• artificial intelligence
• bibliometric analysis
• lymph node
• oncology
• radiomics

• cervix cancer
• deep learning
• diffusion weighted imaging
• grading
• staging

• Neoplasm Staging
• Neoplasm Grading
• Uterine Cervical Neoplasms/diagnostic imaging
• Uterine Cervical Neoplasms/pathology
• Humans
• Female
• Convolutional Neural Networks
• Datasets as Topic

• Cervical cancer
• Magnetic resonance image
• Reduced field-of-view diffusion-weighted imaging
• Synthetic diffusion-weighted image

• MRI
• cervical cancer
• gynecologic malignancies
• gynecology
• oncology

We investigated whether features derived from texture analysis (TA) can distinguish breast density (BD) in spiral photon-counting breast computed tomography (PC-BCT).

In this retrospective single-centre study, we analysed 10,000 images from 400 PC-BCT examinations of 200 patients. Images were categorised into four-level density scale (a–d) using Breast Imaging Reporting and Data System (BI-RADS)-like criteria. After manual definition of representative regions of interest, 19 texture features (TFs) were calculated to analyse the voxel grey-level distribution in the included image area. ANOVA, cluster analysis, and multinomial logistic regression statistics were used. A human readout then was performed on a subset of 60 images to evaluate the reliability of the proposed feature set.

Of the 19 TFs, 4 first-order features and 7 second-order features showed significant correlation with BD and were selected for further analysis. Multinomial logistic regression revealed an overall accuracy of 80% for BD assessment. The majority of TFs systematically increased or decreased with BD. Skewness (rho -0.81), as a first-order feature, and grey-level nonuniformity (GLN, -0.59), as a second-order feature, showed the strongest correlation with BD, independently of other TFs. Mean skewness and GLN decreased linearly from density a to d. Run-length nonuniformity (RLN), as a second-order feature, showed moderate correlation with BD, but resulted in redundant being correlated with GLN. All other TFs showed only weak correlation with BD (range -0.49 to 0.49, p < 0.001) and were neglected.

TA of PC-BCT images might be a useful approach to assess BD and may serve as an observer-independent tool.

• Breast density
• Breast neoplasms
• Image processing (computer-assisted)
• Radiomics
• Tomography (x-ray computed)

• Cervical squamous cell cancer
• FIGO stage
• Overall survival
• Progression-free survival
• Radiomics

• Cervical cancer
• adenocarcinoma
• magnetic resonance imaging
• radiomics
• squamous cell carcinoma

• Carcinoma, Squamous Cell
• Diffusion Magnetic Resonance Imaging
• Female
• Humans
• Magnetic Resonance Imaging
• Multiparametric Magnetic Resonance Imaging
• Retrospective Studies
• Uterine Cervical Neoplasms/diagnostic imaging

The accuracy of discriminating pT3a from pT3b-c rectal cancer using high-resolution magnetic resonance imaging (MRI) remains unsatisfactory, although texture analysis (TA) could improve such discrimination.

To investigate the value of TA on apparent diffusion coefficient (ADC) maps in differentiating pT3a rectal adenocarcinomas from pT3b-c tumors.

This was a case-control study of 59 patients with pT3 rectal adenocarcinoma, who underwent diffusion-weighted imaging (DWI) between October 2016 and December 2018. The inclusion criteria were: (1) Proven pT3 rectal adenocarcinoma; (2) Primary MRI including high-resolution T2-weighted image (T2WI) and DWI; and (3) Availability of pathological reports for surgical specimens. The exclusion criteria were: (1) Poor image quality; (2) Preoperative chemoradiation therapy; and (3) A different pathological type. First-order (ADC values, skewness, kurtosis, and uniformity) and second-order (energy, entropy, inertia, and correlation) texture features were derived from whole-lesion ADC maps. Receiver operating characteristic curves were used to determine the diagnostic value for pT3b-c tumors.

The final study population consisted of 59 patients (34 men and 25 women), with a median age of 66 years (range, 41-85 years). Thirty patients had pT3a, 24 had pT3b, and five had pT3c. Among the ADC first-order textural differences between pT3a and pT3b-c rectal adenocarcinomas, only skewness was significantly lower in the pT3a tumors than in pT3b-c tumors. Among the ADC second-order textural differences, energy and entropy were significantly different between pT3a and pT3b-c rectal adenocarcinomas. For differentiating pT3a rectal adenocarcinomas from pT3b-c tumors, the areas under the curves (AUCs) of skewness, energy, and entropy were 0.686, 0.657, and 0.747, respectively. Logistic regression analysis of all three features yielded a greater AUC (0.775) in differentiating pT3a rectal adenocarcinomas from pT3b-c tumors (69.0% sensitivity and 83.3% specificity).

TA features derived from ADC maps might potentially differentiate pT3a rectal adenocarcinomas from pT3b-c tumors.

• Diffusion-weighted imaging
• Apparent diffusion coefficient
• Rectal cancer
• Cancer stage
• Texture analysis
• case-control study

Radiomics is an emerging field of research that aims to find associations between quantitative information extracted from imaging examinations and clinical data to support the best clinical decision. In the last few years, some papers have been evaluating the role of radiomics in gynecological malignancies, mainly focusing on ovarian cancer. Nonetheless, cervical cancer is the most frequent gynecological malignancy in developing countries and endometrial cancer is the most common in western countries. The purpose of this narrative review is to give an overview of the latest published papers evaluating the role of radiomics in cervical and endometrial cancer, mostly evaluating association with tumor prognostic factors, with response to therapy and with prediction of recurrence and distant metastasis.

MR Radiomics based on cervical lesions from one single scanner has achieved promising results. However, it is a challenge to achieve clinical translation. Considering multi-scanners and non-uniform scanning parameters from different centers in a real-world medical scenario, we should first identify the influence of such conditions on the robustness of MR radiomics features (RFs) based on the female cervix. In this study, 9 healthy female volunteers were enrolled and 3 kiwis were selected as references. Each of them underwent T2 weighted imaging in three different 3.0-T MR scanners with uniform acquisition parameters, and in one MR scanner with various scanning parameters. A total of 396 RFs were extracted from their images with and without decile intensity normalization. The RFs’ reproducibility was evaluated by coefficient of variation (CV) and quartile coefficient of dispersion (QCD). Representative features were selected using the hierarchical cluster analysis and their discrimination abilities were estimated by ROC analysis through retrospective comparison with the junctional zone and the outer muscular layer of healthy cervix in patients (n = 58) with leiomyoma. This study showed that only a few RFs were robust across different MR scanners and acquisition parameters based on females’ cervix, which might be improved by decile intensity normalization method.

Objective: To develop and validate a radiomics predictive model based on multiparameter MR imaging features and clinical features to predict lymph node metastasis (LNM) in patients with cervical cancer.

Material and Methods: A total of 168 consecutive patients with cervical cancer from two centers were enrolled in our retrospective study. A total of 3,930 imaging features were extracted from T2-weighted (T2W), ADC, and contrast-enhanced T1-weighted (cT1W) images for each patient. Four-step procedures, mainly minimum redundancy maximum relevance (MRMR) and least absolute shrinkage and selection operator (LASSO) regression, were applied for feature selection and radiomics signature building in the training set from center I (n = 115). Combining clinical risk factors, a radiomics nomogram was then constructed. The models were then validated in the external validation set comprising 53 patients from center II. The predictive performance was determined by its calibration, discrimination, and clinical usefulness.

Results: The radiomics signature derived from the combination of T2W, ADC, and cT1W images, composed of six LN-status-related features, was significantly associated with LNM and showed better predictive performance than signatures derived from either of them alone in both sets. Encouragingly, the radiomics signature also showed good discrimination in the MRI-reported LN-negative subgroup, with AUC of 0.825 (95% CI: 0.732–0.919). The radiomics nomogram that incorporated radiomics signature and MRI-reported LN status also showed good calibration and discrimination in both sets, with AUCs of 0.865 (95% CI: 0.794–0.936) and 0.861 (95% CI: 0.733–0.990), respectively. Decision curve analysis confirmed its clinical usefulness.

Conclusion: The proposed MRI-based radiomics nomogram has good performance for predicting LN metastasis in cervical cancer and may be useful for improving clinical decision making.

• lymph nodes
• magnetic resonance imaging
• nomograms
• predictive value of tests
• uterine cervical neoplasms

• Lymph nodes
• Radiomics
• Ultrasonography
• Uterine cervical neoplasms

• Adult
• Aged
• Area Under Curve
• Feasibility Studies
• Female
• Humans
• Image Processing, Computer-Assisted/methods
• Lymph Nodes/diagnostic imaging
• Lymph Nodes/pathology
• Lymphatic Metastasis/diagnostic imaging
• Middle Aged
• ROC Curve
• Retrospective Studies
• Ultrasonography
• Uterine Cervical Neoplasms/diagnostic imaging
• Uterine Cervical Neoplasms/pathology

• 11675122 National Natural Science Foundation of China
• 2018ZY016 Wenzhou Municipal Science and Technology Bureau
• Y20190183 Wenzhou Municipal Science and Technology Bureau

• Adenocarcinoma
• Area under the curve
• Entropy
• Magnetic resonance imaging
• Squamous cell carcinoma

It is evident that an accurate evaluation of T and N stage rectal cancer is essential for treatment planning. It has not been extensively investigated whether texture features derived from diffusion-weighted imaging (DWI) images and apparent diffusion coefficient (ADC) maps are associated with the extent of local invasion (pathological stage T1-2 vs T3-4) and nodal involvement (pathological stage N0 vs N1-2) in rectal cancer.

To predict different stages of rectal cancer using texture analysis based on DWI images and ADC maps.

One hundred and fifteen patients with pathologically proven rectal cancer, who underwent preoperative magnetic resonance imaging, including DWI, were enrolled, retrospectively. The ADC measurements (ADC_mean, ADC_min, ADC_max) as well as texture features, including the gray level co-occurrence matrix parameters, the gray level run-length matrix parameters and wavelet parameters were calculated based on DWI (b = 0 and b = 1000) images and the ADC maps. Independent sample t-tests or Mann-Whitney U tests were used for statistical analysis. Multivariate logistic regression analysis was conducted to establish the models. The predictive performance was validated by receiver operating characteristic curve analysis.

Dissimilarity, sum average, information correlation and run-length nonuniformity from DWI_b=0 images, gray level nonuniformity, run percentage and run-length nonuniformity from DWI_b=1000 images, and dissimilarity and run percentage from ADC maps were found to be independent predictors of local invasion (stage T3-4). The area under the operating characteristic curve of the model reached 0.793 with a sensitivity of 78.57% and a specificity of 74.19%. Sum average, gray level nonuniformity and the horizontal components of symlet transform (SymletH) from DWI_b=0 images, sum average, information correlation, long run low gray level emphasis and SymletH from DWI_b=1000 images, and ADC_max, ADC_mean and information correlation from ADC maps were identified as independent predictors of nodal involvement. The area under the operating characteristic curve of the model reached 0.802 with a sensitivity of 80.77% and a specificity of 68.25%.

Texture features extracted from DWI images and ADC maps are useful clues for predicting pathological T and N stages in rectal cancer.

• Rectal cancer
• Diffusion weighted imaging
• Apparent diffusion coefficient
• Texture analysis

• Adult
• Aged
• Aged, 80 and over
• Diffusion Magnetic Resonance Imaging/methods
• Feasibility Studies
• Female
• Humans
• Image Interpretation, Computer-Assisted
• Lymphatic Metastasis/diagnostic imaging
• Lymphatic Metastasis/pathology
• Male
• Middle Aged
• Neoplasm Invasiveness/diagnostic imaging
• Neoplasm Invasiveness/pathology
• Neoplasm Staging
• Preoperative Period
• Proctectomy
• ROC Curve
• Rectal Neoplasms/diagnosis
• Rectal Neoplasms/pathology
• Rectal Neoplasms/surgery
• Rectum/diagnostic imaging
• Rectum/pathology
• Rectum/surgery
• Retrospective Studies

Current chemoradiation regimens for locally advanced cervical cancer are fairly uniform despite a profound diversity of treatment response and recurrence patterns. The wide range of treatment responses and prognoses to standardized concurrent chemoradiation highlights the need for a reliable tool to predict treatment outcomes. We investigated pretreatment magnetic resonance (MR) imaging features of primary tumor and involved lymph node for predicting clinical outcome in cervical cancer patients.

We included 93 node-positive cervical cancer patients treated with definitive chemoradiotherapy at our institution between 2006 and 2017. The median follow-up period was 38 months (range, 5–128). Primary tumor and involved lymph node were manually segmented on axial gadolinium-enhanced T1-weighted images as well as T2-weighted images and saved as 3-dimensional regions of interest (ROI). After the segmentation, imaging features related to histogram, shape, and texture were extracted from each ROI. Using these features, random survival forest (RSF) models were built to predict local control (LC), regional control (RC), distant metastasis-free survival (DMFS), and overall survival (OS) in the training dataset (n = 62). The generated models were then tested in the validation dataset (n = 31).

For predicting LC, models generated from primary tumor imaging features showed better predictive performance (C-index, 0.72) than those from lymph node features (C-index, 0.62). In contrast, models from lymph nodes showed superior performance for predicting RC, DMFS, and OS compared to models of the primary tumor. According to the 3-year time-dependent receiver operating characteristic analysis of LC, RC, DMFS, and OS prediction, the respective area under the curve values for the predicted risk of the models generated from the training dataset were 0.634, 0.796, 0.733, and 0.749 in the validation dataset.

Our results suggest that tumor and lymph node imaging features may play complementary roles for predicting clinical outcomes in node-positive cervical cancer.

• Cervical cancer
• Chemoradiotherapy
• Lymph node
• Predictive modeling
• Radiomics
• Texture analysis

• Cervical Cancer
• Diffusion-weighted imaging
• Intravoxel incoherent motion
• Lymph node metastasis
• Magnetic resonance imaging
• Perfusion

• Adult
• Aged
• Diffusion Magnetic Resonance Imaging/methods
• Diffusion Magnetic Resonance Imaging/standards
• Female
• Humans
• Lymphatic Metastasis/diagnostic imaging
• Lymphatic Metastasis/pathology
• Middle Aged
• Observer Variation
• Uterine Cervical Neoplasms/diagnostic imaging
• Uterine Cervical Neoplasms/pathology

• GRF 17119916 Research Grants Council, University Grants Committee

• apparent diffusion coefficient
• b-value combination
• cervical cancer
• radiomics

• Adult
• Aged
• Cervix Uteri/diagnostic imaging
• Diffusion Magnetic Resonance Imaging/methods
• Female
• Humans
• Image Interpretation, Computer-Assisted/methods
• Middle Aged
• Retrospective Studies
• Uterine Cervical Neoplasms/diagnostic imaging

• PET/MR hybrid imaging
• metastasis
• multiparametric
• pancreas—adenocarcinoma
• texture analysis

• Cervical cancer
• Magnetic resonance imaging
• Nomogram
• Parametrial invasion

• Cervical cancer
• Diffusion-weighted imaging
• Intravoxel incoherent motion
• Magnetic resonance imaging
• b-values

• Adenocarcinoma/pathology
• Adult
• Aged
• Aged, 80 and over
• Algorithms
• Carcinoma, Squamous Cell/pathology
• Female
• Humans
• Image Processing, Computer-Assisted/methods
• Middle Aged
• Neoplasm Staging
• Retrospective Studies
• Signal-To-Noise Ratio
• Uterine Cervical Neoplasms/pathology
• Young Adult

• Council Hong Kong

• 18F-fluorodeoxyglucose positron emission tomography (18F-FDG PET)
• Parkinson’s disease (PD)
• radiomics
• support vector machine (SVM)

Our aims were to determine if features derived from texture analysis (TA) can distinguish normal, benign, and malignant tissue on automated breast ultrasound (ABUS); to evaluate whether machine learning (ML) applied to TA can categorise ABUS findings; and to compare ML to the analysis of single texture features for lesion classification.

This ethically approved retrospective pilot study included 54 women with benign (n = 38) and malignant (n = 32) solid breast lesions who underwent ABUS. After manual region of interest placement along the lesions’ margin as well as the surrounding fat and glandular breast tissue, 47 texture features (TFs) were calculated for each category. Statistical analysis (ANOVA) and a support vector machine (SVM) algorithm were applied to the texture feature to evaluate the accuracy in distinguishing (i) lesions versus normal tissue and (ii) benign versus malignant lesions.

Skewness and kurtosis were the only TF significantly different among all the four categories (p < 0.000001). In subsets (i) and (ii), a maximum area under the curve of 0.86 (95% confidence interval [CI] 0.82–0.88) for energy and 0.86 (95% CI 0.82–0.89) for entropy were obtained. Using the SVM algorithm, a maximum area under the curve of 0.98 for both subsets was obtained with a maximum accuracy of 94.4% in subset (i) and 90.7% in subset (ii).

TA in combination with ML might represent a useful diagnostic tool in the evaluation of breast imaging findings in ABUS. Applying ML techniques to TFs might be superior compared to the analysis of single TF.

The online version of this article (10.1186/s41747-019-0121-6) contains supplementary material, which is available to authorized users.

• Breast neoplasms
• Machine learning
• Ultrasonography

Accurate assessment of nodal metastasis in head and neck squamous cell carcinoma (SCC) is important, and diffusion-weighted imaging (DWI) has emerged as a potential technique in differentiating benign from malignant lymph nodes (LNs). This study aims to evaluate the diagnostic performance of texture analysis using apparent diffusion coefficient (ADC) data of multi-shot echo-planar imaging-based DWI (msEPI-DWI) in predicting metastatic LNs of head and neck SCC. 36 patients with pathologically proven head and neck SCC were included in this study. A total of 204 MRI-detected LNs, including 176 subcentimeter-sized LNs, were assigned to metastatic or benign groups. Texture features of LNs were compared using independent t-test. Hierarchical cluster analysis was performed to exclude redundant features. Multivariate logistic regression and receiver operating characteristic analysis were performed to assess the diagnostic performance. The discriminative texture features for predicting metastatic LNs were complexity, energy and roundness. Areas under the curves (AUCs) for diagnosing metastasis in all/subcentimeter-sized LNs were 0.829/0.767 using complexity, 0.699/0.685 using energy and 0.671/0.638 using roundness, respectively. The combination of three features resulted in higher AUC values of 0.836/0.781. In conclusion, texture analysis of ADC data using msEPI-DWI could be a useful tool for nodal staging in head and neck SCC.

• apparent diffusion coefficient
• diffusion-weighted imaging
• lymph node
• squamous cell carcinoma
• texture analysis

• Cervical cancer
• Diffusion-weighted imaging
• Imaging biomarkers
• Magnetic resonance imaging
• Positron emission tomography
• Transvaginal ultrasound

• Female
• Humans
• Lymphatic Metastasis
• Magnetic Resonance Imaging/methods
• Neoplasm Staging
• Positron Emission Tomography Computed Tomography/methods
• Tomography, X-Ray Computed/methods
• Ultrasonography/methods
• Uterine Cervical Neoplasms/diagnostic imaging
• Uterine Cervical Neoplasms/pathology
• Uterine Cervical Neoplasms/therapy

• Adult
• Aged
• Biomarkers, Tumor/analysis
• Female
• Follow-Up Studies
• Humans
• Image Interpretation, Computer-Assisted
• Logistic Models
• Magnetic Resonance Imaging
• Middle Aged
• Neoplasm Recurrence, Local/diagnostic imaging
• Neoplasm Recurrence, Local/pathology
• Neoplasm Recurrence, Local/therapy
• Neoplasm Staging
• ROC Curve
• Support Vector Machine
• Treatment Outcome
• Uterine Cervical Neoplasms/diagnostic imaging
• Uterine Cervical Neoplasms/pathology
• Uterine Cervical Neoplasms/therapy

• BK20150102 Natural Science Foundation of Jiangsu Province (Jiangsu Provincial Natural Science Foundation)
• BK20150109 Natural Science Foundation of Jiangsu Province (Jiangsu Provincial Natural Science Foundation)
• 81501441 National Natural Science Foundation of China (National Science Foundation of China)
• 81371516, 81671751 National Natural Science Foundation of China (National Science Foundation of China)
• W201306 National Health and Family Planning Commission of the People's Republic of China (Ministry of Health of China)
• Jiangsu Province Health and Family Planning Commission Youth Scientific Research Project (Q201508) Six Talent Peaks Project of Jiangsu Province (2015-WSN-079)
• National Health and Family Planning Commission of the People&apos;s Republic of China (Ministry of Health of China)
• Social Development Foundation of Jiangsu Province (BE2015605) Key Project supported by Medical Science and technology development Foundation, Nanjing Department of Health (YKK15067)