Extent of enhancement on multiphase contrast-enhanced CT images is a potential prognostic factor of stage I–III colon cancer

Wang, Zhanhuai; Yao, Ye; Hu, Yeting; Sun, Long; Xu, Dong; Ding, Kefeng

doi:10.1007/s00330-018-5689-3

Cited by 9 publications

(16 citation statements)

References 21 publications

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“…Li et al proposed a postoperative survival assessment method for colon cancer patients by analyzing the blood test results of colorectal cancer, combined with data classification and machine learning methods [ 3 ]. Wang et al studied the CT images of patients with colon cancer who were admitted to the hospital and established a survival prediction model for colon cancer patients on medical imaging after observing the attenuation of colon cancer indexes by using the regression analysis algorithm [ 4 ]. Akinkuotu et al used the colon cancer database to compare the survival rate of colon cancer patients before and after the age of 25, and found that younger colon cancer patients had a higher survival rate [ 5 ].…”

Section: Introductionmentioning

confidence: 99%

18F-FDG PET/CT Image Deep Learning Predicts Colon Cancer Survival

Tian

Chen

2023

Contrast Media & Molecular Imaging

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Colon cancer is a type of cancer that begins in the large intestine. In the process of efficacy evaluation, postoperative recurrence prediction and metastasis monitoring of colon cancer, traditional medical image analysis methods are highly dependent on the personal ability of the doctors. In the process of patient treatment, it not only increases the workload and work pressure for doctors, but also has some problems with traditional medical image analysis methods. Moreover, the traditional medical image analysis methods have problems such as insufficient prediction accuracy, slow prediction speed, and the risk of errors in prediction. When analyzing 18F-FDG PET/CT images by traditional medical image analysis methods, it is easy to cause problems such as untimely treatment plans and errors in diagnosis, which will adversely affect the survival of colon cancer patients. Although 18F-FDG PET/CT images have certain advantages in image clarity and accuracy compared with traditional medical imaging methods, the analysis method based on 18F-FDG PET/CT images also has certain effects in predicting the survival of colon cancer patients, but there are still many shortcomings: the 18F-FDG PET/CT image analysis method overly relies on the technical advantages of 8F-FDG PET/CT images; in the analysis and prediction of image data, it has not gotten rid of the dependence on the personal medical quality of the doctors; traditional medical image analysis methods are still used when analyzing and predicting images; there is no breakthrough in image analysis effects. In order to solve these problems, this paper combined deep learning theory, using three algorithms of the improved RBM algorithm, image feature extraction method based on deep learning, and regression neural network to analyze and predict 18F-FDG PET/CT images, and applied some algorithms to analyze and predict 18F-FDG PET/CT images, and also established a deep learning-based 18F-FDG PET/CT image survival analysis prediction model. Four aspects survival prediction accuracy, survival prediction speed, survival prediction precision, and physician satisfaction were studied through this model. The research results have shown that compared with traditional medical image analysis methods, the prediction accuracy of 18F-FDG PET/CT image survival analysis prediction model based on deep learning is improved by 0.83%, and the prediction speed is improved by 3.42%, as well as the prediction precision increased by 6.13%. The research results show that the deep learning-based 18F-FDG PET/CT image survival analysis prediction model established in this paper is of great significance to improve the survival rate of colon cancer patients, and also promotes the development of the medical industry.

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Section: Introductionmentioning

confidence: 99%

18F-FDG PET/CT Image Deep Learning Predicts Colon Cancer Survival

Tian

Chen

2023

Contrast Media & Molecular Imaging

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“…Low rectal cancer, as a kind of common malignant tumor in clinic, refers to the rectal cancer occurring less than 8 centimeters from the anal verge [ 1 , 2 ]. Colostomy is the common treatment for this cancer at present.…”

Section: Introductionmentioning

confidence: 99%

Effects of Nutritious Meal Combined with Online Publicity and Education on Postoperative Nutrition and Psychological State in Patients with Low Rectal Cancer After Colostomy

Li³

2022

Computational and Mathematical Methods in Medicine

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Objective. To investigate the effects of nutritious meal combined with online publicity and education on postoperative nutrition and psychological state in patients with low rectal cancer after colostomy. Methods. The clinic data of 88 patients with low rectal cancer who received the colostomy in our hospital (August 2020-August 2021) were retrospectively reviewed. Among them, 44 patients received nutritious meal combined with online publicity and education and they made up the study group, and the others were given conventional care and they made up the reference group. The nutrition indicators, scores of the World Health Organization Quality of Life (WHOQOL)-BREF, and other materials of the patients in the two groups were compared. Results. After intervention, the various nutrition indicators, immune indexes, and WHOQOL-BREF score of the study group were all prominently higher than those of the reference group ( P < 0.001 ). Compared with the reference group, the study group after intervention achieved markedly lower self-rating anxiety scale (SAS) score and self-rating depression scale (SDS) score ( P < 0.001 ) and obviously lower total incidence of complications ( P < 0.05 ). Conclusion. Combining nutritious meal with online publicity and education can effectively improve the postoperative nutrition and immune function of the patients with low rectal cancer after colostomy, and this intervention contributes to releasing the patients’ adverse emotions. Further study helps to provide these patients with favorable solutions.

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“…One of the parameters measured in the form of CAV is the tumor density. 21 The contrast enhancement of tumor density in the ROI under enhanced CT closely matches the physiological effect of tumor angiogenesis. 22 The state of visceral mass contrast enhancement on CT images which could reflect tumor vascularity state is an important parameter.…”

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confidence: 71%

<p>The Preoperative Enhanced Degree of Contrast-enhanced CT Images: A Potential Independent Predictor in Gastric Adenocarcinoma Patients After Radical Gastrectomy</p>

Wang

Yan

et al. 2020

CMAR

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Aim: To discover the value of contrast-enhanced CT parameters in predicting the prognosis of gastric adenocarcinoma (GAC) patients after radical gastrectomy. Methods: The patients with a clinical diagnosis of GAC were retrospectively enrolled. Two radiologists drew the regions of interest (ROIs) in CT images and measured the CT attenuate value (CAV) in each phase and the corrected CAV (cCAV) in each contrast-enhanced phase. Patients were divided into two groups (high/low-enhancement) according to receiver operating characteristic (ROC) curve. Kaplan-Meier curve and Cox proportional hazards regression analysis were performed to evaluate correlation between prognosis and variables. Subgroup analysis was used to further analyze the prognostic value of variables. Results: In total 435 patients were included. According to ROC curve, the cCAV in delayed phase (DP-cCAV) with maximum AUC and Youden index was chosen. A total of 312 patients (71.7%) entered DP-cCAV low group and remaining 123 (28.3%) patients were in DP-cCAV high group. According to univariate (high vs low, HR=2.120, p<0.001) and multivariate (high vs low, HR=1.623, p<0.001) Cox regression analysis, the low-enhancement state was considered as an independent protective factor. Subgroup analysis was based on age, maximum diameter of tumor, differentiation, vascular invasion status, and TNM staging. In most subgroups, the overall survival (OS) of DP-cCAV low group was overwhelmingly satisfactory (all HR >1, expect TNM stage I, IV and differentiated type subgroups). Conclusion: The prognostic effectiveness of CT parameters as biomarkers for OS in GAC patients treated with radical gastrectomy has potential value.

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Extent of enhancement on multiphase contrast-enhanced CT images is a potential prognostic factor of stage I–III colon cancer

Cited by 9 publications

References 21 publications

18F-FDG PET/CT Image Deep Learning Predicts Colon Cancer Survival

18F-FDG PET/CT Image Deep Learning Predicts Colon Cancer Survival

Effects of Nutritious Meal Combined with Online Publicity and Education on Postoperative Nutrition and Psychological State in Patients with Low Rectal Cancer After Colostomy

<p>The Preoperative Enhanced Degree of Contrast-enhanced CT Images: A Potential Independent Predictor in Gastric Adenocarcinoma Patients After Radical Gastrectomy</p>

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