Predicting pathological complete response to neoadjuvant chemoradiotherapy in locally advanced rectal cancer: a systematic review

被引:115
|
作者
Ryan, J. E. [1 ,2 ,3 ]
Warrier, S. K. [1 ]
Lynch, A. C. [1 ]
Ramsay, R. G. [4 ,5 ]
Phillips, W. A. [5 ,6 ]
Heriot, A. G. [1 ]
机构
[1] Peter MacCallum Canc Ctr, Div Canc Surg, Melbourne, Vic, Australia
[2] Epworth Healthcare, Melbourne, Vic, Australia
[3] Univ Melbourne, Austin Acad Ctr, Parkville, Vic 3052, Australia
[4] Peter MacCallum Canc Ctr, Differentiat & Transcript Lab, Melbourne, Vic, Australia
[5] Univ Melbourne, Sir Peter MacCallum Dept Oncol, Parkville, Vic 3052, Australia
[6] Peter MacCallum Canc Ctr, Canc Biol & Surg Oncol Lab, Melbourne, Vic, Australia
关键词
Rectal cancer; pathologic complete response; prediction; POSITRON-EMISSION-TOMOGRAPHY; GROWTH-FACTOR RECEPTOR; PREOPERATIVE CHEMORADIOTHERAPY; CHEMORADIATION THERAPY; TUMOR-RESPONSE; CARCINOEMBRYONIC ANTIGEN; FDG-PET/CT; HISTOPATHOLOGIC RESPONSE; PROGNOSTIC VALUE; KRAS MUTATION;
D O I
10.1111/codi.13207
中图分类号
R57 [消化系及腹部疾病];
学科分类号
摘要
AimApproximately 20% of patients treated with neoadjuvant chemoradiotherapy (nCRT) for locally advanced rectal cancer achieve a pathological complete response (pCR) while the remainder derive the benefit of improved local control and downstaging and a small proportion show a minimal response. The ability to predict which patients will benefit would allow for improved patient stratification directing therapy to those who are likely to achieve a good response, thereby avoiding ineffective treatment in those unlikely to benefit. MethodA systematic review of the English language literature was conducted to identify pathological factors, imaging modalities and molecular factors that predict pCR following chemoradiotherapy. PubMed, MEDLINE and Cochrane Database searches were conducted with the following keywords and MeSH search terms: rectal neoplasm', response', neoadjuvant', preoperative chemoradiation', tumor response'. After review of title and abstracts, 85 articles addressing the prediction of pCR were selected. ResultsClear methods to predict pCR before chemoradiotherapy have not been defined. Clinical and radiological features of the primary cancer have limited ability to predict response. Molecular profiling holds the greatest potential to predict pCR but adoption of this technology will require greater concordance between cohorts for the biomarkers currently under investigation. ConclusionAt present no robust markers of the prediction of pCR have been identified and the topic remains an area for future research. This review critically evaluates existing literature providing an overview of the methods currently available to predict pCR to nCRT for locally advanced rectal cancer. The review also provides a comprehensive comparison of the accuracy of each modality.
引用
收藏
页码:234 / 246
页数:13
相关论文
共 50 条
  • [1] Assessing pathological complete response to neoadjuvant chemoradiotherapy in locally advanced rectal cancer: a systematic review
    Ryan, J. E.
    Warrier, S. K.
    Lynch, A. C.
    Heriot, A. G.
    [J]. COLORECTAL DISEASE, 2015, 17 (10) : 849 - 861
  • [2] Deep Learning Model for Predicting the Pathological Complete Response to Neoadjuvant Chemoradiotherapy of Locally Advanced Rectal Cancer
    Lou, Xiaoying
    Zhou, Niyun
    Feng, Lili
    Li, Zhenhui
    Fang, Yuqi
    Fan, Xinjuan
    Liu, Hailing
    Yao, Jianhua
    Huang, Yan
    [J]. MODERN PATHOLOGY, 2022, 35 : 489 - 490
  • [3] Deep Learning Model for Predicting the Pathological Complete Response to Neoadjuvant Chemoradiotherapy of Locally Advanced Rectal Cancer
    Lou, Xiaoying
    Zhou, Niyun
    Feng, Lili
    Li, Zhenhui
    Fang, Yuqi
    Fan, Xinjuan
    Liu, Hailing
    Yao, Jianhua
    Huang, Yan
    [J]. LABORATORY INVESTIGATION, 2022, 102 (SUPPL 1) : 489 - 490
  • [4] Deep Learning Model for Predicting the Pathological Complete Response to Neoadjuvant Chemoradiotherapy of Locally Advanced Rectal Cancer
    Lou, Xiaoying
    Zhou, Niyun
    Feng, Lili
    Li, Zhenhui
    Fang, Yuqi
    Fan, Xinjuan
    Ling, Yihong
    Liu, Hailing
    Zou, Xuan
    Wang, Jing
    Huang, Junzhou
    Yun, Jingping
    Yao, Jianhua
    Huang, Yan
    [J]. FRONTIERS IN ONCOLOGY, 2022, 12
  • [5] Deep Learning Model for Predicting the Pathological Complete Response to Neoadjuvant Chemoradiotherapy of Locally Advanced Rectal Cancer
    Lou, Xiaoying
    Zhou, Niyun
    Feng, Lili
    Li, Zhenhui
    Fang, Yuqi
    Fan, Xinjuan
    Liu, Hailing
    Yao, Jianhua
    Huang, Yan
    [J]. MODERN PATHOLOGY, 2022, 35 (SUPPL 2) : 489 - 490
  • [6] Morphologic predictors of pathological complete response to neoadjuvant chemoradiotherapy in locally advanced rectal cancer
    Zhang, Chongda
    Ye, Feng
    Liu, Yuan
    Ouyang, Han
    Zhao, Xinming
    Zhang, Hongmei
    [J]. ONCOTARGET, 2018, 9 (04) : 4862 - 4874
  • [7] Machine learning for predicting pathological complete response in patients with locally advanced rectal cancer after neoadjuvant chemoradiotherapy
    Chun-Ming Huang
    Ming-Yii Huang
    Ching-Wen Huang
    Hsiang-Lin Tsai
    Wei-Chih Su
    Wei-Chiao Chang
    Jaw-Yuan Wang
    Hon-Yi Shi
    [J]. Scientific Reports, 10
  • [8] Machine learning for predicting pathological complete response in patients with locally advanced rectal cancer after neoadjuvant chemoradiotherapy
    Huang, Chun-Ming
    Huang, Ming-Yii
    Huang, Ching-Wen
    Tsai, Hsiang-Lin
    Su, Wei-Chih
    Chang, Wei-Chiao
    Wang, Jaw-Yuan
    Shi, Hon-Yi
    [J]. SCIENTIFIC REPORTS, 2020, 10 (01)
  • [9] Significance of MRI-based radiomics in predicting pathological complete response to neoadjuvant chemoradiotherapy of locally advanced rectal cancer: A narrative review
    Li, Y.
    Liu, X.
    Gu, M.
    Xu, T.
    Ge, C.
    Chang, P.
    [J]. CANCER RADIOTHERAPIE, 2024, 28 (04): : 390 - 401
  • [10] Radiomics Analysis for Evaluation of Pathological Complete Response to Neoadjuvant Chemoradiotherapy in Locally Advanced Rectal Cancer
    Liu, Zhenyu
    Zhang, Xiao-Yan
    Shi, Yan-Jie
    Wang, Lin
    Zhu, Hai-Tao
    Tang, Zhenchao
    Wang, Shuo
    Li, Xiao-Ting
    Tian, Jie
    Sun, Ying-Shi
    [J]. CLINICAL CANCER RESEARCH, 2017, 23 (23) : 7253 - 7262