Predicting Chemotherapeutic Response for Far-advanced Gastric Cancer by Radiomics with Deep Learning Semi-automatic Segmentation.
Predicting Chemotherapeutic Response for Far-advanced Gastric Cancer by Radiomics with Deep Learning Semi-automatic Segmentation.
复制标题
通过放射组学和深度学习半自动分割预测晚期胃癌的化疗反应
DOI：
10.7150/jca.46704
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发表时间：
2020
影响因子：
3.9
通讯作者：
Zhang H
中科院分区：
文献类型：
--
作者：
Tan JW;Wang L;Chen Y;Xi W;Ji J;Wang L;Xu X;Zou LK;Feng JX;Zhang J;Zhang H
目的：建立双能计算机断层扫描（DECT） delta放射组学模型，预测远晚期胃癌（GC）患者的化疗反应。设计了一种基于深度学习的半自动图像分割方法，并将其性能与人工分割方法进行了比较。方法：本回顾性研究纳入2016年9月至2017年12月接受化疗的远晚期胃癌患者86例（训练组66例，试验组20例）。基线和第一次随访DECT之间的δ放射组学特征通过随机森林模型来预测第二次随访DECT评估的化疗反应。从混杂因素和δ放射组学特征中选择9个特征子集，在训练队列中选择10倍交叉验证的最佳模型。开发了一种基于深度学习的半自动分割方法来预测化疗反应，并在测试队列中与人工分割进行比较，并在30例患者的独立验证队列中进一步验证。结果：由混杂因素和纹理特征构建的最佳模型在训练队列中的平均AUC达到0.752。我们提出的半自动分割方法比人工分割更省时，测试队列和独立验证队列的平均节省时间分别为11.2333±6.3989 min和9.9889±5.5086 min （p均< 0.05）。在测试队列和独立验证队列中，半自动分割的预测能力也优于手动分割（AUC分别为0.728 vs. 0.687和0.828 vs. 0.749）。结论：DECT δ放射组学是预测晚期胃癌化疗反应的一种有前景的生物标志物。与手动版本相比，基于深度学习的半自动分割具有更高的可重复性和更低的人工成本，具有临床应用的潜力。
Purpose: To build a dual-energy computed tomography (DECT) delta radiomics model to predict chemotherapeutic response for far-advanced gastric cancer (GC) patients. A semi-automatic segmentation method based on deep learning was designed, and its performance was compared with that of manual segmentation. Methods: This retrospective study included 86 patients with far-advanced GC treated with chemotherapy from September 2016 to December 2017 (66 and 20 in the training and testing cohorts, respectively). Delta radiomics features between the baseline and first follow-up DECT were modeled by random forest to predict the chemotherapeutic response evaluated by the second follow-up DECT. Nine feature subsets from confounding factors and delta radiomics features were used to choose the best model with 10-fold cross-validation in the training cohort. A semi-automatic segmentation method based on deep learning was developed to predict the chemotherapeutic response and compared with manual segmentation in the testing cohort, which was further validated in an independent validation cohort of 30 patients. Results: The best model, constructed by confounding factors and texture features, reached an average AUC of 0.752 in the training cohort. Our proposed semi-automatic segmentation method was more time-effective than manual segmentation, with average saving-time of 11.2333 ± 6.3989 minutes and 9.9889 ±5.5086 minutes in the testing cohort and the independent validation cohort, respectively (both p < 0.05). The predictive ability of the semi-automatic segmentation was also better than that of the manual segmentation both in the testing cohort and the independent validation cohort (AUC: 0.728 vs. 0.687 and 0.828 vs. 0.749, respectively). Conclusion: DECT delta radiomics serves as a promising biomarker for predicting chemotherapeutic response for far-advanced GC. Semi-automatic segmentation based on deep learning shows the potential for clinical use with increased reproducibility and decreased labor costs compared to the manual version.
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影响因子：
6
作者：
Rao, Sheng-Xiang;Lambregts, Doenja M. J.;Beets-Tan, Regina G. H.
通讯作者：
Beets-Tan, Regina G. H.
影响因子：
5.7
作者：
Ganguly A;Yang H;Cabral F
通讯作者：
Cabral F
影响因子：
8.4
作者：
Eisenhauer, E. A.;Therasse, P.;Verweij, J.
通讯作者：
Verweij, J.
影响因子：
10.6
作者：
Gibson E;Giganti F;Hu Y;Bonmati E;Bandula S;Gurusamy K;Davidson B;Pereira SP;Clarkson MJ;Barratt DC
通讯作者：
Barratt DC
影响因子：
3.6
作者：
Jeon, Seung Hyuck;Song, Changhoon;Kim, Jae-Sung
通讯作者：
Kim, Jae-Sung