|
1
|
Thiebaut L, Pasquier G, Theret S, Russello J. [Hemophagocytic lymphohistiocytosis: A retrospective analysis of 66 patients]. Rev Med Interne 2024; 45:6-12. [PMID: 37932192 DOI: 10.1016/j.revmed.2023.10.440] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2023] [Revised: 07/02/2023] [Accepted: 10/12/2023] [Indexed: 11/08/2023]
Abstract
CONTEXT Hemophagocytic lymphohistiocytosis is a rare syndrome with a poor prognosis, characterized by an uncontrolled dysregulation of the immune system. The rarity of this disease makes it difficult to obtain large cohorts. In this study, we analyzed the data of 66 patients: the objective was to describe the epidemiological, clinical, biological and therapeutic characteristics and to compare our results with those already published. METHODS We conducted a retrospective study at the University Hospital of Montpellier from 2015 to 2021. Patients were included when the diagnosis of HLH was mentioned on the hospitalization report and when the HSCORE was higher than 50% (169). Prognostic analyses were performed by comparing the patients who died from HMH to those who didn't. RESULTS The mean age the 66 patients included was 49.2 years, 62% were men. The percentage of deaths was 45.9%. Lymphoma was the main etiology, followed by infections, then autoimmune/autoinflammatory diseases. Fever, splenomegaly, hepatomegaly and organ failure were the main clinical manifestations. Pancytopenia was present in 62% of cases. Ferritin, triglycerides, LDH and AST were highly increased. Advanced age, associated lymphoma, and the severity of cytopenias were linked to a poor prognosis. DISCUSSION The study of the clinico-biological, epidemiological and survival data of the patients in our cohort allowed us to confirm previously published data but also to discuss some of them.
Collapse
Affiliation(s)
- L Thiebaut
- Laboratoire d'hématologie, CHU de Montpellier, 80, avenue Augustin-Fliche, 34090 Montpellier, France.
| | - G Pasquier
- Laboratoire de parasitologie-mycologie, CHU de Montpellier, 39, avenue Charles-Flahault, 34295 Montpellier, France
| | - S Theret
- Pharmacie hospitalière, CHU de Montpellier, 80, avenue Augustin-Fliche, 34090 Montpellier, France
| | - J Russello
- Laboratoire d'hématologie, CHU de Montpellier, 80, avenue Augustin-Fliche, 34090 Montpellier, France
| |
Collapse
|
|
2
|
Cheng JA, Lin YC, Lin Y, Wu RC, Lu HY, Yang LY, Chiang HJ, Juan YH, Lai YC, Lin G. Machine Learning Radiomics Signature for Differentiating Lymphoma versus Benign Splenomegaly on CT. Diagnostics (Basel) 2023; 13:3632. [PMID: 38132216 PMCID: PMC10742777 DOI: 10.3390/diagnostics13243632] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2023] [Revised: 12/01/2023] [Accepted: 12/06/2023] [Indexed: 12/23/2023] Open
Abstract
BACKGROUND We aimed to develop and validate a preoperative CT-based radiomics signature for differentiating lymphoma versus benign splenomegaly. METHODS We retrospectively analyzed CT studies from 139 patients (age range 26-93 years, 43% female) between 2011 and 2019 with histopathological diagnosis of the spleen (19 lymphoma, 120 benign) and divided them into developing (n = 79) and testing (n = 60) datasets. The volumetric radiomic features were extracted from manual segmentation of the whole spleen on venous-phase CT imaging using PyRadiomics package. LASSO regression was applied for feature selection and development of the radiomic signature, which was interrogated with the complete blood cell count and differential count. All p values < 0.05 were considered to be significant. RESULTS Seven features were selected for constructing the radiomic signature after feature selection, including first-order statistics (10th percentile and Robust Mean Absolute Deviation), shape-based (Surface Area), and texture features (Correlation, MCC, Small Area Low Gray-level Emphasis and Low Gray-level Zone Emphasis). The radiomic signature achieved an excellent diagnostic accuracy of 97%, sensitivity of 89%, and specificity of 98%, distinguishing lymphoma versus benign splenomegaly in the testing dataset. The radiomic signature significantly correlated with the platelet and segmented neutrophil percentage. CONCLUSIONS CT-based radiomics signature can be useful in distinguishing lymphoma versus benign splenomegaly and can reflect the changes in underlying blood profiles.
Collapse
Affiliation(s)
- Jih-An Cheng
- Department of Medical Imaging and Intervention, Chang Gung Memorial Hospital at Linkou, 5 Fuhsing St., Guishan, Taoyuan 333, Taiwan; (J.-A.C.); (Y.-C.L.); (H.-Y.L.); (H.-J.C.); (Y.-H.J.); (Y.-C.L.)
| | - Yu-Chun Lin
- Department of Medical Imaging and Intervention, Chang Gung Memorial Hospital at Linkou, 5 Fuhsing St., Guishan, Taoyuan 333, Taiwan; (J.-A.C.); (Y.-C.L.); (H.-Y.L.); (H.-J.C.); (Y.-H.J.); (Y.-C.L.)
- Department of Medical Imaging and Radiological Sciences, Chang Gung University, Taoyuan 333, Taiwan
- Clinical Metabolomics Core and Imaging Core Laboratory, Institute for Radiological Research, Chang Gung Memorial Hospital at Linkou and Chang Gung University, 5 Fuhsing St., Guishan, Taoyuan 333, Taiwan
| | - Yenpo Lin
- Department of Medical Imaging and Intervention, Chang Gung Memorial Hospital at Linkou, 5 Fuhsing St., Guishan, Taoyuan 333, Taiwan; (J.-A.C.); (Y.-C.L.); (H.-Y.L.); (H.-J.C.); (Y.-H.J.); (Y.-C.L.)
- Department of Medical Imaging and Radiological Sciences, Chang Gung University, Taoyuan 333, Taiwan
- Clinical Metabolomics Core and Imaging Core Laboratory, Institute for Radiological Research, Chang Gung Memorial Hospital at Linkou and Chang Gung University, 5 Fuhsing St., Guishan, Taoyuan 333, Taiwan
| | - Ren-Chin Wu
- Department of Pathology, Chang Gung Memorial Hospital at Linkou, 5 Fuhsing St., Guishan, Taoyuan 333, Taiwan;
| | - Hsin-Ying Lu
- Department of Medical Imaging and Intervention, Chang Gung Memorial Hospital at Linkou, 5 Fuhsing St., Guishan, Taoyuan 333, Taiwan; (J.-A.C.); (Y.-C.L.); (H.-Y.L.); (H.-J.C.); (Y.-H.J.); (Y.-C.L.)
- Clinical Metabolomics Core and Imaging Core Laboratory, Institute for Radiological Research, Chang Gung Memorial Hospital at Linkou and Chang Gung University, 5 Fuhsing St., Guishan, Taoyuan 333, Taiwan
| | - Lan-Yan Yang
- Clinical Trial Center, Chang Gung Memorial Hospital at Linkou and Chang Gung University, 5 Fuhsing St., Guishan, Taoyuan 333, Taiwan;
| | - Hsin-Ju Chiang
- Department of Medical Imaging and Intervention, Chang Gung Memorial Hospital at Linkou, 5 Fuhsing St., Guishan, Taoyuan 333, Taiwan; (J.-A.C.); (Y.-C.L.); (H.-Y.L.); (H.-J.C.); (Y.-H.J.); (Y.-C.L.)
- Clinical Metabolomics Core and Imaging Core Laboratory, Institute for Radiological Research, Chang Gung Memorial Hospital at Linkou and Chang Gung University, 5 Fuhsing St., Guishan, Taoyuan 333, Taiwan
| | - Yu-Hsiang Juan
- Department of Medical Imaging and Intervention, Chang Gung Memorial Hospital at Linkou, 5 Fuhsing St., Guishan, Taoyuan 333, Taiwan; (J.-A.C.); (Y.-C.L.); (H.-Y.L.); (H.-J.C.); (Y.-H.J.); (Y.-C.L.)
| | - Ying-Chieh Lai
- Department of Medical Imaging and Intervention, Chang Gung Memorial Hospital at Linkou, 5 Fuhsing St., Guishan, Taoyuan 333, Taiwan; (J.-A.C.); (Y.-C.L.); (H.-Y.L.); (H.-J.C.); (Y.-H.J.); (Y.-C.L.)
- Department of Medical Imaging and Radiological Sciences, Chang Gung University, Taoyuan 333, Taiwan
- Clinical Metabolomics Core and Imaging Core Laboratory, Institute for Radiological Research, Chang Gung Memorial Hospital at Linkou and Chang Gung University, 5 Fuhsing St., Guishan, Taoyuan 333, Taiwan
| | - Gigin Lin
- Department of Medical Imaging and Intervention, Chang Gung Memorial Hospital at Linkou, 5 Fuhsing St., Guishan, Taoyuan 333, Taiwan; (J.-A.C.); (Y.-C.L.); (H.-Y.L.); (H.-J.C.); (Y.-H.J.); (Y.-C.L.)
- Department of Medical Imaging and Radiological Sciences, Chang Gung University, Taoyuan 333, Taiwan
- Clinical Metabolomics Core and Imaging Core Laboratory, Institute for Radiological Research, Chang Gung Memorial Hospital at Linkou and Chang Gung University, 5 Fuhsing St., Guishan, Taoyuan 333, Taiwan
| |
Collapse
|
|
3
|
Qiu Q, Li D, Chen Y, Song Y, Lou S, Zhou K, Deng J. Clinical features and prognostic risk prediction of adult hemophagocytic lymphohistiocytosis: a 9-year retrospective study. Ann Hematol 2023; 102:2671-2682. [PMID: 37464139 DOI: 10.1007/s00277-023-05368-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2023] [Accepted: 07/11/2023] [Indexed: 07/20/2023]
Abstract
Hemophagocytic lymphohistiocytosis (HLH) has a low incidence and high mortality. In order to improve our understanding of the clinical features and prognostic risk factors of adult HLH, we analyzed the clinical characteristics and prognostic risk factors of adult HLH and developed a prognostic model to predict the overall survival (OS) of adult HLH. The clinical characteristics and survival statistics of adult patients with HLH identified at The Second Affiliated Hospital of Chongqing Medical University between February 2012 and October 2020 were retrospectively analyzed to constitute the primary cohort, while patients between 25 October 2020 and 20 March 2023 were collected at the same institution as a validation cohort for the prospective study. A total of 142 patients met the inclusion criteria, with 72 and 70 in the primary cohort and validation cohort respectively. In the primary cohort, the median OS was 102 days, with 37.5%, 34.5%, and 28.7% 1-, 2-, and 3-year OS, respectively. Univariate analysis showed that age, interleukin-10 (IL-10), interleukin-2 receptor (IL-2R), prothrombin time (PT), and indirect bilirubin (IBiL) were correlated with prognosis. Multivariate analysis showed that IL-10 and PT were independent factors affecting OS in adult patients with HLH. A prognostic model consisting of IL-2R, PT, and IL-10 and a corresponding prognostic nomogram were developed adopting the principle of minimum value of Akaike information criterion(AIC). The model has a high prediction accuracy letter (C-index = 0.708). The AUC values of 1-year, 2-year, and 3-year were 0.826, 0.865, and 0.882, correspondingly. In the validation cohort, all patients were divided into high-risk and low-risk groups, and the risk of death was significantly higher in the high-risk group than in the low-risk group (p < 0.01). The calibration curve for the model shows that the Nomogram constructed in this study is very reliable to predict the OS of HLH patients. IL-10 and PT have significant prognostic value in adult HLH. The prognostic model and the nomogram built in this study can forecast the OS of adult HLH patients.
Collapse
Affiliation(s)
- Qunxiang Qiu
- Department of Hematology, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, 400010, China
| | - Dan Li
- Department of Hematology, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, 400010, China
| | - Ying Chen
- Department of Hematology, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, 400010, China
| | - Ying Song
- Department of Hematology, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, 400010, China
| | - Shifeng Lou
- Department of Hematology, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, 400010, China
| | - Kang Zhou
- Department of Hematology, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, 400010, China.
| | - Jianchuan Deng
- Department of Hematology, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, 400010, China.
| |
Collapse
|
|
4
|
Fanni SC, Febi M, Francischello R, Caputo FP, Ambrosini I, Sica G, Faggioni L, Masala S, Tonerini M, Scaglione M, Cioni D, Neri E. Radiomics Applications in Spleen Imaging: A Systematic Review and Methodological Quality Assessment. Diagnostics (Basel) 2023; 13:2623. [PMID: 37627882 PMCID: PMC10453085 DOI: 10.3390/diagnostics13162623] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2023] [Revised: 07/25/2023] [Accepted: 08/05/2023] [Indexed: 08/27/2023] Open
Abstract
The spleen, often referred to as the "forgotten organ", plays numerous important roles in various diseases. Recently, there has been an increased interest in the application of radiomics in different areas of medical imaging. This systematic review aims to assess the current state of the art and evaluate the methodological quality of radiomics applications in spleen imaging. A systematic search was conducted on PubMed, Scopus, and Web of Science. All the studies were analyzed, and several characteristics, such as year of publication, research objectives, and number of patients, were collected. The methodological quality was evaluated using the radiomics quality score (RQS). Fourteen articles were ultimately included in this review. The majority of these articles were published in non-radiological journals (78%), utilized computed tomography (CT) for extracting radiomic features (71%), and involved not only the spleen but also other organs for feature extraction (71%). Overall, the included papers achieved an average RQS total score of 9.71 ± 6.37, corresponding to an RQS percentage of 27.77 ± 16.04. In conclusion, radiomics applications in spleen imaging demonstrate promising results in various clinical scenarios. However, despite all the included papers reporting positive outcomes, there is a lack of consistency in the methodological approaches employed.
Collapse
Affiliation(s)
- Salvatore Claudio Fanni
- Department of Translational Research, Academic Radiology, University of Pisa, 56126 Pisa, Italy
| | - Maria Febi
- Department of Translational Research, Academic Radiology, University of Pisa, 56126 Pisa, Italy
| | - Roberto Francischello
- Department of Translational Research, Academic Radiology, University of Pisa, 56126 Pisa, Italy
| | - Francesca Pia Caputo
- Department of Translational Research, Academic Radiology, University of Pisa, 56126 Pisa, Italy
| | - Ilaria Ambrosini
- Department of Translational Research, Academic Radiology, University of Pisa, 56126 Pisa, Italy
| | - Giacomo Sica
- Radiology Unit, Monaldi Hospital, 80131 Napoli, Italy
| | - Lorenzo Faggioni
- Department of Translational Research, Academic Radiology, University of Pisa, 56126 Pisa, Italy
| | - Salvatore Masala
- Department of Medicine, Surgery and Pharmacy, University of Sassari, 07100 Sassari, Italy
| | - Michele Tonerini
- Department of Surgical, Medical, Molecular and Critical Area Pathology, University of Pisa, 56124 Pisa, Italy
| | - Mariano Scaglione
- Department of Medicine, Surgery and Pharmacy, University of Sassari, 07100 Sassari, Italy
| | - Dania Cioni
- Department of Translational Research, Academic Radiology, University of Pisa, 56126 Pisa, Italy
| | - Emanuele Neri
- Department of Translational Research, Academic Radiology, University of Pisa, 56126 Pisa, Italy
| |
Collapse
|