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Radiology Radiology

Development and Validation of a Preoperative Magnetic Resonance Imaging Radiomics-Based Signature to Predict Axillary Lymph Node Metastasis and Disease-Free Survival in Patients With Early-Stage Breast Cancer.

In JAMA network open

Importance : Axillary lymph node metastasis (ALNM) status, typically estimated using an invasive procedure with a high false-negative rate, strongly affects the prognosis of recurrence in breast cancer. However, preoperative noninvasive tools to accurately predict ALNM status and disease-free survival (DFS) are lacking.

Objective : To develop and validate dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) radiomic signatures for preoperative identification of ALNM and to assess individual DFS in patients with early-stage breast cancer.

Design, Setting, and Participants : This retrospective prognostic study included patients with histologically confirmed early-stage breast cancer diagnosed at 4 hospitals in China from July 3, 2007, to September 21, 2019, randomly divided (7:3) into development and vaidation cohorts. All patients underwent preoperative MRI scans, were treated with surgery and sentinel lymph node biopsy or ALN dissection, and were pathologically examined to determine the ALNM status. Data analysis was conducted from February 15, 2019, to March 20, 2020.

Exposure : Clinical and DCE-MRI radiomic signatures.

Main Outcomes and Measures : The primary end points were ALNM and DFS.

Results : This study included 1214 women (median [IQR] age, 47 [42-55] years), split into development (849 [69.9%]) and validation (365 [30.1%]) cohorts. The radiomic signature identified ALNM in the development and validation cohorts with areas under the curve (AUCs) of 0.88 and 0.85, respectively, and the clinical-radiomic nomogram accurately predicted ALNM in the development and validation cohorts (AUC, 0.92 and 0.90, respectively) based on a least absolute shrinkage and selection operator (LASSO)-logistic regression model. The radiomic signature predicted 3-year DFS in the development and validation cohorts (AUC, 0.81 and 0.73, respectively), and the clinical-radiomic nomogram could discriminate high-risk from low-risk patients in the development cohort (hazard ratio [HR], 0.04; 95% CI, 0.01-0.11; P < .001) and the validation cohort (HR, 0.04; 95% CI, 0.004-0.32; P < .001) based on a random forest-Cox regression model. The clinical-radiomic nomogram was associated with 3-year DFS in the development and validation cohorts (AUC, 0.89 and 0.90, respectively). The decision curve analysis demonstrated that the clinical-radiomic nomogram displayed better clinical predictive usefulness than the clinical or radiomic signature alone.

Conclusions and Relevance : This study described the application of MRI-based machine learning in patients with breast cancer, presenting novel individualized clinical decision nomograms that could be used to predict ALNM status and DFS. The clinical-radiomic nomograms were useful in clinical decision-making associated with personalized selection of surgical interventions and therapeutic regimens for patients with early-stage breast cancer.

Yu Yunfang, Tan Yujie, Xie Chuanmiao, Hu Qiugen, Ouyang Jie, Chen Yongjian, Gu Yang, Li Anlin, Lu Nian, He Zifan, Yang Yaping, Chen Kai, Ma Jiafan, Li Chenchen, Ma Mudi, Li Xiaohong, Zhang Rong, Zhong Haitao, Ou Qiyun, Zhang Yiwen, He Yufang, Li Gang, Wu Zhuo, Su Fengxi, Song Erwei, Yao Herui

2020-Dec-01

General General

Addressing taxonomic challenges for Internet Use Disorders in light of changing technologies and diagnostic classifications. •.

In Journal of behavioral addictions ; h5-index 36.0

Drawing a distinction between mobile and non-mobile Internet Use Disorders is an important step to clarify blurred current concepts in the field of behavioral addictions. Similarly, future technological advances related to virtual or augmented reality, artificial intelligence or the Internet of things might lead to further modifications or new taxonomies. Moreover, diagnostic specifiers like offline/online might change with technological advances and trends of use. An important taxonomical approach might be to look for common structural characteristics of games and applications that will be amenable to new technical developments. Diagnostic and taxonomical approaches based on empirical evidence are important goals in the study of behavioral addictions.

Rumpf Hans-Jürgen, Browne Dillon, Brandt Dominique, Rehbein Florian

2020-Dec-08

General General

Web-Based Privacy-Preserving Multicenter Medical Data Analysis Tools Via Threshold Homomorphic Encryption: Design and Development Study.

In Journal of medical Internet research ; h5-index 88.0

BACKGROUND : Data sharing in multicenter medical research can improve the generalizability of research, accelerate progress, enhance collaborations among institutions, and lead to new discoveries from data pooled from multiple sources. Despite these benefits, many medical institutions are unwilling to share their data, as sharing may cause sensitive information to be leaked to researchers, other institutions, and unauthorized users. Great progress has been made in the development of secure machine learning frameworks based on homomorphic encryption in recent years; however, nearly all such frameworks use a single secret key and lack a description of how to securely evaluate the trained model, which makes them impractical for multicenter medical applications.

OBJECTIVE : The aim of this study is to provide a privacy-preserving machine learning protocol for multiple data providers and researchers (eg, logistic regression). This protocol allows researchers to train models and then evaluate them on medical data from multiple sources while providing privacy protection for both the sensitive data and the learned model.

METHODS : We adapted a novel threshold homomorphic encryption scheme to guarantee privacy requirements. We devised new relinearization key generation techniques for greater scalability and multiplicative depth and new model training strategies for simultaneously training multiple models through x-fold cross-validation.

RESULTS : Using a client-server architecture, we evaluated the performance of our protocol. The experimental results demonstrated that, with 10-fold cross-validation, our privacy-preserving logistic regression model training and evaluation over 10 attributes in a data set of 49,152 samples took approximately 7 minutes and 20 minutes, respectively.

CONCLUSIONS : We present the first privacy-preserving multiparty logistic regression model training and evaluation protocol based on threshold homomorphic encryption. Our protocol is practical for real-world use and may promote multicenter medical research to some extent.

Lu Yao, Zhou Tianshu, Tian Yu, Zhu Shiqiang, Li Jingsong

2020-Dec-08

confidentiality, logistic regression, machine learning, threshold homomorphic encryption

General General

Real-time, low-latency closed-loop feedback using markerless posture tracking.

In eLife

The ability to control a behavioral task or stimulate neural activity based on animal behavior in real-time is an important tool for experimental neuroscientists. Ideally, such tools are noninvasive, low-latency, and provide interfaces to trigger external hardware based on posture. Recent advances in pose estimation with deep learning allows researchers to train deep neural networks to accurately quantify a wide variety of animal behaviors. Here we provide a new DeepLabCut-Live! package that achieves low-latency real-time pose estimation (within 15 ms, >100 FPS), with an additional forward-prediction module that achieves zero-latency feedback, and a dynamic-cropping mode that allows for higher inference speeds. We also provide three options for using this tool with ease: (1) a stand-alone GUI (called DLC-Live! GUI, and integration into (2) Bonsai and (3) AutoPilot. Lastly, we benchmarked performance on a wide range of systems so that experimentalists can easily decide what hardware is required for their needs.

Kane Gary A, Lopes Gonçalo, Sanders Jonny L, Mathis Alexander, Mathis Mackenzie

2020-Dec-08

computational biology, mouse, neuroscience, systems biology

Radiology Radiology

Pulmonary Ventilation Maps Generated with Free-breathing Proton MRI and a Deep Convolutional Neural Network.

In Radiology ; h5-index 91.0

Background Hyperpolarized noble gas MRI helps measure lung ventilation, but clinical translation remains limited. Free-breathing proton MRI may help quantify lung function using existing MRI systems without contrast material and may assist in providing information about ventilation not visible to the eye or easily extracted with segmentation methods. Purpose To explore the use of deep convolutional neural networks (DCNNs) to generate synthetic MRI ventilation scans from free-breathing MRI (deep learning [DL] ventilation MRI)-derived specific ventilation maps as a surrogate of noble gas MRI and to validate this approach across a wide range of lung diseases. Materials and Methods In this secondary analysis of prospective trials, 114 paired noble gas MRI and two-dimensional free-breathing MRI scans were obtained in healthy volunteers with no history of chronic or acute respiratory disease and in study participants with a range of different obstructive lung diseases, including asthma, bronchiectasis, chronic obstructive pulmonary disease, and non-small-cell lung cancer between September 2013 and April 2018 (ClinicalTrials.gov identifiers: NCT03169673, NCT02351141, NCT02263794, NCT02282202, NCT02279329, and NCT02002052). A U-Net-based DCNN model was trained to map free-breathing proton MRI to hyperpolarized helium 3 (3He) MRI ventilation and validated using a sixfold validation. During training, the DCNN ventilation maps were compared with noble gas MRI scans using the Pearson correlation coefficient (r) and mean absolute error. DCNN ventilation images were segmented for ventilation and ventilation defects and were compared with noble gas MRI scans using the Dice similarity coefficient (DSC). Relationships were evaluated with the Spearman correlation coefficient (rS). Results One hundred fourteen study participants (mean age, 56 years ± 15 [standard deviation]; 66 women) were evaluated. As compared with 3He MRI, DCNN model ventilation maps had a mean r value of 0.87 ± 0.08. The mean DSC for DL ventilation MRI and 3He MRI ventilation was 0.91 ± 0.07. The ventilation defect percentage for DL ventilation MRI was highly correlated with 3He MRI ventilation defect percentage (rS = 0.83, P < .001, mean bias = -2.0% ± 5). Both DL ventilation MRI (rS = -0.51, P < .001) and 3He MRI (rS = -0.61, P < .001) ventilation defect percentage were correlated with the forced expiratory volume in 1 second. The DCNN model required approximately 2 hours for training and approximately 1 second to generate a ventilation map. Conclusion In participants with diverse pulmonary pathologic findings, deep convolutional neural networks generated ventilation maps from free-breathing proton MRI trained with a hyperpolarized noble-gas MRI ventilation map data set. The maps showed correlation with noble gas MRI ventilation and pulmonary function measurements. © RSNA, 2020 See also the editorial by Vogel-Claussen in this issue.

Capaldi Dante P I, Guo Fumin, Xing Lei, Parraga Grace

2020-Dec-08

General General

Selecting Children with VUR Who are Most Likely to Benefit from Antibiotic Prophylaxis: Application of Machine Learning to RIVUR.

In The Journal of urology ; h5-index 80.0

PURPOSE : Continuous antibiotic prophylaxis (CAP) reduces the risk of recurrent urinary tract infection (rUTI) by 50% in vesicoureteral reflux (VUR) children. However, there may be subgroups in whom CAP could be used more selectively. We sought to develop a machine learning model to identify such subgroups.

MATERIALS AND METHODS : We used RIVUR data and randomly split it into train/test in 4:1 ratio. Two models were developed to predict rUTI risk in scenario with and without CAP. The test set was then used to validate rUTI events and the effectiveness of CAP. Predicted probabilities of rUTI were generated from each model. CAP was assigned at various cutoffs of rUTI risk reduction to evaluate CAP effectiveness.

RESULTS : 607 patients (558 female/49 male, median age 12 months) were included. Predictors included VUR grade, serum creatinine, race/sex, prior UTI symptoms (fever/dysuria), and weight percentiles. The AUC of the prediction model of rUTI (CAP/placebo) was 0.82 (95% CI: 0.74-0.87). Using 10% rUTI risk reduction cutoff, minimal rUTI on population-level can be achieved by giving CAP to 40% VUR patients instead of everyone. In test set (n=121), 51 patients had CAP randomization consistent with model recommendation (CAP if rUTI risk reduction >10%). rUTI incidence was significantly lower among this group compared to those whose CAP assignment differed from model suggestion (7.5% vs 19.4%, p=0.037).

CONCLUSIONS : Our predictive model identifies VUR patients who are more likely to benefit from CAP, which would allow more selective, personalized use of CAP with maximal benefit, while minimizing use in those with least need.

Wang Hsin-Hsiao Scott, Li Michael, Bertsimas Dimitri, Estrada Carlos, Caleb Nelson

2020-Dec-08

Prediction model, RIVUR, antibiotic prophylaxis, machine learning, urinary tract infection, vesico-ureteral reflux