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Abstract:While the development of positron emission tomography (PET) radiopharmaceuticals closely follows that of traditional drug development, there are several key considerations in the chemical and radiochemical synthesis, preclinical assessment, and clinical translation of PET radiotracers. As such, we outline the fundamentals of radiotracer design, with respect to the selection of an appropriate pharmacophore. These concepts will be reinforced by exemplary cases of PET radiotracer development, both with respect to their preclinical and clinical evaluation. We also provide a guideline for the proper selection of a radionuclide and the appropriate labeling strategy to access a tracer with optimal imaging qualities. Finally, we summarize the methodology of their evaluation in in vitro and animal models and the road to clinical translation. This review is intended to be a primer for newcomers to the field and give insight into the workflow of developing radiopharmaceuticals.Keywords: positron emission tomography; diagnostic imaging; radiopharmaceuticals; radiochemistry; personalized medicine
Available data are typically split into three sets: a training, a validation, and a test set (Fig. 8), though there are some variants, such as cross validation. A training set is used to train a network, where loss values are calculated via forward propagation and learnable parameters are updated via backpropagation. A validation set is used to evaluate the model during the training process, fine-tune hyperparameters, and perform model selection. A test set is ideally used only once at the very end of the project in order to evaluate the performance of the final model that was fine-tuned and selected on the training process with training and validation sets.
Available data are typically split into three sets: a training, a validation, and a test set. A training set is used to train a network, where loss values are calculated via forward propagation and learnable parameters are updated via backpropagation. A validation set is used to monitor the model performance during the training process, fine-tune hyperparameters, and perform model selection. A test set is ideally used only once at the very end of the project in order to evaluate the performance of the final model that is fine-tuned and selected on the training process with training and validation sets
Separate validation and test sets are needed because training a model always involves fine-tuning its hyperparameters and performing model selection. As this process is performed based on the performance on the validation set, some information about this validation set leaks into the model itself, i.e., overfitting to the validation set, even though the model is never directly trained on it for the learnable parameters. For that reason, it is guaranteed that the model with fine-tuned hyperparameters on the validation set will perform well on this same validation set. Therefore, a completely unseen dataset, i.e., a separate test set, is necessary for the appropriate evaluation of the model performance, as what we care about is the model performance on never-before-seen data, i.e., generalizability.
In medical image analysis, classification with deep learning usually utilizes target lesions depicted in medical images, and these lesions are classified into two or more classes. For example, deep learning is frequently used for the classification of lung nodules on computed tomography (CT) images as benign or malignant (Fig. 11a). As shown, it is necessary to prepare a large number of training data with corresponding labels for efficient classification using CNN. For lung nodule classification, CT images of lung nodules and their labels (i.e., benign or cancerous) are used as training data. Figure 11b, c show two examples of training data of lung nodule classification between benign lung nodule and primary lung cancer; Fig. 11b shows the training data where each datum includes an axial image and its label, and Fig. 11c shows the training data where each datum includes three images (axial, coronal, and sagittal images of a lung nodule) and their labels. After training CNN, the target lesions of medical images can be specified in the deployment phase by medical doctors or computer-aided detection (CADe) systems [38].
Because 2D images are frequently utilized in computer vision, deep learning networks developed for the 2D images (2D-CNN) are not directly applied to 3D images obtained in radiology [thin-slice CT or 3D-magnetic resonance imaging (MRI) images]. To apply deep learning to 3D radiological images, different approaches such as custom architectures are used. For example, Setio et al. [39] used a multistream CNN to classify nodule candidates of chest CT images between nodules or non-nodules in the databases of the Lung Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI) [40], ANODE09 [41], and the Danish Lung Cancer Screening Trial [42]. They extracted differently oriented 2D image patches based on multiplanar reconstruction from one nodule candidate (one or nine patches per candidate), and these patches were used in separate streams and merged in the fully connected layers to obtain the final classification output. One previous study used 3D-CNN for fully capturing the spatial 3D context information of lung nodules [43]. Their 3D-CNN performed binary classification (benign or malignant nodules) and ternary classification (benign lung nodule, and malignant primary and secondary lung cancers) using the LIDC-IDRI database. They used a multiview strategy in 3D-CNN, whose inputs were obtained by cropping three 3D patches of a lung nodule in different sizes and then resizing them into the same size. They also used the 3D Inception model in their 3D-CNN, where the network path was divided into multiple branches with different convolution and pooling operators.
Deep learning is considered as a black box, as it does not leave an audit trail to explain its decisions. Researchers have proposed several techniques in response to this problem that give insight into what features are identified in the feature maps, called feature visualization, and what part of an input is responsible for the corresponding prediction, called attribution. For feature visualization, Zeiler and Fergus [34] described a way to visualize the feature maps, where the first layers identify small local patterns, such as edges or circles, and subsequent layers progressively combine them into more meaningful structures. For attribution, Zhou et al. proposed a way to produce coarse localization maps, called class activation maps (CAMs), that localize the important regions in an input used for the prediction (Fig. 14) [58, 59]. On the other hand, it is worth noting that researchers have recently that noticed deep neural networks are vulnerable to adversarial examples, which are carefully chosen inputs that cause the network to change output without a visible change to a human (Fig. 15) [60,61,62,63]. Although the impact of adversarial examples in the medical domain is unknown, these studies indicate that the way artificial networks see and predict is different from the way we do. Research on the vulnerability of deep neural networks in medical imaging is crucial because the clinical application of deep learning needs extreme robustness for the eventual use in patients, compared to relatively trivial non-medical tasks, such as distinguishing cats or dogs.
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The motion-corrected fMRI time course is then registered to the corresponding anatomical T1 image. A cohort specific group template discretised in MNI space is iteratively computed by mapping all T1 images with 10 (1 rigid, 9 affine) and 10 non-linear registrations into the MNI image space [86]. The fMRI scan is then transformed into the template space by combining the affine registration from fMRI to T1 image with the transformation that maps the individual T1 image into the group template in MNI space. A Generalised Linear Model (optimised with restricted maximum likelihood estimation (REML) [87]) is used to account for signal drifts and physiological noise using cosine basis functions (highpass filtering of frequencies >0.01 Hz), the demeaned motion-realignment estimates and their derivatives, and RETROICOR regressors, where appropriate [88].
Two methods of analyzing the pre-processed data will be used: a seed-based method and independent component analysis (ICA) [89]. In brief, the remaining residuals are smoothed (Gaussian smoothing kernel with 5 mm FWHM) and mapped into the subsampled group space to create spatial correspondence among individual brains. A seed region is chosen to extract an average time course that is correlated with the time course of every individual voxel. The resulting correlation map per participant is Fisher z-transformed to enable t-test hypothesis testing among participants. For the ICA, time courses of the motion-realigned fMRI scan within a mask of the brain are extracted, centered and variance-normalized, resulting in one voxel-time matrix per participant. All participant matrices are then concatenated in time. The obtained group matrix is reduced to its principal components and whitened. The independent component analysis [90] is applied to the whitened group matrix to obtain spatial components. The representation of all group-independent components in each participant is required for group comparison. Dual regression will be applied to obtain group-independent component representations in each participant. 2ff7e9595c
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