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Radiomic-Based Pathological Response Prediction from Primary Tumors and Lymph Nodes in NSCLC  Thibaud P. Coroller, MSc, Vishesh Agrawal, MD, Elizabeth.

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Presentation on theme: "Radiomic-Based Pathological Response Prediction from Primary Tumors and Lymph Nodes in NSCLC  Thibaud P. Coroller, MSc, Vishesh Agrawal, MD, Elizabeth."— Presentation transcript:

1 Radiomic-Based Pathological Response Prediction from Primary Tumors and Lymph Nodes in NSCLC 
Thibaud P. Coroller, MSc, Vishesh Agrawal, MD, Elizabeth Huynh, PhD, Vivek Narayan, BSc, Stephanie W. Lee, BA, Raymond H. Mak, MD, Hugo J.W.L. Aerts, PhD  Journal of Thoracic Oncology  Volume 12, Issue 3, Pages (March 2017) DOI: /j.jtho Copyright © 2016 International Association for the Study of Lung Cancer Terms and Conditions

2 Figure 1 (A) Schematic of the radiomics quantification workflow demonstrating feature extraction from the lung primary tumor site (green) and lymph nodes (orange) from pretreatment computed tomography (CT) images. (B) Radiomics could enable precision medicine by classifying patients before therapy on the basis of how they will respond to chemoradiation. Journal of Thoracic Oncology  , DOI: ( /j.jtho ) Copyright © 2016 International Association for the Study of Lung Cancer Terms and Conditions

3 Figure 2 Areas under the curve of selected radiomic and conventional imaging features for pathological complete response versus non–pathological complete response (A) and gross residual disease versus non–gross residual disease (B). Selected features are derived from the primary tumor (PRIM) or lymph node(s) (LNs). The predictive power was reported as directly (Prop.) or inversely (Inv.) proportional to the risk for experiencing the event as the feature value is increased. Features reported with an asterisk are significant from a random guess (Noether’s test, p ≤ 0.05, false discovery rate–corrected). Legend colors indicate feature groups. WV, wavelet; glszm, Gray level size zone matrix; LoG, Laplacian of Gaussian; 3D, three-dimensional; MaxProb, maximum probability; GLCM, Gray level co-occurrence matrix; 2D, two-dimensional; Stats, statistics; H, High; L, Low; HLH, High-Low-High. Journal of Thoracic Oncology  , DOI: ( /j.jtho ) Copyright © 2016 International Association for the Study of Lung Cancer Terms and Conditions

4 Figure 3 The performances of random forest classification of models for pathological complete response (A) and gross residual disease (B). Areas under the curve are reported from nested cross validation analysis. Asterisk indicates significant difference between the feature models (permutation test, p ≤ 0.05). ns, not significant; Clin., clinical; Rad., radiomics. Journal of Thoracic Oncology  , DOI: ( /j.jtho ) Copyright © 2016 International Association for the Study of Lung Cancer Terms and Conditions

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