1
|
Chougar L, Faucher A, Faouzi J, Lejeune FX, Gama Lobo G, Jovanovic C, Cormier F, Dupont G, Vidailhet M, Corvol JC, Colliot O, Lehéricy S, Grabli D, Degos B. Contribution of MRI for the Early Diagnosis of Parkinsonism in Patients with Diagnostic Uncertainty. Mov Disord 2024; 39:825-835. [PMID: 38486423 DOI: 10.1002/mds.29760] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2023] [Revised: 01/16/2024] [Accepted: 02/16/2024] [Indexed: 05/18/2024] Open
Abstract
BACKGROUND International clinical criteria are the reference for the diagnosis of degenerative parkinsonism in clinical research, but they may lack sensitivity and specificity in the early stages. OBJECTIVES To determine whether magnetic resonance imaging (MRI) analysis, through visual reading or machine-learning approaches, improves diagnostic accuracy compared with clinical diagnosis at an early stage in patients referred for suspected degenerative parkinsonism. MATERIALS Patients with initial diagnostic uncertainty between Parkinson's disease (PD), progressive supranuclear palsy (PSP), and multisystem atrophy (MSA), with brain MRI performed at the initial visit (V1) and available 2-year follow-up (V2), were included. We evaluated the accuracy of the diagnosis established based on: (1) the international clinical diagnostic criteria for PD, PSP, and MSA at V1 ("Clin1"); (2) MRI visual reading blinded to the clinical diagnosis ("MRI"); (3) both MRI visual reading and clinical criteria at V1 ("MRI and Clin1"), and (4) a machine-learning algorithm ("Algorithm"). The gold standard diagnosis was established by expert consensus after a 2-year follow-up. RESULTS We recruited 113 patients (53 with PD, 31 with PSP, and 29 with MSA). Considering the whole population, compared with clinical criteria at the initial visit ("Clin1": balanced accuracy, 66.2%), MRI visual reading showed a diagnostic gain of 14.3% ("MRI": 80.5%; P = 0.01), increasing to 19.2% when combined with the clinical diagnosis at the initial visit ("MRI and Clin1": 85.4%; P < 0.0001). The algorithm achieved a diagnostic gain of 9.9% ("Algorithm": 76.1%; P = 0.08). CONCLUSION Our study shows the use of MRI analysis, whether by visual reading or machine-learning methods, for early differentiation of parkinsonism. © 2024 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.
Collapse
Affiliation(s)
- Lydia Chougar
- Department of Neuroradiology, Sorbonne Université, Institut du Cerveau-Paris Brain Institute-ICM, CNRS, Inria, Inserm, AP-HP, Hôpital de la Pitié Salpêtrière, DMU DIAMENT, Paris, France
- ICM, Centre de NeuroImagerie de Recherche-CENIR, Paris, France
- ICM, Team "Movement Investigations and Therapeutics" (MOV'IT), Paris, France
- Department of Neuroradiology, Sorbonne Université, Institut du Cerveau-Paris Brain Institute-ICM, CNRS, Inserm, AP-HP, Hôpital de la Pitié Salpêtrière, DMU DIAMENT, Paris, France
| | - Alice Faucher
- Assistance Publique Hôpitaux de Paris, Service de Neurologie, Hôpital Avicenne, Hôpitaux Universitaires de Paris Seine-Saint-Denis, Sorbonne Paris Nord, NS-PARK/FCRIN Network, Bobigny, France
| | - Johann Faouzi
- Sorbonne Université, Institut du Cerveau-Paris Brain Institute-ICM, CNRS, Inria, Inserm, AP-HP, Hôpital de la Pitié Salpêtrière, Paris, France
- CREST, ENSAI, Campus de Ker-Lann, Bruz, France
| | - François-Xavier Lejeune
- Sorbonne Université, Institut du Cerveau-Paris Brain Institute-ICM, CNRS, Inserm, Paris, France
- ICM, Data Analysis Core (DAC), Paris, France
| | - Gonçalo Gama Lobo
- Neuroradiology Department, Centro Hospitalar Universitário de Lisboa Central, Lisboa, Portugal
| | - Carna Jovanovic
- Neurology Clinic, University Clinical Center of Serbia, Belgrade, Serbia
| | - Florence Cormier
- Département de Neurologie, Hôpital Pitié-Salpêtrière, Assistance Publique Hôpitaux de Paris, Clinique des Mouvements Anormaux, Clinical Investigation Center for Neurosciences, Paris, France
| | - Gwendoline Dupont
- Université de Bourgogne, Dijon, France
- Département de Neurologie, Centre Hospitalier Universitaire François Mitterrand, Dijon, France
| | - Marie Vidailhet
- ICM, Team "Movement Investigations and Therapeutics" (MOV'IT), Paris, France
- Sorbonne Université, Institut du Cerveau-Paris Brain Institute-ICM, CNRS, Inserm, Paris, France
| | - Jean-Christophe Corvol
- Sorbonne Université, Institut du Cerveau-Paris Brain Institute-ICM, CNRS, Inserm, Paris, France
- Département de Neurologie, Hôpital Pitié-Salpêtrière, Assistance Publique Hôpitaux de Paris, Clinique des Mouvements Anormaux, Clinical Investigation Center for Neurosciences, Paris, France
| | - Olivier Colliot
- Sorbonne Université, Institut du Cerveau-Paris Brain Institute-ICM, CNRS, Inria, Inserm, AP-HP, Hôpital de la Pitié Salpêtrière, Paris, France
| | - Stéphane Lehéricy
- ICM, Centre de NeuroImagerie de Recherche-CENIR, Paris, France
- ICM, Team "Movement Investigations and Therapeutics" (MOV'IT), Paris, France
- Department of Neuroradiology, Sorbonne Université, Institut du Cerveau-Paris Brain Institute-ICM, CNRS, Inserm, AP-HP, Hôpital de la Pitié Salpêtrière, DMU DIAMENT, Paris, France
| | - David Grabli
- Sorbonne Université, Institut du Cerveau-Paris Brain Institute-ICM, CNRS, Inserm, Paris, France
- Département de Neurologie, Hôpital Pitié-Salpêtrière, Assistance Publique Hôpitaux de Paris, Clinique des Mouvements Anormaux, Clinical Investigation Center for Neurosciences, Paris, France
| | - Bertrand Degos
- Assistance Publique Hôpitaux de Paris, Service de Neurologie, Hôpital Avicenne, Hôpitaux Universitaires de Paris Seine-Saint-Denis, Sorbonne Paris Nord, NS-PARK/FCRIN Network, Bobigny, France
- Dynamics and Pathophysiology of Neuronal Networks Team, Center for Interdisciplinary Research in Biology, Collège de France, CNRS UMR7241/INSERM U1050, Université PSL, Paris, France
| |
Collapse
|
2
|
Seada SA, van der Eerden AW, Boon AJW, Hernandez-Tamames JA. Quantitative MRI protocol and decision model for a 'one stop shop' early-stage Parkinsonism diagnosis: Study design. Neuroimage Clin 2023; 39:103506. [PMID: 37696098 PMCID: PMC10500558 DOI: 10.1016/j.nicl.2023.103506] [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: 03/30/2023] [Revised: 06/21/2023] [Accepted: 09/04/2023] [Indexed: 09/13/2023]
Abstract
Differentiating among early-stage parkinsonisms is a challenge in clinical practice. Quantitative MRI can aid the diagnostic process, but studies with singular MRI techniques have had limited success thus far. Our objective is to develop a multi-modal MRI method for this purpose. In this review we describe existing methods and present a dedicated quantitative MRI protocol, a decision model and a study design to validate our approach ahead of a pilot study. We present example imaging data from patients and a healthy control, which resemble related literature.
Collapse
Affiliation(s)
- Samy Abo Seada
- Department of Radiology and Nuclear Medicine, Erasmus MC, Rotterdam, The Netherlands
| | - Anke W van der Eerden
- Department of Radiology and Nuclear Medicine, Erasmus MC, Rotterdam, The Netherlands
| | - Agnita J W Boon
- Department of Neurology, Erasmus MC, Rotterdam, The Netherlands
| | - Juan A Hernandez-Tamames
- Department of Radiology and Nuclear Medicine, Erasmus MC, Rotterdam, The Netherlands; Department of Imaging Physics, TU Delft, The Netherlands.
| |
Collapse
|
3
|
Chougar L, Lejeune FX, Faouzi J, Morino B, Faucher A, Hoyek N, Grabli D, Cormier F, Vidailhet M, Corvol JC, Colliot O, Degos B, Lehéricy S. Comparison of mean diffusivity, R2* relaxation rate and morphometric biomarkers for the clinical differentiation of parkinsonism. Parkinsonism Relat Disord 2023; 108:105287. [PMID: 36706616 DOI: 10.1016/j.parkreldis.2023.105287] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/09/2022] [Revised: 12/15/2022] [Accepted: 01/14/2023] [Indexed: 01/19/2023]
Abstract
INTRODUCTION Quantitative biomarkers for clinical differentiation of parkinsonian syndromes are still lacking. Our aim was to evaluate the value of combining clinically feasible manual measurements of R2* relaxation rates and mean diffusivity (MD) in subcortical regions and brainstem morphometric measurements to improve the discrimination of parkinsonian syndromes. METHODS Twenty-two healthy controls (HC), 25 patients with Parkinson's disease (PD), 19 with progressive supranuclear palsy (PSP) and 27 with multiple system atrophy (MSA, 21 with the parkinsonian variant -MSAp, 6 with the cerebellar variant -MSAc) were recruited. R2*, MD measurements and morphometric biomarkers including the midbrain to pons area ratio and the Magnetic Resonance Parkinsonism Index (MRPI) were compared between groups and their diagnostic performances were assessed. RESULTS Morphometric biomarkers discriminated better patients with PSP (ratio: AUC 0.89, MRPI: AUC 0.89) and MSAc (ratio: AUC 0.82, MRPI: AUC 0.75) from other groups. R2* and MD measurements in the posterior putamen performed better in separating patients with MSAp from PD (R2*: AUC 0.89; MD: AUC 0.89). For the three-class classification "MSA vs PD vs PSP", the combination of MD and R2* measurements in the posterior putamen with morphometric biomarkers (AUC: 0.841) outperformed each marker separately. At the individual-level, there were seven discordances between imaging-based prediction and clinical diagnosis involving MSA. Using the new Movement Disorder Society criteria for the diagnosis of MSA, three of these seven patients were clinically reclassified as predicted by quantitative imaging. CONCLUSION Combining R2* and MD measurements in the posterior putamen with morphometric biomarkers improves the discrimination of parkinsonism.
Collapse
Affiliation(s)
- Lydia Chougar
- Sorbonne Université, Institut du Cerveau - Paris Brain Institute - ICM, CNRS, Inria, Inserm, AP-HP, Hôpital de la Pitié Salpêtrière, DMU DIAMENT, Department of Neuroradiology, F-75013, Paris, France; ICM, Centre de NeuroImagerie de Recherche-CENIR, Paris, France; ICM, Team "Movement Investigations and Therapeutics" (MOV'IT), Paris, France; Sorbonne Université, Institut du Cerveau - Paris Brain Institute - ICM, CNRS, Inserm, AP-HP, Hôpital de la Pitié Salpêtrière, DMU DIAMENT, Department of Neuroradiology, F-75013, Paris, France.
| | - François-Xavier Lejeune
- Sorbonne Université, Institut du Cerveau - Paris Brain Institute - ICM, CNRS, Inserm, F-75013, Paris, France; ICM, Data and Analysis Core, Paris, France
| | - Johann Faouzi
- Sorbonne Université, Institut du Cerveau - Paris Brain Institute - ICM, CNRS, Inserm, F-75013, Paris, France; Sorbonne Université, Institut du Cerveau - Paris Brain Institute - ICM, CNRS, Inria, Inserm, AP-HP, Hôpital de la Pitié Salpêtrière, F-75013, Paris, France
| | - Benjamin Morino
- Sorbonne Université, Institut du Cerveau - Paris Brain Institute - ICM, CNRS, Inserm, AP-HP, Hôpital de la Pitié Salpêtrière, DMU DIAMENT, Department of Neuroradiology, F-75013, Paris, France
| | - Alice Faucher
- Dynamics and Pathophysiology of Neuronal Networks Team, Center for Interdisciplinary Research in Biology, Collège de France, CNRS UMR7241/INSERM U1050, Université PSL, Paris, France; Service de Neurologie, Hôpital Avicenne, Hôpitaux Universitaires de Paris Seine-Saint-Denis, APHP, Bobigny, France
| | - Nadine Hoyek
- Department of Radiology, Hotel Dieu de France University Hospital, Faculty of Medicine, Saint Joseph University, Beirut, Lebanon
| | - David Grabli
- Clinique des mouvements anormaux, Département de Neurologie, Assistance Publique Hôpitaux de Paris, Hôpital Pitié-Salpêtrière, Paris, France; ICM, Centre d'Investigation Clinique Neurosciences, Paris, France
| | - Florence Cormier
- Clinique des mouvements anormaux, Département de Neurologie, Assistance Publique Hôpitaux de Paris, Hôpital Pitié-Salpêtrière, Paris, France; ICM, Centre d'Investigation Clinique Neurosciences, Paris, France
| | - Marie Vidailhet
- ICM, Team "Movement Investigations and Therapeutics" (MOV'IT), Paris, France; Sorbonne Université, Institut du Cerveau - Paris Brain Institute - ICM, CNRS, Inserm, F-75013, Paris, France; Clinique des mouvements anormaux, Département de Neurologie, Assistance Publique Hôpitaux de Paris, Hôpital Pitié-Salpêtrière, Paris, France; ICM, Centre d'Investigation Clinique Neurosciences, Paris, France
| | - Jean-Christophe Corvol
- Sorbonne Université, Institut du Cerveau - Paris Brain Institute - ICM, CNRS, Inserm, F-75013, Paris, France; Clinique des mouvements anormaux, Département de Neurologie, Assistance Publique Hôpitaux de Paris, Hôpital Pitié-Salpêtrière, Paris, France; ICM, Centre d'Investigation Clinique Neurosciences, Paris, France
| | - Olivier Colliot
- Sorbonne Université, Institut du Cerveau - Paris Brain Institute - ICM, CNRS, Inserm, F-75013, Paris, France; Sorbonne Université, Institut du Cerveau - Paris Brain Institute - ICM, CNRS, Inria, Inserm, AP-HP, Hôpital de la Pitié Salpêtrière, F-75013, Paris, France
| | - Bertrand Degos
- Dynamics and Pathophysiology of Neuronal Networks Team, Center for Interdisciplinary Research in Biology, Collège de France, CNRS UMR7241/INSERM U1050, Université PSL, Paris, France; Service de Neurologie, Hôpital Avicenne, Hôpitaux Universitaires de Paris Seine-Saint-Denis, APHP, Bobigny, France
| | - Stéphane Lehéricy
- ICM, Centre de NeuroImagerie de Recherche-CENIR, Paris, France; ICM, Team "Movement Investigations and Therapeutics" (MOV'IT), Paris, France; Sorbonne Université, Institut du Cerveau - Paris Brain Institute - ICM, CNRS, Inserm, AP-HP, Hôpital de la Pitié Salpêtrière, DMU DIAMENT, Department of Neuroradiology, F-75013, Paris, France
| |
Collapse
|
4
|
Naidoo AK, Wells CD, Rugbeer Y, Naidoo N. The "Hot Cross Bun Sign" in Spinocerebellar Ataxia Types 2 and 7-Case Reports and Review of Literature. Mov Disord Clin Pract 2022; 9:1105-1113. [PMID: 36339304 PMCID: PMC9631856 DOI: 10.1002/mdc3.13550] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2022] [Revised: 07/25/2022] [Accepted: 07/26/2022] [Indexed: 09/19/2023] Open
Abstract
Background The "hot cross bun" sign is a cruciform hyperintensity is seen on T2 weighted imaging within the pons. The sign is considered to be pathognomic for Multiple system atrophy type C. The clinical and radiological features of Multiple system atrophy type C overlap with the autosomal dominant inherited ataxias. We present a case series of 3 African patients with genetically proven Spinocerebellar Ataxia presenting with the Hot cross bun sign and a scoping review of similar studies. Cases We described the phenotypic and radiological presentation of genetically confirmed SCA-2 in two, and SCA-7 in one patient, with the "hot cross bun" sign. Literature Review We performed a scoping review on the Hot Cross Bun Sign.A total of 66 articles were retrieved. We describe the diverse aetiologies of the sign and associated phenotypic and radiological features. We review the Spinocerebellar Ataxias described with a Hot cross bun sign and make comparisons to Multiple System Atrophy Type C [Ref. 1,2]. Conclusions To our knowledge this is the first description of an African cohort presenting with the Hot Cross Bun Sign. We expand the differential diagnosis of the Hot Cross Bun Sign.
Collapse
Affiliation(s)
- Ansuya Kasavelu Naidoo
- Greys Academic HospitalPietermaritzburgSouth Africa
- School of Clinical Medicine, Division NeurologyUniversity of KwaZulu NatalDurbanSouth Africa
| | - Cait‐Lynn Deanne Wells
- Greys Academic HospitalPietermaritzburgSouth Africa
- School of Clinical Medicine, Division NeurologyUniversity of KwaZulu NatalDurbanSouth Africa
| | | | - Neil Naidoo
- Greys Academic HospitalPietermaritzburgSouth Africa
- School of Clinical Medicine, Division NeurologyUniversity of KwaZulu NatalDurbanSouth Africa
| |
Collapse
|
5
|
Abstract
Multiple system atrophy (MSA) is a rare neurodegenerative disease that is characterized by neuronal loss and gliosis in multiple areas of the central nervous system including striatonigral, olivopontocerebellar and central autonomic structures. Oligodendroglial cytoplasmic inclusions containing misfolded and aggregated α-synuclein are the histopathological hallmark of MSA. A firm clinical diagnosis requires the presence of autonomic dysfunction in combination with parkinsonism that responds poorly to levodopa and/or cerebellar ataxia. Clinical diagnostic accuracy is suboptimal in early disease because of phenotypic overlaps with Parkinson disease or other types of degenerative parkinsonism as well as with other cerebellar disorders. The symptomatic management of MSA requires a complex multimodal approach to compensate for autonomic failure, alleviate parkinsonism and cerebellar ataxia and associated disabilities. None of the available treatments significantly slows the aggressive course of MSA. Despite several failed trials in the past, a robust pipeline of putative disease-modifying agents, along with progress towards early diagnosis and the development of sensitive diagnostic and progression biomarkers for MSA, offer new hope for patients.
Collapse
|
6
|
Carlos AF, Sekiya H, Koga S, Pham NTT, Ali F, Botha H, Clark HM, Coon EA, Lowe V, Ahlskog JE, Trejo-Lopez JA, Dickson DW, Whitwell JL, Josephs KA. Tau-PET and multimodal imaging in clinically atypical multiple system atrophy masquerading as progressive supranuclear palsy. Parkinsonism Relat Disord 2022; 101:9-14. [PMID: 35752126 DOI: 10.1016/j.parkreldis.2022.06.008] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/15/2022] [Revised: 06/07/2022] [Accepted: 06/12/2022] [Indexed: 12/20/2022]
Abstract
INTRODUCTION Multiple system atrophy (MSA) typically presents with parkinsonism, ataxia and/or autonomic dysfunction. Occasionally, clinically atypical (ca-MSA) cases masquerade as progressive supranuclear palsy (PSP). We aimed to investigate whether different neuroimaging modalities could facilitate differentiation and whether histopathologic characteristics could explain the atypical presentation. METHODS We identified 3 neuropathologically-defined ca-MSA patients with clinically diagnosed PSP who underwent various antemortem brain imaging: MRI and PET imaging using 11C-Pittsburgh compound B, 18F-flortaucipir, and 18F-fluorodeoxyglucose. We compared clinical features, brainstem planimetry, and radiotracer standardized uptake value ratios in ca-MSA to 10 autopsy-confirmed PSP patients and 10 healthy controls (imaging only). We also compared histologic count of neuronal loss, iron deposition and α-synuclein-immunoreactive glial cytoplasmic inclusion burden to 10 autopsy-confirmed MSA-parkinsonism (MSA-P) cases. RESULTS Ca-MSA had better PSP Saccadic Impairment Scale scores (p = 0.003) and more frequent good levodopa response (p = 0.061) than PSP. Ca-MSA showed higher midbrain-to-pons ratio and lower Magnetic Resonance Parkinsonism Index than PSP (each, p = 0.036) and exhibited lower glucose metabolism in the putamen and globus pallidus versus PSP (p = 0.017) and controls (p = 0.007). These same regions showed higher flortaucipir uptake in ca-MSA than PSP (p = 0.007 for putamen, p = 0.049 for pallidum) and controls (p = 0.012). Lower flortaucipir retention was observed in the subthalamic nucleus versus PSP (p = 0.007). The putamen-to-subthalamic ratio distinguished ca-MSA from PSP. No histopathological differences were observed for ca-MSA versus typical MSA-P. CONCLUSION Severity of saccadic impairment, levodopa responsiveness, MRI planimetric measurements, and different patterns of fluorodeoxyglucose and flortaucipir uptake can help improve antemortem differentiation of MSA masquerading as PSP from true PSP.
Collapse
Affiliation(s)
- Arenn F Carlos
- Department of Neurology, Mayo Clinic, Rochester, MN, USA
| | - Hiroaki Sekiya
- Department of Neuroscience (Neuropathology), Mayo Clinic, Jacksonville, FL, USA
| | - Shunsuke Koga
- Department of Neuroscience (Neuropathology), Mayo Clinic, Jacksonville, FL, USA
| | | | - Farwa Ali
- Department of Neurology, Mayo Clinic, Rochester, MN, USA
| | - Hugo Botha
- Department of Neurology, Mayo Clinic, Rochester, MN, USA
| | | | | | - Val Lowe
- Department of Radiology, Mayo Clinic, Rochester, MN, USA
| | - J Eric Ahlskog
- Department of Neurology, Mayo Clinic, Rochester, MN, USA
| | - Jorge A Trejo-Lopez
- Department of Pathology and Laboratory Medicine, Mayo Clinic, Rochester, MN, USA
| | - Dennis W Dickson
- Department of Neuroscience (Neuropathology), Mayo Clinic, Jacksonville, FL, USA
| | | | | |
Collapse
|
7
|
Lin CY, Yen YT, Huang LT, Chen TY, Liu YS, Tang SY, Huang WL, Chen YY, Lai CH, Fang YHD, Chang CC, Tseng YL. An MRI-Based Clinical-Perfusion Model Predicts Pathological Subtypes of Prevascular Mediastinal Tumors. Diagnostics (Basel) 2022; 12:diagnostics12040889. [PMID: 35453937 PMCID: PMC9026802 DOI: 10.3390/diagnostics12040889] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2022] [Revised: 03/18/2022] [Accepted: 03/31/2022] [Indexed: 12/10/2022] Open
Abstract
This study aimed to build machine learning prediction models for predicting pathological subtypes of prevascular mediastinal tumors (PMTs). The candidate predictors were clinical variables and dynamic contrast–enhanced MRI (DCE-MRI)–derived perfusion parameters. The clinical data and preoperative DCE–MRI images of 62 PMT patients, including 17 patients with lymphoma, 31 with thymoma, and 14 with thymic carcinoma, were retrospectively analyzed. Six perfusion parameters were calculated as candidate predictors. Univariate receiver-operating-characteristic curve analysis was performed to evaluate the performance of the prediction models. A predictive model was built based on multi-class classification, which detected lymphoma, thymoma, and thymic carcinoma with sensitivity of 52.9%, 74.2%, and 92.8%, respectively. In addition, two predictive models were built based on binary classification for distinguishing Hodgkin from non-Hodgkin lymphoma and for distinguishing invasive from noninvasive thymoma, with sensitivity of 75% and 71.4%, respectively. In addition to two perfusion parameters (efflux rate constant from tissue extravascular extracellular space into the blood plasma, and extravascular extracellular space volume per unit volume of tissue), age and tumor volume were also essential parameters for predicting PMT subtypes. In conclusion, our machine learning–based predictive model, constructed with clinical data and perfusion parameters, may represent a useful tool for differential diagnosis of PMT subtypes.
Collapse
Affiliation(s)
- Chia-Ying Lin
- Department of Medical Imaging, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan 704, Taiwan; (C.-Y.L.); (L.-T.H.); (Y.-S.L.)
| | - Yi-Ting Yen
- Division of Thoracic Surgery, Department of Surgery, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan 704, Taiwan; (Y.-T.Y.); (W.-L.H.); (Y.-Y.C.); (Y.-L.T.)
- Division of Trauma and Acute Care Surgery, Department of Surgery, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan 704, Taiwan
| | - Li-Ting Huang
- Department of Medical Imaging, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan 704, Taiwan; (C.-Y.L.); (L.-T.H.); (Y.-S.L.)
| | - Tsai-Yun Chen
- Division of Hematology and Oncology, Department of Internal Medicine, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan 704, Taiwan;
| | - Yi-Sheng Liu
- Department of Medical Imaging, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan 704, Taiwan; (C.-Y.L.); (L.-T.H.); (Y.-S.L.)
| | - Shih-Yao Tang
- Department of Biomedical Engineering, National Cheng Kung University, Tainan 704, Taiwan;
| | - Wei-Li Huang
- Division of Thoracic Surgery, Department of Surgery, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan 704, Taiwan; (Y.-T.Y.); (W.-L.H.); (Y.-Y.C.); (Y.-L.T.)
| | - Ying-Yuan Chen
- Division of Thoracic Surgery, Department of Surgery, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan 704, Taiwan; (Y.-T.Y.); (W.-L.H.); (Y.-Y.C.); (Y.-L.T.)
| | - Chao-Han Lai
- Department of Surgery, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan 704, Taiwan;
| | - Yu-Hua Dean Fang
- Department of Radiology, University of Alabama at Birmingham, Birmingham, AL 35294, USA
- Correspondence: (Y.-H.D.F.); (C.-C.C.)
| | - Chao-Chun Chang
- Division of Thoracic Surgery, Department of Surgery, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan 704, Taiwan; (Y.-T.Y.); (W.-L.H.); (Y.-Y.C.); (Y.-L.T.)
- Correspondence: (Y.-H.D.F.); (C.-C.C.)
| | - Yau-Lin Tseng
- Division of Thoracic Surgery, Department of Surgery, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan 704, Taiwan; (Y.-T.Y.); (W.-L.H.); (Y.-Y.C.); (Y.-L.T.)
| |
Collapse
|