maternal-fetal clinical characteristics and ultrasound parameters were integrated into a model of pre- or postnatal outcomes.

RESULTS

The study enrolled 190 eligible fetuses, including 94 cases of spontaneous regression, 33 cases of conservative management and 63 cases of surgical intervention. For the 63 cases of surgical intervention, prenatal ultrasound was found to identify fetal intra-abdominal cysts with 80.00% sensitivity (95% CI: 67.03%-89.57%), 37.50% specificity (95% CI: 8.52%-75.51%), 89.80% positive predictive value (95% CI: 83.51%-93.86%), 21.43% negative predictive value (95% CI: 8.80%-43.53%) and 74.60% accuracy (95% CI: 62.06%-84.73%). The predictive model of prenatal spontaneous regression was as follows: y = -3.291 + 0.083 × gestational age + 1.252 × initial diameter, with an area under the curve (AUC) of 0.819 (95% CI: 0.739-0.899) and an optimal cut-off value of 0.74. The large cyst diameter before delivery was an independent predictor of postnatal surgical intervention (p < 0.001), with an AUC of 0.710 (95% CI: 0.625-0.794) and an optimal cut-off value of 3.35 cm.

CONCLUSION

Although ultrasound has a limited ability in the accurate diagnosis of fetal abdominal cysts, a simple method of measuring the diameter can predict fetal outcomes and identify the cases that may require surgical intervention or spontaneous regression.

Fetal body MRI and its application to fetal and neonatal treatment: an illustrative review

This Review depicts the evolving role of MRI in the diagnosis and prognostication of anomalies of the fetal body, here including head and neck, thorax, abdomen and spine. A review of the current literature on the latest developments in antenatal imaging for diagnosis and prognostication of congenital anomalies is coupled with illustrative cases in true radiological planes with viewable three-dimensional video models that show the potential of post-acquisition reconstruction protocols. We discuss the benefits and limitations of fetal MRI, from anomaly detection, to classification and prognostication, and defines the role of imaging in the decision to proceed to fetal intervention, across the breadth of included conditions. We also consider the current capabilities of ultrasound and explore how MRI and ultrasound can complement each other in the future of fetal imaging.

Fetal abdominal tumors and cysts

This article reviews the contemporary diagnosis and management of the most common abdominal neoplasms and cystic lesions diagnosed in the fetus. Fetal tumors discussed include teratomas (sacrococcygeal, cervical or mediastinal), mesoblastic nephroma, nephroblastoma (Wilms' tumor), neuroblastoma, and hepatoblastoma. Fetal abdominal cystic lesions discussed include ovarian cyst, choledochal cyst, intestinal duplication cyst, mesenteric cyst, simple hepatic cyst, and meconium pseudocyst. We discuss the rare indications for fetal intervention or fetal surgery and other perinatal management, including prenatal interventions and fetal surgery for sacrococcygeal teratoma. The lesions reviewed are detected by widespread use of screening ultrasonography during pregnancy. Work-up for these abnormalities may include fetal MRI which enhances the diagnostic accuracy of abdominal tumors and cystic lesions and can aid in characterization of the lesion in relationship to surrounding anatomic structures. Accurate prenatal diagnosis of such lesions permits recommendations for optimal location and timing of delivery, and inclusion of appropriate caregivers and expertise to facilitate postnatal management. Perinatal management of the fetus with a neoplasm requires consideration of the optimal timing and mode of delivery, and pediatric oncology and surgical specialty care. The majority of tumors diagnosed antenatally have good prognosis with current multimodality treatment.

Fetal Non-Ovarian Abdominopelvic Cystic Lesions: A Single-Center Report

INTRODUCTION

There is a paucity of reports describing the clinical course and likely postnatal outcomes of prenatally identified simple cystic abdominopelvic lesions which are not associated with the ovary.

OBJECTIVE

The aim of this study was to describe the natural history and postnatal outcomes of prenatally discovered abdominopelvic cystic lesions seen at our center.

METHODS

This study is a retrospective review of all newborns with prenatally discovered non-ovarian simple cystic abdominal or pelvic lesions (September 2012-December 2018). Prenatal solid organ involvement, lesion size, and postnatal clinical outcomes are described.

RESULTS

Sixty-six patients with 68 cystic lesions were identified; 22 patients with 24 lesions met the defined study criteria and were included. Eleven (46%) resolved prenatally, while 5 (21%) resolved by 18 months of age. Of the 10 lesions associated with an organ, 4 (40%) resolved prenatally. Of the remaining 14 lesions not associated with a solid organ, 7 (50%) resolved prenatally. Seven lesions (29%) required postnatal surgical intervention. Larger maximum prenatal lesions tended toward postnatal surgical intervention (one-way ANOVA: p = 0.072).

CONCLUSIONS

The majority of simple non-ovarian cystic abdominopelvic lesions at our center resolved in the perinatal period. Due to the low frequency of these lesions at fetal centers, a larger multicenter study based on a consistent monitoring protocol should be undertaken to better describe the resolution patterns of simple non-ovarian cystic lesions for improved prenatal counseling.