Monitoring of Current Cancer Therapy by Positron Emission Tomography and Possible Role of Radiomics Assessment

被引:7
作者
Oriuchi, Noboru [1 ,2 ]
Endoh, Hideki [3 ]
Kaira, Kyoichi [4 ]
机构
[1] Fukushima Med Univ, Fukushima Global Med Sci Ctr, Adv Clin Res Ctr, Fukushima 9601295, Japan
[2] Fukushima Med Univ Hosp, Dept Nucl Med, Fukushima 9601295, Japan
[3] Saku Cent Hosp Adv Care Ctr, Dept Thorac Surg, Nagano 3850051, Japan
[4] Saitama Med Univ, Dept Resp Med, Ctr Comprehens Canc, Int Med Ctr, Saitama 3501298, Japan
关键词
tumor microenvironment; immunotherapy; FDG-PET; tumor heterogeneity; metabolism; immune-checkpoint inhibitors artificial intelligence; machine learning; radiomics; IMMUNE-RELATED RESPONSE; CELL LUNG-CANCER; F-18-FDG PET/CT; PD-L1; EXPRESSION; PROGNOSTIC VALUE; TUMOR RESPONSE; L-ASPARAGINASE; FDG-PET; CRITERIA; PREDICTION;
D O I
10.3390/ijms23169394
中图分类号
Q5 [生物化学]; Q7 [分子生物学];
学科分类号
071010 ; 081704 ;
摘要
Evaluation of cancer therapy with imaging is crucial as a surrogate marker of effectiveness and survival. The unique response patterns to therapy with immune-checkpoint inhibitors have facilitated the revision of response evaluation criteria using FDG-PET, because the immune response recalls reactive cells such as activated T-cells and macrophages, which show increased glucose metabolism and apparent progression on morphological imaging. Cellular metabolism and function are critical determinants of the viability of active cells in the tumor microenvironment, which would be novel targets of therapies, such as tumor immunity, metabolism, and genetic mutation. Considering tumor heterogeneity and variation in therapy response specific to the mechanisms of therapy, appropriate response evaluation is required. Radiomics approaches, which combine objective image features with a machine learning algorithm as well as pathologic and genetic data, have remarkably progressed over the past decade, and PET radiomics has increased quality and reliability based on the prosperous publications and standardization initiatives. PET and multimodal imaging will play a definitive role in personalized therapeutic strategies by the precise monitoring in future cancer therapy.
引用
收藏
页数:15
相关论文
共 123 条
  • [1] The Potential of Radiomic-Based Phenotyping in PrecisionMedicine A Review
    Aerts, Hugo J. W. L.
    [J]. JAMA ONCOLOGY, 2016, 2 (12) : 1636 - 1642
  • [2] Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach
    Aerts, Hugo J. W. L.
    Velazquez, Emmanuel Rios
    Leijenaar, Ralph T. H.
    Parmar, Chintan
    Grossmann, Patrick
    Cavalho, Sara
    Bussink, Johan
    Monshouwer, Rene
    Haibe-Kains, Benjamin
    Rietveld, Derek
    Hoebers, Frank
    Rietbergen, Michelle M.
    Leemans, C. Rene
    Dekker, Andre
    Quackenbush, John
    Gillies, Robert J.
    Lambin, Philippe
    [J]. NATURE COMMUNICATIONS, 2014, 5
  • [3] 3-D RPET-NET: Development of a 3-D PET Imaging Convolutional Neural Network for Radiomics Analysis and Outcome Prediction
    Amyar, A.
    Ruan, S.
    Gardin, I.
    Chatelain, C.
    Decazes, P.
    Modzelewski, R.
    [J]. IEEE TRANSACTIONS ON RADIATION AND PLASMA MEDICAL SCIENCES, 2019, 3 (02) : 225 - 231
  • [4] Comparison of 18F-FDG PET/CT Criteria for the Prediction of Therapy Response and Clinical Outcome in Patients With Metastatic Melanoma Treated With Ipilimumab and PD-1 Inhibitors
    Annovazzi, Alessio
    Vari, Sabrina
    Giannarelli, Diana
    Pasqualoni, Rosella
    Sciuto, Rosa
    Carpano, Silvia
    Cognetti, Francesco
    Ferraresi, Virginia
    [J]. CLINICAL NUCLEAR MEDICINE, 2020, 45 (03) : 187 - 194
  • [5] FDG PET/CT: EANM procedure guidelines for tumour imaging: version 2.0
    Boellaard, Ronald
    Delgado-Bolton, Roberto
    Oyen, Wim J. G.
    Giammarile, Francesco
    Tatsch, Klaus
    Eschner, Wolfgang
    Verzijlbergen, Fred J.
    Barrington, Sally F.
    Pike, Lucy C.
    Weber, Wolfgang A.
    Stroobants, Sigrid
    Delbeke, Dominique
    Donohoe, Kevin J.
    Holbrook, Scott
    Graham, Michael M.
    Testanera, Giorgio
    Hoekstra, Otto S.
    Zijlstra, Josee
    Visser, Eric
    Hoekstra, Corneline J.
    Pruim, Jan
    Willemsen, Antoon
    Arends, Bertjan
    Kotzerke, Joerg
    Bockisch, Andreas
    Beyer, Thomas
    Chiti, Arturo
    Krause, Bernd J.
    [J]. EUROPEAN JOURNAL OF NUCLEAR MEDICINE AND MOLECULAR IMAGING, 2015, 42 (02) : 328 - 354
  • [6] Exploiting tumour hypoxia in cancer treatment
    Brown, JM
    William, WR
    [J]. NATURE REVIEWS CANCER, 2004, 4 (06) : 437 - 447
  • [7] 18F-FDG PET/CT of Non-eSmall Cell Lung Carcinoma Under Neoadjuvant Chemotherapy: Background-Based Adaptive Volume Metrics Outperform TLG and MTV in Predicting Histopathologic Response
    Burger, Irene A.
    Casanova, Ruben
    Steiger, Seraina
    Husmann, Lars
    Stolzmann, Paul
    Huellner, Martin W.
    Curioni, Alessandra
    Hillinger, Sven
    Schmidtlein, C. Ross
    Soltermann, Alex
    [J]. JOURNAL OF NUCLEAR MEDICINE, 2016, 57 (06) : 849 - 854
  • [8] The current state of cancer metabolism FOREWORD
    Cairns, Rob A.
    Mak, Tak W.
    [J]. NATURE REVIEWS CANCER, 2016, 16 (10) : 613 - 614
  • [9] FDG-PET Radiomics for Response Monitoring in Non-Small-Cell Lung Cancer Treated with Radiation Therapy
    Carles, Montserrat
    Fechter, Tobias
    Radicioni, Gianluca
    Schimek-Jasch, Tanja
    Adebahr, Sonja
    Zamboglou, Constantinos
    Nicolay, Nils H.
    Marti-Bonmati, Luis
    Nestle, Ursula
    Grosu, Anca L.
    Baltas, Dimos
    Mix, Michael
    Gkika, Eleni
    [J]. CANCERS, 2021, 13 (04) : 1 - 15
  • [10] EMT, cell plasticity and metastasis
    Chaffer, Christine L.
    San Juan, Beatriz P.
    Lim, Elgene
    Weinberg, Robert A.
    [J]. CANCER AND METASTASIS REVIEWS, 2016, 35 (04) : 645 - 654