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author:

Hu, Yihuang (Hu, Yihuang.) [1] | Li, Juan (Li, Juan.) [2] | Zhuang, Zhuokai (Zhuang, Zhuokai.) [3] | Xu, Bin (Xu, Bin.) [4] | Wang, Dabiao (Wang, Dabiao.) [5] (Scholars:王大彪) | Yu, Huichuan (Yu, Huichuan.) [6] | Li, Lanlan (Li, Lanlan.) [7] (Scholars:李兰兰)

Indexed by:

Scopus SCIE

Abstract:

Neoadjuvant systemic treatment before surgery is a prevalent regimen in the patients with advanced-stage or high-risk tumor, which has shaped the treatment strategies and cancer survival in the past decades. However, some patients present with poor response to the neoadjuvant treatment. Therefore, it is of great significance to develop tools to help distinguish the patients that could achieve pathological complete response before surgery to avoid inappropriate treat-ment. Here, this study demonstrated a multi-task deep learning tool called DeepInteg. In the DeepInteg framework, the segmentation module was constructed based on the CE-Net with a context extractor to achieve end-to-end delineation of region of interest (ROI) from radiological images, then the features of segmented Magnetic Resonance Imaging (MRI) and Computed To-mography (CT) images of each case were fused and input to the classification module based on a convolution neural network for treatment outcome prediction. The dataset with 1700 MRI and CT slices collected from the prospectively randomized clinical trial (NCT01211210) on systemic treatment for rectal cancer was used to develop and systematically optimize DeepInteg. As a result, DeepInteg achieved automatic segmentation of tumoral ROI with Dices of 0.766 and 0.719 and mIoUs of 0.788 and 0.756 in CT and MRI images, respectively. In addition, DeepInteg ach-ieved AUC of 0.833, accuracy of 0.826 and specificity of 0.856 in the prediction for pathological complete response after treatment, which showed better performance compared with the model based on CT or MRI alone. This study provide a robust framework to develop disease-specific tools for automatic delineation of ROI and clinical outcome prediction. The well-trained Deep-Integ could be readily applied in clinic to predict pathological complete response after neo-adjuvant therapy in rectal cancer patients.

Keyword:

CT Deep learning MRI Neoadjuvant therapy Rectal cancer

Community:

  • [ 1 ] [Hu, Yihuang]Fuzhou Univ, Coll Phys & Informat Engn, Fujian Key Lab Intelligent Proc & Wireless Transmi, Fuzhou 350108, Peoples R China
  • [ 2 ] [Xu, Bin]Fuzhou Univ, Coll Phys & Informat Engn, Fujian Key Lab Intelligent Proc & Wireless Transmi, Fuzhou 350108, Peoples R China
  • [ 3 ] [Li, Lanlan]Fuzhou Univ, Coll Phys & Informat Engn, Fujian Key Lab Intelligent Proc & Wireless Transmi, Fuzhou 350108, Peoples R China
  • [ 4 ] [Li, Juan]Sun Yat Sen Univ, Affiliated Hosp 6, Dept Endoscop Surg, Guangzhou 510655, Guangdong, Peoples R China
  • [ 5 ] [Zhuang, Zhuokai]Sun Yat Sen Univ, Affiliated Hosp 6, Guangdong Inst Gastroenterol, Guangdong Prov Key Lab Colorectal & Pelv Floor Dis, Guangzhou 510655, Guangdong, Peoples R China
  • [ 6 ] [Zhuang, Zhuokai]Sun Yat Sen Univ, Affiliated Hosp 6, Dept Colorectal Surg, Guangzhou 510655, Guangdong, Peoples R China
  • [ 7 ] [Wang, Dabiao]Fuzhou Univ, Coll Chem & Engn, Fuzhou 350108, Peoples R China
  • [ 8 ] [Yu, Huichuan]Sun Yat Sen Univ, Affiliated Hosp 6, Guangdong Inst Gastroenterol, Guangdong Prov Key Lab Colorectal & Pelv Floor Dis, 26 Yuancun Erheng Rd, Guangzhou 510655, Guangdong, Peoples R China

Reprint 's Address:

  • 王大彪 李兰兰

    [Li, Lanlan]Fuzhou Univ, Coll Phys & Informat Engn, Fujian Key Lab Intelligent Proc & Wireless Transmi, Fuzhou 350108, Peoples R China;;[Wang, Dabiao]Fuzhou Univ, Coll Chem & Engn, Fuzhou 350108, Peoples R China;;[Yu, Huichuan]Sun Yat Sen Univ, Affiliated Hosp 6, Guangdong Inst Gastroenterol, Guangdong Prov Key Lab Colorectal & Pelv Floor Dis, 26 Yuancun Erheng Rd, Guangzhou 510655, Guangdong, Peoples R China

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Source :

HELIYON

ISSN: 2405-8440

Year: 2023

Issue: 2

Volume: 9

3 . 4

JCR@2023

3 . 4 0 0

JCR@2023

ESI Discipline: MULTIDISCIPLINARY;

ESI HC Threshold:42

JCR Journal Grade:1

CAS Journal Grade:3

Cited Count:

WoS CC Cited Count: 2

SCOPUS Cited Count: 1

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 2

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