TY - JOUR
T1 - Methodological quality of machine learning-based quantitative imaging analysis studies in esophageal cancer: a systematic review of clinical outcome prediction after concurrent chemoradiotherapy
AU - Shi, Z.W.
AU - Zhang, Z.
AU - Liu, Z.Y.
AU - Zhao, L.J.
AU - Ye, Z.X.
AU - Dekker, A.
AU - Wee, L.
N1 - Funding Information:
This work was supported by the National Science Fund for Distinguished Young Scholars (Grant No. 81925023), the National Natural Science Foundation of China (Grants No. 81771912, 82102034). L.W. acknowledges financial support from the Dutch Research Council NWO (STW-Perspectief STRaTegy 14930, Indo-Dutch projects BIONIC 629.002.205 and TRAIN 629.002.212), the Queen Wilhemina foundation KWF (ProTRaIT), and a personal research grant from the Hanarth Foundation.
Publisher Copyright:
© 2021, The Author(s).
PY - 2022/7
Y1 - 2022/7
N2 - Purpose Studies based on machine learning-based quantitative imaging techniques have gained much interest in cancer research. The aim of this review is to critically appraise the existing machine learning-based quantitative imaging analysis studies predicting outcomes of esophageal cancer after concurrent chemoradiotherapy in accordance with PRISMA guidelines. Methods A systematic review was conducted in accordance with PRISMA guidelines. The citation search was performed via PubMed and Embase Ovid databases for literature published before April 2021. From each full-text article, study characteristics and model information were summarized. We proposed an appraisal matrix with 13 items to assess the methodological quality of each study based on recommended best-practices pertaining to quality. Results Out of 244 identified records, 37 studies met the inclusion criteria. Study endpoints included prognosis, treatment response, and toxicity after concurrent chemoradiotherapy with reported discrimination metrics in validation datasets between 0.6 and 0.9, with wide variation in quality. A total of 30 studies published within the last 5 years were evaluated for methodological quality and we found 11 studies with at least 6 "good" item ratings. Conclusion A substantial number of studies lacked prospective registration, external validation, model calibration, and support for use in clinic. To further improve the predictive power of machine learning-based models and translate into real clinical applications in cancer research, appropriate methodologies, prospective registration, and multi-institution validation are recommended.
AB - Purpose Studies based on machine learning-based quantitative imaging techniques have gained much interest in cancer research. The aim of this review is to critically appraise the existing machine learning-based quantitative imaging analysis studies predicting outcomes of esophageal cancer after concurrent chemoradiotherapy in accordance with PRISMA guidelines. Methods A systematic review was conducted in accordance with PRISMA guidelines. The citation search was performed via PubMed and Embase Ovid databases for literature published before April 2021. From each full-text article, study characteristics and model information were summarized. We proposed an appraisal matrix with 13 items to assess the methodological quality of each study based on recommended best-practices pertaining to quality. Results Out of 244 identified records, 37 studies met the inclusion criteria. Study endpoints included prognosis, treatment response, and toxicity after concurrent chemoradiotherapy with reported discrimination metrics in validation datasets between 0.6 and 0.9, with wide variation in quality. A total of 30 studies published within the last 5 years were evaluated for methodological quality and we found 11 studies with at least 6 "good" item ratings. Conclusion A substantial number of studies lacked prospective registration, external validation, model calibration, and support for use in clinic. To further improve the predictive power of machine learning-based models and translate into real clinical applications in cancer research, appropriate methodologies, prospective registration, and multi-institution validation are recommended.
KW - Quantitative imaging analysis
KW - Radiomics
KW - Esophageal cancer
KW - Concurrent chemoradiotherapy
KW - Clinical outcomes
KW - Methodological assessment
KW - PATHOLOGICAL COMPLETE RESPONSE
KW - TEXTURE ANALYSIS
KW - F-18-FDG PET
KW - PREOPERATIVE CHEMORADIOTHERAPY
KW - NEOADJUVANT CHEMORADIOTHERAPY
KW - RADIATION PNEUMONITIS
KW - GENETIC-VARIANTS
KW - TUMOR RESPONSE
KW - RADIOMICS
KW - FEATURES
U2 - 10.1007/s00259-021-05658-9
DO - 10.1007/s00259-021-05658-9
M3 - (Systematic) Review article
C2 - 34939174
SN - 1619-7070
VL - 49
SP - 2462
EP - 2481
JO - European Journal of Nuclear Medicine and Molecular Imaging
JF - European Journal of Nuclear Medicine and Molecular Imaging
IS - 8
ER -