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O28 - Endoscopy (ID 124)
- Event: WCLC 2013
- Type: Oral Abstract Session
- Track: Pulmonology + Endoscopy/Pulmonary
- Presentations: 1
- Moderators:F.J. Herth, N. Ikeda
- Coordinates: 10/30/2013, 10:30 - 12:00, Parkside Auditorium, Level 1
O28.02 - Grey Scale Texture Analysis of Endobronchial Ultrasound Mini Probe Guide Sheath Images for Prediction of Benign or Malignant Aetiology. (ID 1059)
10:30 - 12:00 | Author(s): R. Ware
Expert analysis of endobronchial ultrasound (EBUS) images obtained with the mini probe (MP) has established certain subjective criteria for predicting benign or malignant disease. Minimal data is available for objective analysis of these images. The aim of this study was to determine if greyscale texture analysis of EBUS-MP images could differentiate between benign and malignant peripheral lung lesions.
Digital EBUS-MP images with contrast set at 4 and gain set at 10 were included in this study. A region of interest (ROI) was mapped for each image and analysed in a prediction set. The ROIs were analysed for the following greyscale texture features in MATLAB (v184.108.40.2067 (R2009a)); mean pixel value, difference between maximum and minimum pixel value, standard deviation of the mean pixel value, entropy, correlation, energy and homogeneity. Significant greyscale texture features were used to assess a validation set. Figure 1
Eighty-five peripheral lung lesions were in the prediction set (47 malignant and 38 benign). Benign lesions had larger differences between maximum and minimum pixel values, larger standard deviations of the mean pixel values and a higher entropy than malignant lesions (p<0.0001 for all values). Eighty two peripheral lesions were in the validation set; 63/82 (76.8%) were correctly classified. Of these 45/49(91.8%) malignant lesions and 18/33 (54.5%) benign lesions were correctly classified. The negative predictive value for malignancy was 82% and the positive predictive value was 75%. Figure 1
Greyscale texture analysis of EBUS-MP images could assist in differentiating between benign and malignant peripheral lung lesions but tissue diagnosis is still important.
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P3.19 - Poster Session 3 - Imaging (ID 181)
- Event: WCLC 2013
- Type: Poster Session
- Track: Imaging, Staging & Screening
- Presentations: 1
- Coordinates: 10/30/2013, 09:30 - 16:30, Exhibit Hall, Ground Level
P3.19-004 - Comparison of objective criteria and expert visual interpretation to classify benign and malignant hilar and mediastinal nodes on 18-F FDG PET/CT. (ID 1398)
09:30 - 16:30 | Author(s): R. Ware
Despite the widespread adoption of FDG-PET/CT in staging of lung cancer, there are no universally accepted criteria for classifying thoracic nodes as malignant. Previous studies have generally shown high negative predictive values, but there are varying reporting criteria and positive predictive values for classifying malignant involvement. Using Endobronchial ultrasound transbronchial needle aspiration (EBUS-TBNA) histology as the gold standard, we evaluated objective 18-F FDG-PET/CT criteria for interpreting mediastinal and hilar nodes and compared this to expert visual interpretation (EVI).
A retrospective review of all patients with proven/suspected primary lung cancer who had both FDG-PET/CT and EBUS-TBNA from 2008-2010 was performed. Scan interpretation was blinded to histology. Separate prediction and validation datasets were used. 104 patients from 2008/2009 formed the prediction set; 48 patients from 2010 formed the validation set. Objective FDG-PET/CT criteria were: - SUVmax lymph node (SUVmaxLN) - Ratio SUVmaxLN/SUVmax primary lung malignancy if evident (R-SUVmax primary) - Ratio SUVmaxLN/SUVaverage liver (R-SUVavg liver) - Ratio SUVmaxLN/SUVmax liver (R-SUVmax liver) - Ratio SUVmaxLN/SUVmax blood pool (R-SUVmaxBP) An experienced Nuclear Medicine Physician visually reviewed all scans and classified each thoracic nodal station as benign, malignant, or equivocal. For statistical analysis, ‘equivocal’ nodes were classified benign.
87 malignant lymph nodes from 75 patients and 41 benign nodes from 21 patients were in the prediction set. All objective 18-F FDG-PET/CT criteria analysed were significantly higher in the malignant group compared to the benign group (p<0.0001 all criteria). EVI had 95.3% accuracy, with 83/87(95.4%) malignant nodes and 39/41(95.1%) benign nodes correctly classified. 34 malignant nodes from 34 patients and 19 benign nodes from 14 patients were in the validation set. The new proposed cut-off values of the objective criteria from the prediction set correctly classified 44/53(83.0%) nodes, with 28/34(82.4%) malignant nodes and 16/19(84.2%) benign nodes correctly classified. EVI had 91% accuracy, with 33/34(97.1%) malignant nodes and 15/19 (79.0%) benign nodes correctly classified. Figure 1
Objective analysis of 18-F FDG PET/CT can differentiate between malignant and benign nodes with high accuracy, but is not superior to EVI. For objective criteria to perform optimally, there may need to be different criteria devised for different patient populations.