Applying image features of proximal paracancerous tissues in predicting prognosis of patients with hepatocellular carcinoma

BACKGROUND

Most of the methods using digital pathological image for predicting Hepatocellular carcinoma (HCC) prognosis have not considered paracancerous tissue microenvironment (PTME), which are potentially important for tumour initiation and metastasis. This study aimed to identify roles of image features of PTME in predicting prognosis and tumour recurrence of HCC patients.

METHODS

We collected whole slide images (WSIs) of 146 HCC patients from Sun Yat-sen Memorial Hospital (SYSM dataset). For each WSI, five types of regions of interests (ROIs) in PTME and tumours were manually annotated. These ROIs were used to construct a Lasso Cox survival model for predicting the prognosis of HCC patients. To make the model broadly useful, we established a deep learning method to automatically segment WSIs, and further used it to construct a prognosis prediction model. This model was tested by the samples of 225 HCC patients from the Cancer Genome Atlas Liver Hepatocellular Carcinoma (TCGA-LIHC).

RESULTS

In predicting prognosis of the HCC patients, using the image features of manually annotated ROIs in PTME achieved C-index 0.668 in the SYSM testing dataset, which is higher than the C-index 0.648 reached by the model only using image features of tumours. Integrating ROIs of PTME and tumours achieved C-index 0.693 in the SYSM testing dataset. The model using automatically segmented ROIs of PTME and tumours achieved C-index of 0.665 (95% CI: 0.556-0.774) in the TCGA-LIHC samples, which is better than the widely used methods, WSISA (0.567), DeepGraphSurv (0.593), and SeTranSurv (0.642). Finally, we found the Texture SumAverage Skew HV on immune cell infiltration and Texture related features on desmoplastic reaction are the most important features of PTME in predicting HCC prognosis. We additionally used the model in prediction HCC recurrence for patients from SYSM-training, SYSM-testing, and TCGA-LIHC datasets, indicating the important roles of PTME in the prediction.

CONCLUSIONS

Our results indicate image features of PTME is critical for improving the prognosis prediction of HCC. Moreover, the image features related with immune cell infiltration and desmoplastic reaction of PTME are the most important factors associated with prognosis of HCC.

Optimal machine learning methods for prediction of high-flow nasal cannula outcomes using image features from electrical impedance tomography

BACKGROUND

High-flow nasal cannula (HNFC) is able to provide ventilation support for patients with hypoxic respiratory failure. Early prediction of HFNC outcome is warranted, since failure of HFNC might delay intubation and increase mortality rate. Existing methods require a relatively long period to identify the failure (approximately 12 h) and electrical impedance tomography (EIT) may help identify the patient's respiratory drive during HFNC.

OBJECTIVES

This study aimed to investigate a proper machine-learning model to predict HFNC outcomes promptly by EIT image features.

METHODS

The Z-score standardization method was adopted to normalize the samples from 43 patients who underwent HFNC and six EIT features were selected as model input variables through the random forest feature selection method. Machine-learning methods including discriminant, ensembles, k-nearest neighbour (KNN), artificial neural network (ANN), support vector machine (SVM), AdaBoost, xgboost, logistic, random forest, bernoulli bayes, gaussian bayes and gradient-boosted decision trees (GBDT) were used to build prediction models with the original data and balanced data proceeded by the synthetic minority oversampling technique.

RESULTS

Prior to data balancing, an extremely low specificity (less than 33.33%) as well as a high accuracy in the validation data set were observed in all the methods. After data balancing, the specificity of KNN, xgboost, random forest, GBDT, bernoulli bayes and AdaBoost significantly reduced (p<0.05) while the area under curve did not improve considerably (p>0.05); and the accuracy and recall decreased significantly (p<0.05).

CONCLUSIONS

The xgboost method showed better overall performance for balanced EIT image features, which may be considered as the ideal machine learning method for early prediction of HFNC outcomes.

MRI image features with an evident relation to low back pain: a narrative review

PURPOSE

Low back pain (LBP) is one of the most prevalent health condition worldwide and responsible for the most years lived with disability, yet the etiology is often unknown. Magnetic resonance imaging (MRI) is frequently used for treatment decision even though it is often inconclusive. There are many different image features that could relate to low back pain. Conversely, multiple etiologies do relate to spinal degeneration but do not actually cause the perceived pain. This narrative review provides an overview of all possible relevant features visible on MRI images and determines their relation to LBP.

METHODS

We conducted a separate literature search per image feature. All included studies were scored using the GRADE guidelines. Based on the reported results per feature an evidence agreement (EA) score was provided, enabling us to compare the collected evidence of separate image features. The various relations between MRI features and their associated pain mechanisms were evaluated to provide a list of features that are related to LBP.

RESULTS

All searches combined generated a total of 4472 hits of which 31 articles were included. Features were divided into five different categories:'discogenic', 'neuropathic','osseous', 'facetogenic', and'paraspinal', and discussed separately.

CONCLUSION

Our research suggests that type I Modic changes, disc degeneration, endplate defects, disc herniation, spinal canal stenosis, nerve compression, and muscle fat infiltration have the highest probability to be related to LBP. These can be used to improve clinical decision-making for patients with LBP based on MRI.

First treatments for Lattice stereotactic body radiation therapy using magnetic resonance image guided radiation therapy

Two abdominal patients were treated with Lattice stereotactic body radiation therapy (SBRT) using magnetic resonance guided radiation therapy (MRgRT). This is one of the first reported treatments of Lattice SBRT with the use of MRgRT. A description of the treatment approach and planning considerations were incorporated into this report. MRgRT Lattice SBRT delivered similar planning quality metrics to established dosimetric parameters for Lattice SBRT. Increased signal intensity were seen in the MRI treatments for one of the patients during the course of treatment.

Rumor Classification through a Multimodal Fusion Framework and Ensemble Learning

The proliferation of rumors on social media has become a major concern due to its ability to create a devastating impact. Manually assessing the veracity of social media messages is a very time-consuming task that can be much helped by machine learning. Most message veracity verification methods only exploit textual contents and metadata. Very few take both textual and visual contents, and more particularly images, into account. Moreover, prior works have used many classical machine learning models to detect rumors. However, although recent studies have proven the effectiveness of ensemble machine learning approaches, such models have seldom been applied. Thus, in this paper, we propose a set of advanced image features that are inspired from the field of image quality assessment, and introduce the Multimodal fusiON framework to assess message veracIty in social neTwORks (MONITOR), which exploits all message features by exploring various machine learning models. Moreover, we demonstrate the effectiveness of ensemble learning algorithms for rumor detection by using five metalearning models. Eventually, we conduct extensive experiments on two real-world datasets. Results show that MONITOR outperforms state-of-the-art machine learning baselines and that ensemble models significantly increase MONITOR's performance.

[Control study of chest CT imaging features of aluminosis and silicosis patients]

Objective: To understand the chest CT features of aluminosis caused by alumina and to improve the understanding of the imaging findings of alumina pneumoconiosis. Methods: The chest CT findings of 17 cases of alumina-induced pneumoconiosis and 30 cases of silicosis (the control group) diagnosed in Zibo Occupational Disease Prevention Hospital from April 2015 to July 2020 were analyzed retrospectively. The characteristics of fibrosis of the two kinds of pneumoconiosis and the incidence of size, density, distribution, tractive bronchiectasis, pleural thickening and interlobular septal thickening of pneumoconiosis nodules were compared. Results: Alumina pneumoconiosis showed nodules with thickened interlobular septal of 66.67% (12/18) , honeycomb lung of 22.22% (4/18) , ground glass shadow of 61.11% (11/18) , simple nodules of 11.11% (2/18) , and no fusion mass. In the control group, the long-line fibrosis of nodules with thickened interlobular septal were 16.67% (5/30) , 6.67% (2/30) with honeycomb lung and ground glass density shadow, 23.33% (7/30) with fusion mass and 53.33% (16/30) with simple nodule. There were significant differences in CT findings of nodules with thickened interlobular septal, ground glass density shadow, fused mass and simple nodules between the two groups (P<0.05) . The interstitial beaded nodules were seen in 18 cases of alumina pneumoconiosis, 50.00% (9/18) of them were beaded nodules, 61.33% (46/75) of low density nodules and 38.89% (7/18) of central lobular nodules were seen in alumina pneumoconiosis. The average width of nodules was (1.29±0.38) mm. Central lobular nodules were seen in all 30 cases of silicosis, 10.00% (3/30) were mainly beaded nodules, low density nodules were 36.29% (90/248) , and the average width diameter of nodules was (1.85±0.58) mm. There were significant differences between the two groups (P<0.05) . Alumina pneumoconiosis was often accompanied by traction bronchiectasis, pleural thickening and interlobular septal thickening (11, 18, 17 cases, 61.11%, 100.00%, 94.44%) , compared with the control group (9, 18, 18 cases, 30.00%, 60.00%, 60.00%) . The differences were statistically significant (P<0.05) . The maximum CT value of noncalcified mediastinal lymphnodes in alumina pneumoconiosis was (103.43±26.33) HU, which was higher than that of the control group[ (75.22±16.70) HU], and the difference was statistically significant (P<0.05) . Conclusion: Alumina pneumoconiosis chest CT shows slightly low-density beaded nodules, thickened interlobular septal, and pulmonary interstitial fibrosis of ground-glass shadows, mostly combines with stretched bronchiectasis, thickened pleura, and mediastinum increased lymph node density.

Inkjet classification based on a few letters

Morphology-based classification of inkjet documents has the characteristics of low cost and high efficiency, but this method usually requires measurement and analysis of a large number of printed characters. This paper proposes a novel method for detecting the source of printed documents using a few printed letters. A dataset containing data pertaining to various inkjet printers, including 27 models of inkjets from HP, Canon, and Epson, and their printed documents were gathered. The specifications of the various brands and models of inkjets are summarised, and the characteristics of the microscopic appearance of the printheads are presented. Principal component analysis (PCA) of the variables was applied to describe the proximity between the specimens, and a two-dimensional kernel density estimation was used to describe the variation between and within printer brands and models. Then, specific cases were simulated by random sampling based on the collected inkjet dataset. Multivariate kernel density estimation was used to estimate the numerator and denominator probability distribution for computing the likelihood ratio (LR). The result of K-nearest neighbour analysis showed classification accuracy as high as 98%. The evaluation of the LR presented a significant result (EER=0, RMEP=0, RMED=0.07). This method helps to find a specific inkjet from even a few letters in the printed document for tactical purposes.

Prediction of microvascular invasion in solitary hepatocellular carcinoma ≤ 5 cm based on computed tomography radiomics

BACKGROUND

Liver cancer is one of the most common malignant tumors, and ranks as the fourth leading cause of cancer death worldwide. Microvascular invasion (MVI) is considered one of the most important factors for recurrence and poor prognosis of liver cancer. Thus, accurately identifying MVI before surgery is of great importance in making treatment strategies and predicting the prognosis of patients with hepatocellular carcinoma (HCC). Radiomics as an emerging field, aims to utilize artificial intelligence software to develop methods that may contribute to cancer diagnosis, treatment improvement and evaluation, and better prediction.

AIM

To investigate the predictive value of computed tomography radiomics for MVI in solitary HCC ≤ 5 cm.

METHODS

A total of 185 HCC patients, including 122 MVI negative and 63 MVI positive patients, were retrospectively analyzed. All patients were randomly assigned to the training group (n = 124) and validation group (n = 61). A total of 1351 radiomic features were extracted based on three-dimensional images. The diagnostic performance of the radiomics model was verified in the validation group, and the Delong test was applied to compare the radiomics and MVI-related imaging features (two-trait predictor of venous invasion and radiogenomic invasion).

RESULTS

A total of ten radiomics features were finally obtained after screening 1531 features. According to the weighting coefficient that corresponded to the features, the radiomics score (RS) calculation formula was obtained, and the RS score of each patient was calculated. The radiomics model exhibited a better correction and identification ability in the training and validation groups [area under the curve: 0.72 (95% confidence interval: 0.58-0.86) and 0.74 (95% confidence interval: 0.66-0.83), respectively]. Its prediction performance was significantly higher than that of the image features (P < 0.05).

CONCLUSION

Computed tomography radiomics has certain predictive value for MVI in solitary HCC ≤ 5 cm, and the predictive ability is higher than that of image features.

Clinical and imaging features of COVID-19

Since December 2019, multiple cases of 2019 coronavirus disease (COVID-19) have been reported in Wuhan in China's Hubei Province, a disease which has subsequently spread rapidly across the entire country. Highly infectious, COVID-19 has numerous transmission channels and humans are highly susceptible to infection. The main clinical symptoms of COVID-19 are fever, fatigue, and a dry cough. Laboratory examination in the early stage of the disease shows a normal or decreased white blood cell count, and a decreased lymphocyte count. While CT examination serves as the screening and diagnostic basis for COVID-19, its accuracy is limited. The nucleic acid testing is the gold standard for the diagnosis of COVID-19, but has a low sensitivity is low. There is clearly a divide between the two means of examination. This paper reviews the published literature, guidelines and consensus, and summarizes the clinical and imaging characteristics of COVID-19, in order to provide a reliable basis for early diagnosis and treatment.

Radiomics Based on Multimodal MRI for the Differential Diagnosis of Benign and Malignant Breast Lesions

BACKGROUND

MRI-based radiomics has been used to diagnose breast lesions; however, little research combining quantitative pharmacokinetic parameters of dynamic contrast-enhanced MRI (DCE-MRI) and diffusion kurtosis imaging (DKI) exists.

PURPOSE

To develop and validate a multimodal MRI-based radiomics model for the differential diagnosis of benign and malignant breast lesions and analyze the discriminative abilities of different MR sequences.

STUDY TYPE

Retrospective.

POPULATION

In all, 207 female patients with 207 histopathology-confirmed breast lesions (95 benign and 112 malignant) were included in the study. Then 159 patients were assigned to the training group, and 48 patients comprised the validation group.

FIELD STRENGTH/SEQUENCE

T₂ -weighted (T₂ W), T₁ -weighted (T₁ W), diffusion-weighted MR imaging (b-values = 0, 500, 800, and 2000 seconds/mm² ) and quantitative DCE-MRI were performed on a 3.0T MR scanner.

ASSESSMENT

Radiomics features were extracted from T₂ WI, T₁ WI, DKI, apparent diffusion coefficient (ADC) maps, and DCE pharmacokinetic parameter maps in the training set. Models based on each sequence or combinations of sequences were built using a support vector machine (SVM) classifier and used to differentiate benign and malignant breast lesions in the validation set.

STATISTICAL TESTS

Optimal feature selection was performed by Spearman's rank correlation coefficients and the least absolute shrinkage and selection operator algorithm (LASSO). Receiver operating characteristic (ROC) curves were used to assess the diagnostic performance of the radiomics models in the validation set.

RESULTS

The area under the ROC curve (AUC) of the optimal radiomics model, including T₂ WI, DKI, and quantitative DCE-MRI parameter maps was 0.921, with an accuracy of 0.833. The AUCs of the models based on T₁ WI, T₂ WI, ADC map, DKI, and DCE pharmacokinetic parameter maps were 0.730, 0.791, 0.770, 0.788, and 0.836, respectively.

DATA CONCLUSION

The model based on radiomics features from T₂ WI, DKI, and quantitative DCE pharmacokinetic parameter maps has a high discriminatory ability for benign and malignant breast lesions.

LEVEL OF EVIDENCE

3 TECHNICAL EFFICACY STAGE: 2 J. Magn. Reson. Imaging 2020;52:596-607.

A unified framework of source camera identification based on features

Source camera identification, which aims at identifying the source camera of an image, has attracted a wide range of attention in the field of digital image forensics recently. Many approaches to source camera identification have been proposed by extracting some image features. However, most of these methods only focused on extracting features from the single artifact of the camera left on the captured images and ignored other artifacts that may help improve final accuracy. Therefore, in this paper, we propose a feature-based framework for source camera identification, which first captures various pure camera-specific artifacts through preprocessing and residual calculation, then extracts discriminative features through image transform, and finally reduces the algorithm complexity through feature reduction. Based on the framework, a novel source camera identification method is proposed, which can identify different camera brands, models and individuals with high accuracy. A large number of comparative experiments show that the proposed method outperforms the state-of-the-art methods.

A quantitative analysis method assisted by image features in laser-induced breakdown spectroscopy

The determination accuracy of alloying elements in high alloy steel is generally poor in laser-induced breakdown spectroscopy (LIBS) due to their matrix effect. To solve this problem, an image quantitative analysis (IQA) method was proposed and verified by determining nickel (Ni) in 17 stainless steel samples in this work. The results showed that the coefficient of determination (R²) was increased from 0.9833 of a conventional spectrum quantitative analysis (SQA) method to 0.9996 of the IQA method, and the average relative error of cross-validation (ARECV) and root mean squared error of cross-validation (RMSECV) were decreased from 56.80% and 1.0818 wt% to 15.93% and 0.9866 wt%, respectively. Besides, the determinations of chromium (Cr) and silicon (Si) demonstrated the generalization ability of the IQA. This study provides an effective approach to improving the quantitative performance of LIBS through the combination of image processing and computer vision technology.

[Radiomics: the process and applications in tumor research]

Radiomics enables extraction of innumerable quantitative features from medical images with high-throughput computing for diagnosis and prediction. The practice of radiomics involves image acquisition, identifying and segmenting the volumes of interest, extracting and analyzing of quantitative features, and classification or prediction model development. Compared with traditional visual interpretation of medical images, the deep mining of medical images by computer technology from radiomics makes feature uptake more efficient, relatively objective and rich in feature types. Whereas, radiomic analysis requires high image quality and consistent scan parameters. The features extracted are confined to the segmented area. Radiomics is promising in tumor screening, early diagnosis, accurate grading and staging, treatment and prognosis, molecular characteristics and so on. Combined with traditional visual interpretation of medical images, radiomics is helpful in tumor diagnosis and prediction.

It's not all black and white: visual scene parameters influence optokinetic reflex performance in Xenopus laevis tadpoles

The maintenance of visual acuity during active and passive body motion is ensured by gaze-stabilizing reflexes that aim at minimizing retinal image slip. For the optokinetic reflex (OKR), large-field visual motion of the surround forms the essential stimulus that activates eye movements. Properties of the moving visual world influence cognitive motion perception and the estimation of visual image velocity. Therefore, the performance of brainstem-mediated visuo-motor behaviors might also depend on image scene characteristics. Employing semi-intact preparations of mid-larval stages of Xenopus laevis tadpoles, we studied the influence of contrast polarity, intensity, contour shape and different motion stimulus patterns on the performance of the OKR and multi-unit optic nerve discharge during motion of a large-field visual scene. At high contrast intensities, the OKR amplitude was significantly larger for visual scenes with a positive contrast (bright dots on a dark background) compared with those with a negative contrast. This effect persisted for luminance-matched pairs of stimuli, and was independent of contour shape. The relative biases of OKR performance along with the independence of the responses from contour shape were closely matched by the optic nerve discharge evoked by the same visual stimuli. However, the multi-unit activity of retinal ganglion cells in response to a small single moving vertical edge was strongly influenced by the light intensity in the vertical neighborhood. This suggests that the underlying mechanism of OKR biases related to contrast polarity directly derives from visual motion-processing properties of the retinal circuitry.

A fully automatic end-to-end method for content-based image retrieval of CT scans with similar liver lesion annotations

PURPOSE

The goal of medical content-based image retrieval (M-CBIR) is to assist radiologists in the decision-making process by retrieving medical cases similar to a given image. One of the key interests of radiologists is lesions and their annotations, since the patient treatment depends on the lesion diagnosis. Therefore, a key feature of M-CBIR systems is the retrieval of scans with the most similar lesion annotations. To be of value, M-CBIR systems should be fully automatic to handle large case databases.

METHODS

We present a fully automatic end-to-end method for the retrieval of CT scans with similar liver lesion annotations. The input is a database of abdominal CT scans labeled with liver lesions, a query CT scan, and optionally one radiologist-specified lesion annotation of interest. The output is an ordered list of the database CT scans with the most similar liver lesion annotations. The method starts by automatically segmenting the liver in the scan. It then extracts a histogram-based features vector from the segmented region, learns the features' relative importance, and ranks the database scans according to the relative importance measure. The main advantages of our method are that it fully automates the end-to-end querying process, that it uses simple and efficient techniques that are scalable to large datasets, and that it produces quality retrieval results using an unannotated CT scan.

RESULTS

Our experimental results on 9 CT queries on a dataset of 41 volumetric CT scans from the 2014 Image CLEF Liver Annotation Task yield an average retrieval accuracy (Normalized Discounted Cumulative Gain index) of 0.77 and 0.84 without/with annotation, respectively.

CONCLUSIONS

Fully automatic end-to-end retrieval of similar cases based on image information alone, rather that on disease diagnosis, may help radiologists to better diagnose liver lesions.

CT imaging features associated with recurrence in non-small cell lung cancer patients after stereotactic body radiotherapy

BACKGROUND

Predicting recurrence after stereotactic body radiotherapy (SBRT) in non-small cell lung cancer (NSCLC) patients is problematic, but critical for the decision of following treatment. This study aims to investigate the association of imaging features derived from the first follow-up computed tomography (CT) on lung cancer patient outcomes following SBRT, and identify patients at high risk of recurrence.

METHODS

Fifty nine biopsy-proven non-small cell lung cancer patients were qualified for this study. The first follow-up CTs were performed about 3 months after SBRT (median time: 91 days). Imaging features included 34 manually scored radiological features (semantics) describing the lesion, lung and thorax and 219 quantitative imaging features (radiomics) extracted automatically after delineation of the lesion. Cox proportional hazard models and Harrel's C-index were used to explore predictors of overall survival (OS), recurrence-free survival (RFS), and loco-regional recurrence-free survival (LR-RFS). Five-fold cross validation was performed on the final prognostic model.

RESULTS

The median follow-up time was 42 months. The model for OS contained Eastern Cooperative Oncology Group (ECOG) performance status (HR = 3.13, 95% CI: 1.17-8.41), vascular involvement (HR = 3.21, 95% CI: 1.29-8.03), lymphadenopathy (HR = 3.59, 95% CI: 1.58-8.16) and the 1st principle component of radiomic features (HR = 1.24, 95% CI: 1.02-1.51). The model for RFS contained vascular involvement (HR = 3.06, 95% CI: 1.40-6.70), vessel attachment (HR = 3.46, 95% CI: 1.65-7.25), pleural retraction (HR = 3.24, 95% CI: 1.41-7.42), lymphadenopathy (HR = 6.41, 95% CI: 2.58-15.90) and relative enhancement (HR = 1.40, 95% CI: 1.00-1.96). The model for LR-RFS contained vascular involvement (HR = 4.96, 95% CI: 2.23-11.03), lymphadenopathy (HR = 2.64, 95% CI: 1.19-5.82), circularity (F13, HR = 1.60, 95% CI: 1.10-2.32) and 3D Laws feature (F92, HR = 1.96, 95% CI: 1.35-2.83). Five-fold cross-validated the areas under the receiver operating characteristic curves (AUC) of these three models were all above 0.8.

CONCLUSIONS

Our analysis reveals disease progression could be prognosticated as early as 3 months after SBRT using CT imaging features, and these features would be helpful in clinical decision-making.

Biomarkers from in vivo molecular imaging of breast cancer: pretreatment 18F-FDG PET predicts patient prognosis, and pretreatment DWI-MR predicts response to neoadjuvant chemotherapy

OBJECTIVE

Human cancers display intra-tumor phenotypic heterogeneity and recent research has focused on developing image processing methods extracting imaging descriptors to characterize this heterogeneity. This work assesses the role of pretreatment 18F-FDG PET and DWI-MR with respect to the prognosis and prediction of neoadjuvant chemotherapy (NAC) outcomes when image features are used to characterize primitive lesions from breast cancer (BC).

MATERIALS AND METHODS

A retrospective protocol included 38 adult women with biopsy-proven BC. Patients underwent a pre-therapy 18F-FDG PET/CT whole-body study and a pre-therapy breast multi-parametric MR study. Patients were then referred for NAC treatment and then for surgical resection, with an evaluation of the therapy response. Segmentation methods were developed in order to identify functional volumes both on 18F-FDG PET images and ADC maps. Macroscopic and histogram features were extracted from the defined functional volumes.

RESULTS

Our work demonstrates that macroscopic and histogram features from 18F-FDG PET are able to biologically characterize primitive BC, and define the prognosis. In addition, histogram features from ADC maps are able to predict the response to NAC.

CONCLUSION

Our work suggests that pre-treatment 18F-FDG PET and pre-treatment DWI-MR provide useful complementary information for biological characterization and NAC response prediction in BC.

Beyond imaging: The promise of radiomics. Phys Med. 2017;38:122-139. [PMID: 28595812 DOI: 10.1016/j.ejmp.2017.05.071]

The domain of investigation of radiomics consists of large-scale radiological image analysis and association with biological or clinical endpoints. The purpose of the present study is to provide a recent update on the status of this rapidly emerging field by performing a systematic review of the literature on radiomics, with a primary focus on oncologic applications. The systematic literature search, performed in Pubmed using the keywords: "radiomics OR radiomic" provided 97 research papers. Based on the results of this search, we describe the methods used for building a model of prognostic value from quantitative analysis of patient images. Then, we provide an up-to-date overview of the results achieved in this field, and discuss the current challenges and future developments of radiomics for oncology.

Fixation durations in scene viewing: Modeling the effects of local image features, oculomotor parameters, and task

Scene perception requires the orchestration of image- and task-related processes with oculomotor constraints. The present study was designed to investigate how these factors influence how long the eyes remain fixated on a given location. Linear mixed models (LMMs) were used to test whether local image statistics (including luminance, luminance contrast, edge density, visual clutter, and the number of homogeneous segments), calculated for 1° circular regions around fixation locations, modulate fixation durations, and how these effects depend on task-related control. Fixation durations and locations were recorded from 72 participants, each viewing 135 scenes under three different viewing instructions (memorization, preference judgment, and search). Along with the image-related predictors, the LMMs simultaneously considered a number of oculomotor and spatiotemporal covariates, including the amplitudes of the previous and next saccades, and viewing time. As a key finding, the local image features around the current fixation predicted this fixation’s duration. For instance, greater luminance was associated with shorter fixation durations. Such immediacy effects were found for all three viewing tasks. Moreover, in the memorization and preference tasks, some evidence for successor effects emerged, such that some image characteristics of the upcoming location influenced how long the eyes stayed at the current location. In contrast, in the search task, scene processing was not distributed across fixation durations within the visual span. The LMM-based framework of analysis, applied to the control of fixation durations in scenes, suggests important constraints for models of scene perception and search, and for visual attention in general.