Deep learning survival model for colorectal cancer patients (DeepCRC) with Asian clinical data compared with different theories

Wei, Li; Lin, Shuye; He, Yuqi; Wang, Jinghui; Pan, Yuanming

doi:10.5114/aoms/156477

Cited by 9 publications

(3 citation statements)

References 16 publications

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“…DeepSurv converges deep neural network and CPH regression, and it can find out about the complex and nonlinear relationships between prognostic clinical variables and an individual's probability of mortality in true world, which has shown huge potential on medical field (24)(25)(26). Our previous studies have also demonstrated DeepSurv may outperform CPH in predicting tumor patients' survival (27,28). Therefore, we construct survival models using both CPH and DeepSurv algorithm this time, using all variables collected or LASSO to filter potential predictive clinical features, and chose the better one to serve as the final model.…”

Section: Discussionmentioning

confidence: 99%

Develop prediction model to help forecast advanced prostate cancer patients’ prognosis after surgery using neural network

Li,

Cai,

Huang

et al. 2024

Front. Endocrinol.

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BackgroundThe effect of surgery on advanced prostate cancer (PC) is unclear and predictive model for postoperative survival is lacking yet.MethodsWe investigate the National Cancer Institute’s Surveillance, Epidemiology, and End Results (SEER) database, to collect clinical features of advanced PC patients. According to clinical experience, age, race, grade, pathology, T, N, M, stage, size, regional nodes positive, regional nodes examined, surgery, radiotherapy, chemotherapy, history of malignancy, clinical Gleason score (composed of needle core biopsy or transurethral resection of the prostate specimens), pathological Gleason score (composed of prostatectomy specimens) and prostate-specific antigen (PSA) are the potential predictive variables. All samples are divided into train cohort (70% of total, for model training) and test cohort (30% of total, for model validation) by random sampling. We then develop neural network to predict advanced PC patients’ overall. Area under receiver operating characteristic curve (AUC) is used to evaluate model’s performance.Results6380 patients, diagnosed with advanced (stage III-IV) prostate cancer and receiving surgery, have been included. The model using all collected clinical features as predictors and based on neural network algorithm performs best, which scores 0.7058 AUC (95% CIs, 0.7021-0.7068) in train cohort and 0.6925 AUC (95% CIs, 0.6906-0.6956) in test cohort. We then package it into a Windows 64-bit software.ConclusionPatients with advanced prostate cancer may benefit from surgery. In order to forecast their overall survival, we first build a clinical features-based prognostic model. This model is accuracy and may offer some reference on clinical decision making.

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Section: Discussionmentioning

confidence: 99%

Develop prediction model to help forecast advanced prostate cancer patients’ prognosis after surgery using neural network

Li,

Cai,

Huang

et al. 2024

Front. Endocrinol.

Self Cite

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“…Katzman et al [ 12 ] proposed the DeepSurv method to solve this problem, which has been applied in some tumor prognosis studies, such as lung cancer and head and neck cancer[ 12 , 29 , 30 ]. Our previous study also demonstrated that it is better than traditional algorithms such as CPH[ 31 ]. Therefore, we built a deep-learning-based model based on the DeepSurv algorithm.…”

Section: Discussionmentioning

confidence: 99%

Unveiling the secrets of gastrointestinal mucous adenocarcinoma survival after surgery with artificial intelligence: A population-based study

Song,

Yan,

Zhang

et al. 2024

World J Gastrointest Oncol

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BACKGROUND Research on gastrointestinal mucosal adenocarcinoma (GMA) is limited and controversial, and there is no reference tool for predicting postoperative survival. AIM To investigate the prognosis of GMA and develop predictive model. METHODS From the Surveillance, Epidemiology, and End Results database, we collected clinical information on patients with GMA. After random sampling, the patients were divided into the discovery (70% of the total, for model training), validation (20%, for model evaluation), and completely blind test cohorts (10%, for further model evaluation). The main assessment metric was the area under the receiver operating characteristic curve (AUC). All collected clinical features were used for Cox proportional hazard regression analysis to determine factors influencing GMA’s prognosis. RESULTS This model had an AUC of 0.7433 [95% confidence intervals (95%CI): 0.7424-0.7442] in the discovery cohort, 0.7244 (GMA: 0.7234-0.7254) in the validation cohort, and 0.7388 (95%CI: 0.7378-0.7398) in the test cohort. We packaged it into Windows software for doctors’ use and uploaded it. Mucinous gastric adenocarcinoma had the worst prognosis, and these were protective factors of GMA: Regional nodes examined [hazard ratio (HR): 0.98, 95%CI: 0.97-0.98, P < 0.001)] and chemotherapy (HR: 0.62, 95%CI: 0.58-0.66, P < 0.001). CONCLUSION The deep learning-based tool developed can accurately predict the overall survival of patients with GMA postoperatively. Combining surgery, chemotherapy, and adequate lymph node dissection during surgery can improve patient outcomes.

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“…Colorectal cancer (CRC) is the third most common cancer and the second leading cause of cancer death worldwide. About 1 in 10 cancer patients aged < 50 years old is diagnosed with CRC [ 1 , 2 ]. The 5-year survival rate is < 15%, which causes a heavy burden on human health [ 3 ].…”

Section: Introductionmentioning

confidence: 99%

Preventive effects of 13 different drugs on colorectal cancer: a network meta-analysis

Liao

et al. 2023

Arch Med Sci

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Objective: To evaluate the preventive effect of 13 drugs on colorectal cancer (CRC) and guide the clinical application of these drugs. Materials and methods: 5 databases were searched for RCTs and cohort studies. The Cochrane bias risk assessment tool and the NOS quality evaluation tool were used to evaluate the quality of the studies. The funnel plot was used to analyze publication bias. A network meta-analysis of the extracted data was conducted by using Stata16.0 software. Results: A total of 57 studies involving 82719 participants were included. The funnel plot showed no obvious publication bias. The network meta-analysis showed that allopurinol was found to have the best effect, followed by berberine, non-aspirin NSAIDs, statins, metformin, calcium, mesalazine, ursodeoxycholic acid, vitamin D, mercaptopurine, aspirin, folic acid, and eicosapentaenoic acid. Conclusions: The preventive effect of allopurinol on CRC was better than that of the other 13 drugs.

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Deep learning survival model for colorectal cancer patients (DeepCRC) with Asian clinical data compared with different theories

Cited by 9 publications

References 16 publications

Develop prediction model to help forecast advanced prostate cancer patients’ prognosis after surgery using neural network

Develop prediction model to help forecast advanced prostate cancer patients’ prognosis after surgery using neural network

Unveiling the secrets of gastrointestinal mucous adenocarcinoma survival after surgery with artificial intelligence: A population-based study

Preventive effects of 13 different drugs on colorectal cancer: a network meta-analysis

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