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J. Erasmus Jr
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P3.01 - Poster Session/ Treatment of Advanced Diseases – NSCLC (ID 208)
- Event: WCLC 2015
- Type: Poster
- Track: Treatment of Advanced Diseases - NSCLC
- Presentations: 1
- Moderators:
- Coordinates: 9/09/2015, 09:30 - 17:00, Exhibit Hall (Hall B+C)
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P3.01-065 - PET Tumor Response by PERCIST Predicts Local-Regional Control in Locally Advanced NSCLC after Concurrent Chemoradiotherapy with Erlotinib (ID 1242)
09:30 - 09:30 | Author(s): J. Erasmus Jr
- Abstract
Background:
Assessing response of locally advanced non-small cell lung cancer (NSCLC) after concurrent chemoradiotherapy by computed tomography (CT) can be complicated by treatment-related pneumonitis or fibrosis. Hypothesizing that measurements of tumor response by [18]F-fluorodeoxyglucose standardized uptake values (SUVs) on positron emission tomography (PET) are more reliably associated with treatment outcomes than those by CT, we compared outcomes and responses according to PET SUV vs. CT among patients in a phase II study of erlotinib+chemoradiation for stage III NSCLC.
Methods:
Trial 2005-1023 enrolled 46 patients in 2007–2010; patients received 63 Gy in 35 fractions over 7 weeks with daily erlotinib and weekly paclitaxel-carboplatin. Tumor response was assessed on diagnostic CT scans with contrast or CT from PET-CT and scored according to RECIST 1.1. Tumor response was also assessed by PERCIST 1.0 (based on SUV) as follows: complete response (CR), disappearance of all measurable tumors; partial response (PR), ≥30% reduction in the sum of SUVs of target lesions; progressive disease (PD), ≥30% increase in the sum of SUVs of target lesions; and stable disease (SD), insufficient change in SUV to qualify for PR or PD. The longest diameter of measurable primary lesions and the short axis of measurable lymph nodes were measured. All non-target lesions were also measured. Two-sided Pearson’s chi-square tests were used to assess frequency associations. Overall survival (OS) and local-regional control (LRC) rates were assessed from treatment start by Kaplan-Meier analysis and log-rank tests; P≤0.05 indicated significance.
Results:
One patient did not have CT and PET after treatment. For the 45 evaluable patients, best response by PET-CT at 6 months after treatment was CR for 15 patients (33%), PR for 19 (42%), SD for 0, PD for 4 (9%), and not available due to did not have baseline or post treatment PET for 7 (16%). Best response by CT at 6 months was CR for 11 (24%), PR for 27 (60), SD for 3 (7%), and PD for 4 (9%) (P<0.001). The 3 patients with SD by CT all died within 7 months after treatment; the 4 patients with PD had new distant metastases. Four-year OS was associated with best overall response on both PET and CT at 6 months (P<0.05) and at 1 year (P<0.05). LRC was associated with best overall response on PET (P<0.01) and best primary tumor response on PET (P<0.05) at 6 and 12 months. Lymph node response was not associated with OS or LRC by PET or CT.
Conclusion:
The CR rate was higher with PET than with CT. Tumor response at 6 months by PET or CT predicted treatment outcomes after chemoradiotherapy for stage III NSCLC. The best overall and primary tumor response by PET within 6 months after treatment was more reliably associated with LRC than was response on CT because of difficulty to assess response due to pneumonitis/lung fibrosis.
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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
- Moderators:
- Coordinates: 9/09/2015, 09:30 - 17:00, Exhibit Hall (Hall B+C)
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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): J. Erasmus Jr
- 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.
