Artificial neural network in predicting bladder cancer recurrence

Borkowska, Edyta; Constantinou, Maria; Jędrzejczyk, Adam; Traczyk, Magdalena; Banaszkiewicz, Monika; Pietrusiński, Michaƚ; Marks, Piotr; Rożniecki, Marek; Kruk, Andrzej; Kałużewski, Bogdan

doi:10.1186/1897-4287-10-s1-a3

Cited by 2 publications

(2 citation statements)

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“…While the purpose of this paper is not to claim methodological superiority, the cautious reporting of positive results serves to demonstrate the proof of concept of the methodology for this indication. The use of MLPs has been established as a valid methodology in predicting outcomes based on genetic markers [25] and in using radiomics to identify disease [26] or to differentiate tumour subtypes [27]. These results are preliminary but contribute to a growing body of evidence demonstrating the potential for the use of deep learning to analyse radiomic data and suggest a benefit to this method in assessing for the local recurrence of lung malignancies.…”

Section: Discussionmentioning

confidence: 95%

Multilayer Perceptron Analysis of Radiomics to Predict Local Recurrence of Lung Cancer After Radiotherapy

Jan

Miller²,

Wright³

et al. 2022

J Radiat Oncol Inform

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Purpose: to assess the likelihood of local recurrence of lung malignancies following stereotactic ablative radiotherapy (SABR) by evaluating clinical and radiomic features with machine learning and novel use of deep learning methods. Methods: pre-treatment CT images were attained from seventy patients with primary lung malignancies. The malignancy was segmented by the treating radiation oncologist and 107 radiomic features were extracted from the image. The data underwent feature reduction via Spearman’s correlation and selection with adapted LASSO regression analysis. A random forest model and a multilayer perceptron (MLP) with cost-sensitive classifier were independently used to assess for local recurrence of malignancy. The recurrence likelihood predictions from each of these were used to stratify patients into groups with high and low risk of recurrence. These were assessed for time-to-event predictions using Kaplan-Meier analyses and Gray’s test to evaluate the separation between the high and low-risk groups. The prognostic capacity of the models was evaluated with a concordance index, 95% confidence intervals and bootstrapping (10,000 iterations). Results: the MLP was able to predict the recurrence of malignancy with 100% sensitivity and 91% specificity (AUC 0.95). The MLP predictions showed statistically significant separation of high and low-risk patients, and robust model fit (p=0.04, c=0.79), which out-performed random forest model predictions (p=0.15, c=0.41) that did not reach statistical significance. Conclusions: radiomic data analysis with an MLP showed improved prediction potential within this dataset compared to random forest models for predicting local recurrence of lung cancer.

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Section: Discussionmentioning

confidence: 95%

Multilayer Perceptron Analysis of Radiomics to Predict Local Recurrence of Lung Cancer After Radiotherapy

Jan

Miller²,

Wright³

et al. 2022

J Radiat Oncol Inform

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“…The majority of the network-based methods are performed to identify distinct patterns within one cancer [ 1 , 2 ]. Compared with only analyzing one cancer, several methods have been used to identify common patterns shared by two or more cancers.…”

Section: Introductionmentioning

confidence: 99%

Identification of cancer prognosis-associated functional modules using differential co-expression networks

Zhao

Wang

et al. 2017

Oncotarget

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The rapid accumulation of cancer-related data owing to high-throughput technologies has provided unprecedented choices to understand the progression of cancer and discover functional networks in multiple cancers. Establishment of co-expression networks will help us to discover the systemic properties of carcinogenesis features and regulatory mechanisms of multiple cancers. Here, we proposed a computational workflow to identify differentially co-expressed gene modules across 8 cancer types by using combined gene differential expression analysis methods and a higher-order generalized singular value decomposition. Four co-expression modules were identified; and oncogenes and tumor suppressors were significantly enriched in these modules. Functional enrichment analysis demonstrated the significantly enriched pathways in these modules, including ECM-receptor interaction, focal adhesion and PI3K-Akt signaling pathway. The top-ranked miRNAs (mir-199, mir-29, mir-200) and transcription factors (FOXO4, E2A, NFAT, and MAZ) were identified, which play an important role in deregulating cellular energetics; and regulating angiogenesis and cancer immune system. The clinical significance of the co-expressed gene clusters was assessed by evaluating their predictability of cancer patients’ survival. The predictive power of different clusters and subclusters was demonstrated. Our results will be valuable in cancer-related gene function annotation and for the evaluation of cancer patients’ prognosis.

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Artificial neural network in predicting bladder cancer recurrence

Cited by 2 publications

References 0 publications

Multilayer Perceptron Analysis of Radiomics to Predict Local Recurrence of Lung Cancer After Radiotherapy

Multilayer Perceptron Analysis of Radiomics to Predict Local Recurrence of Lung Cancer After Radiotherapy

Identification of cancer prognosis-associated functional modules using differential co-expression networks

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