Multimodal imaging for improved diagnosis and treatment of cancers

Technological Frontiers in Brain Cancer: A Systematic Review and Meta-Analysis of Hyperspectral Imaging in Computer-Aided Diagnosis Systems

Brain cancer is a substantial factor in the mortality associated with cancer, presenting difficulties in the timely identification of the disease. The precision of diagnoses is significantly dependent on the proficiency of radiologists and neurologists. Although there is potential for early detection with computer-aided diagnosis (CAD) algorithms, the majority of current research is hindered by its modest sample sizes. This meta-analysis aims to comprehensively assess the diagnostic test accuracy (DTA) of computer-aided design (CAD) models specifically designed for the detection of brain cancer utilizing hyperspectral (HSI) technology. We employ Quadas-2 criteria to choose seven papers and classify the proposed methodologies according to the artificial intelligence method, cancer type, and publication year. In order to evaluate heterogeneity and diagnostic performance, we utilize Deeks' funnel plot, the forest plot, and accuracy charts. The results of our research suggest that there is no notable variation among the investigations. The CAD techniques that have been examined exhibit a notable level of precision in the automated detection of brain cancer. However, the absence of external validation hinders their potential implementation in real-time clinical settings. This highlights the necessity for additional studies in order to authenticate the CAD models for wider clinical applicability.

Recent developments in two-dimensional molybdenum disulfide-based multimodal cancer theranostics

Recent advancements in cancer research have led to the generation of innovative nanomaterials for improved diagnostic and therapeutic strategies. Despite the proven potential of two-dimensional (2D) molybdenum disulfide (MoS2) as a versatile platform in biomedical applications, few review articles have focused on MoS2-based platforms for cancer theranostics. This review aims to fill this gap by providing a comprehensive overview of the latest developments in 2D MoS2 cancer theranostics and emerging strategies in this field. This review highlights the potential applications of 2D MoS2 in single-model imaging and therapy, including fluorescence imaging, photoacoustic imaging, photothermal therapy, and catalytic therapy. This review further classifies the potential of 2D MoS2 in multimodal imaging for diagnostic and synergistic theranostic platforms. In particular, this review underscores the progress of 2D MoS2 as an integrated drug delivery system, covering a broad spectrum of therapeutic strategies from chemotherapy and gene therapy to immunotherapy and photodynamic therapy. Finally, this review discusses the current challenges and future perspectives in meeting the diverse demands of advanced cancer diagnostic and theranostic applications.

Exploring the State of Cancer Imaging Research in Africa

INTRODUCTION

The growing cancer burden in Africa demands urgent action. Medical imaging is crucial for cancer diagnosis and management and is an essential enabler of precision medicine. To understand the readiness for quantitative imaging analysis to support cancer management in Africa, we analyzed the utilization patterns of imaging modalities for cancer research across the continent.

METHODS

We retrieved articles by systematically searching PubMed, using a combination of search terms {"Neoplasm"} AND {"Radiology" or "Diagnostic imaging" or "Radiography" or "Interventional Radiology" or "Radiotherapy" or "Radiation Oncology"} AND {Africa∗ or 54 African countries}. Articles describing cancer diagnosis or management in humans with the utilization of imaging were included. Exclusion criteria were review articles, non-English articles, publications before 2000, noncancer diagnoses, and studies conducted outside Africa.

RESULTS

The analysis of diagnostic imaging in Africa revealed a diverse utilization pattern across different cancer types and regions. The literature search identified 107 publications on cancer imaging in Africa. The studies were carried out in 19 African countries on 12 different cancer types with 6 imaging modalities identified. Most cancer imaging research studies used multiple imaging modalities. Ultrasound was the most used distinct imaging modality and MRI was the least frequently used. Most research studies originated from Nigeria, South Africa, and Egypt.

CONCLUSION

We demonstrate substantial variability in the presence of imaging modalities, widespread utilization of ultrasonography, and limited availability of advanced imaging modalities for cancer research.

Hybrid Positron Emission Tomography and Magnetic Resonance Imaging Guided Microsurgical Management of Glial Tumors: Case Series and Review of the Literature

In this case series, we aimed to report our clinical experience with hybrid positron emission tomography (PET) and magnetic resonance imaging (MRI) navigation in the management of recurrent glial brain tumors. Consecutive recurrent neuroglial brain tumor patients who underwent PET/MRI at preoperative or intraoperative periods were included, whereas patients with non-glial intracranial tumors including metastasis, lymphoma and meningioma were excluded from the study. A total of eight patients (mean age 50.1 ± 11.0 years) with suspicion of recurrent glioma tumor were evaluated. Gross total tumor resection of the PET/MRI-positive area was achieved in seven patients, whereas one patient was diagnosed with radiation necrosis, and surgery was avoided. All patients survived at 1-year follow-up. Five (71.4%) of the recurrent patients remained free of recurrence for the entire follow-up period. Two patients with glioblastoma had tumor recurrence at the postoperative sixth and eighth months. According to our results, hybrid PET/MRI provides reliable and accurate information to distinguish recurrent glial tumor from radiation necrosis. With the help of this differential diagnosis, hybrid imaging may provide the gross total resection of recurrent tumors without harming eloquent brain areas.

HyperMRI: hyperspectral and magnetic resonance fusion methodology for neurosurgery applications

PURPOSE

Magnetic resonance imaging (MRI) is a common technique in image-guided neurosurgery (IGN). Recent research explores the integration of methods like ultrasound and tomography, among others, with hyperspectral (HS) imaging gaining attention due to its non-invasive real-time tissue classification capabilities. The main challenge is the registration process, often requiring manual intervention. This work introduces an automatic, markerless method for aligning HS images with MRI.

METHODS

This work presents a multimodal system that combines RGB-Depth (RGBD) and HS cameras. The RGBD camera captures the patient's facial geometry, which is used for registration with the preoperative MR through ICP. Once MR-depth registration is complete, the integration of HS data is achieved using a calibrated homography transformation. The incorporation of external tracking with a novel calibration method allows camera mobility from the registration position to the craniotomy area. This methodology streamlines the fusion of RGBD, HS and MR images within the craniotomy area.

RESULTS

Using the described system and an anthropomorphic phantom head, the system has been characterised by registering the patient's face in 25 positions and 5 positions resulted in a fiducial registration error of 1.88 ± 0.19 mm and a target registration error of 4.07 ± 1.28 mm, respectively.

CONCLUSIONS

This work proposes a new methodology to automatically register MR and HS information with a sufficient accuracy. It can support the neurosurgeons to guide the diagnosis using multimodal data over an augmented reality representation. However, in its preliminary prototype stage, this system exhibits significant promise, driven by its cost-effectiveness and user-friendly design.

Amide proton transfer-weighted imaging and stretch-exponential model DWI based 18F-FDG PET/MRI for differentiation of benign and malignant solitary pulmonary lesions

OBJECTIVES

To differentiate benign and malignant solitary pulmonary lesions (SPLs) by amide proton transfer-weighted imaging (APTWI), mono-exponential model DWI (MEM-DWI), stretched exponential model DWI (SEM-DWI), and 18F-FDG PET-derived parameters.

METHODS

A total of 120 SPLs patients underwent chest 18F-FDG PET/MRI were enrolled, including 84 in the training set (28 benign and 56 malignant) and 36 in the test set (13 benign and 23 malignant). MTRasym(3.5 ppm), ADC, DDC, α, SUVmax, MTV, and TLG were compared. The area under receiver-operator characteristic curve (AUC) was used to assess diagnostic efficacy. The Logistic regression analysis was used to identify independent predictors and establish prediction model.

RESULTS

SUVmax, MTV, TLG, α, and MTRasym(3.5 ppm) values were significantly lower and ADC, DDC values were significantly higher in benign SPLs than malignant SPLs (all P < 0.01). SUVmax, ADC, and MTRasym(3.5 ppm) were independent predictors. Within the training set, the prediction model based on these independent predictors demonstrated optimal diagnostic efficacy (AUC, 0.976; sensitivity, 94.64%; specificity, 92.86%), surpassing any single parameter with statistical significance. Similarly, within the test set, the prediction model exhibited optimal diagnostic efficacy. The calibration curves and DCA revealed that the prediction model not only had good consistency but was also able to provide a significant benefit to the related patients, both in the training and test sets.

CONCLUSION

The SUVmax, ADC, and MTRasym(3.5 ppm) were independent predictors for differentiation of benign and malignant SPLs, and the prediction model based on them had an optimal diagnostic efficacy.

An [18F]FDG PET/3D-ultrashort echo time MRI-based radiomics model established by machine learning facilitates preoperative assessment of lymph node status in non-small cell lung cancer

OBJECTIVES

To develop an [18F]FDG PET/3D-UTE model based on clinical factors, three-dimensional ultrashort echo time (3D-UTE), and PET radiomics features via machine learning for the assessment of lymph node (LN) status in non-small cell lung cancer (NSCLC).

METHODS

A total of 145 NSCLC patients (training, 101 cases; test, 44 cases) underwent whole-body [18F]FDG PET/CT and chest [18F]FDG PET/MRI were enrolled. Preoperative clinical factors and 3D-UTE, CT, and PET radiomics features were analyzed. The Mann-Whitney U test, LASSO regression, and SelectKBest were used for feature extraction. Five machine learning algorithms were used to establish prediction models, which were evaluated by the area under receiver-operator characteristic (ROC), DeLong test, calibration curves, and decision curve analysis (DCA).

RESULTS

A prediction model based on random forest, consisting of four clinical factors, six 3D-UTE, and six PET radiomics features, was used as the final model for PET/3D-UTE. The AUCs of this model were 0.912 and 0.791 in the training and test sets, respectively, which not only showed different degrees of improvement over individual models such as clinical, 3D-UTE, and PET (AUC-training = 0.838, 0.834, and 0.828, AUC-test = 0.756, 0.745, and 0.768, respectively) but also achieved the similar diagnostic efficacy as the optimal PET/CT model (AUC-training = 0.890, AUC-test = 0.793). The calibration curves and DCA indicated good consistency (C-index, 0.912) and clinical utility of this model, respectively.

CONCLUSION

The [18F]FDG PET/3D-UTE model based on clinical factors, 3D-UTE, and PET radiomics features using machine learning methods could noninvasively assess the LN status of NSCLC.

CLINICAL RELEVANCE STATEMENT

A machine learning model of 18F-fluorodeoxyglucose positron emission tomography/ three-dimensional ultrashort echo time could noninvasively assess the lymph node status of non-small cell lung cancer, which provides a novel method with less radiation burden for clinical practice.

KEY POINTS

• The 3D-UTE radiomics model using the PLS-DA classifier was significantly associated with LN status in NSCLC and has similar diagnostic performance as the clinical, CT, and PET models. • The [18F]FDG PET/3D-UTE model based on clinical factors, 3D-UTE, and PET radiomics features using the RF classifier could noninvasively assess the LN status of NSCLC and showed improved diagnostic performance compared to the clinical, 3D-UTE, and PET models. • In the assessment of LN status in NSCLC, the [18F]FDG PET/3D-UTE model has similar diagnostic efficacy as the [18F]FDG PET/CT model that incorporates clinical factors and CT and PET radiomics features.

Novel Characterization Techniques for Multifunctional Plasmonic-Magnetic Nanoparticles in Biomedical Applications

In the rapidly emerging field of biomedical applications, multifunctional nanoparticles, especially those containing magnetic and plasmonic components, have gained significant attention due to their combined properties. These hybrid systems, often composed of iron oxide and gold, provide both magnetic and optical functionalities and offer promising avenues for applications in multimodal bioimaging, hyperthermal therapies, and magnetically driven selective delivery. This paper focuses on the implementation of advanced characterization methods, comparing statistical analyses of individual multifunctional particle properties with macroscopic properties as a way of fine-tuning synthetic methodologies for their fabrication methods. Special emphasis is placed on the size-dependent properties, biocompatibility, and challenges that can arise from this versatile nanometric system. In order to ensure the quality and applicability of these particles, various novel methods for characterizing the magnetic gold particles, including the analysis of their morphology, optical response, and magnetic response, are also discussed, with the overall goal of optimizing the fabrication of this complex system and thus enhancing its potential as a preferred diagnostic agent.

Tumor-derived systems as novel biomedical tools-turning the enemy into an ally

Cancer is a complex illness that presents significant challenges in its understanding and treatment. The classic definition, "a group of diseases characterized by the uncontrolled growth and spread of abnormal cells in the body," fails to convey the intricate interaction between the many entities involved in cancer. Recent advancements in the field of cancer research have shed light on the role played by individual cancer cells and the tumor microenvironment as a whole in tumor development and progression. This breakthrough enables the utilization of the tumor and its components as biological tools, opening new possibilities. This article delves deeply into the concept of "tumor-derived systems", an umbrella term for tools sourced from the tumor that aid in combatting it. It includes cancer cell membrane-coated nanoparticles (for tumor theranostics), extracellular vesicles (for tumor diagnosis/therapy), tumor cell lysates (for cancer vaccine development), and engineered cancer cells/organoids (for cancer research). This review seeks to offer a complete overview of the tumor-derived materials that are utilized in cancer research, as well as their current stages of development and implementation. It is aimed primarily at researchers working at the interface of cancer biology and biomedical engineering, and it provides vital insights into this fast-growing topic.

Advancing Diabetic Retinopathy Diagnosis: Leveraging Optical Coherence Tomography Imaging with Convolutional Neural Networks

Diabetic retinopathy (DR) is a vision-threatening complication of diabetes, necessitating early and accurate diagnosis. The combination of optical coherence tomography (OCT) imaging with convolutional neural networks (CNNs) has emerged as a promising approach for enhancing DR diagnosis. OCT provides detailed retinal morphology information, while CNNs analyze OCT images for automated detection and classification of DR. This paper reviews the current research on OCT imaging and CNNs for DR diagnosis, discussing their technical aspects and suitability. It explores CNN applications in detecting lesions, segmenting microaneurysms, and assessing disease severity, showing high sensitivity and accuracy. CNN models outperform traditional methods and rival expert ophthalmologists' results. However, challenges such as dataset availability and model interpretability remain. Future directions include multimodal imaging integration and real-time, point-of-care CNN systems for DR screening. The integration of OCT imaging with CNNs has transformative potential in DR diagnosis, facilitating early intervention, personalized treatments, and improved patient outcomes. Abbreviations: DR = Diabetic Retinopathy, OCT = Optical Coherence Tomography, CNN = Convolutional Neural Network, CMV = Cytomegalovirus, PDR = Proliferative Diabetic Retinopathy, AMD = Age-Related Macular Degeneration, VEGF = vascular endothelial growth factor, RAP = Retinal Angiomatous Proliferation, OCTA = OCT Angiography, AI = Artificial Intelligence.

Near-infrared photodynamic and photothermal co-therapy based on organic small molecular dyes

Near-infrared (NIR) organic small molecule dyes (OSMDs) are effective photothermal agents for photothermal therapy (PTT) due to their advantages of low cost and toxicity, good biodegradation, and strong NIR absorption over a wide wavelength range. Nevertheless, OSMDs have limited applicability in PTT due to their low photothermal conversion efficiency and inadequate destruction of tumor regions that are nonirradiated by NIR light. However, they can also act as photosensitizers (PSs) to produce reactive oxygen species (ROS), which can be further eradicated by using ROS-related therapies to address the above limitations of PTT. In this review, the synergistic mechanism, composition, and properties of photodynamic therapy (PDT)-PTT nanoplatforms were comprehensively discussed. In addition, some specific strategies for further improving the combined PTT and PDT based on OSMDs for cancer to completely eradicate cancer cells were outlined. These strategies include performing image-guided co-therapy, enhancing tumor infiltration, increasing H2O2 or O2 in the tumor microenvironment, and loading anticancer drugs onto nanoplatforms to enable combined therapy with phototherapy and chemotherapy. Meanwhile, the intriguing prospects and challenges of this treatment modality were also summarized with a focus on the future trends of its clinical application.

Multifunctional Nanotheranostics for Dual-Modal Imaging-Guided Precision Therapy of Nasopharyngeal Carcinoma

Currently, the low survival rate and poor prognosis of patients with nasopharyngeal carcinoma are ascribed to the lack of early and accurate diagnosis and resistance to radiotherapy. In parallel, the integration of imaging-guided diagnosis and precise treatment has gained much attention in the field of theranostic nanotechnology. However, constructing dual-modal imaging-guided nanotheranostics with desired imaging performance as well as great biocompatibility remains challenging. Therefore, we developed a simple but multifunctional nanotheranostic GdCPP for the early and accurate diagnosis and efficient treatment of nasopharyngeal carcinoma (NPC), which combined fluorescence imaging and magnetic resonance imaging (MRI) onto a single nanoplatform for imaging-guided subsequent photodynamic therapy (PDT). GdCPP had an appropriate particle size (81.93 ± 0.69 nm) and was highly stable, resulting in sufficient tumor accumulation, which along with massive reactive oxygen species (ROS) generation upon irradiation further significantly killed tumor cells. Moreover, GdCPP owned much stronger r1 relaxivity (9.396 mM-1 s-1) compared to clinically used Gd-DTPA (5.034 mM-1 s-1) and exhibited better T1WI MRI performance. Under dual-modal imaging-guided PDT, GdCPP achieved efficient therapeutic outcomes without causing any noticeable tissue damage. The results of in vitro and in vivo studies indicated that GdCPP may be a suitable candidate for dual-modal imaging-guided precision tumor therapy.

A modality-collaborative convolution and transformer hybrid network for unpaired multi-modal medical image segmentation with limited annotations

BACKGROUND

Multi-modal learning is widely adopted to learn the latent complementary information between different modalities in multi-modal medical image segmentation tasks. Nevertheless, the traditional multi-modal learning methods require spatially well-aligned and paired multi-modal images for supervised training, which cannot leverage unpaired multi-modal images with spatial misalignment and modality discrepancy. For training accurate multi-modal segmentation networks using easily accessible and low-cost unpaired multi-modal images in clinical practice, unpaired multi-modal learning has received comprehensive attention recently.

PURPOSE

Existing unpaired multi-modal learning methods usually focus on the intensity distribution gap but ignore the scale variation problem between different modalities. Besides, within existing methods, shared convolutional kernels are frequently employed to capture common patterns in all modalities, but they are typically inefficient at learning global contextual information. On the other hand, existing methods highly rely on a large number of labeled unpaired multi-modal scans for training, which ignores the practical scenario when labeled data is limited. To solve the above problems, we propose a modality-collaborative convolution and transformer hybrid network (MCTHNet) using semi-supervised learning for unpaired multi-modal segmentation with limited annotations, which not only collaboratively learns modality-specific and modality-invariant representations, but also could automatically leverage extensive unlabeled scans for improving performance.

METHODS

We make three main contributions to the proposed method. First, to alleviate the intensity distribution gap and scale variation problems across modalities, we develop a modality-specific scale-aware convolution (MSSC) module that can adaptively adjust the receptive field sizes and feature normalization parameters according to the input. Secondly, we propose a modality-invariant vision transformer (MIViT) module as the shared bottleneck layer for all modalities, which implicitly incorporates convolution-like local operations with the global processing of transformers for learning generalizable modality-invariant representations. Third, we design a multi-modal cross pseudo supervision (MCPS) method for semi-supervised learning, which enforces the consistency between the pseudo segmentation maps generated by two perturbed networks to acquire abundant annotation information from unlabeled unpaired multi-modal scans.

RESULTS

Extensive experiments are performed on two unpaired CT and MR segmentation datasets, including a cardiac substructure dataset derived from the MMWHS-2017 dataset and an abdominal multi-organ dataset consisting of the BTCV and CHAOS datasets. Experiment results show that our proposed method significantly outperforms other existing state-of-the-art methods under various labeling ratios, and achieves a comparable segmentation performance close to single-modal methods with fully labeled data by only leveraging a small portion of labeled data. Specifically, when the labeling ratio is 25%, our proposed method achieves overall mean DSC values of 78.56% and 76.18% in cardiac and abdominal segmentation, respectively, which significantly improves the average DSC value of two tasks by 12.84% compared to single-modal U-Net models.

CONCLUSIONS

Our proposed method is beneficial for reducing the annotation burden of unpaired multi-modal medical images in clinical applications.

A review of recent developments of metal-organic frameworks as combined biomedical platforms over the past decade

Metal-organic frameworks (MOFs), also called porous coordination polymers, represent a class of crystalline porous materials made up of organic ligands and metal ions/metal clusters. Herein, an overview of the preparation of different metal-organic frameworks and the recent advances in MOF-based stimuli-responsive drug delivery systems (DDSs) with the drug release mechanisms including pH-, temperature-, ion-, magnetic-, pressure-, adenosine-triphosphate (ATP)-, H₂S-, redox-, responsive, and photoresponsive MOF were rarely introduced. The combination therapy containing of two or more treatments can be enhanced treatment effectiveness through overcoming limitations of monotherapy. Photothermal therapy (PTT) combined with chemotherapy (CT), chemotherapy in combination with PTT or other combinations were explained to overcome drug resistance and side effects in normal cells as well as enhancing the therapeutic response. Integrated platforms containing of photothermal/drug-delivering functions with magnetic resonance imaging (MRI) properties exhibited great advantages in cancer therapy.

Nanoparticles in the diagnosis and treatment of cancer metastases: Current and future perspectives

Metastasis accounts for greater than 90% of cancer-related deaths. Despite recent advancements in conventional chemotherapy, immunotherapy, targeted therapy, and their rational combinations, metastatic cancers remain essentially untreatable. The distinct obstacles to treat metastases include their small size, high multiplicity, redundancy, therapeutic resistance, and dissemination to multiple organs. Recent advancements in nanotechnology provide the numerous applications in the diagnosis and prophylaxis of metastatic diseases, including the small particle size to penetrate cell membrane and blood vessels and their capacity to transport complex molecular 'cargo' particles to various metastatic regions such as bones, brain, liver, lungs, and lymph nodes. Indeed, nanoparticles (NPs) have demonstrated a significant ability to target specific cells within these organs. In this regard, the purpose of this review is to summarize the present state of nanotechnology in terms of its application in the diagnosis and treatment of metastatic cancer. We intensively reviewed applications of NPs in fluorescent imaging, PET scanning, MRI, and photoacoustic imaging to detect metastasis in various cancer models. The use of targeted NPs for cancer ablation in conjunction with chemotherapy, photothermal treatment, immuno therapy, and combination therapy is thoroughly discussed. The current review also highlights the research opportunities and challenges of leveraging engineering technologies with cancer cell biology and pharmacology to fabricate nanoscience-based tools for treating metastases.

The Impact of Fluorination on the Design of Histone Deacetylase Inhibitors

In recent years, histone deacetylases (HDACs) have emerged as promising targets in the treatment of cancer. The approach is to inhibit HDACs with drugs known as HDAC inhibitors (HDACis). Such HDACis are broadly classified according to their chemical structure, e.g., hydroxamic acids, benzamides, thiols, short-chain fatty acids, and cyclic peptides. Fluorination plays an important role in the medicinal-chemical design of new active representatives. As a result of the introduction of fluorine into the chemical structure, parameters such as potency or selectivity towards isoforms of HDACs can be increased. However, the impact of fluorination cannot always be clearly deduced. Nevertheless, a change in lipophilicity and, hence, solubility, as well as permeability, can influence the potency. The selectivity towards certain HDACs isoforms can be explained by special interactions of fluorinated compounds with the structure of the slightly different enzymes. Another aspect is that for a more detailed investigation of newly synthesized fluorine-containing active compounds, fluorination is often used for the purpose of labeling. Aside from the isotope ¹⁹F, which can be detected by nuclear magnetic resonance spectroscopy, the positron emission tomography of ¹⁸F plays a major role. However, to our best knowledge, a survey of the general effects of fluorination on HDACis development is lacking in the literature to date. Therefore, the aim of this review is to highlight the introduction of fluorine in the course of chemical synthesis and the impact on biological activity, using selected examples of recently developed fluorinated HDACis.

Technological advancements in cancer diagnostics: Improvements and limitations

BACKGROUND

Cancer is characterized by the rampant proliferation, growth, and infiltration of malignantly transformed cancer cells past their normal boundaries into adjacent tissues. It is the leading cause of death worldwide, responsible for approximately 19.3 million new diagnoses and 10 million deaths globally in 2020. In the United States alone, the estimated number of new diagnoses and deaths is 1.9 million and 609 360, respectively. Implementation of currently existing cancer diagnostic techniques such as positron emission tomography (PET), X-ray computed tomography (CT), and magnetic resonance spectroscopy (MRS), and molecular diagnostic techniques, have enabled early detection rates and are instrumental not only for the therapeutic management of cancer patients, but also for early detection of the cancer itself. The effectiveness of these cancer screening programs are heavily dependent on the rate of accurate precursor lesion identification; an increased rate of identification allows for earlier onset treatment, thus decreasing the incidence of invasive cancer in the long-term, and improving the overall prognosis. Although these diagnostic techniques are advantageous due to lack of invasiveness and easier accessibility within the clinical setting, several limitations such as optimal target definition, high signal to background ratio and associated artifacts hinder the accurate diagnosis of specific types of deep-seated tumors, besides associated high cost. In this review we discuss various imaging, molecular, and low-cost diagnostic tools and related technological advancements, to provide a better understanding of cancer diagnostics, unraveling new opportunities for effective management of cancer, particularly in low- and middle-income countries (LMICs).

RECENT FINDINGS

Herein we discuss various technological advancements that are being utilized to construct an assortment of new diagnostic techniques that incorporate hardware, image reconstruction software, imaging devices, biomarkers, and even artificial intelligence algorithms, thereby providing a reliable diagnosis and analysis of the tumor. Also, we provide a brief account of alternative low cost-effective cancer therapy devices (CryoPop®, LumaGEM®, MarginProbe®) and picture archiving and communication systems (PACS), emphasizing the need for multi-disciplinary collaboration among radiologists, pathologists, and other involved specialties for improving cancer diagnostics.

CONCLUSION

Revolutionary technological advancements in cancer imaging and molecular biology techniques are indispensable for the accurate diagnosis and prognosis of cancer.

Mapping Resection Progress by Tool-Tip Tracking during Brain Tumor Surgery for Real-Time Estimation of Residual Tumor

Surgical resection continues to be the primary initial therapeutic strategy in the treatment of patients with brain tumors. Computerized cranial neuronavigation based on preoperative imaging offers precision guidance during craniotomy and early tumor resection but progressively loses validity with brain shift. Intraoperative MRI (iMRI) and intraoperative ultrasound (iUS) can update the imaging used for guidance and navigation but are limited in terms of temporal and spatial resolution, respectively. We present a system that uses time-stamped tool-tip positions of surgical instruments to generate a map of resection progress with high spatial and temporal accuracy. We evaluate this system and present results from 80 cranial tumor resections. Regions of the preoperative tumor segmentation that are covered by the resection map (True Positive Tracking) and regions of the preoperative tumor segmentation not covered by the resection map (True Negative Tracking) are determined for each case. We compare True Negative Tracking, which estimates the residual tumor, with the actual residual tumor identified using iMRI. We discuss factors that can cause False Positive Tracking and False Negative Tracking, which underestimate and overestimate the residual tumor, respectively. Our method provides good estimates of the residual tumor when there is minimal brain shift, and line-of-sight is maintained. When these conditions are not met, surgeons report that it is still useful for identifying regions of potential residual.

Single Shot Lensless Interferenceless Phase Imaging of Biochemical Samples Using Synchrotron near Infrared Beam

Phase imaging of biochemical samples has been demonstrated for the first time at the Infrared Microspectroscopy (IRM) beamline of the Australian Synchrotron using the usually discarded near-IR (NIR) region of the synchrotron-IR beam. The synchrotron-IR beam at the Australian Synchrotron IRM beamline has a unique fork shaped intensity distribution as a result of the gold coated extraction mirror shape, which includes a central slit for rejection of the intense X-ray beam. The resulting beam configuration makes any imaging task challenging. For intensity imaging, the fork shaped beam is usually tightly focused to a point on the sample plane followed by a pixel-by-pixel scanning approach to record the image. In this study, a pinhole was aligned with one of the lobes of the fork shaped beam and the Airy diffraction pattern was used to illuminate biochemical samples. The diffracted light from the samples was captured using a NIR sensitive lensless camera. A rapid phase-retrieval algorithm was applied to the recorded intensity distributions to reconstruct the phase information. The preliminary results are promising to develop multimodal imaging capabilities at the IRM beamline of the Australian Synchrotron.

Progress in large field-of-view interventional planar scintigraphy and SPECT imaging

INTRODUCTION

Handheld gamma cameras and gamma probes have been successfully implemented for enabling nuclear image or radio-guidance in minimally-invasive procedures. There is an opportunity for large field-of-view interventional planar scintigraphy and SPECT imaging to complement these small field-of-view devices for two reasons. First, a large field-of-view camera enables imaging of relatively larger organs and activity accumulations that are not close to the patient's skin. And second, more precise corrections can be implemented in the SPECT reconstruction algorithm, improving its quality.

AREAS COVERED

This review article discusses the progress that has been made in the field of large field-of-view interventional planar scintigraphy and SPECT imaging. First, an overview of planar scintigraphy and SPECT is provided. Second, an exploration is given of the potential applications where large field-of-view interventional planar scintigraphy and SPECT imaging may be employed. And third, the requirements for scanner hardware are discussed and an overview of the possible system configurations is provided.

EXPERT OPINION

We believe that there is an opportunity for large field-of-view interventional planar scintigraphy and SPECT imaging to assist clinical workflows. A major effort is now required to evaluate the prototype systems in clinical studies so that valuable practical experience can be obtained.

Intraoperative MR Imaging during Glioma Resection

One of the major issues in the surgical treatment of gliomas is the concern about maximizing the extent of resection while minimizing neurological impairment. Thus, surgical planning by carefully observing the relationship between the glioma infiltration area and eloquent area of the connecting fibers is crucial. Neurosurgeons usually detect an eloquent area by functional MRI and identify a connecting fiber by diffusion tensor imaging. However, during surgery, the accuracy of neuronavigation can be decreased due to brain shift, but the positional information may be updated by intraoperative MRI and the next steps can be planned accordingly. In addition, various intraoperative modalities may be used to guide surgery, including neurophysiological monitoring that provides real-time information (e.g., awake surgery, motor-evoked potentials, and sensory evoked potential); photodynamic diagnosis, which can identify high-grade glioma cells; and other imaging techniques that provide anatomical information during the surgery. In this review, we present the historical and current context of the intraoperative MRI and some related approaches for an audience active in the technical, clinical, and research areas of radiology, as well as mention important aspects regarding safety and types of devices.

Multimodal Intraoperative Image-Driven Surgery for Skull Base Chordomas and Chondrosarcomas

Given the difficulty and importance of achieving maximal resection in chordomas and chondrosarcomas, all available tools offered by modern neurosurgery are to be deployed for planning and resection of these complex lesions. As demonstrated by the review of our series of skull base chordoma and chondrosarcoma resections in the Advanced Multimodality Image-Guided Operating (AMIGO) suite, as well as by the recently published literature, we describe the use of advanced multimodality intraoperative imaging and neuronavigation as pivotal to successful radical resection of these skull base lesions while preventing and managing eventual complications.

Relation-Aware Shared Representation Learning for Cancer Prognosis Analysis With Auxiliary Clinical Variables and Incomplete Multi-Modality Data

The integrative analysis of complementary phenotype information contained in multi-modality data (e.g., histopathological images and genomic data) has advanced the prognostic evaluation of cancers. However, multi-modality based prognosis analysis confronts two challenges: (1) how to explore underlying relations inherent in different modalities data for learning compact and discriminative multi-modality representations; (2) how to take full consideration of incomplete multi-modality data for constructing accurate and robust prognostic model, since a host of complete multi-modality data are not always available. Additionally, many existing multi-modality based prognostic methods commonly ignore relevant clinical variables (e.g., grade and stage), which, however, may provide supplemental information to promote the performance of model. In this paper, we propose a relation-aware shared representation learning method for prognosis analysis of cancers, which makes full use of clinical information and incomplete multi-modality data. The proposed method learns multi-modal shared space tailored for prognostic model via a dual mapping. Within the shared space, it equips with relational regularizers to explore the potential relations (i.e., feature-label and feature-feature relations) among multi-modality data for inducing discriminatory representations and simultaneously obtaining extra sparsity for alleviating overfitting. Moreover, it regresses and incorporates multiple auxiliary clinical attributes with dynamic coefficients to meliorate performance. Furthermore, in training stage, a partial mapping strategy is employed to extend and train a more reliable model with incomplete multi-modality data. We have evaluated our method on three public datasets derived from The Cancer Genome Atlas (TCGA) project, and the experimental results demonstrate the superior performance of the proposed method.

Adaptive Physics-Based Non-Rigid Registration for Immersive Image-Guided Neuronavigation Systems

Objective: In image-guided neurosurgery, co-registered preoperative anatomical, functional, and diffusion tensor imaging can be used to facilitate a safe resection of brain tumors in eloquent areas of the brain. However, the brain deforms during surgery, particularly in the presence of tumor resection. Non-Rigid Registration (NRR) of the preoperative image data can be used to create a registered image that captures the deformation in the intraoperative image while maintaining the quality of the preoperative image. Using clinical data, this paper reports the results of a comparison of the accuracy and performance among several non-rigid registration methods for handling brain deformation. A new adaptive method that automatically removes mesh elements in the area of the resected tumor, thereby handling deformation in the presence of resection is presented. To improve the user experience, we also present a new way of using mixed reality with ultrasound, MRI, and CT.

Materials and methods: This study focuses on 30 glioma surgeries performed at two different hospitals, many of which involved the resection of significant tumor volumes. An Adaptive Physics-Based Non-Rigid Registration method (A-PBNRR) registers preoperative and intraoperative MRI for each patient. The results are compared with three other readily available registration methods: a rigid registration implemented in 3D Slicer v4.4.0; a B-Spline non-rigid registration implemented in 3D Slicer v4.4.0; and PBNRR implemented in ITKv4.7.0, upon which A-PBNRR was based. Three measures were employed to facilitate a comprehensive evaluation of the registration accuracy: (i) visual assessment, (ii) a Hausdorff Distance-based metric, and (iii) a landmark-based approach using anatomical points identified by a neurosurgeon.

Results: The A-PBNRR using multi-tissue mesh adaptation improved the accuracy of deformable registration by more than five times compared to rigid and traditional physics based non-rigid registration, and four times compared to B-Spline interpolation methods which are part of ITK and 3D Slicer. Performance analysis showed that A-PBNRR could be applied, on average, in <2 min, achieving desirable speed for use in a clinical setting.

Conclusions: The A-PBNRR method performed significantly better than other readily available registration methods at modeling deformation in the presence of resection. Both the registration accuracy and performance proved sufficient to be of clinical value in the operating room. A-PBNRR, coupled with the mixed reality system, presents a powerful and affordable solution compared to current neuronavigation systems.

Interim clinical trial analysis of intraoperative mass spectrometry for breast cancer surgery

Optimal resection of breast tumors requires removing cancer with a rim of normal tissue while preserving uninvolved regions of the breast. Surgical and pathological techniques that permit rapid molecular characterization of tissue could facilitate such resections. Mass spectrometry (MS) is increasingly used in the research setting to detect and classify tumors and has the potential to detect cancer at surgical margins. Here, we describe the ex vivo intraoperative clinical application of MS using a liquid micro-junction surface sample probe (LMJ-SSP) to assess breast cancer margins. In a midpoint analysis of a registered clinical trial, surgical specimens from 21 women with treatment naïve invasive breast cancer were prospectively collected and analyzed at the time of surgery with subsequent histopathological determination. Normal and tumor breast specimens from the lumpectomy resected by the surgeon were smeared onto glass slides for rapid analysis. Lipidomic profiles were acquired from these specimens using LMJ-SSP MS in negative ionization mode within the operating suite and post-surgery analysis of the data revealed five candidate ions separating tumor from healthy tissue in this limited dataset. More data is required before considering the ions as candidate markers. Here, we present an application of ambient MS within the operating room to analyze breast cancer tissue and surgical margins. Lessons learned from these initial promising studies are being used to further evaluate the five candidate biomarkers and to further refine and optimize intraoperative MS as a tool for surgical guidance in breast cancer.

Recurrent Chordoma Resection in the Advanced Multimodality Image Guided Operating Suite: 2-Dimensional Operative Video

Recurrent skull base chordomas are challenging lesions. They already had maximum radiation, and in the absence of any effective medical treatment, surgical resection is the only treatment.1,2 Surgery on recurrent previously radiated chordomas, however, carries much higher risk and the likelihood of subtotal resection. Maximizing tumor resection allows longer tumor control.3-5 The Advanced Multimodality Image Guided Operating Suite developed at the Brigham and Women's Hospital, Harvard Medical School, with the support of the National Institutes of Health, provides an optimal environment to manage these tumors. It offers the capability to obtain and integrate multiple modalities during surgery, including magnetic resonance imaging (MRI), positron emission tomography-computed tomography (PET-CT), endoscopy, ultrasound, fluoroscopy, and the ability to perform emergent endovascular procedures.5-7 The patient is a 39-yr-old male, presenting after 19 yr follow-up of a surgical resection and proton beam treatment for a skull base chordoma. He developed progressive ophthalmoplegia due to recurrence of his chordoma at the right petrous apex and cavernous sinus. Preoperative angiography demonstrated narrowing of the petrous segment of the right carotid artery suspect of radiation-induced angiopathy. The presence of radiation-induced angiopathy increases the risk of intraoperative carotid rupture, and the availability of endovascular intervention in the operative suite added favorable preparedness to deal with such complications if they happen. Given the clinical and radiological progression, surgical intervention was carried out through the prior zygomatic approach with the goal of performing maximum resection.8 The patient had an uneventful postoperative course and remained stable until he had a second recurrence 4 yr later. The patient consented to the procedure.

Challenges and Opportunities of Intraoperative 3D Ultrasound With Neuronavigation in Relation to Intraoperative MRI

Introduction

Neuronavigation greatly improves the surgeons ability to approach, assess and operate on brain tumors, but tends to lose its accuracy as the surgery progresses and substantial brain shift and deformation occurs. Intraoperative MRI (iMRI) can partially address this problem but is resource intensive and workflow disruptive. Intraoperative ultrasound (iUS) provides real-time information that can be used to update neuronavigation and provide real-time information regarding the resection progress. We describe the intraoperative use of 3D iUS in relation to iMRI, and discuss the challenges and opportunities in its use in neurosurgical practice.

Methods

We performed a retrospective evaluation of patients who underwent image-guided brain tumor resection in which both 3D iUS and iMRI were used. The study was conducted between June 2020 and December 2020 when an extension of a commercially available navigation software was introduced in our practice enabling 3D iUS volumes to be reconstructed from tracked 2D iUS images. For each patient, three or more 3D iUS images were acquired during the procedure, and one iMRI was acquired towards the end. The iUS images included an extradural ultrasound sweep acquired before dural incision (iUS-1), a post-dural opening iUS (iUS-2), and a third iUS acquired immediately before the iMRI acquisition (iUS-3). iUS-1 and preoperative MRI were compared to evaluate the ability of iUS to visualize tumor boundaries and critical anatomic landmarks; iUS-3 and iMRI were compared to evaluate the ability of iUS for predicting residual tumor.

Results

Twenty-three patients were included in this study. Fifteen patients had tumors located in eloquent or near eloquent brain regions, the majority of patients had low grade gliomas (11), gross total resection was achieved in 12 patients, postoperative temporary deficits were observed in five patients. In twenty-two iUS was able to define tumor location, tumor margins, and was able to indicate relevant landmarks for orientation and guidance. In sixteen cases, white matter fiber tracts computed from preoperative dMRI were overlaid on the iUS images. In nineteen patients, the EOR (GTR or STR) was predicted by iUS and confirmed by iMRI. The remaining four patients where iUS was not able to evaluate the presence or absence of residual tumor were recurrent cases with a previous surgical cavity that hindered good contact between the US probe and the brainsurface.

Conclusion

This recent experience at our institution illustrates the practical benefits, challenges, and opportunities of 3D iUS in relation to iMRI.

Generation of annotated multimodal ground truth datasets for abdominal medical image registration

PURPOSE

Sparsity of annotated data is a major limitation in medical image processing tasks such as registration. Registered multimodal image data are essential for the diagnosis of medical conditions and the success of interventional medical procedures. To overcome the shortage of data, we present a method that allows the generation of annotated multimodal 4D datasets.

METHODS

We use a CycleGAN network architecture to generate multimodal synthetic data from the 4D extended cardiac-torso (XCAT) phantom and real patient data. Organ masks are provided by the XCAT phantom; therefore, the generated dataset can serve as ground truth for image segmentation and registration. Realistic simulation of respiration and heartbeat is possible within the XCAT framework. To underline the usability as a registration ground truth, a proof of principle registration is performed.

RESULTS

Compared to real patient data, the synthetic data showed good agreement regarding the image voxel intensity distribution and the noise characteristics. The generated T1-weighted magnetic resonance imaging, computed tomography (CT), and cone beam CT images are inherently co-registered. Thus, the synthetic dataset allowed us to optimize registration parameters of a multimodal non-rigid registration, utilizing liver organ masks for evaluation.

CONCLUSION

Our proposed framework provides not only annotated but also multimodal synthetic data which can serve as a ground truth for various tasks in medical imaging processing. We demonstrated the applicability of synthetic data for the development of multimodal medical image registration algorithms.

Tumor-targeted Gd-doped mesoporous Fe3O4 nanoparticles for T1/T2 MR imaging guided synergistic cancer therapy

In this study, a novel intelligent nanoplatform to integrate multiple imaging and therapeutic functions for targeted cancer theranostics. The nanoplatform, DOX@Gd-MFe₃O₄ NPs, was constructed Gd-doped mesoporous Fe₃O₄ nanoparticles following with the doxorubicin (DOX) loading in the mesopores of the NPs. The DOX@Gd-MFe₃O₄ NPs exhibited good properties in colloidal dispersity, photothermal conversion, NIR triggered drug release, and high T₁/T₂ relaxicity rate (r₁=9.64 mM⁻¹s⁻¹, r₂= 177.71 mM⁻¹s⁻¹). Benefiting from the high MR contrast, DOX@Gd-MFe₃O₄ NPs enabled simultaneous T₁/T₂ dual-modal MR imagining on 4T1 bearing mice in vivo and the MR contrast effect was further strengthened by external magnetic field. In addition, the DOX@Gd-MFe₃O₄ NPs revealed the strongest inhibition to the growth of 4T1 in vitro and in vivo under NIR irradiation and guidance of external magnetic field. Moreover, biosafety was also validated by in vitro and in vivo tests. Thus, the prepared DOX@Gd-MFe₃O₄ NPs would provide a promising intelligent nanoplatform for dual-modal MR imagining guided synergistic therapy in cancer theranostics.

Combined Use of X-ray Angiography and Intraprocedural MRI Enables Tissue-based Decision Making Regarding Revascularization during Acute Ischemic Stroke Intervention

Background For patients with acute ischemic stroke undergoing endovascular mechanical thrombectomy with x-ray angiography, the use of adjuncts to maintain vessel patency, such as stents or antiplatelet medications, can increase risk of periprocedural complications. Criteria for using these adjuncts are not well defined. Purpose To evaluate use of MRI to guide critical decision making by using a combined biplane x-ray neuroangiography 3.0-T MRI suite during acute ischemic stroke intervention. Materials and Methods This retrospective observational study evaluated consecutive patients undergoing endovascular intervention for acute ischemic stroke between July 2019 and May 2020 who underwent either angiography with MRI or angiography alone. Cerebral tissue viability was assessed by using MRI as the reference standard. For statistical analysis, Fisher exact test and Student t test were used to compare groups. Results Of 47 patients undergoing acute stroke intervention, 12 patients (median age, 69 years; interquartile range, 60-77 years; nine men) underwent x-ray angiography with MRI whereas the remaining 35 patients (median age, 80 years; interquartile range, 68-86 years; 22 men) underwent angiography alone. MRI results influenced clinical decision making in one of three ways: whether or not to perform initial or additional mechanical thrombectomy, whether or not to place an intracranial stent, and administration of antithrombotic or blood pressure medications. In this initial experience, decision making during endovascular acute stroke intervention in the combined angiography-MRI suite was better informed at MRI, such that therapy was guided in real time by the viability of the at-risk cerebral tissue. Conclusion Integrating intraprocedural 3.0-T MRI into acute ischemic stroke treatment was feasible and guided decisions of whether or not to continue thrombectomy, to place stents, or to administer antithrombotic medication or provide blood pressure medications. © RSNA, 2021 Online supplemental material is available for this article. See also the editorial by Lev and Leslie-Mazwi in this issue.

A colorful approach towards developing new nano-based imaging contrast agents for improved cancer detection

Providing physicians with new imaging agents to help detect cancer with better sensitivity and specificity has the potential to significantly improve patient outcomes. Development of new imaging agents could offer improved early cancer detection during routine screening or help surgeons identify tumor margins for surgical resection. In this study, we evaluate the optical properties of a colorful class of dyes and pigments that humans routinely encounter. The pigments are often used in tattoo inks and the dyes are FDA approved for the coloring of foods, drugs, and cosmetics. We characterized their absorption, fluorescence and Raman scattering properties in the hopes of identifying a new panel of dyes that offer exceptional imaging contrast. We found that some of these coloring agents, coined as "optical inks", exhibit a multitude of useful optical properties, outperforming some of the clinically approved imaging dyes on the market. The best performing optical inks (Green 8 and Orange 16) were further incorporated into liposomal nanoparticles to assess their tumor targeting and optical imaging potential. Mouse xenograft models of colorectal, cervical and lymphoma tumors were used to evaluate the newly developed nano-based imaging contrast agents. After intravenous injection, fluorescence imaging revealed significant localization of the new "optical ink" liposomal nanoparticles in all three tumor models as opposed to their neighboring healthy tissues (p < 0.05). If further developed, these coloring agents could play important roles in the clinical setting. A more sensitive imaging contrast agent could enable earlier cancer detection or help guide surgical resection of tumors, both of which have been shown to significantly improve patient survival.

Hydrophobic insertion-based engineering of tumor cell-derived exosomes for SPECT/NIRF imaging of colon cancer

Background

Tumor cell-derived exosomes (TEx) have emerged as promising nanocarriers for drug delivery. Noninvasive multimodality imaging for tracing the in vivo trafficking of TEx may accelerate their clinical translation. In this study, we developed a TEx-based nanoprobe via hydrophobic insertion mechanism and evaluated its performance in dual single-photon emission computed tomography (SPECT) and near-infrared fluorescence (NIRF) imaging of colon cancer.

Results

TEx were successfully isolated from HCT116 supernatants, and their membrane vesicle structure was confirmed by TEM. The average hydrodynamic diameter and zeta potential of TEx were 110.87 ± 4.61 nm and –9.20 ± 0.41 mV, respectively. Confocal microscopy and flow cytometry findings confirmed the high tumor binding ability of TEx. The uptake rate of ^99mTc-TEx-Cy7 by HCT116 cells increased over time, reaching 14.07 ± 1.31% at 6 h of co-incubation. NIRF and SPECT imaging indicated that the most appropriate imaging time was 18 h after the injection of ^99mTc-TEx-Cy7 when the tumor uptake (1.46% ± 0.06% ID/g) and tumor-to-muscle ratio (8.22 ± 0.65) peaked. Compared with radiolabeled adipose stem cell derived exosomes (^99mTc-AEx-Cy7), ^99mTc-TEx-Cy7 exhibited a significantly higher tumor accumulation in tumor-bearing mice.

Conclusion

Hydrophobic insertion-based engineering of TEx may represent a promising approach to develop and label exosomes for use as nanoprobes in dual SPECT/NIRF imaging. Our findings confirmed that TEx has a higher tumor-targeting ability than AEx and highlight the potential usefulness of exosomes in biomedical applications.

Recent advances in nanoscale materials for antibody-based cancer theranostics

The quest for advanced management tools or options of various cancers has been on the rise to efficiently reduce their risks of mortality without the demerits of conventional treatments (e.g., undesirable side effects of the medications on non-target tissues, non-targeted distribution, slow clearance of the administered drugs, and the development of drug resistance over the duration of therapy). In this context, nanomaterials-antibody conjugates can offer numerous advantages in the development of cancer theranostics over conventional delivery systems (e.g., highly specific and enhanced biodistribution of the drug in targeted tissues, prolonged systemic circulation, low toxicity, and minimally invasive molecular imaging). This review comprehensively discusses and evaluates recent advances in the application of nanomaterial-antibody bioconjugates for cancer theranostics for the further advancement in the control of diverse cancerous diseases. Further, discussion is expanded to cover the various challenges and limitations associated with the design and development of nanomaterial-antibody conjugates applicable towards better management of cancer.

Multimodal VEGF-Targeted Contrast-Enhanced Ultrasound and Photoacoustic Imaging of Rats with Inflammatory Arthritis: Using Dye-VEGF-Antibody-Loaded Microbubbles

Owing to the heavy health burdens from rheumatoid arthritis, a sensitive and objective imaging method is needed for early diagnosis and accurate evaluation of the disease. We aimed to fabricate vascular epithelial growth factor (VEGF)-targeted microbubbles (MBs) to evaluate the expression levels of VEGF within the inflammatory lesions of rats with adjuvant-induced arthritis (AIA) using a multimodal photoacoustic (PA)/ultrasound (US) imaging system. Fluorescein isothiocyanate-biotin double-labeled vascular endothelial growth factor receptor 2 antibodies and Cy5.5-biotin double-labeled VEGF2 antibodies were added to the avidin-labeled MBs to synthesize VEGF-targeted MBs. The antibodies could specifically bind to the MBs according to the flow cytometry and fluorescence imaging. In vitro experiments on the cellular uptake of the target MBs also validated the interaction of the VEGF antibodies and the MBs. Multimodal contrast-enhanced US (CEUS)/PA imaging was performed in sequence on the inflamed paws of the AIA rats with a single PA/US imaging system after the injection of the targeted MBs. The CEUS and PA signals were then quantified and verified by the pathologic results. A CEUS pattern of fast wash in and slow washout was observed in the AIA rats after injection of targeted MBs. Compared with AIA rats injected with unconnected VEGF antibodies and naked MBs, AIA rats injected with targeted MBs presented a higher peak intensity (p = 0.0079 and 0.0079 respectively) and a longer time to peak (p = 0.0117 and 0.0117, respectively). The PA signals were also significantly enhanced after injection of targeted MBs (p = 0.0112 and 0.0119, respectively), which was in accordance with the pathologic and immunohistochemical results. In conclusion, VEGF-targeted MBs can be used as agents for multimodal CEUS/PA imaging and to detect VEGF expression in the inflammatory lesions of AIA rats in vivo. This strategy may be useful in imaging evaluation of arthritis by identifying inflammation-related molecules in different imaging modes.

A Review on Advances in Intra-operative Imaging for Surgery and Therapy: Imagining the Operating Room of the Future

With the advent of Minimally Invasive Surgery (MIS), intra-operative imaging has become crucial for surgery and therapy guidance, allowing to partially compensate for the lack of information typical of MIS. This paper reviews the advancements in both classical (i.e. ultrasounds, X-ray, optical coherence tomography and magnetic resonance imaging) and more recent (i.e. multispectral, photoacoustic and Raman imaging) intra-operative imaging modalities. Each imaging modality was analyzed, focusing on benefits and disadvantages in terms of compatibility with the operating room, costs, acquisition time and image characteristics. Tables are included to summarize this information. New generation of hybrid surgical room and algorithms for real time/in room image processing were also investigated. Each imaging modality has its own (site- and procedure-specific) peculiarities in terms of spatial and temporal resolution, field of view and contrasted tissues. Besides the benefits that each technique offers for guidance, considerations about operators and patient risk, costs, and extra time required for surgical procedures have to be considered. The current trend is to equip surgical rooms with multimodal imaging systems, so as to integrate multiple information for real-time data extraction and computer-assisted processing. The future of surgery is to enhance surgeons eye to minimize intra- and after-surgery adverse events and provide surgeons with all possible support to objectify and optimize the care-delivery process.

Deep Learning of Imaging Phenotype and Genotype for Predicting Overall Survival Time of Glioblastoma Patients

Glioblastoma (GBM) is the most common and deadly malignant brain tumor. For personalized treatment, an accurate pre-operative prognosis for GBM patients is highly desired. Recently, many machine learning-based methods have been adopted to predict overall survival (OS) time based on the pre-operative mono- or multi-modal imaging phenotype. The genotypic information of GBM has been proven to be strongly indicative of the prognosis; however, this has not been considered in the existing imaging-based OS prediction methods. The main reason is that the tumor genotype is unavailable pre-operatively unless deriving from craniotomy. In this paper, we propose a new deep learning-based OS prediction method for GBM patients, which can derive tumor genotype-related features from pre-operative multimodal magnetic resonance imaging (MRI) brain data and feed them to OS prediction. Specifically, we propose a multi-task convolutional neural network (CNN) to accomplish both tumor genotype and OS prediction tasks jointly. As the network can benefit from learning tumor genotype-related features for genotype prediction, the accuracy of predicting OS time can be prominently improved. In the experiments, multimodal MRI brain dataset of 120 GBM patients, with as many as four different genotypic/molecular biomarkers, are used to evaluate our method. Our method achieves the highest OS prediction accuracy compared to other state-of-the-art methods.

Repurposing Molecular Imaging and Sensing for Cancer Image-Guided Surgery

Gone are the days when medical imaging was used primarily to visualize anatomic structures. The emergence of molecular imaging (MI), championed by radiolabeled ¹⁸F-FDG PET, has expanded the information content derived from imaging to include pathophysiologic and molecular processes. Cancer imaging, in particular, has leveraged advances in MI agents and technology to improve the accuracy of tumor detection, interrogate tumor heterogeneity, monitor treatment response, focus surgical resection, and enable image-guided biopsy. Surgeons are actively latching on to the incredible opportunities provided by medical imaging for preoperative planning, intraoperative guidance, and postoperative monitoring. From label-free techniques to enabling cancer-selective imaging agents, image-guided surgery provides surgical oncologists and interventional radiologists both macroscopic and microscopic views of cancer in the operating room. This review highlights the current state of MI and sensing approaches available for surgical guidance. Salient features of nuclear, optical, and multimodal approaches will be discussed, including their strengths, limitations, and clinical applications. To address the increasing complexity and diversity of methods available today, this review provides a framework to identify a contrast mechanism, suitable modality, and device. Emerging low-cost, portable, and user-friendly imaging systems make the case for adopting some of these technologies as the global standard of care in surgical practice.

Gadolinium-containing carbon nanomaterials for magnetic resonance imaging: Trends and challenges

Gadolinium-containing carbon nanomaterials are a new class of contrast agent for magnetic resonance imaging. They are characterized by a superior proton relaxivity to any current commercial gadolinium contrast agent and offer the possibility to design multifunctional contrasts. Intense efforts have been made to develop these nanomaterials because of their potential for better results than the available gadolinium contrast agents. The aim of the present work is to provide a review of the advances in research on gadolinium-containing carbon nanomaterials and their advantages over conventional gadolinium contrast agents. Due to their enhanced proton relaxivity, they can provide a reliable imaging contrast for cells, tissues or organs with much smaller doses than currently used in clinical practice, thus leading to reduced toxicity (as shown by cytotoxicity and biodistribution studies). Their active targeting capability allows for improved MRI of molecular or cellular targets, overcoming the limited labelling capability of available contrast agents (restricted to physiological irregularities during pathological conditions). Their potential of multifunctionality encompasses multimodal imaging and the combination of imaging and therapy.

Deformable MRI-Ultrasound registration using correlation-based attribute matching for brain shift correction: Accuracy and generality in multi-site data

Intraoperative tissue deformation, known as brain shift, decreases the benefit of using preoperative images to guide neurosurgery. Non-rigid registration of preoperative magnetic resonance (MR) to intraoperative ultrasound (iUS) has been proposed as a means to compensate for brain shift. We focus on the initial registration from MR to predurotomy iUS. We present a method that builds on previous work to address the need for accuracy and generality of MR-iUS registration algorithms in multi-site clinical data. High-dimensional texture attributes were used instead of image intensities for image registration and the standard difference-based attribute matching was replaced with correlation-based attribute matching. A strategy that deals explicitly with the large field-of-view mismatch between MR and iUS images was proposed. Key parameters were optimized across independent MR-iUS brain tumor datasets acquired at 3 institutions, with a total of 43 tumor patients and 758 reference landmarks for evaluating the accuracy of the proposed algorithm. Despite differences in imaging protocols, patient demographics and landmark distributions, the algorithm is able to reduce landmark errors prior to registration in three data sets (5.37±4.27, 4.18±1.97 and 6.18±3.38 mm, respectively) to a consistently low level (2.28±0.71, 2.08±0.37 and 2.24±0.78 mm, respectively). This algorithm was tested against 15 other algorithms and it is competitive with the state-of-the-art on multiple datasets. We show that the algorithm has one of the lowest errors in all datasets (accuracy), and this is achieved while sticking to a fixed set of parameters for multi-site data (generality). In contrast, other algorithms/tools of similar performance need per-dataset parameter tuning (high accuracy but lower generality), and those that stick to fixed parameters have larger errors or inconsistent performance (generality but not the top accuracy). Landmark errors were further characterized according to brain regions and tumor types, a topic so far missing in the literature.

Fully automatic catheter segmentation in MRI with 3D convolutional neural networks: application to MRI-guided gynecologic brachytherapy

External-beam radiotherapy followed by high dose rate (HDR) brachytherapy is the standard-of-care for treating gynecologic cancers. The enhanced soft-tissue contrast provided by magnetic resonance imaging (MRI) makes it a valuable imaging modality for diagnosing and treating these cancers. However, in contrast to computed tomography (CT) imaging, the appearance of the brachytherapy catheters, through which radiation sources are inserted to reach the cancerous tissue later on, is often variable across images. This paper reports, for the first time, a new deep-learning-based method for fully automatic segmentation of multiple closely spaced brachytherapy catheters in intraoperative MRI. Represented in the data are 50 gynecologic cancer patients treated by MRI-guided HDR brachytherapy. For each patient, a single intraoperative MRI was used. 826 catheters in the images were manually segmented by an expert radiation physicist who is also a trained radiation oncologist. The number of catheters in a patient ranged between 10 and 35. A deep 3D convolutional neural network (CNN) model was developed and trained. In order to make the learning process more robust, the network was trained 5 times, each time using a different combination of shown patients. Finally, each test case was processed by the five networks and the final segmentation was generated by voting on the obtained five candidate segmentations. 4-fold validation was executed and all the patients were segmented. An average distance error of 2.0  ±  3.4 mm was achieved. False positive and false negative catheters were 6.7% and 1.5% respectively. Average Dice score was equal to 0.60  ±  0.17. The algorithm is available for use in the open source software platform 3D Slicer allowing for wide scale testing and research discussion. In conclusion, to the best of our knowledge, fully automatic segmentation of multiple closely spaced catheters from intraoperative MR images was achieved for the first time in gynecological brachytherapy.

Real-Time Fluorescence Imaging in Thoracic Surgery

Near-infrared (NIR) fluorescence imaging provides a safe and cost-efficient method for immediate data acquisition and visualization of tissues, with technical advantages including minimal autofluorescence, reduced photon absorption, and low scattering in tissue. In this review, we introduce recent advances in NIR fluorescence imaging systems for thoracic surgery that improve the identification of vital tissues and facilitate the resection of tumorous tissues. When coupled with appropriate NIR fluorophores, NIR fluorescence imaging may transform current intraoperative thoracic surgery methods by enhancing the precision of surgical procedures and augmenting postoperative outcomes through improvements in diagnostic accuracy and reductions in the remission rate.

Design and Demonstration of a Configurable Imaging Platform for Combined Laser, Ultrasound, and Elasticity Imaging

This paper introduces a configurable combined laser, ultrasound, and elasticity (CLUE) imaging platform. The CLUE platform enables imaging sequences capable of simultaneously providing quantitative acoustic, optical, and mechanical contrast for comprehensive diagnosis and monitoring of complex diseases, such as cancer. The CLUE imaging platform was developed on a Verasonics ultrasound scanner integrated with a pulsed laser, and it was designed to be modular and scalable to allow researchers to create their own specific imaging sequences efficiently. The CLUE imaging platform and sequence were demonstrated in a tissue-mimicking phantom containing a stiff inclusion labeled with optically-activated nanodroplets and in an ex vivo mouse spleen. We have shown that CLUE imaging can simultaneously capture multi-functional imaging signals providing quantitative information on tissue.

Automatic Needle Segmentation and Localization in MRI With 3-D Convolutional Neural Networks: Application to MRI-Targeted Prostate Biopsy

Image guidance improves tissue sampling during biopsy by allowing the physician to visualize the tip and trajectory of the biopsy needle relative to the target in MRI, CT, ultrasound, or other relevant imagery. This paper reports a system for fast automatic needle tip and trajectory localization and visualization in MRI that has been developed and tested in the context of an active clinical research program in prostate biopsy. To the best of our knowledge, this is the first reported system for this clinical application and also the first reported system that leverages deep neural networks for segmentation and localization of needles in MRI across biomedical applications. Needle tip and trajectory were annotated on 583 T2-weighted intra-procedural MRI scans acquired after needle insertion for 71 patients who underwent transperineal MRI-targeted biopsy procedure at our institution. The images were divided into two independent training-validation and test sets at the patient level. A deep 3-D fully convolutional neural network model was developed, trained, and deployed on these samples. The accuracy of the proposed method, as tested on previously unseen data, was 2.80-mm average in needle tip detection and 0.98° in needle trajectory angle. An observer study was designed in which independent annotations by a second observer, blinded to the original observer, were compared with the output of the proposed method. The resultant error was comparable to the measured inter-observer concordance, reinforcing the clinical acceptability of the proposed method. The proposed system has the potential for deployment in clinical routine.

Stimuli responsive PEGylated bismuth selenide hollow nanocapsules for fluorescence/CT imaging and light-driven multimodal tumor therapy

PEGylated bismuth selenide hollow nanocapsules encapsulating doxorubicin and chlorin e6 for fluorescence/CT imaging and light-driven multimodal tumor therapy.