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S. Swisher



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    ORAL 30 - Community Practice (ID 141)

    • Event: WCLC 2015
    • Type: Oral Session
    • Track: Community Practice
    • Presentations: 1
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      ORAL30.04 - Discussant for ORAL30.01, ORAL30.02, ORAL30.03 (ID 3365)

      17:18 - 17:28  |  Author(s): S. Swisher

      • Abstract
      • Presentation

      Abstract not provided

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    P2.01 - Poster Session/ Treatment of Advanced Diseases – NSCLC (ID 207)

    • Event: WCLC 2015
    • Type: Poster
    • Track: Treatment of Advanced Diseases - NSCLC
    • Presentations: 1
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      P2.01-041 - MD Anderson Oncology Expert Advisor™ System (OEA™): A Cognitive Computing Recommendations Application (App) for Lung Cancer (ID 3106)

      09:30 - 09:30  |  Author(s): S. Swisher

      • Abstract

      Background:
      The OEA[TM] is a clinical support system with a continuous improvement capability. Its objectives are to enable/empower evidence-based decisions/care by disseminating knowledge and expertise to physicians/users tailored to meet the clinical needs of individual patients as if consulting with an expert. Cognitive computing platforms have the potential to disseminate expert knowledge and tertiary level care to patients. This objective is made possible by making available to physicians/providers cognitive computing generated expert recommendations in diagnosis, staging and treatment. The cognitive computing software was trained by MD Anderson experts using currently available consensus guidelines and an iterative feedback process. Here we test the capability of this cognitive computing software program developed at MD Anderson to generate expert recommendations when patients with advanced-stage NSCLC have a targetable molecular aberration.

      Methods:
      We developed a web based prototype of MD Anderson’s Oncology Expert Advisor (OEA[TM]), a cognitive clinical decision support tool powered by IBM Watson. The Watson technology is IBM’s third generation cognitive computing system based on its unique capabilities in natural language processing and deep QA (question-answer). We trained OEA[TM] by loading historical patient cases and assessed the accuracy of targeted treatment suggestions using MD Anderson’s physicians’ decisions as benchmark. A false positive result was defined as a treatment recommendation rendered with high confidence that was non-correct (less optimal), whereas false negative was defined as a correct or more optimal treatment suggestion listed as a low confidence recommendation.

      Results:
      In our preliminary analyses, OEA[TM] demonstrated four core capabilities: 1) Patient Evaluation through interpretation of structured and unstructured clinical data to create a dynamic case summary with longitudinal view of the pertinent events 2) Treatment and management suggestions based on patient profile weighed against consensus guidelines, relevant literature, and MD Anderson expertise, which included approved therapies, genomic based therapies as well as automated matching to appropriate clinical trials at MD Anderson, 3) Care pathway advisory that alerts the user for anticipated toxicities and its early identification and proactive management, and 4) Patient-oriented research functionalities for identification of patient cohorts and hypothesis generation for future potential clinical investigations. Detailed testing continues and the accuracy of standard-of-care (SOC) treatment recommendations of OEA[TM], as well as false positivity and negativity rates will be presented in detail at the meeting.

      Conclusion:
      OEA[TM] is able to generate dynamic patient case summary by interpreting structured and unstructured clinical data and suggest personalized treatment options. Live system evaluation of OEA[TM] is ongoing and the application of OEA[TM] in clinical practice is expected to be piloted at our institution.

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    P2.03 - Poster Session/ Treatment of Locoregional Disease – NSCLC (ID 213)

    • Event: WCLC 2015
    • Type: Poster
    • Track: Treatment of Locoregional Disease – NSCLC
    • Presentations: 1
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      P2.03-017 - Pre-Operative Chemotherapy Followed by Surgery for N2 Non-Small Cell Lung Cancer: A 15-Year Experience (ID 3152)

      09:30 - 09:30  |  Author(s): S. Swisher

      • Abstract

      Background:
      The ideal approach to patients with N2 non-small cell lung cancer (NSCLC) remains controversial. While pathological confirmation of nodal status is advocated, in clinical practice patients with suspicious radiographic evidence of N2 disease are frequently assigned to pre-operative therapy without pathological confirmation. Herein, we review our experience with pre-operative chemotherapy followed by surgery in patients with N2 NSCLC and compare outcomes of biopsy proven N2 disease and those patients who were diagnosed based on PET/CT alone.

      Methods:
      A prospectively entered institutional database was accessed to identify all patients with N2 NSCLC treated by pre-operative chemotherapy followed by surgery from 1999 to 2014. Data were verified by chart review. Patients without biopsy or PET-based evidence of N2 disease were excluded.

      Results:
      We identified 113 patients of whom 57 had biopsy proof of cN2 and 56 were cN2 based on PET-positivity. See Table 1 for patient demographic and clinico-pathologic variables. Median survival for the cohort was 53.3 months and there was only 1 (0.88%) peri-operative death at 90 days. Three and 5-year survival rates were 63.8% and 39.7%, respectively. Locoregional recurrences occurred in 16.8% of patients. Induction chemotherapy resulted in a significant PET response (SUV reduction > 6) in 38.5% of cases (15/39) where pre- and post-treatment imaging was available. Only 8.77% of patients remained pN2 after pre-operative chemotherapy in those patients who had pre-treatment pathological confirmation. No survival differences were noted between patients with biopsy proven N2 and those with PET-positive N2 nodes (Figure 1).

      Demographic and clinico-pathologic variables.
      Variables Biopsy proven N2 (N=57) PET positive N2 (N=56) P value Total cohort (N=113)
      Median age (range) 64(38-80) 62(43-77) 0.763 63(38-80)
      Male gender 25(46.3) 28(54.90) 0.378 53(50.48)
      Mean FEV1 (%pred) 85.78 86.54 0.798 86.16
      Mean DLCO (%pred) 81.89 82.28 0.916 82.08
      Type of surgery 0.743
      Wedge/Segmentectomy 3(5.26) 4(7.14) 7(6.19)
      Lobectomy 48(84.21) 44(78.57) 92(81.42)
      Pneumonectomy 6(10.53) 8(14.29) 14(12.39)
      Post-operative treatment 0.094
      None 24(42.11) 27(48.21) 51(45.13)
      Chemo 1(1.75) 15(26.79) 6(5.31)
      Radiation 6(5.31) 9(16.07) 41(36.28)
      Chemoradiation 6(10.53) 9(16.07) 9(16.07)
      Pathological N stage 0.090
      N0 20(35.09) 22(39.29) 42(37.17)
      N1 32(56.14) 22(39.29) 54(47.79)
      N2 5(8.77) 12(21.43) 17(15.04)
      Figure 1



      Conclusion:
      Pre-operative chemotherapy followed by surgery for N2 NSCLC in a well-selected cohort results in good short and long-term outcomes. When pathological confirmation of N2 disease requires invasive staging, it may be acceptable to forgo such tests without compromising patient outcomes. Further prospective studies are needed to determine the ideal treatment regimen for these complex patients.

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    P2.04 - Poster Session/ Biology, Pathology, and Molecular Testing (ID 234)

    • Event: WCLC 2015
    • Type: Poster
    • Track: Biology, Pathology, and Molecular Testing
    • Presentations: 1
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      P2.04-066 - Programmed Cell Death Ligand 1 (PD-L1) Overexpression and Low Immune Infiltrate Score Correlate with Poor Outcome in Lung Adenocarcinoma (ID 776)

      09:30 - 09:30  |  Author(s): S. Swisher

      • Abstract

      Background:
      PD-L1 is a key immunoregulatory checkpoint which suppresses cytotoxic immune response in a variety of physiologic and pathologic conditions. Thus, inhibition of PD-L1 can lead to reactivating tumor immunity and assist to cancer therapy. PD-L1 overexpression in the tumor cells has been correlated to a lessened immune response and consequent worse prognosis in a variety of cancers. To better understand the immune profiling of PD-L1 expression and its interplay with immune cells, we analyzed the correlation between image analysis-based immunohistochemical (IHC) expression of PD-L1 and tumor infiltrating immune cells density in surgically resected non-small cell lung carcinomas (NSCLC), and the correlation with clinical and pathological features, including patient outcome.

      Methods:
      IHC for PD-L1, PD-1, CD3, CD4, CD8, CD45RO, CD57, CD68, Granzyme B and FOXP3 were performed in 254 surgical resected stages I-III NSCLC, Adenocarcinoma (ADC=146) and Squamous cell Carcinoma (SqCC=108) from formalin-fixed and paraffin-embedded tissues. PD-L1 membrane expression on tumor cells and density of inflammatory cells were quantified using image analysis in intra-tumoral (IT) and peri-tumoral (PT) compartments. H-score > 5 was used as a cut-off for positive PD-L1 expression and an immune-score (IMS) using CD8/CD4/CD68 was devised. PD-L1 expression and inflammatory cells were correlated with clinico-pathologic features and patient outcomes.

      Results:
      Positive PD-L1 expression was seen in 26.84% (n=69) of the entire cohort, 23.29% (n=34) of 146 ADC and 23.40% (n=35) of 115 SqCC. In ADC, higher levels of PD-L1 expression were detected in tumors with solid histology pattern compared with other histology patterns (P=0.034), and in lifetime smokers compared with non-smokers (P<0.0001). In SqCC PD-L1 expression was positive correlation with tumor size (Rho=0.19471, P=0.0435). In overall, PD-L1 expression correlated positively with inflammatory cell density in both IT and PT compartments in ADC and SqCC. Patients with KRAS mutation (P=0.00058), solid tumor (P<0.0001) or smoker (P = 0.0446) were more likely to have positive PD-L1 expression tumor cells in ADC. No correlation was detected between EGFR mutation and immune markers. Using PD-L1 and CD8/CD4/CD68 IMS expression levels, in ADC and SqCC, we identified 4 groups of tumors (Table 1). Multivariate Cox proportional hazard regression analysis demonstrated that tumors with high PD-L1 expression and low IMS in ADC exhibited significantly poor recurrence-free (HR=4.299; P=0.0101) and overall survival (HR=5.632; P=0.0010).

      Table 1. Summary of the correlation between PD-L1 expression levels and immune-score (IMS=CD8/CD4/CD68) in adenocarcinoma (ADC) and squamous cells carcinoma (SQCC).
      PDL-1 H-score (ADC) IMS (Low) IMS (High) Total
      <5 61 (41.78%) 51 (34.93%) 112 (76.71%)
      ≥5 8 (5.48%) 26 (17.81%) 34 (23.29%)
      Total 69 (47.26%) 77 (52.74%) 146 (100.0%)
      PDL-1 H-score (SqCC)
      <5 37 (34.30%) 36 (33.30%) 73 (67.60%)
      ≥5 17 (15.70%) 18 (16.70%) 35 (32.40%)
      Total 54 (50.00%) 54 (50.00%) 108 (100.0%)


      Conclusion:
      Higher PD-L1 expression is associated with solid pattern in adenocarcinoma and higher level of tumoral immune infiltrate. We developed an immune score which when combined with PD-L1 expression significantly correlates with patient outcome in surgically resected ADCs. (Supported by grants UT-Lung SPORE P50CA70907 and CPRIT RP120713).

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    P3.03 - Poster Session/ Treatment of Locoregional Disease – NSCLC (ID 214)

    • Event: WCLC 2015
    • Type: Poster
    • Track: Treatment of Locoregional Disease – NSCLC
    • Presentations: 1
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      P3.03-032 - MD Anderson Oncology Expert Advisor™: A Cognitive Clinical Decision Support Tool for Evidence-Based Multi-Disciplinary Lung Cancer Care (ID 3039)

      09:30 - 09:30  |  Author(s): S. Swisher

      • Abstract
      • Slides

      Background:
      The majority of patients diagnosed with non-small cell lung cancer (NSCLC) receive care in the community setting with limited access to multidisciplinary management common in tertiary care centers. The availability of genomics allows tailored treatments for patients; and with novel, rapidly emerging therapeutic options, it is challenging for busy clinicians to maintain familiarity with current therapy recommendations. Therefore, to empower practicing oncologists in community settings to offer the optimal management at the first intervention, we have developed the MD Anderson Oncology Expert Advisor™ (OEA) application for multi-disciplinary management of lung cancer patients. As the first multi-disciplinary solution for providing comprehensive management of lung cancer, the objective of OEA™ Lung is to leverage cognitive analytics on vast and ever evolving clinical care information and patient big data to disseminate knowledge and expertise, thus enabling physicians to provide evidence-based care and management tailored for the individual patient, similar to consulting an expert. Further, we aimed to create a system for sharing knowledge from more experienced experts to provide care pathways and management recommendations for physicians globally.

      Methods:
      Using cognitive computing, our cancer center partnered with IBM Watson to develop an expert system designed to provide physicians with the tools needed to process high-volume patient and medical information and to stay up-to-date with the latest treatment and management options, so that they can make the best evidence-based treatment decisions for their lung cancer patients. The OEA™ application for lung was built upon core capabilities of the OEA™ applications for leukemia and molecular/targeted therapies. Experts in multiple disciplines including thoracic surgery, medical oncology, and radiation oncology met regularly to design and provide specialized input to the IBM technical team in an agile development cycle. This system was powered to utilize both structured and unstructured data from validated sources; to thoroughly evaluate and stage patients; and to offer eligible clinical trials and personalized therapeutic options. In addition to delivering evidence-based, weighted therapy recommendations, OEA™ Lung provides care pathways for management of toxicities for each treatment modality (surgery, radiation, and medical oncology).

      Results:
      The OEA™ Lung application supports three core functions: 1) dynamic patient summary assimilating complete (structured and unstructured) data to show demographics, labs, genotype, treatment history, and previous treatment responses; 2) weighted evidence-based, multimodality treatment options, with recommendations based on literature support which is provided, along with screening for relevant trials; 3) care pathway advisories, to manage treatment related toxicities for each modality. Further, this product improves quality of care by optimizing outcomes with access to trials and care pathways.

      Conclusion:
      The OEA™ application for lung is a cognitive expert system designed to assimilate multidisciplinary recommendations for care and management of lung cancer patients based on current consensus guidelines and expert recommendations from a quaternary referral cancer center to the community practice setting. By democratizing knowledge from our specialty cancer center, we have taken steps toward achieving an important goal of ending cancer for all, by providing global access to optimal cancer care for patients with this disease. Further evaluation of outcomes following implementation are warranted.

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    P3.04 - Poster Session/ Biology, Pathology, and Molecular Testing (ID 235)

    • Event: WCLC 2015
    • Type: Poster
    • Track: Biology, Pathology, and Molecular Testing
    • Presentations: 1
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      P3.04-031 - Combining CT Texture Analysis with Semantic Imaging Descriptions for the Radiogenomic Detection of EGFR and KRAS Mutations in NSCLC (ID 2965)

      09:30 - 09:30  |  Author(s): S. Swisher

      • Abstract

      Background:
      Existing literature suggests quantitative texture features derived from CT imaging can differentiate tumor genotypes and phenotypes. We combined CT texture analysis with semantic imaging descriptions provided by radiologists, and evaluated their ability to identify EGFR and KRAS mutation status in NSCLC.

      Methods:
      We retrospectively reviewed CT images from 628 patients from the GEMINI (Genomic Marker-Guided Therapy Initiative) cohort. Included were NSCLC patients whose biopsies included genetic testing for EGFR or KRAS mutations, and who underwent contrast-enhanced CT imaging within 90 days of biopsy. Excluded were patients who had undergone therapy or biopsy of their primary tumor before imaging, or whose tumors weren’t segmentable. All CT images were contrast-enhanced, with body kernel reconstruction, and slice thicknesses of 1.25-5mm. Tumor segmentation was done in 3DSlicer (Harvard University, Cambridge MA) using a semi-automatic segmentation algorithm. Image pre-processing and textural feature extraction was performed using IBEX (MDACC, Houston TX). Semantic descriptions of the tumors were recorded by a thoracic radiology fellow and a board-certified thoracic radiologist in consensus. For each patient a set of textural features was calculated, based on the GreyLevel Co-Occurrence Matrix, Run-Length Matrix, voxel intensity histogram, and geometric properties of the tumor. Feature selection was based on existing literature, prior research experience, and excluded those features previously found to be poorly reproducible in lung tissue. These were combined with semantic descriptions (e.g. presence or absence of features such as spiculations, air bronchograms, and pleural effusions), for a total of 51 textural and geometric features, and 11 semantic features. When available, the SUVmax for the tumor was also included. To detect correlations with genetic mutations, these features were combined to train a Random Forest machine learning algorithm. This algorithm output a prediction for the mutation status of each tumor, and the predictive accuracy was assessed based on 10-fold cross-validation.

      Results:
      Included were 121 patients, 113 tested for KRAS mutations (26 positive) and 118 tested for EGFR mutations (31 positive). Maximum tumor dimensions ranged from 1.2–15.5cm (mean 5.6cm). Individual semantic features found to correlate with mutation status included tumor cavitation, pleural effusion, presence of ground glass opacity, and the nature of tumor margins (all p-values <0.05). Used collectively in a Random Forest classifier, textural features alone showed a sensitivity and specificity for KRAS detection of 50% and 81% respectively, with 74% overall accuracy. This increased modestly to a sensitivity and specificity of 50% and 84% respectively when semantic features were added, with accuracy increasing to 77%. For EGFR detection, textural features had sensitivity and specificity of 48% and 77% respectively, giving 69% accuracy. Detection of EGFR did not improve with inclusion of semantic features.

      Conclusion:
      Texture analysis correctly identified EGFR and KRAS mutation status in most patients. Although some semantic features correlated with mutation status, when combined with textural features they provided little or no improvement in predictive accuracy. One possible explanation is that textural features may already be capturing the information contained in the semantic features. Our results suggest oncogenic drivers of NSCLC are associated with distinct imaging features that can be detected radiographically.