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Fibrotic-like abnormalities are present in 60% of a single-center, longitudinal, multi-ethnic cohort 3-years after severe COVID-19. They are independently associated with male sex, low BMI, shorter telomere length, increased severity of illness, and mechanical ventilation; Black race and asthma are protective. Participants with fibrotic-like abnormalities are more likely to have reduced diffusion capacity and 6-minute walk distance. Fibrotic-like abnormalities persist but modestly improve over time. Transbronchial biopsies show small airways histopathology, consistent with high prevalence of air trapping in expiration, and infrequent interstitial thickening. This study highlights the need for continued monitoring of patients with persistent fibrosis after severe COVID-19.

The transforming growth factor-beta (TGF-β) signaling pathway is a vital regulator of cell proliferation, differentiation, apoptosis, and extracellular matrix production. It functions through canonical SMAD-mediated processes and noncanonical pathways involving MAPK cascades, PI3K/AKT, Rho-like GTPases, and NF-κB signaling. This intricate signaling system is finely tuned by interactions between canonical and noncanonical pathways and plays key roles in both physiologic and pathologic conditions including tissue homeostasis, fibrosis, and cancer progression. TGF-β signaling is known to have paradoxical actions. Under normal physiologic conditions, TGF-β signaling promotes cell quiescence and apoptosis, acting as a tumor suppressor. In contrast, in pathological states such as inflammation and cancer, it triggers processes that facilitate cancer progression and tissue remodeling, thus promoting tumor development and fibrosis. Here, we detail the role that TGF-β plays in cancer and fibrosis and highlight the potential for future theranostics targeting this pathway., (© 2024. The Author(s).)

Both cancer and fibrosis are diseases involving dysregulation of cell signaling pathways resulting in an altered cellular microenvironment which ultimately leads to progression of the condition. The two disease entities share common molecular pathophysiology and recent research has illuminated the how each promotes the other. Multiple imaging techniques have been developed to aid in the early and accurate diagnosis of each disease, and given the commonalities between the pathophysiology of the conditions, advances in imaging one disease have opened new avenues to study the other. Here, we detail the most up-to-date advances in imaging techniques for each disease and how they have crossed over to improve detection and monitoring of the other. We explore techniques in positron emission tomography (PET), magnetic resonance imaging (MRI), second generation harmonic Imaging (SGHI), ultrasound (US), radiomics, and artificial intelligence (AI). A new diagnostic imaging tool in PET/computed tomography (CT) is the use of radiolabeled fibroblast activation protein inhibitor (FAPI). SGHI uses high-frequency sound waves to penetrate deeper into the tissue, providing a more detailed view of the tumor microenvironment. Artificial intelligence with the aid of advanced deep learning (DL) algorithms has been highly effective in training computer systems to diagnose and classify neoplastic lesions in multiple organs. Ultimately, advancing imaging techniques in cancer and fibrosis can lead to significantly more timely and accurate diagnoses of both diseases resulting in better patient outcomes., (© 2024. The Author(s).)

Background and Aims: Early identification of cardiac structural abnormalities indicative of heart failure is crucial to improving patient outcomes. Chest X-rays (CXRs) are routinely conducted on a broad population of patients, presenting an opportunity to build scalable screening tools for structural abnormalities indicative of Stage B or worse heart failure with deep learning methods. In this study, a model was developed to identify severe left ventricular hypertrophy (SLVH) and dilated left ventricle (DLV) using CXRs., Methods: A total of 71 589 unique CXRs from 24 689 different patients completed within 1 year of echocardiograms were identified. Labels for SLVH, DLV, and a composite label indicating the presence of either were extracted from echocardiograms. A deep learning model was developed and evaluated using area under the receiver operating characteristic curve (AUROC). Performance was additionally validated on 8003 CXRs from an external site and compared against visual assessment by 15 board-certified radiologists., Results: The model yielded an AUROC of 0.79 (0.76-0.81) for SLVH, 0.80 (0.77-0.84) for DLV, and 0.80 (0.78-0.83) for the composite label, with similar performance on an external data set. The model outperformed all 15 individual radiologists for predicting the composite label and achieved a sensitivity of 71% vs. 66% against the consensus vote across all radiologists at a fixed specificity of 73%., Conclusions: Deep learning analysis of CXRs can accurately detect the presence of certain structural abnormalities and may be useful in early identification of patients with LV hypertrophy and dilation. As a resource to promote further innovation, 71 589 CXRs with adjoining echocardiographic labels have been made publicly available., (© The Author(s) 2024. Published by Oxford University Press on behalf of the European Society of Cardiology.)