Texture analysis of magnetic resonance images of the human placenta throughout gestation: A feasibility study

Interdependence of placenta and fetal cardiac development

The placenta and fetal heart undergo development concurrently during early pregnancy, and, while human studies have reported associations between placental abnormalities and congenital heart disease (CHD), the nature of this relationship remains incompletely understood. Evidence from animal studies suggests a plausible cause and effect connection between placental abnormalities and fetal CHD. Biomechanical models demonstrate the influence of mechanical forces on cardiac development, whereas genetic models highlight the role of confined placental mutations that can cause some forms of CHD. Similar definitive studies in humans are lacking; however, placental pathologies such as maternal and fetal vascular malperfusion and chronic deciduitis are frequently observed in pregnancies complicated by CHD. Moreover, maternal conditions such as diabetes and pre-eclampsia, which affect placental function, are associated with increased risk of CHD in offspring. Bridging the gap between animal models and human studies is crucial to understanding how placental abnormalities may contribute to human fetal CHD. The next steps will require new methodologies and multidisciplinary approaches combining innovative imaging modalities, comprehensive genomic testing, and histopathology. These studies may eventually lead to preventative strategies for some forms of CHD by targeting placental influences on fetal heart development.

Gastric coagulation and postprandial amino acid absorption of milk is affected by mineral composition: a randomized crossover trial

Background: In vitro studies suggest that casein coagulation of milk is influenced by its mineral composition, and may therefore affect the dynamics of protein digestion, gastric emptying and appearance of amino acids (AA) in the blood, but this remains to be confirmed in vivo. Objective: This study aimed to compare gastrointestinal digestion between two milks with the same total calcium content but different casein mineralization (CM). Design: Fifteen males (age 30.9 ± 13.8 years, BMI 22.5 ± 2.2 kg m-2) participated in this randomized cross-over study with two treatments. Participants underwent gastric magnetic resonance imaging (MRI) scans at the baseline and every 10 min up to 90 min after consumption of 600 ml milk with low or high CM. Blood samples were taken at the baseline and up to 5 hours postprandially. Primary outcomes were postprandial plasma AA concentrations and gastric emptying rate. Secondary outcomes were postprandial glucose and insulin levels, gastric coagulation as estimated by image texture metrics, and appetite ratings. Results: Gastric content volume over time was similar for both treatments. However, gastric content image analysis suggested that the liquid fraction emptied quicker in the high CM milk, while the coagulum emptied slower. Relative to high CM, low CM showed earlier appearance of AAs that are more dominant in casein, such as proline (MD 4.18 μmol L-1, 95% CI [2.38-5.98], p < 0.001), while there was no difference in appearance of AAs that are more dominant in whey protein, such as leucine. The image texture metrics homogeneity and busyness differed significantly between treatments (MD 0.007, 95% CI [0.001, 0.012], p = 0.022; MD 0.005, 95% CI [0.001, 0.010], p = 0.012) likely because of a reduced coagulation in the low CM milk. Conclusions: Mineral composition of milk can influence postprandial serum AA kinetics, likely due to differences in coagulation dynamics. The clinical trial registry number is NL8959 (https://clinicaltrials.gov).

Influence of maternal psychological distress during COVID-19 pandemic on placental morphometry and texture

The Coronavirus Disease 2019 (COVID-19) pandemic has been accompanied by increased prenatal maternal distress (PMD). PMD is associated with adverse pregnancy outcomes which may be mediated by the placenta. However, the potential impact of the pandemic on in vivo placental development remains unknown. To examine the impact of the pandemic and PMD on in vivo structural placental development using advanced magnetic resonance imaging (MRI), acquired anatomic images of the placenta from 63 pregnant women without known COVID-19 exposure during the pandemic and 165 pre-pandemic controls. Measures of placental morphometry and texture were extracted. PMD was determined from validated questionnaires. Generalized estimating equations were utilized to compare differences in PMD placental features between COVID-era and pre-pandemic cohorts. Maternal stress and depression scores were significantly higher in the pandemic cohort. Placental volume, thickness, gray level kurtosis, skewness and run length non-uniformity were increased in the pandemic cohort, while placental elongation, mean gray level and long run emphasis were decreased. PMD was a mediator of the association between pandemic status and placental features. Altered in vivo placental structure during the pandemic suggests an underappreciated link between disturbances in maternal environment and perturbed placental development. The long-term impact on offspring is currently under investigation.

Association of intraplacental oxygenation patterns on dual-contrast MRI with placental abnormality and fetal brain oxygenation

OBJECTIVES

Most human in-vivo placental imaging techniques are unable to distinguish and characterize various placental compartments, such as the intervillous space (IVS), placental vessels (PV) and placental tissue (PT), limiting their specificity. We describe a method that employs T2* and diffusion-weighted magnetic resonance imaging (MRI) data to differentiate automatically placental compartments, quantify their oxygenation properties and identify placental lesions (PL) in vivo. We also investigate the association between placental oxygenation patterns and fetal brain oxygenation.

METHODS

This was a prospective study conducted between 2018 and 2021 in which dual-contrast clinical MRI data (T2* and diffusion-weighted MRI) were acquired from patients between 20 and 38 weeks' gestation. We trained a fuzzy clustering method to analyze T2* and diffusion-weighted MRI data and assign placental voxels to one of four clusters, based on their distinct imaging domain features. The new method divided automatically the placenta into IVS, PV, PT and PL compartments and characterized their oxygenation changes throughout pregnancy.

RESULTS

A total of 27 patients were recruited, of whom five developed pregnancy complications. Total placental oxygenation level and T2* did not demonstrate a statistically significant temporal correlation with gestational age (GA) (R²  = 0.060, P = 0.27). In contrast, the oxygenation level reflected by T2* values in the placental IVS (R²  = 0.51, P = 0.0002) and PV (R²  = 0.76, P = 1.1 × 10^-7 ) decreased significantly with advancing GA. Oxygenation levels in the PT did not show any temporal change during pregnancy (R²  = 0.00044, P = 0.93). A strong spatial-dependent correlation between PV oxygenation level and GA was observed. The strongest negative correlation between PV oxygenation and GA (R²  = 0.73, P = 4.5 × 10^-7 ) was found at the fetal-vessel-dominated region close to the chorionic plate. The location and extent of the placental abnormality were automatically delineated and quantified in the five women with clinically confirmed placental pathology. Compared to the averaged total placental oxygenation, placental IVS oxygenation level best reflected fetal brain oxygenation level during fetal development.

CONCLUSION

Based on clinically feasible dual-MRI, our method enables accurate spatiotemporal quantification of placental compartment and fetal brain oxygenation across different GAs. This information should improve our knowledge of human placenta development and its relationship with normal and abnormal pregnancy. © 2022 The Authors. Ultrasound in Obstetrics & Gynecology published by John Wiley & Sons Ltd on behalf of International Society of Ultrasound in Obstetrics and Gynecology.

Radiomics analysis of T2 -weighted images for differentiating invasive placentas in women at high risks

PURPOSE

To develop and validate an MRI-based radiomics model for differentiating invasive placentas in patients with high risks.

METHODS

A total of 181 pregnant women suspected of placenta accreta spectrum (PAS) disorders and who underwent MRI for placenta evaluation were retrospectively enrolled. The data set was randomly divided into the training (n = 125; invasive = 63, noninvasive = 62) and test (n = 56; invasive = 28, noninvasive = 28) groups. Radiomics features were extracted from half-Fourier acquisition single-shot turbo spin echo (HASTE) and sagittal true fast imaging in steady-state precession (TRUFISP) sequences independently and mainly selected based on their correlations with invasive placentas to construct two radiomics signatures including HASTE-Radscore and TRUFISP-Radscore. Then, the predictive performance of radiomic signatures, clinical features, radiographic features, and their combination were evaluated. The model with the best predictive performance was validated with its discrimination ability, calibration, and clinical usefulness.

RESULTS

Five radiomics features from HASTE and three radiomics features from TRUFISP were retained, respectively, for predicting invasive placentas. The combination of radiomics signatures and clinical features including prior cesarean delivery, placenta previa, and radiographic feature, the placental thickness resulted in the best discrimination ability, with area under the curve of 0.898 (95% confidence interval [CI] 0.844-0.9522) and 0.858 (95% confidence interval 0.7514-0.9655) in the training and test cohort, respectively. The combined model showed a significantly better area under the curve performance and clinical usefulness than independent clinical or radiographic model according to DeLong test (p < .05), net reclassification improvement and integrated discrimination improvement analysis (positive improvement) and decision curve analysis (higher net benefit).

CONCLUSIONS

The T₂ -weighted imaging MRI radiomics model could serve as a potential prenatal diagnosis tool for identifying invasive placentas in patients with high risks.

Lung Inflammation Predictors in Combined Immune Checkpoint-Inhibitor and Radiation Therapy—Proof-of-Concept Animal Study

Purpose: Combined radiotherapy (RT) and immune checkpoint-inhibitor (ICI) therapy can act synergistically to enhance tumor response beyond what either treatment can achieve alone. Alongside the revolutionary impact of ICIs on cancer therapy, life-threatening potential side effects, such as checkpoint-inhibitor-induced (CIP) pneumonitis, remain underreported and unpredictable. In this preclinical study, we hypothesized that routinely collected data such as imaging, blood counts, and blood cytokine levels can be utilized to build a model that predicts lung inflammation associated with combined RT/ICI therapy. Materials and Methods: This proof-of-concept investigational work was performed on Lewis lung carcinoma in a syngeneic murine model. Nineteen mice were used, four as untreated controls and the rest subjected to RT/ICI therapy. Tumors were implanted subcutaneously in both flanks and upon reaching volumes of ~200 mm3 the animals were imaged with both CT and MRI and blood was collected. Quantitative radiomics features were extracted from imaging of both lungs. The animals then received RT to the right flank tumor only with a regimen of three 8 Gy fractions (one fraction per day over 3 days) with PD-1 inhibitor administration delivered intraperitoneally after each daily RT fraction. Tumor volume evolution was followed until tumors reached the maximum size allowed by the Institutional Animal Care and Use Committee (IACUC). The animals were sacrificed, and lung tissues harvested for immunohistochemistry evaluation. Tissue biomarkers of lung inflammation (CD45) were tallied, and binary logistic regression analyses were performed to create models predictive of lung inflammation, incorporating pretreatment CT/MRI radiomics, blood counts, and blood cytokines. Results: The treated animal cohort was dichotomized by the median value of CD45 infiltration in the lungs. Four pretreatment radiomics features (3 CT features and 1 MRI feature) together with pre-treatment neutrophil-to-lymphocyte (NLR) ratio and pre-treatment granulocyte-macrophage colony-stimulating factor (GM-CSF) level correlated with dichotomized CD45 infiltration. Predictive models were created by combining radiomics with NLR and GM-CSF. Receiver operating characteristic (ROC) analyses of two-fold internal cross-validation indicated that the predictive model incorporating MR radiomics had an average area under the curve (AUC) of 0.834, while the model incorporating CT radiomics had an AUC of 0.787. Conclusions: Model building using quantitative imaging data, blood counts, and blood cytokines resulted in lung inflammation prediction models justifying the study hypothesis. The models yielded very-good-to-excellent AUCs of more than 0.78 on internal cross-validation analyses.

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.

Prediction of placenta accreta spectrum using texture analysis on coronal and sagittal T2-weighted imaging

PURPOSE

To separately perform visual and texture analyses of the axial, coronal, and sagittal planes of T2-weighted images and identify the optimal method for differentiating between the normal placenta and placenta accreta spectrum (PAS).

METHODS

Eighty consecutive patients (normal group, n = 50; PAS group, n = 30) underwent preoperative MRI. A scoring system (0-2) was used to evaluate the degree of abnormality observed in visual analysis (bulging, abnormal vascularity, T2 dark band, placental heterogeneity). The axial, coronal, and sagittal planes were manually segmented separately to obtain texture features, and seven combinations were obtained: axial; coronal; sagittal; axial and coronal; axial and sagittal; coronal and sagittal; and axial, coronal, and sagittal. Feature selection using the least absolute shrinkage and selection operator method and model construction using a support vector machine algorithm with k-fold cross-validation were performed. AUC was used to evaluate diagnostic performance.

RESULTS

The AUC of visual analysis was 0.75. The model 'coronal and sagittal' had the highest AUC (0.98) amongst the seven combinations. The fivefold cross-validation for the model 'coronal and sagittal' showed AUCs of 0.85 and 0.97 in training and validation sets, respectively. The AUC of the model 'coronal and sagittal' for all subjects was significantly higher than that of visual analysis (0.98 vs. 0.75; p < 0.0001).

CONCLUSION

The model 'coronal and sagittal' can accurately differentiate between the normal placenta and PAS, with a significantly better diagnostic performance than visual analysis. Texture analysis is an optimal method for differentiating between the normal placenta and PAS.

Emerging placental biomarkers of health and disease through advanced magnetic resonance imaging (MRI)

Placental dysfunction is a major cause of fetal demise, fetal growth restriction, and preterm birth, as well as significant maternal morbidity and mortality. Infant survivors of placental dysfunction are at elevatedrisk for lifelong neuropsychiatric morbidity. However, despite the significant consequences of placental disease, there are no clinical tools to directly and non-invasively assess and measure placental function in pregnancy. In this work, we will review advanced MRI techniques applied to the study of the in vivo human placenta in order to better detail placental structure, architecture, and function. We will discuss the potential of these measures to serve as optimal biomarkers of placental dysfunction and review the evidence of these tools in the discrimination of health and disease in pregnancy. Efforts to advance our understanding of in vivo placental development are necessary if we are to optimize healthy pregnancy outcomes and prevent brain injury in successive generations. Current management of many high-risk pregnancies cannot address placental maldevelopment or injury, given the standard tools available to clinicians. Once accurate biomarkers of placental development and function are constructed, the subsequent steps will be to introduce maternal and fetal therapeutics targeting at optimizing placental function. Applying these biomarkers in future studies will allow for real-time assessments of safety and efficacy of novel interventions aimed at improving maternal-fetal well-being.

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.

Toward prediction of abscopal effect in radioimmunotherapy: Pre-clinical investigation

PURPOSE

Immunotherapy (IT) and radiotherapy (RT) can act synergistically, enhancing antitumor response beyond what either treatment can achieve separately. Anecdotal reports suggest that these results are in part due to the induction of an abscopal effect on non-irradiated lesions. Systematic data on incidence of the abscopal effect are scarce, while the existence and the identification of predictive signatures or this phenomenon are lacking. The purpose of this pre-clinical investigational work is to shed more light on the subject by identifying several imaging features and blood counts, which can be utilized to build a predictive binary logistic model.

MATERIALS AND METHODS

This proof-of-principle study was performed on Lewis Lung Carcinoma in a syngeneic, subcutaneous murine model. Nineteen mice were used: four as control and the rest were subjected to combined RT plus IT regimen. Tumors were implanted on both flanks and after reaching volume of ~200 mm3 the animals were CT and MRI imaged and blood was collected. Quantitative imaging features (radiomics) were extracted for both flanks. Subsequently, the treated animals received radiation (only to the right flank) in three 8 Gy fractions followed by PD-1 inhibitor administrations. Tumor volumes were followed and animals exhibiting identical of better tumor growth delay on the non-irradiated (left) flank as compared to the irradiated flank were identified as experiencing an abscopal effect. Binary logistic regression analysis was performed to create models for CT and MRI radiomics and blood counts, which are predictive of the abscopal effect.

RESULTS

Four of the treated animals experienced an abscopal effect. Three CT and two MRI radiomics features together with the pre-treatment neutrophil-to-lymphocyte (NLR) ratio correlated with the abscopal effect. Predictive models were created by combining the radiomics with NLR. ROC analyses indicated that the CT model had AUC of 0.846, while the MRI model had AUC of 0.946.

CONCLUSIONS

The combination of CT and MRI radiomics with blood counts resulted in models with AUCs of 1 on the modeling dataset. Application of the models to the validation dataset exhibited AUCs above 0.84, indicating very good predictive power of the combination between quantitative imaging and blood counts.

Normative placental structure in pregnancy using quantitative Magnetic Resonance Imaging

INTRODUCTION

To characterize normative morphometric, textural and microstructural placental development by applying advanced and quantitative magnetic resonance imaging (qMRI) techniques to the in-vivo placenta.

METHODS

We enrolled 195 women with uncomplicated, healthy singleton pregnancies in a prospective observational study. Women underwent MRI between 16- and 40-weeks' gestation. Morphometric and textural metrics of placental growth were calculated from T2-weighted (T2W) images, while measures of microstructural development were calculated from diffusion-weighted images (DWI). Normative tables and reference curves were constructed for each measured index across gestation and according to fetal sex.

RESULTS

Data from 269 MRI studies from 169 pregnant women were included in the analyses. During the study period, placentas undergo significant increases in morphometric measures of volume, thickness, and elongation. Placental texture reveals increasing variability with advancing gestation as measured by grey level non uniformity, run length non uniformity and long run high grey level emphasis. Placental microstructure did not vary with gestational age. Placental elongation was the only metric that differed significantly between male and female fetuses.

DISCUSSION

We report quantitative metrics of placental morphometry, texture and microstructure in a large cohort of healthy controls during the second and third trimesters of pregnancy. These measures can serve as normative references of in-vivo placental development to better understand placental function in high-risk conditions and allow for the early detection of placental mal-development.

T2* placental MRI in pregnancies complicated with fetal congenital heart disease

BACKGROUND

Congenital heart disease (CHD) is one of the most important and common group of congenital malformations in humans. Concurrent development and close functional links between the fetal heart and placenta emphasise the importance of understanding placental function and its influence in pregnancy outcomes. The aim of this study was to evaluate placental oxygenation by relaxometry (T2*) to assess differences in placental phenotype and function in CHD.

METHODS

In this prospective cross-sectional observational study, 69 women with a fetus affected with CHD and 37 controls, whole placental T2* was acquired using a 1.5-Tesla MRI scanner. Gaussian Process Regression was used to assess differences in placental phenotype in CHD cohorts compared to our controls.

RESULTS

Placental T2* maps demonstrated significant differences in CHD compared to controls at equivalent gestational age. Mean T2* values over the entire placental volume were lowest compared to predicted normal in right sided obstructive lesions (RSOL) (Z-Score 2.30). This cohort also showed highest lacunarity indices (Z-score -1.7), as a marker of lobule size. Distribution patterns of T2* values over the entire placental volume were positively skewed in RSOL (Z-score -4.69) and suspected, not confirmed coarctation of the aorta (CoA-) (Z-score -3.83). Deviations were also reflected in positive kurtosis in RSOL (Z-score -3.47) and CoA- (Z-score -2.86).

CONCLUSION

Placental structure and function appear to deviate from normal development in pregnancies with fetal CHD. Specific patterns of altered placental function assessed by T2* deliver crucial complementary information to antenatal assessments in the presence of fetal CHD.

Radiomic Machine Learning Classifiers in Spine Bone Tumors: A Multi-Software, Multi-Scanner Study

PURPOSE

Spinal lesion differential diagnosis remains challenging even in MRI. Radiomics and machine learning (ML) have proven useful even in absence of a standardized data mining pipeline. We aimed to assess ML diagnostic performance in spinal lesion differential diagnosis, employing radiomic data extracted by different software.

METHODS

Patients undergoing MRI for a vertebral lesion were retrospectively analyzed (n = 146, 67 males, 79 females; mean age 63 ± 16 years, range 8-89 years) and constituted the train (n = 100) and internal test cohorts (n = 46). Part of the latter had additional prior exams which constituted a multi-scanner, external test cohort (n = 35). Lesions were labeled as benign or malignant (2-label classification), and benign, primary malignant or metastases (3-label classification) for classification analyses. Features extracted via 3D Slicer heterogeneityCAD module (hCAD) and PyRadiomics were independently used to compare different combinations of feature selection methods and ML classifiers (n = 19).

RESULTS

In total, 90 and 1548 features were extracted by hCAD and PyRadiomics, respectively. The best feature selection method-ML algorithm combination was selected by 10 iterations of 10-fold cross-validation in the training data. For the 2-label classification ML obtained 94% accuracy in the internal test cohort, using hCAD data, and 86% in the external one. For the 3-label classification, PyRadiomics data allowed for 80% and 69% accuracy in the internal and external test sets, respectively.

CONCLUSIONS

MRI radiomics combined with ML may be useful in spinal lesion assessment. More robust pre-processing led to better consistency despite scanner and protocol heterogeneity.

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.

Assessment of Radiomics and Deep Learning for the Segmentation of Fetal and Maternal Anatomy in Magnetic Resonance Imaging and Ultrasound

Recent advances in fetal imaging open the door to enhanced detection of fetal disorders and computer-assisted surgical planning. However, precise segmentation of womb's tissues is challenging due to motion artifacts caused by fetal movements and maternal respiration during acquisition. This work aims to efficiently segment different intrauterine tissues in fetal magnetic resonance imaging (MRI) and 3D ultrasound (US). First, a large set of ninety-four radiomic features are extracted to characterize the mother uterus, placenta, umbilical cord, fetal lungs, and brain. The optimal features for each anatomy are identified using both K-best and Sequential Forward Feature Selection techniques. These features are then fed to a Support Vector Machine with instance balancing to accurately segment the intrauterine anatomies. To the best of our knowledge, this is the first time that "Radiomics" is expanded from classification tasks to segmentation purposes to deal with challenging fetal images. In addition, we evaluate several state-of-the-art deep learning-based segmentation approaches. Validation is extensively performed on a set of 60 axial MRI and 3D US images from pathological and clinical cases. Our results suggest that combining the selected 10 radiomic features per anatomy along with DeepLabV3+ or BiSeNet architectures for MRI, and PSPNet or Tiramisu for 3D US, can lead to the highest fetal / maternal tissue segmentation performance, robustness, informativeness, and heterogeneity. Therefore, this work opens new avenues for advancement of segmentation techniques and, in particular, for improved fetal surgical planning.

Establishing Correlations between Breast Tumor Response to Radio-Immunotherapy and Radiomics from Multi-Parametric Imaging: An Animal Study

Triple-negative breast cancer (TNBC), which is a type of invasive breast cancer, is characterized by severe disease progression, poor prognosis, high recurrence rate, and short survival. We sought to gain new insight into TNBC by applying computed tomography (CT) and magnetic resonance (MR) quantitative imaging (radiomics) approaches to predict the outcome of radio-immunotherapy treatments in a syngeneic subcutaneous murine breast tumor model. Five Athymic Nude mice were implanted with breast cancer cell lines (4T1) tumors on the right flank. The animals were CT- and MRI-imaged, tumors were contoured, and radiomics features were extracted. All animals were treated with radiotherapy (RT), followed by the administration of PD1 inhibitor. Approximately 10 days later, the animals were sacrificed, tumor volumes were measured, and histopathology evaluation was performed through Ki-67 staining. Linear regression modeling between radiomics and Ki-67 results was performed to establish a correlation between quantitative imaging and post-treatment histochemistry. There was no correlation between tumor volumes and Ki-67 values. Multiple CT- and MRI-derived features, however, correlated with histopathology with correlation coefficients greater than 0.8. MRI imaging helps in tumor delineation as well as an additional orthogonal imaging modality for quantitative imaging purposes. This is the first investigation correlating simultaneously CT- and MRI-derived radiomics to histopathology outcomes of combined radio-immunotherapy treatments in a preclinical setting applied to treatment naïve tumors. The findings indicate that imaging can guide discrimination between responding and non-responding tumors for the combined RT and ImT treatment regimen in TNBC.

Placenta Accreta Spectrum Disorder: Uterine Dehiscence, Not Placental Invasion

For decades, placenta accreta spectrum disorder has been classified, staged, and described as a disorder of placental invasion. In this commentary, we argue that placenta accreta spectrum exists as a disorder of defective decidua and uterine scar dehiscence, not as a disorder of destructive trophoblast invasion. Adopting this understanding of placenta accreta spectrum will help direct research efforts and clinical resources toward the prevention, accurate diagnosis, and safe treatment of this devastating-and increasingly common-disorder.

New Frontiers in Placenta Tissue Imaging

The placenta is a highly vascularized organ with unique structural and metabolic complexities. As the primary conduit of fetal support, the placenta mediates transport of oxygen, nutrients, and waste between maternal and fetal blood. Thus, normal placenta anatomy and physiology is absolutely required for maintenance of maternal and fetal health during pregnancy. Moreover, impaired placental health can negatively impact offspring growth trajectories as well as increase the risk of maternal cardiovascular disease later in life. Despite these crucial roles for the placenta, placental disorders, such as preeclampsia, intrauterine growth restriction (IUGR), and preterm birth, remain incompletely understood. Effective noninvasive imaging and image analysis are needed to advance the obstetrician's clinical reasoning toolkit and improve the utility of the placenta in interpreting maternal and fetal health trajectories. Current paradigms in placental imaging and image analysis aim to improve the traditional imaging techniques that may be time-consuming, costly, or invasive. In concert with conventional clinical approaches such as ultrasound (US), advanced imaging modalities can provide insightful information on the structure of placental tissues. Herein we discuss such imaging modalities, their specific applications in structural, vascular, and metabolic analysis of placental health, and emerging frontiers in image analysis research in both preclinical and clinical contexts.

How and why should the radiologist look at the placenta?

This editorial comment refers to the article "Identification of suspicious invasive placentation based on clinical MRI data using textural features and automated machine learning" by Sun et al. in European Radiology. KEY POINTS: • Understanding how the placenta works is one of the major challenges facing radiologists. • New perspectives are opening up for MRI studies of the placenta. • The authors propose a new approach to placental MRI based on texture analysis and machine learning.

Machine learning analysis of MRI-derived texture features to predict placenta accreta spectrum in patients with placenta previa

PURPOSE

To evaluate whether a machine learning (ML) analysis employing MRI-derived texture analysis (TA) features could be useful in assessing the presence of placenta accreta spectrum (PAS) in patients with placenta previa (PP). The hypothesis is that TA features may reflect histological abnormalities underlying PAS in patients with PP thus helping in differentiating positive from negative cases.

MATERIALS AND METHODS

Pre-operative MRI examinations of 64 patients with PP of which 20 positive (12 accreta, 7 increta and 1 percreta) and 44 negative for PAS were retrospectively selected. Multiple (n = 3) rounded regions of interest (ROIs) were manually positioned on sagittal or coronal T2-weighted images over homogeneous placental tissue close to the placental-myometrial interface for each patient to extract TA features. After balancing the dataset with the Synthetic Minority Over-sampling Technique, training and testing sets were obtained using Hold-out with a 75/25% split. Different algorithms were applied on the training set using the wrapper method, which looks for the best combination of features based on the optimization of a heuristic function in order to get the highest accuracy, and a 10-fold Cross-validation. The accuracy of the best models was also assessed on the test set. Histology was used as the standard of reference.

RESULTS

A total of 192 ROIs were positioned and a ROI-based analysis was then conducted. Among the different algorithms, k-nearest neighbors obtained the highest accuracy (98.1%), precision (98.7%), sensitivity (97.5%) and specificity (98.7%) while exploiting the lowest number of features (n = 26); conversely, the Naïve Bayes algorithm got the lowest scores showing an accuracy of 80.5%.

CONCLUSION

ML analysis using MRI-derived TA features could be a feasible tool in the identification of placental tissue abnormalities underlying PAS in patients with PP. This approach might represent an additional tool in the clinical practice, thus expanding the application field of artificial intelligence to medical images.