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oncology Oncology

Automatic intraprostatic lesion segmentation in multiparametric magnetic resonance images with proposed multiple branch Unet.

In Medical physics ; h5-index 59.0

PURPOSE : Contouring intraprostatic lesions is a prerequisite for dose-escalating these lesions in radiotherapy to improve the local cancer control. In this study, a deep learning-based approach was developed for automatic intraprostatic lesion segmentation in multiparametric magnetic resonance imaging (mpMRI) images contributing to the clinical practice.

METHODS : mpMRI images from 136 patient cases were collected from our institution, and all these cases contained suspicious lesions with Prostate Imaging Reporting and Data System (PI-RADS) score ≥ 4. The contours of the lesion and prostate were manually created on axial T2-weighted (T2W), apparent diffusion coefficient (ADC) and high b-value diffusion-weighted imaging (DWI) images to provide the ground truth data. Then a multiple branch UNet (MB-UNet) was proposed for the segmentation of indistinct target in multi-modality MRI images. An encoder module was designed with three branches for the three MRI modalities separately, to fully extract the high-level features provided by different MRI modalities; an input module was added by using three sub-branches for three consecutive image slices, to consider the contour consistency among different image slices; deep supervision strategy was also integrated into the network to speed up the convergency of the network and improve the performance. The probability maps of the background, normal prostate and lesion were output by the network to generate the segmentation of the lesion, and the performance was evaluated using the Dice similarity coefficient (DSC) as the main metric.

RESULTS : A total of 162 lesions were contoured on 652 image slices, with 119 lesions in the peripheral zone, 38 in the transition zone, 4 in the central zone and 1 in the anterior fibromuscular stroma. All prostates were also contoured on 1,264 image slices. As for the segmentation of lesions in the testing set, MB-UNet achieved a per case DSC of 0.6333, specificity of 0.9993, sensitivity of 0.7056; and global DSC of 0.7205, specificity of 0.9993, sensitivity of 0.7409. All the three deep learning strategies adopted in this study contributed to the performance promotion of the MB-UNet. And missing the DWI modality would degrade the segmentation performance more markedly compared with the other two modalities.

CONCLUSIONS : A deep learning-based approach with proposed MB-UNet was developed to automatically segment suspicious lesions in mpMRI images. This study makes it feasible to adopt boosting intraprostatic lesions in clinical practice to achieve better outcomes.

Chen Yizheng, Xing Lei, Yu Lequan, Bagshaw Hilary P, Buyyounouski Mark K, Han Bin


boosting radiotherapy, deep learning, intraprostatic lesion segmentation, multi-parametric magnetic resonance imaging, multiple branch UNet (MB-UNet)

General General

Rapid screening for hazelnut oil and high oleic sunflower oil in extra virgin olive oil using low-field nuclear magnetic resonance relaxometry and machine learning.

In Journal of the science of food and agriculture

BACKGROUND : As extra virgin olive oil (EVOO) has high commercial value, it is routinely adulterated with other oils. The present study investigated the feasibility of rapidly identifying adulterated EVOO using low-field nuclear magnetic resonance (LF-NMR) relaxometry and machine learning approaches (decision tree, K-nearest neighbor, linear discriminant analysis, support vector machines, and convolutional neural network).

RESULTS : LF-NMR spectroscopy effectively distinguished pure EVOO from that which was adulterated with hazelnut oil (HO) and high oleic sunflower oil (HOSO). The applied convolutional neural network (CNN) algorithm had an accuracy of 89.29%, a precision of 81.25%, a recall of 81.25%, and enabled the rapid (two-minute) discrimination of pure EVOO that was adulterated with HO and HOSO in the volumetric ratio range of 10-100%.

CONCLUSION : LF-NMR coupled with the CNN algorithm is a viable candidate for rapid EVOO authentication. This article is protected by copyright. All rights reserved.

Hou Xuewen, Wang Guangli, Wang Xin, Ge Xinmin, Fan Yiren, Jiang Rui, Nie Shengdong


Adulteration, Classification, Extra virgin olive oil, LF-NMR, Machine learning

General General

Accuracy, uncertainty, and interpretability assessments of ANFIS models to predict dust concentration in semi-arid regions.

In Environmental science and pollution research international

Accurate prediction of the dust concentration (DC) is necessary to reduce its undesirable environmental effects in different geographical areas. Although the adaptive neuro-fuzzy inference system (ANFIS) is a powerful model for predicting dust events, no attempt has been made to investigate its uncertainty and interpretability. In this study, therefore, the uncertainty of the ANFIS model was quantified using uncertainty estimation based on local errors and clustering methods. Furthermore, we used a model-agnostic interpretation to make the ANFIS model interpretable. In addition, we used the bat optimization algorithm (BAT) to increase the prediction accuracy of the ANFIS model. Seven explanatory variables were chosen for predicting DC in the cold and warm months across semi-arid regions of Iran. The results showed that the ANFIS+BAT model increased the correlation coefficient by 10% and 16% for predicting DC in the cold and warm months, respectively, compared with the ANFIS model. Furthermore, the uncertainty analysis indicated a lower prediction interval (i.e., lower uncertainty) for the ANFIS+BAT model compared with the ANFIS model for predicting DC in the cold and warm months. In addition, the model-agnostic interpretation tool findings indicated the highest contributions of air temperature and maximum wind speed for predicting DC in the cold and warm months, respectively. Prediction of DC using the proposed model will allow decision-makers to better plan for measures to mitigate the risks of wind erosion and air pollution.

Ebrahimi-Khusfi Zohre, Taghizadeh-Mehrjardi Ruhollah, Nafarzadegan Ali Reza


ANFIS, Air pollution, Bat optimization algorithm, Interpretability, Machine learning, Uncertainty

Radiology Radiology

Diagnostic performance for pulmonary adenocarcinoma on CT: comparison of radiologists with and without three-dimensional convolutional neural network.

In European radiology ; h5-index 62.0

OBJECTIVES : To compare diagnostic performance for pulmonary invasive adenocarcinoma among radiologists with and without three-dimensional convolutional neural network (3D-CNN).

METHODS : Enrolled were 285 patients with adenocarcinoma in situ (AIS, n = 75), minimally invasive adenocarcinoma (MIA, n = 58), and invasive adenocarcinoma (IVA, n = 152). A 3D-CNN model was constructed with seven convolution-pooling and two max-pooling layers and fully connected layers, in which batch normalization, residual connection, and global average pooling were used. Only the flipping process was performed for augmentation. The output layer comprised two nodes for two conditions (AIS/MIA and IVA) according to prognosis. Diagnostic performance of the 3D-CNN model in 285 patients was calculated using nested 10-fold cross-validation. In 90 of 285 patients, results from each radiologist (R1, R2, and R3; with 9, 14, and 26 years of experience, respectively) with and without the 3D-CNN model were statistically compared.

RESULTS : Without the 3D-CNN model, accuracy, sensitivity, and specificity of the radiologists were as follows: R1, 70.0%, 52.1%, and 90.5%; R2, 72.2%, 75%, and 69%; and R3, 74.4%, 89.6%, and 57.1%, respectively. With the 3D-CNN model, accuracy, sensitivity, and specificity of the radiologists were as follows: R1, 72.2%, 77.1%, and 66.7%; R2, 74.4%, 85.4%, and 61.9%; and R3, 74.4%, 93.8%, and 52.4%, respectively. Diagnostic performance of each radiologist with and without the 3D-CNN model had no significant difference (p > 0.88), but the accuracy of R1 and R2 was significantly higher with than without the 3D-CNN model (p < 0.01).

CONCLUSIONS : The 3D-CNN model can support a less-experienced radiologist to improve diagnostic accuracy for pulmonary invasive adenocarcinoma without deteriorating any diagnostic performances.

KEY POINTS : • The 3D-CNN model is a non-invasive method for predicting pulmonary invasive adenocarcinoma in CT images with high sensitivity. • Diagnostic accuracy by a less-experienced radiologist was better with the 3D-CNN model than without the model.

Yanagawa Masahiro, Niioka Hirohiko, Kusumoto Masahiko, Awai Kazuo, Tsubamoto Mitsuko, Satoh Yukihisa, Miyata Tomo, Yoshida Yuriko, Kikuchi Noriko, Hata Akinori, Yamasaki Shohei, Kido Shoji, Nagahara Hajime, Miyake Jun, Tomiyama Noriyuki


Artificial intelligence, Deep learning, Lung cancer

General General

Do children's expectations about future physical activity predict their physical activity in adulthood?

In International journal of epidemiology ; h5-index 76.0

BACKGROUND : Much of the population fails to meet recommended physical activity (PA) levels, but there remains considerable individual variation. By understanding drivers of different trajectories, interventions can be better targeted and more effective. One such driver may be a person's physical activity identity (PAI)-the extent to which a person perceives PA as central to who they are.

METHODS : Using survey information and a unique body of essays written at age 11 from the National Child Development Study (N = 10 500), essays mentioning PA were automatically identified using the machine learning technique support vector classification and PA trajectories were estimated using latent class analysis. Analyses tested the extent to which childhood PAI correlated with activity levels from age 23 through 55 and with trajectories across adulthood.

RESULTS : 42.2% of males and 33.5% of females mentioned PA in their essays, describing active and/or passive engagement. Active PAI in childhood was correlated with higher levels of activity for men but not women, and was correlated with consistently active PA trajectories for both genders. Passive PAI was not related to PA for either gender.

CONCLUSIONS : This study offers a novel approach for analysing large qualitative datasets to assess identity and behaviours. Findings suggest that at as young as 11 years old, the way a young person conceptualizes activity as part of their identity has a lasting association with behaviour. Still, an active identity may require a supportive sociocultural context to manifest in subsequent behaviour.

Pongiglione Benedetta, Kern Margaret L, Carpentieri J D, Schwartz H Andrew, Gupta Neelaabh, Goodman Alissa


Physical activity, exercise identity, identity, latent class analysis, life course perspective, narratives, natural language processing, sociocultural context

General General

Machine learning-based analysis of adolescent gambling factors.

In Journal of behavioral addictions ; h5-index 36.0

Background and aims : Problem gambling among adolescents has recently attracted attention because of easy access to gambling in online environments and its serious effects on adolescent lives. We proposed a machine learning-based analysis method for predicting the degree of problem gambling.

Methods : Of the 17,520 respondents in the 2018 National Survey on Youth Gambling Problems dataset (collected by the Korea Center on Gambling Problems), 5,045 students who had gambled in the past 3 months were included in this study. The Gambling Problem Severity Scale was used to provide the binary label information. After the random forest-based feature selection method, we trained four models: random forest (RF), support vector machine (SVM), extra trees (ETs), and ridge regression.

Results : The online gambling behavior in the past 3 months, experience of winning money or goods, and gambling of personal relationship were three factors exhibiting the high feature importance. All four models demonstrated an area under the curve (AUC) of >0.7; ET showed the highest AUC (0.755), RF demonstrated the highest accuracy (71.8%), and SVM showed the highest F1 score (0.507) on a testing set.

Discussion : The results indicate that machine learning models can convey meaningful information to support predictions regarding the degree of problem gambling.

Conclusion : Machine learning models trained using important features showed moderate accuracy in a large-scale Korean adolescent dataset. These findings suggest that the method will help screen adolescents at risk of problem gambling. We believe that expandable machine learning-based approaches will become more powerful as more datasets are collected.

Seo Wonju, Kim Namho, Lee Sang-Kyu, Park Sung-Min


adolescents, feature engineering, machine learning-based analysis method, problem gambling