Advertisement
Original Investigation|Articles in Press

Machine Learning Classifier for Preoperative Prediction of Early Recurrence After Bronchial Arterial Chemoembolization Treatment in Lung Cancer Patients

  • Author Footnotes
    1 Chunli Kong, Linqiang Lai and Xiaofeng Jin contributed equally to this work.
    Chunli Kong
    Footnotes
    1 Chunli Kong, Linqiang Lai and Xiaofeng Jin contributed equally to this work.
    Affiliations
    Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, Institute of Imaging Diagnosis and Minimally Invasive Intervention Research, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui 323000, China (C.K., L.L., X.J., W.C., J.D., L.Z., D.Z., X.Y., X.C., M.C., J.T., J.J.)

    Clinical College of The Affiliated Central Hospital, School of Medicine, Lishui University, Lishui 323000, China (C.K., L.L., W.C., J.D., L.Z., D.Z., X.Y., X.C., M.C., J.T., J.J.)

    Department of Radiology, Lishui Hospital of Zhejiang University, Lishui 323000, China (C.K., L.L., W.C., L.Z., D.Z., X.Y., X.C., M.C., J.T., J.J.)
    Search for articles by this author
  • Author Footnotes
    1 Chunli Kong, Linqiang Lai and Xiaofeng Jin contributed equally to this work.
    Linqiang Lai
    Footnotes
    1 Chunli Kong, Linqiang Lai and Xiaofeng Jin contributed equally to this work.
    Affiliations
    Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, Institute of Imaging Diagnosis and Minimally Invasive Intervention Research, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui 323000, China (C.K., L.L., X.J., W.C., J.D., L.Z., D.Z., X.Y., X.C., M.C., J.T., J.J.)

    Clinical College of The Affiliated Central Hospital, School of Medicine, Lishui University, Lishui 323000, China (C.K., L.L., W.C., J.D., L.Z., D.Z., X.Y., X.C., M.C., J.T., J.J.)

    Department of Radiology, Lishui Hospital of Zhejiang University, Lishui 323000, China (C.K., L.L., W.C., L.Z., D.Z., X.Y., X.C., M.C., J.T., J.J.)
    Search for articles by this author
  • Author Footnotes
    1 Chunli Kong, Linqiang Lai and Xiaofeng Jin contributed equally to this work.
    Xiaofeng Jin
    Footnotes
    1 Chunli Kong, Linqiang Lai and Xiaofeng Jin contributed equally to this work.
    Affiliations
    Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, Institute of Imaging Diagnosis and Minimally Invasive Intervention Research, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui 323000, China (C.K., L.L., X.J., W.C., J.D., L.Z., D.Z., X.Y., X.C., M.C., J.T., J.J.)
    Search for articles by this author
  • Weiyue Chen
    Affiliations
    Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, Institute of Imaging Diagnosis and Minimally Invasive Intervention Research, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui 323000, China (C.K., L.L., X.J., W.C., J.D., L.Z., D.Z., X.Y., X.C., M.C., J.T., J.J.)

    Clinical College of The Affiliated Central Hospital, School of Medicine, Lishui University, Lishui 323000, China (C.K., L.L., W.C., J.D., L.Z., D.Z., X.Y., X.C., M.C., J.T., J.J.)

    Department of Radiology, Lishui Hospital of Zhejiang University, Lishui 323000, China (C.K., L.L., W.C., L.Z., D.Z., X.Y., X.C., M.C., J.T., J.J.)
    Search for articles by this author
  • Jiayi Ding
    Affiliations
    Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, Institute of Imaging Diagnosis and Minimally Invasive Intervention Research, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui 323000, China (C.K., L.L., X.J., W.C., J.D., L.Z., D.Z., X.Y., X.C., M.C., J.T., J.J.)

    Clinical College of The Affiliated Central Hospital, School of Medicine, Lishui University, Lishui 323000, China (C.K., L.L., W.C., J.D., L.Z., D.Z., X.Y., X.C., M.C., J.T., J.J.)
    Search for articles by this author
  • Liyun Zheng
    Affiliations
    Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, Institute of Imaging Diagnosis and Minimally Invasive Intervention Research, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui 323000, China (C.K., L.L., X.J., W.C., J.D., L.Z., D.Z., X.Y., X.C., M.C., J.T., J.J.)

    Clinical College of The Affiliated Central Hospital, School of Medicine, Lishui University, Lishui 323000, China (C.K., L.L., W.C., J.D., L.Z., D.Z., X.Y., X.C., M.C., J.T., J.J.)

    Department of Radiology, Lishui Hospital of Zhejiang University, Lishui 323000, China (C.K., L.L., W.C., L.Z., D.Z., X.Y., X.C., M.C., J.T., J.J.)
    Search for articles by this author
  • Dengke Zhang
    Affiliations
    Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, Institute of Imaging Diagnosis and Minimally Invasive Intervention Research, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui 323000, China (C.K., L.L., X.J., W.C., J.D., L.Z., D.Z., X.Y., X.C., M.C., J.T., J.J.)

    Clinical College of The Affiliated Central Hospital, School of Medicine, Lishui University, Lishui 323000, China (C.K., L.L., W.C., J.D., L.Z., D.Z., X.Y., X.C., M.C., J.T., J.J.)

    Department of Radiology, Lishui Hospital of Zhejiang University, Lishui 323000, China (C.K., L.L., W.C., L.Z., D.Z., X.Y., X.C., M.C., J.T., J.J.)
    Search for articles by this author
  • Xihui Ying
    Affiliations
    Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, Institute of Imaging Diagnosis and Minimally Invasive Intervention Research, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui 323000, China (C.K., L.L., X.J., W.C., J.D., L.Z., D.Z., X.Y., X.C., M.C., J.T., J.J.)

    Clinical College of The Affiliated Central Hospital, School of Medicine, Lishui University, Lishui 323000, China (C.K., L.L., W.C., J.D., L.Z., D.Z., X.Y., X.C., M.C., J.T., J.J.)

    Department of Radiology, Lishui Hospital of Zhejiang University, Lishui 323000, China (C.K., L.L., W.C., L.Z., D.Z., X.Y., X.C., M.C., J.T., J.J.)
    Search for articles by this author
  • Xiaoxiao Chen
    Affiliations
    Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, Institute of Imaging Diagnosis and Minimally Invasive Intervention Research, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui 323000, China (C.K., L.L., X.J., W.C., J.D., L.Z., D.Z., X.Y., X.C., M.C., J.T., J.J.)

    Clinical College of The Affiliated Central Hospital, School of Medicine, Lishui University, Lishui 323000, China (C.K., L.L., W.C., J.D., L.Z., D.Z., X.Y., X.C., M.C., J.T., J.J.)

    Department of Radiology, Lishui Hospital of Zhejiang University, Lishui 323000, China (C.K., L.L., W.C., L.Z., D.Z., X.Y., X.C., M.C., J.T., J.J.)
    Search for articles by this author
  • Minjiang Chen
    Affiliations
    Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, Institute of Imaging Diagnosis and Minimally Invasive Intervention Research, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui 323000, China (C.K., L.L., X.J., W.C., J.D., L.Z., D.Z., X.Y., X.C., M.C., J.T., J.J.)

    Clinical College of The Affiliated Central Hospital, School of Medicine, Lishui University, Lishui 323000, China (C.K., L.L., W.C., J.D., L.Z., D.Z., X.Y., X.C., M.C., J.T., J.J.)

    Department of Radiology, Lishui Hospital of Zhejiang University, Lishui 323000, China (C.K., L.L., W.C., L.Z., D.Z., X.Y., X.C., M.C., J.T., J.J.)
    Search for articles by this author
  • Jianfei Tu
    Affiliations
    Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, Institute of Imaging Diagnosis and Minimally Invasive Intervention Research, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui 323000, China (C.K., L.L., X.J., W.C., J.D., L.Z., D.Z., X.Y., X.C., M.C., J.T., J.J.)

    Clinical College of The Affiliated Central Hospital, School of Medicine, Lishui University, Lishui 323000, China (C.K., L.L., W.C., J.D., L.Z., D.Z., X.Y., X.C., M.C., J.T., J.J.)

    Department of Radiology, Lishui Hospital of Zhejiang University, Lishui 323000, China (C.K., L.L., W.C., L.Z., D.Z., X.Y., X.C., M.C., J.T., J.J.)
    Search for articles by this author
  • Jiansong Ji
    Correspondence
    Address correspondence to: J.J., Department of Radiology, the Fifth Affiliated Hospital of Wenzhou Medical University, Lishui 323000, China.
    Affiliations
    Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, Institute of Imaging Diagnosis and Minimally Invasive Intervention Research, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui 323000, China (C.K., L.L., X.J., W.C., J.D., L.Z., D.Z., X.Y., X.C., M.C., J.T., J.J.)

    Clinical College of The Affiliated Central Hospital, School of Medicine, Lishui University, Lishui 323000, China (C.K., L.L., W.C., J.D., L.Z., D.Z., X.Y., X.C., M.C., J.T., J.J.)

    Department of Radiology, Lishui Hospital of Zhejiang University, Lishui 323000, China (C.K., L.L., W.C., L.Z., D.Z., X.Y., X.C., M.C., J.T., J.J.)
    Search for articles by this author
  • Author Footnotes
    1 Chunli Kong, Linqiang Lai and Xiaofeng Jin contributed equally to this work.
Published:May 22, 2023DOI:https://doi.org/10.1016/j.acra.2023.04.011
Machine Learning Classifier for Preoperative Prediction of Early Recurrence After Bronchial Arterial Chemoembolization Treatment in Lung Cancer Patients
Previous ArticleUtility of Six Ultrasound-Based Risk Stratification Systems in the Diagnosis of AUS/FLUS Thyroid Nodules
Next ArticleTeaching Artificial Intelligence Literacy: A Challenge in the Education of Radiology Residents

      Highlights

      • •
        Clinical response to BACE in lung cancer varies among patients with similar features.
      • •
        Radiomics signatures exhibited excellent performance in predicting early recurrence.
      • •
        The combined model exhibits high clinical utility.

      Rationale and Objectives

      Bronchial arterial chemoembolization (BACE) was deemed as an effective and safe approach for advanced standard treatment-ineligible/rejected lung cancer patients. However, the therapeutic outcome of BACE varies greatly and there is no reliable prognostic tool in clinical practice. This study aimed to investigate the effectiveness of radiomics features in predicting tumor recurrence after BACE treatment in lung cancer patients.

      Materials and Methods

      A total of 116 patients with pathologically confirmed lung cancer who received BACE treatment were retrospectively recruited. All patients underwent contrast-enhanced CT within 2 weeks before BACE treatment and were followed up for more than 6 months. We conducted a machine learning-based characterization of each lesion on the preoperative contrast-enhanced CT images. In the training cohort, recurrence-related radiomics features were screened by least absolute shrinkage and selection operator (LASSO) regression. Three predictive radiomics signatures were built with linear discriminant analysis (LDA), support vector machine (SVM) and logistic regression (LR) algorithms, respectively. Univariate and multivariate LR analyses were performed to select the independent clinical predictors for recurrence. The radiomics signature with best predictive performance was integrated with the clinical predictors to form a combined model, which was visualized as a nomogram. The performance of the combined model was assessed by receiver operating characteristic curve (ROC), calibration curve, and decision curve analysis (DCA).

      Results

      Nine recurrence-related radiomics features were screened out, and three radiomics signatures (RadscoreLDA, RadscoreSVM and RadscoreLR) were built based on these features. Patients were classified into the low-risk and high-risk groups based on the optimal threshold of three signatures. Progression-free survival (PFS) analysis showed that patients of low-risk group achieved longer PFS than patients of high-risk group (P < 0.05). The combined model including RadscoreLDA and independent clinical predictors (tumor size, carcinoembryonic antigen and pro-gastrin releasing peptide) achieved the best predictive performance for recurrence after BACE treatment. It yields AUCs of 0.865 and 0.867 in the training and validation cohorts, with accuracy (ACC) of 0.804 and 0.750, respectively. Calibration curves indicated that the probability of recurrence predicted by the model fits well with the actual recurrence probability. DCA showed that the radiomics nomogram was clinically useful.

      Conclusion

      The radiomics and clinical predictors-based nomogram can predict tumor recurrence after BACE treatment effectively, which allowing oncologists to identify potential recurrence and enable better patient management and clinical decision-making.

      Abbreviations:

      BACE (bronchial arterial chemoembolization), ER (early recurrence), CT (computed tomography), MRI (magnetic resonance imaging), CTA (CT angiography), DCA (decision curve analysis), PFS (progression-free survival), mRECIST (modified Response Evaluation Criteria in Solid Tumors), LASSO (least absolute shrinkage and selection operator), LDA (linear discriminant analysis), SVM (support vector machine), LR (logistic regression), ORR (objective response rate), DCR (disease control rate), AUC (area under the curve), ROC (receiver operating characteristic), ECOG (Eastern Cooperative Oncology Group), VOI (volume of interest), 95% CI (95% confidence interval), GLSZM (grey-level size zone matrix), GLCM (grey-level co-occurrence matrix), GLRLM (gray-level run length matrix), GLDM (gray-level dependence matrix), NGTDM (neighbouring gray tone difference matrix), TBIL (total bilirubin), CEA (carcinoembryonic antigen), SCC (squamous cell carcinoma antigen), CYFRA21.1 (cytokeratin-19-fragment CYFRA21-1), NSE (neuron-specific enolase), ProGRP (pro-gastrin releasing peptide)

      Key Words

      To read this article in full you will need to make a payment

      Purchase one-time access:

      Academic & Personal: 24 hour online accessCorporate R&D Professionals: 24 hour online access
      One-time access price info
      • For academic or personal research use, select 'Academic and Personal'
      • For corporate R&D use, select 'Corporate R&D Professionals'

      Subscribe:

      Subscribe to Academic Radiology
      Already a print subscriber? Claim online access
      Already an online subscriber? Sign in
      Register: Create an account
      Institutional Access: Sign in to ScienceDirect

      References

        • Siegel R.L.
        • Miller K.D.
        • Fuchs H.E.
        • et al.
        Cancer statistics, 2021.
        CA Cancer J Clin. 2021; 71: 7-33
        View in Article
        • Fan X.X.
        • Wu Q.
        Decoding lung cancer at single-cell level.
        Front Immunol. 2022; 13883758
        View in Article
        • Cao W.
        • Chen H.D.
        • Yu Y.W.
        • et al.
        Changing profiles of cancer burden worldwide and in China: a secondary analysis of the global cancer statistics 2020.
        Chin Med J. 2021; 134: 783-791
        View in Article
        • Xing P.Y.
        • Zhu Y.X.
        • Wang L.
        • et al.
        What are the clinical symptoms and physical signs for non-small cell lung cancer before diagnosis is made? A nation-wide multicenter 10-year retrospective study in China.
        Cancer Med. 2019; 8: 4055-4069
        View in Article
        • Bray F.
        • Ferlay J.
        • Soerjomataram I.
        • et al.
        Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries.
        CA Cancer J Clin. 2018; 68: 394-424
        View in Article
        • Liu J.
        • Zhang W.
        • Ren J.
        • et al.
        Efficacy and safety of drug-eluting bead bronchial arterial chemoembolization plus anlotinib in patients with advanced non-small-cell lung cancer.
        Front Cell Dev Biol. 2021; 9768943
        View in Article
        • De Ruysscher D.
        • Faivre-Finn C.
        • Nackaerts K.
        • et al.
        Recommendation for supportive care in patients receiving concurrent chemotherapy and radiotherapy for lung cancer.
        Ann Oncol. 2020; 31: 41-49
        View in Article
        • Ramlau R.
        • Gorbunova V.
        • Ciuleanu T.E.
        • et al.
        Aflibercept and docetaxel versus docetaxel alone after platinum failure in patients with advanced or metastatic non-small-cell lung cancer: a randomized, controlled phase III trial.
        J Clin Oncol. 2012; 30: 3640-3647
        View in Article
        • Liu X.F.
        • Lin H.
        • Wang Q.
        • et al.
        Drug-eluting bead bronchial arterial chemoembolization vs. chemotherapy in treating advanced non-small cell lung cancer: comparison of treatment efficacy, safety and quality of life.
        Eur Rev Med Pharmacol Sci. 2021; 25: 2554-2566
        View in Article
        • Cao H.
        • Jin S.
        • Bai B.
        Efficacy analysis of DSA-guided bronchial arterial chemoembolization interventional therapy in patients with middle-advanced primary bronchial lung cancer.
        Comput Math Methods Med. 2022; 20223722703
        View in Article
        • Zhu J.
        • Zhang H.P.
        • Jiang S.
        • et al.
        Neoadjuvant chemotherapy by bronchial arterial infusion in patients with unresectable stage III squamous cell lung cancer.
        Ther Adv Respir Dis. 2017; 11: 301-309
        View in Article
        • Zeng Y.
        • Yin M.
        • Zhao Y.
        • et al.
        Combination of bronchial arterial infusion chemotherapy plus drug-eluting embolic transarterial chemoembolization for treatment of advanced lung cancer – a retrospective analysis of 23 patients.
        J Vasc Interv Radiol. 2020; 31: 1645-1653
        View in Article
        • Zeng Y.W.
        • Liu Y.
        • Qi Y.
        • et al.
        Bronchial arterial infusion chemotherapy plus drug-eluting bead chemoembolization for recurrence of carina region-induced severe right main bronchial stenosis after pneumonectomy.
        Clin Lung Cancer. 2021; 22: e293-e297
        View in Article
        • Chen Y.
        • Li Y.
        • Jia Y.
        • et al.
        Bronchial artery chemoembolization combined with radioactive iodine-125 seed implantation in the treatment of advanced nonsmall cell lung cancer.
        J Cancer Res Ther. 2017; 13: 636-641
        View in Article
        • Bie Z.
        • Li Y.
        • Li B.
        • et al.
        The efficacy of drug-eluting beads bronchial arterial chemoembolization loaded with gemcitabine for treatment of non-small cell lung cancer.
        Thoracic Cancer. 2019; 10: 1770-1778
        View in Article
        • Wilson R.
        • Devaraj A.
        Radiomics of pulmonary nodules and lung cancer.
        Trans Lung Cancer Res. 2017; 6: 86-91
        View in Article
        • Khorrami M.
        • Prasanna P.
        • Gupta A.
        • et al.
        Changes in CT radiomic features associated with lymphocyte distribution predict overall survival and response to immunotherapy in non-small cell lung cancer.
        Cancer Immunol Res. 2020; 8: 108-119
        View in Article
        • Ardila D.
        • Kiraly A.P.
        • Bharadwaj S.
        • et al.
        End-to-end lung cancer screening with three-dimensional deep learning on low-dose chest computed tomography.
        Nat Med. 2019; 25: 954-961
        View in Article
        • Mu W.
        • Jiang L.
        • Zhang J.
        • et al.
        Non-invasive decision support for NSCLC treatment using PET/CT radiomics.
        Nat Commun. 2020; 11: 5228
        View in Article
        • Song J.
        • Wang L.
        • Ng N.N.
        • et al.
        Development and validation of a machine learning model to explore tyrosine kinase inhibitor response in patients with stage IV EGFR variant-positive non-small cell lung cancer.
        JAMA Netw Open. 2020; 3e2030442
        View in Article
        • Yanagimoto H.
        • Satoi S.
        • Yamamoto T.
        • et al.
        Benefits of conversion surgery after multimodal treatment for unresectable pancreatic ductal adenocarcinoma.
        Cancers. 2020; 12
        View in Article
        • Yamamoto T.
        • Sugiura T.
        • Mizuno T.
        • et al.
        Preoperative FDG-PET predicts early recurrence and a poor prognosis after resection of pancreatic adenocarcinoma.
        Ann Surg Oncol. 2015; 22: 677-684
        View in Article
        • Fedorov A.
        • Beichel R.
        • Kalpathy-Cramer J.
        • et al.
        3D Slicer as an image computing platform for the Quantitative Imaging Network.
        Magn Reson Imaging. 2012; 30: 1323-1341
        View in Article
        • van Griethuysen J.J.M.
        • Fedorov A.
        • Parmar C.
        • et al.
        Computational radiomics system to decode the radiographic phenotype.
        Cancer Res. 2017; 77: e104-e107
        View in Article
        • Han K.
        • Yoon K.W.
        • Kim J.H.
        • et al.
        Bronchial artery embolization for hemoptysis in primary lung cancer: a retrospective review of 84 patients.
        J Vasc Interv Radiol. 2019; 30: 428-434
        View in Article
        • Chen C.
        • Wang W.
        • Yu Z.
        • et al.
        Combination of computed tomography-guided iodine-125 brachytherapy and bronchial arterial chemoembolization for locally advanced stage III non-small cell lung cancer after failure of concurrent chemoradiotherapy.
        Lung Cancer. 2020; 146: 290-296
        View in Article
        • Fu Z.
        • Wang C.
        • Wei W.
        • et al.
        Efficacy and safety of drug-eluting beads bronchial arterial chemoembolization versus conventional bronchial arterial chemoembolization in lung cancer patients with hemoptysis.
        Future Oncol. 2022; 18: 2805-2815
        View in Article
        • Trebeschi S.
        • Drago S.G.
        • Birkbak N.J.
        • et al.
        Predicting response to cancer immunotherapy using noninvasive radiomic biomarkers.
        Ann Oncol. 2019; 30: 998-1004
        View in Article
        • Perez-Johnston R.
        • Araujo-Filho J.A.
        • Connolly J.G.
        • et al.
        CT-based radiogenomic analysis of clinical stage I lung adenocarcinoma with histopathologic features and oncologic outcomes.
        Radiology. 2022; 303: 664-672
        View in Article
        • Anagnostopoulos A.K.
        • Gaitanis A.
        • Gkiozos I.
        • et al.
        Radiomics/radiogenomics in lung cancer: basic principles and initial clinical results.
        Cancers. 2022; 14: 7
        View in Article
        • Wood D.E.
        • Kazerooni E.A.
        • Baum S.L.
        • et al.
        Lung cancer screening, version 3.2018, NCCN Clinical Practice Guidelines in Oncology.
        J Natl Compr Cancer Network. 2018; 16: 412-441
        View in Article
        • Mahapatra D.
        • Poellinger A.
        • Shao L.
        • et al.
        Interpretability-driven sample selection using self supervised learning for disease classification and segmentation.
        IEEE Trans Med Imaging. 2021; 40: 2548-2562
        View in Article
        • Nadif M.
        • Role F.
        Unsupervised and self-supervised deep learning approaches for biomedical text mining.
        Brief Bioinform. 2021; 22: 1592-1603
        View in Article
        • Zheng Y.
        • Luo J.
        • Chen W.
        • et al.
        Unsupervised 3D reconstruction with multi-measure and high-resolution loss.
        Sensors (Basel). 2022; 23
        View in Article
        • Cho S.H.
        • Yoon S.
        • Lee D.H.
        • et al.
        Recurrence-associated gene signature in patients with stage I non-small-cell lung cancer.
        Sci Rep. 2021; 11: 19596
        View in Article
        • Schuchert M.J.
        • Normolle D.P.
        • Awais O.
        • et al.
        Factors influencing recurrence following anatomic lung resection for clinical stage I non-small cell lung cancer.
        Lung Cancer. 2019; 128: 145-151
        View in Article
        • Chen H.
        • Fu F.
        • Zhao Y.
        • et al.
        The prognostic value of preoperative serum tumor markers in non-small cell lung cancer varies with radiological features and histological types.
        Front Oncol. 2021; 11645159
        View in Article
        • Okusaka T.
        • Eguchi K.
        • Kasai T.
        • et al.
        Serum levels of pro-gastrin-releasing peptide for follow-up of patients with small cell lung cancer.
        Clin Cancer Res. 1997; 3: 123-127
        View in Article
        • Niho S.
        • Nishiwaki Y.
        • Goto K.
        • et al.
        Significance of serum pro-gastrin-releasing peptide as a predictor of relapse of small cell lung cancer: comparative evaluation with neuron-specific enolase and carcinoembryonic antigen.
        Lung Cancer. 2000; 27: 159-167
        View in Article

      Article info

      Publication history

      Published online: May 22, 2023
      Accepted: April 13, 2023
      Received in revised form: April 12, 2023
      Received: March 4, 2023

      Publication stage

      In Press Corrected Proof

      Identification

      DOI: https://doi.org/10.1016/j.acra.2023.04.011

      Copyright

      © 2023 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved All rights reserved.

      ScienceDirect

      Access this article on ScienceDirect

      The content on this site is intended for healthcare professionals.


      We use cookies to help provide and enhance our service and tailor content. To update your cookie settings, please visit the Cookie Preference Center for this site.
      Copyright © 2023 Elsevier Inc. except certain content provided by third parties.


      RELX