Long-term outcome and prognosis of mixed histiocytosis (Erdheim-Chester disease and Langerhans Cell Histiocytosis).

Background: Erdheim-Chester disease (ECD) is a rare histiocytosis that may overlap with Langerhans Cell Histiocytosis (LCH). This "mixed" entity is poorly characterized. We here investigated the clinical phenotype, outcome, and prognostic factors of a large cohort of patients with mixed ECD-LCH. Methods: This retrospective study was performed at two referral centers in France and Italy (Pitié-Salpêtrière Hospital, Paris; Meyer Children's Hospital, Florence). We included children and adults with ECD diagnosed in 2000-2022 who had biopsy-proven LCH, available data on clinical presentation, treatment and outcome, and a minimum follow-up of one year. Outcomes included differences in clinical presentation and survival between mixed ECD-LCH and isolated ECD; we also investigated response to treatments and predictors of survival in the mixed cohort. Survival was analyzed using the Kaplan-Maier method and differences in survival with the long-rank test. Cox regression models were used to evaluate the potential impact of age and gender on survival and to identify predictors of non-response and survival. Findings: Out of a cohort of 502 ECD patients, 69 (14%) had mixed ECD-LCH. Compared to isolated ECD, mixed ECD-LCH occurred more frequently in females (51 vs. 26%, p < 0.001) and in patients with multisystem disease (≥4 sites). Mixed ECD-LCH more frequently involved long bones (91 vs. 79%, p = 0.014), central nervous system (51 vs. 34%, p = 0.007), facial/orbit (52 vs. 38%, p = 0.031), lungs (43 vs. 28%, p = 0.009), hypothalamic/pituitary axis (51 vs. 26%, p < 0.001), skin (61 vs. 29%, p < 0.001), and lymph nodes (15 vs. 7%, p = 0.028); the BRAFV600E mutation was also more frequent in mixed ECD-LCH (81 vs. 59%, p < 0.001). Targeted treatments (BRAF and/or MEK inhibitors) induced response more frequently than conventional therapies (interferon-α, chemotherapy), either as first-line (77 vs. 29%, p < 0.001) or as any line (75 vs. 24%, p < 0.001). After a median follow-up of 71 months, 24 patients (35%) died. Survival probability was comparable between ECD alone and mixed ECD-LCH (log-rank p = 0.948). At multivariable analysis, age at diagnosis (HR 1.052, 95% CI 1.008-1.096), associated hematologic conditions (HR 3.030, 95% CI 1.040-8.827), and treatment failure (HR 9.736, 95% CI 2.919-32.481) were associated with an increased risk of death, while lytic bone lesions with a lower risk (HR 0.116, 95% CI 0.031-0.432). Interpretation: Mixed ECD-LCH is a multisystem disease driven by the BRAFV600E mutation and targeted treatments are effective. Age at diagnosis, bone lesion patterns, associated hematologic conditions, and treatment failure are the main predictors of death in mixed ECD-LCH. Funding: None.

Fully Integrated Quantitative Multiparametric Analysis of Non-Small Cell Lung Cancer at 3-T PET/MRI: Toward One-Stop-Shop Tumor Biological Characterization at the Supervoxel Level.

INTRODUCTION: The aim of this study was to study the feasibility of a fully integrated multiparametric imaging framework to characterize non-small cell lung cancer (NSCLC) at 3-T PET/MRI. PATIENTS AND METHODS: An 18F-FDG PET/MRI multiparametric imaging framework was developed and prospectively applied to 11 biopsy-proven NSCLC patients. For each tumor, 12 parametric maps were generated, including PET full kinetic modeling, apparent diffusion coefficient, T1/T2 relaxation times, and DCE full kinetic modeling. Gaussian mixture model-based clustering was applied at the whole data set level to define supervoxels of similar multidimensional PET/MRI behaviors. Taking the multidimensional voxel behaviors as input and the supervoxel class as output, machine learning procedure was finally trained and validated voxelwise to reveal the dominant PET/MRI characteristics of these supervoxels at the whole data set and individual tumor levels. RESULTS: The Gaussian mixture model-based clustering clustering applied at the whole data set level (17,316 voxels) found 3 main multidimensional behaviors underpinned by the 12 PET/MRI quantitative parameters. Four dominant PET/MRI parameters of clinical relevance (PET: k2, k3 and DCE: ve, vp) predicted the overall supervoxel behavior with 97% of accuracy (SD, 0.7; 10-fold cross-validation). At the individual tumor level, these dimensionality-reduced supervoxel maps showed mean discrepancy of 16.7% compared with the original ones. CONCLUSIONS: One-stop-shop PET/MRI multiparametric quantitative analysis of NSCLC is clinically feasible. Both PET and MRI parameters are useful to characterize the behavior of tumors at the supervoxel level. In the era of precision medicine, the full capabilities of PET/MRI would give further insight of the characterization of NSCLC behavior, opening new avenues toward image-based personalized medicine in this field.

18F-FDG PET and DCE kinetic modeling and their correlations in primary NSCLC: first voxel-wise correlative analysis of human simultaneous [18F]FDG PET-MRI data.

OBJECTIVES: To decipher the correlations between PET and DCE kinetic parameters in non-small-cell lung cancer (NSCLC), by using voxel-wise analysis of dynamic simultaneous [18F]FDG PET-MRI. MATERIAL AND METHODS: Fourteen treatment-naïve patients with biopsy-proven NSCLC prospectively underwent a 1-h dynamic [18F]FDG thoracic PET-MRI scan including DCE. The PET and DCE data were normalized to their corresponding T1-weighted MR morphological space, and tumors were masked semi-automatically. Voxel-wise parametric maps of PET and DCE kinetic parameters were computed by fitting the dynamic PET and DCE tumor data to the Sokoloff and Extended Tofts models respectively, by using in-house developed procedures. Curve-fitting errors were assessed by computing the relative root mean square error (rRMSE) of the estimated PET and DCE signals at the voxel level. For each tumor, Spearman correlation coefficients (rs) between all the pairs of PET and DCE kinetic parameters were estimated on a voxel-wise basis, along with their respective bootstrapped 95% confidence intervals (n = 1000 iterations). RESULTS: Curve-fitting metrics provided fit errors under 20% for almost 90% of the PET voxels (median rRMSE = 10.3, interquartile ranges IQR = 8.1; 14.3), whereas 73.3% of the DCE voxels showed fit errors under 45% (median rRMSE = 31.8%, IQR = 22.4; 46.6). The PET-PET, DCE-DCE, and PET-DCE voxel-wise correlations varied according to individual tumor behaviors. Beyond this wide variability, the PET-PET and DCE-DCE correlations were mainly high (absolute rs values > 0.7), whereas the PET-DCE correlations were mainly low to moderate (absolute rs values < 0.7). Half the tumors showed a hypometabolism with low perfused/vascularized profile, a hallmark of hypoxia, and tumor aggressiveness. CONCLUSION: A dynamic "one-stop shop" procedure applied to NSCLC is technically feasible in clinical practice. PET and DCE kinetic parameters assessed simultaneously are not highly correlated in NSCLC, and these correlations showed a wide variability among tumors and patients. These results tend to suggest that PET and DCE kinetic parameters might provide complementary information. In the future, this might make PET-MRI a unique tool to characterize the individual tumor biological behavior in NSCLC.

Diffusion-weighted Imaging Voxelwise-matched Analyses of Lung Cancer at 3.0-T PET/MRI: Reverse Phase Encoding Approach for Echo-planar Imaging Distortion Correction.

Background PET/MRI has drawn increasing interest in thoracic oncology due to the simultaneous acquisition of PET and MRI data. Geometric distortions related to diffusion-weighted imaging (DWI) limit the evaluation of voxelwise multimodal analyses. Purpose To assess the effectiveness of reverse phase encoding in correcting DWI geometric distortion for multimodal PET/MRI voxelwise lung tumor analyses. Materials and Methods In this prospective study, reverse phase encoding method was implemented with 3.0-T PET/MRI to correct geometric distortions related to DWI. The method was validated in dedicated phantom and then applied to 12 consecutive patients (mean age, 66 years ± 13 [standard deviation]; 10 men) suspected of having lung cancer who underwent fluorodeoxyglucose PET/MRI between October 2018 and April 2019. The effects on DWI-related image matching and apparent diffusion coefficient (ADC) regional map computation were assessed. Consequences on multimodal PET/MRI voxelwise lung tumor analyses were evaluated. Spearman correlation coefficients (rs) between the standardized uptake value (SUV) and ADC data corrected for distortion were computed from optimal realigned DWI PET data, along with bootstrap confidence intervals. Results Phantom results showed that in highly distorted areas, correcting the distortion significantly reduced the mean error against the ground truth (-25% ± 10.6 to -18.4% ± 12.6; P < .001) and the number of voxels with more than 20% error (from 85.3% to 31.4%). In the 12 patients, the coregistration of multimodal PET/MRI tumor data was improved by using the reverse phase encoding method (0.4%-44%). In all tumors, voxelwise correlations (rs) between ADC and SUV revealed null or weak monotonic relationships (mean rs of 0.016 ± 0.24 with none above 0.5). Conclusion Reverse phase encoding is a simple-to-implement method for improved diffusion-weighted multimodal PET/MRI voxelwise-matched analyses in lung cancer. © RSNA, 2020 Online supplemental material is available for this article. See also the editorial by Colletti in this issue.

Rapid Contour-based Segmentation for 18F-FDG PET Imaging of Lung Tumors by Using ITK-SNAP: Comparison to Expert-based Segmentation.

Purpose To assess the performance of the ITK-SNAP software for fluorodeoxyglucose (FDG) positron emission tomography (PET) segmentation of complex-shaped lung tumors compared with an optimized, expert-based manual reference standard. Materials and Methods Seventy-six FDG PET images of thoracic lesions were retrospectively segmented by using ITK-SNAP software. Each tumor was manually segmented by six raters to generate an optimized reference standard by using the simultaneous truth and performance level estimate algorithm. Four raters segmented 76 FDG PET images of lung tumors twice by using ITK-SNAP active contour algorithm. Accuracy of ITK-SNAP procedure was assessed by using Dice coefficient and Hausdorff metric. Interrater and intrarater reliability were estimated by using intraclass correlation coefficients of output volumes. Finally, the ITK-SNAP procedure was compared with currently recommended PET tumor delineation methods on the basis of thresholding at 41% volume of interest (VOI; VOI41) and 50% VOI (VOI50) of the tumor's maximal metabolism intensity. Results Accuracy estimates for the ITK-SNAP procedure indicated a Dice coefficient of 0.83 (95% confidence interval: 0.77, 0.89) and a Hausdorff distance of 12.6 mm (95% confidence interval: 9.82, 15.32). Interrater reliability was an intraclass correlation coefficient of 0.94 (95% confidence interval: 0.91, 0.96). The intrarater reliabilities were intraclass correlation coefficients above 0.97. Finally, VOI41 and VOI50 accuracy metrics were as follows: Dice coefficient, 0.48 (95% confidence interval: 0.44, 0.51) and 0.34 (95% confidence interval: 0.30, 0.38), respectively, and Hausdorff distance, 25.6 mm (95% confidence interval: 21.7, 31.4) and 31.3 mm (95% confidence interval: 26.8, 38.4), respectively. Conclusion ITK-SNAP is accurate and reliable for active-contour-based segmentation of heterogeneous thoracic PET tumors. ITK-SNAP surpassed the recommended PET methods compared with ground truth manual segmentation.

Adenosine deaminase is a useful biomarker to diagnose pleural tuberculosis in low to medium prevalence settings.

Adenosine deaminase (ADA) activity measurement in pleural fluid is a relevant test to diagnose pleural tuberculosis (pTB) in high tuberculosis prevalence settings. We investigated the diagnostic utility of pleural ADA using a retrospective analysis of patients admitted with newly diagnosed pleural effusion without identified etiology between 2001 and 2008 in Paris suburb, a low to medium tuberculosis prevalence area. 104 adults (mean age 55 years; 34 with pTB, 70 with other diagnoses) were analyzed. Median follow-up was 15.6 months. Mean [interquartile range] pleural ADA was 119 U/L [IQR: 83-143] in pTB and 24 U/L [IQR: 15-31] in non-tuberculous effusions (P<0.001). With an optimal pleural ADA cut-off value of 41.5 U/L for pTB diagnosis, sensitivity and specificity were 97.1% and 92.9%, while positive and negative predictive values were 86.8% and 98.5%, respectively. We conclude that pleural ADA activity could be integrated in the diagnostic procedures of pTB in low to medium tuberculosis prevalence settings.

Pulmonary arterial hypertension in patients treated with interferon.

Isolated cases of pulmonary arterial hypertension (PAH) in patients treated with interferon (IFN) α or ß have been reported in the literature. The aim of this study was to describe all consecutive cases of PAH patients with a history of IFN exposure identified in the French reference centre for severe pulmonary hypertension between 1998 and 2012. A total of 53 patients with PAH and a history of IFN therapy were identified. 48 patients had been treated with IFNα for chronic hepatitis C. Most of them had portal hypertension (85%) and 56% had HIV co-infection. Five additional patients had been treated with IFNß for multiple sclerosis. The diagnosis of PAH was made within 3 years after IFN therapy in 66% of patients. Repeated haemodynamic assessment was available in 13 out of 16 patients exposed to IFN after the diagnosis of PAH. Increased pulmonary vascular resistance >20% was observed in 11 out of 13 cases (median 43% increase; IQR 32-67%). In five of these patients, IFN withdrawal resulted in spontaneous haemodynamic improvement. This retrospective analysis suggests that IFN therapy may trigger PAH. However, most of these patients had other risk factors for PAH. A prospective case-control study is necessary to definitively establish a link between IFN exposure and PAH.