Radiomic phenotype features predict pathological response in non-small cell lung cancer.
发表日期： 2016.06.01 来源：Radiother Oncol. 2016 Jun;119(3):480-6.
Coroller TP1, Agrawal V22, Narayan V22, Hou Y22, Grossmann P22, Lee SW22, Mak RH2,2 Aerts HJ33.
1. Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, Harvard Medical School, Boston, USA. Electronic address: firstname.lastname@example.org.
2. Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, Harvard Medical School, Boston, USA.
3. Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, Harvard Medical School, Boston, USA; Department of Radiology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, Harvard Medical School, Boston, USA.
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.
7个特征可预测对总体病理残留病变（AUC > 0.6，p值 < 0.05），以及1个用于病理完全反应（AUC = 0.63，p值 = 0.01）。常规成像特征没有预测性（范围AUC = 0.51-0.59，p值 > 0.05）。对新辅助放化疗没有很好响应的肿瘤更可能呈现更圆的形状（不成比例球形，AUC = 0.63，p值 = 0.009）和非均匀结构（LOG 5毫米3D - GLCM熵，AUC = 0.61，p值 = 0.03）。
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).
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.
Biomarkers; NSCLC; Pathological response; Quantitative imaging; Radiomics