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Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery
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Artificial Neural Network Individualised Prediction of Time to Colorectal Cancer Surgery
N J Curtis1,2, G Dennison2, E Salib3
1Department of Surgery and Cancer, Imperial College London, Level 10, St. Mary's Hospital, Praed Street, London W2 1NY, UK.
Gastroenterology Research and Practice
|July 31, 2019
Summary
Artificial neural networks can predict individual colorectal cancer surgery wait times, enabling personalized prehabilitation. This advance helps optimize patient care by anticipating delays and tailoring interventions before surgery.
Area of Science:
- Surgical Oncology
- Artificial Intelligence in Medicine
- Predictive Analytics
Background:
- Colorectal cancer (CRC) treatment requires timely surgery, but unpredictable wait times hinder patient optimization.
- Current systems lack reliable prediction of individual surgical wait times.
- Knowing surgical wait times could facilitate tailored prehabilitation programs.
Purpose of the Study:
- To develop a predictive system for individual patient wait times for elective colorectal cancer surgery.
- To explore the utility of artificial neural networks (ANNs) in predicting surgical wait times.
- To identify factors influencing prolonged surgical waits.
Main Methods:
- Utilized a prospectively populated database of elective laparoscopic colorectal cancer surgeries.
- Trained and tested a multilayered perceptron artificial neural network (ANN) model.
- Performed univariate and multivariate analyses to validate ANN predictions.
Main Results:
- The ANN model accurately predicted surgery times within 8 and 12 weeks for training and testing cohorts.
- Area under the receiver operating curves reached 0.793 and 0.865, respectively.
- American Society of Anesthesiologists (ASA) physical status score was a key modifiable risk factor for longer waits post-neoadjuvant therapy.
Conclusions:
- Artificial neural networks effectively predict individual colorectal cancer surgery wait times using demographic and diagnostic data.
- This predictive capability can personalize preoperative care.
- The findings support the integration of prehabilitation interventions based on predicted wait times.
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