Stephanie W. Lee

Radiomic phenotype features predict pathological response in non-small cell lung cancer.

BACKGROUND AND PURPOSE Radiomics can quantify tumor phenotype characteristics non-invasively by applying advanced imaging feature algorithms. 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 AND METHODS 127 NSCLC patients were included in this study. Fifteen radiomic features selected based on stability and variance were evaluated for its power to predict pathological response. Predictive power was evaluated using area under the curve (AUC). Conventional imaging features (tumor volume and diameter) were used for comparison. RESULTS Seven features were predictive for pathologic gross residual disease (AUC>0.6, p-value<0.05), and one for pathologic complete response (AUC=0.63, p-value=0.01). No conventional imaging features were predictive (range AUC=0.51-0.59, p-value>0.05). Tumors that did not respond well to neoadjuvant chemoradiation were more likely to present a rounder shape (spherical disproportionality, AUC=0.63, p-value=0.009) and heterogeneous texture (LoG 5mm 3D - GLCM entropy, AUC=0.61, p-value=0.03). CONCLUSION We identified predictive radiomic features for pathological response, although no conventional features were significantly predictive. This study demonstrates that radiomics can provide valuable clinical information, and performed better than conventional imaging features.

Radiomic-Based Pathological Response Prediction from Primary Tumors and Lymph Nodes in NSCLC

Introduction: Noninvasive biomarkers that capture the total tumor burden could provide important complementary information for precision medicine to aid clinical decision making. We investigated the value of radiomic data extracted from pretreatment computed tomography images of the primary tumor and lymph nodes in predicting pathological response after neoadjuvant chemoradiation before surgery. Methods: A total of 85 patients with resectable locally advanced (stage II–III) NSCLC (median age 60.3 years, 65% female) treated from 2003 to 2013 were included in this institutional review board–approved study. Radiomics analysis was performed on 85 primary tumors and 178 lymph nodes to discriminate between pathological complete response (pCR) and gross residual disease (GRD). Twenty nonredundant and stable features (10 from each site) were evaluated by using the area under the curve (AUC) (all p values were corrected for multiple hypothesis testing). Classification performance of each feature set was evaluated by random forest and nested cross validation. Results: Three radiomic features (describing primary tumor sphericity and lymph node homogeneity) were significantly predictive of pCR with similar performances (all AUC = 0.67, p < 0.05). Two features (quantifying lymph node homogeneity) were predictive of GRD (AUC range 0.72–0.75, p < 0.05) and performed significantly better than the primary features (AUC = 0.62). Multivariate analysis showed that for pCR, the radiomic features set alone had the best‐performing classification (median AUC = 0.68). Furthermore, for GRD classification, the combination of radiomic and clinical data significantly outperformed all other feature sets (median AUC = 0.73). Conclusion: Lymph node phenotypic information was significantly predictive for pathological response and showed higher classification performance than radiomic features obtained from the primary tumor.

Radiologic-pathologic correlation of response to chemoradiation in resectable locally advanced NSCLC

OBJECTIVES Accurate assessment of tumor response to chemoradiation has the potential to guide clinical decision-making regarding surgical resection and/or dose escalation for patients. Early assessment has implications for Optimal local therapy for operable locally advanced non-small cell lung cancer (LA-NSCLC) is controversial. This study evaluated quantitative CT-based tumor measurements to predict pathologic response. MATERIALS AND METHODS Patients with operable LA-NSCLC treated with chemoradiation followed by surgical resection were assessed. Tumor diameter and volume were quantified from CT imaging obtained prior to chemoradiation and post-chemoradiation prior to surgical resection. Univariate and multivariate logistic regression were used to determine association with the primary endpoint of pathologic complete response (pCR). Overall survival, locoregional recurrence, and distant metastasis were assessed as secondary endpoints. RESULTS 101 LA-NSCLC patients were identified and treated with preoperative chemoradiation and surgical resection. The median RT dose was 54Gy (range, 46-70) and 98% of patients received concurrent chemoradiation as part of their preoperative treatment. Reduction of CT-defined tumor volume was associated with pCR (OR 1.06 [1.02-1.09], p=0.002) and LRR (HR 1.01 [1.00-1.02], p=0.048). Conventional response assessment determined by RECIST (p=0.213) was not associated with pCR or any secondary endpoints. CONCLUSION CT-measured reductions in tumor volume after chemoradiation are associated with pCR and provide greater clinical information about tumor response than conventional response assessment (RECIST) or absolute tumor sizes or volumes. This study demonstrates that change in tumor volumes provides better radiologic-pathologic correlation and is thus an additional tool to assess tumor response following chemoradiation.

Lymph node volume predicts survival but not nodal clearance in Stage IIIA-IIIB NSCLC

Background Locally advanced non-small cell lung cancer (LA-NSCLC) patients have poorer survival and local control with mediastinal node (N2) tumor involvement at resection. Earlier assessment of nodal burden could inform clinical decision-making prior to surgery. This study evaluated the association between clinical outcomes and lymph node volume before and after neoadjuvant therapy. Materials and methods CT imaging of patients with operable LA-NSCLC treated with chemoradiation and surgical resection was assessed. Clinically involved lymph node stations were identified by FDG-PET or mediastinoscopy. Locoregional recurrence (LRR), distant metastasis (DM), progression free survival (PFS) and overall survival (OS) were analyzed by the Kaplan Meier method, concordance index and Cox regression. Results 73 patients with Stage IIIA-IIIB NSCLC treated with neoadjuvant chemoradiation and surgical resection were identified. The median RT dose was 54 Gy and all patients received concurrent chemotherapy. Involved lymph node volume was significantly associated with LRR and OS but not DM on univariate analysis. Additionally, lymph node volume greater than 10.6 cm3 after the completion of preoperative chemoradiation was associated with increased LRR (p<0.001) and decreased OS (p = 0.04). There was no association between nodal volumes and nodal clearance. Conclusion For patients with LA-NSCLC, large volume nodal disease post-chemoradiation is associated with increased risk of locoregional recurrence and decreased survival. Nodal volume can thus be used to further stratify patients within the heterogeneous Stage IIIA-IIIB population and potentially guide clinical decision-making.

SU-E-J-246: CT-Based Volumetric Features Are Associated with Somatic Mutations in Lung Cancer

Purpose: Subsets of non-small cell lung cancer (NSCLC) are driven by mutations in key oncogenes, with unique biology including susceptibility to targeted treatment. Additionally, those mutations could lead to phenotypic differences of the primary tumor that can be assess with quantitative imaging. In this study, we investigated whether somatic mutation are associated with, and hence can be predicted by CT tumor volume-based features of NSCLC patients. Methods: We included 117 NSCLC patients with treatment-planning CT scans in our analysis and clinical genotyping for the epidermal growth factor receptor (EGFR) and Kirsten rat sarcoma viral oncogene homolog (KRAS) oncogenes. We extracted four volumetric features describing volume and diameters (x/y axis and 3D) of the primary tumor. Volumetric differences between mutant and wild-type tumors were assessed using Wilcoxon test. Predictive value of the volumetric features for mutation status was assessed using the area under the curve (AUC). Results: Genotype distribution included 14 (12%) EGFR mutant, 35 (30%) KRAS mutant, and 68 (58%) wild-type tumors. All volumetric features for EGFR mutant were significantly (p-value <0.05) lower than for KRAS mutant and Wild-Type. No volumetric features were significantly different between KRAS and Wild-Type. The median (Q1–Q3) for volume was 10.2(6.1– 29.6), 39.3(14.3–89.7) and 49(10.7–119) for EGFR, KRAS and Wild-Type respectively. All volumetric features were also significant predictive features for EGFR mutation with median (range) AUC of 0.69(0.67–0.70) and all p-value<0.05. However, the AUC was only 0.51(0.50–0.51) for KRAS mutation. Conclusion: EGFR mutant primary tumors were significantly smaller (for all volumetric features) than KRAS or Wild-Type. Moreover, all volumetric features were significantly predictive for EGFR. KRAS and Wild-type could not be discriminated only based on volumetric features. A larger set of imaging features (e.g. Radiomics) would help find more predictive biomarkers for tumor mutation status.

Clinical outcomes and toxicity following palliative radiotherapy for childhood cancers

Few reports of palliative radiotherapy (RT) for pedialltric malignancies have been published. We described clinical indications, outcomes, and toxicities for children who received palliative RT.

SU-D-207B-03: A PET-CT Radiomics Comparison to Predict Distant Metastasis in Lung Adenocarcinoma

PURPOSE Early prediction of distant metastasis may provide crucial information for adaptive therapy, subsequently improving patient survival. Radiomic features that extracted from PET and CT images have been used for assessing tumor phenotype and predicting clinical outcomes. This study investigates the values of radiomic features in predicting distant metastasis (DM) in non-small cell lung cancer (NSCLC). METHODS A total of 108 patients with stage II-III lung adenocarcinoma were included in this retrospective study. Twenty radiomic features were selected (10 from CT and 10 from PET). Conventional features (metabolic tumor volume, SUV, volume and diameter) were included for comparison. Concordance index (CI) was used to evaluate features prognostic value. Noether test was used to compute p-value to consider CI significance from random (CI = 0.5) and were adjusted for multiple testing using false rate discovery (FDR). RESULTS A total of 70 patients had DM (64.8%) with a median time to event of 8.8 months. The median delivered dose was 60 Gy (range 33-68 Gy). None of the conventional features from PET (CI ranged from 0.51 to 0.56) or CT (CI ranged from 0.57 to 0.58) were significant from random. Five radiomics features were significantly prognostic from random for DM (p-values < 0.05). Four were extracted from CT (CI = 0.61 to 0.63, p-value <0.01) and one from PET which was also the most prognostic (CI = 0.64, p-value <0.001). CONCLUSION This study demonstrated significant association between radiomic features and DM for patients with locally advanced lung adenocarcinoma. Moreover, conventional (clinically utilized) metrics were not significantly associated with DM. Radiomics can potentially help classify patients at higher risk of DM, allowing clinicians to individualize treatment, such as intensification of chemotherapy) to reduce the risk of DM and improve survival. R.M. has consulting interests with Amgen.

SU-E-J-266: Cone Beam Computed Tomography (CBCT) Inter-Scan and Inter-Observer Tumor Volume Variability Assessment in Patients Treated with Stereotactic Body Radiation Therapy (SBRT) for Early Stage Non-Small Cell Lung Cancer (NSCLC)

Purpose: Quantification of volume changes on CBCT during SBRT for NSCLC may provide a useful radiological marker for radiation response and adaptive treatment planning, but the reproducibility of CBCT volume delineation is a concern. This study is to quantify inter-scan/inter-observer variability in tumor volume delineation on CBCT. Methods: Twenty earlystage (stage I and II) NSCLC patients were included in this analysis. All patients were treated with SBRT with a median dose of 54 Gy in 3 to 5 fractions. Two physicians independently manually contoured the primary gross tumor volume on CBCTs taken immediately before SBRT treatment (Pre) and after the same SBRT treatment (Post). Absolute volume differences (AVD) were calculated between the Pre and Post CBCTs for a given treatment to quantify inter-scan variability, and then between the two observers for a given CBCT to quantify inter-observer variability. AVD was also normalized with respect to average volume to obtain relative volume differences (RVD). Bland-Altman approach was used to evaluate variability. All statistics were calculated with SAS version 9.4. Results: The 95% limit of agreement (mean ± 2SD) on AVD and RVD measurements between Pre and Post scans were −0.32cc to 0.32cc and −0.5% to 0.5% versus −1.9 cc to 1.8 cc and −15.9% to 15.3% for the two observers respectively. The 95% limit of agreement of AVD and RVD between the two observers were −3.3 cc to 2.3 cc and −42.4% to 28.2% respectively. The greatest variability in inter-scan RVD was observed with very small tumors (< 5 cc). Conclusion: Inter-scan variability in RVD is greatest with small tumors. Inter-observer variability was larger than inter-scan variability. The 95% limit of agreement for inter-observer and inter-scan variability (∼15–30%) helps define a threshold for clinically meaningful change in tumor volume to assess SBRT response, with larger thresholds needed for very small tumors. Part of the work was funded by a Kaye award; Disclosure/Conflict of interest: Raymond H. Mak: Stock ownership: Celgene, Inc. Consulting: Boehringer-Ingelheim, Inc.