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Tobias Peikert
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MA23 - Preclinical Models and Genetics of Malignant Pleural Mesothelioma (ID 353)
- Event: WCLC 2019
- Type: Mini Oral Session
- Track: Mesothelioma
- Presentations: 1
- Now Available
- Moderators:Ramon Palmero Sánchez, Raphael Bueno
- Coordinates: 9/10/2019, 14:30 - 16:00, Copenhagen (1980)
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MA23.07 - Loss of Expression of BAP1 and/or MTAP Aids in the Diagnosis of Malignant Mesothelioma Metastatic to Lymph Nodes (Now Available) (ID 1121)
14:30 - 16:00 | Author(s): Tobias Peikert
- Abstract
- Presentation
Background
Stage and histology are the strongest prognostic parameters in malignant pleural mesothelioma and aid management of patients. However, the distinction between reactive intranodal mesothelial cells and metastatic malignant mesothelioma (MM) can be challenging. Loss of BRCA1 associated protein-1 (BAP1) and/or methylthioadenosine phosphorylase (MTAP) expression has been identified in a subset of MM but not in reactive mesothelial proliferation. We investigated the value of these markers in the distinction between reactive mesothelial cells and metastatic MM in lymph nodes.
Method
Surgical files of Mayo Clinic Rochester (1996-2018) were searched for metastatic MM in lymph nodes. All cases and if available corresponding primary MM were reviewed by a thoracic pathologist (ACR) to confirm the diagnosis. Primary MM and lymph nodes were stained with BAP1 (clone C-4) and MTAP (2G4). Absence of nuclear staining of BAP1 and absence of nuclear and cytoplasmic staining of MTAP in essentially all tumor cells was considered as loss of expression.
Result
Forty-four patients (25 males, 56.8%) had a median age of 64 years (range, 24-75) at time of surgery. Tissue was available from nodal metastases in all cases, either paired with the primary MM at time of nodal sampling (N=37) or at a different time (N=4) (time between tissue collections, range, 1day- 4 years, respectively), or without paired primary MM (N=3). Thirty-seven pleural, 6 peritoneal and 1 pericardial MM were of epithelioid (N=39) or biphasic (N=5) subtype. Patients underwent extrapleural pneumonectomy (N=17), pleurectomy (N=7), resection (N=9), debulking (N=2), biopsy (N=8), or autopsy (N=1). In nodal metastases, BAP1 and/or MTAP expression was lost in 29 (of 43, 67.4%) cases; specifically, BAP1 expression was lost in 28 (of 44, 63.6%), MTAP was lost in 14 (of 43, 32.6%), and both were lost in 12 (of 43, 27.9%) cases. Agreement in expression/loss of expression of BAP1 and/or MTAP in primary and metastatic MM occurred in all cases. During a median follow up of patients who underwent extrapleural pneumonectomy or pleurectomy (available in N=23) of 14.8 months (range, 1-119) 17 patients died within a median time of 16 months.
Conclusion
BAP1 and MTAP immunostains are helpful in the distinction between metastatic MM and reactive mesothelial cells in lymph nodes when one or both markers lost expression in the mesothelial cells. Expression of both markers does not exclude the possibility of metastatic MM.
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OA06 - Refining Lung Cancer Screening (ID 131)
- Event: WCLC 2019
- Type: Oral Session
- Track: Screening and Early Detection
- Presentations: 1
- Now Available
- Moderators:Tomasz Grodzki, Lluis Esteban Tejero
- Coordinates: 9/09/2019, 11:00 - 12:30, Hilton Head (1978)
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OA06.06 - Independent Validation of a Novel High-Resolution Computed Tomography-Based Radiomic Classifier for Indeterminate Lung Nodules (Now Available) (ID 2862)
11:00 - 12:30 | Presenting Author(s): Tobias Peikert
- Abstract
- Presentation
Background
Optimization of the clinical management of incidentally- and screen-identified lung nodules is urgently needed to limit the number of unnecessary invasive diagnostic interventions, and therefore morbidity, mortality and healthcare costs. We recently developed and internally validated a novel radiomics-based approach for the classification of screen-detected indeterminate nodules, and present herein validation of this algorithm in an independent cohort.
Method
In a previous study, we developed a multivariate prediction model evaluating independent quantitative variables assessing various radiologic nodule features such as sphericity, flatness, elongation, spiculation, lobulation and curvature. Nodules between 7 and 30 mm of largest diameter were selected from the National Lung Screening Trial (n=726 indeterminate nodules, benign (n = 318) and malignant (n = 408)) were used to derive this model using least absolute shrinkage and selection operator (LASSO) method with bootstrapping method applied for the internal validation. Eight variables capturing vertical location, size, shape, density and surface characteristics were included with an optimism-correct area under the curve (AUC) of 0.94. For this study, an independent dataset of 203 incidentally-identified lung nodules obtained from the indeterminate pulmonary nodule registry at Vanderbilt University was identified. CT datasets were transferred to Mayo Clinic (Rochester, MN) for analysis. Nodules were segmented manually using the ANALYZE software (Biomedical Imaging Resource, Mayo Clinic, Rochester, MN), and radiomic analysis was performed using the 8-variable radiomic diagnostic algorithm derived from the NLST. The Brock model was also used to calculate probability of malignancy for all NLST and Vanderbilt nodules.
Result
Brock scores were calculated for 685 NLST nodules (excluded: interval cancers, n=12; missing values needed for Brock score, n=29). The AUC for the Brock score (AUC Brock) for NLST nodules was 0.83 which was inferior to the AUC for the radiomic model (AUC Radiomic =0.94, P<0.001). When the subset of intermediate pre-test probability of lung cancer was considered (Brock score > 10 but <= 60), the AUC Brock was 0.61 (95% CI: 0.54-0.68) whereas the AUC Radiomic was 0.88 (95% CI: 0.84-0.93). A total of 203 incidentally found pulmonary nodules with available clinical information and biopsy or surgery-proven histology identified in the Vanderbilt indeterminate pulmonary nodule registry, and all histology data and corresponding CT images were reviewed. CT images were transferred to Mayo Clinic for analysis. After exclusion of duplicate CT datasets, unanalyzable CT images and not identifiable nodules (n=27 cases), 176 nodules were segmented and analyzed, including 84 benign and 92 malignant nodules. The AUC was 0.89 (95% CI: 0.85-0.94). For comparison, the AUC Brock was 0.88 (95% CI: 0.83-0.94). When the subset of intermediate pre-test probability of lung cancer was considered (Brock score > 10 but <= 60), the AUC Brock was 0.76 (95% CI: 0.63-0.89) whereas the AUC Radiomic was 0.85 (95% CI: 0.74-0.95).
Conclusion
Our radiomic classifier demonstrates good performance characteristics on an independent retrospective validation dataset. If prospectively validated, integration into clinical decision making algorithm could significantly impact patient care.
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