Automated Segmentation of Lymph Nodes on Neck CT Scans Using Deep Learning.

Early and accurate detection of cervical lymph nodes is essential for the optimal management and staging of patients with head and neck malignancies. Pilot studies have demonstrated the potential for radiomic and artificial intelligence (AI) approaches in increasing diagnostic accuracy for the detection and classification of lymph nodes, but implementation of many of these approaches in real-world clinical settings would necessitate an automated lymph node segmentation pipeline as a first step. In this study, we aim to develop a non-invasive deep learning (DL) algorithm for detecting and automatically segmenting cervical lymph nodes in 25,119 CT slices from 221 normal neck contrast-enhanced CT scans from patients without head and neck cancer. We focused on the most challenging task of segmentation of small lymph nodes, evaluated multiple architectures, and employed U-Net and our adapted spatial context network to detect and segment small lymph nodes measuring 5-10 mm. The developed algorithm achieved a Dice score of 0.8084, indicating its effectiveness in detecting and segmenting cervical lymph nodes despite their small size. A segmentation framework successful in this task could represent an essential initial block for future algorithms aiming to evaluate small objects such as lymph nodes in different body parts, including small lymph nodes looking normal to the naked human eye but harboring early nodal metastases.

Appropriateness and imaging outcomes of ultrasound, CT, and MR in the emergency department: a retrospective analysis from an urban academic center.

PURPOSE: To evaluate the appropriateness and outcomes of ultrasound (US), computed tomography (CT), and magnetic resonance (MR) orders in the ED. METHODS: We retrospectively reviewed consecutive US, CT, and MR orders for adult ED patients at a tertiary care urban academic center from January to March 2019. The American College of Radiology Appropriateness Criteria (ACRAC) guidelines were primarily used to classify imaging orders as "appropriate" or "inappropriate". Two radiologists in consensus judged specific clinical scenarios that were unavailable in the ACRAC. Final imaging reports were compared with the initial clinical suspicion for imaging and categorized into "normal", "compatible with initial diagnosis", "alternative diagnosis", or "inconclusive". The sample was powered to show a prevalence of inappropriate orders of 30% with a margin of error of 5%. RESULTS: The rate of inappropriate orders was 59.4% for US, 29.1% for CT, and 33.3% for MR. The most commonly imaged systems for each modality were neuro (130/330) and gastrointestinal (95/330) for CT, genitourinary (132/330) and gastrointestinal (121/330) for US, neuro (273/330) and gastrointestinal (37/330) for MR. Compared to inappropriately ordered tests, the final reports of appropriate orders were nearly three times more likely to demonstrate findings compatible with the initial diagnosis for all modalities: US (45.5 vs. 14.3%, p < 0.001), CT (46.6 vs. 14.6%, p < 0.001), and MR (56.3 vs. 21.8%, p < 0.001). Inappropriate orders were more likely to show no abnormalities compared to appropriate orders: US (65.8 vs. 38.8%, p < 0.001), CT (62.5 vs. 34.2%, p < 0.001), and MR (61.8 vs. 38.7%, p < 0.001). CONCLUSION: The prevalence of inappropriate imaging orders in the ED was 59.4% for US, 29.1% for CT, and 33.3% for MR. Appropriately ordered imaging was three times more likely to yield findings compatible with the initial diagnosis across all modalities.

Diagnostic Performance of MRI for the Detection of Pulmonary Nodules: A Systematic Review and Meta-Analysis.

Purpose To perform a meta-analysis of the diagnostic performance of MRI for the detection of pulmonary nodules, with use of CT as the reference standard. Materials and Methods PubMed, Embase, Scopus, and other databases were systematically searched for studies published from January 2000 to March 2023 evaluating the performance of MRI for diagnosis of lung nodules measuring 4 mm or larger, with CT as reference. Studies including micronodules, nodules without size stratification, or those from which data for contingency tables could not be extracted were excluded. Primary outcomes were the per-lesion sensitivity of MRI and the rate of false-positive nodules per patient (FPP). Subgroup analysis by size and meta-regression with other covariates were performed. The study protocol was registered in the International Prospective Register of Systematic Reviews, or PROSPERO (no. CRD42023437509). Results Ten studies met inclusion criteria (1354 patients and 2062 CT-detected nodules). Overall, per-lesion sensitivity of MRI for nodules measuring 4 mm or larger was 87.7% (95% CI: 81.1, 92.2), while the FPP rate was 12.4% (95% CI: 7.0, 21.1). Subgroup analyses demonstrated that MRI sensitivity was 98.5% (95% CI: 90.4, 99.8) for nodules measuring at least 8-10 mm and 80.5% (95% CI: 71.5, 87.1) for nodules less than 8 mm. Conclusion MRI demonstrated a good overall performance for detection of pulmonary nodules measuring 4 mm or larger and almost equal performance to CT for nodules measuring at least 8-10 mm, with a low rate of FPP. Systematic review registry no. CRD42023437509 Keywords: Lung Nodule, Lung Cancer, Lung Cancer Screening, MRI, CT Supplemental material is available for this article. © RSNA, 2024.

Lung cancer screening in Brazil: recommendations from the Brazilian Society of Thoracic Surgery, Brazilian Thoracic Association, and Brazilian College of Radiology and Diagnostic Imaging.

Although lung cancer (LC) is one of the most common and lethal tumors, only 15% of patients are diagnosed at an early stage. Smoking is still responsible for more than 85% of cases. Lung cancer screening (LCS) with low-dose CT (LDCT) reduces LC-related mortality by 20%, and that reduction reaches 38% when LCS by LDCT is combined with smoking cessation. In the last decade, a number of countries have adopted population-based LCS as a public health recommendation. Albeit still incipient, discussion on this topic in Brazil is becoming increasingly broad and necessary. With the aim of increasing knowledge and stimulating debate on LCS, the Brazilian Society of Thoracic Surgery, the Brazilian Thoracic Association, and the Brazilian College of Radiology and Diagnostic Imaging convened a panel of experts to prepare recommendations for LCS in Brazil. The recommendations presented here were based on a narrative review of the literature, with an emphasis on large population-based studies, systematic reviews, and the recommendations of international guidelines, and were developed after extensive discussion by the panel of experts. The following topics were reviewed: reasons for screening; general considerations about smoking; epidemiology of LC; eligibility criteria; incidental findings; granulomatous lesions; probabilistic models; minimum requirements for LDCT; volumetric acquisition; risks of screening; minimum structure and role of the multidisciplinary team; practice according to the Lung CT Screening Reporting and Data System; costs versus benefits of screening; and future perspectives for LCS.

Evaluating ChatGPT's Accuracy in Providing Screening Mammography Recommendations among Older Women: Artificial Intelligence and Cancer Communication.

Abstract Objective: The U.S. Preventive Services Task Force (USPSTF) recommends biennial screening mammography through age 74. Guidelines vary as to whether or not they recommended mammography screening to women aged 75 and older. This study aims to determine the ability of ChatGPT to provide appropriate recommendations for breast cancer screening in patients aged 75 years and older. Methods: 12 questions and 4 clinical vignettes addressing fundamental concepts about breast cancer screening and prevention in patients aged 75 years and older were created and asked to ChatGPT three consecutive times to generate 3 sets of responses. The responses were graded by a multi-disciplinary panel of experts in the intersection of breast cancer screening and aging . The responses were graded as 'appropriate', 'inappropriate', or 'unreliable' based on the reviewer's clinical judgment, content of the response, and whether the content was consistent across the three responses . Appropriateness was determined through a majority consensus. Results: The responses generated by ChatGPT were appropriate for 11/17 questions (64%). Three questions were graded as inappropriate (18%) and 2 questions were graded as unreliable (12%). A consensus was not reached on one question (6%) and was graded as no consensus. Conclusions: While recognizing the limitations of ChatGPT, it has potential to provide accurate health care information and could be utilized by healthcare professionals to assist in providing recommendations for breast cancer screening in patients age 75 years and older. Physician oversight will be necessary, due to the possibility of ChatGPT to provide inappropriate and unreliable responses, and the importance of accuracy in medicine.

The International Association for the Study of Lung Cancer Early Lung Imaging Confederation Open-Source Deep Learning and Quantitative Measurement Initiative.

INTRODUCTION: With global adoption of computed tomography (CT) lung cancer screening, there is increasing interest to use artificial intelligence (AI) deep learning methods to improve the clinical management process. To enable AI research using an open-source, cloud-based, globally distributed, screening CT imaging data set and computational environment that are compliant with the most stringent international privacy regulations that also protect the intellectual properties of researchers, the International Association for the Study of Lung Cancer sponsored development of the Early Lung Imaging Confederation (ELIC) resource in 2018. The objective of this report is to describe the updated capabilities of ELIC and illustrate how this resource can be used for clinically relevant AI research. METHODS: In this second phase of the initiative, metadata and screening CT scans from two time points were collected from 100 screening participants in seven countries. An automated deep learning AI lung segmentation algorithm, automated quantitative emphysema metrics, and a quantitative lung nodule volume measurement algorithm were run on these scans. RESULTS: A total of 1394 CTs were collected from 697 participants. The LAV950 quantitative emphysema metric was found to be potentially useful in distinguishing lung cancer from benign cases using a combined slice thickness more than or equal to 2.5 mm. Lung nodule volume change measurements had better sensitivity and specificity for classifying malignant from benign lung nodules when applied to solid lung nodules from high-quality CT scans. CONCLUSIONS: These initial experiments revealed that ELIC can support deep learning AI and quantitative imaging analyses on diverse and globally distributed cloud-based data sets.

MRI with DWI improves detection of liver metastasis and selection of surgical candidates with pancreatic cancer: a systematic review and meta-analysis.

OBJECTIVE: To perform a systematic review and meta-analysis to evaluate if magnetic resonance imaging (MRI) with diffusion weighted imaging (DWI) adds value compared to contrast-enhanced computed tomography (CECT) alone in the preoperative evaluation of pancreatic cancer. METHODS: MEDLINE, EMBASE, and Cochrane databases were searched for relevant published studies through October 2022. Studies met eligibility criteria if they evaluated the per-patient diagnostic performance of MRI with DWI in the preoperative evaluation of newly diagnosed pancreatic cancer compared to CECT. Our primary outcome was the number needed to treat (NNT) to prevent one futile surgery using MRI with DWI, defined as those in which CECT was negative and MRI with DWI was positive for liver metastasis (i.e., surgical intervention in metastatic disease missed by CECT). The secondary outcomes were to determine the diagnostic performance and the NNT of MRI with DWI to change management in pancreatic cancer. RESULTS: Nine studies met the inclusion criteria with a total of 1121 patients, of whom 172 had liver metastasis (15.3%). The proportion of futile surgeries reduced by MRI with DWI was 6.0% (95% CI, 3.0-11.6%), yielding an NNT of 16.6. The proportion of cases that MRI with DWI changed management was 18.1% (95% CI, 9.9-30.7), corresponding to an NNT of 5.5. The per-patient sensitivity and specificity of MRI were 92.4% (95% CI, 87.4-95.6%) and 97.3% (95% CI, 96.0-98.1). CONCLUSION: MRI with DWI may prevent futile surgeries in pancreatic cancer by improving the detection of occult liver metastasis on preoperative CECT with an NNT of 16.6. CLINICAL RELEVANCE STATEMENT: MRI with DWI complements the standard preoperative CECT evaluation for liver metastasis in pancreatic cancer, improving the selection of surgical candidates and preventing unnecessary surgeries. KEY POINTS: • The NNT of MRI with DWI to prevent potential futile surgeries due to occult liver metastasis on CECT, defined as those in which CECT was negative and MRI with DWI was positive for liver metastasis, in patients with pancreatic cancer was 16.6. • The higher performance of MRI with DWI to detect liver metastasis occult on CECT can be attributed to an increased detection of subcentimeter liver metastasis.

Lung cancer screening in Brazil: recommendations from the Brazilian Society of Thoracic Surgery, Brazilian Thoracic Association, and Brazilian College of Radiology and Diagnostic Imaging / Recomendações da Sociedade Brasileira de Cirurgia Torácica, Sociedade Brasileira de Pneumologia e Tisiologia e Colégio Brasileiro de Radiologia e Diagnóstico por Imagem para o rastreamento do câncer de pulmão no Brasil

ABSTRACT Although lung cancer (LC) is one of the most common and lethal tumors, only 15% of patients are diagnosed at an early stage. Smoking is still responsible for more than 85% of cases. Lung cancer screening (LCS) with low-dose CT (LDCT) reduces LC-related mortality by 20%, and that reduction reaches 38% when LCS by LDCT is combined with smoking cessation. In the last decade, a number of countries have adopted population-based LCS as a public health recommendation. Albeit still incipient, discussion on this topic in Brazil is becoming increasingly broad and necessary. With the aim of increasing knowledge and stimulating debate on LCS, the Brazilian Society of Thoracic Surgery, the Brazilian Thoracic Association, and the Brazilian College of Radiology and Diagnostic Imaging convened a panel of experts to prepare recommendations for LCS in Brazil. The recommendations presented here were based on a narrative review of the literature, with an emphasis on large population-based studies, systematic reviews, and the recommendations of international guidelines, and were developed after extensive discussion by the panel of experts. The following topics were reviewed: reasons for screening; general considerations about smoking; epidemiology of LC; eligibility criteria; incidental findings; granulomatous lesions; probabilistic models; minimum requirements for LDCT; volumetric acquisition; risks of screening; minimum structure and role of the multidisciplinary team; practice according to the Lung CT Screening Reporting and Data System; costs versus benefits of screening; and future perspectives for LCS.

RESUMO O câncer de pulmão (CP) é uma das neoplasias mais comuns e letais no Brasil, e apenas 15% dos pacientes são diagnosticados nos estágios iniciais. O tabagismo persiste como o responsável por mais de 85% de todos os casos. O rastreamento do CP (RCP) por meio da TC de baixa dosagem de radiação (TCBD) reduz a mortalidade do CP em 20%, e, quando combinado com a cessação do tabagismo, essa redução chega a 38%. Na última década, diversos países adotaram o RCP como recomendação de saúde populacional. No Brasil, embora ainda incipiente, a discussão sobre o tema é cada vez mais ampla e necessária. Com o intuito de aumentar o conhecimento e estimular o debate sobre o RCP, a Sociedade Brasileira de Cirurgia Torácica, a Sociedade Brasileira de Pneumologia e Tisiologia e o Colégio Brasileiro de Radiologia e Diagnóstico por Imagem constituíram um painel de especialistas para elaborar as recomendações para o RCP. As recomendações aqui apresentadas foram baseadas em revisão narrativa da literatura, com ênfase em grandes estudos populacionais, em revisões sistemáticas e em recomendações de diretrizes internacionais, sendo construídas após ampla discussão pelo grupo de especialistas. Os temas revisados foram os seguintes: porque rastrear, considerações gerais sobre tabagismo, epidemiologia do CP, critérios de elegibilidade, achados incidentais, lesões granulomatosas, modelos probabilísticos, requisitos mínimos da TCBD, aquisições volumétricas, riscos do rastreamento, estrutura mínima e papel da equipe multidisciplinar, conduta segundo o Lung CT Screening Reporting and Data System (Lung-RADS), custos vs. benefícios e perspectivas do rastreamento.

Detection of Liver Lesions in Colorectal Cancer Patients Using 18F-FDG PET/CT Dual-Time-Point Scan Imaging.

OBJECTIVE: The aim of this study was to evaluate the diagnostic performance of dual-time-point fluorine-18-fluorodeoxyglucose positron emission computed tomography/computed tomography (18F-FDG PET/CT) compared to conventional early imaging for detecting colorectal liver metastases (CRLM) in colorectal cancer (CRC) patients. METHODS: One hundred twenty-four consecutive CRC patients underwent dual-time-point imaging scans on a retrospective basis. Histopathological confirmation and/or clinical follow-up were accepted as the gold standard. Standard uptake values (SUV), signal-to-noise ratio (SNR), retention index (RI), tumor-to-normal liver ratio (TNR), and lesion sizes were measured for early and delayed PET scans. The diagnostic performance of early and delayed images was calculated on a per-patient basis and compared using McNemar's test. RESULTS: Among the 124 patients, 57 (46%) had CRLM, 6 (4.8%) had benign lesions, and 61 (49.2%) had no concerning lesions detected. Smaller CRLM lesions (<5 cm3) showed significantly higher uptake in the delayed scans relative to early imaging (p < 0.001). The SUV and TNR increased significantly in delayed imaging of all metastatic lesions (p < 0.001). The retention index of all CRLM was high (40.8%), especially for small lesions (54.8%). A total of 177 lesions in delayed images and 124 in standard early images were identified. In a per-patient analysis, delayed imaging had significantly higher sensitivity (100% vs. 87.7%) and specificity (91.0% vs. 94.0%) compared to early imaging (p-value = 0.04). CONCLUSIONS: The detection of liver lesions using dual-time-point PET/CT scan improves the sensitivity and specificity for the detection of colorectal liver metastasis.

Medical imaging and multimodal artificial intelligence models for streamlining and enhancing cancer care: opportunities and challenges.

INTRODUCTION: Artificial intelligence (AI) has the potential to transform oncologic care. There have been significant developments in AI applications in medical imaging and increasing interest in multimodal models. These are likely to enable improved oncologic care through more precise diagnosis, increasingly in a more personalized and less invasive manner. In this review, we provide an overview of the current state and challenges that clinicians, administrative personnel and policy makers need to be aware of and mitigate for the technology to reach its full potential. AREAS COVERED: The article provides a brief targeted overview of AI, a high-level review of the current state and future potential AI applications in diagnostic radiology and to a lesser extent digital pathology, focusing on oncologic applications. This is followed by a discussion of emerging approaches, including multimodal models. The article concludes with a discussion of technical, regulatory challenges and infrastructure needs for AI to realize its full potential. EXPERT OPINION: There is a large volume of promising research, and steadily increasing commercially available tools using AI. For the most advanced and promising precision diagnostic applications of AI to be used clinically, robust and comprehensive quality monitoring systems and informatics platforms will likely be required.

Computed tomography aspects of thoracic metastases from osteosarcoma: pictorial essay.

Osteosarcoma is the most common primary bone tumor, with a higher incidence in the second decade of life, and it often leads to pulmonary metastases. The most common pattern seen on computed tomography is one of multiple well-defined nodules in the lung parenchyma, often with calcifications. Because of the variety of presentations of pulmonary metastases in osteosarcoma, including atypical forms, knowledge of the computed tomography aspects of these lesions is important for characterizing and evaluating the extent of the disease, as well as for distinguishing metastatic disease from other benign or malignant lung diseases. This essay discusses the main tomographic findings of pulmonary metastases from osteosarcoma.

O osteossarcoma é o tumor ósseo primário mais comum, com maior incidência na segunda década de vida, sendo as metástases pulmonares achado frequente. O padrão tomográfico mais comum das metástases pulmonares de osteossarcoma é o de múltiplos nódulos bem definidos no parênquima pulmonar, frequentemente com calcificações. Em razão da multiplicidade de apresentações das metástases pulmonares do osteossarcoma, inclusive com formas atípicas, o conhecimento dos aspectos dessas lesões na tomografia computadorizada do tórax é importante para a caracterização e avaliação da extensão da doença, além de permitir a diferenciação entre doença metastática e outras doenças pulmonares benignas ou malignas. Este ensaio discute os principais achados tomográficos das metástases pulmonares de osteossarcoma.

Factors influencing the outcome of volumetry tools for pulmonary nodule analysis: a systematic review and attempted meta-analysis.

Health systems worldwide are implementing lung cancer screening programmes to identify early-stage lung cancer and maximise patient survival. Volumetry is recommended for follow-up of pulmonary nodules and outperforms other measurement methods. However, volumetry is known to be influenced by multiple factors. The objectives of this systematic review (PROSPERO CRD42022370233) are to summarise the current knowledge regarding factors that influence volumetry tools used in the analysis of pulmonary nodules, assess for significant clinical impact, identify gaps in current knowledge and suggest future research. Five databases (Medline, Scopus, Journals@Ovid, Embase and Emcare) were searched on the 21st of September, 2022, and 137 original research studies were included, explicitly testing the potential impact of influencing factors on the outcome of volumetry tools. The summary of these studies is tabulated, and a narrative review is provided. A subset of studies (n = 16) reporting clinical significance were selected, and their results were combined, if appropriate, using meta-analysis. Factors with clinical significance include the segmentation algorithm, quality of the segmentation, slice thickness, the level of inspiration for solid nodules, and the reconstruction algorithm and kernel in subsolid nodules. Although there is a large body of evidence in this field, it is unclear how to apply the results from these studies in clinical practice as most studies do not test for clinical relevance. The meta-analysis did not improve our understanding due to the small number and heterogeneity of studies testing for clinical significance. CRITICAL RELEVANCE STATEMENT: Many studies have investigated the influencing factors of pulmonary nodule volumetry, but only 11% of these questioned their clinical relevance in their management. The heterogeneity among these studies presents a challenge in consolidating results and clinical application of the evidence. KEY POINTS: • Factors influencing the volumetry of pulmonary nodules have been extensively investigated. • Just 11% of studies test clinical significance (wrongly diagnosing growth). • Nodule size interacts with most other influencing factors (especially for smaller nodules). • Heterogeneity among studies makes comparison and consolidation of results challenging. • Future research should focus on clinical applicability, screening, and updated technology.

Natural Language Processing for the Identification of Incidental Lung Nodules in Computed Tomography Reports: A Quality Control Tool.

PURPOSE: To evaluate the diagnostic performance of a natural language processing (NLP) model in detecting incidental lung nodules (ILNs) in unstructured chest computed tomography (CT) reports. METHODS: All unstructured consecutive reports of chest CT scans performed at a tertiary hospital between 2020 and 2021 were retrospectively reviewed (n = 21,542) to train the NLP tool. Internal validation was performed using reference readings by two radiologists of both CT scans and reports, using a different external cohort of 300 chest CT scans. Second, external validation was performed in a cohort of all random unstructured chest CT reports from 57 different hospitals conducted in May 2022. A review by the same thoracic radiologists was used as the gold standard. The sensitivity, specificity, and accuracy were calculated. RESULTS: Of 21,542 CT reports, 484 mentioned at least one ILN (mean age, 71 ± 17.6 [standard deviation] years; women, 52%) and were included in the training set. In the internal validation (n = 300), the NLP tool detected ILN with a sensitivity of 100.0% (95% CI, 97.6 to 100.0), a specificity of 95.9% (95% CI, 91.3 to 98.5), and an accuracy of 98.0% (95% CI, 95.7 to 99.3). In the external validation (n = 977), the NLP tool yielded a sensitivity of 98.4% (95% CI, 94.5 to 99.8), a specificity of 98.6% (95% CI, 97.5 to 99.3), and an accuracy of 98.6% (95% CI, 97.6 to 99.2). Twelve months after the initial reports, 8 (8.60%) patients had a final diagnosis of lung cancer, among which 2 (2.15%) would have been lost to follow-up without the NLP tool. CONCLUSION: NLP can be used to identify ILNs in unstructured reports with high accuracy, allowing a timely recall of patients and a potential diagnosis of early-stage lung cancer that might have been lost to follow-up.

Machine learning for malignant versus benign focal liver lesions on US and CEUS: a meta-analysis.

OBJECTIVES: To perform a meta-analysis of the diagnostic performance of learning (ML) algorithms (conventional and deep learning algorithms) for the classification of malignant versus benign focal liver lesions (FLLs) on US and CEUS. METHODS: Available databases were searched for relevant published studies through September 2022. Studies met eligibility criteria if they evaluate the diagnostic performance of ML for the classification of malignant and benign focal liver lesions on US and CEUS. The pooled per-lesion sensitivities and specificities for each modality with 95% confidence intervals were calculated. RESULTS: A total of 8 studies on US, 11 on CEUS, and 1 study evaluating both methods met the inclusion criteria with a total of 34,245 FLLs evaluated. The pooled sensitivity and specificity of ML for the malignancy classification of FLLs were 81.7% (95% CI, 77.2-85.4%) and 84.8% (95% CI, 76.0-90.8%) for US, compared to 87.1% (95% CI, 81.8-91.0%) and 87.0% (95% CI, 83.1-90.1%) for CEUS. In the subgroup analysis of studies that evaluated deep learning algorithms, the sensitivity and specificity of CEUS (n = 4) increased to 92.4% (95% CI, 88.5-95.0%) and 88.2% (95% CI, 81.1-92.9%). CONCLUSIONS: The diagnostic performance of ML algorithms for the malignant classification of FLLs was high for both US and CEUS with overall similar sensitivity and specificity. The similar performance of US may be related to the higher prevalence of DL models in that group.

Computed tomography findings of pulmonary histoplasmosis: pictorial essay / Aspectos tomográficos da histoplasmose pulmonar: ensaio iconográfico

Abstract Endemic systemic mycoses are prevalent in specific geographic areas of the world and are responsible for high rates of morbidity and mortality in the populations of such areas, as well as in immigrants and travelers returning from endemic regions. Pulmonary histoplasmosis is an infection caused by Histoplasma capsulatum, a dimorphic fungus. This infection has a worldwide distribution, being endemic in Brazil. Histoplasmosis can affect the lungs, and its diagnosis and management remain challenging, especially in non-endemic areas. Therefore, recognition of the various radiological manifestations of pulmonary histoplasmosis, together with the clinical and epidemiological history of the patient, is essential to narrowing the differential diagnosis. This essay discusses the main computed tomography findings of pulmonary histoplasmosis.

Resumo As micoses sistêmicas endêmicas são prevalentes em áreas geográficas específicas do mundo e são responsáveis por altas taxas de morbidade e mortalidade nessas populações e em imigrantes e viajantes que retornam de regiões endêmicas. A histoplasmose pulmonar é uma infecção causada pelo Histoplasma capsulatum, um fungo dimórfico. Essa infecção tem distribuição mundial, apresentando-se de forma endêmica no Brasil. A histoplasmose pode afetar os pulmões de pacientes, e seu diagnóstico e manejo permanecem desafiadores, especialmente em áreas não endêmicas. Portanto, o reconhecimento das várias manifestações radiológicas da histoplasmose pulmonar associadas a história clínica e epidemiológica dos pacientes é fundamental para estreitar o diagnóstico diferencial. Este ensaio discute os principais achados tomográficos da histoplasmose pulmonar.