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Pierre P Massion
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MA 14 - Diagnostic Radiology, Staging and Screening for Lung Cancer I (ID 672)
- Event: WCLC 2017
- Type: Mini Oral
- Track: Radiology/Staging/Screening
- Presentations: 2
- Moderators:H. Kondo, Hong Kwan Kim
- Coordinates: 10/17/2017, 15:45 - 17:30, F205 + F206 (Annex Hall)
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MA 14.08 - Hematology/Oncology Providers’ Practices and Attitudes of Lung Cancer Screening And Tobacco Cessation at an Academic Medical Center and VA (ID 8827)
16:30 - 16:35 | Author(s): Pierre P Massion
- Abstract
- Presentation
Background:
Advances in cancer screening and therapeutics have led to an estimated 15.5 million US cancer survivors. A history of cancer is a known risk factor for lung cancer. Lung cancer screening (LCS) with low-dose CT (LDCT) and smoking cessation in high-risk populations are recommended standard-of-care practices for cancer survivors, yet knowledge and practice of these interventions is low among PCPs. Hematologists and oncologists commonly provide cancer survivorship care, and yet their practices of and attitudes toward LCS are unknown. Based on prior data, we hypothesized that very few providers (<25%) would report performing LDCT screening while most (>75%) would report providing tobacco cessation services in the last year, and that knowledge of LCS guidelines would be associated with LDCT screening.
Method:
We electronically surveyed all Hematology/Oncology providers (n = 104) at a large academic institution in the Mid-South and its affiliated VA from February to May 2017. The survey queried: LCS/tobacco cessation practices (LDCT screening as primary outcome), perceived cancer screening/tobacco cessation effectiveness, knowledge of USPSTF LCS guideline recommendations and CMS coverage, perceived barriers to LDCT screening, and interest in future provider/patient LCS education and reminder tools. Data were summarized using counts, proportions, means, and medians. We used logistic regression to evaluate the association of LCS guideline knowledge (primary predictor) with reported LDCT screening.
Result:
The overall survey response rate was 73%. Few providers (38%) reported performing LDCT screening in the past year, while almost all providers (95%) reported providing tobacco cessation services. In unadjusted analysis, providers who knew at least three LCS guideline components were more likely to perform LDCT screening (OR 5.96, CI 2.03-17.49; P = 0.001). Only 55% of providers knew at least three LCS guideline components. More providers rated Pap-smear (75%), colonoscopy (71%), smoking cessation (68%), and mammography (39%) as very effective at reducing cancer-specific mortality compared to LDCT (24%). Major perceived barriers included: lack of patient awareness (74%) and patient financial cost (51%). More VA providers (37%) rated lack of a multi-disciplinary screening program as a major screening barrier compared to academic providers (7%) (P = 0.002). Majority of providers (≥ 56%) reported interest in future provider/patient LCS education and reminders.
Conclusion:
LDCT screening is currently an uncommon practice among hematology/oncology providers. Future interventions aimed at the provider, patient, and health system levels are needed to ensure standard-of-care LCS practices in the cancer survivor population. Provider level interventions should incorporate education on screening/tobacco cessation effectiveness and screening guideline recommendations.
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MA 14.13 - Nodule Size Isn't Everything: Imaging Features Other Than Size Contribute to AI Based Risk Stratification of Solid Nodules (ID 8177)
17:05 - 17:10 | Author(s): Pierre P Massion
- Abstract
- Presentation
Background:
Previously proposed risk models for the malignancy of Indeterminate Pulmonary Nodules (IPNs) detected on Computed Tomography (CT) typically incorporate a mixture of clinical factors, such as age and smoking history, and radiological factors such as nodule size and location. Of the latter, size is considered one of the most significant. Artificial Intelligence based risk stratification software has been previously proposed that uses Texture Analysis with Machine Learning to predict IPN malignancy and has been shown to achieve high classification performance. While it is assumed that such techniques can capture image texture patterns that separate benignity from malignancy, such methods also intrinsically measure nodule size. The contribution of texture to classifier performance beyond size has not been studied and we seek to quantify this. We show, for the first time, the relative contributions of texture and size on the performance of Artificial Intelligence risk stratification of solid nodules.
Method:
Two datasets were created from the US National Lung Screening Trial (NLST). The first (A), comprising 640 solid nodules, was built to remove size as a discriminatory factor between benign and malignant; all malignant solid nodules between 4 and 20 mm diameter were selected, and for each, a benign solid nodule was selected that most closely matched it in diameter. Any malignant nodule for which an equivalently sized benign could not be found within 0.8 mm was rejected. Sizes were measured using automated volumetric segmentation. The second dataset (B), also comprising 640 subjects, included all malignant nodules in A but benign nodules were randomly selected following the empirical size distribution of the whole NLST dataset. Therefore, nodule size cannot be a discriminative factor in A but would be in B. Two nodule stratification algorithms were developed using Texture Analysis combined with Machine Learning (Support Vector Regression) integrating 20 variables including 3D Haralick, Gabor and Shape features, from A and B respectively using five-fold cross validation and the performance compared measuring Area-Under-the-Curve (AUC).
Result:
The average AUC for the algorithm trained on dataset A was 0.70 whereas using size alone on the same dataset gave an AUC of 0.50. The AUC was 0.91 for the algorithm trained on B.
Conclusion:
On this data, Texture Analysis with Machine Learning contributes 0.20 AUC points to classfication performance. Artificial Intelligence based risk classification can identify radiological features that are predictive of solid nodule malignancy that are independent of nodule size.
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P3.15 - SCLC/Neuroendocrine Tumors (ID 731)
- Event: WCLC 2017
- Type: Poster Session with Presenters Present
- Track: SCLC/Neuroendocrine Tumors
- Presentations: 1
- Moderators:
- Coordinates: 10/18/2017, 09:30 - 16:00, Exhibit Hall (Hall B + C)
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P3.15-004 - Distinct Role of FAK Kinase and C-Terminal Domains on Small-Cell Lung Cancer Proliferation (ID 9951)
09:30 - 09:30 | Author(s): Pierre P Massion
- Abstract
Background:
Small-cell lung cancer (SCLC) is a devastating illness with a median five-year overall survival of 5%. Focal Adhesion Kinase (FAK) is a non-receptor tyrosine kinase which regulates integrin and growth factor signaling pathways involved in cell proliferation, survival, migration, and invasion. FAK is overexpressed/activated in many cancers, including SCLC. We hypothesized that FAK overexpression/activation in SCLC contributes to its aggressive behavior and that FAK may represent a novel therapeutic target in SCLC.
Method:
Two SCLC cell lines, one growing in suspension (NCI-H82) and one adherent (NCI-H446), were treated with a FAK small-molecule inhibitor, PF-573,228 (PF-228) (1 to 5µM), or stably transfected with FAK shRNA and/or FAK-related non kinase (FRNK) domain (doxycycline-inducible) using lentivirus vector. Cell proliferation, cell cycle, apoptosis, motility, and invasion were studied by standard methodologies. FAK expression/activity was evaluated by WB. Active Rac1 expression was evaluated by WB after enrichment of cell lysates in active GTP-bound Rac using Rac pull down columns.
Result:
PF-228 decreased FAK activity by decreasing phospho-FAK (Tyr397) expression in both SCLC cell lines, without modifying total FAK expression. This induced significant inhibition of cell proliferation and DNA synthesis, cell cycle arrest in G2/M phases, and increase of apoptosis dose-dependently. PF-228 also decreased motility in the adherent cell line NCI-H446. Transfection of FAK shRNA decreased total and phospho-FAK expression but this did not affect cell proliferation, DNA synthesis, and cell cycle. We hypothesized that the absence of anti-proliferative effects was due to the loss of the FAK-targeting domain (FAT), normally attached to the focal adhesion complex where it inhibits other proteins supporting proliferation (such as Rac). To test this, we restored FAT function by transfecting FRNK, a physical repressor of FAK activity, into cells stably transfected with FAK shRNA and demonstrated inhibition of cell proliferation and DNA synthesis. Expression of FRNK in SCLC cell lines not previously transfected with FAK shRNA also significantly decreased cell proliferation and DNA synthesis. Moreover, FAK shRNA transfection increased active Rac1 levels, while FRNK re-expression in the cell lines previously transfected with FAK shRNA decreased it.
Conclusion:
This work demonstrates that FAK has a dual role in SCLC: 1/ it supports proliferation, migration, invasion, and inhibits apoptosis through the kinase domain, suggesting that inhibition of FAK kinase activity may represent a suitable therapeutic target for SCLC, and 2/ it inhibits SCLC proliferation through the non-kinase C-terminal domain FRNK, which keeps Rac inactive.
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WS 02 - IASLC Symposium on the Advances in Lung Cancer CT Screening (Ticketed Session SOLD OUT) (ID 631)
- Event: WCLC 2017
- Type: Symposium
- Track: Radiology/Staging/Screening
- Presentations: 1
- Moderators:
- Coordinates: 10/14/2017, 09:00 - 18:15, F201 + F202 (Annex Hall)
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WS 02.12 - Lung Cancer Biomarkers - Do We Have Good Candidates for Early Detection? (ID 10625)
14:00 - 14:30 | Presenting Author(s): Pierre P Massion
- Abstract
- Presentation
Abstract:
Are we screening the at risk population? How can we bring imaging and molecular tools to improve the early detection/treatment rates of lung cancer and decrease the false positive rates? The National Lung Screening Trial (NLST) demonstrated that low dose CT screening among high risk individuals reduces the relative risk for lung cancer mortality by 20%. Yet the poor specificity of chest CT, which forces us to deal with large proportions of false positive results, morbidity and cost, pushes us to improve the risk assessment and diagnostic accuracy of the tests offered. A large proportion of individuals will be diagnosed with lung cancer and still do not meet the population criteria studied in NLST trial. So the scientific community is charged to improving early detection of invasive lung cancers to a definitive treatment. An estimated 43% of individuals diagnosed with lung cancer meet the NLST criteria, thus missing an opportunity to screen another large at-risk population. There are currently no accepted strategies for screening patients who fall outside of these criteria. Therefore tools of risk assessment and early detection could profoundly reduce lung cancer mortality. Considerations for familial history with or without germline DNA mutation carriers, exposure to carcinogens, chronic pulmonary obstructive lung disease, are being proposed for integration in risk prediction strategies. The reporting tools for findings at the time of CT screening have been replaced by the American Radiology Association’s LungRADS score which reduces the false positives rate among the most highly suspicious lesions from 27% to 13%. On the imaging diagnostic side, the emerging field of radiomics involves computational analysis of extracted quantitative data from clinical radiology images. Rapid progress in this field offers the promise of diagnostic accuracies that will surpass the one of expert radiologists. On the molecular diagnostic side, diagnostic tools for risk adjustment and to augment current lung cancer detection strategies are urgently needed. Circulating tumor cells are shed from primary tumors into the blood stream, so is circulating tumor DNA naked or in microvesicles. Proteins, RNA moieties and epigenetic changes can be captured in the circulation and also have the promise of changing the landscape of non-invasive diagnosis of early lung cancer. Some of these strategies will be discussed to illustrate the impressive and rapid progress soon coming to the clinic to address the primary goals of early detection.
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