Literature DB >> 31375452

Assessment of a Radiomic Signature Developed in a General NSCLC Cohort for Predicting Overall Survival of ALK-Positive Patients With Different Treatment Types.

Lyu Huang1, Jiayan Chen1, Weigang Hu1, Xinyan Xu1, Di Liu1, Junmiao Wen1, Jiayu Lu1, Jianzhao Cao1, Junhua Zhang1, Yu Gu1, Jiazhou Wang2, Min Fan3.   

Abstract

BACKGROUND: The purpose of the study was to investigate the potential of a radiomic signature developed in a general non-small-cell lung cancer (NSCLC) cohort for predicting the overall survival of anaplastic lymphoma kinase (ALK)-positive (ALK+) patients with different treatment types.
MATERIALS AND METHODS: After test-retest in the Reference Image Database to Evaluate Therapy Response data set, 132 features (intraclass correlation coefficient > 0.9) were selected in the least absolute shrinkage and selection operator Cox regression model with a leave-one-out cross-validation. The NSCLC radiomics collection from The Cancer Imaging Archive was randomly divided into a training set (n = 254) and a validation set (n = 63) to develop a general radiomic signature for NSCLC. In our ALK+ set, 35 patients received targeted therapy and 19 patients received nontargeted therapy. The developed signature was tested later in this ALK+ set. Performance of the signature was evaluated with the concordance index (C-index) and stratification analysis.
RESULTS: The general signature had good performance (C-index > 0.6; log rank P < .05) in the NSCLC radiomics collection. It includes 5 features: Geom_va_ratio, W_GLCM_Std, W_GLCM_DV, W_GLCM_IM2, and W_his_mean. Its accuracy of predicting overall survival in the ALK+ set achieved 0.649 (95% confidence interval [CI], 0.640-0.658). Nonetheless, impaired performance was observed in the targeted therapy group (C-index = 0.573; 95% CI, 0.556-0.589) whereas significantly improved performance was observed in the nontargeted therapy group (C-index = 0.832; 95% CI, 0.832-0.852). Stratification analysis also showed that the general signature could only identify high- and low-risk patients in the nontargeted therapy group (log rank P = .00028).
CONCLUSION: This preliminary study suggests that the applicability of a general signature to ALK+ patients is limited. The general radiomic signature seems to be only applicable to ALK+ patients who had received nontargeted therapy, which indicates that developing special radiomics signatures for patients treated with tyrosine kinase inhibitors might be necessary.
Copyright © 2019. Published by Elsevier Inc.

Entities:  

Keywords:  ALK rearrangement; Carcinoma; Multislice computed tomography; Non–small-cell lung; Survival analysis

Year:  2019        PMID: 31375452     DOI: 10.1016/j.cllc.2019.05.005

Source DB:  PubMed          Journal:  Clin Lung Cancer        ISSN: 1525-7304            Impact factor:   4.785


  6 in total

1.  Reliability as a Precondition for Trust-Segmentation Reliability Analysis of Radiomic Features Improves Survival Prediction.

Authors:  Gustav Müller-Franzes; Sven Nebelung; Justus Schock; Christoph Haarburger; Firas Khader; Federico Pedersoli; Maximilian Schulze-Hagen; Christiane Kuhl; Daniel Truhn
Journal:  Diagnostics (Basel)       Date:  2022-01-19

2.  A CT-based radiomics model to predict subsequent brain metastasis in patients with ALK-rearranged non-small cell lung cancer undergoing crizotinib treatment.

Authors:  Yongluo Jiang; Yixing Wang; Sha Fu; Tao Chen; Yixin Zhou; Xuanye Zhang; Chen Chen; Li-Na He; Wei Du; Haifeng Li; Zuan Lin; Yuanyuan Zhao; Yunpeng Yang; Hongyun Zhao; Wenfeng Fang; Yan Huang; Shaodong Hong; Li Zhang
Journal:  Thorac Cancer       Date:  2022-04-18       Impact factor: 3.223

Review 3.  Radiomics as a personalized medicine tool in lung cancer: Separating the hope from the hype.

Authors:  Isabella Fornacon-Wood; Corinne Faivre-Finn; James P B O'Connor; Gareth J Price
Journal:  Lung Cancer       Date:  2020-06-02       Impact factor: 5.705

4.  MRI radiomic signature predicts intracranial progression-free survival in patients with brain metastases of ALK-positive non-small cell lung cancer.

Authors:  Shijun Zhao; Donghui Hou; Xiaomin Zheng; Wei Song; Xiaoqing Liu; Sicong Wang; Lina Zhou; Xiuli Tao; Lv Lv; Qi Sun; Yujing Jin; Lieming Ding; Li Mao; Ning Wu
Journal:  Transl Lung Cancer Res       Date:  2021-01

5.  An Adaptive Transfer-Learning-Based Deep Cox Neural Network for Hepatocellular Carcinoma Prognosis Prediction.

Authors:  Hua Chai; Long Xia; Lei Zhang; Jiarui Yang; Zhongyue Zhang; Xiangjun Qian; Yuedong Yang; Weidong Pan
Journal:  Front Oncol       Date:  2021-09-27       Impact factor: 6.244

6.  Radiomics-Based Deep Learning Prediction of Overall Survival in Non-Small-Cell Lung Cancer Using Contrast-Enhanced Computed Tomography.

Authors:  Kuei-Yuan Hou; Jyun-Ru Chen; Yung-Chen Wang; Ming-Huang Chiu; Sen-Ping Lin; Yuan-Heng Mo; Shih-Chieh Peng; Chia-Feng Lu
Journal:  Cancers (Basel)       Date:  2022-08-04       Impact factor: 6.575

  6 in total

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