Machine learning for prediction of chemoradiation therapy response in rectal cancer using pre-treatment and mid-radiation multi-parametric MRI.
Machine learning for prediction of chemoradiation therapy response in rectal cancer using pre-treatment and mid-radiation multi-parametric MRI.
复制标题
使用预处理和中期多参数MRI预测直肠癌化学放疗治疗反应的机器学习。
DOI：
10.1016/j.mri.2019.05.003
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发表时间：
2019-09
影响因子：
2.5
通讯作者：
Su MY
中科院分区：
文献类型：
--
作者：
Shi L;Zhang Y;Nie K;Sun X;Niu T;Yue N;Kwong T;Chang P;Chow D;Chen JH;Su MY
To predict the neoadjuvant chemoradiation therapy (CRT) response in patients with locally advanced rectal cancer (LARC) using radiomics and deep learning based on pre-treatment MRI and a mid-radiation follow-up MRI taken 3–4 weeks after the start of CRT. A total of 51 patients were included, 45 with pre-treatment, 41 with mid-radiation therapy (RT), and 35 with both MRI sets. The multi-parametric MRI protocol included T2, diffusion weighted imaging (DWI) with b-values of 0 and 800 s/mm2, and dynamic-contrast-enhanced (DCE) MRI. After completing CRT and surgery, the specimen was examined to determine the pathological response based on the tumor regression grade. The tumor ROI was manually drawn on the post-contrast image and mapped to other sequences. The total tumor volume and mean apparent diffusion coefficient (ADC) were measured. Radiomics using GLCM texture and histogram parameters, and deep learning using a convolutional neural network (CNN), were performed to differentiate pathologic complete response (pCR) vs. non-pCR, and good response (GR) vs. non-GR. Tumor volume decreased and ADC increased significantly in the mid-RT MRI compared to the pre-treatment MRI. For predicting pCR vs. non-pCR, combining ROI and radiomics features achieved an AUC of 0.80 for pre-treatment, 0.82 for mid-RT, and 0.86 for both MRI together. For predicting GR vs. non-GR, the AUC was 0.91 for pre-treatment, 0.92 for mid-RT, and 0.93 for both MRI together. In deep learning using CNN, combining pre-treatment and mid-RT MRI achieved a higher accuracy compared to using either dataset alone, with AUC of 0.83 for predicting pCR vs. non-pCR. Radiomics based on pre-treatment and early follow-up multi-parametric MRI in LARC patients receiving CRT could extract comprehensive quantitative information to predict final pathologic response.
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DOI：
10.1186/s13058-017-0846-1
发表时间：
2017-05-18
期刊：
Breast cancer research : BCR
影响因子：
--
作者：
Braman NM;Etesami M;Prasanna P;Dubchuk C;Gilmore H;Tiwari P;Plecha D;Madabhushi A
通讯作者：
Madabhushi A
DOI：
10.1016/j.ijrobp.2010.12.063
发表时间：
2012-02-01
影响因子：
7
作者：
Lambrecht, Maarten;Vandecaveye, Vincent;Haustermans, Karin
通讯作者：
Haustermans, Karin
影响因子：
19.7
作者：
Kim, Seung Ho;Lee, Jeong Min;Choi, Byung Ihn
通讯作者：
Choi, Byung Ihn
影响因子：
8.9
作者：
Cusumano, Davide;Dinapoli, Nicola;Valentini, Vincenzo
通讯作者：
Valentini, Vincenzo
影响因子：
3.7
作者：
Maas M;Lambregts DM;Nelemans PJ;Heijnen LA;Martens MH;Leijtens JW;Sosef M;Hulsewé KW;Hoff C;Breukink SO;Stassen L;Beets-Tan RG;Beets GL
通讯作者：
Beets GL