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Radiomics Predict Pathological Response in Non-Small Cell Lung Cancer
E586
International Journal of Radiation Oncology Biology Physics
tumor stage was a significant predictor only for DMFS (HR-1.55 P<0.01). On Multivariate analysis, TLG (at cut-off of 820) was an independent predictor of OS (P-0.04). Stage IV was an independent predictor of DFS (P-0.007). Stage IV, total GTV and total MTV were independent predictors for DMFS (P<0.00). Post treatment metabolic response (MR) had no impact on DFS, DMFS and OS but patients with partial MR (PMR) had a significantly inferior LRC (82% vs. 65% PZ0.004) compared to patients with complete MR (CMR). There was a significant association between total MTV and MR, patients with PMR having higher mean MTV than patients with CMR (103.4 59.2 vs. 73.8 65.8, PZ0.013). Conclusion: Total GTV and TLG are predictors of DFS, DMFS and OS. MTV is a predictor of DMFS. Traditional TNM based prognostic parameters like tumor stage and nodal stage influenced DFS and DMFS but did not impact on OS. TNM and PET-CT parameters are complementary in determining prognosis in NPC patients treated with definitive chemo radiotherapy. Author Disclosure: A. Pilar: None. S.G. Laskar: None. V. Rangarajan: None. N. Purandare: None. A. Budrukkar: None. T. Gupta: None. V. Murthy: None. P. Pai: None. A. Deshmukh: None. D. Chaukar: None. A.K. D’Cruz: director; Tata Memorial Centre. J. Agarwal: None.
and rounder shape (Spherical disproportionality, AUC Z 0.63, P <0.01). On cross validation, a combined model of radiomic and conventional imaging features performed significantly better than conventional measurements for prediction of pCR (0.68 vs 0.57, P<0.05) and GRD (0.65 vs 0.60, P<0.05) Conclusion: We identified CT radiomic features associated with pathological response in NSCLC patients undergoing trimodality therapy. Clinically used conventional imaging measurements, such as tumor diameter, were not associated with pathological outcome. This study demonstrates that radiomics can provide valuable complementary clinical information, and perform better than conventional imaging features for clinical outcomes. Author Disclosure: T.P. Coroller: None. V. Agrawal: None. V. Narayan: None. P. Grossmann: None. Y. Hou: None. S. Lee: None. R.H. Mak: Consultant; Amgen. H. Aerts: None.
3436 Radiomics Predict Pathological Response in Non-Small Cell Lung Cancer T.P. Coroller,1 V. Agrawal,1 V. Narayan,1 P. Grossmann,1 Y. Hou,1 S. Lee,1 R.H. Mak,2 and H. Aerts1; 1Dana-Farber Cancer Institute, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, 2Dana-Farber Cancer Institute, Brigham and Women’s Cancer Center, Boston, MA Purpose/Objective(s): Radiomics is an emerging field of quantitative imaging that aims to describe tumors using a large set of advanced imaging features. Pathological response is a direct measure of tumor response to neoadjuvant chemoradiation assessed at time of surgery. Predicting pathological response at an early time point would allow for potential modification of the treatment regimen. In this study we assessed if pre-treatment radiomics data are able to predict pathological response after neoadjuvant chemoradiation in patients with locally advanced non-small cell lung cancer (NSCLC). Materials/Methods: 127 NSCLC patients undergoing with trimodality treatment at a single institution were included in this retrospective study. The median radiation dose was 54 Gy with 107 (84%) and 20 (16%) patients that respectively had concurrent or induction chemoradiation. The median time to surgery after completion of chemoradiation was 1.4 months (0.3 to 5 months). Pathological response was evaluated at the time of surgery and scored as gross residual disease (GRD) or pathological complete response (pCR). Radiomic features (over 1600) were extracted from CT imaging obtained at the time of radiation treatment planning and reduced using unsupervised selection (principal component analysis). Association of selected features with pathological response was evaluated using area under the curve (AUC) analysis. Cross-validation (CV) was computed to evaluate model performance (100 splits, 80% training and 20% validation). Permutation test (1000 CV) was used to control model performance from random. Results: At the time of surgery, 27 (21.3%) patients had a complete pathological response and 67 (52.7%) had a gross residual disease. PCA analysis selected 15 features for further analysis. Of these, seven features were predictive associated with GRD (AUC range from 0.61 to 0.66, P<0.05), and one with pCR (AUCZ0.63, PZ0.01). No conventional imaging features were predictive associated with pathological response (range AUCZ0.51 to 0.59, P >0.05). Tumors that did not respond well to neoadjuvant chemoradiation were more likely to present with heterogeneous texture (GLCM Entropy, AUC Z 0.61, P Z 0.029)
3437 Inhibition of PI3K and MEK in Combination With Radiation Therapy in Murine Model of Head and Neck Squamous Cell Carcinoma (HNSCC) K.G. Blas,1 S. Galoforo,1 S.A. Krueger,1 T.G. Wilson,1 I.S. Grills,2 B. Marples,1 and G.D. Wilson1; 1Beaumont Health, Royal Oak, MI, 2 Beaumont Health System, Royal Oak, MI Purpose/Objective(s): To study the effectiveness of dual targeted PI3K and MEK inhibition alone and in combination with radiotherapy in HNSCC models as a novel clinical treatment strategy. Materials/Methods: MTT assays were used to establish growth inhibition and scheduling of RT with Buparlisib (BKM120) the pan PI3K-inhibitor and Binimetnib (MEK162) a MEK1/2-inhibitor. Then, clonogenic survival assays were used to assess the impact of BKM120 alone, MEK162 alone, and both drugs in combination in two HNSCC cell lines (UT14 and UT15). Subcutaneous xenografts were established in female NIH III HO mice using the UT14 cell line. Tumors grew to 200-400 mm3 before treatment: +BKM120 (10mg/kg oral gavage), +MEK162 (5mg/kg oral gavage), or combined +BKM120 (10mg/kg), +MEK162 (5mg/kg oral gavage) RT. In radiation treated cohorts, a sub-curative 30 Gy dose (2 Gy/day 5 days a week) was delivered 4 hours before drug treatment. The primary endpoint was tumor regrowth at 90 days post treatment. Results: The IC50 concentrations for BKM120 were 0.4 mM and 0.5 mM for UT14 and UT15, respectively. For MEK162, the IC50 was 0.08 mM for UT14 and above 10 mM for UT15. Delivery of BKM120 (0.5 mM) 4 hours post radiotherapy produced the greatest reduction in cell viability compared to radiation alone (75% to 50% in UT14 and 100% to 80% in UT15). Clonogenic survival assay confirmed UT14 to be more sensitive to MEK162 than BKM120. In the UT14 flank tumor model, RT+ MEK162 and RT+ (BKM120 & MEK162) had slower growth rates compared to RT alone. Time to 2x pre-treatment volume was (66.1 days, PZ0.03) for RT+ MEK162 and (65.31 days, PZ0.0004) for RT+ (BKM120 & MEK162) compared to (32.39 days) for RT alone. There was no difference in growth rates between +BKM120 (18.65 days), +MEK162 (14.65 days), and controls (17.95 days). The combination drug treatment did slow growth rates compared to controls (27.0 days), however, had faster regrowth than RT alone (NS). Conclusion: Although the combination of PI3K and MEK inhibition showed delay tumor regrowth over radiotherapy alone in the UT14 xenograft model, it was the addition of MEK inhibition to radiotherapy that appeared to be the most effective combination tested in this study. However, a therapeutic response with increased doses of BKM120 has not been examined and merits further study. Ongoing experiments will determine whether this observation is also observed in the second, more radioresistant, UT15 model.
International Journal of Radiation Oncology Biology Physics
tumor stage was a significant predictor only for DMFS (HR-1.55 P<0.01). On Multivariate analysis, TLG (at cut-off of 820) was an independent predictor of OS (P-0.04). Stage IV was an independent predictor of DFS (P-0.007). Stage IV, total GTV and total MTV were independent predictors for DMFS (P<0.00). Post treatment metabolic response (MR) had no impact on DFS, DMFS and OS but patients with partial MR (PMR) had a significantly inferior LRC (82% vs. 65% PZ0.004) compared to patients with complete MR (CMR). There was a significant association between total MTV and MR, patients with PMR having higher mean MTV than patients with CMR (103.4 59.2 vs. 73.8 65.8, PZ0.013). Conclusion: Total GTV and TLG are predictors of DFS, DMFS and OS. MTV is a predictor of DMFS. Traditional TNM based prognostic parameters like tumor stage and nodal stage influenced DFS and DMFS but did not impact on OS. TNM and PET-CT parameters are complementary in determining prognosis in NPC patients treated with definitive chemo radiotherapy. Author Disclosure: A. Pilar: None. S.G. Laskar: None. V. Rangarajan: None. N. Purandare: None. A. Budrukkar: None. T. Gupta: None. V. Murthy: None. P. Pai: None. A. Deshmukh: None. D. Chaukar: None. A.K. D’Cruz: director; Tata Memorial Centre. J. Agarwal: None.
and rounder shape (Spherical disproportionality, AUC Z 0.63, P <0.01). On cross validation, a combined model of radiomic and conventional imaging features performed significantly better than conventional measurements for prediction of pCR (0.68 vs 0.57, P<0.05) and GRD (0.65 vs 0.60, P<0.05) Conclusion: We identified CT radiomic features associated with pathological response in NSCLC patients undergoing trimodality therapy. Clinically used conventional imaging measurements, such as tumor diameter, were not associated with pathological outcome. This study demonstrates that radiomics can provide valuable complementary clinical information, and perform better than conventional imaging features for clinical outcomes. Author Disclosure: T.P. Coroller: None. V. Agrawal: None. V. Narayan: None. P. Grossmann: None. Y. Hou: None. S. Lee: None. R.H. Mak: Consultant; Amgen. H. Aerts: None.
3436 Radiomics Predict Pathological Response in Non-Small Cell Lung Cancer T.P. Coroller,1 V. Agrawal,1 V. Narayan,1 P. Grossmann,1 Y. Hou,1 S. Lee,1 R.H. Mak,2 and H. Aerts1; 1Dana-Farber Cancer Institute, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, 2Dana-Farber Cancer Institute, Brigham and Women’s Cancer Center, Boston, MA Purpose/Objective(s): Radiomics is an emerging field of quantitative imaging that aims to describe tumors using a large set of advanced imaging features. Pathological response is a direct measure of tumor response to neoadjuvant chemoradiation assessed at time of surgery. Predicting pathological response at an early time point would allow for potential modification of the treatment regimen. In this study we assessed if pre-treatment radiomics data are able to predict pathological response after neoadjuvant chemoradiation in patients with locally advanced non-small cell lung cancer (NSCLC). Materials/Methods: 127 NSCLC patients undergoing with trimodality treatment at a single institution were included in this retrospective study. The median radiation dose was 54 Gy with 107 (84%) and 20 (16%) patients that respectively had concurrent or induction chemoradiation. The median time to surgery after completion of chemoradiation was 1.4 months (0.3 to 5 months). Pathological response was evaluated at the time of surgery and scored as gross residual disease (GRD) or pathological complete response (pCR). Radiomic features (over 1600) were extracted from CT imaging obtained at the time of radiation treatment planning and reduced using unsupervised selection (principal component analysis). Association of selected features with pathological response was evaluated using area under the curve (AUC) analysis. Cross-validation (CV) was computed to evaluate model performance (100 splits, 80% training and 20% validation). Permutation test (1000 CV) was used to control model performance from random. Results: At the time of surgery, 27 (21.3%) patients had a complete pathological response and 67 (52.7%) had a gross residual disease. PCA analysis selected 15 features for further analysis. Of these, seven features were predictive associated with GRD (AUC range from 0.61 to 0.66, P<0.05), and one with pCR (AUCZ0.63, PZ0.01). No conventional imaging features were predictive associated with pathological response (range AUCZ0.51 to 0.59, P >0.05). Tumors that did not respond well to neoadjuvant chemoradiation were more likely to present with heterogeneous texture (GLCM Entropy, AUC Z 0.61, P Z 0.029)
3437 Inhibition of PI3K and MEK in Combination With Radiation Therapy in Murine Model of Head and Neck Squamous Cell Carcinoma (HNSCC) K.G. Blas,1 S. Galoforo,1 S.A. Krueger,1 T.G. Wilson,1 I.S. Grills,2 B. Marples,1 and G.D. Wilson1; 1Beaumont Health, Royal Oak, MI, 2 Beaumont Health System, Royal Oak, MI Purpose/Objective(s): To study the effectiveness of dual targeted PI3K and MEK inhibition alone and in combination with radiotherapy in HNSCC models as a novel clinical treatment strategy. Materials/Methods: MTT assays were used to establish growth inhibition and scheduling of RT with Buparlisib (BKM120) the pan PI3K-inhibitor and Binimetnib (MEK162) a MEK1/2-inhibitor. Then, clonogenic survival assays were used to assess the impact of BKM120 alone, MEK162 alone, and both drugs in combination in two HNSCC cell lines (UT14 and UT15). Subcutaneous xenografts were established in female NIH III HO mice using the UT14 cell line. Tumors grew to 200-400 mm3 before treatment: +BKM120 (10mg/kg oral gavage), +MEK162 (5mg/kg oral gavage), or combined +BKM120 (10mg/kg), +MEK162 (5mg/kg oral gavage) RT. In radiation treated cohorts, a sub-curative 30 Gy dose (2 Gy/day 5 days a week) was delivered 4 hours before drug treatment. The primary endpoint was tumor regrowth at 90 days post treatment. Results: The IC50 concentrations for BKM120 were 0.4 mM and 0.5 mM for UT14 and UT15, respectively. For MEK162, the IC50 was 0.08 mM for UT14 and above 10 mM for UT15. Delivery of BKM120 (0.5 mM) 4 hours post radiotherapy produced the greatest reduction in cell viability compared to radiation alone (75% to 50% in UT14 and 100% to 80% in UT15). Clonogenic survival assay confirmed UT14 to be more sensitive to MEK162 than BKM120. In the UT14 flank tumor model, RT+ MEK162 and RT+ (BKM120 & MEK162) had slower growth rates compared to RT alone. Time to 2x pre-treatment volume was (66.1 days, PZ0.03) for RT+ MEK162 and (65.31 days, PZ0.0004) for RT+ (BKM120 & MEK162) compared to (32.39 days) for RT alone. There was no difference in growth rates between +BKM120 (18.65 days), +MEK162 (14.65 days), and controls (17.95 days). The combination drug treatment did slow growth rates compared to controls (27.0 days), however, had faster regrowth than RT alone (NS). Conclusion: Although the combination of PI3K and MEK inhibition showed delay tumor regrowth over radiotherapy alone in the UT14 xenograft model, it was the addition of MEK inhibition to radiotherapy that appeared to be the most effective combination tested in this study. However, a therapeutic response with increased doses of BKM120 has not been examined and merits further study. Ongoing experiments will determine whether this observation is also observed in the second, more radioresistant, UT15 model.