BACKGROUND: Historically, clinicians have relied on medical risk factors and clinical symptoms for preterm birth risk assessment. In nulliparous women, clinicians may rely solely on reported symptoms to assess for the risk of preterm birth. The routine use of ultrasound during pregnancy offers the opportunity to incorporate quantitative ultrasound scanning of the cervix to potentially improve assessment of preterm birth risk. OBJECTIVE: This study aimed to investigate the efficiency of quantitative ultrasound measurements at relatively early stages of pregnancy to enhance identification of women who might be at risk for spontaneous preterm birth. STUDY DESIGN: A prospective cohort study of pregnant women was conducted with volunteer participants receiving care from the University of Illinois Hospital in Chicago, Illinois. Participants received a standard clinical screening followed by 2 research screenings conducted at 20 +/- 2 and 24 +/- 2 weeks. Quantitative ultrasound scans were performed during research screenings by registered diagnostic medical sonographers using a standard cervical length approach. Quantitative ultrasound features were computed from calibrated raw radiofrequency backscattered signals. Full-term birth outcomes and spontaneous preterm birth outcomes were included in the analysis. Medically indicated preterm births were excluded from the analysis. Using data from each visit, logistic regression with Akaike information criterion feature selection was conducted to derive predictive models for each time frame based on historical clinical and quantitative ultrasound features. Model evaluations included a likelihood ratio test of quantitative ultrasound features, cross-validated receiver operating characteristic curve analysis, sensitivity, and specificity. RESULTS: On the basis of historical clinical features alone, the best predictive model had an estimated receiver operating characteristic area under the curve of 0.56 +/- 0.03. By the time frame of Visit 1, a predictive model using both historical clinical and quantitative ultrasound features provided a modest improvement in the area under the curve (0.63 +/- 0.03) relative to that of the predictive model using only historical clinical features. By the time frame of Visit 2, the predictive model using historical clinical and quantitative ultrasound features provided significant improvement (likelihood ratio test, P<.01), with an area under the curve of 0.69 +/- 0.03. CONCLUSION: Accurate identification of women at risk for spontaneous preterm birth solely through historical clinical features has been proven to be difficult. In this study, a history of preterm birth was the most significant historical clinical predictor of preterm birth risk, but the historical clinical predictive model performance was not statistically significantly better than the no-skill level. According to our study results, including quantitative ultrasound yields a statistically significant improvement in risk prediction as the pregnancy progresses.

OBJECTIVE:Women with a history of spontaneous preterm birth (sPTB) face an increased risk of recurrence. Yet, the factors contributing to the increased risk are unknown, hampering the development of targeted interventions. Noninvasive quantitative ultrasound (QUS) has been validated in the characterization of cervical tissue and has the potential to provide information about postpartum cervical remodeling. The objective of this study was to determine the postpartum cervical remodeling trajectories of women over 12 mo post-delivery and to determine whether there were differences between women who delivered full-term and spontaneous preterm that were sensitive to QUS biomarkers. METHODS:Data were collected prospectively from 55 women: 41 who delivered full-term and 14 who delivered spontaneously preterm at 6 wk, 3, 6, 9 and 12 mo (±2 wk) postpartum. Data from QUS biomarkers: Attenuation Coefficient; Backscatter Coefficient; Shear Wave Speed; and Lizzi-Feleppa Slope, Intercept and Midband were analyzed from the acquired radiofrequency data using a Siemens S2000 ultrasound system with a transvaginal MC 9-4 MHz probe. The biomarkers were analyzed using descriptive statistics and linear mixed-effects models. RESULTS:QUS biomarkers, Backscatter Coefficient and Lizzi-Feleppa Intercept showed significant differences during the year after delivery between women who had a full-term birth and sPTB (p < 0.05), suggesting that there are differences in the cervical remodeling trajectories between the two groups. All QUS biomarkers demonstrated significant variations between the full-term birth and sPTB groups over time (p < 0.05), indicating ongoing cervical remodeling for both groups during the 12-mo postpartum period. CONCLUSION:QUS biomarkers identified cervical microstructure differences and trajectories in the year after delivery between women who delivered full-term and spontaneous preterm.

OBJECTIVE:Quantitative ultrasound (QUS) analysis of the human cervix is valuable for predicting spontaneous preterm birth risk. However, this approach currently requires an offline processing step wherein a medically trained analyst manually draws a free-hand field of interest (Manual FOI) for QUS computation. This offline step hinders the clinical adoption of QUS. To address this challenge, we developed a method to determine automatically the cervical FOI (Auto FOI). This study's objective is to evaluate the agreement between QUS results obtained from the Auto and Manual FOIs and assess the feasibility of using the Auto FOI to replace the Manual FOI for cervical QUS computation. METHODS:The auto FOI method was developed and evaluated using cervical ultrasound data from 527 pregnant women, using Manual FOIs as the reference. A deep learning model was developed using the cervical B-mode image as the input to determine automatically the FOI. RESULTS:Quantitative comparison between the Auto and Manual FOIs yielded a high pixel accuracy of 97% and a Dice coefficient of 87%. Further, the Auto FOI yielded QUS biomarker values that were highly correlated with those obtained from the Manual FOIs. For example, the Pearson correlation coefficient was 0.87 between attenuation coefficient values obtained using Auto and Manual FOIs. Further, Bland-Altman analyses showed negligible bias between QUS biomarker values computed using the Auto and Manual FOIs. CONCLUSION:The results support the feasibility of using Auto FOIs to replace Manual FOIs in QUS computation, an important step toward the clinical adoption of QUS technology.

Hypothesis: Predicting the spontaneous preterm birth (sPTB) risk level is enhanced when using both historical clinical (HC) data and quantitative ultrasound (QUS) data compared to using only HC data. HC data defined herein includebirth history prior to that of the current pregnancy as well as, from the current pregnancy, a clinical cervical length assessment, and physical examination data. Study population included 248 full-term births (FTBs) and 26 sPTBs. Advanced statistical analyses were performed for supervised classification containing 53 scaled candidate features (48 QUS, 5 HC) using nested fivefold cross-validation of L1-penalized linear logistic regression with 1000 repetitions to identify potential predictors. Statistical models for HC data alone and HC + QUS data were compared with likelihood-ratio test, cross-validated receiver operating characteristic (ROC) area under the curve (AUC), sensitivity, and specificity. To assess performance, the ROC-AUC was estimated with 10-fold cross-validation logistic regression and 1000 repetitions. Averaged ROC curves plus AUCs were computed using threshold averaging. AUC confidence intervals and test statistics to test the two ROC curves’ differences were constructed via DeLong method. Combined HC and QUS data identified women at sPTB risk with better AUC (0.68; 95% CI, 0.57–0.78) than those of HC data alone (0.53; 95% CI, 0.40–0.66). [Work supported by NIHR01HD089935.]

Predicting women at risk for spontaneous preterm birth (sPTB) has been medically challenging due to lack of signs and symptoms of preterm labor until intervention is too late. Hypothesis: advanced statistical modeling predicts sPTB risk from quantitative ultrasound (QUS) plus prior data (prior birth history through first clinical cervical length) better than that of prior data alone. Study population included 250 full-term births (FTBs) and 25sPTBs. QUS scans (Siemens S2000 & MC9-4) were performed using a standard cervical length approach by registered diagnostic medical sonographers. Two cervical QUS scans were conducted at 20 ± 2 and 24 ± 2 wk gestation. Multiple QUS features were processed from calibrated raw radiofrequency backscattered ultrasonic signals. Two statistical models designed to determine sPTB risk were compared: (1) QUS plus prior data and (2) prior data alone. Test ROC AUC compared both models. Using statistical methods, QUS plus prior data identified women at risk for sPTB with better AUC (0.68; std error 0.01; 95% CI, 0.66–0.70) than that of prior data alone (0.63; std error 0.01; 95% CI, 0.61–0.65). Even with only 25 sPTBs, data suggest that there is value added for predicting sPTB when QUS data are included with prior data. [R01HD089935.]

The past two decades have witnessed a great change in the statistics community, as we have become more inclusive and appreciative of different types and areas of research. Interdisciplinary research, including statistical climatology, has developed rapidly in such a background. The Institute for Mathematical and Statistical Innovation (IMSI), funded by the National Science Foundation, is managed by the University of Chicago, Northwestern University, the University of Illinois Chicago, and the University of Illinois Urbana-Champaign, and is hosted at the University of Chicago. IMSI launched in fall 2020 with a mission “to apply rigorous mathematics and statistics to urgent scientific and societal problems, and to spur transformational change in the mathematical sciences.” This article describes the work of IMSI and gives our perspective on the future of the field of statistical climatology.

Globally, ∼15 million babies are born preterm every year. In the United States, the preterm birth rate remains stubbornly high (10–15%) and refractory to interventions. Consequences of preterm birth (PTB) account for the second leading cause of infant mortality, with 1 million deaths annually. While the costs of PTB to society are more than for any other disease, the impact on families is devastating. Preterm babies suffer both immediate and lifelong physiological, cognitive, and developmental health problems. It is estimated that with the proper tools and technology, we could reduce the preterm birth and survival rates. Due to lack of technology, clinicians have had few interventions that have been rigorously studied to prevent preterm birth. Ineffective interventions have historically been based on opinion and patient symptoms rather than tissue based reliable and repeatable scientific studies. Our group has rigorously studied quantitative ultrasound’s (QUS’s) role for assessing PTB risk in animals and humans. QUS provided added value to currently available health and traditional ultrasound risk assessment methods. Basing clinical decision-making on tissue microstructure has the potential to reduce the PTB rate and provides a scientific basis for developing and objectively evaluating present and new treatments to prevent PTB. [Work supported by NIHR01HD089935.]

Having a history of a previous spontaneous preterm birth (sPTB) is the strongest risk factor for recurrent sPTB. It is unknown if there are differences in postpartum cervical remodeling between women who have delivered spontaneous preterm (sPT) and full-term. No studies have evaluated the role of postpartum remodeling between the two groups. Quantitative ultrasound (QUS) is a noninvasive ultrasound technology used to quantify tissue microstructure and function. QUS biomarkers were used to evaluate postpartum cervical microstructure in women who delivered sPT and full-term. Data were collected from 54 women:14 who delivered sPT and 40 who delivered full-term. Transvaginal QUS scans were performed at 6 weeks (±2 weeks) after delivery. Attenuation coefficient (AC), backscatter coefficient (BSC), and shear wave speed (SWS) QUS biomarkers were collected. BSC was significantly higher at six weeks postpartum in women who delivered sPT versus full-term (p = 0.01), while the AC approached statistical significance (p = 0.09). QUS biomarker BSC was able to identify cervical microstructure differences at six weeks postpartum between women who delivered sPT and full-term. QUS technology may improve our understanding of postpartum cervical remodeling and has the potential to noninvasively direct precision-health approaches for recurrent sPTB. [Work supported by NIH 5F31NR019716.]

Spontaneous preterm birth (sPTB) is one of the leading causes of infant morbidity. Medical interventions can prevent death caused by preterm birth if the risk is predicted at early stages. Quantitative ultrasound (QUS) is found valuable for predicting sPTB risk with a limitation of requiring a medically trained image analyst to manually draw a region of interest (ROI) on the cervix of a B-mode ultrasound image. An automated ROI placement algorithm was designed and trained to reduce the reliance on human annotations. The algorithm utilized a deep neural network with an optimized U-Net architecture to locate cervical tissues. A total of 8670 ultrasound images with sonographer-drawn ROIs were used for algorithm training and testing, followed by several postprocessing steps to yield the final ROIs. Quantitative comparison between algorithm-generated and sonographer-drawn ROIs yielded an average pixel accuracy of 96% and a dice coefficient of 88%. In addition, the QUS’s attenuation coefficient (AC) and backscatter coefficient (BSC) obtained from the algorithm-generated ROIs were highly correlated to those obtained from the sonographer-drawn ROIs with a Pearson correlation coefficient of 0.93 and 0.85, respectively. The results support the feasibility of automating QUS imaging for sPTB risk assessment. [Work supported by NIHR01HD089935.]

Hypothesis testing procedures are developed to assess linear operator constraints in function-on-scalar regression when incomplete functional responses are observed. The approach enables statistical inferences about the shape and other aspects of the functional regression coefficients within a unified framework encompassing three incomplete sampling scenarios: (i) partially observed response functions as curve segments over random sub-intervals of the domain; (ii) discretely observed functional responses with additive measurement errors; and (iii) the composition of former two scenarios, where partially observed response segments are observed discretely with measurement error. The latter scenario has been little explored to date, although such structured data is increasingly common in applications. For statistical inference, deviations from the constraint space are measured via integrated $L^2$-distance between the model estimates from the constrained and unconstrained model spaces. Large sample properties of the proposed test procedure are established, including the consistency, asymptotic distribution and local power of the test statistic. Finite sample power and level of the proposed test are investigated in a simulation study covering a variety of scenarios. The proposed methodologies are illustrated by applications to U.S. obesity prevalence data, analyzing the functional shape of its trends over time, and motion analysis in a study of automotive ergonomics.

Irregular functional data in which densely sampled curves are observed over different ranges pose a challenge for modeling and inference, and sensitivity to outlier curves is a concern in applications. Motivated by applications in quantitative ultrasound signal analysis, this paper investigates a class of robust M-estimators for partially observed functional data including functional location and quantile estimators. Consistency of the estimators is established under general conditions on the partial observation process. Under smoothness conditions on the class of M-estimators, asymptotic Gaussian process approximations are established and used for large sample inference. The large sample approximations justify a bootstrap approximation for robust inferences about the functional response process. The performance is demonstrated in simulations and in the analysis of irregular functional data from quantitative ultrasound analysis.

SIGNIFICANCE:Optical coherence tomography (OCT) is widely used as a potential diagnostic tool for a variety of diseases including various types of cancer. However, sensitivity and specificity analyses of OCT in different cancers yield results varying from 11% to 100%. Hence, there is a need for more detailed statistical analysis of blinded reader studies.AIM:Extensive statistical analysis is performed on results from a blinded study involving OCT of breast tumor margins to assess the impact of reader variability on sensitivity and specificity.APPROACH:Five readers with varying levels of experience reading OCT images assessed 50 OCT images of breast tumor margins collected using an intraoperative OCT system. Statistical modeling and analysis was performed using the R language to analyze reader experience and variability.RESULTS:Statistical analysis showed that the readers' prior experience with OCT images was directly related to the probability of the readers' assessment agreeing with histology. Additionally, results from readers with prior experience specific to OCT in breast cancer had a higher probability of agreement with histology compared to readers with experience with OCT in other (noncancer) diseases.CONCLUSIONS:The results from this study demonstrate the potential impact of reader training and experience in the assessment of sensitivity and specificity. They also demonstrate even greater potential improvement in diagnostic performance by combining results from multiple readers. These preliminary findings suggest valuable directions for further study.

A two-level group-specific curve model is such that the mean response of each member of a group is a separate smooth function of a predictor of interest. The three-level extension is such that one grouping variable is nested within another one, and higher level extensions are analogous. Streamlined variational inference for higher level group-specific curve models is a challenging problem. We confront it by systematically working through two-level and then three-level cases and making use of the higher level sparse matrix infrastructure laid down in (Nolan and Wand (2020),ANZIAM Journal, doi: 10.1017/S1446181120000061). A motivation is analysis of data from ultrasound technology for which three-level group-specific curve models are appropriate. Whilst extension to the number of levels exceeding three is not covered explicitly, the pattern established by our systematic approach sheds light on what is required for even higher level group-specific curve models.

This article evaluated the repeatability and reproducibility (R&R) of quantitative ultrasound (QUS) biomarkers attenuation coefficient (AC) and backscatter coefficient (BSC) in transvaginal QUS reference phantoms for obstetric applications. Five phantoms were scanned by three sonographers according to the scanning protocol. Each sonographer scanned each phantom with four transvaginal transducers of the same model (MC9-4) and three probe cover types (latex cover, nonlatex cover, and no cover). The AC and BSC were estimated by using a reference phantom method. The R&R analysis was performed for the frequency-averaged AC and logBSC (= 10log(10)BSC) (5.4-5.8 MHz) by using three-factor random effects Analysis of Variance with interaction. The total R&R variabilities for AC and logBSC are small (AC: 0.042-0.065 dB/cm-MHz; logBSC: 0.50-0.68 dB), indicating high measurement precision. These values are small compared to the ranges of AC (0.28-0.99 dB/cm-MHz) and logBSC (-33.16 to -20.35 dB) of the five phantoms. The AC and logBSC biomarkers measured on transvaginal QUS phantoms using the reference phantom method are repeatable, and reproducible between sonographers, transducers, and probe covers.

This study uses in vivo radiofrequency ultrasound data acquired from human cervices to compare two commonly used spectral-based techniques for estimating the ultrasonic attenuation coefficient (AC): the spectral difference (SD) and the spectral log difference (SLD) techniques. The AC is a fundamental quantitative ultrasound (QUS) parameter useful for tissue characterization to improve diagnostics, e.g., cervix characterization to predict preterm birth. The selection of appropriate AC techniques is a critical step for QUS tissue characterization. The advantages and disadvantages of various AC estimation techniques have been studied using physical phantoms and computational simulations. However, the heterogeneous nature of real tissue cannot be fully simulated with phantoms and computations. In this study, the SD and SLD techniques were evaluated using human cervices from 214 pregnant women (enrollment still ongoing). Each participant underwent 1-2 cervical QUS exams during each visit. In each exam, 10 acquisitions of radiofrequency ultrasound and one acquisition of a physical phantom were made using a Siemens MC9-4 transvaginal ultrasound transducer (center frequency: 5.25 MHz) with a Siemens S2000 ultrasound system. Preliminary analysis yielded correlated AC estimates (Pearson’s r = 0.66; <0.001) between the two AC techniques and better precision (e.g., inter-sonographer reproducibility) with the SLD technique. [No. R01HD089935.]

A robust probabilistic classifier for functional data is developed to predict class membership based on functional input measurements and to provide a reliable probability estimates for class membership. The method combines a Bayes classifier and semi-parametric mixed effects model with robust tuning parameter to make the method robust to outlying curves, and to improve the accuracy of the risk or uncertainty estimates, which is crucial in medical diagnostic applications. The approach applies to functional data with varying ranges and irregular sampling without making parametric assumptions on the within-curve covariance. Simulation studies evaluate the proposed method and competitors in terms of sensitivity to heavy tailed functional distributions and outlying curves. Classification performance is evaluated by both error rate and logloss, the latter of which imposes heavier penalties on highly confident errors than on less confident errors. Runtime experiments on the R implementation indicate that the proposed method scales well computationally. Illustrative applications include data from quantitative ultrasound analysis and phoneme recognition.

Background: Evaluation of lymph node (LN) status is an important factor for detecting metastasis and thereby staging breast cancer. Currently utilized clinical techniques involve the surgical disruption and resection of lymphatic structure, whether nodes or axillary contents, for histological examination. While reasonably effective at detection of macrometastasis, the majority of the resected lymph nodes are histologically negative. Improvements need to be made to better detect micrometastasis, minimize or eliminate lymphatic disruption complications, and provide immediate and accurate intraoperative feedback for in vivo cancer staging to better guide surgery.Methods: We evaluated the use of optical coherence tomography (OCT), a high-resolution, real-time, label-free imaging modality for the intraoperative assessment of human LNs for metastatic disease in patients with breast cancer. We assessed the sensitivity and specificity of double-blinded trained readers who analyzed intraoperative OCT LN images for presence of metastatic disease, using co-registered post-operative histopathology as the gold standard.Results: Our results suggest that intraoperative OCT examination of LNs is an appropriate real-time, label-free, non-destructive alternative to frozen-section analysis, potentially offering faster interpretation and results to empower superior intraoperative decision-making.Conclusions: Intraoperative OCT has strong potential to supplement current post-operative histopathology with real-time in situ assessment of LNs to preserve both non-cancerous nodes and their lymphatic vessels, and thus reduce the associated risks and complications from surgical disruption of lymphoid structures following biopsy.

Techniques have been developed to localize sentinel lymph nodes during cancer surgery and for post-operative histology. Intraoperative OCT, however, uniquely offers microscopic label-free in vivo assessment of lymph nodes for metastatic disease.

Objectives-Quantitative ultrasound estimates such as the frequency-dependent backscatter coefficient (BSC) have the potential to enhance noninvasive tissue characterization and to identify tumors better than traditional B-mode imaging. Thus, investigating system independence of BSC estimates from multiple imaging platforms is important for assessing their capabilities to detect tissue differences.Methods-Mouse and rat mammary tumor models, 4T1 and MAT, respectively, were used in a comparative experiment using 3 imaging systems (Siemens, Ultrasonix, and VisualSonics) with 5 different transducers covering a range of ultrasonic frequencies.Results-Functional analysis of variance of the MAT and 4T1 BSC-versus-frequency curves revealed statistically significant differences between the two tumor types. Variations also were found among results from different transducers, attributable to frequency range effects. At 3 to 8 MHz, tumor BSC functions using different systems showed no differences between tumor type, but at 10 to 20 MHz, there were differences between 4T1 and MAT tumors. Fitting an average spline model to the combined BSC estimates (3-22 MHz) demonstrated that the BSC differences between tumors increased with increasing frequency, with the greatest separation above 15 MHz. Confining the analysis to larger tumors resulted in better discrimination over a wider bandwidth.Conclusions-Confining the comparison to higher ultrasonic frequencies or larger tumor sizes allowed for separation of BSC-versus-frequency curves from 4T1 and MAT tumors. These constraints ensure that a greater fraction of the backscattered signals originated from within the tumor, thus demonstrating that statistically significant tumor differences were detected.

The purpose of this study was to determine whether cervical ultrasonic attenuation could identify women at risk of spontaneous preterm birth. During pregnancy, women (n = 67) underwent from one to five transvaginal ultrasonic examinations to estimate cervical ultrasonic attenuation and cervical length. Ultrasonic data were obtained with a Zonare ultrasound system with a 5- to 9-MHz endovaginal transducer and processed offline. Cervical ultrasonic attenuation was lower at 17-21 wk of gestation in the SPTB group (1.02 dB/cm-MHz) than in the full-term birth groups (1.34 dB/cm-MHz) (p = 0.04). Cervical length was shorter (3.16 cm) at 22-26 wk in the SPTB group than in the women delivering full term (3.68 cm) (p = 0.004); cervical attenuation was not significantly different at this time point. These findings suggest that low attenuation may be an additional early cervical marker to identify women at risk for SPTB.