Non-invasive decision support for clinical treatment of non-small cell lung cancer using a multiscale radiomics approach.

Autor: Zhang X; School of Medical Information Engineering, Gannan Medical University, 341000, Ganzhou, China; Cyberspace Institute of Advanced Technology, Guangzhou University, 510006 Guangzhou, China; Institute for Sustainable Industries and Liveable Cities, Victoria University, 3011, Melbourne, Australia; Department of New Networks, Peng Cheng Laboratory, 518000, Shenzhen, China., Zhang G; Department of Respiratory and Critical Care, First Affiliated Hospital of Gannan Medical University, 341000, Ganzhou, China., Qiu X; Department of Radiology, First Affiliated Hospital of Gannan Medical University, 341000, Ganzhou, China., Yin J; Institute for Sustainable Industries and Liveable Cities, Victoria University, 3011, Melbourne, Australia., Tan W; Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Northeastern University, 110189, Shenyang, China., Yin X; Cyberspace Institute of Advanced Technology, Guangzhou University, 510006 Guangzhou, China., Yang H; Cyberspace Institute of Advanced Technology, Guangzhou University, 510006 Guangzhou, China., Wang H; Institute for Sustainable Industries and Liveable Cities, Victoria University, 3011, Melbourne, Australia. Electronic address: hua.wang@vu.edu.au., Zhang Y; Institute for Sustainable Industries and Liveable Cities, Victoria University, 3011, Melbourne, Australia; School of Computer Science and Technology, Zhejiang Normal University, 321000, Jinhua, China; Department of New Networks, Peng Cheng Laboratory, 518000, Shenzhen, China. Electronic address: yanchun.zhang@vu.edu.au.
Jazyk: angličtina
Zdroj: Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology [Radiother Oncol] 2024 Feb; Vol. 191, pp. 110082. Date of Electronic Publication: 2024 Jan 07.
DOI: 10.1016/j.radonc.2024.110082
Abstrakt: Background: Selecting therapeutic strategies for cancer patients is typically based on key target-molecule biomarkers that play an important role in cancer onset, progression, and prognosis. Thus, there is a pressing need for novel biomarkers that can be utilized longitudinally to guide treatment selection.
Methods: Using data from 508 non-small cell lung cancer (NSCLC) patients across three institutions, we developed and validated a comprehensive predictive biomarker that distinguishes six genotypes and infiltrative immune phenotypes. These features were analyzed to establish the association between radiological phenotypes and tumor genotypes/immune phenotypes and to create a radiological interpretation of molecular features. In addition, we assessed the sensitivity of the models by evaluating their performance at five different voxel intervals, resulting in improved generalizability of the proposed approach.
Findings: The radiomics model we developed, which integrates clinical factors and multi-regional features, outperformed the conventional model that only uses clinical and intratumoral features. Our combined model showed significant performance for EGFR, KRAS, ALK, TP53, PIK3CA, and ROS1 mutation status with AUCs of 0.866, 0.874, 0.902, 0.850, 0.860, and 0.900, respectively. Additionally, the predictive performance for PD-1/PD-L1 was 0.852. Although the performance of all models decreased to different degrees at five different voxel space resolutions, the performance advantage of the combined model did not change.
Conclusions: We validated multiscale radiomic signatures across tumor genotypes and immunophenotypes in a multi-institutional cohort. This imaging-based biomarker offers a non-invasive approach to select patients with NSCLC who are sensitive to targeted therapies or immunotherapy, which is promising for developing personalized treatment strategies during therapy.
Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
(Copyright © 2024 Elsevier B.V. All rights reserved.)
Databáze: MEDLINE
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