. 2023 Sep 10;7(4):387–432. doi: 10.1007/s41666-023-00144-3

Table 6.

CT studies summarized

Pre-processing technique	Pre-trained model used	Novel technique	Performance	Ref., year
–	–	Deep learning algorithm based on RetinaNet	For internal test set- per-image SN: 91.9 $%$ , per-lesion SN: 96.5 $%$ , precision: 18.2 $%$ , FPs/case: 13.5. for external test set- per-image SN: 90.3 $%$ , per-lesion SN: 96.1 $%$ , precision: 34.2 $%$ , FPs/case: 15.6.	[122], 2022
Processed and augmented with code written in Python 3.7.0 and Python imaging library of Pillow 3.3.1	ImageNet pretraining model	Xpection CNN. Applied fine-tuned and Stochastic gradient descent optimizer	4-degree model’s SN: 96 $%$ , SP: 80 $%$ , AUC = 0.936 (CI: 95 $%$ , 0.890 $-$ 0.982 ). 0-degree model’s SN: 82 $%$ , SP: 88 $%$ , AUC = 0.918 (CI: 95 $%$ , 0.859 $-$ 0.968, p = 0.078).	[123], 2022
Dual-phase contrast-enhanced (reconstructed by blending factor of 0.5) DECT scan of the thorax.	–	Univariate analysis, logistic regression, XGBoost, SGD, LDA, AdaBoost, RF, decision tree, and SVM-based model.	On training dataset, AUROC: 0.88 $-$ 0.99, SN: 0.85 $-$ 0.98, SP: 0.92 $-$ 1.0, F1 score: 0.87 $-$ 0.98. On testing dataset, AUROC: 0.83 $-$ 0.96, SN: 0.72 $-$ 0.92, SP: 0.76 $-$ 1.0, F1 score: 0.75 $-$ 0.91.	[134], 2022
Data augmentation: 90 $^{\circ}$ rotation, grayscale-value reversing, 90 $^{\circ}$ rotation of generated images.	–	Primal-dual hybrid gradient (PDHG) methods based algorithm. FI-Net to replace the computation.	Structural similarity measure (SSIM): 0.94, root mean squared error (RMSE): 0.1.	[130], 2022
Data augmentation: horizontal and vertical shifting, flipping.	Compared with pre-trained ResNet models	Neural architecture search (NAS)-generated CNN	AUC: 0.727, SN: 80 $%$ (95 $%$ CI), SP: 60 $%$ (95 $%$ CI).	[135], 2021
Data augmentation: flipped the training set images horizontally and vertically. labeled samples preparation.	Pre-trained VGG16; pre-trained in ImageNet	Deformable attention, DA-VGG19 is proposed	AUC: 0.9696, acc: 0.9088, PPV: 0.8786, NPV: 0.9469, SN: 0.9500, SP: 0.8675.	[121], 2021
–	–	3D residual CNN equipped with an attention mechanism.	SN: 68.6 $%$ & 64.2 $%$	[124], 2021
Data augmentation: elastic deformations, random scaling, random rotation, gamma augmentation.	–	U-Net $_{BL}$ & U-Net $_{FU}$ network’s architectures	For $Δ$ SUL $_{pesk}$ biomarker; AUC: 0.89, SN: 87 $%$ , SP: 87 $%$ , optimal cutoff value: -32 $%$ , $p -$ value: $\leq$ 0.001.	[125], 2021
Contrast-enhanced. image reconstruction-ordered subset expectation maximization algorithm.	Pre-trained on lymphoma and lung cancer $^{18}$ F-FDG PET/CT data.	PET-Assisted Reporting System (PARS) prototype that uses a neural network.	SN: 92 $%$ , SP: 98 $%$ , acc: 98 $%$ , region:88 $%$	[119], 2021
Image optimization with fuzzy C-means clustering algorithm (FCM). Gray-gradient two-dimensional histogram generated.	–	Convolution and deconvolution neural network (CDNN) through the CNN.	SN: 80 $%$ (FP rate: 0,1). Detection acc: 78.4 $%$ (CI: 0.95)	[126], 2021
Cropping, resizing, manual segmentation of ROI.	CNN-F pre-trained in ILSVRC-2012 dataset	CNN-F- consisted of five convolutional layers and three fully connected layers.	Combined model brier score: 0.159 (primary cohort) & 0.211 (validation cohort).	[128], 2020
Manual segmentation, rotation (1–20 $^{\circ}$ , 10–30 $^{\circ}$ ,20–40 $^{\circ}$ ), mirroring, shearing. Generative Adversarial Network (GAN) for data augmentation.	-	U-Net based CNN architecture	Average DICE: 0.93 ± 0.03, SN: 0.92 ± 0.03, precision: 0.93 ± 0.05, conformity: 0.85 ± 0.06.	[129], 2020
PET/CT fusion images attenuation-corrected by radiologists, Define spherical ROI with a radius of 2.4 cm., data augmentation.	–	Novel 3D CNN	Predicted SUV $_{\max}$ associated with real SUV $_{\max}$ ( $β$ estimate = 0.83, p !‘ 0.0001) and with FDG avidity (p !‘ 0.0001), ROC AUC: 0.85.	[133], 2019
No adjustment	–	AlexNet trained from scratch with 3D CT case.	Classify breast density correctly 72 $%$ (training samples) & 76 $%$ (testing samples).	[131], 2017