Vishesh Agrawal

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.

CT-based radiomic analysis of stereotactic body radiation therapy patients with lung cancer

BACKGROUND Radiomics uses a large number of quantitative imaging features that describe the tumor phenotype to develop imaging biomarkers for clinical outcomes. Radiomic analysis of pre-treatment computed-tomography (CT) scans was investigated to identify imaging predictors of clinical outcomes in early stage non-small cell lung cancer (NSCLC) patients treated with stereotactic body radiation therapy (SBRT). MATERIALS AND METHODS CT images of 113 stage I-II NSCLC patients treated with SBRT were analyzed. Twelve radiomic features were selected based on stability and variance. The association of features with clinical outcomes and their prognostic value (using the concordance index (CI)) was evaluated. Radiomic features were compared with conventional imaging metrics (tumor volume and diameter) and clinical parameters. RESULTS Overall survival was associated with two conventional features (volume and diameter) and two radiomic features (LoG 3D run low gray level short run emphasis and stats median). One radiomic feature (Wavelet LLH stats range) was significantly prognostic for distant metastasis (CI=0.67, q-value<0.1), while none of the conventional and clinical parameters were. Three conventional and four radiomic features were prognostic for overall survival. CONCLUSION This exploratory analysis demonstrates that radiomic features have potential to be prognostic for some outcomes that conventional imaging metrics cannot predict in SBRT patients.

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.

Associations of Radiomic Data Extracted from Static and Respiratory-Gated CT Scans with Disease Recurrence in Lung Cancer Patients Treated with SBRT.

Radiomics aims to quantitatively capture the complex tumor phenotype contained in medical images to associate them with clinical outcomes. This study investigates the impact of different types of computed tomography (CT) images on the prognostic performance of radiomic features for disease recurrence in early stage non-small cell lung cancer (NSCLC) patients treated with stereotactic body radiation therapy (SBRT). 112 early stage NSCLC patients treated with SBRT that had static free breathing (FB) and average intensity projection (AIP) images were analyzed. Nineteen radiomic features were selected from each image type (FB or AIP) for analysis based on stability and variance. The selected FB and AIP radiomic feature sets had 6 common radiomic features between both image types and 13 unique features. The prognostic performances of the features for distant metastasis (DM) and locoregional recurrence (LRR) were evaluated using the concordance index (CI) and compared with two conventional features (tumor volume and maximum diameter). P-values were corrected for multiple testing using the false discovery rate procedure. None of the FB radiomic features were associated with DM, however, seven AIP radiomic features, that described tumor shape and heterogeneity, were (CI range: 0.638–0.676). Conventional features from FB images were not associated with DM, however, AIP conventional features were (CI range: 0.643–0.658). Radiomic and conventional multivariate models were compared between FB and AIP images using cross validation. The differences between the models were assessed using a permutation test. AIP radiomic multivariate models (median CI = 0.667) outperformed all other models (median CI range: 0.601–0.630) in predicting DM. None of the imaging features were prognostic of LRR. Therefore, image type impacts the performance of radiomic models in their association with disease recurrence. AIP images contained more information than FB images that were associated with disease recurrence in early stage NSCLC patients treated with SBRT, which suggests that AIP images may potentially be more optimal for the development of an imaging biomarker.

Use of frailty to predict survival in elderly patients with early stage non-small-cell lung cancer treated with stereotactic body radiation therapy

OBJECTIVES Frailty has been shown to increase morbidity and mortality independent of age, but studies are lacking in radiation oncology. This study evaluates a modified frailty index (mFI) in predicting overall survival (OS) and non-cancer death for Stage I/II [N0M0] Non-Small-Cell Lung Cancer (NSCLC) patients treated with Stereotactic Body Radiation Therapy (SBRT). MATERIALS AND METHODS Medical records for all patients with Stage I/II NSCLC treated at our institution with SBRT from 2009 to 2014 were reviewed. A validated mFI score, consisting of 11 variables was calculated, classifying patients as non-frail (0-1) or frail (≥2). Primary endpoint (OS) was analyzed using Kaplan-Meier method and log-rank. Secondary endpoint, non-cancer death, was analyzed using Fine-Grays method, with death from lung cancer as a competing risk. RESULTS Patient cohort consisted of 38 (27.3%) non-frail and 101 (72.7%) frail [median total mFI score 3.0 (range 0-7)]. Median age and pack-year history was 74 and 46years, respectively. Median follow-up among survivors was 38.5months (range 4.0-74.1months). Frailty was associated with a lower 3-year OS (37.3% vs. 74.7%; p=0.003) and 3-year cumulative incidence of non-cancer death (36.7% vs. 12.5%; p=0.02). Frailty remained significant in the multivariate model [OS HR for mFI ≥2: 2.25 (1.14-4.44); p=0.02]. CONCLUSION Frailty is associated with lower OS in older patients with early stage NSCLC treated with SBRT, yet frail patients survived a median 2.5years, and were more likely to die of causes unrelated to the primary lung cancer, suggesting SBRT should be considered even in older patients deemed unfit for surgery.

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.

TU‐D‐207B‐06: Pathological Response Prediction by Radiomic Data From Primary and Lymph Nodes in NSCLC

PURPOSE In advanced non-small cell lung cancer (NSCLC) patient, metastasis can spread from the primary tumor to the lymph nodes and hence could have a distinct phenotype compared to unaffected nodes. In this study we investigated the complementary information of radiomics extracted from lymph nodes and the primary tumor in order to predict pathological response at time of surgery after chemoradiation in patients with stage II-III NSCLC. METHODS 86 NSCLC patients with primary tumor and involved lymph nodes (LN) were included in this study. Twenty radiomic features were selected based on stability and variance. Predictive power was evaluated using AUC and false discovery rate (FDR) corrected p-values. Conventional imaging features (total tumor / LN volume and axial tumor diameter) and clinical characteristics were included for comparison. Classification power was investigated using random forest. Performances were assessed using cross validation (1000 iterations, 70% training / 30% validation). RESULTS Three radiomic features were predictive for pathologic (GRD) gross residual disease (AUC range 0.69-0.75, p<0.05) and two pathologic (pCR) complete response (AUC range 0.65-0.68, p<0.05). No conventional imaging features were predictive of either outcome (range AUC = 0.51 to 0.61, p>0.05). Patients with pCR were likely to have more homogeneous LN (large area emphasis, AUC=0.74, p<0.05) when in the other hand patients with GRD present rounder primary tumor shape (spherical disproportionality, AUC= 0.68 p<0.05). Cross validation AUC values shown that the combined radiomics-clinical features set outperformed for each outcomes (median AUC = 0.68 and 0.74 respectively for pCR and GRD). CONCLUSION We demonstrate that LN phenotypic information, ascertained from radiomic features, is complementary to imaging features obtained from the primary tumor. These features are strongly associated with response to chemoradiation as determined by pathologic response and provide greater predictive performance than clinical characteristics and conventional assessment of tumor burden by diameter or volume. R.M. have consulting interest with Amgen.

Inter-scan and inter-observer tumour volume delineation variability on cone beam computed tomography in patients treated with stereotactic body radiation therapy for early-stage non-small cell lung cancer.

Quantification of volume changes on cone beam computed tomography (CBCT) during lung stereotactic body radiation therapy (SBRT) for non‐small cell lung cancer (NSCLC) may provide a useful radiological marker for radiation response and for adaptive treatment planning. This study quantifies inter‐scan and inter‐observer variability in tumour volume delineation on CBCT.

SU-F-R-52: A Comparison of the Performance of Radiomic Features From Free Breathing and 4DCT Scans in Predicting Disease Recurrence in Lung Cancer SBRT Patients

PURPOSE There is a clinical need to identify patients who are at highest risk of recurrence after being treated with stereotactic body radiation therapy (SBRT). Radiomics offers a non-invasive approach by extracting quantitative features from medical images based on tumor phenotype that is predictive of an outcome. Lung cancer patients treated with SBRT routinely undergo free breathing (FB image) and 4DCT (average intensity projection (AIP) image) scans for treatment planning to account for organ motion. The aim of the current study is to evaluate and compare the prognostic performance of radiomic features extracted from FB and AIP images in lung cancer patients treated with SBRT to identify which image type would generate an optimal predictive model for recurrence. METHODS FB and AIP images of 113 Stage I-II NSCLC patients treated with SBRT were analysed. The prognostic performance of radiomic features for distant metastasis (DM) was evaluated by their concordance index (CI). Radiomic features were compared with conventional imaging metrics (e.g. diameter). All p-values were corrected for multiple testing using the false discovery rate. RESULTS All patients received SBRT and 20.4% of patients developed DM. From each image type (FB or AIP), nineteen radiomic features were selected based on stability and variance. Both image types had five common and fourteen different radiomic features. One FB (CI=0.70) and five AIP (CI range=0.65-0.68) radiomic features were significantly prognostic for DM (p<0.05). None of the conventional features derived from FB images (range CI=0.60-0.61) were significant but all AIP conventional features were (range CI=0.64-0.66). CONCLUSION Features extracted from different types of CT scans have varying prognostic performances. AIP images contain more prognostic radiomic features for DM than FB images. These methods can provide personalized medicine approaches at low cost, as FB and AIP data are readily available within a large number of radiation oncology departments. R.M. had consulting interest with Amgen (ended in 2015).