Multi-institutional validation of a novel textural analysis tool for preoperative stratification of suspected thyroid tumors on diffusion-weighted MRI

Radiomics Applications in Head and Neck Tumor Imaging: A Narrative Review

Recent advances in machine learning and artificial intelligence technology have ensured automated evaluation of medical images. As a result, quantifiable diagnostic and prognostic biomarkers have been created. We discuss radiomics applications for the head and neck region in this paper. Molecular characterization, categorization, prognosis and therapy recommendation are given special consideration. In a narrative manner, we outline the fundamental technological principles, the overall idea and usual workflow of radiomic analysis and what seem to be the present and potential challenges in normal clinical practice. Clinical oncology intends for all of this to ensure informed decision support for personalized and useful cancer treatment. Head and neck cancers present a unique set of diagnostic and therapeutic challenges. These challenges are brought on by the complicated anatomy and heterogeneity of the area under investigation. Radiomics has the potential to address these barriers. Future research must be interdisciplinary and focus on the study of certain oncologic functions and outcomes, with external validation and multi-institutional cooperation in order to achieve this.

Radiomic analysis based on multi-phase magnetic resonance imaging to predict preoperatively microvascular invasion in hepatocellular carcinoma

BACKGROUND

The prognosis of hepatocellular carcinoma (HCC) remains poor and relapse occurs in more than half of patients within 2 years after hepatectomy. In terms of recent studies, microvascular invasion (MVI) is one of the potential predictors of recurrence. Accurate preoperative prediction of MVI is potentially beneficial to the optimization of treatment planning.

AIM

To develop a radiomic analysis model based on pre-operative magnetic resonance imaging (MRI) data to predict MVI in HCC.

METHODS

A total of 113 patients recruited to this study have been diagnosed as having HCC with histological confirmation, among whom 73 were found to have MVI and 40 were not. All the patients received preoperative examination by Gd-enhanced MRI and then curative hepatectomy. We manually delineated the tumor lesion on the largest cross-sectional area of the tumor and the adjacent two images on MRI, namely, the regions of interest. Quantitative analyses included most discriminant factors (MDFs) developed using linear discriminant analysis algorithm and histogram analysis with MaZda software. Independent significant variables of clinical and radiological features and MDFs for the prediction of MVI were estimated and a discriminant model was established by univariate and multivariate logistic regression analysis. Prediction ability of the above-mentioned parameters or model was then evaluated by receiver operating characteristic (ROC) curve analysis. Five-fold cross-validation was also applied via R software.

RESULTS

The area under the ROC curve (AUC) of the MDF (0.77-0.85) outperformed that of histogram parameters (0.51-0.74). After multivariate analysis, MDF values of the arterial and portal venous phase, and peritumoral hypointensity in the hepatobiliary phase were identified to be independent predictors of MVI (P < 0.05). The AUC value of the model was 0.939 [95% confidence interval (CI): 0.893-0.984, standard error: 0.023]. The result of internal five-fold cross-validation (AUC: 0.912, 95%CI: 0.841-0.959, standard error: 0.0298) also showed favorable predictive efficacy.

CONCLUSION

Noninvasive MRI radiomic model based on MDF values and imaging biomarkers may be useful to make preoperative prediction of MVI in patients with primary HCC.

Relevance of apparent diffusion coefficient features for a radiomics-based prediction of response to induction chemotherapy in sinonasal cancer

In this paper, several radiomics-based predictive models of response to induction chemotherapy (IC) in sinonasal cancers (SNCs) are built and tested. Models were built as a combination of radiomic features extracted from three types of MRI images: T1-weighted images, T2-weighted images and apparent diffusion coefficient (ADC) maps. Fifty patients (aged 54 ± 12 years, 41 men) were included in this study. Patients were classified according to their response to IC (25 responders and 25 nonresponders). Not all types of images were acquired for all of the patients: 49 had T1-weighted images, 50 had T2-weighted images and 34 had ADC maps. Only in a subset of 33 patients were all three types of image acquired. Eighty-nine radiomic features were extracted from the MRI images. Dimensionality reduction was performed by using principal component analysis (PCA) and by selecting only the three main components. Different algorithms (trees ensemble, K-nearest neighbors, support vector machine, naïve Bayes) were used to classify the patients as either responders or nonresponders. Several radiomic models (either monomodality or multimodality obtained by a combination of T1-weighted, T2-weighted and ADC images) were developed and the performance was assessed through 100 iterations of train and test split. The area under the curve (AUC) of the models ranged from 0.56 to 0.78. Trees ensemble, support vector machine and naïve Bayes performed similarly, but in all cases ADC-based models performed better. Trees ensemble gave the highest AUC (0.78 for the T1-weighted+T2-weighted+ADC model) and was used for further analyses. For trees ensemble, the models based on ADC features performed better than those models that did not use those features (P < 0.02 for one-tail Hanley test, AUC range 0.68-0.78 vs 0.56-0.69) except the T1-weighted+ADC model (AUC 0.71 vs 0.69, nonsignificant differences). The results suggest the relevance of ADC-based radiomics for prediction of response to IC in SNCs.

Refining Tumor Treatment in Sinonasal Cancer Using Delta Radiomics of Multi-Parametric MRI after the First Cycle of Induction Chemotherapy

Background: Response to induction chemotherapy (IC) has been predicted in patients with sinonasal cancer using early delta radiomics obtained from T1- and T2-weighted images and apparent diffusion coefficient (ADC) maps, comparing results with early radiological evaluation by RECIST. Methods: Fifty patients were included in the study. For each image (at baseline and after the first IC cycle), 536 radiomic features were extracted as follows: semi-supervised principal component analysis components, explaining 97% of the variance, were used together with a support vector machine (SVM) to develop a radiomic signature. One signature was developed for each sequence (T1-, T2-weighted and ADC). A multiagent decision-making algorithm was used to merge multiple signatures into one score. Results: The area under the curve (AUC) for mono-modality signatures was 0.79 (CI: 0.65–0.88), 0.76 (CI: 0.62–0.87) and 0.93 (CI: 0.75–1) using T1-, T2-weighted and ADC images, respectively. The fuse signature improved the AUC when an ADC-based signature was added. Radiological prediction using RECIST criteria reached an accuracy of 0.78. Conclusions: These results suggest the importance of early delta radiomics and of ADC maps to predict the response to IC in sinonasal cancers.

Magnetic Fields and Cancer: Epidemiology, Cellular Biology, and Theranostics

Humans are exposed to a complex mix of man-made electric and magnetic fields (MFs) at many different frequencies, at home and at work. Epidemiological studies indicate that there is a positive relationship between residential/domestic and occupational exposure to extremely low frequency electromagnetic fields and some types of cancer, although some other studies indicate no relationship. In this review, after an introduction on the MF definition and a description of natural/anthropogenic sources, the epidemiology of residential/domestic and occupational exposure to MFs and cancer is reviewed, with reference to leukemia, brain, and breast cancer. The in vivo and in vitro effects of MFs on cancer are reviewed considering both human and animal cells, with particular reference to the involvement of reactive oxygen species (ROS). MF application on cancer diagnostic and therapy (theranostic) are also reviewed by describing the use of different magnetic resonance imaging (MRI) applications for the detection of several cancers. Finally, the use of magnetic nanoparticles is described in terms of treatment of cancer by nanomedical applications for the precise delivery of anticancer drugs, nanosurgery by magnetomechanic methods, and selective killing of cancer cells by magnetic hyperthermia. The supplementary tables provide quantitative data and methodologies in epidemiological and cell biology studies. Although scientists do not generally agree that there is a cause-effect relationship between exposure to MF and cancer, MFs might not be the direct cause of cancer but may contribute to produce ROS and generate oxidative stress, which could trigger or enhance the expression of oncogenes.

Texture and shape analysis of diffusion-weighted imaging for thyroid nodules classification using machine learning

PURPOSE

To assess whether the integration between (a) functional imaging features that will be extracted from diffusion-weighted imaging (DWI); and (b) shape and texture imaging features as well as volumetric features that will be extracted from T2-weighted magnetic resonance imaging (MRI) can noninvasively improve the diagnostic accuracy of thyroid nodules classification.

PATIENTS AND METHODS

In a retrospective study of 55 patients with pathologically proven thyroid nodules, T2-weighted and diffusion-weighted MRI scans of the thyroid gland were acquired. Spatial maps of the apparent diffusion coefficient (ADC) were reconstructed in all cases. To quantify the nodules' morphology, we used spherical harmonics as a new parametric shape descriptor to describe the complexity of the thyroid nodules in addition to traditional volumetric descriptors (e.g., tumor volume and cuboidal volume). To capture the inhomogeneity of the texture of the thyroid nodules, we used the histogram-based statistics (e.g., kurtosis, entropy, skewness, etc.) of the T2-weighted signal. To achieve the main goal of this paper, a fusion system using an artificial neural network (NN) is proposed to integrate both the functional imaging features (ADC) with the structural morphology and texture features. This framework has been tested on 55 patients (20 patients with malignant nodules and 35 patients with benign nodules), using leave-one-subject-out (LOSO) for training/testing validation tests.

RESULTS

The functionality, morphology, and texture imaging features were estimated for 55 patients. The accuracy of the computer-aided diagnosis (CAD) system steadily improved as we integrate the proposed imaging features. The fusion system combining all biomarkers achieved a sensitivity, specificity, positive predictive value, negative predictive value, F1-score, and accuracy of 92.9 % (confidence interval [CI]: 78.9 % -- 99.5 % ), 95.8 % (CI: 87.4 % -- 99.7 % ), 93 % (CI: 80.7 % -- 99.5 % ), 96 % (CI: 88.8 % -- 99.7 % ), 92.8 % (CI: 83.5 % -- 98.5 % ), and 95.5 % (CI: 88.8 % -- 99.2 % ), respectively, using the LOSO cross-validation approach.

CONCLUSION

The results demonstrated in this paper show the promise that integrating the functional features with morphology as well as texture features by using the current state-of-the-art machine learning approaches will be extremely useful for identifying thyroid nodules as well as diagnosing their malignancy.

Artificial Intelligence and Deep Learning of Head and Neck Cancer

Artificial intelligence (AI) algorithms, particularly deep learning, have developed to the point that they can be applied in image recognition tasks. The use of AI in medical imaging can guide radiologists to more accurate image interpretation and diagnosis in radiology. The software will provide data that we cannot extract from the images. The rapid development in computational capabilities supports the wide applications of AI in a range of cancers. Among those are its widespread applications in head and neck cancer.

Machine Learning: Applications and Advanced Progresses of Radiomics in Endocrine Neoplasms

Endocrine neoplasms remain a great threat to human health. It is extremely important to make a clear diagnosis and timely treatment of endocrine tumors. Machine learning includes radiomics, which has long been utilized in clinical cancer research. Radiomics refers to the extraction of valuable information by analyzing a large amount of standard data with high-throughput medical images mainly including computed tomography, positron emission tomography, magnetic resonance imaging, and ultrasound. With the quantitative imaging analysis and model building, radiomics can reflect specific underlying characteristics of a disease that otherwise could not be evaluated visually. More and more promising results of radiomics in oncological practice have been seen in recent years. Radiomics may have the potential to supplement traditional imaging analysis and assist in providing precision medicine for patients. Radiomics had developed rapidly in endocrine neoplasms practice in the past decade. In this review, we would introduce the general workflow of radiomics and summarize the applications and developments of radiomics in endocrine neoplasms in recent years. The limitations of current radiomic research studies and future development directions would also be discussed.

Texture Analysis and its Applications in Biomedical Imaging: A Survey

Texture analysis describes a variety of image analysis techniques that quantify the variation in intensity and pattern. This paper provides an overview of several texture analysis approaches addressing the rationale supporting them, their advantages, drawbacks, and applications. This surveys emphasis is in collecting and categorising over five decades of active research on texture analysis. Brief descriptions of different approaches are presented along with application examples. From a broad range of texture analysis applications, this surveys final focus is on biomedical image analysis. An up-to-date list of biological tissues and organs in which disorders produce texture changes that may be used to spot disease onset and progression is provided. Finally, the role of texture analysis methods as biomarkers of disease is summarised.

Establishing ADC-Based Histogram and Texture Features for Early Treatment-Induced Changes in Head and Neck Squamous Cell Carcinoma

The purpose of this study was to assess baseline variability in histogram and texture features derived from apparent diffusion coefficient (ADC) maps from diffusion-weighted MRI (DW-MRI) examinations and to identify early treatment-induced changes to these features in patients with head and neck squamous cell carcinoma (HNSCC) undergoing definitive chemoradiation. Patients with American Joint Committee on Cancer Stage III–IV (7^th edition) HNSCC were prospectively enrolled on an IRB-approved study to undergo two pre-treatment baseline DW-MRI examinations, performed 1 week apart, and a third early intra-treatment DW-MRI examination during the second week of chemoradiation. Forty texture and six histogram features were derived from ADC maps. Repeatability of the features from the baseline ADC maps was assessed with the intra-class correlation coefficient (ICC). A Wilcoxon signed-rank test compared average baseline and early treatment feature changes. Data from nine patients were used for this study. Comparison of the two baseline ADC maps yielded 11 features with an ICC ≥ 0.80, indicating that these features had excellent repeatability: Run Gray-Level Non-Uniformity, Coarseness, Long Zone High Gray-Level, Variance (Histogram Feature), Cluster Shade, Long Zone, Variance (Texture Feature), Run Length Non-Uniformity, Correlation, Cluster Tendency, and ADC Median. The Wilcoxon signed-rank test resulted in four features with significantly different early treatment-induced changes compared to the baseline values: Run Gray-Level Non-Uniformity (p = 0.005), Run Length Non-Uniformity (p = 0.005), Coarseness (p = 0.006), and Variance (Histogram) (p = 0.006). The feasibility of histogram and texture analysis as a potential biomarker is dependent on the baseline variability of each metric, which disqualifies many features.

Radiomics feature stability of open-source software evaluated on apparent diffusion coefficient maps in head and neck cancer

Radiomics is a promising technique for discovering image based biomarkers of therapy response in cancer. Reproducibility of radiomics features is a known issue that is addressed by the image biomarker standardisation initiative (IBSI), but it remains challenging to interpret previously published radiomics signatures. This study investigates the reproducibility of radiomics features calculated with two widely used radiomics software packages (IBEX, MaZda) in comparison to an IBSI compliant software package (PyRadiomics). Intensity histogram, shape and textural features were extracted from 334 diffusion weighted magnetic resonance images of 59 head and neck cancer (HNC) patients from the PREDICT-HN observational radiotherapy study. Based on name and linear correlation, PyRadiomics shares 83 features with IBEX and 49 features with MaZda, a sub-set of well correlated features are considered reproducible (IBEX: 15 features, MaZda: 18 features). We explore the impact of including non-reproducible radiomics features in a HNC radiotherapy response model. It is possible to classify equivalent patient groups using radiomic features from either software, but only when restricting the model to reliable features using a correlation threshold method. This is relevant for clinical biomarker validation trials as it provides a framework to assess the reproducibility of reported radiomic signatures from existing trials.

Diagnostic Utility of Radiomics in Thyroid and Head and Neck Cancers

Radiomics is an emerging field in radiology that utilizes advanced statistical data characterizing algorithms to evaluate medical imaging and objectively quantify characteristics of a given disease. Due to morphologic heterogeneity and genetic variation intrinsic to neoplasms, radiomics have the potential to provide a unique insight into the underlying tumor and tumor microenvironment. Radiomics has been gaining popularity due to potential applications in disease quantification, predictive modeling, treatment planning, and response assessment - paving way for the advancement of personalized medicine. However, producing a reliable radiomic model requires careful evaluation and construction to be translated into clinical practices that have varying software and/or medical equipment. We aim to review the diagnostic utility of radiomics in otorhinolaryngology, including both cancers of the head and neck as well as the thyroid.

Novel MRI-Based CAD System for Early Detection of Thyroid Cancer Using Multi-Input CNN

Early detection of thyroid nodules can greatly contribute to the prediction of cancer burdening and the steering of personalized management. We propose a novel multimodal MRI-based computer-aided diagnosis (CAD) system that differentiates malignant from benign thyroid nodules. The proposed CAD is based on a novel convolutional neural network (CNN)-based texture learning architecture. The main contribution of our system is three-fold. Firstly, our system is the first of its kind to combine T2-weighted MRI and apparent diffusion coefficient (ADC) maps using a CNN to model thyroid cancer. Secondly, it learns independent texture features for each input, giving it more advanced capabilities to simultaneously extract complex texture patterns from both modalities. Finally, the proposed system uses multiple channels for each input to combine multiple scans collected into the deep learning process using different values of the configurable diffusion gradient coefficient. Accordingly, the proposed system would enable the learning of more advanced radiomics with an additional advantage of visualizing the texture patterns after learning. We evaluated the proposed system using data collected from a cohort of 49 patients with pathologically proven thyroid nodules. The accuracy of the proposed system has also been compared against recent CNN models as well as multiple machine learning (ML) frameworks that use hand-crafted features. Our system achieved the highest performance among all compared methods with a diagnostic accuracy of 0.87, specificity of 0.97, and sensitivity of 0.69. The results suggest that texture features extracted using deep learning can contribute to the protocols of cancer diagnosis and treatment and can lead to the advancement of precision medicine.

Investigation of MRI-based radiomics model in differentiation between sinonasal primary lymphomas and squamous cell carcinomas

PURPOSE

To develop and validate an MRI-based radiomics model in differentiation between sinonasal primary lymphomas and squamous cell carcinomas (SCCs).

MATERIALS AND METHODS

One-hundred-and-fifty-four patients were enrolled (74 individuals with SCCs and 80 with lymphomas). After feature analysis and feature selection with variance threshold and least absolute shrinkage and selection operator (LASSO) methods, an MRI-based radiomics model with the support vector machine (SVM) classifier was constructed in differentiation between lymphomas and SCCs. Areas under the receiver operating characteristic curves (AUCs) of the MRI-based radiomics model were compared with those of radiologists using Delong test.

RESULTS

Five features (T1 original shape Compactness2, T1 wavelet-HHH first-order Total Energy, T2 wavelet-HLH GLCM Informational Measure of Correlation1, T1 wavelet-LHL GLCM Inverse Variance and T1 square GLRLM Long Run Low Gray Level Emphasis) were finally selected in the radiomics model. The AUC values in differentiation between lymphomas and SCCs were 0.94 for the training dataset and 0.85 for the validation dataset, respectively. For all the patient datasets, the AUC values of radiomics model, readers 1, 2 and 3 were 0.92, 0.76, 0.77 and 0.80, respectively. For the validation datasets, no significant difference was found between the AUCs of the radiomics model and those of the three radiologist (P = 0.459, 0.469, 0.738 for radiologist 1, 2 and 3, respectively).

CONCLUSION

An MRI-based radiomics model can help to differentiate sinonasal lymphomas from SCCs with high accuracy.

Malignancy rates and initial management of Thy3 thyroid nodules in a district general hospital: The 'Reading' experience

Background

Ultrasound-guided fine-needle aspiration cytology is the gold standard for investigating thyroid nodules. Stratifying the Thy3 thyroid nodule risk of malignancy is essential for clinical decision-making. According to the Royal College of Pathologists Guidance (2016), the rate of malignancy for Thy3a is 5-15% and for Thy3f 15-30%. Our aim was to investigate the malignancy rate and the initial management of Thy3 nodules in our institution.

Methods

A retrospective review was undertaken of 115 patients with Thy3 cytology results from thyroid fine-needle aspirations performed between January 2015 and June 2020 at a single centre. A total of 90 out of 115 patients underwent surgery.

Results

Of the 90 patients, we had a 40% malignant rate (36/90). Specifically, 14 of 34 (41.1%) Thy3a and 22 of 56 (39.2%) Thy3f nodules were malignant. Of the malignant lesions, 52.7% (19/36) were follicular thyroid carcinoma. 58.8% (10/17) of male patients and 35.6% (26/73) of female patients had a malignant histology. Eighteen patients eventually needed a completion thyroidectomy.

Conclusion

Compared with national data, we showed a higher risk of malignancy in Thy3 nodules in our centre. Our study should encourage other centres to audit their own data. We propose setting up a national Thy3 registry as a basis to promote research in improving preoperative diagnosis of indeterminate thyroid nodules.

Prediction of Cervical Lymph Node Metastasis Using MRI Radiomics Approach in Papillary Thyroid Carcinoma: A Feasibility Study

BACKGROUND

Cervical lymph node (LN) metastasis of papillary thyroid carcinoma (PTC) is critical for treatment and prognosis. To examine the feasibility of MRI radiomics to preoperatively predict cervical LN metastasis in patients with PTC.

METHODS

Between January 2015 and March 2018, a total of 61 patients with pathologically confirmed PTC were analyzed retrospectively. The patients were divided into cervical LN metastasis group (n = 37) and no cervical LN metastasis (n = 24). T2WI and T2WI-fat-suppression (T2WI-FS) images were collected. A number of radiomic features were automatically extracted from the largest section of tumor. Three types of classifier (the random forests, the support vector machine classifier and the generalized linear model) based on T2WI and T2WI-FS images of cervical LN metastasis and no cervical LN metastasis were constructed and evaluated with a nested cross-validation scheme.

RESULTS

Radiomic features extracted from T2WI images were more discriminative than T2WI-FS images. The random forests model showed the best discriminate performance with the highest area under the curve (0.85, CI:0.76 -1), accuracy (0.87), sensitivity (0.83), specificity (1.00), positive predictive value (PPV = 1.00) and negative predictive value (NPV = 0.88).

CONCLUSION

MRI radiomics analysis based on conventional T2WI and T2WI-FS can predict cervical LN metastasis in patients with PTC, and the radiomics is shown to be an assistant diagnosis tool for radiologists.

Radiomic biomarkers for head and neck squamous cell carcinoma

Tumor heterogeneity is a well-known prognostic factor in head and neck squamous cell carcinoma (HNSCC). A major limitation of tissue- and blood-derived tumor markers is the lack of spatial resolution to image tumor heterogeneity. Tissue markers derived from tumor biopsies usually represent only a small tumor subregion at a single timepoint and are therefore often not representative of the tumors' biology or the biological alterations during and after treatment. Similarly, liquid biopsies give an overall picture of the tumors' secreted factors but completely lack any spatial resolution. Radiomics has the potential to give complete three-dimensional information about the tumor. We conducted a comprehensive literature search to assess the correlation of radiomics to tumor biology and treatment outcome in HNSCC and to assess current limitations of the radiomic biomarkers. In total, 25 studies that explored the ability of radiomics to predict tumor biology and phenotype in HNSCC and 28 studies that explored radiomics to predict post-treatment events were identified. Out of these 53 studies, only three failed to show a significant correlation. The major technical challenges are currently artifacts due to metal implants, non-standardized contrast injection, and delineation uncertainties. All studies to date were retrospective and none of the above-mentioned radiomics signatures have been validated in an independent cohort using an independent software implementation, which shows that transferability due to the numerous technical challenges is currently a major limitation. However, radiomics is a very young field and these studies hopefully pave the way for clinical implementation of radiomics for HNSCC in the future.

Apparent diffusion coefficient values and non-homogeneity of diffusion in brain tumors in diffusion-weighted MRI

Background

The values that have been received from apparent diffusion coefficient (ADC) maps of diffusion-weighted magnetic resonance imaging (DW-MRI) might play a vital role in evaluating tumors and their grading scale.

Purpose

To investigate the predictive role of this heterogeneity in brain tumor pathologies and its correlation with Ki-67.

Material and Methods

A total of 124 patients with brain tumors underwent brain MRI with gadolinium injection. ADC and standard deviation of each lesion have been obtained from manual localization of the region of interest on the ADC map. A receiver operating characteristic analysis was conducted to determine the minimum cut-off values of the mean ADC and mean standard deviation of ADC maps having the highest sensitivity and specificity to differentiate high-grade and low-grade tumors.

Results

Mean ADC values in the region of interest were significantly lower for malignant tumors (grade IV and metastasis) than grade I brain tumors, while a higher mean standard deviation was observed. In a more detailed comparison of tumor groups, the mean standard deviation of the ADC for glioblastoma multiform was significantly higher than meningioma grade I ( P < 0.001) and metastasis was significantly higher than grade III and IV astrocytic tumors ( P = 0.004). The analysis of Ki-67 proliferation index and mean ADC values in gliomas showed a significant inverse correlation between the parameters (r = –0.0429, P < 0.001) and direct correlation between Ki-67 and mean standard deviation of the ADC (r = 0.551, P < 0.001). As an index for the ADC to differentiate high-grade and low-grade tumors, the cut-off values of 1.40*10−3 mm2/s for mean ADC and 45*10−3 mm2/s for mean standard deviation have the highest combination of sensitivity, specificity, and area under the curve.

Conclusion

The mean value and standard deviation of the ADC could be considered for differentiating between low-grade and high-grade brain tumors, as two available non-invasive methods.

Using Diffusion-Weighted MRI to Predict Central Lymph Node Metastasis in Papillary Thyroid Carcinoma: A Feasibility Study

Objective: To investigate whether diffusion-weighted imaging (DWI) with multi b values can be used as a quantitative assessment tool to predict central lymph node metastasis (CLNM) in papillary thyroid carcinoma (PTC). Method: A total of 214 PTC patients were enrolled from January 2015 to April 2018. Each patient underwent multi b value DWI (300, 500, and 800 s/mm²) preoperatively and then clinical treatment of central LN dissection at the Thyroid Surgery Department. These patients were divided as two groups based on with and without CLNM. The corresponding apparent diffusion coefficients (ADCs) were evaluated with separated b value, i.e., 300, 500, or 800 s/mm². Clinicopathological variables and ADC values were analyzed retrospectively by using univariate and binary logistic regression. The corresponding obtained variables with statistical significance were further applied to create a nomogram in which the bootstrap resampling method was used for correction. Results: PTCs with CLNM had significantly lower ADC₃₀₀, ADC₅₀₀, and ADC₈₀₀ values compared with PTCs without CLNM. Using receiver operating characteristic (ROC) analysis, the ADC₅₀₀ value (0.817) showed a higher area under the curve (AUC) than those of the ADC₃₀₀ and ADC₈₀₀ values (0.610 and 0.641, respectively) in differentiating patients with CLNM and without CLNM. The corresponding cutoff value for ADC₅₀₀ was also determined (1.444 × 10^-3 mm²/s), with respective sensitivity and specificity of 88.6 and 66%. The nomogram was generated by binary logistic regression results, incorporating four variables: gender, primary tumor size, extrathyroidal extension (ETE), and ADC₅₀₀ value. The AUC of the nomogram was 0.894 in predicting CLNM. Moreover, as shown in the calibration curve between nomogram and clinical findings, a strong agreement was observed in the prediction of CLNM. Conclusions: In summary, the ADC value is a valuable noninvasive imaging biomarker for evaluating CLNM in PTCs. The nomogram, as a clinical predictive model, is able to provide an effective evaluation of CLNM risk in PTC patients preoperatively.

Epithelial salivary gland tumors: Utility of radiomics analysis based on diffusion-weighted imaging for differentiation of benign from malignant tumors

OBJECTIVE

To evaluate the utility of radiomics analysis for differentiating benign and malignant epithelial salivary gland tumors on diffusion-weighted imaging (DWI).

METHODS

A retrospective dataset involving 218 and 51 patients with histology-confirmed benign and malignant epithelial salivary gland tumors was used in this study. A total of 396 radiomic features were extracted from the DW images. Analysis of variance (ANOVA) and least-absolute shrinkage and selection operator regression (LASSO) were used to select optimal radiomic features. The selected features were used to build three classification models namely, logistic regression method (LR), support vector machine (SVM), and K-nearest neighbor (KNN) by using a five-fold cross validation strategy on the training dataset. The diagnostic performance of each classification model was quantified by receiver operating characteristic (ROC) curve and area under the ROC curve (AUC) in the training and validation datasets.

RESULTS

Eight most valuable features were selected by LASSO. LR and SVM models yielded optimally diagnostic performance. In the training dataset, LR and SVM yielded AUC values of 0.886 and 0.893 via five-fold cross validation, respectively, while KNN model showed relatively lower AUC (0.796). In the testing dataset, a similar result was found, where AUC values for LR, SVM, and KNN were 0.876, 0.870, and 0.791, respectively.

CONCLUSIONS

Classification models based on optimally selected radiomics features computed from DW images present a promising predictive value in distinguishing benign and malignant epithelial salivary gland tumors and thus have potential to be used for preoperative auxiliary diagnosis.

Radiomics and Texture Analysis in Laryngeal Cancer. Looking for New Frontiers in Precision Medicine through Imaging Analysis

Radiomics and texture analysis represent a new option in our biomarkers arsenal. These techniques extract a large number of quantitative features, analyzing their properties to incorporate them in clinical decision-making. Laryngeal cancer represents one of the most frequent cancers in the head and neck area. We hypothesized that radiomics features can be included as a laryngeal cancer precision medicine tool, as it is able to non-invasively characterize the overall tumor accounting for heterogeneity, being a prognostic and/or predictive biomarker derived from routine, standard of care, imaging data, and providing support during the follow up of the patient, in some cases avoiding the need for biopsies. The larynx represents a unique diagnostic and therapeutic challenge for clinicians due to its complex tridimensional anatomical structure. Its complex regional and functional anatomy makes it necessary to enhance our diagnostic tools in order to improve decision-making protocols, aimed at better survival and functional results. For this reason, this technique can be an option for monitoring the evolution of the disease, especially in surgical and non-surgical organ preservation treatments. This concise review article will explain basic concepts about radiomics and discuss recent progress and results related to laryngeal cancer.

In vivo textural and morphometric analysis of placental development in healthy & growth-restricted pregnancies using magnetic resonance imaging

BACKGROUND

The objective of this study was to characterize structural changes in the healthy in vivo placenta by applying morphometric and textural analysis using magnetic resonance imaging (MRI), and to explore features that may be able to distinguish placental insufficiency in fetal growth restriction (FGR).

METHODS

Women with healthy pregnancies or pregnancies complicated by FGR underwent MRI between 20 and 40 weeks gestation. Measures of placental morphometry (volume, elongation, depth) and digital texture (voxel-wise geometric and signal-intensity analysis) were calculated from T2W MR images.

RESULTS

We studied 66 pregnant women (32 healthy controls, 34 FGR); during the study period, placentas undergo significant increases in size; signal intensity remains relatively constant, however there is increasing variation in spatial arrangements, suggestive of progressive microstructural heterogeneity. In FGR, placental size is smaller, with great homogeneity of signal intensity and spatial arrangements.

CONCLUSION

We report quantitative textural and morphometric changes in the in vivo placenta in healthy controls over the second half of pregnancy. These MRI features demonstrate important differences in placental development in the setting of placental insufficiency that relate to onset and severity of FGR, as well as neonatal outcome.

Radiomic Machine Learning and Texture Analysis - New Horizons for Head and Neck Oncology

Radiomics is a relatively new concept that consists of extracting data from images and applies advanced characterization algorithms to generate imaging features. These features are biomarkers with prognostic and predictive value, which provide a characterization of tumor phenotypes in a non-invasive manner. The clinical application of radiomics is hampered by challenges such as lack of image acquisition and analysis standardization. Textural features extracted from computed tomography (CT), magnetic resonance imaging (MRI) and positron emission tomography-computed tomography (PET-CT) images of patients diagnosed with head and neck cancers can be used in the pre-therapeutic evaluation of the response to multimodal chemo-radiotherapy. For patients with positive HPV-oropharyngeal cancers, the correlation of the radiomic textural features from the tumor with p16 values from the pathological sample can identify tumor specific signatures in CT imaging, an entity with favorable prognosis and a better response to chemo-radiotherapy. Pretreatment contrast CT-scans were extracted and radiomics analysis of gross tumor volume were performed using MaZda package apart from MaZda software containing B11 program for texture analysis and visualization. Data set was randomly divided into a training dataset and a test dataset and machine learning algorithms were applied to identify a textural radiomic signature. Radiomic texture analysis and machine learning algorithms demonstrate a predictive potential related to the capability of stratification for subclasses of platinum-chemotherapy resistance and radioresistant head and neck cancers requiring an intensification of multimodal treatment.

Diagnostic value of high b-value (2000 s/mm2) DWI for thyroid micronodules

The aim of the study was to assess the diagnostic value of high b-value (2000 s/mm) diffusion-weighted imaging (DWI) in differentiating malignant from benign thyroid micronodules.Consecutive patients with thyroid micronodules scheduled for Ultrasound (US)-guided fine-needle aspiration biopsy (FNAB) or surgery were underwent high b-value DWI with 3 b-values: 0, 800, and 2000 s/mm. Signal intensity ratios (SIRs) of thyroid micronodules to adjacent normal thyroid tissue on DWI were measured as SIRb0, SIRb800 and SIRb2000. Apparent diffusion coefficients (ADCs) according to the three different b-values were acquired as: ADCb0-800, ADCb0-2000 and ADCb0-800-2000. The 6 diagnostic indicators were evaluated by receiver operating characteristic (ROC) and diagnostic ability was compared between the high b-value DWI and US.Sixty-two malignant thyroid micronodules (48 patients, 13 men and 35 women, aged 44.8 ± 11.7 years) and 57 benign thyroid micronodules (40 patients, 6 men and 34 women, aged 49.6 ± 12.5 years) were enrolled into the final statistical analysis. Among the alone diagnostic indicators, SIRb2000 had the highest diagnostic ability in differentiating malignant from benign thyroid micronodules with area under curve (AUC) of 0.975, sensitivity of 90.32% and specificity of 96.49%. Compared to US, SIRb2000 had a significantly better diagnostic ability US for thyroid micronodules (P < .001) with dramatically raised positive predict value (96.6% vs 78.9%) and reduced false-positive rate (3.51% vs 26.32%).High b-value (2000 s/mm) DWI can contribute to differentiating malignant from benign thyroid micronodules.

Texture analysis on conventional MRI images accurately predicts early malignant transformation of low-grade gliomas

OBJECTIVES

Texture analysis performed on MRI images can provide additional quantitative information that is invisible to human assessment. This study aimed to evaluate the feasibility of texture analysis on preoperative conventional MRI images in predicting early malignant transformation from low- to high-grade glioma and compare its utility to histogram analysis alone.

METHODS

A total of 68 patients with low-grade glioma (LGG) were included in this study, 15 of which showed malignant transformation. Patients were randomly divided into training (60%) and testing (40%) sets. Texture analyses were performed to obtain the most discriminant factor (MDF) values for both training and testing data. Receiver operating characteristic (ROC) curve analyses were performed on MDF values and 9 histogram parameters in the training data to obtain cutoff values for determining the correct rates of discrimination between two groups in the testing data.

RESULTS

The ROC analyses on MDF values resulted in an area under the curve (AUC) of 0.90 (sensitivity 85%, specificity 84%) for T2w FLAIR, 0.92 (86%, 94%) for ADC, 0.96 (97%, 84%) for T1w, and 0.82 (78%, 75%) for T1w + Gd and correctly discriminated between the two groups in 93%, 100%, 93%, and 92% of cases in testing data, respectively. In the astrocytoma subgroup, AUCs were 0.92 (88%, 83%) for T2w FLAIR and 0.90 (92%, 74%) for T1w + Gd and correctly discriminated two groups in 100% and 92% of cases. The MDF outperformed all 9 of the histogram parameters.

CONCLUSION

Texture analysis on conventional preoperative MRI images can accurately predict early malignant transformation of LGGs, which may guide therapeutic planning.

KEY POINTS

• Texture analysis performed on MRI images can provide additional quantitative information that is invisible to human assessment. • Texture analysis based on conventional preoperative MR images can accurately predict early malignant transformation from low- to high-grade glioma. • Texture analysis is a clinically feasible technique that may provide an alternative and effective way of determining the likelihood of early malignant transformation and help guide therapeutic decisions.

Diagnostic efficacy of multiple MRI parameters in differentiating benign vs. malignant thyroid nodules

Background

Diffusion weighted imaging (DWI) has a good diagnostic value for malignant thyroid nodules, but the published protocols suffer from flaws and focus on the apparent diffusion coefficient (ADC). This study investigated the diagnostic performance of multiple MRI parameters in differentiating malignant from benign thyroid nodules.

Methods

This was a retrospective study of 181 consecutive patients (148 benign and 111 malignant nodules, confirmed by pathological results). The patients underwent conventional MRI, DWI, and dynamic contrast-enhanced MRI before surgery. The chi-square test and the Student t test were used to compare the conventional features and ADC value between malignant and benign groups. Multivariate logistic regression was used to identify the independent predictors and to construct a model. Receiver operator characteristic (ROC) curve analysis was used to assess the diagnostic performance of the independent variables and model.

Results

Tumor diameter, ADC value, cystic degeneration, pseudocapsule sign, high signal cystic area on T1-weighted imaging, ring sign in the delayed phase, and irregular shape showed significant differences between two groups (all P < 0.05). The multivariable analysis revealed that ADC value (OR = 694.006, P < 0.001), irregular shape (OR = 32.798, P < 0.001), ring sign in the delayed phase (OR = 20.381, P = 0.004), and cystic degeneration (OR = 8.468, P = 0.016) were independent predictors. Among them, ADC performed the best in discriminating benign from malignant nodules, with an area under the curve (AUC) of 0.95, 0.90 sensitivity, and 0.91 specificity. When the independent factors were combined, the diagnostic performance was improved with an AUC of 0.99, 0.97 sensitivity, and 0.95 specificity.

Conclusions

ADC value could discriminate between benign and malignant thyroid nodules with a good performance. Subjective features such as the ring sign, irregular shape, and cystic degeneration associated with malignant thyroid nodules could provide complementary information for differentiation.

MRI based texture analysis to classify low grade gliomas into astrocytoma and 1p/19q codeleted oligodendroglioma

PURPOSE

Texture analysis performed on MR images can detect quantitative features that are imperceptible to human visual assessment. The purpose of this study was to evaluate the feasibility of texture analysis on preoperative conventional MRI to discriminate between histological subtypes in low-grade gliomas (LGGs), and to determine the utility of texture analysis compared to histogram analysis alone.

METHODS

A total of 41 patients with LGG, 21 astrocytoma and 20 1p/19q codeleted oligodendroglioma were included in this study. Patients were randomly divided into training (60%) and testing (40%) sets. Texture analysis was performed on conventional MRI sequences to obtain the most discriminant factor (MDF) values for both the training and testing data. Receiver operating characteristic (ROC) curve analyses were then performed using the MDF values and 9 histogram parameters in the training data to obtain cut-off values for determining the correct rate of discriminating between astrocytoma and oligodendroglioma in the testing data.

RESULTS

The ROC analyses using MDF values resulted in an area under the curve (AUC) of 0.91 (sensitivity 86%, specificity 87%) for T2w FLAIR, 0.94 (87%, 89%) for ADC, 0.98 (93%, 95%) for T1w, and 0.88 (78%, 86%) for T1w + Gd sequences. Using the best cut-off values, MDF correctly discriminated between the two groups in 94%, 82%, 100%, and 88% of cases in the testing data, respectively. The MDF outperformed all 9 of the histogram parameters.

CONCLUSION

Texture analysis performed on conventional preoperative MRI images can accurately predict histological subtype of LGGs, which would have an impact on clinical management.

Utility of high b-value (2000 sec/mm2) DWI with RESOLVE in differentiating papillary thyroid carcinomas and papillary thyroid microcarcinomas from benign thyroid nodules

Purpose

The aim of the study was to evaluate the role of high b-value (2000 sec/mm²) diffusion-weighted imaging (DWI) by using Readout Segmentation of Long Variable Echo-trains (RESOLVE) in differentiating papillary thyroid carcinomas (PTCs) and papillary thyroid microcarcinomas (PTMCs) from benign thyroid nodules.

Materials and methods

Consecutive patients with thyroid nodules scheduled for surgery underwent high b-value DWI with 3 b-values: 0, 800 and 2000 sec/mm². Signal intensity ratios (SIRs) of thyroid nodules to adjacent normal thyroid tissue on DWI were measured as: SIR_b0, SIR_b800 and SIR_b2000. Apparent diffusion coefficient (ADC) values based on the 3 different b-values were acquired as: ADC_b0-800, ADC_b0-2000, and ADC_b0-800-2000. The 6 diagnostic indicators were evaluated by receiver operating characteristic (ROC) and diagnostic ability was compared between high b-value DWI and Ultrasound (US).

Results

A total of 52 PTCs including 33 PTMCs (38 patients, 8 men and 30 women, aged 45.68 ± 11.93 years) and 62 benign thyroid nodules (46 patients, 7 men and 39 women, aged 48.73 ± 11.98 years) were enrolled into the final statistical analysis. ADC_b0-800-2000 had the highest diagnostic ability in differentiating PTCs from benign thyroid nodules with area under curve (AUC) of 0.944, sensitivity of 96.15% and specificity of 85.48%, and PTMCs from benign thyroid nodules with AUC of 0.940, sensitivity of 93.94% and specificity of 85.48%. On the strength of lower false-positive rates than US (14.52% vs. 32.26% for PTCs and 14.52% vs. 32.26% for PTMCs), ADC_b0-800-2000 had significantly better diagnostic ability in PTCs (P = 0.002) and PTMCs (P = 0.005).

Conclusion

High b-value (2000 sec/mm²) DWI can contribute to differentiating PTCs and PTMCs from benign thyroid nodules and can be potentially used as an active surveillance imaging method for PTMCs.

Exploring Applications of Radiomics in Magnetic Resonance Imaging of Head and Neck Cancer: A Systematic Review

Background

Radiomics has been widely investigated for non-invasive acquisition of quantitative textural information from anatomic structures. While the vast majority of radiomic analysis is performed on images obtained from computed tomography, magnetic resonance imaging (MRI)-based radiomics has generated increased attention. In head and neck cancer (HNC), however, attempts to perform consistent investigations are sparse, and it is unclear whether the resulting textural features can be reproduced. To address this unmet need, we systematically reviewed the quality of existing MRI radiomics research in HNC.

Methods

Literature search was conducted in accordance with guidelines established by Preferred Reporting Items for Systematic Reviews and Meta-Analyses. Electronic databases were examined from January 1990 through November 2017 for common radiomic keywords. Eligible completed studies were then scored using a standardized checklist that we developed from Enhancing the Quality and Transparency of Health Research guidelines for reporting machine-learning predictive model specifications and results in biomedical research, defined by Luo et al. (1). Descriptive statistics of checklist scores were populated, and a subgroup analysis of methodology items alone was conducted in comparison to overall scores.

Results

Sixteen completed studies and four ongoing trials were selected for inclusion. Of the completed studies, the nasopharynx was the most common site of study (37.5%). MRI modalities varied with only four of the completed studies (25%) extracting radiomic features from a single sequence. Study sample sizes ranged between 13 and 118 patients (median of 40), and final radiomic signatures ranged from 2 to 279 features. Analyzed endpoints included either segmentation or histopathological classification parameters (44%) or prognostic and predictive biomarkers (56%). Liu et al. (2) addressed the highest number of our checklist items (total score: 48), and a subgroup analysis of methodology checklist items alone did not demonstrate any difference in scoring trends between studies [Spearman’s ρ = 0.94 (p < 0.0001)].

Conclusion

Although MRI radiomic applications demonstrate predictive potential in analyzing diverse HNC outcomes, methodological variances preclude accurate and collective interpretation of data.

Diagnostic Utility of Molecular and Imaging Biomarkers in Cytological Indeterminate Thyroid Nodules

Indeterminate thyroid cytology (Bethesda III and IV) corresponds to follicular-patterned benign and malignant lesions, which are particularly difficult to differentiate on cytology alone. As ~25% of these nodules harbor malignancy, diagnostic hemithyroidectomy is still custom. However, advanced preoperative diagnostics are rapidly evolving.This review provides an overview of additional molecular and imaging diagnostics for indeterminate thyroid nodules in a preoperative clinical setting, including considerations regarding cost-effectiveness, availability, and feasibility of combining techniques. Addressed diagnostics include gene mutation analysis, microRNA, immunocytochemistry, ultrasonography, elastosonography, computed tomography, sestamibi scintigraphy, [18F]-2-fluoro-2-deoxy-d-glucose positron emission tomography (FDG-PET), and diffusion-weighted magnetic resonance imaging.The best rule-out tests for malignancy were the Afirma® gene expression classifier and FDG-PET. The most accurate rule-in test was sole BRAF mutation analysis. No diagnostic had both near-perfect sensitivity and specificity, and estimated cost-effectiveness. Molecular techniques are rapidly advancing. However, given the currently available techniques, a multimodality stepwise approach likely offers the most accurate diagnosis, sequentially applying one sensitive rule-out test and one specific rule-in test. Geographical variations in cytology (e.g., Hürthle cell neoplasms) and tumor genetics strongly influence local test performance and clinical utility. Multidisciplinary collaboration and implementation studies can aid the local decision for one or more eligible diagnostics.

Skeletal Muscle Quantitative Nuclear Magnetic Resonance Imaging and Spectroscopy as an Outcome Measure for Clinical Trials

Recent years have seen tremendous progress towards therapy of many previously incurable neuromuscular diseases. This new context has acted as a driving force for the development of novel non-invasive outcome measures. These can be organized in three main categories: functional tools, fluid biomarkers and imagery. In the latest category, nuclear magnetic resonance imaging (NMRI) offers a considerable range of possibilities for the characterization of skeletal muscle composition, function and metabolism. Nowadays, three NMR outcome measures are frequently integrated in clinical research protocols. They are: 1/ the muscle cross sectional area or volume, 2/ the percentage of intramuscular fat and 3/ the muscle water T2, which quantity muscle trophicity, chronic fatty degenerative changes and oedema (or more broadly, “disease activity”), respectively. A fourth biomarker, the contractile tissue volume is easily derived from the first two ones. The fat fraction maps most often acquired with Dixon sequences have proven their capability to detect small changes in muscle composition and have repeatedly shown superior sensitivity over standard functional evaluation. This outcome measure will more than likely be the first of the series to be validated as an endpoint by regulatory agencies. The versatility of contrast generated by NMR has opened many additional possibilities for characterization of the skeletal muscle and will result in the proposal of more NMR biomarkers. Ultra-short TE (UTE) sequences, late gadolinium enhancement and NMR elastography are being investigated as candidates to evaluate skeletal muscle interstitial fibrosis. Many options exist to measure muscle perfusion and oxygenation by NMR. Diffusion NMR as well as texture analysis algorithms could generate complementary information on muscle organization at microscopic and mesoscopic scales, respectively. ³¹P NMR spectroscopy is the reference technique to assess muscle energetics non-invasively during and after exercise. In dystrophic muscle, ³¹P NMR spectrum at rest is profoundly perturbed, and several resonances inform on cell membrane integrity. Considerable efforts are being directed towards acceleration of image acquisitions using a variety of approaches, from the extraction of fat content and water T2 maps from one single acquisition to partial matrices acquisition schemes. Spectacular decreases in examination time are expected in the near future. They will reinforce the attractiveness of NMR outcome measures and will further facilitate their integration in clinical research trials.

MRI Texture Analysis Reflects Histopathology Parameters in Thyroid Cancer - A First Preliminary Study

OBJECT

Thyroid cancer represents the most frequent malignancy of the endocrine system with an increasing incidence worldwide. Novel imaging techniques are able to further characterize tumors and even predict histopathology features. Texture analysis is an emergent imaging technique to extract extensive data from an radiology images. The present study was therefore conducted to identify possible associations between texture analysis and histopathology parameters in thyroid cancer.

METHODS

The radiological database was retrospectively reviewed for thyroid carcinoma. Overall, 13 patients (3 females, 23.1%) with a mean age of 61.6 years were identified. The MaZda program was used for texture analysis. The T1-precontrast and T2-weighted images were analyzed and overall 279 texture feature for each sequence was investigated. For every patient cell count, Ki67-index and p53 count were investigated.

RESULTS

Several significant correlations between texture features and histopathology were identified. Regarding T1-weighted images, S(0;1)Sum Averg correlated the most with cell count (r=0.82). An inverse correlations with S(5;0)AngScMom, S(5;0)DifVarnc S(5;0), DiffEntrp and GrNonZeros (r=-0.69, -0.66, -0.69 and -0.63, respectively) was also identified. For T2-weighted images, Variance with r=0.63 was the highest coefficient, WavEnLL_S3 correlated inversely with cell count (r=-0.57). WavEnLL_S2 derived from T1-weighted images was the highest coefficient r=-0.80, S(0;5)SumVarnc was positively with r=0.74. Regarding T2-weighted images WavEnHL_s-1 was inverse correlated with Ki67 index (r=-0.77). S(1;0)Correlat was with r=0.75 the best correlation with Ki67 index. For T1-weighed images S(5;0)SumofSqs was the best with r=0.65 with p53 count. For T2-weighted images S(1;-1)SumEntrp was the inverse correlation with r=-0.72, whereas S(0;4)AngScMom correlated positively with r=0.63.

CONCLUSIONS

MRI texture analysis derived from conventional sequences reflects histopathology features in thyroid cancer. This technique might be a novel noninvasive modality to further characterize thyroid cancer in clinical oncology.

MRI-Based Texture Analysis to Differentiate Sinonasal Squamous Cell Carcinoma from Inverted Papilloma

BACKGROUND AND PURPOSE

Because sinonasal inverted papilloma can harbor squamous cell carcinoma, differentiating these tumors is relevant. The objectives of this study were to determine whether MR imaging-based texture analysis can accurately classify cases of noncoexistent squamous cell carcinoma and inverted papilloma and to compare this classification performance with neuroradiologists' review.

MATERIALS AND METHODS

Adult patients who had inverted papilloma or squamous cell carcinoma resected were eligible (coexistent inverted papilloma and squamous cell carcinoma were excluded). Inclusion required tumor size of >1.5 cm and preoperative MR imaging with axial T1, axial T2, and axial T1 postcontrast sequences. Five well-established texture analysis algorithms were applied to an ROI from the largest tumor cross-section. For a training dataset, machine-learning algorithms were used to identify the most accurate model, and performance was also evaluated in a validation dataset. On the basis of 3 separate blinded reviews of the ROI, isolated tumor, and entire images, 2 neuroradiologists predicted tumor type in consensus.

RESULTS

The inverted papilloma (n = 24) and squamous cell carcinoma (n = 22) cohorts were matched for age and sex, while squamous cell carcinoma tumor volume was larger (P = .001). The best classification model achieved similar accuracies for training (17 squamous cell carcinomas, 16 inverted papillomas) and validation (7 squamous cell carcinomas, 6 inverted papillomas) datasets of 90.9% and 84.6%, respectively (P = .537). For the combined training and validation cohorts, the machine-learning accuracy (89.1%) was better than that of the neuroradiologists' ROI review (56.5%, P = .0004) but not significantly different from the neuroradiologists' review of the tumors (73.9%, P = .060) or entire images (87.0%, P = .748).

CONCLUSIONS

MR imaging-based texture analysis has the potential to differentiate squamous cell carcinoma from inverted papilloma and may, in the future, provide incremental information to the neuroradiologist.

Texture analysis of medical images for radiotherapy applications

The high-throughput extraction of quantitative information from medical images, known as radiomics, has grown in interest due to the current necessity to quantitatively characterize tumour heterogeneity. In this context, texture analysis, consisting of a variety of mathematical techniques that can describe the grey-level patterns of an image, plays an important role in assessing the spatial organization of different tissues and organs. For these reasons, the potentiality of texture analysis in the context of radiotherapy has been widely investigated in several studies, especially for the prediction of the treatment response of tumour and normal tissues. Nonetheless, many different factors can affect the robustness, reproducibility and reliability of textural features, thus limiting the impact of this technique. In this review, an overview of the most recent works that have applied texture analysis in the context of radiotherapy is presented, with particular focus on the assessment of tumour and tissue response to radiations. Preliminary, the main factors that have an influence on features estimation are discussed, highlighting the need of more standardized image acquisition and reconstruction protocols and more accurate methods for region of interest identification. Despite all these limitations, texture analysis is increasingly demonstrating its ability to improve the characterization of intratumour heterogeneity and the prediction of clinical outcome, although prospective studies and clinical trials are required to draw a more complete picture of the full potential of this technique.

Texture analysis of the developing human brain using customization of a knowledge-based system

Background: Pattern recognition software originally designed for geospatial and other technical applications could be trained by physicians and used as texture-analysis tools for evidence-based practice, in order to improve diagnostic imaging examination during pregnancy.Methods: Various machine-learning techniques and customized datasets were assessed for training of an integrable knowledge-based system (KBS), to determine a hypothetical methodology for texture classification of closely-related anatomical structures in fetal brain magnetic resonance (MR) images. Samples were manually categorized according to the magnetic field of the MRI scanner (i.e. 1.5-tesla (1.5T), 3-tesla (3T)), rotational planes (i.e. coronal, sagittal and axial), and signal weighting (i.e. spin-lattice, spin-spin, relaxation, proton density). In the machine-learning sessions, the operator manually selected relevant regions of interest (ROI) in 1.5/3T MR images. Semi-automatic procedures in MaZda/B11 were performed to determine optimal parameter sets for ROI classification. Four classes were defined: ventricles, thalamus, grey matter, and white matter. Various textures analysis methods were tested. The KBS performed automatic data pre-processing and semi-automatic classification of ROIs.Results: After testing 3456 ROIs, statistical binary classification revealed that combination of reduction techniques with linear discriminant algorithms (LDA) or nonlinear discriminant algorithms (NDA) yielded the best scoring in terms of sensitivity (both 100%, 95% CI: 99.79-100), specificity (both 100%, 95% CI: 99.79-100) and Fisher coefficient (≈E+4, ≈E+5, respectively). Conclusions: LDA and NDA in MaZda can be useful data mining tools for screening a population of interest subjected to a clinical test.

Radiomics in head and neck cancer: from exploration to application

In the context of clinical oncology, a fundamental goal of radiomics is the extraction of large amounts of quantitative features whose subsequent analysis can be used for decision support towards personalized and actionable cancer care. Head and neck cancers present a unique set of diagnostic and therapeutic challenges by nature of its complex anatomy and heterogeneity. Radiomics holds the potential to address these barriers, but only if as a collective field we direct future effort towards investigating specific oncologic function and oncologic outcomes, with external validation and collaborative multi-institutional efforts to begin standardizing and refining radiomic signatures. Here we present an overview of radiomic texture analysis methods as well as the software infrastructure, review the developments of radiomics in head and neck cancer applications, discuss unmet challenges, and propose key recommendations for moving the field forward.

Diffusion-weighted MRI in differentiating malignant from benign thyroid nodules: a meta-analysis

OBJECTIVES

To perform a meta-analysis to evaluate the diagnostic efficacy of diffusion-weighted imaging (DWI) in differentiating malignant from benign thyroid nodules.

DESIGN

A meta-analysis.

DATA SOURCES AND STUDY SELECTION

Medical and scientific literature databases were searched for original articles published up to August 2015. Studies were selected if they (1) included diagnostic DWI for differentiating malignant from benign thyroid lesions, (2) included patients who later underwent biopsy and (3) presented sufficient data to enable the construction of contingency tables.

DATA SYNTHESIS

For each study, the true-positive, false-positive, true-negative and false-negative values were extracted or derived, and 2×2 contingency tables were constructed. Methodological quality was assessed using the Quality Assessment of Diagnostic Accuracy Studies (QUADAS) instrument. The heterogeneity test, threshold effect test, subgroup analyses and publication bias analyses were performed.

RESULTS

From the 113 identified search results, 15 studies, representing a total of 765 lesions, were included in the meta-analysis. We detected heterogeneity between studies but found no evidence of publication bias. The methodological quality was moderate. The pooled weighted sensitivity was 0.90 (95% CI 0.85 to 0.93); the specificity was 0.95 (95% CI 0.88 to 0.98); the positive likelihood ratio was 16.49 (95% CI 7.37 to 36.86); the negative likelihood ratio was 0.11 (95% CI 0.08 to 0.16); and the diagnostic OR was 150.73 (95% CI 64.96 to 349.75). The area under the receiver operator characteristic curve was 0.95 (95% CI 0.93 to 0.97).

CONCLUSIONS

Quantitative DWI may be a non-invasive, non-radiative and accurate method of distinguishing malignant from benign thyroid nodules. Nevertheless, large-scale trials are necessary to assess its clinical value and to establish standards regarding b values and cut-off values for DWI-based diagnosis.