Detection of Solid Pigment in Dermatoscopy Images using Texture Analysis

Background/aims: Epiluminescence microscopy (ELM), also known as dermoscopy or dermatoscopy, is a non-invasive, in vivo technique, that permits visualization of features of pigmented melanocytic neoplasms that are not discernable by examination with the naked eye. ELM offers a completely new range of visual features. One such feature is the solid pigment, also called the blotchy pigment or dark structureless area. Our goal was to automatically detect this feature and determine whether its presence is useful in distinguishing benign from malignant pigmented lesions. Methods: Here, a texture-based algorithm is developed for the detection of solid pigment. The factors d and a used in calculating neighboring gray level dependence matrix (NGLDM) numbers were chosen as optimum by experimentation. The algorithms are tested on a set of 37 images. A new index is presented for separation of benign and malignant lesions, based on the presence of solid pigment in the periphery. Results: The NGLDM large number emphasis N2 was satisfactory for the detection of the solid pigment. Nine lesions had solid pigment detected, and among our 37 lesions, no melanoma lacked solid pigment. The index for separation of benign and malignant lesions was applied to the nine lesions. We were able to separate the benign lesions with solid pigment from the malignant lesions with the exception of only one lesion, a Spitz nevus that mimicked a malignant melanoma. Conclusion: Texture methods may be useful in detecting important dermatoscopy features in digitized images and a new index may be useful in separating benign from malignant lesions. Testing on a larger set of lesions is needed before further conclusions can be made

Developing a Novel Image Marker to Predict the Responses of Neoadjuvant Chemotherapy (NACT) for Ovarian Cancer Patients

Objective: Neoadjuvant chemotherapy (NACT) is one kind of treatment for advanced stage ovarian cancer patients. However, due to the nature of tumor heterogeneity, the patients' responses to NACT varies significantly among different subgroups. To address this clinical challenge, the purpose of this study is to develop a novel image marker to achieve high accuracy response prediction of the NACT at an early stage. Methods: For this purpose, we first computed a total of 1373 radiomics features to quantify the tumor characteristics, which can be grouped into three categories: geometric, intensity, and texture features. Second, all these features were optimized by principal component analysis algorithm to generate a compact and informative feature cluster. Using this cluster as the input, an SVM based classifier was developed and optimized to create a final marker, indicating the likelihood of the patient being responsive to the NACT treatment. To validate this scheme, a total of 42 ovarian cancer patients were retrospectively collected. A nested leave-one-out cross-validation was adopted for model performance assessment. Results: The results demonstrate that the new method yielded an AUC (area under the ROC [receiver characteristic operation] curve) of 0.745. Meanwhile, the model achieved overall accuracy of 76.2%, positive predictive value of 70%, and negative predictive value of 78.1%. Conclusion: This study provides meaningful information for the development of radiomics based image markers in NACT response prediction

Evaluation de la dépendance spatiale locale pour la caractérisation de la texture

- Nous proposons une modélisation des distributions locales de la proximité de niveaux de gris entre un pixel et ses voisins sous hypothèses d'indépendance des pixels du voisinage. Le modèle proposé permet, au moyen du test du Chi-deux, de caractériser les aspects aléatoire et isotrope de la texture. Nous considérons le cas de l'indépendance stricte des pixels voisins et celui de l'indépendance conditionnelle relativement au pixel central

Robustness Analysis of DCE-MRI-Derived Radiomic Features in Breast Masses: Assessing Quantization Levels and Segmentation Agreement

Featured Application The use of highly robust radiomic features is fundamental to reduce intrinsic dependencies and to provide reliable predictive models. This work presents a study on breast tumor DCE-MRI considering the radiomic feature robustness against the quantization settings and segmentation methods. Machine learning models based on radiomic features allow us to obtain biomarkers that are capable of modeling the disease and that are able to support the clinical routine. Recent studies have shown that it is fundamental that the computed features are robust and reproducible. Although several initiatives to standardize the definition and extraction process of biomarkers are ongoing, there is a lack of comprehensive guidelines. Therefore, no standardized procedures are available for ROI selection, feature extraction, and processing, with the risk of undermining the effective use of radiomic models in clinical routine. In this study, we aim to assess the impact that the different segmentation methods and the quantization level (defined by means of the number of bins used in the feature-extraction phase) may have on the robustness of the radiomic features. In particular, the robustness of texture features extracted by PyRadiomics, and belonging to five categories-GLCM, GLRLM, GLSZM, GLDM, and NGTDM-was evaluated using the intra-class correlation coefficient (ICC) and mean differences between segmentation raters. In addition to the robustness of each single feature, an overall index for each feature category was quantified. The analysis showed that the level of quantization (i.e., the 'bincount' parameter) plays a key role in defining robust features: in fact, in our study focused on a dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) dataset of 111 breast masses, sets with cardinality varying between 34 and 43 robust features were obtained with 'binCount' values equal to 256 and 32, respectively. Moreover, both manual segmentation methods demonstrated good reliability and agreement, while automated segmentation achieved lower ICC values. Considering the dependence on the quantization level, taking into account only the intersection subset among all the values of 'binCount' could be the best selection strategy. Among radiomic feature categories, GLCM, GLRLM, and GLDM showed the best overall robustness with varying segmentation methods

A comparative study of machine learning methods for time-to-event survival data for radiomics risk modelling

Radiomics applies machine learning algorithms to quantitative imaging data to characterise the tumour phenotype and predict clinical outcome. For the development of radiomics risk models, a variety of different algorithms is available and it is not clear which one gives optimal results. Therefore, we assessed the performance of 11 machine learning algorithms combined with 12 feature selection methods by the concordance index (C-Index), to predict loco- regional tumour control (LRC) and overall survival for patients with head and neck squamous cell carcinoma. The considered algorithms are able to deal with continuous time-to-event survival data. Feature selection and model building were performed on a multicentre cohort (213 patients) and validated using an independent cohort (80 patients). We found several combinations of machine learning algorithms and feature selection methods which achieve similar results, e.g., MSR-RF: C-Index = 0.71 and BT-COX: C-Index = 0.70 in combination with Spearman feature selection. Using the best performing models, patients were stratified into groups of low and high risk of recurrence. Significant differences in LRC were obtained between both groups on the validation cohort. Based on the presented analysis, we identified a subset of algorithms which should be considered in future radiomics studies to develop stable and clinically relevant predictive models for time-to-event endpoints

Класифікатор стану печінки у дітей з патологією гепатобіліарної системи за текстурними статистиками ультразвукового дослідження

За результатами ультразвукового дослідження печінки можна діагностувати наявність дифузних захворювань печінки.На вході було отримано набір медичних зображень ультразвукової діагностики печінки. В даній роботі до зображень було застосовано методи текстурного аналізу, внаслідок чого було отримано 96 статистичних ознак. Всі отримані дані були приведені до стандартного розподілу. Кількість ознак була скорочена при збереженні вагомої інформації за допомогою ядрової методики головних компонент. Розглянуто побудову моделі класифікатора, наведено порівняння ефективності моделі випадкового лісу, логістичної регресії та багатошарового перцептрону. Проведено оцінювання точності моделі за допомогою методу перехресної перевірки. Найкращий результат показала модель мультиноміальної логістичної регресії – 77%. Розроблено систему для прогнозування можливих патології печінки у дітей за допомогою текстурного аналізу результатів сонографії. Отримана система візуалізує результати прогнозування та їх точність для спрощення процесу прийняття рішень лікарем з ультразвукової діагностики.With the results of ultrasound diagnostics of the liver, it is possible to diagnose the presence of diffuse liver diseases. At the entrance it was received a set of medical images of ultrasound diagnostics of the liver. In this work, texture analysis methods were applied to the images, therefore it was received 96 statistical features. All received data were brought to the standard distribution. The number of attributes has been reduced while retaining significant information through the principal component analysis. The construction of the classifier model is considered, the comparison of the efficiency of the model of random forest, logistic regression and multilayer perceptron is given. The accuracy of the model was evaluated using the cross-validation. The best result got the model of multivariate logistic regression - 77%. The system for prediction of possible liver disease in children is developed with the help of texture analysis of the results of sonography. The obtained system visualizes the results of prediction and their accuracy in order to simplify the decision-making process of the ultrasound diagnostics doctor.По результатам ультразвукового исследования печенки можно диагностировать наличие диффузных заболеваний печени. На входе было получено набор медицински зображений ультразвуковой діагностики печени. В даннойработе к изображениям были применены методы текстурного анализа, в результате чего было получено 96 статистических признаков. Все полученные данные были приведены к стандартному распределению. Количество признаков было сокращена при сохранении значимой информации с помощью ядерной методики главных компонентов. Рассмотрено построение модели классификатора, приведено сравнение эффективности модели случайного леса, логистической регрессии и многослойного персептрона. Проведена оценк точности модели с помощью метода перекрестной проверки. Лучший результат показала модель мультиномиальной логистической регрессии - 77%. Разработана система для прогнозирования возможных патологии печени у детей с помощью текстурного анализа результатов сонографии. Полученная система визуализирует результат прогнозирования и их точность для упрощени процесса приняти решений врачом ультразвуковой диагностики

Artificial intelligence-based, semi-automated segmentation for the extraction of ultrasound-derived radiomics features in breast cancer: a prospective multicenter study

Purpose: To investigate the feasibility of an artificial intelligence (AI)-based semi-automated segmentation for the extraction of ultrasound (US)-derived radiomics features in the characterization of focal breast lesions (FBLs). Material and methods: Two expert radiologists classified according to US BI-RADS criteria 352 FBLs detected in 352 patients (237 at Center A and 115 at Center B). An AI-based semi-automated segmentation was used to build a machine learning (ML) model on the basis of B-mode US of 237 images (center A) and then validated on an external cohort of B-mode US images of 115 patients (Center B). Results: A total of 202 of 352 (57.4%) FBLs were benign, and 150 of 352 (42.6%) were malignant. The AI-based semi-automated segmentation achieved a success rate of 95.7% for one reviewer and 96% for the other, without significant difference (p = 0.839). A total of 15 (4.3%) and 14 (4%) of 352 semi-automated segmentations were not accepted due to posterior acoustic shadowing at B-Mode US and 13 and 10 of them corresponded to malignant lesions, respectively. In the validation cohort, the characterization made by the expert radiologist yielded values of sensitivity, specificity, PPV and NPV of 0.933, 0.9, 0.857, 0.955, respectively. The ML model obtained values of sensitivity, specificity, PPV and NPV of 0.544, 0.6, 0.416, 0.628, respectively. The combined assessment of radiologists and ML model yielded values of sensitivity, specificity, PPV and NPV of 0.756, 0.928, 0.872, 0.855, respectively. Conclusion: AI-based semi-automated segmentation is feasible, allowing an instantaneous and reproducible extraction of US-derived radiomics features of FBLs. The combination of radiomics and US BI-RADS classification led to a potential decrease of unnecessary biopsy but at the expense of a not negligible increase of potentially missed cancers

Meta Aprendizagem Aplicada ao Diagnóstico de Glaucoma / Learning Goal Applied to Glaucoma Diagnosis

O glaucoma é uma doença silenciosa que pode levar a cegueira caso não seja tratada com urgência. Métodos de diagnóstico que utilizam inteligên- cia computacional têm sido propostos com a finalidade de aumentar a taxa de detecções da doença ainda na sua fase inicial, e proporcionar melhor qualidade de vida aos pacientes. Porém, a descoberta de melhores técnicas e métodos de diagnóstico automatizado, é necessária grande quantidade de testes de diferen- tes metodologias e abordagens sobre o problema, tornando o processo lento e sujeito a erros. Este trabalho propõe uma solução através da meta aprendiza- gem de métodos de pré processamento, decomposição, extração de caracterís- ticas que devem ser usados de maneira eficiente para solucionar o problema. Os resultados obtidos são promissores, atingindo 93,40% de acurácia após 144 execuções e deve melhorar proporcionalmente à quantidade de testes realiza- dos

Impact of Wavelet Kernels on Predictive Capability of Radiomic Features: A Case Study on COVID-19 Chest X-ray Images

Radiomic analysis allows for the detection of imaging biomarkers supporting decision-making processes in clinical environments, from diagnosis to prognosis. Frequently, the original set of radiomic features is augmented by considering high-level features, such as wavelet transforms. However, several wavelets families (so called kernels) are able to generate different multi-resolution representations of the original image, and which of them produces more salient images is not yet clear. In this study, an in-depth analysis is performed by comparing different wavelet kernels and by evaluating their impact on predictive capabilities of radiomic models. A dataset composed of 1589 chest X-ray images was used for COVID-19 prognosis prediction as a case study. Random forest, support vector machine, and XGBoost were trained (on a subset of 1103 images) after a rigorous feature selection strategy to build-up the predictive models. Next, to evaluate the models generalization capability on unseen data, a test phase was performed (on a subset of 486 images). The experimental findings showed that Bior1.5, Coif1, Haar, and Sym2 kernels guarantee better and similar performance for all three machine learning models considered. Support vector machine and random forest showed comparable performance, and they were better than XGBoost. Additionally, random forest proved to be the most stable model, ensuring an appropriate balance between sensitivity and specificity

Impact of lesion delineation and intensity quantisation on the stability of texture features from lung nodules on ct: A reproducible study

Computer-assisted analysis of three-dimensional imaging data (radiomics) has received a lot of research attention as a possible means to improve the management of patients with lung cancer. Building robust predictive models for clinical decision making requires the imaging features to be stable enough to changes in the acquisition and extraction settings. Experimenting on 517 lung lesions from a cohort of 207 patients, we assessed the stability of 88 texture features from the following classes: first-order (13 features), Grey-level Co-Occurrence Matrix (24), Grey-level Difference Matrix (14), Grey-level Run-length Matrix (16), Grey-level Size Zone Matrix (16) and Neighbouring Grey-tone Difference Matrix (five). The analysis was based on a public dataset of lung nodules and open-access routines for feature extraction, which makes the study fully reproducible. Our results identified 30 features that had good or excellent stability relative to lesion delineation, 28 to intensity quantisation and 18 to both. We conclude that selecting the right set of imaging features is critical for building clinical predictive models, particularly when changes in lesion delineation and/or intensity quantisation are involved