Application of a Neural Network Whole Transcriptome–Based Pan-Cancer Method for Diagnosis of Primary and Metastatic Cancers

Grewal, Jasleen K.; Tessier‐Cloutier, Basile; Jones, Steven J.M.; Gakkhar, Sitanshu; Ma, Yussanne; Moore, Richard A.; Mungall, Andrew J.; Zhao, Yongjun; Taylor, Michael D.; Gelmon, Karen A.; Lim, Howard John; Renouf, Daniel J.; Laskin, Janessa; Marra, Marco A.; Yip, Stephen; Jones, Steven J.M.

doi:10.1001/jamanetworkopen.2019.2597

Cited by 78 publications

(66 citation statements)

References 38 publications

(91 reference statements)

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“…In addition, nonnormally distributed and highly correlated data can be used to develop nonlinear and linear ANN models, with extensive application in medical big data analysis. Clinical studies have commonly used ANN models for prognosis prediction 11,22,24 . This study's comparison of various models indicated that the ANN model had the best performance in terms of expanding the set of predictive variables; this facilitates evaluation of the effectiveness of research methods and enables comprehensive prediction of pCR occurrence.…”

Section: Discussionmentioning

confidence: 99%

Machine learning for predicting pathological complete response in patients with locally advanced rectal cancer after neoadjuvant chemoradiotherapy

Huang

Tsai

et al. 2020

Sci Rep

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For patients with locally advanced rectal cancer (LARC), achieving a pathological complete response (pCR) after neoadjuvant chemoradiotherapy (CRT) provides them with the optimal prognosis. However, no reliable prediction model is presently available. We evaluated the performance of an artificial neural network (ANN) model in pCR prediction in patients with LARC. Predictive accuracy was compared between the ANN, k-nearest neighbor (KNN), support vector machine (SVM), naïve Bayes classifier (NBC), and multiple logistic regression (MLR) models. Data from two hundred seventy patients with LARC were used to compare the efficacy of the forecasting models. We trained the model with an estimation data set and evaluated model performance with a validation data set. The ANN model significantly outperformed the KNN, SVM, NBC, and MLR models in pCR prediction. Our results revealed that the post-CRT carcinoembryonic antigen is the most influential pCR predictor, followed by intervals between CRT and surgery, chemotherapy regimens, clinical nodal stage, and clinical tumor stage. The ANN model was a more accurate pCR predictor than other conventional prediction models. The predictors of pCR can be used to identify which patients with LARC can benefit from watch-and-wait approaches. Neoadjuvant chemoradiotherapy (CRT) has benefited patients with locally advanced rectal cancer (LARC) with specific respect to improvements in local control, disease-free survival, and sphincter preservation rates 1-3. However, the patterns of tumor regression after neoadjuvant CRT vary widely, ranging from a pathological complete response (pCR) to disease progression. Patients with a pCR have the most favorable survival and tumor control, but only 10-30% of patients with LARC achieve a pCR to neoadjuvant CRT 4-7. Furthermore, mounting evidence has demonstrated that in patients who achieve a pCR, radical surgery can be omitted without compromising

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Section: Discussionmentioning

confidence: 99%

Machine learning for predicting pathological complete response in patients with locally advanced rectal cancer after neoadjuvant chemoradiotherapy

Huang

Tsai

et al. 2020

Sci Rep

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“…In addition, we can use non-normally distributed and highly correlated data to develop nonlinear and linear ANN models, with extensive applications in medical big data analysis. In medicine, clinical studies have commonly used the ANN model for prognosis prediction [11,22,24]. This study's comparison of various models indicated that the ANN model exhibited the best performance of expanding the set of predictive variables; this facilitates the evaluation of the effectiveness of research methods and comprehensive prediction of pCR occurrence.…”

Section: Discussionmentioning

confidence: 93%

“…Few studies have compared the artificial neural network (ANN), k-nearest neighbor (KNN), support vector machine (SVM), naïve Bayes Classifier (NBC), and multiple logistic regression (MLR) prediction models with respect to internal validity (reproducibility). The validity is an important performance metric [10,11]. However, numerous predictive models yield insufficiently reliable predictions of pCR occurrence in patients with LARC after neoadjuvant CRT.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning for Predicting Pathological Complete Response in Patients with Locally Advanced Rectal Cancer after Neoadjuvant Chemoradiotherapy

Huang

et al. 2020

Preprint

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BACKGROUND For patients with locally advanced rectal cancer (LARC), achieving pathological complete response (pCR) after neoadjuvant chemoradiotherapy (CRT) results in best prognosis. So far, no reliable prediction model has been available. We aim to evaluate the performance of an artificial neural network (ANN) model in the prediction of pCR in patients with LARC. METHODS Predictive accuracy was compared between the ANN, k-earest neighbor (KNN), support vector machines (SVM), naïve Bayes classifier (NBC), and multiple logistic regression (MLR) models. RESULTS A total of 236 patients with LARC were used to compare the forecasting models. We trained the model with an estimation data set, and evaluated model performance with a validation data set. The ANN model significantly outperformed the KNN, SVM, NBC, and MLR models in predicting pCR. Our results revealed that post-CRT carcinoembryonic antigen was the most influential predictor of pCR, followed by intervals between CRT and surgery, chemotherapy regimens, clinical nodal stage, and clinical tumor stage. CONCLUSIONS Compared with conventional prediction models, the ANN model was more accurate in the prediction of pCR. The predictors of pCR can be used to identify which patients with LARC can benefit from watch-and-wait approaches.

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“…Then, confidence scores were assigned in their performance evolution. The weight analysis of the neural network was used to identify the genes most important in class prediction of tumor subtype (Kourou et al, 2015 [217]; Grewal et al, 2019 [216]).…”

Section: Network Reconstructionmentioning

confidence: 99%

A Complex Systems Analysis of Cancer Networks

Uthamacumaran¹

2019

Preprint

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Cancers are complex, adaptive ecosystems. It remains the lead cause of disease-related, pediatric death in North America. The emerging field of complexity science has redefined cancer as a computational system with intractable, algorithmic complexity. Herein, a tumor and its heterogeneous phenotypes are discussed as dynamical systems having multiple, chaotic attractors. Machine learning, Network science and information theory are discussed as current tools for cancer network reconstruction. The fluid dynamics of cancer-cell fate transitions and chemical pattern formation are briefly reviewed. Deep Learning architectures, delay-embedding algorithms and computational fluid models are proposed for better forecasting gene expression patterns in cancer ecosystems. Cancer cell decision-making is investigated within the framework of complexity theory.

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Application of a Neural Network Whole Transcriptome–Based Pan-Cancer Method for Diagnosis of Primary and Metastatic Cancers

Abstract: This cross-sectional diagnostic study evaluates the accuracy of a machine learning method that uses the whole transcriptome to identify gene markers in primary and metastatic tumors.

Cited by 78 publications

References 38 publications

Machine learning for predicting pathological complete response in patients with locally advanced rectal cancer after neoadjuvant chemoradiotherapy

Machine learning for predicting pathological complete response in patients with locally advanced rectal cancer after neoadjuvant chemoradiotherapy

Machine Learning for Predicting Pathological Complete Response in Patients with Locally Advanced Rectal Cancer after Neoadjuvant Chemoradiotherapy

A Complex Systems Analysis of Cancer Networks

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