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Author(s): Marc A. Judson [sup.1], Jianwei Qiu [sup.2], Camille L. Dumas [sup.3], Jun Yang [sup.4], Brion Sarachan [sup.2], Jhimli Mitra [sup.2] Author Affiliations: (1) https://ror.org/0307crw42, grid.413558.e, 0000 0001 0427 8745, [...], Purpose To determine the reliability of an artificial intelligence, deep learning (AI/DL)-based method of chest computer tomography (CT) scan analysis to distinguish pulmonary sarcoidosis from negative lung cancer screening chest CT scans (Lung Imaging Reporting and Data System score 1, Lung-RADS score 1). Methods Chest CT scans of pulmonary sarcoidosis were evaluated by a clinician experienced with sarcoidosis and a chest radiologist for clinical and radiologic evidence of sarcoidosis and exclusion of alternative or concomitant pulmonary diseases. The AI/DL based method used an ensemble network architecture combining Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs). The method was applied to 126 pulmonary sarcoidosis and 96 Lung-RADS score 1 CT scans. The analytic approach of training and validation of the AI/DL method used a fivefold cross-validation technique, where 4/5th of the available data set was used to train a diagnostic model and tested on the remaining 1/5th of the data set, and repeated 4 more times with non-overlapping validation/test data. The probability values were used to generate Receiver Operating Characteristic (ROC) curves to assess the model's discriminatory power. Results The sensitivity, specificity, positive and negative predictive value of the AI/DL method for the 5 folds of the training/validation sets and the entire set of CT scans were all over 94% to distinguish pulmonary sarcoidosis from LUNG-RADS score 1 chest CT scans. The area under the curve for the corresponding ROC curves were all over 97%. Conclusion This AL/DL model shows promise to distinguish sarcoidosis from alternative pulmonary conditions using minimal radiologic data.

Background: Lesion detection is one of the most important clinical tasks in positron emission tomography (PET) for oncology. An anthropomorphic model observer (MO) designed to replicate human observers (HOs) in a detection task is an important tool for assessing task‐based image quality. The channelized Hotelling observer (CHO) has been the most popular anthropomorphic MO. Recently, deep learning MOs (DLMOs), mostly based on convolutional neural networks (CNNs), have been investigated for various imaging modalities. However, there have been few studies on DLMOs for PET. Purpose: The goal of the study is to investigate whether DLMOs can predict HOs better than conventional MOs such as CHO in a two‐alternative forced‐choice (2AFC) detection task using PET images with real anatomical variability. Methods: Two types of DLMOs were implemented: (1) CNN DLMO, and (2) CNN‐SwinT DLMO that combines CNN and Swin Transformer (SwinT) encoders. Lesion‐absent PET images were reconstructed from clinical data, and lesion‐present images were reconstructed with adding simulated lesion sinogram data. Lesion‐present and lesion‐absent PET image pairs were labeled by eight HOs consisting of four radiologists and four image scientists in a 2AFC detection task. In total, 2268 pairs of lesion‐present and lesion‐absent images were used for training, 324 pairs for validation, and 324 pairs for test. CNN DLMO, CNN‐SwinT DLMO, CHO with internal noise, and non‐prewhitening matched filter (NPWMF) were compared in the same train‐test paradigm. For comparison, six quantitative metrics including prediction accuracy, mean squared errors (MSEs) and correlation coefficients, which measure how well a MO predicts HOs, were calculated in a 9‐fold cross‐validation experiment. Results: In terms of the accuracy and MSE metrics, CNN DLMO and CNN‐SwinT DLMO showed better performance than CHO and NPWMF, and CNN‐SwinT DLMO showed the best performance among the MOs evaluated. Conclusions: DLMO can predict HOs more accurately than conventional MOs such as CHO in PET lesion detection. Combining SwinT and CNN encoders can improve the DLMO prediction performance compared to using CNN only. [ABSTRACT FROM AUTHOR]

Pulmonary sarcoidosis is a multisystem granulomatous interstitial lung disease (ILD) with a variable presentation and prognosis. The early accurate detection of pulmonary sarcoidosis may prevent progression to pulmonary fibrosis, a serious and potentially life-threatening form of the disease. However, the lack of a gold-standard diagnostic test and specific radiographic findings poses challenges in diagnosing pulmonary sarcoidosis. Chest computed tomography (CT) imaging is commonly used but requires expert, chest-trained radiologists to differentiate pulmonary sarcoidosis from lung malignancies, infections, and other ILDs. In this work, we develop a multichannel, CT and radiomics-guided ensemble network (RadCT-CNNViT) with visual explainability for pulmonary sarcoidosis vs. lung cancer (LCa) classification using chest CT images. We leverage CT and hand-crafted radiomics features as input channels, and a 3D convolutional neural network (CNN) and vision transformer (ViT) ensemble network for feature extraction and fusion before a classification head. The 3D CNN sub-network captures the localized spatial information of lesions, while the ViT sub-network captures long-range, global dependencies between features. Through multichannel input and feature fusion, our model achieves the highest performance with accuracy, sensitivity, specificity, precision, F1-score, and combined AUC of 0.93 ± 0.04, 0.94 ± 0.04, 0.93 ± 0.08, 0.95 ± 0.05, 0.94 ± 0.04, and 0.97, respectively, in a five-fold cross-validation study with pulmonary sarcoidosis (n = 126) and LCa (n = 93) cases. A detailed ablation study showing the impact of CNN + ViT compared to CNN or ViT alone, and CT + radiomics input, compared to CT or radiomics alone, is also presented in this work. Overall, the AI model developed in this work offers promising potential for triaging the pulmonary sarcoidosis patients for timely diagnosis and treatment from chest CT. [ABSTRACT FROM AUTHOR]