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T. Henzler
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MTE22 - Perspectives in Lung Cancer Imaging (Ticketed Session) (ID 315)
- Event: WCLC 2016
- Type: Meet the Expert Session (Ticketed Session)
- Track: Radiology/Staging/Screening
- Presentations: 1
- Moderators:
- Coordinates: 12/07/2016, 07:30 - 08:30, Schubert 2
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MTE22.01 - Perspectives in Lung Cancer Imaging (ID 6578)
07:30 - 08:30 | Author(s): T. Henzler
- Abstract
- Presentation
Abstract:
Lung cancer is still the leading cause of cancer-related death in both men and women with 80% to 85% of cases being non-small-cell lung cancer (NSCLC).[1]Over the past years, the IASLC Staging and Prognostic Factors Committee has collected a new database of 94,708 cases of lung cancer as the backbone for the upcoming 8[th] edition of the TNM classification for lung cancer due to be published late 2016 [2,3]. The 8[th] edition will significantly impact lung cancer staging with CT and/or PET-CT due to the subclassification of T1 and T2 into a,b and c categories, the reclassification of tumors more than 5 cm but not more than 7 cm in greatest dimension as T3, the reclassification of tumors more than 7 cm in greatest dimension as T4, the grouping of the involvement of the main bronchus as a T2 descriptor, regardless of distance from the carina, but without invasion of the carina, the grouping of partial and total atelectasis or pneumonitis as a T2 descriptor, the reclassification of diaphragm invasion as T4 and the elimination of mediastinal pleura invasion as a T descriptor [2,3]. Moreover, the upcoming 8[th] edition will also lead to a novel classification of distant metastasis, in which single extrathoracic metastasis will be classified as M1b whereas multiple extrathoracic metastasis are classified as M1c. The changes made within the proposal of the 8[th] edition of the TNM will be discussed within the presentation using clinical examples. Beside the accurate staging of patients with lung cancer early detection using CT screening with novel low radiation dose CT technologies will also be discussed. Within this context, a special focus will be given on novel methods that may improve a more accurate characterization of detected lung nodules using deep machine learning and Radiomics. Radiomics refers to the comprehensive quantification of lung nodule and tumour phenotypes by applying a large number of quantitative image features that are standardized collected with specific software algorithms. Radiomics features have the capability to further enhance imaging data regarding prognostic tumour signatures, detection of tumour heterogeneity as well as the detection of underlying gene expression patterns which is of special interest in patients with metastatic disease. The third part of the presentation will focus on novel techniques in lung cancer imaging. The past fifteen years have brought significant breakthroughs in the understanding of the molecular biology of lung cancer. Signalling pathways and genetic driver mutations that are vital for tumour growth have been identified and can be effectively targeted by novel pharmacologic agents, resulting in significantly improved survival of patients with lung cancer[4]. Parallel to the progress in lung cancer treatment, imaging techniques aiming at improving diagnosis, staging, response evaluation, and detection of tumour recurrence have also considerably advanced in recent years[5]. However, standard morphologic computed tomography (CT) and magnetic resonance imaging (MRI) as well as fluor-18-fluorodeoxyglucose ([18]F-FDG) positron emission tomography CT (PET-CT) are still the currently most frequently utilized imaging modalities in clinical practice and most clinical trials [6,7]. Novel state-of-the-art functional imaging techniques such as dual-energy CT (DECT), dynamic contrast enhanced CT (DCE-CT), diffusion weighted MRI (DW-MRI), perfusion MRI, and PET-CT with more specific tracers that visualize angiogenesis, tumour oxygenation or tumour cell proliferation have not yet been broadly implemented, neither in clinical practice nor in phase I–III clinical trials. In this context, Nishino et al.[4] published an article on personalized tumour response assessment in the era of molecular treatment in oncology. The authors showed that the concept of personalized medicine with regard to cancer treatment has been well applied in therapeutic decision-making and patient management in clinical oncology. With regard to imaging techniques, however, it was criticized that the developments in tumour response assessment that should parallel the advances in cancer treatment are not sufficient to produce state-of-the-art functional information that directly reflect treatment targets. Functional information on tumour response is highly required because there is growing evidence that the current objective criteria for treatment response assessment may not reliably indicate treatment failure and do not adequately capture disease biology. Molecular-targeted therapies and novel immunotherapies induce effects that differ from those induced by classic cytotoxic treatment including intratumorale haemorrhage, changes in vascularity, and tumour cavitation. Thus, conventional approaches for therapy response assessment such as RECIST or WHO criteria that exclusively focus on the change in tumour size are of decreasing value for drug response assessment in clinical trials[8,9]. In summary, the aim of of this presentation is to provide an overview on the changes made within the upcoming 8[th] of the TNM classification as well as to provide an overview on state-of-the-art imaging techniques for lung cancer screening, staging, response evaluation as well as surveillance in patients with lung cancer. The various techniques will be discussed regarding their pros and cons to further provide functional information that best reflects specific targeted therapies including anti-angiogenetic treatment, immunotherapies and stereotactic body radiation therapy. Literature: 1. Rami-Porta R, Crowley JJ, Goldstraw P. The revised TNM staging system for lung cancer. Ann Thorac Cardiovasc Surg 2009;15:4-9. 2. Asamura H, Chansky K, Crowley J, et al. The International Association for the Study of Lung Cancer Lung Cancer Staging Project: Proposals for the Revision of the N Descriptors in the Forthcoming 8th Edition of the TNM Classification for Lung Cancer. Journal of thoracic oncology : official publication of the International Association for the Study of Lung Cancer 2015;10:1675-84. 3. Rami-Porta R, Bolejack V, Crowley J, et al. The IASLC Lung Cancer Staging Project: Proposals for the Revisions of the T Descriptors in the Forthcoming Eighth Edition of the TNM Classification for Lung Cancer. Journal of thoracic oncology : official publication of the International Association for the Study of Lung Cancer 2015;10:990-1003. 4. Rengan R, Maity AM, Stevenson JP, Hahn SM. New strategies in non-small cell lung cancer: improving outcomes in chemoradiotherapy for locally advanced disease. Clin Cancer Res 2011;17:4192-9. 5. Miles K. Can imaging help improve the survival of cancer patients? Cancer Imaging 2011;11 Spec No A:S86-92. 6. Nishino M, Jackman DM, Hatabu H, Janne PA, Johnson BE, Van den Abbeele AD. Imaging of lung cancer in the era of molecular medicine. Acad Radiol 2011;18:424-36. 7. Nishino M, Jagannathan JP, Ramaiya NH, Van den Abbeele AD. Revised RECIST guideline version 1.1: What oncologists want to know and what radiologists need to know. AJR Am J Roentgenol 2010;195:281-9. 8. Oxnard GR, Morris MJ, Hodi FS, et al. When progressive disease does not mean treatment failure: reconsidering the criteria for progression. J Natl Cancer Inst 2012;104:1534-41. 9. Stacchiotti S, Collini P, Messina A, et al. High-grade soft-tissue sarcomas: tumor response assessment--pilot study to assess the correlation between radiologic and pathologic response by using RECIST and Choi criteria. Radiology 2009;251:447-56.
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