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oncology Oncology

The Tumor Target Segmentation of Nasopharyngeal Cancer in CT Images Based on Deep Learning Methods.

In Technology in cancer research & treatment

Radiotherapy is the main treatment strategy for nasopharyngeal carcinoma. A major factor affecting radiotherapy outcome is the accuracy of target delineation. Target delineation is time-consuming, and the results can vary depending on the experience of the oncologist. Using deep learning methods to automate target delineation may increase its efficiency. We used a modified deep learning model called U-Net to automatically segment and delineate tumor targets in patients with nasopharyngeal carcinoma. Patients were randomly divided into a training set (302 patients), validation set (100 patients), and test set (100 patients). The U-Net model was trained using labeled computed tomography images from the training set. The U-Net was able to delineate nasopharyngeal carcinoma tumors with an overall dice similarity coefficient of 65.86% for lymph nodes and 74.00% for primary tumor, with respective Hausdorff distances of 32.10 and 12.85 mm. Delineation accuracy decreased with increasing cancer stage. Automatic delineation took approximately 2.6 hours, compared to 3 hours, using an entirely manual procedure. Deep learning models can therefore improve accuracy, consistency, and efficiency of target delineation in T stage, but additional physician input may be required for lymph nodes.

Li Shihao, Xiao Jianghong, He Ling, Peng Xingchen, Yuan Xuedong

automatic delineation, deep learning, nasopharyngeal cancer

General General

Hep G2 cell culture confluence measurement in phase contrast micrographs - a user-friendly, open-source software-based approach.

In Toxicology mechanisms and methods

Phase-contrast micrographs are often used for confirmation of proliferation and viability assays. However, they are usually only a qualitative tool and fail to exclude with certainty the presence of assay interference by test substances. The complexity of image analysis workflows hinders life scientists from routinely utilizing micrograph data. Here, we present an open-source software-based, combined ilastik segmentation/ImageJ measurement of area (ISIMA) approach for cell monolayer segmentation and confluence percentage measurement of phase-contrast micrographs of HepG2 cells. The aim of this study is to test whether the proposed approach is suitable for quantitative confirmation of proliferation data, acquired by the 3-(4,5-Dimethyl-2-thiazolyl)-2,5-diphenyl-2H-tetrazolium bromide (MTT) assay. Our results show that ISIMA is user-friendly and provides reproducible data, which strongly correlates to the results of the MTT assay. In conclusion, ISIMA is an affordable, simple and fast approach for confluence quantification by researchers without image analysis background.

Yordanov Yordan I


Fiji, ImageJ, ilastik, image analysis, machine learning, segmentation

General General

Salience Models: A Computational Cognitive Neuroscience Review.

In Vision (Basel, Switzerland)

The seminal model by Laurent Itti and Cristoph Koch demonstrated that we can compute the entire flow of visual processing from input to resulting fixations. Despite many replications and follow-ups, few have matched the impact of the original model-so what made this model so groundbreaking? We have selected five key contributions that distinguish the original salience model by Itti and Koch; namely, its contribution to our theoretical, neural, and computational understanding of visual processing, as well as the spatial and temporal predictions for fixation distributions. During the last 20 years, advances in the field have brought up various techniques and approaches to salience modelling, many of which tried to improve or add to the initial Itti and Koch model. One of the most recent trends has been to adopt the computational power of deep learning neural networks; however, this has also shifted their primary focus to spatial classification. We present a review of recent approaches to modelling salience, starting from direct variations of the Itti and Koch salience model to sophisticated deep-learning architectures, and discuss the models from the point of view of their contribution to computational cognitive neuroscience.

Krasovskaya Sofia, MacInnes W Joseph


Itti and Koch, computational modelling, deep learning, salience

General General

An Adaptive Homeostatic Algorithm for the Unsupervised Learning of Visual Features.

In Vision (Basel, Switzerland)

The formation of structure in the visual system, that is, of the connections between cells within neural populations, is by and large an unsupervised learning process. In the primary visual cortex of mammals, for example, one can observe during development the formation of cells selective to localized, oriented features, which results in the development of a representation in area V1 of images' edges. This can be modeled using a sparse Hebbian learning algorithms which alternate a coding step to encode the information with a learning step to find the proper encoder. A major difficulty of such algorithms is the joint problem of finding a good representation while knowing immature encoders, and to learn good encoders with a nonoptimal representation. To solve this problem, this work introduces a new regulation process between learning and coding which is motivated by the homeostasis processes observed in biology. Such an optimal homeostasis rule is implemented by including an adaptation mechanism based on nonlinear functions that balance the antagonistic processes that occur at the coding and learning time scales. It is compatible with a neuromimetic architecture and allows for a more efficient emergence of localized filters sensitive to orientation. In addition, this homeostasis rule is simplified by implementing a simple heuristic on the probability of activation of neurons. Compared to the optimal homeostasis rule, numerical simulations show that this heuristic allows to implement a faster unsupervised learning algorithm while retaining much of its effectiveness. These results demonstrate the potential application of such a strategy in machine learning and this is illustrated by showing the effect of homeostasis in the emergence of edge-like filters for a convolutional neural network.

Perrinet Laurent U


computer vision, neuroscience, sparseness, unsupervised learning, vision

Radiology Radiology

Machine-learning analysis of contrast-enhanced CT radiomics predicts recurrence of hepatocellular carcinoma after resection: A multi-institutional study.

In EBioMedicine

BACKGROUND : Current guidelines recommend surgical resection as the first-line option for patients with solitary hepatocellular carcinoma (HCC); unfortunately, postoperative recurrence rate remains high and there is no reliable prediction tool. We explored the potential of radiomics coupled with machine-learning algorithms to improve the predictive accuracy for HCC recurrence.

METHODS : A total of 470 patients who underwent contrast-enhanced CT and curative resection for solitary HCC were recruited from 3 independent institutions. In the training phase of 210 patients from Institution 1, a radiomics-derived signature was generated based on 3384 engineered features extracted from primary tumor and its periphery using aggregated machine-learning framework. We employed Cox modeling to build predictive models. The models were then validated using an internal dataset of 107 patients and an external dataset of 153 patients from Institution 2 and 3.

FINDINGS : Using the machine-learning framework, we identified a three-feature signature that demonstrated favorable prediction of HCC recurrence across all datasets, with C-index of 0.633-0.699. Serum alpha-fetoprotein, albumin-bilirubin grade, liver cirrhosis, tumor margin, and radiomics signature were selected for preoperative model; postoperative model incorporated satellite nodules into above-mentioned predictors. The two models showed superior prognostic performance, with C-index of 0.733-0.801 and integrated Brier score of 0.147-0.165, compared with rival models without radiomics and widely used staging systems (all P < 0.05); they also gave three risk strata for recurrence with distinct recurrence patterns.

INTERPRETATION : When integrated with clinical data sources, our three-feature radiomics signature promises to accurately predict individual recurrence risk that may facilitate personalized HCC management.

Ji Gu-Wei, Zhu Fei-Peng, Xu Qing, Wang Ke, Wu Ming-Yu, Tang Wei-Wei, Li Xiang-Cheng, Wang Xue-Hao


Hepatocellular carcinoma, Machine learning, Prediction model, Radiomics, Recurrence

General General

Influences of victimization and comorbid conditions on latency to illicit drug use among adolescents and young adults.

In Drug and alcohol dependence

OBJECTIVE : Exposure to violent victimization is associated with higher rates of mental health and substance use disorders (SUD). Some youth who experience multiple victimizations and associated characteristics (i.e. poly-victims) are at heightened risk for long term problems. Thus, we conducted the first study to examine how heterogeneity in experiences of victimization vary in terms of latency to illicit drug use following treatment completion. We also examined if victimization profiles vary across gender and if comorbid conditions (e.g., posttraumatic stress disorder and major depressive disorder) differentially predict latentcy to illicit drug use across groups.

METHODS : Adolescents and young adults (N = 5956; Mage  = 17.5 years; 64.0% male) with SUDs in treatment for illicit drug use completed a battery of measures at baseline. At 3-, 6- and 12-month follow-ups, they reported on the number of days before they used any illicit drug following their last assessment.

RESULTS : Continuous time survival mixture modeling revealed that, as hypothesized, females who experienced high rates of all victimization and related characteristics had a higher hazard for latency to first illicit drug use as compared to females in the low victimization group. This was not the case for males; rather, those who experienced high rates of sexual abuse were quickest to return to illicit drug use. Finally, comorbid conditions led to a higher hazard rate, but only for certain profiles across females.

DISCUSSION : Findings emphasize the necessity for professionals to more fully integrate poly-victimization research and theory into their clinical practices and research.

Davis Jordan P, Christie Nina C, Dworkin Emily R, Prindle John, Dumas Tara M, DiGuiseppi Graham, Helton Jesse J, Ring Colin


Depression, Illicit drug use, PTSD, Poly-Victimization, Relapse, Substance use disorders