Advances in neuro-oncology imaging

Predicting Survival in Patients with Brain Tumors: Current State-of-the-Art of AI Methods Applied to MRI

Given growing clinical needs, in recent years Artificial Intelligence (AI) techniques have increasingly been used to define the best approaches for survival assessment and prediction in patients with brain tumors. Advances in computational resources, and the collection of (mainly) public databases, have promoted this rapid development. This narrative review of the current state-of-the-art aimed to survey current applications of AI in predicting survival in patients with brain tumors, with a focus on Magnetic Resonance Imaging (MRI). An extensive search was performed on PubMed and Google Scholar using a Boolean research query based on MeSH terms and restricting the search to the period between 2012 and 2022. Fifty studies were selected, mainly based on Machine Learning (ML), Deep Learning (DL), radiomics-based methods, and methods that exploit traditional imaging techniques for survival assessment. In addition, we focused on two distinct tasks related to survival assessment: the first on the classification of subjects into survival classes (short and long-term or eventually short, mid and long-term) to stratify patients in distinct groups. The second focused on quantification, in days or months, of the individual survival interval. Our survey showed excellent state-of-the-art methods for the first, with accuracy up to ∼98%. The latter task appears to be the most challenging, but state-of-the-art techniques showed promising results, albeit with limitations, with C-Index up to ∼0.91. In conclusion, according to the specific task, the available computational methods perform differently, and the choice of the best one to use is non-univocal and dependent on many aspects. Unequivocally, the use of features derived from quantitative imaging has been shown to be advantageous for AI applications, including survival prediction. This evidence from the literature motivates further research in the field of AI-powered methods for survival prediction in patients with brain tumors, in particular, using the wealth of information provided by quantitative MRI techniques.

DEGRO practical guideline for central nervous system radiation necrosis part 1: classification and a multistep approach for diagnosis

PURPOSE

The Working Group for Neuro-Oncology of the German Society for Radiation Oncology in cooperation with members of the Neuro-Oncology Working Group of the German Cancer Society aimed to define a practical guideline for the diagnosis and treatment of radiation-induced necrosis (RN) of the central nervous system (CNS).

METHODS

Panel members of the DEGRO working group invited experts, participated in a series of conferences, supplemented their clinical experience, performed a literature review, and formulated recommendations for medical treatment of RN including bevacizumab in clinical routine.

CONCLUSION

Diagnosis and treatment of RN requires multidisciplinary structures of care and defined processes. Diagnosis has to be made on an interdisciplinary level with the joint knowledge of a neuroradiologist, radiation oncologist, neurosurgeon, neuropathologist, and neuro-oncologist. A multistep approach as an opportunity to review as many characteristics as possible to improve diagnostic confidence is recommended. Additional information about radiotherapy (RT) techniques is crucial for the diagnosis of RN. Misdiagnosis of untreated and progressive RN can lead to severe neurological deficits. In this practice guideline, we propose a detailed nomenclature of treatment-related changes and a multistep approach for their diagnosis.

Differentiating EGFR from ALK mutation status using radiomics signature based on MR sequences of brain metastasis

PURPOSE

More and more small brain metastases (BMs) in asymptomatic patients can be detected even prior to their primary lung cancer with the development of MRI. The aim of this study was to develop a predictive radiomics model to identify epidermal growth factor receptor (EGFR) and anaplastic lymphoma kinase (ALK) mutation status in BM and explore the optimal MR sequence for predication.

METHODS

This retrospective study included 186 patients with proven BM of lung cancer (training cohort: 70 patients with EGFR mutations and 65 patients with ALK rearrangements; testing cohort: 26 patients with EGFR mutations and 25 patients with ALK rearrangements). Radiomics features were separately extracted from contrast-enhanced T1-weighted imaging (T1-CE), T2 fluid-attenuated inversion recovery (T2-FLAIR) and T2WI sequences. The model for three MR sequences were constructed using a random forest classifier. ROC curves were used to validate the capability of the models in the training and testing cohorts.

RESULTS

The AUCs of the T2-FLAIR model were significantly higher than those of the T1-CE model in training cohort (0.991 versus 0.954) and testing cohort (0.950 versus 0.867) and much higher than those of the T2WI model in training cohort (0.991 versus 0.880) and testing cohort (0.950 versus 0.731). Besides, the F1 scores of the T1-CE model were slightly higher than the T2-FLAIR model and much higher than the T2WI model in two cohorts.

CONCLUSION

T2-FLAIR and T1-CE radiomics models that can be used as noninvasive tools for identifying EGFR and ALK mutation status are helpful to guide therapeutic strategies.

Liquid Biopsy in Glioblastoma

Simple Summary

Glioblastoma is the most common and malignant primary brain tumor. Despite intensive research for new treatments, the survival of patients has not significantly improved in recent decades. Currently, glioblastoma is mainly diagnosed by neuroimaging techniques followed by histopathological and molecular analysis of the resected or biopsied tissue. Both imaging and tissue-based methods have, despite their advantages, some important limitations highlighting the necessity for alternative techniques such as liquid biopsy. It appears as an attractive and non-invasive alternative to support the diagnosis and the follow-up of patients with glioblastoma and to identify early recurrence. Liquid biopsy, primarily through blood tests, involves the detection and quantification of tumoral content released by tumors into the biofluids. The aim of the present review is to discuss the biological bases, the advantages, and the disadvantages of the most important circulating biomarkers so far proposed for glioblastoma.

Abstract

Glioblastoma (GBM) is the most common and aggressive primary brain tumor. Despite recent advances in therapy modalities, the overall survival of GBM patients remains poor. GBM diagnosis relies on neuroimaging techniques. However, confirmation via histopathological and molecular analysis is necessary. Given the intrinsic limitations of such techniques, liquid biopsy (mainly via blood samples) emerged as a non-invasive and easy-to-implement alternative that could aid in both the diagnosis and the follow-up of GBM patients. Cancer cells release tumoral content into the bloodstream, such as circulating tumor DNA, circulating microRNAs, circulating tumor cells, extracellular vesicles, or circulating nucleosomes: all these could serve as a marker of GBM. In this narrative review, we discuss the current knowledge, the advantages, and the disadvantages of each circulating biomarker so far proposed.

Two Decades of Brain Tumour Imaging with O-(2-[18F]fluoroethyl)-L-tyrosine PET: The Forschungszentrum Jülich Experience

Simple Summary

PET using radiolabelled amino acids has become an essential tool for diagnosing brain tumours in addition to MRI. O-(2-[¹⁸F]fluoroethyl)-L-tyrosine (FET) is one of the most successful tracers in the field. We analysed our database of 6534 FET PET examinations regarding the diagnostic needs and preferences of the referring physicians for FET PET in the clinical decision-making process. The demand for FET PET increased considerably in the last decade, especially for differentiating tumour progress from treatment-related changes in gliomas. Accordingly, referring physicians rated the diagnostics of recurrent glioma and recurrent brain metastases as the most relevant indication for FET PET. The analysis and survey results confirm the high relevance of FET PET in the clinical diagnosis of brain tumours and support the need for approval for routine use.

Abstract

O-(2-[¹⁸F]fluoroethyl)-L-tyrosine (FET) is a widely used amino acid tracer for positron emission tomography (PET) imaging of brain tumours. This retrospective study and survey aimed to analyse our extensive database regarding the development of FET PET investigations, indications, and the referring physicians’ rating concerning the role of FET PET in the clinical decision-making process. Between 2006 and 2019, we performed 6534 FET PET scans on 3928 different patients against a backdrop of growing demand for FET PET. In 2019, indications for the use of FET PET were as follows: suspected recurrent glioma (46%), unclear brain lesions (20%), treatment monitoring (19%), and suspected recurrent brain metastasis (13%). The referring physicians were neurosurgeons (60%), neurologists (19%), radiation oncologists (11%), general oncologists (3%), and other physicians (7%). Most patients travelled 50 to 75 km, but 9% travelled more than 200 km. The role of FET PET in decision-making in clinical practice was evaluated by a questionnaire consisting of 30 questions, which was filled out by 23 referring physicians with long experience in FET PET. Fifty to seventy per cent rated FET PET as being important for different aspects of the assessment of newly diagnosed gliomas, including differential diagnosis, delineation of tumour extent for biopsy guidance, and treatment planning such as surgery or radiotherapy, 95% for the diagnosis of recurrent glioma, and 68% for the diagnosis of recurrent brain metastases. Approximately 50% of the referring physicians rated FET PET as necessary for treatment monitoring in patients with glioma or brain metastases. All referring physicians stated that the availability of FET PET is essential and that it should be approved for routine use. Although the present analysis is limited by the fact that only physicians who frequently referred patients for FET PET participated in the survey, the results confirm the high relevance of FET PET in the clinical diagnosis of brain tumours and support the need for its approval for routine use.

Investigational PET tracers in neuro-oncology-What's on the horizon? A report of the PET/RANO group

Many studies in patients with brain tumors evaluating innovative PET tracers have been published in recent years, and the initial results are promising. Here, the Response Assessment in Neuro-Oncology (RANO) PET working group provides an overview of the literature on novel investigational PET tracers for brain tumor patients. Furthermore, newer indications of more established PET tracers for the evaluation of glucose metabolism, amino acid transport, hypoxia, cell proliferation, and others are also discussed. Based on the preliminary findings, these novel investigational PET tracers should be further evaluated considering their promising potential. In particular, novel PET probes for imaging of translocator protein and somatostatin receptor overexpression as well as for immune system reactions appear to be of additional clinical value for tumor delineation and therapy monitoring. Progress in developing these radiotracers may contribute to improving brain tumor diagnostics and advancing clinical translational research.

Standard clinical approaches and emerging modalities for glioblastoma imaging

Glioblastoma (GBM) is the most common primary adult intracranial malignancy and carries a dismal prognosis despite an aggressive multimodal treatment regimen that consists of surgical resection, radiation, and adjuvant chemotherapy. Radiographic evaluation, largely informed by magnetic resonance imaging (MRI), is a critical component of initial diagnosis, surgical planning, and post-treatment monitoring. However, conventional MRI does not provide information regarding tumor microvasculature, necrosis, or neoangiogenesis. In addition, traditional MRI imaging can be further confounded by treatment-related effects such as pseudoprogression, radiation necrosis, and/or pseudoresponse(s) that preclude clinicians from making fully informed decisions when structuring a therapeutic approach. A myriad of novel imaging modalities have been developed to address these deficits. Herein, we provide a clinically oriented review of standard techniques for imaging GBM and highlight emerging technologies utilized in disease characterization and therapeutic development.

Aggregation-induced emission: An emerging concept in brain science

As an emerging concept in brain science, aggregation-induced emission (AIE) has captivated much interest by virtue of the unique superiority of AIE fluorophores in terms of emission intensity, imaging resolution, biocompatibility and photosensitivity. This review mainly overviews the current state-of-art advances of AIE fluorophores achieving the superb performance in brain imaging and therapy, which facilitate deep tissue penetration, high contrast to autofluorescence and efficient blood-brain barrier (BBB) crossing by rational molecular design and functionalized strategies. We expect this review serve as a modest spur to push forward the blooming growth of research in this fertile field.

Safety and efficacy of irradiation boost based on 18F-FET-PET in patients with newly diagnosed glioblastoma

PURPOSE

Dual timepoint FET-PET acquisition (10 and 60 minutes after FET injection) improves the definition of glioblastoma location and shape. Here we evaluated the safety and efficacy of simultaneous integrated boost (SIB) planned using dual FET-PET for postoperative glioblastoma treatment.

EXPERIMENTAL DESIGN

In this prospective pilot study (March 2017-December 2020), 17 patients qualified for FET-PET-based SIB intensity-modulated radiotherapy after resection. The prescribed dose was 78 and 60 Gy (2.6 and 2.0 Gy per fraction, respectively) for the FET-PET- and MR-based target volumes. Eleven patients had FET-PET within nine months to precisely define biological responses. Progression-free survival (PFS), overall survival (OS), toxicities, and radiation necrosis were evaluated. Six patients (35%) had tumors with MGMT promoter methylation.

RESULTS

The one- and two-year OS and PFS rates were 73% and 43% and 53% and 13%, respectively. The median OS and PFS were 24 (95%CI 9-26) and 12 (95%CI 6-18) months, respectively. Two patients developed uncontrolled seizures during radiotherapy and could not receive treatment per protocol. In patients treated per protocol, 7/15 presented with new or increased neurological deficits in the first month after irradiation. Radiation necrosis was diagnosed by MRI three months after SIB in five patients and later in another two patients. In two patients, the tumor was larger in FET-PET images after six months.

CONCLUSIONS

Survival outcomes using our novel dose escalation concept (total 78 Gy) were promising, even within the MGMTunmethylated subgroup. Excessive neurotoxicity was not observed, but radionecrosis was common and must be considered in future trials.

Nuclear medicine and radiotherapy in the clinical management of glioblastoma patients

Introduction

The aim of the narrative review was to analyse the applications of nuclear medicine (NM) techniques such as PET/CT with different tracers in combination with radiotherapy for the clinical management of glioblastoma patients.

Materials and methods

Key references were derived from a PubMed query. Hand searching and clinicaltrials.gov were also used.

Results

This paper contains a narrative report and a critical discussion of NM approaches in combination with radiotherapy in glioma patients.

Conclusions

NM can provide the Radiation Oncologist several aids that can be useful in the clinical management of glioblastoma patients. At the same, these results need to be validated in prospective and multicenter trials.

Relationship between histological tumor margins and magnetic resonance imaging signal intensities in brain neoplasia of dogs

BACKGROUND

Intracranial neoplasia is relatively common in dogs and stereotactic radiotherapy, surgical debulking, or both, are the most successful treatment approaches. A key component of treatment planning involves delineating tumor margin on magnetic resonance imaging (MRI) examinations. How MRI signal intensity alterations relate to histological tumor margins is unknown.

OBJECTIVES

Directly compare histological brain sections to MRI sequence images and determine which sequence alteration best correlates with tumor margins.

ANIMALS

Five dogs with glioma, 4 dogs with histiocytic sarcoma, and 3 dogs with meningioma.

METHODS

Retrospective cohort study. Histological brain sections were registered to in vivo MRI scan images obtained within 7 days of necropsy. Margins of signal intensity alterations (T2-weighted, fluid-attenuating inversion recovery [FLAIR], T1-weighted and contrast enhancement) were compared directly to solid tumor and surgical margins identified on histology. Jacquard similarity metrics (JSM) and cross-sectional areas were calculated.

RESULTS

In glioma cases, margins drawn around T2-weighted hyperintensity were most similar to surgical margins (JSM, 0.66 ± 0.17) when compared to other sequences. In both meningioma (JSM, 0.57 ± 0.21) and histiocytic sarcoma (JSM, 0.75 ± 0.11) margins of contrast enhancement were most similar to surgical margins.

CONCLUSIONS AND CLINICAL IMPORTANCE

Signal intensities correspond to tumor margins for different tumor types and facilitate surgical and radiation therapy planning using MRI images.

Navigate Towards the Immunotherapy Era: Value of Immune Checkpoint Inhibitors in Non-Small Cell Lung Cancer Patients With Brain Metastases

Brain metastases (BMs) in non-small-cell lung cancer (NSCLC) patients are associated with significant morbidity and poor prognosis. Immune checkpoint inhibitors (ICIs) have resulted in a paradigm shift in the management of advanced NSCLC. However, the value of ICIs in NSCLC patients with BMs remains unclear because patients with BMs are routinely excluded in numerous prospective trials on ICIs. Here, starting from the mechanisms of ICIs for BMs, we will reveal the value of ICIs by reviewing the efficacy and adverse effects of ICIs monotherapy as well as promising combination strategies, such as combinations with chemotherapy, radiotherapy, and anti-angiogenic drugs, etc. In addition, the methods of patient selection and response assessment will be summarized to assist clinical practice and further studies.

Efficient Radiomics-Based Classification of Multi-Parametric MR Images to Identify Volumetric Habitats and Signatures in Glioblastoma: A Machine Learning Approach

Simple Summary

Glioblastomas carry a poor prognosis and usually presents with heterogeneous regions in the brain tumor. Multi-parametric MR images can show morphological characteristics. Radiomics features refer to the extraction of a large number of quantitative measurements that describe the geometry, intensity, and texture which were extracted from contrast-enhanced T1-weighted images from anatomical MRI and metabolic features from PET. It also provides a qualitative image interpretation as well as cellular, molecular, and tumor properties. Thus, it derives additional information about the entire tumor volume which is generally of irregular shape and size from routinely evaluated “non-invasive” imaging biomarkers techniques. We demonstrated volumetric habitats and signatures in necrosis, solid tumor, peritumoral tissue, and edema with key biological processes and phenotype features. This provides physicians with key information on how the disease is progressing in the brain and can also give an indication of how well treatment is working.

Abstract

Glioblastoma (GBM) is a fast-growing and aggressive brain tumor of the central nervous system. It encroaches on brain tissue with heterogeneous regions of a necrotic core, solid part, peritumoral tissue, and edema. This study provided qualitative image interpretation in GBM subregions and radiomics features in quantitative usage of image analysis, as well as ratios of these tumor components. The aim of this study was to assess the potential of multi-parametric MR fingerprinting with volumetric tumor phenotype and radiomic features to underlie biological process and prognostic status of patients with cerebral gliomas. Based on efficiently classified and retrieved cerebral multi-parametric MRI, all data were analyzed to derive volume-based data of the entire tumor from local cohorts and The Cancer Imaging Archive (TCIA) cohorts with GBM. Edema was mainly enriched for homeostasis whereas necrosis was associated with texture features. The proportional volume size of the edema was about 1.5 times larger than the size of the solid part tumor. The volume size of the solid part was approximately 0.7 times in the necrosis area. Therefore, the multi-parametric MRI-based radiomics model reveals efficiently classified tumor subregions of GBM and suggests that prognostic radiomic features from routine MRI examination may also be significantly associated with key biological processes as a practical imaging biomarker.

The diagnostic accuracy of O-(2-18F-fluoroethyl)-L-tyrosine parameters for the differentiation of brain tumour progression from treatment-related changes

BACKGROUND

18F-fluoro-ethyl-tyrosine (18F-FET) is recommended to distinguish brain tumours post-therapeutic true progression (including recurrent and metastatic brain tumours) and treatment-related change (TRC). However, many parameters of 18F-FET can be used for this differential diagnosis. Our purpose was to investigate the diagnostic accuracy of various 18F-FET parameters to differentiate true progression from TRC.

METHODS

We performed a literature search using the following databases: the PubMed, Embase and Web of Science databases up to 29 November 2020. We included studies that reported the diagnostic test results of 18F-FET to distinguish true progression from TRC. The Quality Assessment of Diagnostic Accuracy Studies-2 tool was used to evaluate the quality of the included studies. The diagnostic accuracy of various parameters was pooled using a random-effects model.

RESULTS

We included 17 eligible studies (nine parameters). For static parameters of 18F-FET, the maximum and mean tumour-to-brain ratios (TBRmax and TBRmean) showed similar pooled sensitivities of 82% [95% confidence interval (CI), 80-85%) and 82% (95% CI, 78-85%), respectively. Among the three kinetic parameters (slope, time to peak and kinetic pattern), the kinetic pattern presented the optimal diagnostic value with a pooled sensitivity of 81% (95% CI, 75-86%). When combining the static and kinetic parameters, the diagnostic performance of 18F-FET was significantly improved, with a pooled sensitivity of 90% (95% CI, 84-94%) in the combination of TBR and kinetic patterns.

CONCLUSIONS

18F-FET static parameters alone showed a comparably high sensitivity in the differentiation between brain tumour true progression and TRC. Combining static and kinetic parameters provided improved diagnostic performance.

Use of advanced neuroimaging and artificial intelligence in meningiomas

Anatomical cross‐sectional imaging methods such as contrast‐enhanced MRI and CT are the standard for the delineation, treatment planning, and follow‐up of patients with meningioma. Besides, advanced neuroimaging is increasingly used to non‐invasively provide detailed insights into the molecular and metabolic features of meningiomas. These techniques are usually based on MRI, e.g., perfusion‐weighted imaging, diffusion‐weighted imaging, MR spectroscopy, and positron emission tomography. Furthermore, artificial intelligence methods such as radiomics offer the potential to extract quantitative imaging features from routinely acquired anatomical MRI and CT scans and advanced imaging techniques. This allows the linking of imaging phenotypes to meningioma characteristics, e.g., the molecular‐genetic profile. Here, we review several diagnostic applications and future directions of these advanced neuroimaging techniques, including radiomics in preclinical models and patients with meningioma.

Preoperative [11C]methionine PET to personalize treatment decisions in patients with lower-grade gliomas

BACKGROUND

PET with radiolabelled amino acids is used in the preoperative evaluation of patients with glial neoplasms. This study aimed to assess the role of [ 11C]methionine (MET) PET in assessing molecular features, tumour extent, and prognosis in newly-diagnosed lower-grade gliomas (LGGs) surgically treated.

METHODS

153 patients with a new diagnosis of grade 2/3 glioma who underwent surgery at our Institution and were imaged preoperatively using [ 11C]MET PET/CT were retrospectively included. [ 11C]MET PET images were qualitatively and semiquantitatively analyzed using tumour-to-background ratio (TBR). Progression-free survival (PFS) rates were estimated using the Kaplan-Meier method and Cox proportional-hazards regression was used to test the association of clinicopathological and imaging data to PFS.

RESULTS

Overall, 111 lesions (73%) were positive, while thirty-two (21%) and ten (6%) were isometabolic and hypometabolic at [ 11C]MET PET, respectively. [ 11C]MET uptake was more common in oligodendrogliomas than IDH-mutant astrocytomas (87% vs 50% of cases, respectively). Among [ 11C]MET-positive gliomas, grade 3 oligodendrogliomas had the highest median TBRmax (3.22). In 25% of patients, PET helped to better delineate tumour margins compared to MRI only. In IDH-mutant astrocytomas, higher TBRmax values at [ 11C]MET PET were independent predictors of shorter PFS.

CONCLUSIONS

This work highlights the role of preoperative [ 11C]MET PET in estimating the type, assessing tumour extent, and predicting biological behaviour and prognosis of LGGs. Our findings support the implementation of [ 11C]MET PET in routine clinical practice to better manage these neoplasms.

A systematic review of amino acid PET in assessing treatment response to temozolomide in glioma

The response assessment in neuro-oncology (RANO) criteria have been the gold standard for monitoring treatment response in glioblastoma (GBM) and differentiating tumor progression from pseudoprogression. While the RANO criteria have played a key role in detecting early tumor progression, their ability to identify pseudoprogression is limited by post-treatment damage to the blood-brain barrier (BBB), which often leads to contrast enhancement on MRI and correlates poorly to tumor status. Amino acid positron emission tomography (AA PET) is a rapidly growing imaging modality in neuro-oncology. While contrast-enhanced MRI relies on leaky vascularity or a compromised BBB for delivery of contrast agents, amino acid tracers can cross the BBB, making AA PET particularly well-suited for monitoring treatment response and diagnosing pseudoprogression. The authors performed a systematic review of PubMed, MEDLINE, and Embase through December 2021 with the search terms “temozolomide” OR “Temodar,” “glioma” OR “glioblastoma,” “PET,” and “amino acid.” There were 19 studies meeting inclusion criteria. Thirteen studies utilized [¹⁸F]FET, five utilized [¹¹C]MET, and one utilized both. All studies used static AA PET parameters to evaluate TMZ treatment in glioma patients, with nine using dynamic tracer parameters in addition. Throughout these studies, AA PET demonstrated utility in TMZ treatment monitoring and predicting patient survival.

What Does PET Imaging Bring to Neuro-Oncology in 2022? A Review

Simple Summary

Positron emission tomography (PET) imaging is increasingly used to supplement MRI in the management of patient with brain tumors. In this article, we provide a review of the current place and perspectives of PET imaging for the diagnosis and follow-up of from primary brain tumors such as gliomas, meningiomas and central nervous system lymphomas, as well as brain metastases. Different PET radiotracers targeting different biological processes are used to accurately depict these brain tumors and provide unique metabolic and biologic information. Radiolabeled amino acids such as [¹⁸F]FDOPA or [¹⁸F]FET are used for imaging of gliomas while both [¹⁸F]FDG and amino acids can be used for brain metastases. Meningiomas can be seen with a high contrast using radiolabeled ligands of somatostatin receptors, which they usually carry. Unconventional tracers that allow the study of other biological processes such as cell proliferation, hypoxia, or neo-angiogenesis are currently being studied for brain tumors imaging.

Abstract

PET imaging is being increasingly used to supplement MRI in the clinical management of brain tumors. The main radiotracers implemented in clinical practice include [¹⁸F]FDG, radiolabeled amino acids ([¹¹C]MET, [¹⁸F]FDOPA, [¹⁸F]FET) and [⁶⁸Ga]Ga-DOTA-SSTR, targeting glucose metabolism, L-amino-acid transport and somatostatin receptors expression, respectively. This review aims at addressing the current place and perspectives of brain PET imaging for patients who suffer from primary or secondary brain tumors, at diagnosis and during follow-up. A special focus is given to the following: radiolabeled amino acids PET imaging for tumor characterization and follow-up in gliomas; the role of amino acid PET and [¹⁸F]FDG PET for detecting brain metastases recurrence; [⁶⁸Ga]Ga-DOTA-SSTR PET for guiding treatment in meningioma and particularly before targeted radiotherapy.

Brain Tumor Classification Using a Combination of Variational Autoencoders and Generative Adversarial Networks

Brain tumors are a pernicious cancer with one of the lowest five-year survival rates. Neurologists often use magnetic resonance imaging (MRI) to diagnose the type of brain tumor. Automated computer-assisted tools can help them speed up the diagnosis process and reduce the burden on the health care systems. Recent advances in deep learning for medical imaging have shown remarkable results, especially in the automatic and instant diagnosis of various cancers. However, we need a large amount of data (images) to train the deep learning models in order to obtain good results. Large public datasets are rare in medicine. This paper proposes a framework based on unsupervised deep generative neural networks to solve this limitation. We combine two generative models in the proposed framework: variational autoencoders (VAEs) and generative adversarial networks (GANs). We swap the encoder–decoder network after initially training it on the training set of available MR images. The output of this swapped network is a noise vector that has information of the image manifold, and the cascaded generative adversarial network samples the input from this informative noise vector instead of random Gaussian noise. The proposed method helps the GAN to avoid mode collapse and generate realistic-looking brain tumor magnetic resonance images. These artificially generated images could solve the limitation of small medical datasets up to a reasonable extent and help the deep learning models perform acceptably. We used the ResNet50 as a classifier, and the artificially generated brain tumor images are used to augment the real and available images during the classifier training. We compared the classification results with several existing studies and state-of-the-art machine learning models. Our proposed methodology noticeably achieved better results. By using brain tumor images generated artificially by our proposed method, the classification average accuracy improved from 72.63% to 96.25%. For the most severe class of brain tumor, glioma, we achieved 0.769, 0.837, 0.833, and 0.80 values for recall, specificity, precision, and F1-score, respectively. The proposed generative model framework could be used to generate medical images in any domain, including PET (positron emission tomography) and MRI scans of various parts of the body, and the results show that it could be a useful clinical tool for medical experts.

Static FET PET radiomics for the differentiation of treatment-related changes from glioma progression

PURPOSE

To investigate the potential of radiomics applied to static clinical PET data using the tracer O-(2-[¹⁸F]fluoroethyl)-L-tyrosine (FET) to differentiate treatment-related changes (TRC) from tumor progression (TP) in patients with gliomas.

PATIENTS AND METHODS

One hundred fifty-one (151) patients with histologically confirmed gliomas and post-therapeutic progressive MRI findings according to the response assessment in neuro-oncology criteria underwent a dynamic amino acid PET scan using the tracer O-(2-[¹⁸F]fluoroethyl)-L-tyrosine (FET). Thereof, 124 patients were investigated on a stand-alone PET scanner (data used for model development and validation), and 27 patients on a hybrid PET/MRI scanner (data used for model testing). Mean and maximum tumor to brain ratios (TBR_mean, TBR_max) were calculated using the PET data from 20 to 40 min after tracer injection. Logistic regression models were evaluated for the FET PET parameters TBR_mean, TBR_max, and for radiomics features of the tumor areas as well as combinations thereof to differentiate between TP and TRC. The best performing models in the validation dataset were finally applied to the test dataset. The diagnostic performance was assessed by receiver operating characteristic analysis.

RESULTS

Thirty-seven patients (25%) were diagnosed with TRC, and 114 (75%) with TP. The logistic regression model comprising the conventional FET PET parameters TBR_mean and TBR_max resulted in an AUC of 0.78 in both the validation (sensitivity, 64%; specificity, 80%) and the test dataset (sensitivity, 64%; specificity, 80%). The model combining the conventional FET PET parameters and two radiomics features yielded the best diagnostic performance in the validation dataset (AUC, 0.92; sensitivity, 91%; specificity, 80%) and demonstrated its generalizability in the independent test dataset (AUC, 0.85; sensitivity, 81%; specificity, 70%).

CONCLUSION

The developed radiomics classifier allows the differentiation between TRC and TP in pretreated gliomas based on routinely acquired static FET PET scans with a high diagnostic accuracy.

Tracers progress for positron emission tomography imaging of glial-related disease

Glial cells play an essential part in the neuron system. They can not only serve as structural blocks in the human brain but also participate in many biological processes. Extensive studies have shown that astrocytes and microglia play an important role in neurodegenerative diseases, such as Alzheimer's disease, Parkinson's disease, Huntington's disease, as well as glioma, epilepsy, ischemic stroke, and infections. Positron emission tomography is a functional imaging technique providing molecular-level information before anatomic changes are visible and has been widely used in many above-mentioned diseases. In this review, we focus on the positron emission tomography tracers used in pathologies related to glial cells, such as glioma, Alzheimer's disease, and neuroinflammation.

PET/MR in recurrent glioblastoma patients treated with regorafenib: [18F]FET and DWI-ADC for response assessment and survival prediction

Objective:

The use of regorafenib in recurrent glioblastoma patients has been recently approved by the Italian Medicines Agency (AIFA) and added to the National Comprehensive Cancer Network (NCCN) 2020 guidelines as a preferred regimen. Given its complex effects at the molecular level, the most appropriate imaging tools to assess early response to treatment is still a matter of debate. Diffusion-weighted imaging and O-(2-¹⁸F-fluoroethyl)-L-tyrosine positron emission tomography ([¹⁸F]FET PET) are promising methodologies providing additional information to the currently used RANO criteria. The aim of this study was to evaluate the variations in diffusion-weighted imaging/apparent diffusion coefficient (ADC) and [¹⁸F]FET PET-derived parameters in patients who underwent PET/MR at both baseline and after starting regorafenib.

Methods:

We retrospectively reviewed 16 consecutive GBM patients who underwent [¹⁸F]FET PET/MR before and after two cycles of regorafenib. Patients were sorted into stable (SD) or progressive disease (PD) categories in accordance with RANO criteria. We were also able to analyze four SD patients who underwent a third PET/MR after another four cycles of regorafenib. [¹⁸F]FET uptake greater than 1.6 times the mean background activity was used to define an area to be superimposed on an ADC map at baseline and after treatment. Several metrics were then derived and compared. Log-rank test was applied for overall survival analysis.

Results:

Percentage difference in FET volumes correlates with the corresponding percentage difference in ADC (R = 0.54). Patients with a twofold increase in FET after regorafenib showed a significantly higher increase in ADC pathological volume than the remaining subjects (p = 0.0023). Kaplan–Meier analysis, performed to compare the performance in overall survival prediction, revealed that the percentage variations of FET- and ADC-derived metrics performed at least as well as RANO criteria (p = 0.02, p = 0.024 and p = 0.04 respectively) and in some cases even better. TBR Max and TBR mean are not able to accurately predict overall survival.

Conclusion

In recurrent glioblastoma patients treated with regorafenib, [¹⁸F]FET and ADC metrics, are able to predict overall survival and being obtained from completely different measures as compared to RANO, could serve as semi-quantitative independent biomarkers of response to treatment.

Advances in knowledge

Simultaneous evaluation of [¹⁸F]FET and ADC metrics using PET/MR allows an early and reliable identification of response to treatment and predict overall survival.

Broadband near-infrared persistent luminescence in Ni2+-doped transparent glass-ceramic ZnGa2O4

ZnGa2O4:Ni2+ glass-ceramic exhibits a second near-infrared emission band peaking at 1240 nm and persistent luminescence properties.

Case Report: Detection of Symptomatic Treatment-Related Changes in a Patient With Anaplastic Oligodendroglioma Using FET PET

Following local and systemic treatment of gliomas, the differentiation between glioma relapse and treatment-related changes such as pseudoprogression or radiation necrosis using conventional MRI is limited. To overcome this limitation, various amino acid PET tracers such as O-[2-(¹⁸F)-fluoroethyl]-L-tyrosine (FET) are increasingly used and provide valuable additional clinical information. We here report neuroimaging findings in a clincally symptomatic 53-year-old woman with a recurrent anaplastic oligodendroglioma with MRI findings highly suspicious for tumor progression. In contrast, FET PET imaging suggested treatment-related changes considerably earlier than the regression of contrast enhancement on MRI. In patients with oligodendroglioma, the phenomenon of symptomatic treatment-related changes is not well described, making these imaging findings unique and important for clinical decision-making.

The role of feature-based radiomics for predicting response and radiation injury after stereotactic radiation therapy for brain metastases: A critical review by the Young Group of the Italian Association of Radiotherapy and Clinical Oncology (yAIRO)

Introduction

differential diagnosis of tumor recurrence and radiation injury after stereotactic radiotherapy (SRT) is challenging. The advances in imaging techniques and feature-based radiomics could aid to discriminate radionecrosis from progression.

Methods

we performed a systematic review of current literature, key references were obtained from a PubMed query. Data extraction was performed by 3 researchers and disagreements were resolved with a discussion among the authors.

Results

we identified 15 retrospective series, one prospective trial, one critical review and one editorial paper. Radiomics involves a wide range of imaging features referred to necrotic regions, rate of contrast-enhancing area or the measure of edema surrounding the metastases. Features were mainly defined through a multistep extraction/reduction/selection process and a final validation and comparison.

Conclusions

feature-based radiomics has an optimal potential to accurately predict response and radionecrosis after SRT of BM and facilitate differential diagnosis. Further validation studies are eagerly awaited to confirm radiomics reliability.

Aggregation-induced emission luminogens for image-guided surgery in non-human primates

During the past two decades, aggregation-induced emission luminogens (AIEgens) have been intensively exploited for biological and biomedical applications. Although a series of investigations have been performed in non-primate animal models, there is few pilot studies in non-human primate animal models, strongly hindering the clinical translation of AIE luminogens (AIEgens). Herein, we present a systemic and multifaceted demonstration of an optical imaging-guided surgical operation via AIEgens from small animals (e.g., mice and rabbits) to rhesus macaque, the typical non-human primate animal model. Specifically, the folic conjugated-AIE luminogen (folic-AIEgen) generates strong and stable fluorescence for the detection and surgical excision of sentinel lymph nodes (SLNs). Moreover, with the superior tumor/normal tissue ratio and rapid tumor accumulation, folic-AIEgen successfully images and guides the precise resection of invisible cancerous metastases. Taken together, the presented strategies of folic-AIEgen based fluorescence intraoperative imaging and visualization-guided surgery show potential for clinical applications.

Most applications of aggregation-induced emission luminogens (AIEgens) have been limited in small animal models. Here, the authors show the versatility of AIEgens-based imaging-guided surgical operation from small animals to rhesus macaque, in support of the clinical translation of AIEgens.

Enzymatic synthesis of fluorinated compounds

Fluorinated compounds are widely used in the fields of molecular imaging, pharmaceuticals, and materials. Fluorinated natural products in nature are rare, and the introduction of fluorine atoms into organic compound molecules can give these compounds new functions and make them have better performance. Therefore, the synthesis of fluorides has attracted more and more attention from biologists and chemists. Even so, achieving selective fluorination is still a huge challenge under mild conditions. In this review, the research progress of enzymatic synthesis of fluorinated compounds is summarized since 2015, including cytochrome P450 enzymes, aldolases, fluoroacetyl coenzyme A thioesterases, lipases, transaminases, reductive aminases, purine nucleoside phosphorylases, polyketide synthases, fluoroacetate dehalogenases, tyrosine phenol-lyases, glycosidases, fluorinases, and multienzyme system. Of all enzyme-catalyzed synthesis methods, the direct formation of the C-F bond by fluorinase is the most effective and promising method. The structure and catalytic mechanism of fluorinase are introduced to understand fluorobiochemistry. Furthermore, the distribution, applications, and future development trends of fluorinated compounds are also outlined. Hopefully, this review will help researchers to understand the significance of enzymatic methods for the synthesis of fluorinated compounds and find or create excellent fluoride synthase in future research.Key points• Fluorinated compounds are distributed in plants and microorganisms, and are used in imaging, medicine, materials science.• Enzyme catalysis is essential for the synthesis of fluorinated compounds.• The loop structure of fluorinase is the key to forming the C-F bond.

Unconventional non-amino acidic PET radiotracers for molecular imaging in gliomas

PURPOSE

The objective of this review was to explore the potential clinical application of unconventional non-amino acid PET radiopharmaceuticals in patients with gliomas.

METHODS

A comprehensive search strategy was used based on SCOPUS and PubMed databases using the following string: ("perfusion" OR "angiogenesis" OR "hypoxia" OR "neuroinflammation" OR proliferation OR invasiveness) AND ("brain tumor" OR "glioma") AND ("Positron Emission Tomography" OR PET). From all studies published in English, the most relevant articles were selected for this review, evaluating the mostly used PET radiopharmaceuticals in research centers, beyond amino acid radiotracers and 2-[¹⁸F]fluoro-2-deoxy-D-glucose ([¹⁸F]FDG), for the assessment of different biological features, such as perfusion, angiogenesis, hypoxia, neuroinflammation, cell proliferation, tumor invasiveness, and other biological characteristics in patients with glioma.

RESULTS

At present, the use of non-amino acid PET radiopharmaceuticals specifically designed to assess perfusion, angiogenesis, hypoxia, neuroinflammation, cell proliferation, tumor invasiveness, and other biological features in glioma is still limited.

CONCLUSION

The use of investigational PET radiopharmaceuticals should be further explored considering their promising potential and studies specifically designed to validate these preliminary findings are needed. In the clinical scenario, advancements in the development of new PET radiopharmaceuticals and new imaging technologies (e.g., PET/MR and the application of the artificial intelligence to medical images) might contribute to improve the clinical translation of these novel radiotracers in the assessment of gliomas.

Combining 18F-DOPA PET and MRI with perfusion-weighted imaging improves delineation of high-grade subregions in enhancing and non-enhancing gliomas prior treatment: a biopsy-controlled study

PURPOSE

We aimed to compare spatial extent of high-grade subregions detected with combined [¹⁸F]-dihydroxyphenylalanine (¹⁸F-DOPA) PET and MRI to the one provided by advanced multimodal MRI alone including Contrast-enhanced (CE) and Perfusion weighted imaging (PWI). Then, we compared the accuracy between imaging modalities, in a per biopsy analysis.

METHODS

Participants with suspected diffuse glioma were prospectively included between June 2018 and September 2019. Volumes of high-grade subregions were delineated respectively on ¹⁸F-DOPA PET and MRI (CE and PWI). Up to three per-surgical neuronavigation-guided biopsies were performed per patient.

RESULTS

Thirty-eight biopsy samples from sixteen participants were analyzed. Six participants (38%) had grade IV IDH wild-type glioblastoma, six (38%) had grade III IDH-mutated astrocytoma and four (24%) had grade II IDH-mutated gliomas. Three patients had intratumoral heterogeneity with coexisting high- and low-grade tumor subregions. High-grade volumes determined with combined ¹⁸F-DOPA PET/MRI (median of 1.7 [interquartile range (IQR) 0.0, 19.1] mL) were larger than with multimodal MRI alone (median 1.3 [IQR 0.0, 12.8] mL) with low overlap (median Dice's coefficient 0.24 [IQR 0.08, 0.59]). Delineation volumes were substantially increased in five (31%) patients. In a per biopsy analysis, combined ¹⁸F-DOPA PET/MRI detected high-grade subregions with an accuracy of 58% compared to 42% (p = 0.03) with CE MRI alone and 50% (p = 0.25) using multimodal MRI (CE + PWI).

CONCLUSIONS

The addition of ¹⁸F-DOPA PET to multimodal MRI (CE and PWI) enlarged the delineation volumes and enhanced overall accuracy for detection of high-grade subregions. Thus, combining ¹⁸F-DOPA with advanced MRI may improve treatment planning in newly diagnosed gliomas.

Prognostic value of pre-irradiation FET PET in patients with not completely resectable IDH-wildtype glioma and minimal or absent contrast enhancement

In glioma patients, complete resection of the contrast-enhancing portion is associated with improved survival, which, however, cannot be achieved in a considerable number of patients. Here, we evaluated the prognostic value of O-(2-[¹⁸F]-fluoroethyl)-L-tyrosine (FET) PET in not completely resectable glioma patients with minimal or absent contrast enhancement before temozolomide chemoradiation. Dynamic FET PET scans were performed in 18 newly diagnosed patients with partially resected (n = 8) or biopsied (n = 10) IDH-wildtype astrocytic glioma before initiation of temozolomide chemoradiation. Static and dynamic FET PET parameters, as well as contrast-enhancing volumes on MRI, were calculated. Using receiver operating characteristic analyses, threshold values for which the product of paired values for sensitivity and specificity reached a maximum were obtained. Subsequently, the prognostic values of FET PET parameters and contrast-enhancing volumes on MRI were evaluated using univariate Kaplan–Meier and multivariate Cox regression (including the MTV, age, MGMT promoter methylation, and contrast-enhancing volume) survival analyses for progression-free and overall survival (PFS, OS). On MRI, eight patients had no contrast enhancement; the remaining patients had minimal contrast-enhancing volumes (range, 0.2–5.3 mL). Univariate analyses revealed that smaller pre-irradiation FET PET tumor volumes were significantly correlated with a more favorable PFS (7.9 vs. 4.2 months; threshold, 14.8 mL; P = 0.012) and OS (16.6 vs. 9.0 months; threshold, 23.8 mL; P = 0.002). In contrast, mean tumor-to-brain ratios and time-to-peak values were only associated with a longer PFS (P = 0.048 and P = 0.045, respectively). Furthermore, the pre-irradiation FET PET tumor volume remained significant in multivariate analyses (P = 0.043), indicating an independent predictor for OS. Our results suggest that pre-irradiation FET PET parameters have a prognostic impact in this subgroup of patients.

Cellular senescence in cancer: from mechanisms to detection

Senescence refers to a cellular state featuring a stable cell-cycle arrest triggered in response to stress. This response also involves other distinct morphological and intracellular changes including alterations in gene expression and epigenetic modifications, elevated macromolecular damage, metabolism deregulation and a complex pro-inflammatory secretory phenotype. The initial demonstration of oncogene-induced senescence in vitro established senescence as an important tumour-suppressive mechanism, in addition to apoptosis. Senescence not only halts the proliferation of premalignant cells but also facilitates the clearance of affected cells through immunosurveillance. Failure to clear senescent cells owing to deficient immunosurveillance may, however, lead to a state of chronic inflammation that nurtures a pro-tumorigenic microenvironment favouring cancer initiation, migration and metastasis. In addition, senescence is a response to post-therapy genotoxic stress. Therefore, tracking the emergence of senescent cells becomes pivotal to detect potential pro-tumorigenic events. Current protocols for the in vivo detection of senescence require the analysis of fixed or deep-frozen tissues, despite a significant clinical need for real-time bioimaging methods. Accuracy and efficiency of senescence detection are further hampered by a lack of universal and more specific senescence biomarkers. Recently, in an attempt to overcome these hurdles, an assortment of detection tools has been developed. These strategies all have significant potential for clinical utilisation and include flow cytometry combined with histo- or cytochemical approaches, nanoparticle-based targeted delivery of imaging contrast agents, OFF-ON fluorescent senoprobes, positron emission tomography senoprobes and analysis of circulating SASP factors, extracellular vesicles and cell-free nucleic acids isolated from plasma. Here, we highlight the occurrence of senescence in neoplasia and advanced tumours, assess the impact of senescence on tumorigenesis and discuss how the ongoing development of senescence detection tools might improve early detection of multiple cancers and response to therapy in the near future.

Multimodal Contrast Agents for Optoacoustic Brain Imaging in Small Animals

Optoacoustic (photoacoustic) imaging has demonstrated versatile applications in biomedical research, visualizing the disease pathophysiology and monitoring the treatment effect in an animal model, as well as toward applications in the clinical setting. Given the complex disease mechanism, multimodal imaging provides important etiological insights with different molecular, structural, and functional readouts in vivo. Various multimodal optoacoustic molecular imaging approaches have been applied in preclinical brain imaging studies, including optoacoustic/fluorescence imaging, optoacoustic imaging/magnetic resonance imaging (MRI), optoacoustic imaging/MRI/Raman, optoacoustic imaging/positron emission tomography, and optoacoustic/computed tomography. There is a rapid development in molecular imaging contrast agents employing a multimodal imaging strategy for pathological targets involved in brain diseases. Many chemical dyes for optoacoustic imaging have fluorescence properties and have been applied in hybrid optoacoustic/fluorescence imaging. Nanoparticles are widely used as hybrid contrast agents for their capability to incorporate different imaging components, tunable spectrum, and photostability. In this review, we summarize contrast agents including chemical dyes and nanoparticles applied in multimodal optoacoustic brain imaging integrated with other modalities in small animals, and provide outlook for further research.

Advanced MRI assessment of non-enhancing peritumoral signal abnormality in brain lesions

Evaluation of Central Nervous System (CNS) focal lesions has been classically made focusing on the assessment solid or enhancing component. However, the assessment of solitary peripherally enhancing lesions where the differential diagnosis includes High-Grade Gliomas (HGG) and metastasis, is usually challenging. Several studies have tried to address the characteristics of peritumoral non-enhancing areas, for better characterization of these lesions. Peritumoral hyperintense T2/FLAIR signal abnormality predominantly contains infiltrating tumor cells in HGG whereas CNS metastasis induce pure vasogenic edema. In addition, the accurate determination of the real extension of HGG is critical for treatment selection and outcome. Conventional MRI sequences are limited in distinguishing infiltrating neoplasm from vasogenic edema. Advanced MRI sequences like Diffusion Weighted Imaging (DWI), Diffusion Tensor Imaging (DTI), Perfusion Weighted Imaging (PWI) and MR spectroscopy (MRS) have all been utilized for this aim with acceptable results. Other advanced MRI approaches, less explored for this task such as Arterial Spin Labelling (ASL), Diffusion Kurtosis Imaging (DKI), T2 relaxometry or Amide Proton Transfer (APT) are also showning promising results in this scenario. In this article, we will discuss the physiopathological basis of peritumoral T2/FLAIR signal abnormality and review potential applications of advanced MRI sequences for its evaluation.

Investigation of Cerebral O-(2-[18F]Fluoroethyl)-L-Tyrosine Uptake in Rat Epilepsy Models

PURPOSE

A recent study reported on high, longer lasting and finally reversible cerebral uptake of O-(2-[¹⁸F]fluoroethyl)-L-tyrosine ([¹⁸F]FET) induced by epileptic activity. Therefore, we examined cerebral [¹⁸F]FET uptake in two chemically induced rat epilepsy models and in patients with focal epilepsy to further investigate whether this phenomenon represents a major pitfall in brain tumor diagnostics and whether [¹⁸F]FET may be a potential marker to localize epileptic foci.

PROCEDURES

Five rats underwent kainic acid titration to exhibit 3 to 3.5 h of class IV-V motor seizures (status epilepticus, SE). Rats underwent 4× [¹⁸F]FET PET and 4× MRI on the following 25 days. Six rats underwent kindling with pentylenetetrazol (PTZ) 3 to 8×/week over 10 weeks, and hence, seizures increased from class I to class IV. [¹⁸F]FET PET and MRI were performed regularly on days with and without seizures. Four rats served as healthy controls. Additionally, five patients with focal epilepsy underwent [¹⁸F]FET PET within 12 days after the last documented seizure.

RESULTS

No abnormalities in [¹⁸F]FET PET or MRI were detected in the kindling model. The SE model showed significantly decreased [¹⁸F]FET uptake 3 days after SE in all examined brain regions, and especially in the amygdala region, which normalized within 2 weeks. Corresponding signal alterations in T₂-weighted MRI were noted in the amygdala and hippocampus, which recovered 24 days post-SE. No abnormality of cerebral [¹⁸F]FET uptake was noted in the epilepsy patients.

CONCLUSIONS

There was no evidence for increased cerebral [¹⁸F]FET uptake after epileptic seizures neither in the rat models nor in patients. The SE model even showed decreased [¹⁸F]FET uptake throughout the brain. We conclude that epileptic seizures per se do not cause a longer lasting increased [¹⁸F]FET accumulation and are unlikely to be a major cause of pitfall for brain tumor diagnostics.

Design and Construction of a PET-Compatible Double-Tuned 1H/31P MR Head Coil

Simultaneous MR-PET is an increasingly popular multimodal imaging technique that is able to combine metabolic information obtained from PET with anatomical/functional information from MRI. One of the key technological challenges of the technique is the integration of a PET-transparent MR coil system, a solution to which is demonstrated here for a double-tuned ¹H/³¹P head coil at 3 T. Two single-resonant birdcage coils tuned to the ¹H and ³¹P resonances were arranged in an interleaved fashion and electrically decoupled with the use of trap circuits. All high 511 keV quanta absorbing components were arranged outside the PET field-of-view in order to minimize count rate reduction. The materials inside the PET field-of-view were carefully evaluated and chosen for minimum impact on the PET image quality. As far as possible, the coil case was geometrically optimized to avoid sharp transitions in attenuation, which may potentially result in streaking artefacts during PET image reconstruction. The coil caused a count rate loss of just above 5% when inserted into the PET detector ring. Except for the anterior region, which was designed to maintain free openings for increased patient comfort, an almost uniform distribution of 511 keV attenuation was maintained around the circumference of the coil. MR-related performance for both nuclei was similar or slightly better than that of a commercial double-tuned coil, despite the MR-PET coil having a close-fitting RF screen to shield the PET and MR electronics from possible electromagnetic interferences.

Value of PET imaging for radiation therapy

This comprehensive review written by experts in their field gives an overview on the current status of incorporating positron emission tomography (PET) into radiation treatment planning. Moreover, it highlights ongoing studies for treatment individualisation and per-treatment tumour response monitoring for various primary tumours. Novel tracers and image analysis methods are discussed. The authors believe this contribution to be of crucial value for experts in the field as well as for policy makers deciding on the reimbursement of this powerful imaging modality.

Hahn-PCNN-CNN: an end-to-end multi-modal brain medical image fusion framework useful for clinical diagnosis

Background

In medical diagnosis of brain, the role of multi-modal medical image fusion is becoming more prominent. Among them, there is no lack of filtering layered fusion and newly emerging deep learning algorithms. The former has a fast fusion speed but the fusion image texture is blurred; the latter has a better fusion effect but requires higher machine computing capabilities. Therefore, how to find a balanced algorithm in terms of image quality, speed and computing power is still the focus of all scholars.

Methods

We built an end-to-end Hahn-PCNN-CNN. The network is composed of feature extraction module, feature fusion module and image reconstruction module. We selected 8000 multi-modal brain medical images downloaded from the Harvard Medical School website to train the feature extraction layer and image reconstruction layer to enhance the network’s ability to reconstruct brain medical images. In the feature fusion module, we use the moments of the feature map combined with the pulse-coupled neural network to reduce the information loss caused by convolution in the previous fusion module and save time.

Results

We choose eight sets of registered multi-modal brain medical images in four diease to verify our model. The anatomical structure images are from MRI and the functional metabolism images are SPECT and 18F-FDG. At the same time, we also selected eight representative fusion models as comparative experiments. In terms of objective quality evaluation, we select six evaluation metrics in five categories to evaluate our model.

Conclusions

The fusion image obtained by our model can retain the effective information in source images to the greatest extent. In terms of image fusion evaluation metrics, our model is superior to other comparison algorithms. In terms of time computational efficiency, our model also performs well. In terms of robustness, our model is very stable and can be generalized to multi-modal image fusion of other organs.

Value of PET imaging for radiation therapy

This comprehensive review written by experts in their field gives an overview on the current status of incorporating positron emission tomography (PET) into radiation treatment planning. Moreover, it highlights ongoing studies for treatment individualisation and per-treatment tumour response monitoring for various primary tumours. Novel tracers and image analysis methods are discussed. The authors believe this contribution to be of crucial value for experts in the field as well as for policy makers deciding on the reimbursement of this powerful imaging modality.

Optical tissue clearing and machine learning can precisely characterize extravasation and blood vessel architecture in brain tumors

Precise methods for quantifying drug accumulation in brain tissue are currently very limited, challenging the development of new therapeutics for brain disorders. Transcardial perfusion is instrumental for removing the intravascular fraction of an injected compound, thereby allowing for ex vivo assessment of extravasation into the brain. However, pathological remodeling of tissue microenvironment can affect the efficiency of transcardial perfusion, which has been largely overlooked. We show that, in contrast to healthy vasculature, transcardial perfusion cannot remove an injected compound from the tumor vasculature to a sufficient extent leading to considerable overestimation of compound extravasation. We demonstrate that 3D deep imaging of optically cleared tumor samples overcomes this limitation. We developed two machine learning-based semi-automated image analysis workflows, which provide detailed quantitative characterization of compound extravasation patterns as well as tumor angioarchitecture in large three-dimensional datasets from optically cleared samples. This methodology provides a precise and comprehensive analysis of extravasation in brain tumors and allows for correlation of extravasation patterns with specific features of the heterogeneous brain tumor vasculature.

A Linearized Fit Model for Robust Shape Parameterization of FET-PET TACs

The kinetic analysis of [Formula: see text]-FET time-activity curves (TAC) can provide valuable diagnostic information in glioma patients. The analysis is most often limited to the average TAC over a large tissue volume and is normally assessed by visual inspection or by evaluating the time-to-peak and linear slope during the late uptake phase. Here, we derived and validated a linearized model for TACs of [Formula: see text]-FET in dynamic PET scans. Emphasis was put on the robustness of the numerical parameters and how reliably automatic voxel-wise analysis of TAC kinetics was possible. The diagnostic performance of the extracted shape parameters for the discrimination between isocitrate dehydrogenase (IDH) wildtype (wt) and IDH-mutant (mut) glioma was assessed by receiver-operating characteristic in a group of 33 adult glioma patients. A high agreement between the adjusted model and measured TACs could be obtained and relative, estimated parameter uncertainties were small. The best differentiation between IDH-wt and IDH-mut gliomas was achieved with the linearized model fitted to the averaged TAC values from dynamic FET PET data in the time interval 4-50 min p.i.. When limiting the acquisition time to 20-40 min p.i., classification accuracy was only slightly lower (-3%) and was comparable to classification based on linear fits in this time interval. Voxel-wise fitting was possible within a computation time ≈ 1 min per image slice. Parameter uncertainties smaller than 80% for all fits with the linearized model were achieved. The agreement of best-fit parameters when comparing voxel-wise fits and fits of averaged TACs was very high (p < 0.001).

A Questionnaire-based Survey of Clinical Neuro-oncological Practice in India

Background

Neuro-oncology is a relatively young subspecialty of neurosurgery. 2018 was the 10^th year since the founding of the Indian Society of Neuro-oncology.

Objective

To assess patterns in neuro-oncology practice in India.

Methods

This was an online survey covering various domains of neuro-oncology such as demographics and practice setting, protocols for the medical management of patients with brain tumors, protocols for surgery and the perioperative period (including antibiotic prophylaxis, dural closure techniques, etc.), technological adjuncts used for brain/spine tumors (including intraoperative neurologic monitoring-IONM), and management protocols for certain specific clinical scenarios.

Results

The response rate was 13%. Although 37% of the respondents' institutions could be considered as having reasonable surgical volumes (>1 procedure/day), only about half of these had high volumes of malignant brain tumor surgery. A wide variation was seen in medical management, perioperative protocols, use of adjuncts and intraoperative technologies, and paradigms for specific clinical scenarios.

Conclusions

There is a need to standardize the protocols in neuro-oncology. This could be achieved by strengthening the formal training process in surgical neuro-oncology.

Long-term metabolic evolution of brain metastases with suspected radiation necrosis following stereotactic radiosurgery: longitudinal assessment by F-DOPA PET

BACKGROUND

The evolution of radiation necrosis (RN) varies depending on the combination of radionecrotic tissue and active tumor cells. In this study, we characterized the long-term metabolic evolution of RN by sequential PET/CT imaging with 3,4-dihydroxy-6-[18F]-fluoro-l-phenylalanine (F-DOPA) in patients with brain metastases following stereotactic radiosurgery (SRS).

METHODS

Thirty consecutive patients with 34 suspected radionecrotic brain metastases following SRS repeated F-DOPA PET/CT every 6 months or yearly in addition to standard MRI monitoring. Diagnoses of local progression (LP) or RN were confirmed histologically or by clinical follow-up. Semi-quantitative parameters of F-DOPA uptake were extracted at different time points, and their diagnostic performances were compared with those of corresponding contrast-enhanced MRI.

RESULTS

Ninety-nine F-DOPA PET scans were acquired over a median period of 18 (range: 12-66) months. Median follow-up from the baseline F-DOPA PET/CT was 48 (range 21-95) months. Overall, 24 (70.6%) and 10 (29.4%) lesions were classified as RN and LP, respectively. LP occurred after a median of 18 (range: 12-30) months from baseline PET. F-DOPA tumor-to-brain ratio (TBR) and relative standardized uptake value (rSUV) increased significantly over time in LP lesions, while remaining stable in RN lesions. The parameter showing the best diagnostic performance was rSUV (accuracy = 94.1% for the optimal threshold of 1.92). In contrast, variations of the longest tumor dimension measured on contrast-enhancing MRI did not distinguish between RN and LP.

CONCLUSION

F-DOPA PET has a high diagnostic accuracy for assessing the long-term evolution of brain metastases following SRS.

Contribution of PET imaging to radiotherapy planning and monitoring in glioma patients - a report of the PET/RANO group

The management of patients with glioma usually requires multimodality treatment including surgery, radiotherapy, and systemic therapy. Accurate neuroimaging plays a central role for radiotherapy planning and follow-up after radiotherapy completion. In order to maximize the radiation dose to the tumor and to minimize toxic effects on the surrounding brain parenchyma, reliable identification of tumor extent and target volume delineation is crucial. The use of positron emission tomography (PET) for radiotherapy planning and monitoring in gliomas has gained considerable interest over the last several years, but Class I data are not yet available. Furthermore, PET has been used after radiotherapy for response assessment and to distinguish tumor progression from pseudoprogression or radiation necrosis. Here, the Response Assessment in Neuro-Oncology (RANO) working group provides a summary of the literature and recommendations for the use of PET imaging for radiotherapy of patients with glioma based on published studies, constituting levels 1-3 evidence according to the Oxford Centre for Evidence-based Medicine.

Determining optimal clinical target volume margins in high-grade glioma based on microscopic tumor extension and magnetic resonance imaging

Introduction

In this study, we performed a consecutive macropathologic analysis to assess microscopic extension (ME) in high-grade glioma (HGG) to determine appropriate clinical target volume (CTV) margins for radiotherapy.

Materials and methods

The study included HGG patients with tumors located in non-functional areas, and supratotal resection was performed. The ME distance from the edge of the tumor to the microscopic tumor cells surrounding brain tissue was measured. Associations between the extent of ME and clinicopathological characteristics were evaluated by multivariate linear regression (MVLR) analysis. An ME predictive model was developed based on the MVLR model.

Results

Between June 2017 and July 2019, 652 pathologic slides obtained from 30 HGG patients were analyzed. The mean ME distance was 1.70 cm (range, 0.63 to 2.87 cm). The MVLR analysis identified that pathologic grade, subventricular zone (SVZ) contact and O⁶-methylguanine-DNA methyltransferase (MGMT) methylation, isocitrate dehydrogenase (IDH) mutation and 1p/19q co-deletion status were independent variables predicting ME (all P < 0.05). A multivariable prediction model was developed as follows: Y_ME = 0.672 + 0.513X_Grade + 0.380X_SVZ + 0.439X_MGMT + 0.320X_IDH + 0.333X_1p/19q. The R-square value of goodness of fit was 0.780. The receiver operating characteristic curve proved that the area under the curve was 0.964 (P < 0.001).

Conclusion

ME was heterogeneously distributed across different grades of gliomas according to the tumor location and molecular marker status, which indicated that CTV delineation should be individualized. The model could predict the ME of HGG, which may help clinicians determine the CTV for individual patients.

Trial registration The trial was registered with Chinese Clinical Trial Registry (ChiCTR2100046106). Registered 4 May 2021-Retrospectively registered.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13014-021-01819-0.

Attenuated Microcirculation in Small Metastatic Tumors in Murine Liver

Metastatic cancer disease is the major cause of death in cancer patients. Because those small secondary tumors are clinically hardly detectable in their early stages, little is known about drug biodistribution and permeation into those metastatic tumors potentially contributing to insufficient clinical success against metastatic disease. Our recent studies indicated that breast cancer liver metastases may have compromised perfusion of intratumoral capillaries hindering the delivery of therapeutics for yet unknown reasons. To understand the microcirculation of small liver metastases, we have utilized computational simulations to study perfusion and oxygen concentration fields in and around the metastases smaller than 700 µm in size at the locations of portal vessels, central vein, and liver lobule acinus. Despite tumor vascularization, the results show that blood flow in those tumors can be substantially reduced indicating the presence of inadequate blood pressure gradients across tumors. A low blood pressure may contribute to the collapsed intratumoral capillary lumen limiting tumor perfusion that phenomenologically corroborates with our previously published in vivo studies. Tumors that are smaller than the liver lobule size and originating at different lobule locations may possess a different microcirculation environment and tumor perfusion. The acinus and portal vessel locations in the lobule were found to be the most beneficial to tumor growth based on tumor access to blood flow and intratumoral oxygen. These findings suggest that microcirculation states of small metastatic tumors can potentially contribute to physiological barriers preventing efficient delivery of therapeutic substances into small tumors.

On-Chip Replication of Extremely Early-Stage Tumor Behavior

Cancer is a multistep progressive disease that generally involves tumor growth, invasion, and metastasis. It is crucial to understand tumor progression for tumor diagnosis and therapy. However, tumor progression at an extremely early stage (EES) is barely demonstrated because EES tumors are too small to be detected by imaging. Herein, we, for the first time, replicated tumor progression at the EES on a microfluidic chip and uncovered the tumor behaviors affected by the tumor microenvironment. To mimic the progression of a single solid tumor at the EES, a HeLa cell spheroid was seeded and cultured on the chip, and a microvascular network was developed to integrate the microphysiological contexts around the tumor. We revealed not only the growth patterns and cell behaviors of tumor spheroids of different sizes under angiogenesis and fibroblast conditions but also the effect of tumor progression on peritumoral angiogenesis. We found that smaller tumors were more aggressive and that endotheliocytes and fibroblasts significantly accelerated both the proliferation and migration of tumor cells. In addition, we also first present the dynamic epithelial-mesenchymal transition process of tumor cells and the formation of vasculogenic mimicry at the EES. This work can provide insights for understanding tumor progression at the EES and offer new ideas for tumor therapy.

Diagnosis of Pseudoprogression Following Lomustine-Temozolomide Chemoradiation in Newly Diagnosed Glioblastoma Patients Using FET-PET

PURPOSE

The CeTeG/NOA-09 phase III trial demonstrated a significant survival benefit of lomustine-temozolomide chemoradiation in patients with newly diagnosed glioblastoma with methylated O⁶-methylguanine-DNA methyltransferase (MGMT) promoter. Following lomustine-temozolomide chemoradiation, late and prolonged pseudoprogression may occur. We here evaluated the value of amino acid PET using O-(2-[¹⁸F]fluoroethyl)-l-tyrosine (FET) for differentiating pseudoprogression from tumor progression.

EXPERIMENTAL DESIGN

We retrospectively identified patients (i) who were treated off-study according to the CeTeG/NOA-09 protocol, (ii) had equivocal MRI findings after radiotherapy, and (iii) underwent additional FET-PET imaging for diagnostic evaluation (number of scans, 1-3). Maximum and mean tumor-to-brain ratios (TBR_max, TBR_mean) and dynamic FET uptake parameters (e.g., time-to-peak) were calculated. In patients with more than one FET-PET scan, relative changes of TBR values were evaluated, that is, an increase or decrease of >10% compared with the reference scan was considered as tumor progression or pseudoprogression. Diagnostic performances were evaluated using ROC curve analyses and Fisher exact test. Diagnoses were confirmed histologically or clinicoradiologically.

RESULTS

We identified 23 patients with 32 FET-PET scans. Within 5-25 weeks after radiotherapy (median time, 9 weeks), pseudoprogression occurred in 11 patients (48%). The parameter TBR_mean calculated from the FET-PET performed 10 ± 7 days after the equivocal MRI showed the highest accuracy (87%) to identify pseudoprogression (threshold, <1.95; P = 0.029). The integration of relative changes of TBR_mean further improved the accuracy (91%; P < 0.001). Moreover, the combination of static and dynamic parameters increased the specificity to 100% (P = 0.005).

CONCLUSIONS

The data suggest that FET-PET parameters are of significant clinical value to diagnose pseudoprogression related to lomustine-temozolomide chemoradiation.