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General General

Construction of a convolutional neural network classifier developed by computed tomography images for pancreatic cancer diagnosis.

In World journal of gastroenterology ; h5-index 103.0

BACKGROUND : Efforts should be made to develop a deep-learning diagnosis system to distinguish pancreatic cancer from benign tissue due to the high morbidity of pancreatic cancer.

AIM : To identify pancreatic cancer in computed tomography (CT) images automatically by constructing a convolutional neural network (CNN) classifier.

METHODS : A CNN model was constructed using a dataset of 3494 CT images obtained from 222 patients with pathologically confirmed pancreatic cancer and 3751 CT images from 190 patients with normal pancreas from June 2017 to June 2018. We established three datasets from these images according to the image phases, evaluated the approach in terms of binary classification (i.e., cancer or not) and ternary classification (i.e., no cancer, cancer at tail/body, cancer at head/neck of the pancreas) using 10-fold cross validation, and measured the effectiveness of the model with regard to the accuracy, sensitivity, and specificity.

RESULTS : The overall diagnostic accuracy of the trained binary classifier was 95.47%, 95.76%, 95.15% on the plain scan, arterial phase, and venous phase, respectively. The sensitivity was 91.58%, 94.08%, 92.28% on three phases, with no significant differences (χ2 = 0.914, P = 0.633). Considering that the plain phase had same sensitivity, easier access, and lower radiation compared with arterial phase and venous phase , it is more sufficient for the binary classifier. Its accuracy on plain scans was 95.47%, sensitivity was 91.58%, and specificity was 98.27%. The CNN and board-certified gastroenterologists achieved higher accuracies than trainees on plain scan diagnosis (χ2 = 21.534, P < 0.001; χ2 = 9.524, P < 0.05; respectively). However, the difference between CNN and gastroenterologists was not significant (χ2 = 0.759, P = 0.384). In the trained ternary classifier, the overall diagnostic accuracy of the ternary classifier CNN was 82.06%, 79.06%, and 78.80% on plain phase, arterial phase, and venous phase, respectively. The sensitivity scores for detecting cancers in the tail were 52.51%, 41.10% and, 36.03%, while sensitivity for cancers in the head was 46.21%, 85.24% and 72.87% on three phases, respectively. Difference in sensitivity for cancers in the head among the three phases was significant (χ2 = 16.651, P < 0.001), with arterial phase having the highest sensitivity.

CONCLUSION : We proposed a deep learning-based pancreatic cancer classifier trained on medium-sized datasets of CT images. It was suitable for screening purposes in pancreatic cancer detection.

Ma Han, Liu Zhong-Xin, Zhang Jing-Jing, Wu Feng-Tian, Xu Cheng-Fu, Shen Zhe, Yu Chao-Hui, Li You-Ming


Computed tomography, Convolutional neural networks, Deep learning, Pancreatic cancer

General General

Artificial intelligence in COVID-19 drug repurposing.

In The Lancet. Digital health

Drug repurposing or repositioning is a technique whereby existing drugs are used to treat emerging and challenging diseases, including COVID-19. Drug repurposing has become a promising approach because of the opportunity for reduced development timelines and overall costs. In the big data era, artificial intelligence (AI) and network medicine offer cutting-edge application of information science to defining disease, medicine, therapeutics, and identifying targets with the least error. In this Review, we introduce guidelines on how to use AI for accelerating drug repurposing or repositioning, for which AI approaches are not just formidable but are also necessary. We discuss how to use AI models in precision medicine, and as an example, how AI models can accelerate COVID-19 drug repurposing. Rapidly developing, powerful, and innovative AI and network medicine technologies can expedite therapeutic development. This Review provides a strong rationale for using AI-based assistive tools for drug repurposing medications for human disease, including during the COVID-19 pandemic.

Zhou Yadi, Wang Fei, Tang Jian, Nussinov Ruth, Cheng Feixiong


Pathology Pathology

Unsupervised machine learning reveals lesional variability in focal cortical dysplasia at mesoscopic scale.

In NeuroImage. Clinical

OBJECTIVE : Focal cortical dysplasia (FCD) is the most common epileptogenic developmental malformation and a prevalent cause of surgically amenable epilepsy. While cellular and molecular biology data suggest that FCD lesional characteristics lie along a spectrum, this notion remains to be verified in vivo. We tested the hypothesis that machine learning applied to MRI captures FCD lesional variability at a mesoscopic scale.

METHODS : We studied 46 patients with histologically verified FCD Type II and 35 age- and sex-matched healthy controls. We applied consensus clustering, an unsupervised learning technique that identifies stable clusters based on bootstrap-aggregation, to 3 T multicontrast MRI (T1-weighted MRI and FLAIR) features of FCD normalized with respect to distributions in controls.

RESULTS : Lesions were parcellated into four classes with distinct structural profiles variably expressed within and across patients: Class-1 with isolated white matter (WM) damage; Class-2 combining grey matter (GM) and WM alterations; Class-3 with isolated GM damage; Class-4 with GM-WM interface anomalies. Class membership was replicated in two independent datasets. Classes with GM anomalies impacted local function (resting-state fMRI derived ALFF), while those with abnormal WM affected large-scale connectivity (assessed by degree centrality). Overall, MRI classes reflected typical histopathological FCD characteristics: Class-1 was associated with severe WM gliosis and interface blurring, Class-2 with severe GM dyslamination and moderate WM gliosis, Class-3 with moderate GM gliosis, Class-4 with mild interface blurring. A detection algorithm trained on class-informed data outperformed a class-naïve paradigm.

SIGNIFICANCE : Machine learning applied to widely available MRI contrasts uncovers FCD Type II variability at a mesoscopic scale and identifies tissue classes with distinct structural dimensions, functional and histopathological profiles. Integrating in vivo staging of FCD traits with automated lesion detection is likely to inform the development of novel personalized treatments.

Lee Hyo M, Gill Ravnoor S, Fadaie Fatemeh, Cho Kyoo H, Guiot Marie C, Hong Seok-Jun, Bernasconi Neda, Bernasconi Andrea


Cortical dysplasia, Epilepsy, MRI

General General

Inter-subject pattern analysis for multivariate group analysis of functional neuroimaging. A unifying formalization.

In Computer methods and programs in biomedicine

BACKGROUND AND OBJECTIVE : In medical imaging, population studies have to overcome the differences that exist between individuals to identify invariant image features that can be used for diagnosis purposes. In functional neuroimaging, an appealing solution to identify neural coding principles that hold at the population level is inter-subject pattern analysis, i.e. to learn a predictive model on data from multiple subjects and evaluate its generalization performance on new subjects. Although it has gained popularity in recent years, its widespread adoption is still hampered by the blatant lack of a formal definition in the literature. In this paper, we precisely introduce the first principled formalization of inter-subject pattern analysis targeted at multivariate group analysis of functional neuroimaging.

METHODS : We propose to frame inter-subject pattern analysis as a multi-source transductive transfer question, thus grounding it within several well defined machine learning settings and broadening the spectrum of usable algorithms. We describe two sets of inter-subject brain decoding experiments that use several open datasets: a magneto-encephalography study with 16 subjects and a functional magnetic resonance imaging paradigm with 100 subjects. We assess the relevance of our framework by performing model comparisons, where one brain decoding model exploits our formalization while others do not.

RESULTS : The first set of experiments demonstrates the superiority of a brain decoder that uses subject-by-subject standardization compared to state of the art models that use other standardization schemes, making the case for the interest of the transductive and the multi-source components of our formalization The second set of experiments quantitatively shows that, even after such transformation, it is more difficult for a brain decoder to generalize to new participants rather than to new data from participants available in the training phase, thus highlighting the transfer gap that needs to be overcome.

CONCLUSION : This paper describes the first formalization of inter-subject pattern analysis as a multi-source transductive transfer learning problem. We demonstrate the added value of this formalization using proof-of-concept experiments on several complementary functional neuroimaging datasets. This work should contribute to popularize inter-subject pattern analysis for functional neuroimaging population studies and pave the road for future methodological innovations.

Wang Qi, Artières Thierry, Takerkart Sylvain


Functional neuroimaging, Machine learning, Neuroinformatics, Population studies

Surgery Surgery

Clinical data classification using an enhanced SMOTE and chaotic evolutionary feature selection.

In Computers in biology and medicine

Class imbalance and the presence of irrelevant or redundant features in training data can pose serious challenges to the development of a classification framework. This paper proposes a framework for developing a Clinical Decision Support System (CDSS) that addresses class imbalance and the feature selection problem. Under this framework, the dataset is balanced at the data level and a wrapper approach is used to perform feature selection. The following three clinical datasets from the University of California Irvine (UCI) machine learning repository were used for experimentation: the Indian Liver Patient Dataset (ILPD), the Thoracic Surgery Dataset (TSD) and the Pima Indian Diabetes (PID) dataset. The Synthetic Minority Over-sampling Technique (SMOTE), which was enhanced using Orchard's algorithm, was used to balance the datasets. A wrapper approach that uses Chaotic Multi-Verse Optimisation (CMVO) was proposed for feature subset selection. The arithmetic mean of the Matthews correlation coefficient (MCC) and F-score (F1), which was measured using a Random Forest (RF) classifier, was used as the fitness function. After selecting the relevant features, a RF, which comprises 100 estimators and uses the Information Gain Ratio as the split criteria, was used for classification. The classifier achieved a 0.65 MCC, a 0.84 F1 and 82.46% accuracy for the ILPD; a 0.74 MCC, a 0.87 F1 and 86.88% accuracy for the TSD; and a 0.78 MCC, a 0.89 F1and 89.04% accuracy for the PID dataset. The effects of balancing and feature selection on the classifier were investigated and the performance of the framework was compared with the existing works in the literature. The results showed that the proposed framework is competitive in terms of the three performance measures used. The results of a Wilcoxon test confirmed the statistical superiority of the proposed method.

Sreejith S, Khanna Nehemiah H, Kannan A


Chaotic maps, Class imbalance, Classification, Clinical decision support system, Feature selection, Multi Verse Optimisation, SMOTE

General General

A comprehensive review of deep learning in colon cancer.

In Computers in biology and medicine

Deep learning has emerged as a leading machine learning tool in object detection and has attracted attention with its achievements in progressing medical image analysis. Convolutional Neural Networks (CNNs) are the most preferred method of deep learning algorithms for this purpose and they have an essential role in the detection and potential early diagnosis of colon cancer. In this article, we hope to bring a perspective to progress in this area by reviewing deep learning practices for colon cancer analysis. This study first presents an overview of popular deep learning architectures used in colon cancer analysis. After that, all studies related to colon cancer analysis are collected under the field of colon cancer and deep learning, then they are divided into five categories that are detection, classification, segmentation, survival prediction, and inflammatory bowel diseases. Then, the studies collected under each category are summarized in detail and listed. We conclude our work with a summary of recent deep learning practices for colon cancer analysis, a critical discussion of the challenges faced, and suggestions for future research. This study differs from other studies by including 135 recent academic papers, separating colon cancer into five different classes, and providing a comprehensive structure. We hope that this study is beneficial to researchers interested in using deep learning techniques for the diagnosis of colon cancer.

Pacal Ishak, Karaboga Dervis, Basturk Alper, Akay Bahriye, Nalbantoglu Ufuk


Colon cancer, Colorectal cancer, Convolutional neural networks, Deep learning, Inflammatory bowel diseases, Medical image analysis, Rectal cancer