A deep-learning model to predict the completeness of cytoreductive surgery in colorectal cancer with peritoneal metastasis☆.

Authors

Lin Q,Chen C,Li K,Cao W,Wang R,Fichera A,Han S,Zou X,Li T,Zou P,Wang H,Ye Z,Yuan Z

Affiliations (12)

  • Department of Colorectal Surgery and Guangdong Provincial Key Laboratory of Colorectal and Pelvic Floor Diseases, The Sixth Affiliated Hospital, Sun Yat-Sen University, Guangzhou, Guangdong Province, China.
  • College of Mathematics and Informatics, South China Agricultural University, Guangzhou, China; College of Computers, Central South University, Changsha, China.
  • College of Mathematics and Informatics, South China Agricultural University, Guangzhou, China.
  • Department of Radiology, The Sixth Affiliated Hospital, Sun Yat-Sen University, Guangzhou, China.
  • Department of Colorectal Surgery, Fudan University Shanghai Cancer Center, Shanghai, China.
  • Colon and Rectal Surgery, Baylor University Medical Center, Dallas, TX, USA.
  • General Surgery Center, Department of Gastrointestinal Surgery, Zhujiang Hospital, Southern Medical University, Guangzhou, China.
  • College of Intelligent Manufacturing and Modern Industry (School of Mechanical Engineering), Xinjiang University, Urumqi, China.
  • Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, China.
  • Department of Colorectal Surgery and Guangdong Provincial Key Laboratory of Colorectal and Pelvic Floor Diseases, The Sixth Affiliated Hospital, Sun Yat-Sen University, Guangzhou, Guangdong Province, China. Electronic address: [email protected].
  • Department of Gastrointestinal Surgical Oncology, Fujian Cancer Hospital and Fujian Medical University Cancer Hospital, Fuzhou, China. Electronic address: [email protected].
  • Department of Colorectal Surgery and Guangdong Provincial Key Laboratory of Colorectal and Pelvic Floor Diseases, The Sixth Affiliated Hospital, Sun Yat-Sen University, Guangzhou, Guangdong Province, China. Electronic address: [email protected].

Abstract

Colorectal cancer (CRC) with peritoneal metastasis (PM) is associated with poor prognosis. The Peritoneal Cancer Index (PCI) is used to evaluate the extent of PM and to select Cytoreductive Surgery (CRS). However, PCI score is not accurate to guide patient's selection for CRS. We have developed a novel AI framework of decoupling feature alignment and fusion (DeAF) by deep learning to aid selection of PM patients and predict surgical completeness of CRS. 186 CRC patients with PM recruited from four tertiary hospitals were enrolled. In the training cohort, deep learning was used to train the DeAF model using Simsiam algorithms by contrast CT images and then fuse clinicopathological parameters to increase performance. The accuracy, sensitivity, specificity, and AUC by ROC were evaluated both in the internal validation cohort and three external cohorts. The DeAF model demonstrated a robust accuracy to predict the completeness of CRS with AUC of 0.9 (95 % CI: 0.793-1.000) in internal validation cohort. The model can guide selection of suitable patients and predict potential benefits from CRS. The high predictive performance in predicting CRS completeness were validated in three external cohorts with AUC values of 0.906(95 % CI: 0.812-1.000), 0.960(95 % CI: 0.885-1.000), and 0.933 (95 % CI: 0.791-1.000), respectively. The novel DeAF framework can aid surgeons to select suitable PM patients for CRS and predict the completeness of CRS. The model can change surgical decision-making and provide potential benefits for PM patients.

Topics

Cytoreduction Surgical ProceduresPeritoneal NeoplasmsColorectal NeoplasmsDeep LearningJournal Article

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