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G. Walsh
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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
- Moderators:
- Coordinates: 9/08/2015, 09:30 - 17:00, Exhibit Hall (Hall B+C)
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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): G. Walsh
- 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
- Moderators:
- Coordinates: 9/08/2015, 09:30 - 17:00, Exhibit Hall (Hall B+C)
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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): G. Walsh
- 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.
Figure 1Variables 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)
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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P3.03 - Poster Session/ Treatment of Locoregional Disease – NSCLC (ID 214)
- Event: WCLC 2015
- Type: Poster
- Track: Treatment of Locoregional Disease – NSCLC
- Presentations: 1
- Moderators:
- Coordinates: 9/09/2015, 09:30 - 17:00, Exhibit Hall (Hall B+C)
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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): G. Walsh
- Abstract
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.
