Advanced imaging techniques for neuro-oncologic tumor diagnosis, with an emphasis on PET-MRI imaging of malignant brain tumors

Prediction of treatment response after stereotactic radiosurgery of brain metastasis using deep learning and radiomics on longitudinal MRI data

We developed artificial intelligence models to predict the brain metastasis (BM) treatment response after stereotactic radiosurgery (SRS) using longitudinal magnetic resonance imaging (MRI) data and evaluated prediction accuracy changes according to the number of sequential MRI scans. We included four sequential MRI scans for 194 patients with BM and 369 target lesions for the Developmental dataset. The data were randomly split (8:2 ratio) for training and testing. For external validation, 172 MRI scans from 43 patients with BM and 62 target lesions were additionally enrolled. The maximum axial diameter (Dmax), radiomics, and deep learning (DL) models were generated for comparison. We evaluated the simple convolutional neural network (CNN) model and a gated recurrent unit (Conv-GRU)-based CNN model in the DL arm. The Conv-GRU model performed superior to the simple CNN models. For both datasets, the area under the curve (AUC) was significantly higher for the two-dimensional (2D) Conv-GRU model than for the 3D Conv-GRU, Dmax, and radiomics models. The accuracy of the 2D Conv-GRU model increased with the number of follow-up studies. In conclusion, using longitudinal MRI data, the 2D Conv-GRU model outperformed all other models in predicting the treatment response after SRS of BM.

The detrimental effect of biopsy preceding resection in surgically accessible glioblastoma: results from the national cancer database

PURPOSE

Aggressive resection in surgically-accessible glioblastoma (GBM) correlates with improved survival over less extensive resections. However, the clinical impact of performing a biopsy before definitive resection have not been previously evaluated.

METHODS

We analyzed 17,334 GBM patients from the NCDB from 2010-2014. We categorized them into: "upfront resection" and "biopsy followed by resection". The outcomes of interes included OS, 30-day readmission/mortality, 90-day mortality, and length of hospital stay (LOS). The Kaplan-Meier methods and accelerated failure time (AFT) models were applied for survival analysis. Multivariable binary logistic regression were performed to compare differences among groups. Multiple imputation and propensity score matching (PSM) were conducted for validation.

RESULTS

"Upfront resection" had superior OS over "biopsy followed by resection" (median OS:12.4 versus 11.1 months, log-rank p = 0.001). Similarly, multivariable AFT models favored "upfront resection" (time ratio[TR]:0.83, 95%CI: 0.75-0.93, p = 0.001). Patients undergoing "upfront gross-total resection (GTR)" had higher OS over "upfront subtotal resection (STR)", "GTR following STR", and "GTR or STR following initial biopsy" (14.4 vs. 10.3, 13.5, 13.3, and 9.1 months;TR: 1.00 [Ref.], 0.75, 0.82, 0.88, and 0.67). Recent years of diagnosis, higher income, facilities located in Southern regions, and treatment at academic facilities were significantly associated with the higher likelihood of undergoing upfront resection. Multivariable regression showed a decreased 30 and 90-day mortality for patients undergoing "upfront resection", 73% and 44%, respectively (p < 0.001).

CONCLUSIONS

Pre-operative biopsies for surgically accessible GBM are associated with worse survival despite subsequent resection compared to patients undergoing upfront resection.

Biocompatible and bioactivable terpolymer-lipid-MnO2 Nanoparticle-based MRI contrast agent for improving tumor detection and delineation

Early and precise detection of solid tumor cancers is critical for improving therapeutic outcomes. In this regard, magnetic resonance imaging (MRI) has become a useful tool for tumor diagnosis and image-guided therapy. However, its effectiveness is limited by the shortcomings of clinically available gadolinium-based contrast agents (GBCAs), i.e. poor tumor penetration and retention, and safety concerns. Thus, we have developed a novel nanoparticulate contrast agent using a biocompatible terpolymer and lipids to encapsulate manganese dioxide nanoparticles (TPL-MDNP). The TPL-MDNP accumulated in tumor tissue and produced paramagnetic Mn2+ ions, enhancing T1-weight MRI contrast via the reaction with H2O2 rich in the acidic tumor microenvironment. Compared to the clinically used GBCA, Gadovist®1.0, TPL-MDNP generated stronger T1-weighted MR signals by over 2.0-fold at 30 % less of the recommended clinical dose with well-defined tumor delineation in preclinical orthotopic tumor models of brain, breast, prostate, and pancreas. Importantly, the MRI signals were retained for 60 min by TPL-MDNP, much longer than Gadovist®1.0. Biocompatibility of TPL-MDNP was evaluated and found to be safe up to 4-fold of the dose used for MRI. A robust large-scale manufacturing process was developed with batch-to-batch consistency. A lyophilization formulation was designed to maintain the nanostructure and storage stability of the new contrast agent.

[18]F-fluoroethyl-l-tyrosine positron emission tomography for radiotherapy target delineation: Results from a Radiation Oncology credentialing program

Background and purpose

The [18]F-fluoroethyl-l-tyrosine (FET) PET in Glioblastoma (FIG) study is an Australian prospective, multi-centre trial evaluating FET PET for newly diagnosed glioblastoma management. The Radiation Oncology credentialing program aimed to assess the feasibility in Radiation Oncologist (RO) derivation of standard-of-care target volumes (TVMR) and hybrid target volumes (TVMR+FET) incorporating pre-defined FET PET biological tumour volumes (BTVs).

Materials and methods

Central review and analysis of TVMR and TVMR+FET was undertaken across three benchmarking cases. BTVs were pre-defined by a sole nuclear medicine expert. Intraclass correlation coefficient (ICC) confidence intervals (CIs) evaluated volume agreement. RO contour spatial and boundary agreement were evaluated (Dice similarity coefficient [DSC], Jaccard index [JAC], overlap volume [OV], Hausdorff distance [HD] and mean absolute surface distance [MASD]). Dose plan generation (one case per site) was assessed.

Results

Data from 19 ROs across 10 trial sites (54 initial submissions, 8 resubmissions requested, 4 conditional passes) was assessed with an initial pass rate of 77.8 %; all resubmissions passed. TVMR+FET were significantly larger than TVMR (p < 0.001) for all cases. RO gross tumour volume (GTV) agreement was moderate-to-excellent for GTVMR (ICC = 0.910; 95 % CI, 0.708-0.997) and good-to-excellent for GTVMR+FET (ICC = 0.965; 95 % CI, 0.871-0.999). GTVMR+FET showed greater spatial overlap and boundary agreement compared to GTVMR. For the clinical target volume (CTV), CTVMR+FET showed lower average boundary agreement versus CTVMR (MASD: 1.73 mm vs. 1.61 mm, p = 0.042). All sites passed the planning exercise.

Conclusions

The credentialing program demonstrated feasibility in successful credentialing of 19 ROs across 10 sites, increasing national expertise in TVMR+FET delineation.

Distinguishing brain tumors by Label-free confocal micro-Raman spectroscopy

BACKGROUND

Brain tumors have serious adverse effects on public health and social economy. Accurate detection of brain tumor types is critical for effective and proactive treatment, and thus improve the survival of patients.

METHODS

Four types of brain tumor tissue sections were detected by Raman spectroscopy. Principal component analysis (PCA) has been used to reduce the dimensionality of the Raman spectra data. Linear discriminant analysis (LDA) and quadratic discriminant analysis (QDA) methods were utilized to discriminate different types of brain tumors.

RESULTS

Raman spectra were collected from 40 brain tumors. Variations in intensity and shift were observed in the Raman spectra positioned at 721, 854, 1004, 1032, 1128, 1248, 1449 cm-1 for different brain tumor tissues. The PCA results indicated that glioma, pituitary adenoma, and meningioma are difficult to differentiate from each other, whereas acoustic neuroma is clearly distinguished from the other three tumors. Multivariate analysis including QDA and LDA methods showed the classification accuracy rate of the QDA model was 99.47 %, better than the rate of LDA model was 95.07 %.

CONCLUSIONS

Raman spectroscopy could be used to extract valuable fingerprint-type molecular and chemical information of biological samples. The demonstrated technique has the potential to be developed to a rapid, label-free, and intelligent approach to distinguish brain tumor types with high accuracy.

PET/MRI in colorectal and anal cancers: an update

Positron emission tomography (PET) in the era of personalized medicine has a unique role in the management of oncological patients and offers several advantages over standard anatomical imaging. However, the role of molecular imaging in lower GI malignancies has historically been limited due to suboptimal anatomical evaluation on the accompanying CT, as well as significant physiological 18F-flurodeoxyglucose (FDG) uptake in the bowel. In the last decade, technological advancements have made whole-body FDG-PET/MRI a feasible alternative to PET/CT and MRI for lower GI malignancies. PET/MRI combines the advantages of molecular imaging with excellent soft tissue contrast resolution. Hence, it constitutes a unique opportunity to improve the imaging of these cancers. FDG-PET/MRI has a potential role in initial diagnosis, assessment of local treatment response, and evaluation for metastatic disease. In this article, we review the recent literature on FDG-PET/MRI for colorectal and anal cancers; provide an example whole-body FDG-PET/MRI protocol; highlight potential interpretive pitfalls; and provide recommendations on particular clinical scenarios in which FDG-PET/MRI is likely to be most beneficial for these cancer types.

Delineation and agreement of FET PET biological volumes in glioblastoma: results of the nuclear medicine credentialing program from the prospective, multi-centre trial evaluating FET PET In Glioblastoma (FIG) study-TROG 18.06

PURPOSE

The O-(2-[18F]-fluoroethyl)-L-tyrosine (FET) PET in Glioblastoma (FIG) trial is an Australian prospective, multi-centre study evaluating FET PET for glioblastoma patient management. FET PET imaging timepoints are pre-chemoradiotherapy (FET1), 1-month post-chemoradiotherapy (FET2), and at suspected progression (FET3). Before participant recruitment, site nuclear medicine physicians (NMPs) underwent credentialing of FET PET delineation and image interpretation.

METHODS

Sites were required to complete contouring and dynamic analysis by ≥ 2 NMPs on benchmarking cases (n = 6) assessing biological tumour volume (BTV) delineation (3 × FET1) and image interpretation (3 × FET3). Data was reviewed by experts and violations noted. BTV definition includes tumour-to-background ratio (TBR) threshold of 1.6 with crescent-shaped background contour in the contralateral normal brain. Recurrence/pseudoprogression interpretation (FET3) required assessment of maximum TBR (TBRmax), dynamic analysis (time activity curve [TAC] type, time to peak), and qualitative assessment. Intraclass correlation coefficient (ICC) assessed volume agreement, coefficient of variation (CoV) compared maximum/mean TBR (TBRmax/TBRmean) across cases, and pairwise analysis assessed spatial (Dice similarity coefficient [DSC]) and boundary agreement (Hausdorff distance [HD], mean absolute surface distance [MASD]).

RESULTS

Data was accrued from 21 NMPs (10 centres, n ≥ 2 each) and 20 underwent review. The initial pass rate was 93/119 (78.2%) and 27/30 requested resubmissions were completed. Violations were found in 25/72 (34.7%; 13/12 minor/major) of FET1 and 22/74 (29.7%; 14/8 minor/major) of FET3 reports. The primary reasons for resubmission were as follows: BTV over-contour (15/30, 50.0%), background placement (8/30, 26.7%), TAC classification (9/30, 30.0%), and image interpretation (7/30, 23.3%). CoV median and range for BTV, TBRmax, and TBRmean were 21.53% (12.00-30.10%), 5.89% (5.01-6.68%), and 5.01% (3.37-6.34%), respectively. BTV agreement was moderate to excellent (ICC = 0.82; 95% CI, 0.63-0.97) with good spatial (DSC = 0.84 ± 0.09) and boundary (HD = 15.78 ± 8.30 mm; MASD = 1.47 ± 1.36 mm) agreement.

CONCLUSION

The FIG study credentialing program has increased expertise across study sites. TBRmax and TBRmean were robust, with considerable variability in BTV delineation and image interpretation observed.

Machine Learning Predicts Pathologic Complete Response to Neoadjuvant Chemotherapy for ER+HER2- Breast Cancer: Integrating Tumoral and Peritumoral MRI Radiomic Features

BACKGROUND

This study aimed to predict pathologic complete response (pCR) in neoadjuvant chemotherapy for ER+HER2- locally advanced breast cancer (LABC), a subtype with limited treatment response.

METHODS

We included 265 ER+HER2- LABC patients (2010-2020) with pre-treatment MRI, neoadjuvant chemotherapy, and confirmed pathology. Using data from January 2016, we divided them into training and validation cohorts. Volumes of interest (VOI) for the tumoral and peritumoral regions were segmented on preoperative MRI from three sequences: T1-weighted early and delayed contrast-enhanced sequences and T2-weighted fat-suppressed sequence (T2FS). We constructed seven machine learning models using tumoral, peritumoral, and combined texture features within and across the sequences, and evaluated their pCR prediction performance using AUC values.

RESULTS

The best single sequence model was SVM using a 1 mm tumor-to-peritumor VOI in the early contrast-enhanced phase (AUC = 0.9447). Among the combinations, the top-performing model was K-Nearest Neighbor, using 1 mm tumor-to-peritumor VOI in the early contrast-enhanced phase and 3 mm peritumoral VOI in T2FS (AUC = 0.9631).

CONCLUSIONS

We suggest that a combined machine learning model that integrates tumoral and peritumoral radiomic features across different MRI sequences can provide a more accurate pretreatment pCR prediction for neoadjuvant chemotherapy in ER+HER2- LABC.

Recent advancements and theranostics strategies in glioblastoma therapy

Glioblastoma (GBM) is the most aggressive and lethal malignant brain tumor, and it is challenging to cure with surgery and treatment. The prevention of permanent brain damage and tumor invasion, which is the ultimate cause of recurrence, are major obstacles in GBM treatment. Besides, emerging treatment modalities and newer genetic findings are helping to understand and manage GBM in patients. Accordingly, researchers are focusing on advanced nanomaterials-based strategies for tackling the various problems associated with GBM. In this context, researchers explored novel strategies with various alternative treatment approaches such as early detection techniques and theranostics approaches. In this review, we have emphasized the recent advancement of GBM cellular models and their roles in designing GBM therapeutics. We have added a special emphasis on the novel genetic and drug target findings as well as strategies for early detection. Besides, we have discussed various theranostic approaches such as hyperthermia therapy, phototherapy and image-guided therapy. Approaches utilized for targeted drug delivery to the GBM were also discussed. This article also describes the recentin vivo, in vitroandex vivoadvances using innovative theranostic approaches.

Advances in the application of neuroinflammatory molecular imaging in brain malignancies

The prevalence of brain cancer has been increasing in recent decades, posing significant healthcare challenges. The introduction of immunotherapies has brought forth notable diagnostic imaging challenges for brain tumors. The tumor microenvironment undergoes substantial changes in induced immunosuppression and immune responses following the development of primary brain tumor and brain metastasis, affecting the progression and metastasis of brain tumors. Consequently, effective and accurate neuroimaging techniques are necessary for clinical practice and monitoring. However, patients with brain tumors might experience radiation-induced necrosis or other neuroinflammation. Currently, positron emission tomography and various magnetic resonance imaging techniques play a crucial role in diagnosing and evaluating brain tumors. Nevertheless, differentiating between brain tumors and necrotic lesions or inflamed tissues remains a significant challenge in the clinical diagnosis of the advancements in immunotherapeutics and precision oncology have underscored the importance of clinically applicable imaging measures for diagnosing and monitoring neuroinflammation. This review summarizes recent advances in neuroimaging methods aimed at enhancing the specificity of brain tumor diagnosis and evaluating inflamed lesions.

Hybrid 18F-Fluoroethyltyrosine PET and MRI with Perfusion to Distinguish Disease Progression from Treatment-Related Change in Malignant Brain Tumors: The Quest to Beat the Toughest Cases

Conventional MRI has important limitations when assessing for progression of disease (POD) versus treatment-related changes (TRC) in patients with malignant brain tumors. We describe the observed impact and pitfalls of implementing 18F-fluoroethyltyrosine (18F-FET) perfusion PET/MRI into routine clinical practice. Methods: Through expanded-access investigational new drug use of 18F-FET, hybrid 18F-FET perfusion PET/MRI was performed during clinical management of 80 patients with World Health Organization central nervous system grade 3 or 4 gliomas or brain metastases of 6 tissue origins for which the prior brain MRI results were ambiguous. The diagnostic performance with 18F-FET PET/MRI was dually evaluated within routine clinical service and for retrospective parametric evaluation. Various 18F-FET perfusion PET/MRI parameters were assessed, and patients were monitored for at least 6 mo to confirm the diagnosis using pathology, imaging, and clinical progress. Results: Hybrid 18F-FET perfusion PET/MRI had high overall accuracy (86%), sensitivity (86%), and specificity (87%) for difficult diagnostic cases for which conventional MRI accuracy was poor (66%). 18F-FET tumor-to-brain ratio static metrics were highly reliable for distinguishing POD from TRC (area under the curve, 0.90). Dynamic tumor-to-brain intercept was more accurate (85%) than SUV slope (73%) or time to peak (73%). Concordant PET/MRI findings were 89% accurate. When PET and MRI conflicted, 18F-FET PET was correct in 12 of 15 cases (80%), whereas MRI was correct in 3 of 15 cases (20%). Clinical management changed after 88% (36/41) of POD diagnoses, whereas management was maintained after 87% (34/39) of TRC diagnoses. Conclusion: Hybrid 18F-FET PET/MRI positively impacted the routine clinical care of challenging malignant brain tumor cases at a U.S. institution. The results add to a growing body of literature that 18F-FET PET complements MRI, even rescuing MRI when it fails.

Precision Radiation for Brain Metastases With a Focus on Hypofractionated Stereotactic Radiosurgery

There are multiple published randomized controlled trials supporting single-fraction stereotactic radiosurgery (SF-SRS) for patients presenting with 1 to 4 brain metastases, with the benefit of minimizing radiation-induced neurocognitive sequelae as compared to whole brain radiotherapy . More recently, the dogma of SF-SRS as the only means of delivering an SRS treatment has been challenged by hypofractionated SRS (HF-SRS). The ability to deliver 25-35 Gy in 3-5 HF-SRS fractions is a direct consequence of the evolution of radiation technologies to allow image guidance, specialized treatment planning, robotic delivery and/or patient positioning corrections in all 6 degrees-of-freedom, and frameless head immobilization. The intent is to mitigate the potentially devastating complication of radiation necrosis and improve rates of local control for larger metastases. This narrative review provides an overview of outcomes specific to HF-SRS in addition to the more recent developments of staged SRS, preoperative SRS, and hippocampal avoidance-whole brain radiotherapy with simultaneous integrated boost.

Imaging the uptake of deuterated methionine in Drosophila with stimulated Raman scattering

Introduction: Visualizing small individual biomolecules at subcellular resolution in live cells and tissues can provide valuable insights into metabolic activity in heterogeneous cells, but is challenging.

Methods: Here, we used stimulated Raman scattering (SRS) microscopy to image deuterated methionine (d-Met) incorporated into Drosophila tissues in vivo.

Results: Our results demonstrate that SRS can detect a range of previously uncharacterized cell-to-cell differences in d-Met distribution within a tissue at the subcellular level.

Discussion: These results demonstrate the potential of SRS microscopy for metabolic imaging of less abundant but important amino acids such as methionine in tissue.

Diffusion Weighted Imaging in Neuro-Oncology: Diagnosis, Post-Treatment Changes, and Advanced Sequences-An Updated Review

DWI is an imaging technique commonly used for the assessment of acute ischemia, inflammatory disorders, and CNS neoplasia. It has several benefits since it is a quick, easily replicable sequence that is widely used on many standard scanners. In addition to its normal clinical purpose, DWI offers crucial functional and physiological information regarding brain neoplasia and the surrounding milieu. A narrative review of the literature was conducted based on the PubMed database with the purpose of investigating the potential role of DWI in the neuro-oncology field. A total of 179 articles were included in the study.

Revisiting the Role of Surgical Resection for Brain Metastasis

Brain metastasis (BM) is the most common type of brain tumor in adults. The contemporary management of BM remains challenging. Advancements in systemic cancer treatment have increased the survival of patients with cancer. Although the treatment of BM is still complicated, advances in radiotherapy, including stereotactic radiosurgery and chemotherapy, have improved treatment outcomes. Surgical resection is the traditional treatment for BM and its role in the surgical resection of BM has been well established. However, refinement of the surgical resection technique and strategy for BM is needed. Herein, we discuss the evolving role of surgery in patients with BM and the future of BM treatment.

Evolution from Medical Imaging to Visualized Medicine

The discovery of X-ray in 1895 and the first X-ray image of Mrs. Röntgen's hand opened up a new era of radiology and the research of medical imaging. The evolution of traditional medical imaging has been lasting for over 100 years, serving the detection, diagnosis, and treatments of human diseases with a clear view of the anatomy information. In late 1990s, the concept of molecular imaging was proposed as the science and technology of molecular biology and bio-engineering rapidly developed, and it directly gave birth to the emergence of precision medicine for clinical lesion-targeted treatments against various cancers and cardiocerebrovascular diseases. Physiological and pathological changes in live bodies from zebrafish to human beings can be imaged to ensure an efficient image-guided therapy. Nowadays, the philosophy of medical and molecular imaging has been a powerful tool and indispensable modality for doctors to make their decisions and give patients reliable advices. With the ever-emerging developments of advanced intelligent technologies such as flexible sensors, medical meta-data analysis, brain sciences, surgical robots, VR/AR, etc., modern medicine has been evolving from traditional medical and molecular imaging to visualized medicine, which has created novel accessible approaches along with cutting-edge techniques for the revolutionized diagnostic and therapeutic paradigms. In this context, the history and milestones from medical imaging to visualized medicine will be elucidated. And in particular, representative visualized medicine advances including its application to COVID-19 epidemics will be discussed in order to look for its important contributions and a future perspective to modern medicine.

Principles of Lung Cancer Metastasis to Brain

Lung cancer is a disease associated with significant morbidity and mortality on a global setting. This form of cancer commonly gives raise to metastatic lesions the brain, which can further worsen outcomes. In this focused review, we discuss an overview of lung cancers that metastasize to the brain: known risk factors; means of detection and diagnosis; and options for treatment including a comparison between surgical resection, stereotactic radiosurgery, and whole-brain radiation therapy. These interventions are still being assessed by clinical trials and continue to be modified through evidence-based practice.

Noninvasive Delineation of Glioma Infiltration with Combined 7T Chemical Exchange Saturation Transfer Imaging and MR Spectroscopy: A Diagnostic Accuracy Study

For precise delineation of glioma extent, amino acid PET is superior to conventional MR imaging. Since metabolic MR sequences such as chemical exchange saturation transfer (CEST) imaging and MR spectroscopy (MRS) were developed, we aimed to evaluate the diagnostic accuracy of combined CEST and MRS to predict glioma infiltration. Eighteen glioma patients of different tumor grades were enrolled in this study; 18F-fluoroethyltyrosine (FET)-PET, amide proton transfer CEST at 7 Tesla(T), MRS and conventional MR at 3T were conducted preoperatively. Multi modalities and their association were evaluated using Pearson correlation analysis patient-wise and voxel-wise. Both CEST (R = 0.736, p < 0.001) and MRS (R = 0.495, p = 0.037) correlated with FET-PET, while the correlation between CEST and MRS was weaker. In subgroup analysis, APT values were significantly higher in high grade glioma (3.923 ± 1.239) and IDH wildtype group (3.932 ± 1.264) than low grade glioma (3.317 ± 0.868, p < 0.001) or IDH mutant group (3.358 ± 0.847, p < 0.001). Using high FET uptake as the standard, the CEST/MRS combination (AUC, 95% CI: 0.910, 0.907−0.913) predicted tumor infiltration better than CEST (0.812, 0.808−0.815) or MRS (0.888, 0.885−0.891) alone, consistent with contrast-enhancing and T2-hyperintense areas. Probability maps of tumor presence constructed from the CEST/MRS combination were preliminarily verified by multi-region biopsies. The combination of 7T CEST/MRS might serve as a promising non-radioactive alternative to delineate glioma infiltration, thus reshaping the guidance for tumor resection and irradiation.

Biomimetic semiconducting polymer dots for highly specific NIR-II fluorescence imaging of glioma

Glioma with very short medium survival time consists of 80% of primary malignant types of brain tumors. The unique microenvironment such as the existence of the blood-brain barrier (BBB) makes the glioma theranostics exhibit low sensitivity in diagnosis, a poor prognosis and low treatment efficacy. Therefore, the development of multifunctional nanoplatform that can cross BBB and target the glioma is essential for the high-sensitivity detection and ablation of cancer cells. In this study, C6 cell membrane-coated conjugated polymer dots (Pdots-C6) were constructed for targeted glioma tumor detection. As a new kind of biomimetic and biocompatible nanoprobes, Pdots-C6 preserve the complex biological functions of natural cell membranes while possessing physicochemical properties for NIR-II fluorescence imaging of glioma. After encapsulating C6 cell membrane on the surface of conjugated Pdots, Pdots-C6 exhibited the most favorable specific targeting capabilities in vitro and in vivo. In particular, this pilot study demonstrates that biomimetic nanoparticles offer a potential tool to enhance specific targeting to the brain, hence improving glioma tumor detection accuracy.

Brain Tumor Diagnosis Using Machine Learning, Convolutional Neural Networks, Capsule Neural Networks and Vision Transformers, Applied to MRI: A Survey

Management of brain tumors is based on clinical and radiological information with presumed grade dictating treatment. Hence, a non-invasive assessment of tumor grade is of paramount importance to choose the best treatment plan. Convolutional Neural Networks (CNNs) represent one of the effective Deep Learning (DL)-based techniques that have been used for brain tumor diagnosis. However, they are unable to handle input modifications effectively. Capsule neural networks (CapsNets) are a novel type of machine learning (ML) architecture that was recently developed to address the drawbacks of CNNs. CapsNets are resistant to rotations and affine translations, which is beneficial when processing medical imaging datasets. Moreover, Vision Transformers (ViT)-based solutions have been very recently proposed to address the issue of long-range dependency in CNNs. This survey provides a comprehensive overview of brain tumor classification and segmentation techniques, with a focus on ML-based, CNN-based, CapsNet-based, and ViT-based techniques. The survey highlights the fundamental contributions of recent studies and the performance of state-of-the-art techniques. Moreover, we present an in-depth discussion of crucial issues and open challenges. We also identify some key limitations and promising future research directions. We envisage that this survey shall serve as a good springboard for further study.

Somatostatin Receptor Targeted PET-Imaging for Diagnosis, Radiotherapy Planning and Theranostics of Meningiomas: A Systematic Review of the Literature

The aims of the present systematic review are to: (1) assess the diagnostic performance of somatostatin receptor (SSR)targeted positron emission tomography (PET) with different tracers and devices in patients affected by meningiomas; and (2) to evaluate the theranostic applications of peptide receptor radionuclide therapy (PRRT) in meningiomas. A systematic literature search according to PRISMA criteria was made by using two main databases. Only studies published from 2011 up to March 2022 in the English language with ≥10 enrolled patients were selected. Following our research strategy, 17 studies were included for the assessment. Fourteen studies encompassed 534 patients, harboring 733 meningiomas, submitted to SSR-targeted PET/CT (n = 10) or PET/MRI (n = 4) for de novo diagnosis, recurrence detection, or radiation therapy (RT) planning (endpoint 1), while 3 studies included 69 patients with therapy-refractory meningiomas submitted to PRRT (endpoint 2). A relevant variation in methodology was registered among diagnostic studies, since only a minority of them reported histopathology as a reference standard. PET, especially when performed through PET/MRI, resulted particularly useful for the detection of meningiomas located in the skull base (SB) or next to the falx cerebri, significantly influencing RT planning. As far as it concerns PRRT studies, stable disease was obtained in the 66.6% of the treated patients, being grade 1–2 hematological toxicity the most common side effect. Of note, the wide range of the administered activities, the various utilized radiopharmaceuticals (⁹⁰Y-DOTATOC and/or ¹⁷⁷Lu-DOTATATE), the lack of dosimetric studies hamper a clear definition of PRRT potential on meningiomas’ management.

Facts and Fictions About [18F]FDG versus Other Tracers in Managing Patients with Brain Tumors: It Is Time to Rectify the Ongoing Misconceptions

MRI is the first-choice imaging technique for brain tumors. Positron emission tomography can be combined together with multiparametric MRI to increase diagnostic confidence. Radiolabeled amino acids have gained wide clinical acceptance. The reported pooled specificity of [¹⁸F]FDG positron emission tomography is high and [¹⁸F]FDG might still be the first-choice positron emission tomography tracer in cases of World Health Organization grade 3 to 4 gliomas or [¹⁸F]FDG-avid tumors, avoiding the use of more expensive and less available radiolabeled amino acids. The present review discusses the additional value of positron emission tomography with a focus on [¹⁸F]FDG and radiolabeled amino acids.

Update on the Use of PET/MRI Contrast Agents and Tracers in Brain Oncology: A Systematic Review

The recent advancements in hybrid positron emission tomography–magnetic resonance imaging systems (PET/MRI) have brought massive value in the investigation of disease processes, in the development of novel treatments, in the monitoring of both therapy response and disease progression, and, not least, in the introduction of new multidisciplinary molecular imaging approaches. While offering potential advantages over PET/CT, the hybrid PET/MRI proved to improve both the image quality and lesion detectability. In particular, it showed to be an effective tool for the study of metabolic information about lesions and pathological conditions affecting the brain, from a better tumor characterization to the analysis of metabolic brain networks. Based on the PRISMA guidelines, this work presents a systematic review on PET/MRI in basic research and clinical differential diagnosis on brain oncology and neurodegenerative disorders. The analysis includes literature works and clinical case studies, with a specific focus on the use of PET tracers and MRI contrast agents, which are usually employed to perform hybrid PET/MRI studies of brain tumors. A systematic literature search for original diagnostic studies is performed using PubMed/MEDLINE, Scopus and Web of Science. Patients, study, and imaging characteristics were extracted from the selected articles. The analysis included acquired data pooling, heterogeneity testing, sensitivity analyses, used tracers, and reported patient outcomes. Our analysis shows that, while PET/MRI for the brain is a promising diagnostic method for early diagnosis, staging and recurrence in patients with brain diseases, a better definition of the role of tracers and imaging agents in both clinical and preclinical hybrid PET/MRI applications is needed and further efforts should be devoted to the standardization of the contrast imaging protocols, also considering the emerging agents and multimodal probes.

A Systematic Review of the Current Status and Quality of Radiomics for Glioma Differential Diagnosis

Simple Summary

Gliomas can be difficult to discern clinically and radiologically from other brain lesions (either neoplastic or non-neoplastic) since their clinical manifestations as well as preoperative imaging features often overlap and appear misleading. Radiomics could be extremely helpful for non-invasive glioma differential diagnosis (DDx). However, implementation in clinical practice is still distant and concerns have been raised regarding the methodological quality of radiomic studies. In this context, we aimed to summarize the current status and quality of radiomic studies concerning glioma DDx in a systematic review. In total, 42 studies were selected and examined in our work. Our study revealed that, despite promising and encouraging results, current studies on radiomics for glioma DDx still lack the quality required to allow its introduction into clinical practice. This work could provide new insights and help to reach a consensus on the use of the radiomic approach for glioma DDx.

Abstract

Radiomics is a promising tool that may increase the value of imaging in differential diagnosis (DDx) of glioma. However, implementation in clinical practice is still distant and concerns have been raised regarding the methodological quality of radiomic studies. Therefore, we aimed to systematically review the current status of radiomic studies concerning glioma DDx, also using the radiomics quality score (RQS) to assess the quality of the methodology used in each study. A systematic literature search was performed to identify original articles focused on the use of radiomics for glioma DDx from 2015. Methodological quality was assessed using the RQS tool. Spearman’s correlation (ρ) analysis was performed to explore whether RQS was correlated with journal metrics and the characteristics of the studies. Finally, 42 articles were selected for the systematic qualitative analysis. Selected articles were grouped and summarized in terms of those on DDx between glioma and primary central nervous system lymphoma, those aiming at differentiating glioma from brain metastases, and those based on DDx of glioma and other brain diseases. Median RQS was 8.71 out 36, with a mean RQS of all studies of 24.21%. Our study revealed that, despite promising and encouraging results, current studies on radiomics for glioma DDx still lack the quality required to allow its introduction into clinical practice. This work could provide new insights and help to reach a consensus on the use of the radiomic approach for glioma DDx.

Integrin α6-Targeted Molecular Imaging of Central Nervous System Leukemia in Mice

Central nervous system leukemia (CNS-L) is caused by leukemic cells infiltrating into the meninges or brain parenchyma and remains the main reason for disease relapse. Currently, it is hard to detect CNS-L accurately by clinically available imaging models due to the relatively low amount of tumor cells, confined blood supply, and the inferior glucose metabolism intensity. Recently, integrin α6-laminin interactions have been identified to mediate CNS-L, which suggests that integrin α6 may be a promising molecular imaging target for the detection of CNS-L. The acute lymphoblastic leukemia (ALL) cell line NALM6 stabled and transfected with luciferase was used to establish the CNS-L mouse model. CNS-L-bearing mice were monitored and confirmed by bioluminescence imaging. Three of our previously developed integrin α6-targeted peptide-based molecular imaging agents, Cy5-S5 for near-infrared fluorescence (NIRF), Gd-S5 for magnetic resonance (MR), and ¹⁸F-S5 for positron emission tomography (PET) imaging, were employed for the molecular imaging of these CNS-L-bearing mice. Bioluminescence imaging showed a local intensive signal in the heads among CNS-L-bearing mice; meanwhile, Cy5-S5/NIRF imaging produced intensive fluorescence intensity in the same head regions. Moreover, Gd-S5/MR imaging generated superior MR signal enhancement at the site of meninges, which were located between the skull bone and brain parenchyma. Comparatively, MR imaging with the clinically available MR enhancer Gd-DTPA did not produce the distinguishable MR signal in the same head regions. Additionally, ¹⁸F-S5/PET imaging also generated focal radio-concentration at the same head regions, which generated nearly 5-times tumor-to-background ratio compared to the clinically available PET radiotracer ¹⁸F-FDG. Finally, pathological examination identified layer-displayed leukemic cells in the superficial part of the brain parenchyma tissue, and immunohistochemical staining confirmed the overexpression of the integrin α6 within the lesion. These findings suggest the potential application of these integrin α6-targeted molecular imaging agents for the accurate detection of CNS-L.

Probing Methionine Uptake in Live Cells by Deuterium Labeling and Stimulated Raman Scattering

The small biomolecule methionine (Met) is a fundamental amino acid required for a vast range of biological processes such as protein synthesis, cancer metabolism, and epigenetics. However, it is still difficult to visualize the subcellular distribution of small biomolecules including Met in a minimally invasive manner. Here, we demonstrate stimulated Raman scattering (SRS) imaging of cellular uptake of deuterated methionine (d₈-Met) in live HeLa cells by way of comparison to the previously used alkyne-labeled Met analogue─homopropargylglycine (Hpg). We show that the solutions of d₈-Met and Hpg have similar SRS signal intensities. Furthermore, by careful image analysis with background subtraction, we succeed in the SRS imaging of cellular uptake of d₈-Met with a much greater signal intensity than Hpg, possibly reflecting the increased and minimally invasive uptake kinetics of d₈-Met compared with Hpg. We anticipate that d₈-Met and other deuterated biomolecules will be useful for investigating metabolic processes with subcellular resolution.

Recent Advances in Classification of Brain Tumor from MR Images - State of the Art Review from 2017 to 2021

BACKGROUND

The task of identifying a tumor in the brain is a complex problem that requires sophisticated skills and inference mechanisms to accurately locate the tumor region. The complex nature of the brain tissue makes the problem of locating, segmenting, and ultimately classifying Magnetic Resonance (MR) images a complex problem. The aim of this review paper is to consolidate the details of the most relevant and recent approaches proposed in this domain for the binary and multi-class classification of brain tumors using brain MR images.

OBJECTIVE

In this review paper, a detailed summary of the latest techniques used for brain MR image feature extraction and classification is presented. A lot of research papers have been published recently with various techniques proposed for identifying an efficient method for the correct recognition and diagnosis of brain MR images. The review paper allows researchers in the field to familiarize themselves with the latest developments and be able to propose novel techniques that have not yet been explored in this research domain. In addition, the review paper will facilitate researchers, who are new to machine learning algorithms for brain tumor recognition, to understand the basics of the field and pave the way for them to be able to contribute to this vital field of medical research.

RESULTS

In this paper, the review is performed for all recently proposed methods for both feature extraction and classification. It also identifies the combination of feature extraction methods and classification methods that when combined would be the most efficient technique for the recognition and diagnosis of brain tumor from MR images. In addition, the paper presents the performance metrics particularly the recognition accuracy, of selected research published between 2017- 2021.

Tracking the Corticospinal Tract in Patients With High-Grade Glioma: Clinical Evaluation of Multi-Level Fiber Tracking and Comparison to Conventional Deterministic Approaches

While the diagnosis of high-grade glioma (HGG) is still associated with a considerably poor prognosis, neurosurgical tumor resection provides an opportunity for prolonged survival and improved quality of life for affected patients. However, successful tumor resection is dependent on a proper surgical planning to avoid surgery-induced functional deficits whilst achieving a maximum extent of resection (EOR). With diffusion magnetic resonance imaging (MRI) providing insight into individual white matter neuroanatomy, the challenge remains to disentangle that information as correctly and as completely as possible. In particular, due to the lack of sensitivity and accuracy, the clinical value of widely used diffusion tensor imaging (DTI)-based tractography is increasingly questioned. We evaluated whether the recently developed multi-level fiber tracking (MLFT) technique can improve tractography of the corticospinal tract (CST) in patients with motor-eloquent HGGs. Forty patients with therapy-naïve HGGs (mean age: 62.6 ± 13.4 years, 57.5% males) and preoperative diffusion MRI [repetition time (TR)/echo time (TE): 5000/78 ms, voxel size: 2x2x2 mm3, one volume at b=0 s/mm2, 32 volumes at b=1000 s/mm2] underwent reconstruction of the CST of the tumor-affected and unaffected hemispheres using MLFT in addition to deterministic DTI-based and deterministic constrained spherical deconvolution (CSD)-based fiber tractography. The brain stem was used as a seeding region, with a motor cortex mask serving as a target region for MLFT and a region of interest (ROI) for the other two algorithms. Application of the MLFT method substantially improved bundle reconstruction, leading to CST bundles with higher radial extent compared to the two other algorithms (delineation of CST fanning with a wider range; median radial extent for tumor-affected vs. unaffected hemisphere - DTI: 19.46° vs. 18.99°, p=0.8931; CSD: 30.54° vs. 27.63°, p=0.0546; MLFT: 81.17° vs. 74.59°, p=0.0134). In addition, reconstructions by MLFT and CSD-based tractography nearly completely included respective bundles derived from DTI-based tractography, which was however favorable for MLFT compared to CSD-based tractography (median coverage of the DTI-based CST for affected vs. unaffected hemispheres - CSD: 68.16% vs. 77.59%, p=0.0075; MLFT: 93.09% vs. 95.49%; p=0.0046). Thus, a more complete picture of the CST in patients with motor-eloquent HGGs might be achieved based on routinely acquired diffusion MRI data using MLFT.

Imaging of Complications of Chemoradiation

Chemoradiation for head and neck cancer is associated with a variety of early and late complications. Toxicities may affect the aero-digestive tract (mucositis, salivary gland injury), regional osseous and cartilaginous structures (osteoradionecrosis (ORN) and chondronecrosis), vasculature (progressive radiation vasculopathy and carotid blow out syndromes), and neural structures (optic neuritis, myelitis, and brain injury). These may be difficult to distinguish from tumor recurrence on imaging, and may necessitate the use of advanced MRI and molecular imaging techniques to reach the correct diagnosis. Secondary radiation-induced malignancies include thyroid cancer and a variety of sarcomas that may manifest several years after treatment. Checkpoint inhibitors can cause a variety of adverse immune events, including autoimmune hypophysitis and encephalitis.

Imaging and treatment of brain tumors through molecular targeting: Recent clinical advances

Molecular imaging techniques have rapidly progressed over recent decades providing unprecedented in vivo characterization of metabolic pathways and molecular biomarkers. Many of these new techniques have been successfully applied in the field of neuro-oncological imaging to probe tumor biology. Targeting specific signaling or metabolic pathways could help to address several unmet clinical needs that hamper the management of patients with brain tumors. This review aims to provide an overview of the recent advances in brain tumor imaging using molecular targeting with positron emission tomography and magnetic resonance imaging, as well as the role in patient management and possible therapeutic implications.

Repeatability of image features extracted from FET PET in application to post-surgical glioblastoma assessment

Positron emission tomography (PET) imaging using the amino acid tracer O-[2-(¹⁸F)fluoroethyl]-L-tyrosine (FET) has gained increased popularity within the past decade in the management of glioblastoma (GBM). Radiomics features extracted from FET PET images may be sensitive to variations when imaging at multiple time points. It is therefore necessary to assess feature robustness to test-retest imaging. Eight patients with histologically confirmed GBM that had undergone post-surgical test-retest FET PET imaging were recruited. In total, 1578 radiomic features were extracted from biological tumour volumes (BTVs) delineated using a semi-automatic contouring method. Feature repeatability was assessed using the intraclass correlation coefficient (ICC). The effect of both bin width and filter choice on feature repeatability was also investigated. 59/106 (55.7%) features from the original image and 843/1472 (57.3%) features from filtered images had an ICC ≥ 0.85. Shape and first order features were most stable. Choice of bin width showed minimal impact on features defined as stable. The Laplacian of Gaussian (LoG, σ = 5 mm) and Wavelet filters (HLL and LHL) significantly improved feature repeatability (p ≪ 0.0001, p = 0.003, p = 0.002, respectively). Correlation of textural features with tumour volume was reported for transparency. FET PET radiomic features extracted from post-surgical images of GBM patients that are robust to test-retest imaging were identified. An investigation with a larger dataset is warranted to validate the findings in this study.

Current perspectives on neuroimaging techniques used to identify stroke mimics in clinical practice

INTRODUCTION

Urgent clinical assessment and brain imaging are essential for differentiating stroke mimics from stroke and to avoid unnecessary initiation of reperfusion and other therapies in stroke mimic patients.

AREAS COVERED

In this article, the authors will review acute stroke imaging and then the imaging patterns of the most common stroke mimics. The authors have focused our review on brain CT scan, and more specifically CT perfusion, as this is the most commonly available and emerging tool in emergency settings. The authors also provide information on acute brain MRI and MR perfusion.

EXPERT OPINION

Imaging can contribute to the detection and diagnosis of acute stroke mimics. Knowledge of imaging findings in different stroke mimics can help distinguish these from patients with stroke who require timely reperfusion therapy. CT and MRI perfusion and diffusion-weighted imaging (DWI) MRI are useful imaging modalities for the assessment of acute stroke patients as they provide more accurate information than plain CT scan. Some of these modalities should be available in the emergency setting. The authors recommended CT perfusion as a useful tool for stroke management and differentiation with stroke mimics.