Literature DB >> 34726531

Radiomics for Survival Risk Stratification of Clinical and Pathologic Stage IA Pure-Solid Non-Small Cell Lung Cancer.

Tingting Wang1, Yunlang She1, Yang Yang1, Xinyue Liu1, Shouyu Chen1, Yifan Zhong1, Jiajun Deng1, Mengmeng Zhao1, Xiwen Sun1, Dong Xie1, Chang Chen1.   

Abstract

Background Radiomics-based biomarkers enable the prognostication of resected non-small cell lung cancer (NSCLC), but their effectiveness in clinical stage and pathologic stage IA pure-solid tumors requires further determination. Purpose To construct an efficient radiomics signature for survival risk stratification personalized for patients with clinical stage and pathologic stage IA pure-solid NSCLC. Materials and Methods In this retrospective study, six radiomics signatures were constructed for patients with stage IA pure-solid NSCLC who underwent resection between January 2011 and December 2013 at authors' institution and were tested in the radiogenomics data set. The radiomics features were extracted from the intratumoral two-dimensional region, three-dimensional volume, and peritumoral area using PyRadiomics. The discriminative abilities of the signatures were quantified using the area under the time-dependent receiver operating characteristic curve (AUC), and the optimal signature was selected for patient stratification. Results The study included 592 patients with stage IA pure-solid NSCLC (median age, 61 years; interquartile range, 55-66 years; 269 women) for radiomics analysis: 381 patients for training, 163 for internal validation, and 48 for external validation. The radiomics signature combining three-region features yielded the highest 3- and 5-year AUCs of 0.77 and 0.78, respectively, in the internal validation set and 0.76 and 0.75, respectively, in the external validation set. Multivariable analysis suggested that the radiomics signature remained an independent prognostic factor (hazard ratio, 6.2; 95% CI: 3.5, 11.0; P < .001) and improved the discriminative ability and clinical usefulness of conventional clinical predictors. Conclusion The radiomics signature with multiregional features helped stratify the survival risk of patients with clinical stage and pathologic stage IA pure-solid non-small cell lung cancer. Published under a CC BY 4.0 license. Online supplemental material is available for this article. See also the editorial by Hsu and Sohn in this issue.

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Year:  2021        PMID: 34726531     DOI: 10.1148/radiol.2021210109

Source DB:  PubMed          Journal:  Radiology        ISSN: 0033-8419            Impact factor:   11.105


  5 in total

1.  Response to: Correspondence on 'Novel imaging biomarkers predict outcomes in stage III unresectable non-small cell lung cancer treated with chemoradiation and durvalumab' by Zheng et al.

Authors:  Vidya Sankar Viswanathan; Mohammadhadi Khorrami; Khalid Jazieh; Pingfu Fu; Nathan Pennell; Anant Madabhushi
Journal:  J Immunother Cancer       Date:  2022-05       Impact factor: 12.469

2.  Using Radiomics for Risk Stratification: Where We Need to Go.

Authors:  William Hsu; Jae Ho Sohn
Journal:  Radiology       Date:  2021-11-02       Impact factor: 29.146

3.  A triple-classification for the evaluation of lung nodules manifesting as pure ground-glass sign: a CT-based radiomic analysis.

Authors:  Ziyang Yu; Chenxi Xu; Ying Zhang; Fengying Ji
Journal:  BMC Med Imaging       Date:  2022-07-27       Impact factor: 2.795

4.  Integrating Radiomics with Genomics for Non-Small Cell Lung Cancer Survival Analysis.

Authors:  Wei Chen; Xu Qiao; Shang Yin; Xianru Zhang; Xin Xu
Journal:  J Oncol       Date:  2022-08-27       Impact factor: 4.501

5.  A Noninvasive Tool Based on Magnetic Resonance Imaging Radiomics for the Preoperative Prediction of Pathological Complete Response to Neoadjuvant Chemotherapy in Breast Cancer.

Authors:  Chenchen Li; Nian Lu; Zifan He; Yujie Tan; Yajing Liu; Yongjian Chen; Zhuo Wu; Jingwen Liu; Wei Ren; Luhui Mao; Yunfang Yu; Chuanmiao Xie; Herui Yao
Journal:  Ann Surg Oncol       Date:  2022-06-30       Impact factor: 4.339

  5 in total

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