Magnetic resonance imaging improves diagnosis of placenta accreta spectrum requiring hysterectomy compared to ultrasound.

BACKGROUND: Magnetic resonance imaging has been used increasingly as an adjunct for ultrasound imaging for placenta accreta spectrum assessment and preoperative surgical planning, but its value has not been established yet. The ultrasound-based placenta accreta index is a well-validated standardized approach for placenta accreta spectrum evaluation. Placenta accreta spectrum-magnetic resonance imaging markers have been outlined in a joint guideline from the Society of Abdominal Radiology and the European Society of Urogenital Radiology. OBJECTIVE: This study aimed to compare placenta accreta spectrum-magnetic resonance imaging parameters with the ultrasound-based placenta accreta index in pregnancies at high risk for placenta accreta spectrum and to assess the additional diagnostic value of magnetic resonance imaging for placenta accreta spectrum that requires a cesarean hysterectomy. STUDY DESIGN: This was a single-center, retrospective study of pregnant patients who underwent magnetic resonance imaging, in addition to ultrasonography, because of suspected placenta accreta spectrum. The ultrasound-based placenta accreta index and placenta accreta spectrum-magnetic resonance imaging parameters were obtained. Student's t test and Fisher's exact test were used to compare the groups in terms of the primary outcome (hysterectomy vs no hysterectomy). The diagnostic performance of magnetic resonance imaging and the ultrasound-based placenta accreta index was assessed using multivariable logistic regressions, receiver operating characteristics curves, the DeLong test, McNemar test, and the relative predictive value test. RESULTS: A total of 82 patients were included in the study, 41 of whom required a hysterectomy. All patients who underwent a hysterectomy met the International Federation of Gynecology and Obstetrics clinical evidence of placenta accreta spectrum at the time of delivery. Multiple parameters of the ultrasound-based placenta accreta index and placenta accreta spectrum-magnetic resonance imaging were able to predict hysterectomy, and the parameter of greatest dimension of invasion by magnetic resonance imaging was the best quantitative predictor. At 96% sensitivity for hysterectomy, the cutoff values were 3.5 for the ultrasound-based placenta accreta index and 2.5 cm for the greatest dimension of invasion by magnetic resonance imaging. Using this sensitivity, the parameter of greatest dimension of invasion measured by magnetic resonance imaging had higher specificity (P=.0016) and a higher positive predictive value (P=.0018) than the ultrasound-based placenta accreta index, indicating an improved diagnostic threshold. CONCLUSION: In a suspected high-risk group for placenta accreta spectrum, magnetic resonance imaging identified more patients who will not need a hysterectomy than when using the ultrasound-based placenta accrete index only. Magnetic resonance imaging has the potential to aid patient counseling, surgical planning, and delivery timing, including preterm delivery decisions for patients with placenta accreta spectrum requiring hysterectomy.

The human placenta project: Funded studies, imaging technologies, and future directions.

The placenta plays a critical role in fetal development. It serves as a multi-functional organ that protects and nurtures the fetus during pregnancy. However, despite its importance, the intricacies of placental structure and function in normal and diseased states have remained largely unexplored. Thus, in 2014, the National Institute of Child Health and Human Development launched the Human Placenta Project (HPP). As of May 2023, the HPP has awarded over $101 million in research funds, resulting in 41 funded studies and 459 publications. We conducted a comprehensive review of these studies and publications to identify areas of funded research, advances in those areas, limitations of current research, and continued areas of need. This paper will specifically review the funded studies by the HPP, followed by an in-depth discussion on advances and gaps within placental-focused imaging. We highlight the progress within magnetic reasonance imaging and ultrasound, including development of tools for the assessment of placental function and structure.

Longitudinal assessment of placental perfusion in normal and hypertensive pregnancies using pseudo-continuous arterial spin-labeled MRI: preliminary experience.

OBJECTIVES: To evaluate longitudinal placental perfusion using pseudo-continuous arterial spin-labeled (pCASL) MRI in normal pregnancies and in pregnancies affected by chronic hypertension (cHTN), who are at the greatest risk for placental-mediated disease conditions. METHODS: Eighteen normal and 23 pregnant subjects with cHTN requiring antihypertensive therapy were scanned at 3 T using free-breathing pCASL-MRI at 16-20 and 24-28 weeks of gestational age. RESULTS: Mean placental perfusion was 103.1 ± 48.0 and 71.4 ± 18.3 mL/100 g/min at 16-20 and 24-28 weeks respectively in normal pregnancies and 79.4 ± 27.4 and 74.9 ± 26.6 mL/100 g/min in cHTN pregnancies. There was a significant decrease in perfusion between the first and second scans in normal pregnancies (p = 0.004), which was not observed in cHTN pregnancies (p = 0.36). The mean perfusion was not statistically different between normal and cHTN pregnancies at both scans, but the absolute change in perfusion per week was statistically different between these groups (p = 0.044). Furthermore, placental perfusion was significantly lower at both time points (p = 0.027 and 0.044 respectively) in the four pregnant subjects with cHTN who went on to have infants that were small for gestational age (52.7 ± 20.4 and 50.4 ± 20.9 mL/100 g/min) versus those who did not (85 ± 25.6 and 80.0 ± 25.1 mL/100 g/min). CONCLUSION: pCASL-MRI enables longitudinal assessment of placental perfusion in pregnant subjects. Placental perfusion in the second trimester declined in normal pregnancies whereas it remained unchanged in cHTN pregnancies, consistent with alterations due to vascular disease pathology. Perfusion was significantly lower in those with small for gestational age infants, indicating that pCASL-MRI-measured perfusion may be an effective imaging biomarker for placental insufficiency. CLINICAL RELEVANCE STATEMENT: pCASL-MRI enables longitudinal assessment of placental perfusion without administering exogenous contrast agent and can identify placental insufficiency in pregnant subjects with chronic hypertension that can lead to earlier interventions. KEY POINTS: • Arterial spin-labeled (ASL) magnetic resonance imaging (MRI) enables longitudinal assessment of placental perfusion without administering exogenous contrast agent. • ASL-MRI-measured placental perfusion decreased significantly between 16-20 week and 24-28 week gestational age in normal pregnancies, while it remained relatively constant in hypertensive pregnancies, attributed to vascular disease pathology. • ASL-MRI-measured placental perfusion was significantly lower in subjects with hypertension who had a small for gestational age infant at 16-20-week gestation, indicating perfusion as an effective biomarker of placental insufficiency.

Prenatal Ultrasound Findings of Circumvallate Placenta and Pregnancy Outcomes.

OBJECTIVE: Circumvallate placenta has a suggested association with adverse pregnancy outcomes (antenatal bleeding, placental abruption, preterm birth, emergency cesarean, small for gestational age infants, and stillbirth). The aim was to determine if prenatal diagnosis of circumvallate placenta is associated with these adverse pregnancy outcomes. STUDY DESIGN: Pregnancies with a singleton gestation prenatally diagnosed with circumvallate placenta between January 1, 2012 and March 31, 2021 were identified. Adverse pregnancy outcomes were obtained. Rates of adverse pregnancy outcomes were compared among those with prenatally diagnosed circumvallate placentas to those without this prenatal diagnosis with a 4:1 control matched group. Pregnancies with known fetal anomalies or other placental abnormalities were excluded. Statistical analyses included Student's t-test and Χ 2 with p < 0.05 considered significant. RESULTS: Prenatal ultrasound findings of circumvallate placenta were seen in 179 pregnant people (0.20% of all anatomic US studies and 0.17% of all deliveries). Diagnosis was made at a mean gestational age of 19.8 ± 2.4 weeks. Adverse pregnancy outcomes were similar between groups. CONCLUSION: Prenatal ultrasound findings of circumvallate placenta do not correlate with adverse pregnancy outcomes. Given overall good prognosis, prenatal diagnosis of circumvallate placenta may not warrant additional surveillance during pregnancy. KEY POINTS: · The risk of prenatally diagnosed circumvallate placenta was previously unclear.. · Prenatally diagnosed circumvallate placenta is not associated with adverse pregnancy outcomes.. · No change in management may be necessary with prenatally diagnosed circumvallate placenta..

The Cesarean Scar of Pregnancy: Ultrasound Findings and Expectant Management Outcomes.

OBJECTIVE: The nomenclature has evolved from low implantation to cesarean scar pregnancy (CSP) and criteria are recommended for identification and management. Management guidelines include pregnancy termination due to life-threatening complications. This article applies ultrasound (US) parameters recommended by the Society for Maternal Fetal Medicine (SMFM) in women who were expectantly managed. STUDY DESIGN: Pregnancies were identified between March 1, 2013 and December 31, 2020. Inclusion criteria were women with CSP or low implantation identified on US. Studies were reviewed for niche, smallest myometrial thickness (SMT), and location of basalis blinded to clinical data. Clinical outcomes, pregnancy outcome, need for intervention, hysterectomy, transfusion, pathologic findings, and morbidities were obtained by chart review. RESULTS: Of 101 pregnancies with low implantation, 43 met the SMFM criteria at < 10 weeks and 28 at 10 to 14 weeks. At < 10 weeks, SMFM criteria identified 45out of 76 women; of these 13 required hysterectomy; there were 6 who required hysterectomy but did not meet the SMFM criteria. At 10 to < 14 weeks, SMFM criteria identified 28 out of 42 women; of these 15 required hysterectomy. US parameters yielded significant differences in women requiring hysterectomy, at < 10 weeks and 10 to < 14 weeks' gestational age epochs, but the sensitivity, specificity, positive (PPV), and negative predictive values (NPV) of these US parameters have limitations in identifying invasion to determine management. Of the 101 pregnancies, 46 (46%) failed < 20 weeks, 16 (35%) required medical/surgical management including 6 hysterectomies, and 30 (65%) required no intervention. There were 55 pregnancies (55%) that progressed beyond 20 weeks. Of these, 16 required hysterectomy (29%) while 39 (71%) did not. In the overall cohort of 101, 22 (21.8%) required hysterectomy and an additional16 (15.8%) required some type of intervention, while 66.7% required no intervention. CONCLUSION: SMFM US criteria for CSP have limitations for discerning clinical management due to lack of discriminatory threshold. KEY POINTS: · The SMFM US criteria for CSP at <10 or <14 weeks have limitations for clinical management.. · The sensitivity and specificity of the ultrasound findings limit the utility for management. · The SMT of <1 mm is more discriminating than <3 mm for hysterectomy..

Topography-based feature extraction of the human placenta from prenatal MR images.

Magnetic resonance imaging (MRI) has gained popularity in the field of prenatal imaging due to the ability to provide high quality images of soft tissue. In this paper, we presented a novel method for extracting different textural and morphological features of the placenta from MRI volumes using topographical mapping. We proposed polar and planar topographical mapping methods to produce common placental features from a unique point of observation. The features extracted from the images included the entire placenta surface, as well as the thickness, intensity, and entropy maps displayed in a convenient two-dimensional format. The topography-based images may be useful for clinical placental assessments as well as computer-assisted diagnosis, and prediction of potential pregnancy complications.

Deep learning based automatic segmentation of the placenta and uterine cavity on prenatal MR images.

Magnetic resonance imaging (MRI) has potential benefits in understanding fetal and placental complications in pregnancy. An accurate segmentation of the uterine cavity and placenta can help facilitate fast and automated analyses of placenta accreta spectrum and other pregnancy complications. In this study, we trained a deep neural network for fully automatic segmentation of the uterine cavity and placenta from MR images of pregnant women with and without placental abnormalities. The two datasets were axial MRI data of 241 pregnant women, among whom, 101 patients also had sagittal MRI data. Our trained model was able to perform fully automatic 3D segmentation of MR image volumes and achieved an average Dice similarity coefficient (DSC) of 92% for uterine cavity and of 82% for placenta on the sagittal dataset and an average DSC of 87% for uterine cavity and of 82% for placenta on the axial dataset. Use of our automatic segmentation method is the first step in designing an analytics tool for to assess the risk of pregnant women with placenta accreta spectrum.

Neurocysticercosis Complicating Pregnancy.

OBJECTIVE: Infection with the pork tapeworm can result in neurocysticercosis caused by infestation of central nervous system tissues by the parasite cysts. Parenchymal brain infection can cause symptoms from mass effect that include headache, seizures, confusion, and even coma. Our objective was to describe the clinical course of neurocysticercosis infections in pregnant women. METHODS: This was a case series that described clinical findings and pregnancy outcomes of women diagnosed with neurocysticercosis from 1994 through 2016 at a single institution. Their medical records were reviewed, cranial computed tomography and magnetic resonance imaging scans were assessed, and the infections were classified according to the criteria of the 2018 Infectious Diseases Society of America and the American Society of Tropical Medicine and Hygiene. RESULTS: Overall, 37 pregnant women with neurocysticercosis were identified. Of the 37 women, 32 were symptomatic, and 16 each had severe headaches or new-onset seizures or other neurologic sequelae. Some of these women had multiple symptoms. Others were diagnosed when neuroimaging was done for a history of neurocysticercosis or evaluation of a preexisting seizure disorder. The most common treatment was with anthelmintics-albendazole, praziquantel, or both-usually given with anticonvulsants and corticosteroids to decrease inflammation. There were eight women in whom invasive neurosurgical procedures were performed, usually for hydrocephaly. Overall, 32 healthy neonates were born at term, and there was one preterm delivery at 34 weeks of gestation. The four pregnancy losses included two molar pregnancies, one anembryonic miscarriage, and a 23-week stillbirth of a pregnancy complicated by preeclampsia with severe features. CONCLUSION: Neurocysticercosis in pregnant women may be asymptomatic or result in headaches, seizures, confusion, nausea and vomiting, altered mental status, or obtundation. Anthelmintic treatment is usually successful, but in some women neurosurgical procedures are necessary to relieve obstructive hydrocephaly.

Automatic Segmentation of Uterine Cavity and Placenta on MR Images Using Deep Learning.

Magnetic resonance imaging (MRI) is useful for the detection of abnormalities affecting maternal and fetal health. In this study, we used a fully convolutional neural network for simultaneous segmentation of the uterine cavity and placenta on MR images. We trained the network with MR images of 181 patients, with 157 for training and 24 for validation. The segmentation performance of the algorithm was evaluated using MR images of 60 additional patients that were not involved in training. The average Dice similarity coefficients achieved for the uterine cavity and placenta were 92% and 80%, respectively. The algorithm could estimate the volume of the uterine cavity and placenta with average errors of less than 1.1% compared to manual estimations. Automated segmentation, when incorporated into clinical use, has the potential to quantify, standardize, and improve placental assessment, resulting in improved outcomes for mothers and fetuses.

CascadeNet for hysterectomy prediction in pregnant women due to placenta accreta spectrum.

In severe cases, placenta accreta spectrum (PAS) requires emergency hysterectomy, endangering the life of both mother and fetus. Early prediction may reduce complications and aid in management decisions in these high-risk pregnancies. In this work, we developed a novel convolutional network architecture to combine MRI volumes, radiomic features, and custom feature maps to predict PAS severe enough to result in hysterectomy after fetal delivery in pregnant women. We trained, optimized, and evaluated the networks using data from 241 patients, in groups of 157, 24, and 60 for training, validation, and testing, respectively. We found the network using all three paths produced the best performance, with an AUC of 87.8, accuracy 83.3%, sensitivity of 85.0, and specificity of 82.5. This deep learning algorithm, deployed in clinical settings, may identify women at risk before birth, resulting in improved patient outcomes.

Neuroplacentology in congenital heart disease: placental connections to neurodevelopmental outcomes.

Children with congenital heart disease (CHD) are living longer due to effective medical and surgical management. However, the majority have neurodevelopmental delays or disorders. The role of the placenta in fetal brain development is unclear and is the focus of an emerging field known as neuroplacentology. In this review, we summarize neurodevelopmental outcomes in CHD and their brain imaging correlates both in utero and postnatally. We review differences in the structure and function of the placenta in pregnancies complicated by fetal CHD and introduce the concept of a placental inefficiency phenotype that occurs in severe forms of fetal CHD, characterized by a myriad of pathologies. We propose that in CHD placental dysfunction contributes to decreased fetal cerebral oxygen delivery resulting in poor brain growth, brain abnormalities, and impaired neurodevelopment. We conclude the review with key areas for future research in neuroplacentology in the fetal CHD population, including (1) differences in structure and function of the CHD placenta, (2) modifiable and nonmodifiable factors that impact the hemodynamic balance between placental and cerebral circulations, (3) interventions to improve placental function and protect brain development in utero, and (4) the role of genetic and epigenetic influences on the placenta-heart-brain connection. IMPACT: Neuroplacentology seeks to understand placental connections to fetal brain development. In fetuses with CHD, brain growth abnormalities begin in utero. Placental microstructure as well as perfusion and function are abnormal in fetal CHD.

Placenta Accreta Spectrum and Hysterectomy Prediction Using MRI Radiomic Features.

In women with placenta accreta spectrum (PAS), patient management may involve cesarean hysterectomy at delivery. Magnetic resonance imaging (MRI) has been used for further evaluation of PAS and surgical planning. This work tackles two prediction problems: predicting presence of PAS and predicting hysterectomy using MR images of pregnant patients. First, we extracted approximately 2,500 radiomic features from MR images with two regions of interest: the placenta and the uterus. In addition to analyzing two regions of interest, we dilated the placenta and uterus masks by 5, 10, 15, and 20 mm to gain insights from the myometrium, where the uterus and placenta overlap in the case of PAS. This study cohort includes 241 pregnant women. Of these women, 89 underwent hysterectomy while 152 did not; 141 with suspected PAS, and 100 without suspected PAS. We obtained an accuracy of 0.88 for predicting hysterectomy and an accuracy of 0.92 for classifying suspected PAS. The radiomic analysis tool is further validated, it can be useful for aiding clinicians in decision making on the care of pregnant women.

Deep learning-based segmentation of the placenta and uterus on MR images.

Purpose: Magnetic resonance imaging has been recently used to examine the abnormalities of the placenta during pregnancy. Segmentation of the placenta and uterine cavity allows quantitative measures and further analyses of the organs. The objective of this study is to develop a segmentation method with minimal user interaction. Approach: We developed a fully convolutional neural network (CNN) for simultaneous segmentation of the uterine cavity and placenta in three dimensions (3D) while a minimal operator interaction was incorporated for training and testing of the network. The user interaction guided the network to localize the placenta more accurately. In the experiments, we trained two CNNs, one using 70 normal training cases and the other using 129 training cases including normal cases as well as cases with suspected placenta accreta spectrum (PAS). We evaluated the performance of the segmentation algorithms on two test sets: one with 20 normal cases and the other with 50 images from both normal women and women with suspected PAS. Results: For the normal test data, the average Dice similarity coefficient (DSC) was 92% and 82% for the uterine cavity and placenta, respectively. For the combination of normal and abnormal cases, the DSC was 88% and 83% for the uterine cavity and placenta, respectively. The 3D segmentation algorithm estimated the volume of the normal and abnormal uterine cavity and placenta with average volume estimation errors of 4% and 9%, respectively. Conclusions: The deep learning-based segmentation method provides a useful tool for volume estimation and analysis of the placenta and uterus cavity in human placental imaging.

Transvaginal Color Mapping Ultrasound in the First Trimester Predicts Placenta Accreta Spectrum: A Retrospective Cohort Study.

OBJECTIVES: Ultrasound (US) prediction of placenta accreta spectrum (PAS) in the first trimester may be aided by postprocessing mechanisms employing color pixel quantification near the bladder-uterine serosal interface. Our objective was to create a postprocessing algorithm of color images to identify findings associated with PAS and compare quantification to sonologist impression in prospectively obtained cine US images. METHODS: Transverse transvaginal (TV) US color cines obtained in the first trimester as part of a prospective study were reviewed. Investigators blinded to clinical outcomes reviewed anonymized cines that were archived and labeled the bladder-uterine serosal interface. Color pixels within 2 cm of the defined bladder-uterine serosal interface were ascertained using a Python-based plugin in the Horos open-source DICOM viewer. A sonologist classified the findings as suspicious for invasion, indeterminate, or normal. Statistical analysis was performed using Wilcoxon rank-sum test, Cochran-Armitage trend test, and calculation of receiver-operating characteristic (ROC) curves. RESULTS: Fifty-four studies met inclusion criteria. Of those, six (11%) required hysterectomy with pathologic confirmation of PAS. Women requiring hysterectomy had a significantly higher color Doppler pixel area than those not requiring hysterectomy (P = .0205). A significant trend was identified in the sonologist impression of invasion (P = .0003). ROC's comparing sonologist impression to Doppler color imaging areas were comparable (P = .054). CONCLUSIONS: Color Doppler mapping in the first trimester showed an increase in color pixel area near the bladder-uterine serosal interface in women requiring cesarean hysterectomy with histologically confirmed PAS at time of delivery, compared to women without hysterectomy or pathologic evidence of PAS.

Fetal liver and lung volume index of neonatal survival with congenital diaphragmatic hernia.

BACKGROUND: Magnetic resonance imaging (MRI) assesses pulmonary hypoplasia in fetal congenital diaphragmatic hernia (CDH). Neonatal mortality may occur with CDH. OBJECTIVE: To quantify MRI parameters associated with neonatal survival in fetuses with isolated CDH. MATERIALS AND METHODS: Fetal MRI for assessing CDH included region of interest (ROI) measurements for total lung volume (TLV), herniated liver volume, herniated other organ volume and predicted lung volume. Ratios of observed lung volume and liver up volume to predicted lung volume (observed to predicted TLV, percentage of the thorax occupied by liver) were calculated and compared to neonatal outcomes. Analyses included Wilcoxon rank sum test, multivariate logistic regression and receiver operating characteristic (ROC) curves. RESULTS: Of 61 studies, the median observed to predicted TLV was 0.25 in survivors and 0.16 in non-survivors (P=0.001) with CDH. The median percentage of the thorax occupied by liver was 0.02 in survivors and 0.22 in non-survivors (P<0.001). The association of observed to predicted TLV and percentage of the thorax occupied by liver with survival for gestational age (GA) >28 weeks was greater compared to GA ≤28 weeks. The ROC analysis demonstrated an area under the curve of 0.96 (95% confidence interval 0.91-1.00) for the combined observed to predicted TLV, percentage of the thorax occupied by liver and GA. CONCLUSION: The percentage of the thorax occupied by liver and observed to predicted TLV was predictive of neonatal survival in fetuses with CDH.

Assessing reproducibility in Magnetic Resonance (MR) Radiomics features between Deep-Learning segmented and Expert Manual segmented data and evaluating their diagnostic performance in Pregnant Women with suspected Placenta Accreta Spectrum (PAS).

A Deep-Learning (DL) based segmentation tool was applied to a new magnetic resonance imaging dataset of pregnant women with suspected Placenta Accreta Spectrum (PAS). Radiomic features from DL segmentation were compared to those from expert manual segmentation via intraclass correlation coefficients (ICC) to assess reproducibility. An additional imaging marker quantifying the placental location within the uterus (PLU) was included. Features with an ICC > 0.7 were used to build logistic regression models to predict hysterectomy. Of 2059 features, 781 (37.9%) had ICC >0.7. AUC was 0.69 (95% CI 0.63-0.74) for manually segmented data and 0.78 (95% CI 0.73-0.83) for DL segmented data.