Abstract 388P
Background
More and more evidences suggest that circulating tumor cells (CTC) may be a good biomarker, not only as a prognostic indicator for tumors, but also for monitoring therapeutic effect and recurrence. However, the detection of CTC usually requires the final artificial judgment. This requires experienced pathologists and increases their workload. The application of machine learning in medical image recognition can effectively improve the level of automation and reduce the workload. So we hope to use machine learning to identify CTCs.
Methods
First, the python's openCV software package is used to segment the images of CTCs by image denoising, image filtering, edge detection, image expansion and contraction techniques. Secondly, the segmented cell images as a training set are trained using the CNN deep learning network. The CNN deep learning network includes input layer, intermediate hidden layer, and output layer. The middle hidden layer contains three layers, namely layer1, layer2 and lager3. Each intermediate hidden layer further includes convolution layer, excitation layer, and pooling layer. After the input layer, the cell images first enter the first intermediate hidden layer. The convolution layer of the first intermediate hidden layer is composed of 32 5x5 convolution kernels, which are then output to the pooling layer for dimension reduction through the ReLU excitation layer. After dimension reduction, the data is output from the first hidden layer to complete an entire feature extraction process. Then, through the second and third intermediate hidden layers in sequence, all feature extraction is completed. Finally, it enters the output layer and output the result, ie, CTCs or non-CTCs.
Results
We took 2920 cells from 732 patients for training and testing. Among them, 2000 cells were used as training set and 920 cells were used as testing set. The sensitivity and specificity of recognition reached 86.1% and 84.5%, respectively.
Conclusions
To identify CTC by machine learning can reach high sensitivity and specificity. We are further revising our methods of deep learning to achieve greater recognition effect.
Clinical trial identification
Editorial acknowledgement
Legal entity responsible for the study
The authors.
Funding
Has not received any funding.
Disclosure
All authors have declared no conflicts of interest.
Resources from the same session
87P - Negative to positive lymph node ratio-prognostic marker of survival in node positive rectal cancer
Presenter: Pavan Jonnada
Session: Poster display session
Resources:
Abstract
88P - The Sidra LUMC advanced colon cancer NGS cohort
Presenter: Wouter Hendrickx
Session: Poster display session
Resources:
Abstract
89P - A phase II trial of adjuvant chemoradiotherapy for patients with high-risk rectal submucosal invasive cancer after local resection
Presenter: Masaaki Noguchi
Session: Poster display session
Resources:
Abstract
90P - High MICB expression confers prognostic benefit in colorectal cancer
Presenter: Shanchao Yu
Session: Poster display session
Resources:
Abstract
91P - Adjuvant therapy for high-risk stage II or stage III colon adenocarcinoma: A propensity score-matched, nationwide, population-based cohort study
Presenter: Chien-Hsin Chen
Session: Poster display session
Resources:
Abstract
92P - Prospective randomized controlled study comparing primary surgery versus neoadjuvant chemotherapy followed by surgery in gastric carcinoma
Presenter: Vipin Goel
Session: Poster display session
Resources:
Abstract
93P - Biomarker selection of liver metastatic colorectal patients for anti-EGFR monoclonal antibodies: A machine learning analysis
Presenter: Yijiao Chen
Session: Poster display session
Resources:
Abstract
94P - NORTH/HGCSG1003: North Japan multicenter phase II study of oxaliplatin-containing regimen as adjuvant chemotherapy for stage III colon cancer: Final analysis
Presenter: Michio Nakamura
Session: Poster display session
Resources:
Abstract
95P - Anatomical resections improve relapse-free survival in patients with KRAS/NRAS/BRAF- mutated colorectal liver metastases
Presenter: Ye Wei
Session: Poster display session
Resources:
Abstract
96P - Incidence, characteristics and prognosis in colorectal cancer with CNS metastases
Presenter: Nicola Taylor
Session: Poster display session
Resources:
Abstract