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This study aimed to assess the potential of state-of-the-art ultrasound analysis techniques to non-invasively diagnose axillary lymph nodes involvement in breast cancer. After exclusion criteria, 105 patients were selected from two different hospitals. The 118 lymph node ultrasound images taken from these patients were divided into 53 cases and 65 controls, which made up the study series. The clinical outcome of each node was verified by ultrasound-guided fine needle aspiration, core needle biopsy or surgical biopsy. The achieved accuracy of the proposed method was 86.4%, with 84.9% sensitivity and 87.7% specificity. When tested on breast cancer patients only, the proposed method improved the accuracy of the sonographic assessment of axillary lymph nodes performed by expert radiologists by 9% (87.0% vs 77.9%). In conclusion, the results demonstrate the potential of ultrasound image analysis to detect the microstructural and compositional changes that occur in lymph nodes because of metastatic involvement.

To evaluate the reproducibility of ultrasound cervical length (CL) measurement at the second trimester.A set of 565 cervical ultrasound images were collected at 19 + 0-24 + 6 weeks' gestation. Two senior maternal-fetal specialists measured CL in each image on three occasions 2 weeks apart. In the interval between the first and following two measures, the clinicians reviewed 20 images together to agree on the criteria for measurement. Measurements were analyzed for intra- and inter-observer disagreement. The robustness of patient classification when CL measure was used with different cutoff thresholds was analyzed.Average intra-observer deviation was 2.8 mm for clinician 1 and 3.7 mm for clinician 2. Inter-observer deviation among the two clinicians was 5.2 and 3.2 mm before and after reviewing measurement criteria together. When cutoffs were used to classify as "short" cervix, disagreement ranged from 22 to 70% depending on operator and threshold used.Ultrasound CL measurements by experts showed moderate intra- and inter-observer reproducibility. The use of specific cutoffs to classify patients as high or low risk resulted in wide disagreements. The results stress the importance of training and quality assessments on considering universal screening application of CL measurement.

Objectives: To investigate the effectiveness of mid-trimester sonographic cervical consistency index (CCI) for the prediction of spontaneous preterm birth (sPTB) in low-risk pregnancies and to compare its performance with that of mid-trimester sonographic cervical-length (CL) measurement. Methods: This was a prospective cohort study of women with a singleton pregnancy examined by ultrasound at 19+0 to 24+6weeks' gestation. All women underwent transvaginal ultrasound examination of the cervix, but CCI and CL were measured, offline, only in women without a risk factor for sPTB. Staff and participants were blinded to CL and CCI results. CCI was obtained by calculating the ratio between the anteroposterior diameter of the uterine cervix at maximum compression and at rest. The primary outcome was prediction of sPTB before 37+0weeks. Receiver-operating characteristics (ROC) curves were produced and sensitivity and specificity were calculated for the optimal cut-off based on the ROC curve and for the 1st, 5th and 10th centiles of CCI and CL. Intraclass correlation coefficients (ICC) and Bland-Altman plots were used to estimate intra- and interobserver agreement and reliability for measurement of CCI and CL. Results: Of the 749 women who underwent ultrasound examination of the cervix, 532 were included for analysis. The rates of sPTB before 37+0 and before 34+0weeks were 4.1% (22/532) and 1.3% (7/532), respectively. The rates of short cervix 0.90 both for CCI and CL, while interobserver ICC was 0.89 for CCI and 0.90 for CL. Conclusions: Second-trimester CCI is a better predictor of sPTB

OBJECTIVE New tools are required to improve the identification of women who are at increased risk for spontaneous preterm birth (sPTB). Quantitative analysis of tissue texture on ultrasound has been used to extract robust features from the ultrasound image to detect subtle changes in its microstructure. This may be applied to the cervix. The aim of this study was to determine if there is an association between quantitative analysis of cervical texture (CTx) on mid-trimester ultrasound and sPTB

The objective of this study was to evaluate the performance of a new version of quantusFLM®, a software tool for prediction of neonatal respiratory morbidity (NRM) by ultrasound, which incorporates a fully automated fetal lung delineation based on Deep Learning techniques. A set of 790 fetal lung ultrasound images obtained at 24 + 0–38 + 6 weeks’ gestation was evaluated. Perinatal outcomes and the occurrence of NRM were recorded. quantusFLM® version 3.0 was applied to all images to automatically delineate the fetal lung and predict NRM risk. The test was compared with the same technology but using a manual delineation of the fetal lung, and with a scenario where only gestational age was available. The software predicted NRM with a sensitivity, specificity, and positive and negative predictive value of 71.0%, 94.7%, 67.9%, and 95.4%, respectively, with an accuracy of 91.5%. The accuracy for predicting NRM obtained with the same texture analysis but using a manual delineation of the lung was 90.3%, and using only gestational age was 75.6%. To sum up, automated and non-invasive software predicted NRM with a performance similar to that reported for tests based on amniotic fluid analysis and much greater than that of gestational age alone.

Objective To develop and evaluate the performance of a novel method for predicting neonatal respiratory morbidity based on quantitative analysis of the fetal lung by ultrasound. Methods More than 13 000 non-clinical images and 900 fetal lung images were used to develop a computerized method based on texture analysis and machine learning algorithms, trained to predict neonatal respiratory morbidity risk on fetal lung ultrasound images. The method, termed ‘quantitative ultrasound fetal lung maturity analysis’ (quantusFLM™), was then validated blindly in 144 neonates, delivered at 28 + 0 to 39 + 0 weeks' gestation. Lung ultrasound images in DICOM format were obtained within 48 h of delivery and the ability of the software to predict neonatal respiratory morbidity, defined as either respiratory distress syndrome or transient tachypnea of the newborn, was determined. Results Mean (SD) gestational age at delivery was 36 + 1 (3 + 3) weeks. Among the 144 neonates, there were 29 (20.1%) cases of neonatal respiratory morbidity. Quantitative texture analysis predicted neonatal respiratory morbidity with a sensitivity, specificity, positive predictive value and negative predictive value of 86.2%, 87.0%, 62.5% and 96.2%, respectively. Conclusions Quantitative ultrasound fetal lung maturity analysis predicted neonatal respiratory morbidity with an accuracy comparable to that of current tests using amniotic fluid. Copyright © 2014 ISUOG. Published by John Wiley & Sons Ltd.

BACKGROUND: Prediction of neonatal respiratory morbidity may be useful to plan delivery in complicated pregnancies. The limited predictive performance of the current diagnostic tests together with the risks of an invasive procedure restricts the use of fetal lung maturity assessment. OBJECTIVE: The objective of the study was to evaluate the performance of quantitative ultrasound texture analysis of the fetal lung (quantusFLM) to predict neonatal respiratory morbidity in preterm and early-term (

Respiratory disorders in premature are outlined as one of the major causes of newborn morbidity. Therefore, estimation in utero of the risk that a particular newborn will present respiratory distress syndrome is critical in perinatal medicine. Fetal lung maturity assessment by non-invasive methods is an unsolved problem since 80's. There has been a significant effort reporting that ultrasound images carry information associated with changes of fetal lung microstructure but it required the lung-to-liver measurements to be calibrated, which limits clinical feasibility. In this work, we explored the association between image features only from a fetal lung computed by a previously reported probabilistic model and fetal lung maturity. Two studies validated image features obtained from different US equipments: 957 scans were correlated to gestational age (r>0.97); and 83 scans were modeled to estimate a normalized lung maturity index in 7 blind samples. Bootstrapping provided median r>0.82.

Objective: To evaluate the feasibility and reproducibility of fetal lung texture analysis using a novel automatic quantitative ultrasound analysis and to assess its correlation with gestational age. Methods: Prospective cross-sectional observational study. To evaluate texture features, 957 left and right lung images in a 2D four-cardiac-chamber view plane were previously delineated from fetuses between 20 and 41 weeks of gestation. Quantification of lung texture was performed by the Automatic Quantitative Ultrasound Analysis (AQUA) software to extract image features. A standard learning approach composed of feature transformation and a regression model was used to evaluate the association between texture features and gestational age. Results: The association between weeks of gestation and fetal lung texture quantified by the AQUA software presented a Pearson correlation of 0.97. The association was not influenced by delineation parameters such as region of interest (ROI) localization, ROI size, right/left lung selected or sonographic parameters such as ultrasound equipment or transducer used. Conclusions: Fetal lung texture analysis measured by the AQUA software demonstrated a strong correlation with gestational age. This supports further research to explore the use of this technology to the noninvasive prediction of fetal lung maturity.