Classification of glioblastoma versus primary central nervous system lymphoma using convolutional neural networks

Multicenter investigation of preoperative distinction between primary central nervous system lymphomas and glioblastomas through interpretable artificial intelligence models

OBJECTIVE

Research into the effectiveness and applicability of deep learning, radiomics, and their integrated models based on Magnetic Resonance Imaging (MRI) for preoperative differentiation between Primary Central Nervous System Lymphoma (PCNSL) and Glioblastoma (GBM), along with an exploration of the interpretability of these models.

MATERIALS AND METHODS

A retrospective analysis was performed on MRI images and clinical data from 261 patients across two medical centers. The data were split into a training set (n = 153, medical center 1) and an external test set (n = 108, medical center 2). Radiomic features were extracted using Pyradiomics to build the Radiomics Model. Deep learning networks, including the transformer-based MobileVIT Model and Convolutional Neural Networks (CNN) based ConvNeXt Model, were trained separately. By applying the "late fusion" theory, the radiomics model and deep learning model were fused to produce the optimal Max-Fusion Model. Additionally, Shapley Additive exPlanations (SHAP) and Grad-CAM were employed for interpretability analysis.

RESULTS

In the external test set, the Radiomics Model achieved an Area under the receiver operating characteristic curve (AUC) of 0.86, the MobileVIT Model had an AUC of 0.91, the ConvNeXt Model demonstrated an AUC of 0.89, and the Max-Fusion Model showed an AUC of 0.92. The Delong test revealed a significant difference in AUC between the Max-Fusion Model and the Radiomics Model (P = 0.02).

CONCLUSION

The Max-Fusion Model, combining different models, presents superior performance in distinguishing PCNSL and GBM, highlighting the effectiveness of model fusion for enhanced decision-making in medical applications.

CLINICAL RELEVANCE STATEMENT

The preoperative non-invasive differentiation between PCNSL and GBM assists clinicians in selecting appropriate treatment regimens and clinical management strategies.

Leveraging State-of-the-Art AI Algorithms in Personalized Oncology: From Transcriptomics to Treatment

BACKGROUND

Continuous breakthroughs in computational algorithms have positioned AI-based models as some of the most sophisticated technologies in the healthcare system. AI shows dynamic contributions in advancing various medical fields involving data interpretation and monitoring, imaging screening and diagnosis, and treatment response and survival prediction. Despite advances in clinical oncology, more effort must be employed to tailor therapeutic plans based on each patient's unique transcriptomic profile within the precision/personalized oncology frame. Furthermore, the standard analysis method is not compatible with the comprehensive deciphering of significant data streams, thus precluding the prediction of accurate treatment options.

METHODOLOGY

We proposed a novel approach that includes obtaining different tumour tissues and preparing RNA samples for comprehensive transcriptomic interpretation using specifically trained, programmed, and optimized AI-based models for extracting large data volumes, refining, and analyzing them. Next, the transcriptomic results will be scanned against an expansive drug library to predict the response of each target to the tested drugs. The obtained target-drug combination/s will be then validated using in vitro and in vivo experimental models. Finally, the best treatment combination option/s will be introduced to the patient. We also provided a comprehensive review discussing AI models' recent innovations and implementations to aid in molecular diagnosis and treatment planning.

RESULTS

The expected transcriptomic analysis generated by the AI-based algorithms will provide an inclusive genomic profile for each patient, containing statistical and bioinformatics analyses, identification of the dysregulated pathways, detection of the targeted genes, and recognition of molecular biomarkers. Subjecting these results to the prediction and pairing AI-based processes will result in statistical graphs presenting each target's likely response rate to various treatment options. Different in vitro and in vivo investigations will further validate the selection of the target drug/s pairs.

CONCLUSIONS

Leveraging AI models will provide more rigorous manipulation of large-scale datasets on specific cancer care paths. Such a strategy would shape treatment according to each patient's demand, thus fortifying the avenue of personalized/precision medicine. Undoubtedly, this will assist in improving the oncology domain and alleviate the burden of clinicians in the coming decade.

Differentiation between multifocal CNS lymphoma and glioblastoma based on MRI criteria

PURPOSE

Differentiating between glioblastoma (GB) with multiple foci (mGB) and multifocal central nervous system lymphoma (mCNSL) can be challenging because these cancers share several features at first appearance on magnetic resonance imaging (MRI). The aim of this study was to explore morphological differences in MRI findings for mGB versus mCNSL and to develop an interpretation algorithm with high diagnostic accuracy.

METHODS

In this retrospective study, MRI characteristics were compared between 50 patients with mGB and 50 patients with mCNSL treated between 2015 and 2020. The following parameters were evaluated: size, morphology, lesion location and distribution, connections between the lesions on the fluid-attenuated inversion recovery sequence, patterns of contrast enhancement, and apparent diffusion coefficient (ADC) values within the tumor and the surrounding edema, as well as MR perfusion and susceptibility weighted imaging (SWI) whenever available.

RESULTS

A total of 187 mCNSL lesions and 181 mGB lesions were analyzed. The mCNSL lesions demonstrated frequently a solid morphology compared to mGB lesions, which showed more often a cystic, mixed cystic/solid morphology and a cortical infiltration. The mean measured diameter was significantly smaller for mCNSL than mGB lesions (p < 0.001). Tumor ADC ratios were significantly smaller in mCNSL than in mGB (0.89 ± 0.36 vs. 1.05 ± 0.35, p < 0.001). The ADC ratio of perilesional edema was significantly higher (p < 0.001) in mCNSL than in mGB. In SWI / T2*-weighted imaging, tumor-associated susceptibility artifacts were more often found in mCNSL than in mGB (p < 0.001).

CONCLUSION

The lesion size, ADC ratios of the lesions and the adjacent tissue as well as the vascularization of the lesions in the MR-perfusion were found to be significant distinctive patterns of mCNSL and mGB allowing a radiological differentiation of these two entities on initial MRI. A diagnostic algorithm based on these parameters merits a prospective validation.

Convolutional neural network for identifying common bile duct stones based on magnetic resonance cholangiopancreatography

AIMS

To develop an auto-categorization system based on machine learning for three-dimensional magnetic resonance cholangiopancreatography (3D MRCP) to detect choledocholithiasis from healthy and symptomatic individuals.

MATERIALS AND METHODS

3D MRCP sequences from 254 cases with common bile duct (CBD) stones and 251 cases with normal CBD were enrolled to train the 3D Convolutional Neural Network (3D-CNN) model. Then 184 patients from three different hospitals (91 with positive CBD stone and 93 with normal CBD) were prospectively included to test the performance of 3D-CNN.

RESULTS

With a cutoff value of 0.2754, 3D-CNN achieved the sensitivity, specificity, and accuracy of 94.51%, 92.47%, and 93.48%, respectively. In the receiver operating characteristic curve analysis, the area under the curve (AUC) for the presence or absence of CBD stones was 0.974 (95% CI, 0.940-0.992). There was no significant difference in sensitivity, specificity, and accuracy between 3D-CNN and radiologists. In addition, the performance of 3D-CNN was also evaluated in the internal test set and the external test set, respectively. The internal test set yielded an accuracy of 94.74% and AUC of 0.974 (95% CI, 0.919-0.996), and the external test set yielded an accuracy of 92.13% and AUC of 0.970 (95% CI, 0.911-0.995).

CONCLUSIONS

An artificial intelligence-assisted diagnostic system for CBD stones was constructed using 3D-CNN model for 3D MRCP images. The performance of 3D-CNN model was comparable to that of radiologists in diagnosing CBD stones. 3D-CNN model maintained high performance when applied to data from other hospitals.

Deep learning aided preoperative diagnosis of primary central nervous system lymphoma

The preoperative distinction between glioblastoma (GBM) and primary central nervous system lymphoma (PCNSL) can be difficult, even for experts, but is highly relevant. We aimed to develop an easy-to-use algorithm, based on a convolutional neural network (CNN) to preoperatively discern PCNSL from GBM and systematically compare its performance to experienced neurosurgeons and radiologists. To this end, a CNN-based on DenseNet169 was trained with the magnetic resonance (MR)-imaging data of 68 PCNSL and 69 GBM patients and its performance compared to six trained experts on an external test set of 10 PCNSL and 10 GBM. Our neural network predicted PCNSL with an accuracy of 80% and a negative predictive value (NPV) of 0.8, exceeding the accuracy achieved by clinicians (73%, NPV 0.77). Combining expert rating with automated diagnosis in those cases where experts dissented yielded an accuracy of 95%. Our approach has the potential to significantly augment the preoperative radiological diagnosis of PCNSL.

Artificial intelligence performance in detecting lymphoma from medical imaging: a systematic review and meta-analysis

BACKGROUND

Accurate diagnosis and early treatment are essential in the fight against lymphatic cancer. The application of artificial intelligence (AI) in the field of medical imaging shows great potential, but the diagnostic accuracy of lymphoma is unclear. This study was done to systematically review and meta-analyse researches concerning the diagnostic performance of AI in detecting lymphoma using medical imaging for the first time.

METHODS

Searches were conducted in Medline, Embase, IEEE and Cochrane up to December 2023. Data extraction and assessment of the included study quality were independently conducted by two investigators. Studies that reported the diagnostic performance of an AI model/s for the early detection of lymphoma using medical imaging were included in the systemic review. We extracted the binary diagnostic accuracy data to obtain the outcomes of interest: sensitivity (SE), specificity (SP), and Area Under the Curve (AUC). The study was registered with the PROSPERO, CRD42022383386.

RESULTS

Thirty studies were included in the systematic review, sixteen of which were meta-analyzed with a pooled sensitivity of 87% (95%CI 83-91%), specificity of 94% (92-96%), and AUC of 97% (95-98%). Satisfactory diagnostic performance was observed in subgroup analyses based on algorithms types (machine learning versus deep learning, and whether transfer learning was applied), sample size (≤ 200 or >  200), clinicians versus AI models and geographical distribution of institutions (Asia versus non-Asia).

CONCLUSIONS

Even if possible overestimation and further studies with a better standards for application of AI algorithms in lymphoma detection are needed, we suggest the AI may be useful in lymphoma diagnosis.

The molecular diagnostic yield of frame-based stereotactic biopsies in the age of precision neuro-oncology: a cross-sectional study

PURPOSE

With the increasing role of molecular genetics in the diagnostics of intracranial tumors, delivering sufficient representative tissue for such analyses is of paramount importance. This study explored the rate of successful diagnosis after frame-based stereotactic biopsies of intracranial lesions.

METHODS

Consecutive patients undergoing frame-based stereotactic biopsies in 2020 and 2021 were included in this retrospective analysis. Cases were classified into three groups: conclusive, diagnosis with missing molecular genetics (MG) data, and inconclusive neuropathological diagnosis.

RESULTS

Of 145 patients, a conclusive diagnosis was possible in n = 137 cases (94.5%). For 3 cases (2.0%), diagnosis was established with missing MG data. In 5 cases (3.5%), an inconclusive (tumor) diagnosis was met. Diagnoses comprised mainly WHO 4 glioblastomas (n = 73, 56%), CNS lymphomas (n = 23, 16%), inflammatory diseases (n = 14, 10%), and metastases (n = 5, 3%). Methylomics were applied in 49% (n = 44) of tumor cases (panel sequencing in n = 28, 30% of tumors). The average number of specimens used for MG diagnostics was 5, while the average number of specimens provided was 15. In a univariate analysis, insufficient DNA was associated with an inconclusive diagnosis or a diagnosis with missing MG data (p < 0.001). Analyses of planned and implemented trajectories of cases with diagnosis with missing MG data or inconclusive diagnosis (n = 8) revealed that regions of interest were reached in almost all cases (n = 7).

CONCLUSION

Although stereotactic frame-based biopsies deliver a limited amount of tissue, they bear high histopathological and molecular genetic diagnostic yields. Given the proven surgical precision of the planned biopsy trajectories, optimizing surveyed lesion regions could help improve the rate of conclusive diagnoses.

Artificial Intelligence in Neurosurgery: A State-of-the-Art Review from Past to Future

In recent years, there has been a significant surge in discussions surrounding artificial intelligence (AI), along with a corresponding increase in its practical applications in various facets of everyday life, including the medical industry. Notably, even in the highly specialized realm of neurosurgery, AI has been utilized for differential diagnosis, pre-operative evaluation, and improving surgical precision. Many of these applications have begun to mitigate risks of intraoperative and postoperative complications and post-operative care. This article aims to present an overview of the principal published papers on the significant themes of tumor, spine, epilepsy, and vascular issues, wherein AI has been applied to assess its potential applications within neurosurgery. The method involved identifying high-cited seminal papers using PubMed and Google Scholar, conducting a comprehensive review of various study types, and summarizing machine learning applications to enhance understanding among clinicians for future utilization. Recent studies demonstrate that machine learning (ML) holds significant potential in neuro-oncological care, spine surgery, epilepsy management, and other neurosurgical applications. ML techniques have proven effective in tumor identification, surgical outcomes prediction, seizure outcome prediction, aneurysm prediction, and more, highlighting its broad impact and potential in improving patient management and outcomes in neurosurgery. This review will encompass the current state of research, as well as predictions for the future of AI within neurosurgery.

One Step Forward-The Current Role of Artificial Intelligence in Glioblastoma Imaging

Artificial intelligence (AI) is rapidly integrating into diagnostic methods across many branches of medicine. Significant progress has been made in tumor assessment using AI algorithms, and research is underway on how image manipulation can provide information with diagnostic, prognostic and treatment impacts. Glioblastoma (GB) remains the most common primary malignant brain tumor, with a median survival of 15 months. This paper presents literature data on GB imaging and the contribution of AI to the characterization and tracking of GB, as well as recurrence. Furthermore, from an imaging point of view, the differential diagnosis of these tumors can be problematic. How can an AI algorithm help with differential diagnosis? The integration of clinical, radiomics and molecular markers via AI holds great potential as a tool for enhancing patient outcomes by distinguishing brain tumors from mimicking lesions, classifying and grading tumors, and evaluating them before and after treatment. Additionally, AI can aid in differentiating between tumor recurrence and post-treatment alterations, which can be challenging with conventional imaging methods. Overall, the integration of AI into GB imaging has the potential to significantly improve patient outcomes by enabling more accurate diagnosis, precise treatment planning and better monitoring of treatment response.

Deep learning for image classification between primary central nervous system lymphoma and glioblastoma in corpus callosal tumors

Objectives

It can be challenging in some situations to distinguish primary central nervous system lymphoma (PCNSL) from glioblastoma (GBM) based on magnetic resonance imaging (MRI) scans, especially those involving the corpus callosum. The objective of this study was to assess the diagnostic performance of deep learning (DL) models between PCNSLs and GBMs in corpus callosal tumors.

Materials and Methods

The axial T1-weighted gadolinium-enhanced MRI scans of 274 individuals with pathologically confirmed PCNSL (n = 94) and GBM (n = 180) were examined. After image pooling, pre-operative MRI scans were randomly split with an 80/20 procedure into a training dataset (n = 709) and a testing dataset (n = 177) for DL model development. Therefore, the DL model was deployed as a web application and validated with the unseen images (n = 114) and area under the receiver operating characteristic curve (AUC); other outcomes were calculated to assess the discrimination performance.

Results

The first baseline DL model had an AUC of 0.77 for PCNSL when evaluated with unseen images. The 2nd model with ridge regression regularization and the 3rd model with drop-out regularization increased an AUC of 0.83 and 0.84. In addition, the last model with data augmentation yielded an AUC of 0.57.

Conclusion

DL with regularization may provide useful diagnostic information to help doctors distinguish PCNSL from GBM.

Recapitulating the Key Advances in the Diagnosis and Prognosis of High-Grade Gliomas: Second Half of 2021 Update

High-grade gliomas (World Health Organization grades III and IV) are the most frequent and fatal brain tumors, with median overall survivals of 24–72 and 14–16 months, respectively. We reviewed the progress in the diagnosis and prognosis of high-grade gliomas published in the second half of 2021. A literature search was performed in PubMed using the general terms “radio* and gliom*” and a time limit from 1 July 2021 to 31 December 2021. Important advances were provided in both imaging and non-imaging diagnoses of these hard-to-treat cancers. Our prognostic capacity also increased during the second half of 2021. This review article demonstrates slow, but steady improvements, both scientifically and technically, which express an increased chance that patients with high-grade gliomas may be correctly diagnosed without invasive procedures. The prognosis of those patients strictly depends on the final results of that complex diagnostic process, with widely varying survival rates.

The application of machine learning to imaging in hematological oncology: A scoping review

Background

Here, we conducted a scoping review to (i) establish which machine learning (ML) methods have been applied to hematological malignancy imaging; (ii) establish how ML is being applied to hematological cancer radiology; and (iii) identify addressable research gaps.

Methods

The review was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analysis Extension for Scoping Reviews guidelines. The inclusion criteria were (i) pediatric and adult patients with suspected or confirmed hematological malignancy undergoing imaging (population); (ii) any study using ML techniques to derive models using radiological images to apply to the clinical management of these patients (concept); and (iii) original research articles conducted in any setting globally (context). Quality Assessment of Diagnostic Accuracy Studies 2 criteria were used to assess diagnostic and segmentation studies, while the Newcastle-Ottawa scale was used to assess the quality of observational studies.

Results

Of 53 eligible studies, 33 applied diverse ML techniques to diagnose hematological malignancies or to differentiate them from other diseases, especially discriminating gliomas from primary central nervous system lymphomas (n=18); 11 applied ML to segmentation tasks, while 9 applied ML to prognostication or predicting therapeutic responses, especially for diffuse large B-cell lymphoma. All studies reported discrimination statistics, but no study calculated calibration statistics. Every diagnostic/segmentation study had a high risk of bias due to their case-control design; many studies failed to provide adequate details of the reference standard; and only a few studies used independent validation.

Conclusion

To deliver validated ML-based models to radiologists managing hematological malignancies, future studies should (i) adhere to standardized, high-quality reporting guidelines such as the Checklist for Artificial Intelligence in Medical Imaging; (ii) validate models in independent cohorts; (ii) standardize volume segmentation methods for segmentation tasks; (iv) establish comprehensive prospective studies that include different tumor grades, comparisons with radiologists, optimal imaging modalities, sequences, and planes; (v) include side-by-side comparisons of different methods; and (vi) include low- and middle-income countries in multicentric studies to enhance generalizability and reduce inequity.

Progress of radiological‑pathological workflows in the differential diagnosis between primary central nervous system lymphoma and high‑grade glioma (Review)

Primary central nervous system lymphoma (PCNSL) and high‑grade glioma (HGG) are distinct entities of the CNS with completely distinct treatments. The treatment of PCNSL is chemotherapy‑based, while surgery is the first choice for HGG. However, the clinical features of the two entities often overlap, and a clear pathological diagnosis is important for subsequent management, especially for the management of PCNSL. Stereotactic biopsy is recognized as one of the minimally invasive alternatives for evaluating the involvement of the CNS. However, in the case of limited tissue materials, the differential diagnosis between the two entities is still difficult. In addition, some patients are too ill to tolerate a needle biopsy. Therefore, combining imaging, histopathology and laboratory examinations is essential in order to make a clear diagnosis as soon as possible. The present study reviews the progress of comparative research on both imaging and laboratory tests based on the pathophysiological changes of the two entities, and proposes an integrative and optimized diagnostic process, with the purpose of building a better understanding for neurologists, hematologists, radiologists and pathologists.

Glioblastoma-derived extracellular vesicle subpopulations following 5-aminolevulinic acid treatment bear diagnostic implications

Liquid biopsy is a minimally invasive alternative to surgical biopsy, encompassing different analytes including extracellular vesicles (EVs), circulating tumour cells (CTCs), circulating tumour DNA (ctDNA), proteins, and metabolites. EVs are released by virtually all cells, but at a higher rate by faster cycling, malignant cells. They encapsulate cargo native to the originating cell and can thus provide a window into the tumour landscape. EVs are often analysed in bulk which hinders the analysis of rare, tumour-specific EV subpopulations from the large host EV background. Here, we fractionated EV subpopulations in vitro and in vivo and characterized their phenotype and generic cargo. We used 5-aminolevulinic acid (5-ALA) to induce release of endogenously fluorescent tumour-specific EVs (EVPpIX ). Analysis of five different subpopulations (EVPpIX , EVCD63 , EVCD9 , EVEGFR , EVCFDA ) from glioblastoma (GBM) cell lines revealed unique transcriptome profiles, with the EVPpIX transcriptome demonstrating closer alignment to tumorigenic processes over the other subpopulations. Similarly, isolation of tumour-specific EVs from GBM patient plasma showed enrichment in GBM-associated genes, when compared to bulk EVs from plasma. We propose that fractionation of EV populations facilitates detection and isolation of tumour-specific EVs for disease monitoring.

Segmentation Algorithm of Magnetic Resonance Imaging Glioma under Fully Convolutional Densely Connected Convolutional Networks

This work focused on the application value of magnetic resonance imaging (MRI) image segmentation algorithm based on fully convolutional DenseNet neural network (FCDNN) in glioma diagnosis. In this work, based on the fully convolutional DenseNet algorithm, a new MRI image automatic semantic segmentation method cerebral gliomas semantic segmentation network (CGSSNet) was established and was applied to glioma MRI image segmentation by using the BraTS public dataset as research data. Under the same conditions, compare the differences of dice similarity coefficient (DSC), sensitivity, and Hausdroff distance (HD) between this algorithm and other algorithms in MRI image processing. The results showed that the CGSSNet network segmentation algorithm significantly improved the segmentation accuracy of glioma MRI images. In addition, its DSC, sensitivity, and HD values for glioma MRI images were 0.937, 0.811, and 1.201, respectively. Under different iteration times, the DSC, sensitivity, and HD values of the CGSSNet network segmentation algorithm are significantly better than other algorithms. It showed that the CGSSNet model based on the DenseNet can improve the segmentation accuracy of glioma MRI images, and has potential application value in clinical practice.

Artemether-loaded polymeric lipid-core nanocapsules reduce cell viability and alter the antioxidant status of U-87 MG cells

Glioblastomas are tumors that present a high mortality rate. Artemether (ART) is a lactone with antitumor properties, demonstrating low bioavailability and water solubility. In the present study, we developed lipid-core nanocapsules (LNC) containing pequi oil (Caryocar brasiliense Cambess) as the oily core for ART-loaded LNCs (LNC_ART) and evaluated their effect on human glioblastoma cells (U-87 MG). LNCs were developed by interfacial deposition of a preformed polymer, followed by physicochemical characterization. LNC_ART revealed a diameter of 0.216 µm, polydispersity index of 0.161, zeta potential of -12.0 mV, and a pH of 5.53. Furthermore, mitochondrial viability, proliferation, total antioxidant status, and antioxidant enzyme activity were evaluated. ART reduced cell viability after 24 h and proliferation after 48 h of treatment at concentrations equal to or above 40 µg . mL^-1. LNC_ART, at 1.25 µg . mL^-1, reduced these parameters after 24 h of treatment. Furthermore, superoxide dismutase (SOD) activity was elevated, while glutathione reductase (GR) activity was reduced. These findings suggest that ART loaded into LNC may be a promising alternative to improve its pharmacological action and possible application as a therapeutic agent for glioblastoma.

Classifying primary central nervous system lymphoma from glioblastoma using deep learning and radiomics based machine learning approach - a systematic review and meta-analysis

Background

Glioblastoma (GBM) and primary central nervous system lymphoma (PCNSL) are common in elderly yet difficult to differentiate on MRI. Their management and prognosis are quite different. Recent surge of interest in predictive analytics, using machine learning (ML) from radiomic features and deep learning (DL) for diagnosing, predicting response and prognosticating disease has evinced interest among radiologists and clinicians. The objective of this systematic review and meta-analysis was to evaluate the deep learning &amp; ML algorithms in classifying PCNSL from GBM.

Methods

The authors performed a systematic review of the literature from MEDLINE, EMBASE and the Cochrane central trials register for the search strategy in accordance with PRISMA guidelines to select and evaluate studies that included themes of ML, DL, AI, GBM, PCNSL. All studies reporting on ML algorithms or DL that for differentiating PCNSL from GBM on MR imaging were included. These studies were further narrowed down to focus on works published between 2018 and 2021. Two researchers independently conducted the literature screening, database extraction and risk bias assessment. The extracted data was synthesised and analysed by forest plots. Outcomes assessed were test characteristics such as accuracy, sensitivity, specificity and balanced accuracy.

Results

Ten articles meeting the eligibility criteria were identified addressing use of ML and DL in training and validation classifiers to distinguish PCNSL from GBM on MR imaging. The total sample size was 1311 in the included studies. ML approach was used in 6 studies while DL in 4 studies. The lowest reported sensitivity was 80%, while the highest reported sensitivity was 99% in studies in which ML and DL was directly compared with the gold standard histopathology. The lowest reported specificity was 87% while the highest reported specificity was 100%. The highest reported balanced accuracy was 100% and the lowest was 84%.

Conclusions

Extensive search of the database revealed a limited number of studies that have applied ML or DL to differentiate PCNSL from GBM. Of the currently published studies, Both DL &amp; ML algorithms have demonstrated encouraging results and certainly have the potential to aid neurooncologists in taking preoperative decisions in the future leading to not only reduction in morbidities but also be cost effective.

Artificial intelligence in the radiomic analysis of glioblastomas: A review, taxonomy, and perspective

Radiological imaging techniques, including magnetic resonance imaging (MRI) and positron emission tomography (PET), are the standard-of-care non-invasive diagnostic approaches widely applied in neuro-oncology. Unfortunately, accurate interpretation of radiological imaging data is constantly challenged by the indistinguishable radiological image features shared by different pathological changes associated with tumor progression and/or various therapeutic interventions. In recent years, machine learning (ML)-based artificial intelligence (AI) technology has been widely applied in medical image processing and bioinformatics due to its advantages in implicit image feature extraction and integrative data analysis. Despite its recent rapid development, ML technology still faces many hurdles for its broader applications in neuro-oncological radiomic analysis, such as lack of large accessible standardized real patient radiomic brain tumor data of all kinds and reliable predictions on tumor response upon various treatments. Therefore, understanding ML-based AI technologies is critically important to help us address the skyrocketing demands of neuro-oncology clinical deployments. Here, we provide an overview on the latest advancements in ML techniques for brain tumor radiomic analysis, emphasizing proprietary and public dataset preparation and state-of-the-art ML models for brain tumor diagnosis, classifications (e.g., primary and secondary tumors), discriminations between treatment effects (pseudoprogression, radiation necrosis) and true progression, survival prediction, inflammation, and identification of brain tumor biomarkers. We also compare the key features of ML models in the realm of neuroradiology with ML models employed in other medical imaging fields and discuss open research challenges and directions for future work in this nascent precision medicine area.

Convolutional Neural Network Techniques for Brain Tumor Classification (from 2015 to 2022): Review, Challenges, and Future Perspectives

Convolutional neural networks (CNNs) constitute a widely used deep learning approach that has frequently been applied to the problem of brain tumor diagnosis. Such techniques still face some critical challenges in moving towards clinic application. The main objective of this work is to present a comprehensive review of studies using CNN architectures to classify brain tumors using MR images with the aim of identifying useful strategies for and possible impediments in the development of this technology. Relevant articles were identified using a predefined, systematic procedure. For each article, data were extracted regarding training data, target problems, the network architecture, validation methods, and the reported quantitative performance criteria. The clinical relevance of the studies was then evaluated to identify limitations by considering the merits of convolutional neural networks and the remaining challenges that need to be solved to promote the clinical application and development of CNN algorithms. Finally, possible directions for future research are discussed for researchers in the biomedical and machine learning communities. A total of 83 studies were identified and reviewed. They differed in terms of the precise classification problem targeted and the strategies used to construct and train the chosen CNN. Consequently, the reported performance varied widely, with accuracies of 91.63–100% in differentiating meningiomas, gliomas, and pituitary tumors (26 articles) and of 60.0–99.46% in distinguishing low-grade from high-grade gliomas (13 articles). The review provides a survey of the state of the art in CNN-based deep learning methods for brain tumor classification. Many networks demonstrated good performance, and it is not evident that any specific methodological choice greatly outperforms the alternatives, especially given the inconsistencies in the reporting of validation methods, performance metrics, and training data encountered. Few studies have focused on clinical usability.

Digital Pathology and Artificial Intelligence Applications in Pathology

Digital pathology is revolutionizing pathology. The introduction of digital pathology made it possible to comprehensively change the pathology diagnosis workflow, apply and develop pathological artificial intelligence (AI) models, generate pathological big data, and perform telepathology. AI algorithms, including machine learning and deep learning, are used for the detection, segmentation, registration, processing, and classification of digitized pathological images. Pathological AI algorithms can be helpfully utilized for diagnostic screening, morphometric analysis of biomarkers, the discovery of new meanings of prognosis and therapeutic response in pathological images, and improvement of diagnostic efficiency. In order to develop a successful pathological AI model, it is necessary to consider the selection of a suitable type of image for a subject, utilization of big data repositories, the setting of an effective annotation strategy, image standardization, and color normalization. This review will elaborate on the advantages and perspectives of digital pathology, AI-based approaches, the applications in pathology, and considerations and challenges in the development of pathological AI models.

12 Plagues of AI in Healthcare: A Practical Guide to Current Issues With Using Machine Learning in a Medical Context

The healthcare field has long been promised a number of exciting and powerful applications of Artificial Intelligence (AI) to improve the quality and delivery of health care services. AI techniques, such as machine learning (ML), have proven the ability to model enormous amounts of complex data and biological phenomena in ways only imaginable with human abilities alone. As such, medical professionals, data scientists, and Big Tech companies alike have all invested substantial time, effort, and funding into these technologies with hopes that AI systems will provide rigorous and systematic interpretations of large amounts of data that can be leveraged to augment clinical judgments in real time. However, despite not being newly introduced, AI-based medical devices have more than often been limited in their true clinical impact that was originally promised or that which is likely capable, such as during the current COVID-19 pandemic. There are several common pitfalls for these technologies that if not prospectively managed or adjusted in real-time, will continue to hinder their performance in high stakes environments outside of the lab in which they were created. To address these concerns, we outline and discuss many of the problems that future developers will likely face that contribute to these failures. Specifically, we examine the field under four lenses: approach, data, method and operation. If we continue to prospectively address and manage these concerns with reliable solutions and appropriate system processes in place, then we as a field may further optimize the clinical applicability and adoption of medical based AI technology moving forward.

A Deep Learning Model for Preoperative Differentiation of Glioblastoma, Brain Metastasis and Primary Central Nervous System Lymphoma: A Pilot Study

Background

Neuroimaging differentiation of glioblastoma, primary central nervous system lymphoma (PCNSL) and solitary brain metastasis (BM) remains challenging in specific cases showing similar appearances or atypical features. Overall, advanced MRI protocols have high diagnostic reliability, but their limited worldwide availability, coupled with the overlapping of specific neuroimaging features among tumor subgroups, represent significant drawbacks and entail disparities in the planning and management of these oncological patients.

Objective

To evaluate the classification performance metrics of a deep learning algorithm trained on T1-weighted gadolinium-enhanced (T1Gd) MRI scans of glioblastomas, atypical PCNSLs and BMs.

Materials and Methods

We enrolled 121 patients (glioblastoma: n=47; PCNSL: n=37; BM: n=37) who had undergone preoperative T1Gd-MRI and histopathological confirmation. Each lesion was segmented, and all ROIs were exported in a DICOM dataset. The patient cohort was then split in a training and hold-out test sets following a 70/30 ratio. A Resnet101 model, a deep neural network (DNN), was trained on the training set and validated on the hold-out test set to differentiate glioblastomas, PCNSLs and BMs on T1Gd-MRI scans.

Results

The DNN achieved optimal classification performance in distinguishing PCNSLs (AUC: 0.98; 95%CI: 0.95 - 1.00) and glioblastomas (AUC: 0.90; 95%CI: 0.81 - 0.97) and moderate ability in differentiating BMs (AUC: 0.81; 95%CI: 0.70 - 0.95). This performance may allow clinicians to correctly identify patients eligible for lesion biopsy or surgical resection.

Conclusion

We trained and internally validated a deep learning model able to reliably differentiate ambiguous cases of PCNSLs, glioblastoma and BMs by means of T1Gd-MRI. The proposed predictive model may provide a low-cost, easily-accessible and high-speed decision-making support for eligibility to diagnostic brain biopsy or maximal tumor resection in atypical cases.