Centrilobular nodules are predictive chest computed tomography (CT) findings related to adult T-cell leukemia/lymphoma development in human T-lymphotropic virus type I carriers

AIM: To evaluate predictive chest computed tomography (CT) findings associated with the development of adult T-cell leukemia/lymphoma (ATLL) in human T-lymphotropic virus type I (HTLV-1) carriers. MATERIALS AND METHODS: This retrospective study examined 171 adult T-cell leukemia/ lymphoma and 158 HTLV-1 carriers who were treated from November 2004 to April 2021. The radiological features of 888 chest CT scans in total were retrospectively assessed by two chest radiologists who were unaware of the underlying diagnoses and compared between the groups. RESULTS: In patients with aggressive type ATLL (acute type and lymphoma type), lymph node enlargement was the most frequently observed abnormality (65.2%), followed by ground-glass opacity (33.3%) and pleural effusion (30.4%). In patients with indolent type (chronic type and smoldering type), lymph node enlargement, and bronchiectasis were the most frequently observed abnormalities (5.6% and 5.6%, respectively). In each type, centrilobular nodules were observed in none and in one patient, respectively. In the 158 HTLV-1 carriers, centrilobular nodules (n 1 / 4 62; 39.2%) were the most frequently observed abnormality. Centrilobular nodules were significantly frequently observed in HTLV-1 carriers compared with ATLL patients. No HTLV-1 carrier with centrilobular nodules on CT developed ATLL during the duration of care. A comparative analysis between CT scans performed before ATLL development in ATLL patients and those of HTLV-1 carriers showed that no centrilobular nodules were observed on the preATLL CT scans, and a statistically-significant difference in centrilobular nodules was found between these two groups. CONCLUSION: The presence of centrilobular nodules may be an indicative CT finding in HTLV-1 carriers who are less likely to develop ATLL. (c) 2024 The Royal College of Radiologists. Published by Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.