High-resolution CT findings of pulmonary infections in patients with hematologic malignancy: comparison between patients with or without hematopoietic stem cell transplantation

How accurate is high-resolution computed tomography of the chest in differentiating between pulmonary invasive fungal infections and other pulmonary infections in children with cancer?

BACKGROUND

Pulmonary invasive fungal infections pose a serious risk for immunocompromised patients. Although diagnostic imaging plays an important role in the early detection of pulmonary invasive fungal infections, radiological differentiation between invasive fungal infection and other pulmonary infections is challenging.

OBJECTIVE

The aim of this study was to assess the accuracy of chest high-resolution computed tomography (HRCT) in the differentiation between pulmonary invasive fungal infections and other pulmonary infections in paediatric cancer patients.

MATERIALS AND METHODS

In this retrospective study, baseline HRCTs of patients with probable or proven invasive fungal infections and other pulmonary infections were blindly assessed by two radiologists, followed by a consensus reading. The scoring form included imaging characteristics and radiological invasive fungal infection probability assessment. Inter-rater reliability was determined with Cohen's kappa.

RESULTS

Chest HRCTs (n = 77) of paediatric cancer patients with pulmonary invasive fungal infections (n = 45) and with other pulmonary infections (n = 32) were evaluated. In the consensus reading, nodules with halo sign and wedge-shaped consolidations were observed significantly more in pulmonary invasive fungal infections than in other pulmonary infections (86.7% vs. 34.4% and 28.9% vs. 9.4%), and ground-glass opacities were observed less frequently (61.4% vs. 87.5%). The kappa values for the individual imaging characteristics ranged from 0.121 to 0.408. Sensitivity of the HRCT to diagnose a pulmonary invasive fungal infection ranged from 0.78 to 0.80, and specificity from 0.66 to 0.88.

CONCLUSION

The accuracy of chest HRCTs in differentiating between invasive fungal infections and other pulmonary infections is poor. There are two main reasons for this: no individual imaging characteristic was found to be able to fully distinguish between invasive fungal infections and other pulmonary infections, and the agreement between radiologists was only moderate.

The Role of Bronchoalveolar Lavage in Therapeutic Antimicrobial Choices for Hematologic Patients with Pulmonary Infiltrates

Background and Objectives: Lower respiratory tract infections are particularly frequent in hematological patients; their early diagnosis and the timely start of targeted therapy are essential. Bronchoalveolar lavage (BAL) can provide a microbiological sample from the lower airways in a minimally invasive way. This study aimed to determine the diagnostic yield of BAL in hematological patients for microbiological purposes and its influence on modifying the therapeutic strategy. Material and Methods: This multicenter, retrospective, observational study included data from 193 consecutive patients from two centers from January 2020 to October 2022. The patients underwent a bronchoscopy with BAL in cases of pulmonary infiltrates suspicious of pulmonary infection. The demographic data, presenting symptoms, level of immunosuppression, chest CT changes, BAL sampling results, and antimicrobiological drug administration were analyzed. Results: Of the 193 procedures, 143 (74.1%) were performed on hospitalized patients, while 50 were performed on outpatients. In 53.9% of the cases, the BAL isolated at least one germ; in particular, if the procedure was carried out within 72 h of presenting symptoms, the probability of isolating the germ increased significantly (74.3%, p = 0.04). Among the isolated germs, 59.4% were viruses, 28.6% were bacteria, and 12% were fungi. The patients with higher immunosuppression and the febrile ones underwent BAL earlier than the patients with mild immunosuppression (p = 0.01) and those with other presenting symptoms (p = 0.0001). BAL positivity led to a change in empirical antimicrobial therapy in 79 out of 104 cases (77% vs. 36.3%; p < 0.001); these data were also confirmed among the hospitalized patients (81% vs. 44%; p < 0.001). The isolation of a pathogen through BAL and the degree of patient immunosuppression negatively influenced patient survival (p < 0.05 and p < 0.01, respectively). Conclusions: BAL is confirmed as a valid approach for evaluating pulmonary infiltrates in hematological patients, given the excellent clinical impact and high diagnostic yield, mainly if performed early after symptom presentation. However, ongoing antimicrobial treatments at the time of BAL may have potentially affected the diagnostic yield of the procedure.

The Differential Diagnostic Value of Chest Computed Tomography for the Identification of Pathogens Causing Pulmonary Infections in Patients with Hematological Malignancies

Objective

The role of chest computed tomography (CT) in distinguishing the causative pathogens of pulmonary infections in patients with hematological malignancies (HM) is unclear. The aim of our study was to compare and assess the clinical characteristics, radiologic features and potential differential diagnostic value of CT in HM patients and other different immune statuses patients with pulmonary infections.

Methods

Patients were divided into immunocompetent (105 cases) and immunocompromised groups (99 cases) according to immune status. Immunocompromised patients included the HM group (63 cases) and the non-HM group (42 cases). The basic clinical data and CT findings were collected and statistically analyzed.

Results

Regarding the pathogen distribution, viral, Pneumocystis jirovecii and mixed infections were more common in the immunocompromised group than the immunocompetent (p < 0.01), but viral infections were more common in the HM group than in the non-HM group (p=0.013). Immunocompromised patients had more diverse CT findings and more serious lesions (mostly graded 2-4) than immunocompetent patients. The most common CT findings in HM patients were consolidation and ground-glass opacities (GGO), which were also found in the non-HM group. The overall diagnostic accuracy of CT was lower in immunocompromised patients than in immunocompetent patients (25.7% vs 50.5%, p< 0.01). CT had better diagnostic efficacy for fungi and Pneumocystis jirovecii in HM patients.

Conclusion

CT diagnosis is less efficient in distinguishing the causative pathogens of HM patients. However, CT can help distinguish fungal pneumonia and Pneumocystis jirovecii pneumonia in HM patients.

Clinical Relevance Statement

Our study might facilitate clinical decision-making in fungal pneumonia and Pneumocystis jirovecii pneumonia in HM patients.

Adoption value of support vector machine algorithm-based computed tomography imaging in the diagnosis of secondary pulmonary fungal infections in patients with malignant hematological disorders

This study aimed to assess the feasibility of diagnosing secondary pulmonary fungal infections (PFIs) in patients with hematological malignancies (HM) using computerized tomography (CT) imaging and a support vector machine (SVM) algorithm. A total of 100 patients with HM complicated by secondary PFI underwent CT scans, and they were included in the training group. Concurrently, 80 patients with the same underlying disease who were treated at our institution were included in the test group. The types of pathogens among different PFI patients and the CT imaging features were compared. Radiomic features were extracted from the CT imaging data of patients, and a diagnostic SVM model was constructed by integrating these features with clinical characteristics. Aspergillus was the most common pathogen responsible for PFIs, followed by Candida, Pneumocystis jirovecii, Mucor, and Cryptococcus, in descending order of occurrence. Patients typically exhibited bilateral diffuse lung lesions. Within the SVM algorithm model, six radiomic features, namely the square root of the inverse covariance of the gray-level co-occurrence matrix (square root IV), the square root of the inverse covariance of the gray-level co-occurrence matrix, and small dependency low gray-level emphasis, significantly influenced the diagnosis of secondary PFIs in patients with HM. The area under the curve values for the training and test sets were 0.902 and 0.891, respectively. Therefore, CT images based on the SVM algorithm demonstrated robust predictive capability in diagnosing secondary PFIs in conjunction with HM.

New trend in artificial intelligence-based assistive technology for thoracic imaging

Although there is no solid agreement for artificial intelligence (AI), it refers to a computer system with intelligence similar to that of humans. Deep learning appeared in 2006, and more than 10 years have passed since the third AI boom was triggered by improvements in computing power, algorithm development, and the use of big data. In recent years, the application and development of AI technology in the medical field have intensified internationally. There is no doubt that AI will be used in clinical practice to assist in diagnostic imaging in the future. In qualitative diagnosis, it is desirable to develop an explainable AI that at least represents the basis of the diagnostic process. However, it must be kept in mind that AI is a physician-assistant system, and the final decision should be made by the physician while understanding the limitations of AI. The aim of this article is to review the application of AI technology in diagnostic imaging from PubMed database while particularly focusing on diagnostic imaging in thorax such as lesion detection and qualitative diagnosis in order to help radiologists and clinicians to become more familiar with AI in thorax.

Feasibility of thoracic CT in assessing anemia for aplastic anemia patients undergoing allogeneic hematopoietic stem cell transplantation

BACKGROUND

Anemia is an important clinical symptom for aplastic anemia (AA) patients who are suffered with peripheral pancytopenia.

OBJECTIVE

To evaluate the accuracy of diagnosing anemia with non-invasive chest computed tomography (CT) for AA patients.

METHODS

The CT attenuation of left ventricular (LV) cavity and interventricular septum (IVS) on unenhanced thoracic CT images of AA patients are retrospectively analyzed, including 84 AA patients in pre-transplant and 1-month (n = 82), 2-month (n = 72), 3-month (n = 75), 6-month (n = 74) and 12-month (n = 70) followed patients in post-transplant. The difference (IVS-LV) and ratio (LV/IVS) of the CT attenuation between LV cavity and interventricular septum are calculated. Serum hemoglobin is estimated within 24 hours of CT imaging. The CT attenuations of IVS-LV and LV/IVS are correlated with hemoglobin, and their variation tendency is analyzed during the treatment of a-HSCT. A receiver operating characteristic (ROC) curve analysis is then performed for the diagnosis of anemia.

RESULTS

The CT attenuations of IVS-LV and LV/IVS well correlate with hemoglobin (r = -0.618 and 0.628, respectively, P <  0.001). The variation tendency of IVS-LV and LV/IVS is similar to that of hemoglobin with opposite directions during one-year follow-up of a-HSCT. When a threshold of CT attenuation of IVS-LV and LV/IVS is set at 11.5HU and 0.77, respectively, both the sensitivity and specificity in diagnosing anemia are good (74.7% and 73.8% in CT attenuation of IVS-LV; 77.4% and 70.4% in LV/LVS, respectively).

CONCLUSIONS

Both CT attenuation of LV/IVS and IVS-LV had similar accuracy in diagnosing anemia for AA patients. The non-invasive chest CT can offer a new possibility to complementarily evaluate anemia for AA patients in the diagnostic radiology reports.