Clinical utility of the updated European Organization for Research and Treatment of Cancer/Invasive Fungal Infections Cooperative Group and the National Institute of Allergy and the Mycoses Study Group Education and Research Consortium computed tomography criteria of invasive pulmonary aspergillosis in hematological malignancies

Fungal-associated pneumonia in patients with hematological malignancies

PURPOSE

Patients with hematologic malignancies (HM) are at high risk of invasive lung fungal infections (ILFI). To describe the main characteristics, treatment, and outcomes for five years in adult patients with HM and fungal pneumonia.

METHODS

We conducted a retrospective study at Instituto Nacional de Cancerología (INCan), a referral tertiary care oncology hospital with 135 beds in Mexico City, Mexico. We included all cases of fungal pneumonia in patients with HM from January 1, 2017, to December 31, 2022. Cases were classified as proven, probable, and possible according to EORTC/MSG criteria 2021.

RESULTS

Two hundred ten patients were included; the mean age was 40 years. The most frequent HM was acute lymphoblastic leukemia (n = 74) and acute myeloid leukemia (n = 68). One hundred forty patients (66.7%) had severe neutropenia for a median of 16 days. All patients had a CT thorax scan; in 132 (62.9%), multiple nodules were documented. Serum galactomannan (GM) was positive in 21/192 (10.9%) and bronchoalveolar lavage in 9/36 (25%). Fifty-three patients (25.2%) died in the first month. In the multivariate analysis for mortality in the first 30 days, hypoalbuminemia, shock, possible ILFI, and inappropriate antifungal treatment were statistically associated.

CONCLUSIONS

In high-risk HM patients, CT thorax scan and GM help diagnose ILFI. An appropriate antifungal improves mortality.

Causal relationship between type 1 diabetes mellitus and mycoses: a Mendelian randomization study

Background

Type 1 diabetes mellitus (T1DM) is frequently associated with various infections, including mycoses; however, the direct link between T1DM and fungal infections remains under-researched. This study utilizes a Mendelian randomization (MR) approach to investigate the potential causal relationship between T1DM and mycoses.

Methods

Genetic variants associated with T1DM were sourced from the European Bioinformatics Institute database, while those related to fungal infections such as candidiasis, pneumocystosis, and aspergillosis were obtained from the Finngen database, focusing on European populations. The primary analysis was conducted using the inverse variance weighted (IVW) method, with additional insight from Mendelian randomization Egger regression (MR-Egger). Extensive sensitivity analyses assessed the robustness, diversity, and potential horizontal pleiotropy of our findings. Multivariable Mendelian randomization (MVMR) was employed to adjust for confounders, using both MVMR-IVW and MVMR-Egger to evaluate heterogeneity and pleiotropy.

Results

Genetically, the odds of developing candidiasis increased by 5% in individuals with T1DM, as determined by the IVW method (OR = 1.05; 95% CI 1.02-1.07, p = 0.0001), with a Bonferroni-adjusted p-value of 0.008. Sensitivity analyses indicated no significant issues with heterogeneity or pleiotropy. Adjustments for confounders such as body mass index, glycated hemoglobin levels, and white blood cell counts further supported these findings (OR = 1.08; 95% CI:1.03-1.13, p = 0.0006). Additional adjustments for immune cell counts, including CD4 and CD8 T cells and natural killer cells, also demonstrated significant results (OR = 1.04; 95% CI: 1.02-1.06, p = 0.0002). No causal associations were found between T1DM and other fungal infections like aspergillosis or pneumocystosis.

Conclusion

This MR study suggests a genetic predisposition for increased susceptibility to candidiasis in individuals with T1DM. However, no causal links were established between T1DM and other mycoses, including aspergillosis and pneumocystosis.

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.