Evaluation of stenoses using AI video models applied to coronary angiography.

The coronary angiogram is the gold standard for evaluating the severity of coronary artery disease stenoses. Presently, the assessment is conducted visually by cardiologists, a method that lacks standardization. This study introduces DeepCoro, a ground-breaking AI-driven pipeline that integrates advanced vessel tracking and a video-based Swin3D model that was trained and validated on a dataset comprised of 182,418 coronary angiography videos spanning 5 years. DeepCoro achieved a notable precision of 71.89% in identifying coronary artery segments and demonstrated a mean absolute error of 20.15% (95% CI: 19.88-20.40) and a classification AUROC of 0.8294 (95% CI: 0.8215-0.8373) in stenosis percentage prediction compared to traditional cardiologist assessments. When compared to two expert interventional cardiologists, DeepCoro achieved lower variability than the clinical reports (19.09%; 95% CI: 18.55-19.58 vs 21.00%; 95% CI: 20.20-21.76, respectively). In addition, DeepCoro can be fine-tuned to a different modality type. When fine-tuned on quantitative coronary angiography assessments, DeepCoro attained an even lower mean absolute error of 7.75% (95% CI: 7.37-8.07), underscoring the reduced variability inherent to this method. This study establishes DeepCoro as an innovative video-based, adaptable tool in coronary artery disease analysis, significantly enhancing the precision and reliability of stenosis assessment.

Robot-assisted biopsy sampling for online Raman spectroscopy cancer confirmation in the operating room.

PURPOSE: Cancer confirmation in the operating room (OR) is crucial to improve local control in cancer therapies. Histopathological analysis remains the gold standard, but there is a lack of real-time in situ cancer confirmation to support margin confirmation or remnant tissue. Raman spectroscopy (RS), as a label-free optical technique, has proven its power in cancer detection and, when integrated into a robotic assistance system, can positively impact the efficiency of procedures and the quality of life of patients, avoiding potential recurrence. METHODS: A workflow is proposed where a 6-DOF robotic system (optical camera + MECA500 robotic arm) assists the characterization of fresh tissue samples using RS. Three calibration methods are compared for the robot, and the temporal efficiency is compared with standard hand-held analysis. For healthy/cancerous tissue discrimination, a 1D-convolutional neural network is proposed and tested on three ex vivo datasets (brain, breast, and prostate) containing processed RS and histopathology ground truth. RESULTS: The robot achieves a minimum error of 0.20 mm (0.12) on a set of 30 test landmarks and demonstrates significant time reduction in 4 of the 5 proposed tasks. The proposed classification model can identify brain, breast, and prostate cancer with an accuracy of 0.83 (0.02), 0.93 (0.01), and 0.71 (0.01), respectively. CONCLUSION: Automated RS analysis with deep learning demonstrates promising classification performance compared to commonly used support vector machines. Robotic assistance in tissue characterization can contribute to highly accurate, rapid, and robust biopsy analysis in the OR. These two elements are an important step toward real-time cancer confirmation using RS and OR integration.

A Comprehensive Comparison of Tissue Processing Methods for High-Quality MALDI Imaging of Lipids in Reconstructed Human Epidermis.

Matrix-assisted laser desorption/ionization (MALDI) mass spectrometry imaging (MSI) has become an important tool for skin analysis, as it allows the simultaneous detection and localization of diverse molecular species within a sample. The use of in vivo and ex vivo human skin models is costly and presents ethical issues; therefore, reconstructed human epidermis (RHE) models, which mimic the upper part of native human skin, represent a suitable alternative to investigate adverse effects of chemicals applied to the skin. However, there are few publications investigating the feasibility of using MALDI MSI on RHE models. Therefore, the aim of this study was to investigate the effect of sample preparation techniques, i.e., substrate, sample thickness, washing, and matrix recrystallization, on the quality of MALDI MSI for lipids analysis of the SkinEthic RHE model. Images were generated using an atmospheric pressure MALDI source coupled to a high-resolution mass spectrometer with a pixel size of 5 µm. Masses detected in a defined region of interest were analyzed and annotated using the LipostarMSI platform. The results indicated that the combination of (1) coated metallic substrates, such as APTES-coated stainless-steel plates, (2) tissue sections of 6 µm thickness, and (3) aqueous washing before HCCA matrix spraying (without recrystallization), resulted in images with a significant signal intensity as well as numerous m/z values. This refined methodology using AP-MALDI coupled to a high-resolution mass spectrometer should improve the current sample preparation workflow to evaluate changes in skin composition after application of dermatocosmetics.

Imaging With PET/CT of Diffuse CD8 T-Cell Infiltration of Skeletal Muscle in Patients With Inclusion Body Myositis.

BACKGROUND AND OBJECTIVES: Inclusion body myositis (IBM) is a progressive autoimmune skeletal muscle disease in which cytotoxic CD8+ T cells infiltrate muscle and destroy myofibers. IBM has required a muscle biopsy for diagnosis. Here, we administered to patients with IBM a novel investigational PET tracer 89Zr-Df-crefmirlimab for in vivo imaging of whole body skeletal muscle CD8 T cells. This technology has not previously been applied to patients with autoimmune disease. METHODS: Four patients with IBM received 89Zr-Df-crefmirlimab followed by PET/CT imaging 24 hours later, and the results were compared with similar imaging of age-matched patients with cancer. Mean standardized uptake value (SUVmean) was measured for reference tissues using spherical regions of interest (ROIs). RESULTS: 89Zr-Df-crefmirlimab was safe and well-tolerated. PET imaging demonstrated diffusely increased uptake qualitatively and quantitatively in IBM limb musculature. Quantitation of 89Zr-Df-crefmirlimab intensity in ROIs demonstrated particularly increased CD8 T-cell infiltration in patients with IBM compared with patients with cancer in quadriceps (SUVmean 0.55 vs 0.20, p < 0.0001), biceps brachii (0.62 vs 0.26, p < 0.0001), triceps (0.61 vs 0.25, p = 0.0005), and forearm finger flexors (0.71 vs 0.23, p = 0.008). DISCUSSION: 89Zr-Df-crefmirlimab uptake in muscles of patients with IBM was present at an intensity greater than the comparator population. The ability to visualize whole body in vivo cytotoxic T-cell tissue infiltration in the autoimmune disease IBM may hold utility as a biomarker for diagnosis, disease activity, and therapeutic development and potentially be applicable to other diseases with cytotoxic T-cell autoimmunity.

Artificial intelligence to predict outcomes of head and neck radiotherapy.

Head and neck radiotherapy induces important toxicity, and its efficacy and tolerance vary widely across patients. Advancements in radiotherapy delivery techniques, along with the increased quality and frequency of image guidance, offer a unique opportunity to individualize radiotherapy based on imaging biomarkers, with the aim of improving radiation efficacy while reducing its toxicity. Various artificial intelligence models integrating clinical data and radiomics have shown encouraging results for toxicity and cancer control outcomes prediction in head and neck cancer radiotherapy. Clinical implementation of these models could lead to individualized risk-based therapeutic decision making, but the reliability of the current studies is limited. Understanding, validating and expanding these models to larger multi-institutional data sets and testing them in the context of clinical trials is needed to ensure safe clinical implementation. This review summarizes the current state of the art of machine learning models for prediction of head and neck cancer radiotherapy outcomes.

Cross-institutional outcome prediction for head and neck cancer patients using self-attention neural networks.

In radiation oncology, predicting patient risk stratification allows specialization of therapy intensification as well as selecting between systemic and regional treatments, all of which helps to improve patient outcome and quality of life. Deep learning offers an advantage over traditional radiomics for medical image processing by learning salient features from training data originating from multiple datasets. However, while their large capacity allows to combine high-level medical imaging data for outcome prediction, they lack generalization to be used across institutions. In this work, a pseudo-volumetric convolutional neural network with a deep preprocessor module and self-attention (PreSANet) is proposed for the prediction of distant metastasis, locoregional recurrence, and overall survival occurrence probabilities within the 10 year follow-up time frame for head and neck cancer patients with squamous cell carcinoma. The model is capable of processing multi-modal inputs of variable scan length, as well as integrating patient data in the prediction model. These proposed architectural features and additional modalities all serve to extract additional information from the available data when availability to additional samples is limited. This model was trained on the public Cancer Imaging Archive Head-Neck-PET-CT dataset consisting of 298 patients undergoing curative radio/chemo-radiotherapy and acquired from 4 different institutions. The model was further validated on an internal retrospective dataset with 371 patients acquired from one of the institutions in the training dataset. An extensive set of ablation experiments were performed to test the utility of the proposed model characteristics, achieving an AUROC of [Formula: see text], [Formula: see text] and [Formula: see text] for DM, LR and OS respectively on the public TCIA Head-Neck-PET-CT dataset. External validation was performed on a retrospective dataset with 371 patients, achieving [Formula: see text] AUROC in all outcomes. To test for model generalization across sites, a validation scheme consisting of single site-holdout and cross-validation combining both datasets was used. The mean accuracy across 4 institutions obtained was [Formula: see text], [Formula: see text] and [Formula: see text] for DM, LR and OS respectively. The proposed model demonstrates an effective method for tumor outcome prediction for multi-site, multi-modal combining both volumetric data and structured patient clinical data.

Performance of an integrated multimodality image guidance and dose-planning system supporting tumor-targeted HDR brachytherapy for prostate cancer.

BACKGROUND AND PURPOSE: Advances in high-dose-rate brachytherapy to treat prostate cancer hinge on improved accuracy in navigation and targeting while optimizing a streamlined workflow. Multimodal image registration and electromagnetic (EM) tracking are two technologies integrated into a prototype system in the early phase of clinical evaluation. We aim to report on the system's accuracy and workflow performance in support of tumor-targeted procedures. MATERIALS AND METHODS: In a prospective study, we evaluated the system in 43 consecutive procedures after clinical deployment. We measured workflow efficiency and EM catheter reconstruction accuracy. We also evaluated the system's MRI-TRUS registration accuracy with/without deformation, and with/without y-axis rotation for urethral alignment at initialization. RESULTS: The cohort included 32 focal brachytherapy and 11 integrated boost whole-gland implants. Mean procedure time excluding dose delivery was 38 min (range: 21-83) for focal, and 56 min (range: 38-89) for whole-gland implants; stable over time. EM catheter reconstructions achieved a mean difference between computed and measured free-length of 0.8 mm (SD 0.8, no corrections performed), and mean axial manual corrections 1.3 mm (SD 0.7). EM also enabled the clinical use of a non or partially visible catheter in 21% of procedures. Registration accuracy improved with y-axis rotation for urethral alignment at initialization and with the elastic registration (mTRE 3.42 mm, SD 1.49). CONCLUSION: The system supported tumor-targeting and was implemented with no demonstrable learning curve. EM reconstruction errors were small, correctable, and improved with calibration and control of external distortion sources; increasing confidence in the use of partially visible catheters. Image registration errors remained despite rotational alignment and deformation, and should be carefully considered.

Population-based 3D respiratory motion modelling from convolutional autoencoders for 2D ultrasound-guided radiotherapy.

Radiotherapy is a widely used treatment modality for various types of cancers. A challenge for precise delivery of radiation to the treatment site is the management of internal motion caused by the patient's breathing, especially around abdominal organs such as the liver. Current image-guided radiation therapy (IGRT) solutions rely on ionising imaging modalities such as X-ray or CBCT, which do not allow real-time target tracking. Ultrasound imaging (US) on the other hand is relatively inexpensive, portable and non-ionising. Although 2D US can be acquired at a sufficient temporal frequency, it doesn't allow for target tracking in multiple planes, while 3D US acquisitions are not adapted for real-time. In this work, a novel deep learning-based motion modelling framework is presented for ultrasound IGRT. Our solution includes an image similarity-based rigid alignment module combined with a deep deformable motion model. Leveraging the representational capabilities of convolutional autoencoders, our deformable motion model associates complex 3D deformations with 2D surrogate US images through a common learned low dimensional representation. The model is trained on a variety of deformations and anatomies which enables it to generate the 3D motion experienced by the liver of a previously unseen subject. During inference, our framework only requires two pre-treatment 3D volumes of the liver at extreme breathing phases and a live 2D surrogate image representing the current state of the organ. In this study, the presented model is evaluated on a 3D+t US data set of 20 volunteers based on image similarity as well as anatomical target tracking performance. We report results that surpass comparable methodologies in both metric categories with a mean tracking error of 3.5±2.4 mm, demonstrating the potential of this technique for IGRT.

CD8-Targeted PET Imaging of Tumor-Infiltrating T Cells in Patients with Cancer: A Phase I First-in-Humans Study of 89Zr-Df-IAB22M2C, a Radiolabeled Anti-CD8 Minibody.

There is a need for in vivo diagnostic imaging probes that can noninvasively measure tumor-infiltrating CD8+ leukocytes. Such imaging probes could be used to predict early response to cancer immunotherapy, help select effective single or combination immunotherapies, and facilitate the development of new immunotherapies or immunotherapy combinations. This study was designed to optimize conditions for performing CD8 PET imaging with 89Zr-Df-IAB22M2C and determine whether CD8 PET imaging could provide a safe and effective noninvasive method of visualizing the whole-body biodistribution of CD8+ leukocytes. Methods: We conducted a phase 1 first-in-humans PET imaging study using an anti-CD8 radiolabeled minibody, 89Zr-Df-IAB22M2C, to detect whole-body and tumor CD8+ leukocyte distribution in patients with metastatic solid tumors. Patients received 111 MBq of 89Zr-Df-IAB22M2C followed by serial PET scanning over 5-7 d. A 2-stage design included a dose-escalation phase and a dose-expansion phase. Biodistribution, radiation dosimetry, and semiquantitative evaluation of 89Zr-Df-IAB22M2C uptake were performed in all patients. Results: Fifteen subjects with metastatic melanoma, non-small cell lung cancer, and hepatocellular carcinoma were enrolled. No drug-related adverse events or abnormal laboratory results were noted except for a transient increase in antidrug antibodies in 1 subject. 89Zr-Df-IAB22M2C accumulated in tumors and CD8-rich tissues (e.g., spleen, bone marrow, nodes), with maximum uptake at 24-48 h after injection and low background activity in CD8-poor tissues (e.g., muscle and lung). Radiotracer uptake in tumors was noted in 10 of 15 subjects, including 7 of 8 subjects on immunotherapy, 1 of 2 subjects on targeted therapy, and 2 of 5 treatment-naïve subjects. In 3 patients with advanced melanoma or hepatocellular carcinoma on immunotherapy, posttreatment CD8 PET/CT scans demonstrated increased 89Zr-Df-IAB22M2C uptake in tumor lesions, which correlated with response. Conclusion: CD8 PET imaging with 89Zr-Df-IAB22M2C is safe and has the potential to visualize the whole-body biodistribution of CD8+ leukocytes in tumors and reference tissues, and may predict early response to immunotherapy.

A feature invariant generative adversarial network for head and neck MRI/CT image synthesis.

With the emergence of online MRI radiotherapy treatments, MR-based workflows have increased in importance in the clinical workflow. However proper dose planning still requires CT images to calculate dose attenuation due to bony structures. In this paper, we present a novel deep image synthesis model that generates in an unsupervised manner CT images from diagnostic MRI for radiotherapy planning. The proposed model based on a generative adversarial network (GAN) consists of learning a new invariant representation to generate synthetic CT (sCT) images based on high frequency and appearance patterns. This new representation encodes each convolutional feature map of the convolutional GAN discriminator, leading the training of the proposed model to be particularly robust in terms of image synthesis quality. Our model includes an analysis of common histogram features in the training process, thus reinforcing the generator such that the output sCT image exhibits a histogram matching that of the ground-truth CT. This CT-matched histogram is embedded then in a multi-resolution framework by assessing the evaluation over all layers of the discriminator network, which then allows the model to robustly classify the output synthetic image. Experiments were conducted on head and neck images of 56 cancer patients with a wide range of shape sizes and spatial image resolutions. The obtained results confirm the efficiency of the proposed model compared to other generative models, where the mean absolute error yielded by our model was 26.44(0.62), with a Hounsfield unit error of 45.3(1.87), and an overall Dice coefficient of 0.74(0.05), demonstrating the potential of the synthesis model for radiotherapy planning applications.

Overview of Machine Learning: Part 2: Deep Learning for Medical Image Analysis.

Deep learning has contributed to solving complex problems in science and engineering. This article provides the fundamental background required to understand and develop deep learning models for medical imaging applications. The authors review the main deep learning architectures such as multilayer perceptron, convolutional neural networks, autoencoders, recurrent neural networks, and generative adversarial neural networks. They also discuss the strategies for training deep learning models when the available datasets are imbalanced or of limited size and conclude with a discussion of the obstacles and challenges hindering the deployment of deep learning solutions in clinical settings.

Machine Learning Applications for Head and Neck Imaging.

The head and neck (HN) consists of a large number of vital anatomic structures within a compact area. Imaging plays a central role in the diagnosis and management of major disorders affecting the HN. This article reviews the recent applications of machine learning (ML) in HN imaging with a focus on deep learning approaches. It categorizes ML applications in HN imaging into deep learning and traditional ML applications and provides examples of each category. It also discusses the main challenges facing the successful deployment of ML-based applications in the clinical setting and provides suggestions for addressing these challenges.

First-in-Humans Imaging with 89Zr-Df-IAB22M2C Anti-CD8 Minibody in Patients with Solid Malignancies: Preliminary Pharmacokinetics, Biodistribution, and Lesion Targeting.

Immunotherapy is becoming the mainstay for treatment of a variety of malignancies, but only a subset of patients responds to treatment. Tumor-infiltrating CD8-positive (CD8+) T lymphocytes play a central role in antitumor immune responses. Noninvasive imaging of CD8+ T cells may provide new insights into the mechanisms of immunotherapy and potentially predict treatment response. We are studying the safety and utility of 89Zr-IAB22M2C, a radiolabeled minibody against CD8+ T cells, for targeted imaging of CD8+ T cells in patients with cancer. Methods: The initial dose escalation phase of this first-in-humans prospective study included 6 patients (melanoma, 1; lung, 4; hepatocellular carcinoma, 1). Patients received approximately 111 MBq (3 mCi) of 89Zr-IAB22M2C (at minibody mass doses of 0.2, 0.5, 1.0, 1.5, 5, or 10 mg) as a single dose, followed by PET/CT scans at approximately 1-2, 6-8, 24, 48, and 96-144 h after injection. Biodistribution in normal organs, lymph nodes, and lesions was evaluated. In addition, serum samples were obtained at approximately 5, 30, and 60 min and later at the times of imaging. Patients were monitored for safety during infusion and up to the last imaging time point. Results:89Zr-IAB22M2C infusion was well tolerated, with no immediate or delayed side effects observed after injection. Serum clearance was typically biexponential and dependent on the mass of minibody administered. Areas under the serum time-activity curve, normalized to administered activity, ranged from 1.3 h/L for 0.2 mg to 8.9 h/L for 10 mg. Biodistribution was dependent on the minibody mass administered. The highest uptake was always in spleen, followed by bone marrow. Liver uptake was more pronounced with higher minibody masses. Kidney uptake was typically low. Prominent uptake was seen in multiple normal lymph nodes as early as 2 h after injection, peaking by 24-48 h after injection. Uptake in tumor lesions was seen on imaging as early as 2 h after injection, with most 89Zr-IAB22M2C-positive lesions detectable by 24 h. Lesions were visualized early in patients receiving treatment, with SUV ranging from 5.85 to 22.8 in 6 target lesions. Conclusion:89Zr-IAB22M2C imaging is safe and has favorable kinetics for early imaging. Biodistribution suggests successful targeting of CD8+ T-cell-rich tissues. The observed targeting of tumor lesions suggests this may be informative for CD8+ T-cell accumulation within tumors. Further evaluation is under way.

Serum apoptosis markers in acute liver failure: a pilot study.

BACKGROUND & AIMS: We sought to determine whether circulating apoptotic markers are altered in acute liver failure (ALF), differ with etiology, or predict clinical outcome in this condition. METHODS: Serum levels of soluble Fas (sFas), tumor necrosis factor-alpha (TNF-alpha), hepatocyte growth factor (HGF), and interleukin-6 (IL-6) were measured in 67 acute liver failure patients, as well as controls. In a subset of the groups, we measured serum M-30 antigen, an exposed neoepitope from caspase cleavage. We also assessed M-30 immunoreactivity in liver tissue of ALF patients and controls. RESULTS: Median levels for TNF-alpha, HGF, IL-6, and M-30 antigen were at least 10-fold greater in ALF than in hepatitis C virus or normal controls (P < .0001). Median day 1 sFas, day 3 sFas, and day 1 HGF levels varied according to etiology of acute liver failure (P = .004, P = .011, and P = .019, respectively), with values for drug-induced liver injury and acetaminophen-related ALF higher than other etiologies. Median M-30 antigen levels were significantly higher in patients who were transplanted and/or died (2183 U/L) than spontaneous survivors (1004 U/L) (P = .026). M-30 immunoreactivity in liver tissue was significantly greater in ALF patients than HCV controls (P = .004). CONCLUSIONS: TNF-alpha, HGF, IL-6, and M-30 antigen were significantly elevated in ALF. High levels of sFas and HGF might help to confirm a diagnosis of drug-induced liver injury or acetaminophen-related ALF. Higher levels of M-30 antigen are associated with poor clinical outcomes in ALF.