Predicting pathological complete response by comparing MRI-based radiomics pre- and postneoadjuvant radiotherapy for locally advanced rectal cancer
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Predicting pathological complete response by comparing MRI-based radiomics pre- and postneoadjuvant radiotherapy for locally advanced rectal cancer. / Li, Yuqiang; Liu, Wenxue; Pei, Qian; Zhao, Lilan; Güngör, Cenap; Zhu, Hong; Song, Xiangping; Li, Chenglong; Zhou, Zhongyi; Xu, Yang; Wang, Dan; Tan, Fengbo; Yang, Pei; Pei, Haiping.
in: CANCER MED-US, Jahrgang 8, Nr. 17, 12.2019, S. 7244-7252.Publikationen: SCORING: Beitrag in Fachzeitschrift/Zeitung › SCORING: Zeitschriftenaufsatz › Forschung › Begutachtung
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TY - JOUR
T1 - Predicting pathological complete response by comparing MRI-based radiomics pre- and postneoadjuvant radiotherapy for locally advanced rectal cancer
AU - Li, Yuqiang
AU - Liu, Wenxue
AU - Pei, Qian
AU - Zhao, Lilan
AU - Güngör, Cenap
AU - Zhu, Hong
AU - Song, Xiangping
AU - Li, Chenglong
AU - Zhou, Zhongyi
AU - Xu, Yang
AU - Wang, Dan
AU - Tan, Fengbo
AU - Yang, Pei
AU - Pei, Haiping
N1 - © 2019 The Authors. Cancer Medicine published by John Wiley & Sons Ltd.
PY - 2019/12
Y1 - 2019/12
N2 - BACKGROUND: Total mesorectal excision following neoadjuvant chemoradiotherapy (nCRT) is recommended in the latest treatment of locally advanced rectal cancer (LARC).OBJECTIVE: To predict whether patients with LARC can achieve pathologic complete response (pCR), comparing MRI-based radiomics between before and after neoadjuvant radiotherapy (nRT) was performed.METHODS: One hundred and sixty-five MRI-based radiomics features in axial T2-weighted images were obtained quantitatively from Imaging Biomarker Explorer Software. The specific features of conventional and developing radiomics were selected with the analysis of least absolute shrinkage and selection operator logistic regression, of which the predictive performance was analyzed with receiver operating curve and calibration curve, and applied to an independent cohort.RESULTS: One hundred and thirty-one target patients were enrolled in the present study. A radiomics signature founded on seven radiomics features was generated in the primary cohort. A remarkable difference about Rad-score between pCR and non-pCR group occurred in both of primary (P < .001) or validation cohorts (P < .001). The value of area under the curves was 0.92 (95% CI, 0.86-0.99) and 0.87 (95% CI, 0.74-1.00) in the primary and validation cohorts, respectively. The Rad-score (OR = 23.581; P < .001) from multivariate logistic regression analysis was significant as an independent factor of pCR.CONCLUSION: Our predictive model based on radiomics features was an independent predictor for pCR in LARC and could be a candidate in clinical practice.
AB - BACKGROUND: Total mesorectal excision following neoadjuvant chemoradiotherapy (nCRT) is recommended in the latest treatment of locally advanced rectal cancer (LARC).OBJECTIVE: To predict whether patients with LARC can achieve pathologic complete response (pCR), comparing MRI-based radiomics between before and after neoadjuvant radiotherapy (nRT) was performed.METHODS: One hundred and sixty-five MRI-based radiomics features in axial T2-weighted images were obtained quantitatively from Imaging Biomarker Explorer Software. The specific features of conventional and developing radiomics were selected with the analysis of least absolute shrinkage and selection operator logistic regression, of which the predictive performance was analyzed with receiver operating curve and calibration curve, and applied to an independent cohort.RESULTS: One hundred and thirty-one target patients were enrolled in the present study. A radiomics signature founded on seven radiomics features was generated in the primary cohort. A remarkable difference about Rad-score between pCR and non-pCR group occurred in both of primary (P < .001) or validation cohorts (P < .001). The value of area under the curves was 0.92 (95% CI, 0.86-0.99) and 0.87 (95% CI, 0.74-1.00) in the primary and validation cohorts, respectively. The Rad-score (OR = 23.581; P < .001) from multivariate logistic regression analysis was significant as an independent factor of pCR.CONCLUSION: Our predictive model based on radiomics features was an independent predictor for pCR in LARC and could be a candidate in clinical practice.
U2 - 10.1002/cam4.2636
DO - 10.1002/cam4.2636
M3 - SCORING: Journal article
C2 - 31642204
VL - 8
SP - 7244
EP - 7252
JO - CANCER MED-US
JF - CANCER MED-US
SN - 2045-7634
IS - 17
ER -