Prediction Performance of Deep Learning for Colon Cancer Survival Prediction on SEER Data

Gupta, Surbhi; Kalaivani, S.; Rajasundaram, Archana; Ameta, Gaurav Kumar; Oleiwi, Ahmed Kareem; Dugbakie, Betty Nokobi

doi:10.1155/2022/1467070

Cited by 20 publications

(10 citation statements)

References 47 publications

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“…Our model enables us to identify high-risk patients and enhance the treatment process by providing dynamic prognosis evaluation. Furthermore, through the use of techniques like the C-index, calibration plot, ROC analysis, and DCA analysis ( 10 , 29 , 30 ), we have verified the outstanding predictive capabilities and practical value of our newly developed CS-nomogram model. As a result, the CS-based nomogram offers numerous advantages over conventional methods, particularly in terms of its ability to provide dynamic responses in survival analysis.…”

Section: Discussionmentioning

confidence: 66%

Conditional survival analysis and dynamic prediction of long-term survival in Merkel cell carcinoma patients

Zhang,

Xiang,

Chen

et al. 2024

Front. Med.

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BackgroundMerkel cell carcinoma (MCC) is a rare type of invasive neuroendocrine skin malignancy with high mortality. However, with years of follow-up, what is the actual survival rate and how can we continually assess an individual’s prognosis? The purpose of this study was to estimate conditional survival (CS) for MCC patients and establish a novel CS-based nomogram model.MethodsThis study collected MCC patients from the Surveillance, Epidemiology, and End Results (SEER) database and divided these patients into training and validation groups at the ratio of 7:3. CS refers to the probability of survival for a specific timeframe (y years), based on the patient’s survival after the initial diagnosis (x years). Then, we attempted to describe the CS pattern of MCCs. The Least absolute shrinkage and selection operator (LASSO) regression was employed to screen predictive factors. The Multivariate Cox regression analysis was applied to demonstrate these predictors’ effect on overall survival and establish a novel CS-based nomogram.ResultsA total of 3,843 MCC patients were extracted from the SEER database. Analysis of the CS revealed that the 7-year survival rate of MCC patients progressively increased with each subsequent year of survival. The rates progressed from an initial 41–50%, 61, 70, 78, 85%, and finally to 93%. And the improvement of survival rate was nonlinear. The LASSO regression identified five predictors including patient age, sex, AJCC stage, surgery and radiotherapy as predictors for CS-nomogram development. And this novel survival prediction model was successfully validated with good predictive performance.ConclusionCS of MCC patients was dynamic and increased with time since the initial diagnosis. Our newly established CS-based nomogram can provide a dynamic estimate of survival, which has implications for follow-up guidelines and survivorship planning, enabling clinicians to guide treatment for these patients better.

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Section: Discussionmentioning

confidence: 66%

Conditional survival analysis and dynamic prediction of long-term survival in Merkel cell carcinoma patients

Zhang,

Xiang,

Chen

et al. 2024

Front. Med.

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“…The second indicator is Root Mean Square Error (RMSE) [32], which is used to calculate the average of the square root of the error between the true and predicted values. It is an absolute indicator that is often used in logistic regression tasks.…”

Section: Methodsmentioning

confidence: 99%

Multigranularity Building Energy Consumption Prediction Method Based on Convolutional Recurrent Neural Network

Wang

Gao³

et al. 2022

Wireless Communications and Mobile Computing

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For the building energy consumption models with complex scale sensitivity, it is difficult to achieve ideal prediction effect with single-granularity prediction model. Therefore, this paper proposed a multigranularity MgHa-LSTM model based on convolutional recurrent neural network, including a multigranularity feature extraction module and a long-term dependency capture module. Multigranularity feature extraction included granularity segmentation, feedback mechanism, and parallel convolutional modules, which can capture short-term scale sensitivity dependencies. Long-term dependency capture consists of a hybrid attention mechanism and long-short term memory layers, which are able to capture long-term dependencies. For building energy consumption patterns with different scale sensitivity, MgHa-LSTM, MLP, CNN, LSTM, and MsC-LSTM models were constructed on the IHEPC building energy consumption dataset used in this paper for comparative experiments. The experimental results showed that on the IHEPC dataset, the MSE of the building energy consumption prediction model is 0.2821 based on the MgHa-LSTM model proposed in this paper, which is equivalent to 93.72% of the MsC-LSTM model with the smallest MSE among other deep learning prediction models. Compared with other deep learning prediction models, the prediction results of the MgHa-LSTM building energy consumption prediction model are more accurate.

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“…Deep auto encoders had the highest performance with 97% accuracy and 95% area under curve-receiver operating characteristic (AUC-ROC). 15 Kanavati et al presented the classification of WSIs of LBC specimens into malignant and non-neoplastic using a deep learning model. For the same a data set of 1605 cervical WSIs are used.…”

Section: Gupta Et Al Presented and Compared Various Deep Learning Modelsmentioning

confidence: 99%

“…presented and compared various deep learning models for prediction and diagnosis of colon cancer. Deep auto encoders had the highest performance with 97% accuracy and 95% area under curve‐receiver operating characteristic (AUC‐ROC) 15 . Kanavati et al.…”

Section: Introductionmentioning

confidence: 99%

A systematic review on deep learning‐based automated cancer diagnosis models

Tandon,

Agrawal,

Rathore

et al. 2024

J Cellular Molecular Medi

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Deep learning is gaining importance due to its wide range of applications. Many researchers have utilized deep learning (DL) models for the automated diagnosis of cancer patients. This paper provides a systematic review of DL models for automated diagnosis of cancer patients. Initially, various DL models for cancer diagnosis are presented. Five major categories of cancers such as breast, lung, liver, brain and cervical cancer are considered. As these categories of cancers have a very high percentage of occurrences with high mortality rate. The comparative analysis of different types of DL models is drawn for the diagnosis of cancer at early stages by considering the latest research articles from 2016 to 2022. After comprehensive comparative analysis, it is found that most of the researchers achieved appreciable accuracy with implementation of the convolutional neural network model. These utilized the pretrained models for automated diagnosis of cancer patients. Various shortcomings with the existing DL‐based automated cancer diagnosis models are also been presented. Finally, future directions are discussed to facilitate further research for automated diagnosis of cancer patients.

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Prediction Performance of Deep Learning for Colon Cancer Survival Prediction on SEER Data

Cited by 20 publications

References 47 publications

Conditional survival analysis and dynamic prediction of long-term survival in Merkel cell carcinoma patients

Conditional survival analysis and dynamic prediction of long-term survival in Merkel cell carcinoma patients

Multigranularity Building Energy Consumption Prediction Method Based on Convolutional Recurrent Neural Network

A systematic review on deep learning‐based automated cancer diagnosis models

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