Radiomics in paediatric neuro-oncology: A multicentre study on MRI texture analysis

Applications of machine learning to MR imaging of pediatric low-grade gliomas

INTRODUCTION

Machine learning (ML) shows promise for the automation of routine tasks related to the treatment of pediatric low-grade gliomas (pLGG) such as tumor grading, typing, and segmentation. Moreover, it has been shown that ML can identify crucial information from medical images that is otherwise currently unattainable. For example, ML appears to be capable of preoperatively identifying the underlying genetic status of pLGG.

METHODS

In this chapter, we reviewed, to the best of our knowledge, all published works that have used ML techniques for the imaging-based evaluation of pLGGs. Additionally, we aimed to provide some context on what it will take to go from the exploratory studies we reviewed to clinically deployed models.

RESULTS

Multiple studies have demonstrated that ML can accurately grade, type, and segment and detect the genetic status of pLGGs. We compared the approaches used between the different studies and observed a high degree of variability throughout the methodologies. Standardization and cooperation between the numerous groups working on these approaches will be key to accelerating the clinical deployment of these models.

CONCLUSION

The studies reviewed in this chapter detail the potential for ML techniques to transform the treatment of pLGG. However, there are still challenges that need to be overcome prior to clinical deployment.

Peri-lesion regions in differentiating suspicious breast calcification-only lesions specifically on contrast enhanced mammography

PURPOSE

The explore the added value of peri-calcification regions on contrast-enhanced mammography (CEM) in the differential diagnosis of breast lesions presenting as only calcification on routine mammogram.

METHODS

Patients who underwent CEM because of suspicious calcification-only lesions were included. The test set included patients between March 2017 and March 2019, while the validation set was collected between April 2019 and October 2019. The calcifications were automatically detected and grouped by a machine learning-based computer-aided system. In addition to extracting radiomic features on both low-energy (LE) and recombined (RC) images from the calcification areas, the peri-calcification regions, which is generated by extending the annotation margin radially with gradients from 1 mm to 9 mm, were attempted. Machine learning (ML) models were built to classify calcifications into malignant and benign groups. The diagnostic matrices were also evaluated by combing ML models with subjective reading.

RESULTS

Models for LE (significant features: wavelet-LLL_glcm_Imc2_MLO; wavelet-HLL_firstorder_Entropy_MLO; wavelet-LHH_glcm_DifferenceVariance_CC; wavelet-HLL_glcm_SumEntropy_MLO;wavelet-HLH_glrlm_ShortRunLowGray LevelEmphasis_MLO; original_firstorder_Entropy_MLO; original_shape_Elongation_MLO) and RC (significant features: wavelet-HLH_glszm_GrayLevelNonUniformityNormalized_MLO; wavelet-LLH_firstorder_10Percentile_CC; original_firstorder_Maximum_MLO; wavelet-HHH_glcm_Autocorrelation_MLO; original_shape_Elongation_MLO; wavelet-LHL_glszm_GrayLevelNonUniformityNormalized_MLO; wavelet-LLH_firstorder_RootMeanSquared_MLO) images were set up with 7 features. Areas under the curve (AUCs) of RC models are significantly better than those of LE models with compact and expanded boundary (RC v.s. LE, compact: 0.81 v.s. 0.73, p < 0.05; expanded: 0.89 v.s. 0.81, p < 0.05) and RC models with 3 mm boundary extension yielded the best performance compared to those with other sizes (AUC = 0.89). Combining with radiologists' reading, the 3mm-boundary RC model achieved a sensitivity of 0.871 and negative predictive value of 0.937 with similar accuracy of 0.843 in predicting malignancy.

CONCLUSIONS

The machine learning model integrating intra- and peri-calcification regions on CEM has the potential to aid radiologists' performance in predicting malignancy of suspicious breast calcifications.

Radio-pathomic approaches in pediatric neuro-oncology: Opportunities and challenges

With medical software platforms moving to cloud environments with scalable storage and computing, the translation of predictive artificial intelligence (AI) models to aid in clinical decision-making and facilitate personalized medicine for cancer patients is becoming a reality. Medical imaging, namely radiologic and histologic images, has immense analytical potential in neuro-oncology, and models utilizing integrated radiomic and pathomic data may yield a synergistic effect and provide a new modality for precision medicine. At the same time, the ability to harness multi-modal data is met with challenges in aggregating data across medical departments and institutions, as well as significant complexity in modeling the phenotypic and genotypic heterogeneity of pediatric brain tumors. In this paper, we review recent pathomic and integrated pathomic, radiomic, and genomic studies with clinical applications. We discuss current challenges limiting translational research on pediatric brain tumors and outline technical and analytical solutions. Overall, we propose that to empower the potential residing in radio-pathomics, systemic changes in cross-discipline data management and end-to-end software platforms to handle multi-modal data sets are needed, in addition to embracing modern AI-powered approaches. These changes can improve the performance of predictive models, and ultimately the ability to advance brain cancer treatments and patient outcomes through the development of such models.

Beyond Glioma: The Utility of Radiomic Analysis for Non-Glial Intracranial Tumors

Simple Summary

Tumor qualities, such as growth rate, firmness, and intrusion into healthy tissue, can be very important for operation planning and further treatment. Radiomics is a promising new method that allows the determination of some of these qualities on images performed before surgery. In this article, we provide a review of the use of radiomics in various tumors of the central nervous system, such as metastases, lymphoma, meningioma, medulloblastoma, and pituitary tumors.

Abstract

The field of radiomics is rapidly expanding and gaining a valuable role in neuro-oncology. The possibilities related to the use of radiomic analysis, such as distinguishing types of malignancies, predicting tumor grade, determining the presence of particular molecular markers, consistency, therapy response, and prognosis, can considerably influence decision-making in medicine in the near future. Even though the main focus of radiomic analyses has been on glial CNS tumors, studies on other intracranial tumors have shown encouraging results. Therefore, as the main focus of this review, we performed an analysis of publications on PubMed and Web of Science databases, focusing on radiomics in CNS metastases, lymphoma, meningioma, medulloblastoma, and pituitary tumors.

Radiomic signatures based on multiparametric MR images for predicting Ki-67 index expression in medulloblastoma

BACKGROUND

Medulloblastoma (MB) is a common central nervous system tumor in children with extensive heterogeneity and different prognoses. This study aimed to classify the Ki-67 index in MB with radiomic characteristics based on multi-parametric magnetic resonance imaging to guide treatment and assess the prognosis of patients.

METHODS

Three sequences of T1W, CE-T1W, and T2W were used as test data. Two experienced radiologists manually segmented the tumors according to T2W images from 90 patients. The patients were divided into training and test sets at a ratio of 7:3, and 833 dimensional image features were extracted for each patient. Five models were trained using the feature set selected in three ways. Finally, the area under the curve (AUC) and accuracy (ACC) were used on the test set to evaluate the performance of the different models.

RESULTS

A random forest (RF) model combining three sequence features achieved the best performance (ACC: 0.771, 95% CI: 0.727 to 0.816; AUC: 0.697, 95% CI: 0.614 to 0.78). The voting model that combined a RF and a support vector machine (SVM) had higher performance than the other models (ACC: 0.796, 95% CI: 0.76 to 0.833; AUC: 0.689, 95% CI: 0.615 to 0.763). The best prediction model that used only one sequence feature was voting in the T2W sequence (ACC: 0.736, 95% CI: 0.705 to 0.766; AUC: 0.636, 95% CI: 0.585 to 0.688). The ensemble model was better than the single training model, and a multi-sequence combination was better than a single sequence prediction. The multiple feature selection methods were better than a combination of the two methods.

CONCLUSIONS

A model obtained by machine learning could help doctors predict the Ki-67 values of patients more efficiently to make targeted judgments for subsequent treatments.

Differentiation of paediatric posterior fossa tumours by the multiregional and multiparametric MRI radiomics approach: a study on the selection of optimal multiple sequences and multiregions

OBJECTIVE

To evaluate the diagnostic performance of a radiomics model based on multiregional and multiparametric MRI to classify paediatric posterior fossa tumours (PPFTs), explore the contribution of different MR sequences and tumour subregions in tumour classification, and examine whether contrast-enhanced T₁ weighted (T1C) images have irreplaceable added value.

METHODS

This retrospective study of 136 PPFTs extracted 11,958 multiregional (enhanced, non-enhanced, and total tumour) features from multiparametric MRI (T₁- and T₂ weighted, T1C, fluid-attenuated inversion recovery, and diffusion-weighted images). These features were subjected to fast correlation-based feature selection and classified by a support vector machine based on different tasks. Diagnostic performances of multiregional and multiparametric MRI features, different sequences, and different tumoral regions were evaluated using multiclass and one-vs-rest strategies.

RESULTS

The established model achieved an overall area under the curve (AUC) of 0.977 in the validation cohort. The performance of PPFTs significantly improved after replacing T1C with apparent diffusion coefficient maps added into the plain scan sequences (AUC from 0.812 to 0.917). When oedema features were added to contrast-enhancing tumour volume, the performance did not significantly improve.

CONCLUSION

The radiomics model built by multiregional and multiparametric MRI features allows for the excellent distinction of different PPFTs and provides valuable references for the rational adoption of MR sequences.

ADVANCES IN KNOWLEDGE

This study emphasized that T1C has limited added value in predicting PPFTs and should be cautiously adopted. Selecting optimal MR sequences may help guide clinicians to better allocate acquisition sequences and reduce medical costs.

The role of artificial intelligence in paediatric neuroradiology

Imaging plays a fundamental role in the managing childhood neurologic, neurosurgical and neuro-oncological disease. Employing multi-parametric MRI techniques, such as spectroscopy and diffusion- and perfusion-weighted imaging, to the radiophenotyping of neuroradiologic conditions is becoming increasingly prevalent, particularly with radiogenomic analyses correlating imaging characteristics with molecular biomarkers of disease. However, integration into routine clinical practice remains elusive. With modern multi-parametric MRI now providing additional data beyond anatomy, informing on histology, biology and physiology, such metric-rich information can present as information overload to the treating radiologist and, as such, information relevant to an individual case can become lost. Artificial intelligence techniques are capable of modelling the vast radiologic, biological and clinical datasets that accompany childhood neurologic disease, such that this information can become incorporated in upfront prognostic modelling systems, with artificial intelligence techniques providing a plausible approach to this solution. This review examines machine learning approaches than can be used to underpin such artificial intelligence applications, with exemplars for each machine learning approach from the world literature. Then, within the specific use case of paediatric neuro-oncology, we examine the potential future contribution for such artificial intelligence machine learning techniques to offer solutions for patient care in the form of decision support systems, potentially enabling personalised medicine within this domain of paediatric radiologic practice.

Magnetic Resonance Imaging in the Contemporary Management of Medulloblastoma: Current and Emerging Applications

Medulloblastoma, the most common malignant primary brain tumor in children, is now considered to comprise of four distinct molecular subgroups-wingless (WNT), sonic hedgehog (SHH), Group 3, and Group 4 medulloblastoma, each associated with distinct developmental origins, unique transcriptional profiles, diverse phenotypes, and variable clinical behavior. Due to its exquisite anatomic resolution, multiparametric nature, and ability to image the entire craniospinal axis, magnetic resonance imaging (MRI) is the preferred and recommended first-line imaging modality for suspected brain tumors including medulloblastoma. Preoperative MRI can reliably differentiate medulloblastoma from other common childhood posterior fossa masses such as ependymoma, pilocytic astrocytoma, and brainstem glioma. On T1-weighted images, medulloblastoma is generally iso- to hypointense, while on T2-weighted images, the densely packed cellular component of the tumor is significantly hypointense and displays restricted diffusion on diffusion-weighted imaging. Following intravenous gadolinium, medulloblastoma shows significant but variable and heterogeneous contrast enhancement. Given the propensity of neuraxial spread in medulloblastoma, sagittal fat-suppressed T1-postcontrast spinal MRI is recommended to rule out leptomeningeal metastases for accurate staging. Following neurosurgical excision, postoperative MRI done within 24-48 h confirms the extent of resection, accurately quantifying residual tumor burden imperative for risk assignment. Post-treatment MRI is needed to assess response and effectiveness of adjuvant radiotherapy and systemic chemotherapy. After completion of planned therapy, surveillance MRI is recommended periodically on follow-up for early detection of recurrence for timely institution of salvage therapy, as well as for monitoring treatment-related late complications. Recent studies suggest that preoperative MRI can reliably identify SHH and Group 4 medulloblastoma but has suboptimal predictive accuracy for WNT and Group 3 tumors. In this review, we focus on the role of MRI in the diagnosis, staging, and quantifying residual disease; post-treatment response assessment; and periodic surveillance, and provide a brief summary on radiogenomics in the contemporary management of medulloblastoma.

Radiomics and radiogenomics in pediatric neuro-oncology: A review

The current era of advanced computing has allowed for the development and implementation of the field of radiomics. In pediatric neuro-oncology, radiomics has been applied in determination of tumor histology, identification of disseminated disease, prognostication, and molecular classification of tumors (ie, radiogenomics). The field also comes with many challenges, such as limitations in study sample sizes, class imbalance, generalizability of the methods, and data harmonization across imaging centers. The aim of this review paper is twofold: first, to summarize existing literature in radiomics of pediatric neuro-oncology; second, to distill the themes and challenges of the field and discuss future directions in both a clinical and technical context.

Molecular Biology in Treatment Decision Processes-Neuro-Oncology Edition

Computational approaches including machine learning, deep learning, and artificial intelligence are growing in importance in all medical specialties as large data repositories are increasingly being optimised. Radiation oncology as a discipline is at the forefront of large-scale data acquisition and well positioned towards both the production and analysis of large-scale oncologic data with the potential for clinically driven endpoints and advancement of patient outcomes. Neuro-oncology is comprised of malignancies that often carry poor prognosis and significant neurological sequelae. The analysis of radiation therapy mediated treatment and the potential for computationally mediated analyses may lead to more precise therapy by employing large scale data. We analysed the state of the literature pertaining to large scale data, computational analysis, and the advancement of molecular biomarkers in neuro-oncology with emphasis on radiation oncology. We aimed to connect existing and evolving approaches to realistic avenues for clinical implementation focusing on low grade gliomas (LGG), high grade gliomas (HGG), management of the elderly patient with HGG, rare central nervous system tumors, craniospinal irradiation, and re-irradiation to examine how computational analysis and molecular science may synergistically drive advances in personalised radiation therapy (RT) and optimise patient outcomes.

Applications of Artificial Intelligence in Pediatric Oncology: A Systematic Review

PURPOSE

There is a need for an improved understanding of clinical and biologic risk factors in pediatric cancer to improve patient outcomes. Machine learning (ML) represents the application of computational inference from advanced statistical methods that can be applied to increasing amount of data available for study in pediatric oncology. The goal of this systematic review was to systematically characterize the state of ML in pediatric oncology and highlight advances and opportunities in the field.

METHODS

We conducted a systematic review of the Embase, Scopus, and MEDLINE databases for applications of ML in pediatric oncology. Query results from all three databases were aggregated and duplicate studies were removed.

RESULTS

A total of 42 unique articles that examined the applications of ML in pediatric oncology met inclusion criteria for review. We identified 20 studies of CNS tumors, 13 of solid tumors, and nine of leukemia. ML tasks included classification, prediction of treatment response, and dose optimization with a variety of methods being used including neural network, k-nearest neighbor, random forest, naive Bayes, and support vector machines. Strengths of the identified studies included matching or outperforming physician comparators via automated analysis and predicting therapeutic response. Common limitations included significant heterogeneity in reporting standards, clinical applicability, small sample sizes, and missing external validation cohorts.

CONCLUSION

We identified areas where ML can enhance clinical care in ways that may not otherwise be achievable. Although ML promises enormous potential in improving diagnostics, decision making, and monitoring for children with cancer, the field remains in early stages and future work will be aided by standards and guidelines to ensure rigorous methodologic design and maximizing clinical utility.

Clinical applications of artificial intelligence and radiomics in neuro-oncology imaging

This article is a comprehensive review of the basic background, technique, and clinical applications of artificial intelligence (AI) and radiomics in the field of neuro-oncology. A variety of AI and radiomics utilized conventional and advanced techniques to differentiate brain tumors from non-neoplastic lesions such as inflammatory and demyelinating brain lesions. It is used in the diagnosis of gliomas and discrimination of gliomas from lymphomas and metastasis. Also, semiautomated and automated tumor segmentation has been developed for radiotherapy planning and follow-up. It has a role in the grading, prediction of treatment response, and prognosis of gliomas. Radiogenomics allowed the connection of the imaging phenotype of the tumor to its molecular environment. In addition, AI is applied for the assessment of extra-axial brain tumors and pediatric tumors with high performance in tumor detection, classification, and stratification of patient's prognoses.

Evolving Role and Translation of Radiomics and Radiogenomics in Adult and Pediatric Neuro-Oncology

Exponential technologic advancements in imaging, high-performance computing, and artificial intelligence, in addition to increasing access to vast amounts of diverse data, have revolutionized the role of imaging in medicine. Radiomics is defined as a high-throughput feature-extraction method that unlocks microscale quantitative data hidden within standard-of-care medical imaging. Radiogenomics is defined as the linkage between imaging and genomics information. Multiple radiomics and radiogenomics studies performed on conventional and advanced neuro-oncology image modalities show that they have the potential to differentiate pseudoprogression from true progression, classify tumor subgroups, and predict recurrence, survival, and mutation status with high accuracy. In this article, we outline the technical steps involved in radiomics and radiogenomics analyses with the use of artificial intelligence methods and review current applications in adult and pediatric neuro-oncology.

Artificial Intelligence Applications in Pediatric Brain Tumor Imaging: A Systematic Review

OBJECTIVE

Artificial intelligence (AI) has facilitated the analysis of medical imaging given increased computational capacity and medical data availability in recent years. Although many applications for AI in the imaging of brain tumors have been proposed, their potential clinical impact remains to be explored. A systematic review was performed to examine the role of AI in the analysis of pediatric brain tumor imaging.

METHODS

PubMed, Embase, and Scopus were searched for relevant articles up to January 27, 2021.

RESULTS

Literature search identified 298 records, of which 22 studies were included. The most commonly studied tumors were posterior fossa tumors including brainstem glioma, ependymoma, medulloblastoma, and pilocytic astrocytoma (15, 68%). Tumor diagnosis was the most frequently performed task (14, 64%), followed by tumor segmentation (3, 14%) and tumor detection (3, 14%). Of the 6 studies comparing AI to clinical experts, 5 demonstrated superiority of AI for tumor diagnosis. Other tasks including tumor segmentation, attenuation correction of positron emission tomography scans, image registration for patient positioning, and dose calculation for radiotherapy were performed with high accuracy comparable with clinical experts. No studies described use of the AI tool in routine clinical practice.

CONCLUSIONS

AI methods for analysis of pediatric brain tumor imaging have increased exponentially in recent years. However, adoption of these methods in clinical practice requires further characterization of validity and utility. Implementation of these methods may streamline clinical workflows by improving diagnostic accuracy and automating basic imaging analysis tasks.

Radiomics Analysis of Multi-Sequence MR Images For Predicting Microsatellite Instability Status Preoperatively in Rectal Cancer

Background

Immunotherapy, adjuvant chemotherapy, and prognosis of colorectal cancer are associated with MSI. Biopsy pathology cannot fully reflect the MSI status and heterogeneity of rectal cancer.

Purpose

To develop a radiomic-based model to preoperatively predict MSI status in rectal cancer on MRI.

Assessment

The patients were divided into two cohorts (training and testing) at a 7:3 ratio. Radiomics features, including intensity, texture, and shape, were extracted from the segmented volumes of interest based on T2-weighted and ADC imaging.

Statistical Tests

Independent sample t test, Mann-Whitney test, the chi-squared test, Receiver operating characteristic curves, calibration curves, decision curve analysis and multi-variate logistic regression analysis

Results

The radiomics models were significantly associated with MSI status. The T2-based model showed an area under the curve of 0.870 with 95% CI: 0.794–0.945 (accuracy, 0.845; specificity, 0.714; sensitivity, 0.976) in training set and 0.895 with 95% CI, 0.777–1.000 (accuracy, 0.778; specificity, 0.887; sensitivity, 0.772) in testing set. The ADC-based model had an AUC of 0.790 with 95% CI: 0.794–0.945 (accuracy, 0.774; specificity, 0.714; sensitivity, 0.976) in training set and 0.796 with 95% CI, 0.777–1.000 (accuracy, 0.778; specificity, 0.889; sensitivity, 0.772) in testing set. The combined model integrating T2 and ADC features showed an AUC of 0.908 with 95% CI: 0.845–0.971 (accuracy, 0.857; specificity, 0.762; sensitivity, 0.952) in training set and 0.926 with 95% CI: 0.813-1.000 (accuracy, 0.852; specificity, 1.000; sensitivity, 0.778) in testing set. Calibration curve showed that the combined score had a good calibration degree, and the decision curve demonstrated that the combined score was of benefit for clinical use.

Data Conclusion

Radiomics analysis of T2W and ADC images showed significant relevance in the prediction of microsatellite status, and the accuracy of combined model of ADC and T2W features was better than either alone.

Radiomics Nomogram for Identifying Sub-1 cm Benign and Malignant Thyroid Lesions

PURPOSE

To develop and validate a radiomics nomogram for identifying sub-1 cm benign and malignant thyroid lesions.

METHOD

A total of 171 eligible patients with sub-1 cm thyroid lesions (56 benign and 115 malignant) who were treated in Yantai Yuhuangding Hospital between January and September 2019 were retrospectively collected and randomly divided into training (n = 136) and validation sets (n = 35). The radiomics features were extracted from unenhanced and arterial contrast-enhanced computed tomography images of each patient. In the training set, one-way analysis of variance and least absolute shrinkage and selection operator (LASSO) logistic regression were used to select the features related to benign and malignant lesions, and the LASSO algorithm was used to construct the radiomics signature. Combined with clinical independent predictive factors, a radiomics nomogram was constructed with a multivariate logistic regression model. The performance of the radiomics nomogram was evaluated by using the receiver operating characteristic (ROC) and calibration curves in the training and validation sets. The clinical usefulness was evaluated by using decision curve analysis (DCA).

RESULTS

The radiomics signature consisting of 13 selected features achieved favorable prediction efficiency. The radiomics nomogram, which incorporated radiomics signature and clinical independent predictive factors including age and Thyroid Imaging Reporting and Data System category, showed good calibration and discrimination in the training (area under the ROC [AUC]: 0.853; 95% confidence interval [CI]: 0.797, 0.899) and validation sets (AUC: 0.851; 95% CI: 0.735, 0.931). DCA demonstrated that the nomogram was clinically useful.

CONCLUSION

As a noninvasive preoperative prediction tool, the radiomics nomogram incorporating radiomics signature and clinical predictive factors shows favorable predictive efficiency for identifying sub-1 cm benign and malignant thyroid lesions.

Contrast-Enhanced Mammography and Radiomics Analysis for Noninvasive Breast Cancer Characterization: Initial Results

PURPOSE

To investigate the potential of contrast-enhanced mammography (CEM) and radiomics analysis for the noninvasive differentiation of breast cancer invasiveness, hormone receptor status, and tumor grade.

PROCEDURES

This retrospective study included 100 patients with 103 breast cancers who underwent pretreatment CEM. Radiomics analysis was performed using MAZDA software. Lesions were manually segmented. Radiomic features were derived from first-order histogram (HIS), co-occurrence matrix (COM), run length matrix (RLM), absolute gradient, autoregressive model, the discrete Haar wavelet transform (WAV), and lesion geometry. Fisher, probability of error and average correlation (POE+ACC), and mutual information (MI) coefficients informed feature selection. Linear discriminant analysis followed by k-nearest neighbor classification (with leave-one-out cross-validation) was used for pairwise texture-based separation of tumor invasiveness and hormone receptor status using histopathology as the standard of reference.

RESULTS

Radiomics analysis achieved the highest accuracies of 87.4 % for differentiating invasive from noninvasive cancers based on COM+HIS/MI, 78.4 % for differentiating HR positive from HR negative cancers based on COM+HIS/Fisher, 97.2 % for differentiating human epidermal growth factor receptor 2 (HER2)-positive/HR-negative from HER2-negative/HR-positive cancers based on RLM+WAV/MI, 100 % for differentiating triple-negative from triple-positive breast cancers mainly based on COM+WAV+HIS/POE+ACC, and 82.1 % for differentiating triple-negative from HR-positive cancers mainly based on WAV+HIS/Fisher. Accuracies for differentiating grade 1 vs. grades 2 and 3 cancers were 90 % for invasive cancers (based on COM/MI) and 100 % for noninvasive cancers (almost entirely based on COM/MI).

CONCLUSIONS

Radiomics analysis with CEM has potential for noninvasive differentiation of tumors with different degrees of invasiveness, hormone receptor status, and tumor grade.

CoMB-Deep: Composite Deep Learning-Based Pipeline for Classifying Childhood Medulloblastoma and Its Classes

Childhood medulloblastoma (MB) is a threatening malignant tumor affecting children all over the globe. It is believed to be the foremost common pediatric brain tumor causing death. Early and accurate classification of childhood MB and its classes are of great importance to help doctors choose the suitable treatment and observation plan, avoid tumor progression, and lower death rates. The current gold standard for diagnosing MB is the histopathology of biopsy samples. However, manual analysis of such images is complicated, costly, time-consuming, and highly dependent on the expertise and skills of pathologists, which might cause inaccurate results. This study aims to introduce a reliable computer-assisted pipeline called CoMB-Deep to automatically classify MB and its classes with high accuracy from histopathological images. This key challenge of the study is the lack of childhood MB datasets, especially its four categories (defined by the WHO) and the inadequate related studies. All relevant works were based on either deep learning (DL) or textural analysis feature extractions. Also, such studies employed distinct features to accomplish the classification procedure. Besides, most of them only extracted spatial features. Nevertheless, CoMB-Deep blends the advantages of textural analysis feature extraction techniques and DL approaches. The CoMB-Deep consists of a composite of DL techniques. Initially, it extracts deep spatial features from 10 convolutional neural networks (CNNs). It then performs a feature fusion step using discrete wavelet transform (DWT), a texture analysis method capable of reducing the dimension of fused features. Next, the CoMB-Deep explores the best combination of fused features, enhancing the performance of the classification process using two search strategies. Afterward, it employs two feature selection techniques on the fused feature sets selected in the previous step. A bi-directional long-short term memory (Bi-LSTM) network; a DL-based approach that is utilized for the classification phase. CoMB-Deep maintains two classification categories: binary category for distinguishing between the abnormal and normal cases and multi-class category to identify the subclasses of MB. The results of the CoMB-Deep for both classification categories prove that it is reliable. The results also indicate that the feature sets selected using both search strategies have enhanced the performance of Bi-LSTM compared to individual spatial deep features. CoMB-Deep is compared to related studies to verify its competitiveness, and this comparison confirmed its robustness and outperformance. Hence, CoMB-Deep can help pathologists perform accurate diagnoses, reduce misdiagnosis risks that could occur with manual diagnosis, accelerate the classification procedure, and decrease diagnosis costs.

MRI-based radiomics for prognosis of pediatric diffuse intrinsic pontine glioma: an international study

Background

Diffuse intrinsic pontine gliomas (DIPGs) are lethal pediatric brain tumors. Presently, MRI is the mainstay of disease diagnosis and surveillance. We identify clinically significant computational features from MRI and create a prognostic machine learning model.

Methods

We isolated tumor volumes of T1-post-contrast (T1) and T2-weighted (T2) MRIs from 177 treatment-naïve DIPG patients from an international cohort for model training and testing. The Quantitative Image Feature Pipeline and PyRadiomics was used for feature extraction. Ten-fold cross-validation of least absolute shrinkage and selection operator Cox regression selected optimal features to predict overall survival in the training dataset and tested in the independent testing dataset. We analyzed model performance using clinical variables (age at diagnosis and sex) only, radiomics only, and radiomics plus clinical variables.

Results

All selected features were intensity and texture-based on the wavelet-filtered images (3 T1 gray-level co-occurrence matrix (GLCM) texture features, T2 GLCM texture feature, and T2 first-order mean). This multivariable Cox model demonstrated a concordance of 0.68 (95% CI: 0.61–0.74) in the training dataset, significantly outperforming the clinical-only model (C = 0.57 [95% CI: 0.49–0.64]). Adding clinical features to radiomics slightly improved performance (C = 0.70 [95% CI: 0.64–0.77]). The combined radiomics and clinical model was validated in the independent testing dataset (C = 0.59 [95% CI: 0.51–0.67], Noether’s test P = .02).

Conclusions

In this international study, we demonstrate the use of radiomic signatures to create a machine learning model for DIPG prognostication. Standardized, quantitative approaches that objectively measure DIPG changes, including computational MRI evaluation, could offer new approaches to assessing tumor phenotype and serve a future role for optimizing clinical trial eligibility and tumor surveillance.

Differentiation Between Ependymoma and Medulloblastoma in Children with Radiomics Approach

RATIONALE AND OBJECTIVES

Ependymoma (EP) and medulloblastoma (MB) of children are similar in age, location, manifestations and symptoms. Therefore, it is difficult to differentiate them through visual observation in clinical diagnosis. The aim of this study is to investigate the effectiveness of radiomics and machine-learning techniques on multimodal magnetic resonance imaging (MRI) in distinguish EP from MB.

MATERIALS AND METHODS

Three dimensional (3D) tumors were semi-automatic segmented by radiologists from postcontrast T1-weighted images and apparent diffusion coefficient maps in 51 patients (24 EPs, 27 MBs). Then, we extracted radiomics features and further reduced them by three feature selection methods. For each feature selection method, 4 classifiers were adopted which yield 12 different models. After extensive crossvalidation, pairwise test were carried out in receiver operating characteristic curves to explore performance of these models.

RESULTS

The radiomics model built with multivariable logistic regression as feature selection method and random forests as classifier had the best performance, area under the curve achieved 0.91 (95 % confidence interval 0.787-0.968). Five relevant features were highly correlated to discriminate EP and MB, which may used as imaging biomarkers to predict the kinds of tumors.

CONCLUSION

The combination of radiomics and machine-learning approach on 3D multimodal MRI could well distinguish EP and MB of childhood, which assistant doctors in clinical diagnosis. Since there is no uniform model to obtained best performance for every specific data set, it is necessary to try different combination methods.

MB-AI-His: Histopathological Diagnosis of Pediatric Medulloblastoma and its Subtypes via AI

Medulloblastoma (MB) is a dangerous malignant pediatric brain tumor that could lead to death. It is considered the most common pediatric cancerous brain tumor. Precise and timely diagnosis of pediatric MB and its four subtypes (defined by the World Health Organization (WHO)) is essential to decide the appropriate follow-up plan and suitable treatments to prevent its progression and reduce mortality rates. Histopathology is the gold standard modality for the diagnosis of MB and its subtypes, but manual diagnosis via a pathologist is very complicated, needs excessive time, and is subjective to the pathologists' expertise and skills, which may lead to variability in the diagnosis or misdiagnosis. The main purpose of the paper is to propose a time-efficient and reliable computer-aided diagnosis (CADx), namely MB-AI-His, for the automatic diagnosis of pediatric MB and its subtypes from histopathological images. The main challenge in this work is the lack of datasets available for the diagnosis of pediatric MB and its four subtypes and the limited related work. Related studies are based on either textural analysis or deep learning (DL) feature extraction methods. These studies used individual features to perform the classification task. However, MB-AI-His combines the benefits of DL techniques and textural analysis feature extraction methods through a cascaded manner. First, it uses three DL convolutional neural networks (CNNs), including DenseNet-201, MobileNet, and ResNet-50 CNNs to extract spatial DL features. Next, it extracts time-frequency features from the spatial DL features based on the discrete wavelet transform (DWT), which is a textural analysis method. Finally, MB-AI-His fuses the three spatial-time-frequency features generated from the three CNNs and DWT using the discrete cosine transform (DCT) and principal component analysis (PCA) to produce a time-efficient CADx system. MB-AI-His merges the privileges of different CNN architectures. MB-AI-His has a binary classification level for classifying among normal and abnormal MB images, and a multi-classification level to classify among the four subtypes of MB. The results of MB-AI-His show that it is accurate and reliable for both the binary and multi-class classification levels. It is also a time-efficient system as both the PCA and DCT methods have efficiently reduced the training execution time. The performance of MB-AI-His is compared with related CADx systems, and the comparison verified the powerfulness of MB-AI-His and its outperforming results. Therefore, it can support pathologists in the accurate and reliable diagnosis of MB and its subtypes from histopathological images. It can also reduce the time and cost of the diagnosis procedure which will correspondingly lead to lower death rates.

Classification of paediatric brain tumours by diffusion weighted imaging and machine learning

To determine if apparent diffusion coefficients (ADC) can discriminate between posterior fossa brain tumours on a multicentre basis. A total of 124 paediatric patients with posterior fossa tumours (including 55 Medulloblastomas, 36 Pilocytic Astrocytomas and 26 Ependymomas) were scanned using diffusion weighted imaging across 12 different hospitals using a total of 18 different scanners. Apparent diffusion coefficient maps were produced and histogram data was extracted from tumour regions of interest. Total histograms and histogram metrics (mean, variance, skew, kurtosis and 10th, 20th and 50th quantiles) were used as data input for classifiers with accuracy determined by tenfold cross validation. Mean ADC values from the tumour regions of interest differed between tumour types, (ANOVA P < 0.001). A cut off value for mean ADC between Ependymomas and Medulloblastomas was found to be of 0.984 × 10⁻³ mm² s⁻¹ with sensitivity 80.8% and specificity 80.0%. Overall classification for the ADC histogram metrics were 85% using Naïve Bayes and 84% for Random Forest classifiers. The most commonly occurring posterior fossa paediatric brain tumours can be classified using Apparent Diffusion Coefficient histogram values to a high accuracy on a multicentre basis.

MRI-based diagnosis and treatment of pediatric brain tumors: is tissue sample always needed?

Traditional management of newly diagnosed pediatric brain tumors (PBTs) consists of cranial imaging, typically magnetic resonance imaging (MRI), and is frequently followed by tissue diagnosis, through either surgical biopsy or tumor resection. Therapy regimes are typically dependent on histological diagnosis. To date, many treatment regimens are based on molecular biology. The scope of this article is to discuss the role of diagnosis and further treatment of PBTs based solely on MRI features, in light of the latest treatment protocols. Typical MRI findings and indications for surgical biopsy of these lesions are described.

Impact of different scanners and acquisition parameters on robustness of MR radiomics features based on women's cervix

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.

Distinguishing between paediatric brain tumour types using multi-parametric magnetic resonance imaging and machine learning: A multi-site study

The imaging and subsequent accurate diagnosis of paediatric brain tumours presents a radiological challenge, with magnetic resonance imaging playing a key role in providing tumour specific imaging information. Diffusion weighted and perfusion imaging are commonly used to aid the non-invasive diagnosis of children's brain tumours, but are usually evaluated by expert qualitative review. Quantitative studies are mainly single centre and single modality. The aim of this work was to combine multi-centre diffusion and perfusion imaging, with machine learning, to develop machine learning based classifiers to discriminate between three common paediatric tumour types. The results show that diffusion and perfusion weighted imaging of both the tumour and whole brain provide significant features which differ between tumour types, and that combining these features gives the optimal machine learning classifier with >80% predictive precision. This work represents a step forward to aid in the non-invasive diagnosis of paediatric brain tumours, using advanced clinical imaging.

Radiomics-Based Machine Learning Technology Enables Better Differentiation Between Glioblastoma and Anaplastic Oligodendroglioma

Purpose: The aim of this study was to test whether radiomics-based machine learning can enable the better differentiation between glioblastoma (GBM) and anaplastic oligodendroglioma (AO). Methods: This retrospective study involved 126 patients histologically diagnosed as GBM (n = 76) or AO (n = 50) in our institution from January 2015 to December 2018. A total number of 40 three-dimensional texture features were extracted from contrast-enhanced T1-weighted images using LIFEx package. Six diagnostic models were established with selection methods and classifiers. The optimal radiomics features were separately selected into three datasets with three feature selection methods [distance correlation, least absolute shrinkage and selection operator (LASSO), and gradient boosting decision tree (GBDT)]. Then datasets were separately adopted into linear discriminant analysis (LDA) and support vector machine (SVM) classifiers. Specificity, sensitivity, accuracy, and area under curve (AUC) of each model were calculated to evaluate their diagnostic performances. Results: The diagnostic performance of machine learning models was superior to human readers. Both classifiers showed promising ability in discrimination with AUC more than 0.900 when combined with suitable feature selection method. For LDA-based models, the AUC of models were 0.986, 0.994, and 0.970 in the testing group, respectively. For the SVM-based models, the AUC of models were 0.923, 0.817, and 0.500 in the testing group, respectively. The over-fitting model was GBDT + SVM, suggesting that this model was too volatile that unsuitable for classification. Conclusion: This study indicates radiomics-based machine learning has the potential to be utilized in clinically discriminating GBM from AO.

Artificial intelligence applications for pediatric oncology imaging

Machine learning algorithms can help to improve the accuracy and efficiency of cancer diagnosis, selection of personalized therapies and prediction of long-term outcomes. Artificial intelligence (AI) describes a subset of machine learning that can identify patterns in data and take actions to reach pre-set goals without specific programming. Machine learning tools can help to identify high-risk populations, prescribe personalized screening tests and enrich patient populations that are most likely to benefit from advanced imaging tests. AI algorithms can also help to plan personalized therapies and predict the impact of genomic variations on the sensitivity of normal and tumor tissue to chemotherapy or radiation therapy. The two main bottlenecks for successful AI applications in pediatric oncology imaging to date are the needs for large data sets and appropriate computer and memory power. With appropriate data entry and processing power, deep convolutional neural networks (CNNs) can process large amounts of imaging data, clinical data and medical literature in very short periods of time and thereby accelerate literature reviews, correct diagnoses and personalized treatments. This article provides a focused review of emerging AI applications that are relevant for the pediatric oncology imaging community.

Multisite evaluation of radiomic feature reproducibility and discriminability for identifying peripheral zone prostate tumors on MRI

Recent advances in the field of radiomics have enabled the development of a number of prognostic and predictive imaging-based tools for a variety of diseases. However, wider clinical adoption of these tools is contingent on their generalizability across multiple sites and scanners. This may be particularly relevant in the context of radiomic features derived from T1- or T2-weighted magnetic resonance images (MRIs), where signal intensity values are known to lack tissue-specific meaning and vary based on differing acquisition protocols between institutions. We present the first empirical study of benchmarking five different radiomic feature families in terms of both reproducibility and discriminability in a multisite setting, specifically, for identifying prostate tumors in the peripheral zone on MRI. Our cohort comprised 147 patient T2-weighted MRI datasets from four different sites, all of which are first preprocessed to correct for acquisition-related artifacts such as bias field, differing voxel resolutions, and intensity drift (nonstandardness). About 406 three-dimensional voxel-wise radiomic features from five different families (gray, Haralick, gradient, Laws, and Gabor) were evaluated in a cross-site setting to determine (a) how reproducible they are within a relatively homogeneous nontumor tissue region and (b) how well they could discriminate tumor regions from nontumor regions. Our results demonstrate that a majority of the popular Haralick features are reproducible in over 99% of all cross-site comparisons, as well as achieve excellent cross-site discriminability (classification accuracy of ≈ 0.8 ). By contrast, a majority of Laws features are highly variable across sites (reproducible in < 75 % of all cross-site comparisons) as well as resulting in low cross-site classifier accuracies ( < 0.6 ), likely due to a large number of noisy filter responses that can be extracted. These trends suggest that only a subset of radiomic features and associated parameters may be both reproducible and discriminable enough for use within machine learning classifier schemes.

Multi-parametric MRI-based radiomics signature for discriminating between clinically significant and insignificant prostate cancer: Cross-validation of a machine learning method

PURPOSE

To evaluate the performance of a multi-parametric MRI (mp-MRI)-based radiomics signature for discriminating between clinically significant prostate cancer (csPCa) and insignificant PCa (ciPCa).

MATERIALS AND METHODS

Two hundred and eighty patients with pathology-proven PCa were enrolled and were randomly divided into training and test cohorts. Eight hundred and nineteen radiomics features were extracted from mp-MRI for each patient. The minority group in the training cohort was balanced via the synthetic minority over-sampling technique (SMOTE) method. We used minimum-redundancy maximum-relevance (mRMR) selection and the LASSO algorithm for feature selection and radiomics signature building. The classification performance of the radiomics signature for csPCa and ciPCa was evaluated by receiver operating characteristic curve analysis in the training and test cohorts.

RESULTS

Nine features were selected for the radiomics signature building. Significant differences in the radiomics signature existed between the csPCa and ciPCa groups in both the training and test cohorts (p < 0.01 for both). The AUC, sensitivity and specificity of the radiomics signature were 0.872 (95% CI: 0.823-0.921), 0.883, and 0.753, respectively, in the training cohort, and 0.823 (95% CI: 0.669-0.976), 0.841, and 0.727, respectively, in the test cohort.

CONCLUSION

Mp-MRI-based radiomics signature have the potential to noninvasively discriminate between csPCa and ciPCa.