Malignant Pleural Diseases Advances Clinicians Should Know F Gleeson

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1 Malignant Pleural Diseases Advances Clinicians Should Know F Gleeson The following relevant disclosures, conflicts of interest and/ or financial relationships exist related to this presentation: Consultant to Alliance Medical Limited Medical Board Member Blue Earth Diagnostics Radiology of Malignant Pleural Diseases Fergus V Gleeson Radiology Department Churchill Hospital Oxford 1

2 Pleural Imaging PET-CT and MRI US, CT, MRI and PET-CT Pleural thickening Pleural effusion Mesothelioma Non-asbestos related 2

3 Thoracic US in Malignant Pleural Effusion Can US be used in a similar way to CT to assess the pleural surface? CT criteria for malignancy - Nodularity - Thickness > 1cm - Circumferential - Involves mediastinal surface Qureshi et al. Thorax 2009;64: Thoracic US in Malignant Pleural Effusion Can US be used in a similar way to CT to assess the pleural surface? CT criteria for malignancy - Nodularity - Thickness > 1cm - Circumferential - Involves mediastinal surface - Involves the diaphragm Qureshi et al. Thorax 2009;64:

4 Thoracic US in Malignant Pleural Effusion 41 patients 24 malignant and 17 benign 11 adenocarcinoma 7 mesothelioma Parietal and Visceral pleural thickening Nodular and smooth thickening Diaphragmatic changes Qureshi et al. Thorax 2009;64: Thoracic US in Malignant Pleural Effusion Sens 75%, Spec 100%, PPV 100%, NPV 73% Pleural and diaphragmatic thickening is common in this pt group Nodularity and irregularity are strongly suggestive of malignancy Qureshi et al. Thorax 2009;64:

5 CT in Benign v Malignant Pleural Thickening CT criteria for malignancy - Nodularity - Thickness > 1cm - Circumferential - Involves mediastinal surface Leung A.N et al. Am J Roentgenol 1990; 154: Scott et al. Radiology 1995; 194: Traill Z et al. Clin Radiol 2001;56:

6 CT in Benign v Malignant Pleural Thickening 370 patients underwent thoracoscopy and CT report reviewed 211 (57%) malignant disease Sensitivity 68%, specificity 78% PPV 80%, NPV 65% Halifax et al. Thorax 2015;70: Traill Z et al. Clin Radiol 2001;56: CT Guided Biopsy in Malignant Mesothelioma 83 pts post EPP, pre-epp histology reviewed 64 epithelial, biphasic 19 Pre-EPP diagnosis correct in 84% Epithelial, 26% biphasic Overall subtype misclassification 20% Diagnostic histological subtype accuracy Thoracotomy performed in 4 (7%) accurate in 83% Thoracoscopy performed in 69 (81%) accurate in 74% CT biopsy performed in 7 (11%) accurate in 44% Kao et al. J Thorac Oncol 2011;6:

7 CT Guided Biopsy in Malignant Mesothelioma 83 pts post EPP, pre-epp histology reviewed 64 epithelial, biphasic 19 Pre-EPP diagnosis correct in 84% Epithelial, 26% biphasic Overall subtype misclassification 20% Diagnostic histological subtype accuracy Thoracotomy performed in 4 (7%) accurate in 83% Thoracoscopy performed in 69 (81%) accurate in 74% CT biopsy performed in 7 (11%) accurate in 44% Kao et al. J Thorac Oncol 2011;6: CT assessment of disease response in malignant mesothelioma Subjective Modified RECIST Volumetric measurements Byrne et al. Annals of Oncology 2004;15: Armato et al. AJR 2006;186: Plathow et al. Eur Radiol 2008;18: Liu et al. J Thorac Oncol 2010;5: Labby et al. J Thorac Oncol 2012;7:

8 Armato et al. J Thorac Oncol 2014;9:

9 Evaluation for lung image registration Results for Empire Challenge Workshop (19 contestants) Lung Boundaries Fissures Landmarks Singularities Score % Rank Score % Rank Score mm Rank Score % Rank My method Range

10 Determine tumour volume changes pre post Tumour segmentation Local deformation magnitude Local volume change (tumour) 4/8/2015 Reduction Volume of tumour after treatment 78.6% Expansion CT assessment of disease response in malignant mesothelioma Compared modified RECIST, semiautomated tumour volume and lung volumes Labby et al. J Thorac Oncol 2013;8:

11 Conclusions: US and CT US may be used to detect pleural tumour CT may have a high false negative rate CT biopsy may have sampling errors Volumetric CT may confer benefits in mesothelioma assessment PET-CT in Malignant Pleural Disease Distinguishing between benign and malignant disease Identifying areas for biopsy, disease detection and seeding Staging malignancy Prognostic information Disease response 11

12 PET Standardised Uptake Value Measure of attenuation corrected FDG uptake normalised to the injected dose of FDG and the patients body weight SUV max, mean, % change Time dependent 16% increase between 40 and 60 minutes Glucose dependent Scanner dependent For set parameters Measurement variability approx 10% 12

13 SUVmax 30 p< TOF-OSEM QClear Reconstruction Algorithm 13

14 PET-CT Distinguishing between benign and malignant disease Asbestos related pleural thickening Sens 94%, Spec 100%, PPV 100%, NPV 93% FP due to infection, talc pleurodesis Duysinx et al. Chest 2004;125: Yildirim et al. J Thorac Oncol 2009;4: PET-CT in Malignant Pleural Disease Yildirim et al. J Thorac Oncol 2009;4:

15 Treglia et al. Lung Cancer 2014;83:1-7 15

16 16

17 17

18 18

19 Roca et al. Lung Cancer 2013;79:

20 PET-CT in Malignant Pleural Disease Histological classification 78 patients Kadota et al. J Thorac Oncol 2012;7: PET-CT in Malignant Pleural Disease Histological classification 78 patients Kadota et al. J Thorac Oncol 2012;7:

21 PET-CT in Malignant Pleural Disease Distinguishing between benign and malignant disease Identifying areas for biopsy, disease detection and seeding Staging malignancy Prognostic information Disease response 21

22 22

23 PET-CT in Malignant Pleural Disease Distinguishing between benign and malignant disease Identifying areas for biopsy, disease detection and seeding Staging malignancy Prognostic information Disease response 23

24 PET-CT staging in mesothelioma Staging Retrospective studies Surgical patients May improve nodal and metastatic staging Erasmus et al. J Thorac Cardiovasc Surg 2005;6: Truong et al. J Thorac Imaging 2006;21: Plathow et al. Invest Radiol 43:10; Sorensen et al. Eur J Cardiothorac Surg 2008;35(5):

25 PET-CT in Malignant Pleural Disease Distinguishing between benign and malignant disease Identifying areas for biopsy, disease detection and seeding Staging malignancy Prognostic information Disease response PET-CT Prognosis and Response Prognosis Low SUV and epithelioid histology Best survival Chemoresponse TGV Early MR correlated with TTP No correlation with CT Responders 14 months Non-responders 7 months Gerbaudo et al. Thorax 2003;12: Ceresoli et al. J Clin Oncol 2006;24: Francis et al. J Nucl Med 2007;48:

26 26

27 PET-CT Prognosis and Response Prognostic information 60 patients, MOS 14.1 months, range months Standardised uptake values Metabolic tumour volume (MTV) Total lesion glycolysis (TLG) SUVmax MTV TLG High Low I Armato et al. Lung Cancer 2013;82: PET-CT Prognosis and Response Prognostic information Neoadjuvant therapy assessment 50 patients T1-3 N0-2 MPM EPP and DXT Decrease of > 30% in SUVmax = metabolic responder Tsutani et al. Annals of Oncology 2013:24:

28 PET-CT in Malignant Pleural Disease Prognostic information Tsutani et al. Annals of Oncology 2013:24: PET-CT Prognosis and Response Prognostic information Tsutani et al. Annals of Oncology 2013:24:

29 PET-CT Prognosis and Response Prognostic information Tsutani et al. Annals of Oncology 2013:24: PET-CT Prognosis and Response Disease response 131 patients FDG PET at baseline and post 2 cycles of permetrexed PFS 7.2 months, OS 14.3 months Baseline SUVmax, TLG correlated with PFS and OS (p<0.001) ΔSUVmax and ΔTLG correlated with PFS and OS (p<0.001) Lopci et al. EJNMMI 2015;42:

30 MRI in Malignant Pleural Disease Distinguishing between benign and malignant disease Prognostic information Disease response MRI in Malignant Pleural Disease Sequences T1 and T2 Fat saturated T1 post gadolinium DCE MRI DWI ADC Pointillism 30

31 T2 signal intensity > muscle T1 signal intensity intermediate Falaschi et al AJR 1996; 166: Plathow et al. Eur Radiol 2008;18:

32 DWI MRI in Malignant Pleural Disease Mu & Collins. AJR 2007;188: MRI in Malignant Pleural Disease Distinguishing between benign and malignant disease - DWI 76 pts 28 benign 48 malignant (42 MPM) Sens 91.7%, Spec 85.7%, PPV 91.7% Coolen et al. Radiology 2012;263:

33 MRI in Malignant Pleural Disease Coolen et al. Radiology 2012;263: MRI in Malignant Pleural Disease Pointillism 100 pts 33 benign, 77 malignant, 67 MPM Benign v Malignant CT Sens CT Spec CT Acc MRI Sens MRI Spec MRI Acc Coolen et al. Radiology 2012;263:

34 MRI in Malignant Pleural Disease Coolen et al. Radiology 2012;263:

35 35

36 36

37 MRI in Malignant Pleural Disease Gill et al. AJR 2010;195:W

38 J Thorac Oncol 2014;9(3):

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