A 10-Year Probability Deep Neural Network Prediction Model for Lung Cancer

Lee, Hsiu-An; Chao, Louis R.; Hsu, Chien‐Yeh

doi:10.3390/cancers13040928

Cited by 5 publications

(4 citation statements)

References 24 publications

(32 reference statements)

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“…Four results were possible. 53 • True Positive (TP) refers to a situation where both the actual value and the prediction's results are positive. • False Positive (FP) refers to a situation where a prediction provides positive results even though the actual value is negative.…”

Section: Resultsmentioning

confidence: 99%

Evaluation of artificial neural network and adaptive-network-based fuzzy inference system for ovarian and lung cancer prediction

İpek,

Göktürk

2024

Journal of Health Sciences and Medicine

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Aims: Every year, a significant number of individuals lose their lives due to cancer or undergo challenging treatments. Indeed, the development of an effective cancer prediction method holds great importance in the field of healthcare. Methods: Machine learning methods have played a significant role in advancing cancer prediction models. In this context, this study focuses on exploring the potential of two machine learning methods: Artificial neural network (ANN) and adaptive-network-based fuzzy inference system (ANFIS) for cancer prediction. In this study, two different types of cancer, ovarian cancer and lung cancer, are taken into consideration. For the prediction of ovarian cancer, three specific biomarkers, namely human epididymis protein 4 (HE4), carbohydrate antigen 125 (CA-125), and carcinoembryonic antigen (CEA), are used to develop a prediction model. For the prediction of lung cancer, six different variables are utilized in the development of both the ANN and ANFIS methods. Results: The findings demonstrated that the proposed methods had an accuracy rate of at least 93.9% in predicting ovarian cancer. With an accuracy rate of at least 89%, the proposed methods predicted lung cancer. Also, the proposed ANN method outperforms the ANFIS method in terms of predictive accuracy for both ovarian cancer and lung cancer. Conclusion: This study suggests that the ANN method provides more reliable and accurate predictions for these specific cancer types based on the chosen variables or biomarkers. This study highlights the potential of machine learning methods, particularly ANN, in improving cancer prediction models and aiding in the early detection and effective management of ovarian and lung cancers.

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Section: Resultsmentioning

confidence: 99%

Evaluation of artificial neural network and adaptive-network-based fuzzy inference system for ovarian and lung cancer prediction

İpek,

Göktürk

2024

Journal of Health Sciences and Medicine

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“…The prognostic prediction of NSCLC patients using deep learning models has been applied with several biomarkers such as radiologic, histopathologic, genetic, or molecular evidence [39][40][41][42] Signal intensity loss on the diffusion-sensitive sequence can be quantified by calculating ADC [45]. Based on non-linear transformation of the voxel values of each DWI, the deep learning model could generate ADC-like feature maps.…”

Section: Discussionmentioning

confidence: 99%

Predicting Non-Small-Cell Lung Cancer Survival after Curative Surgery via Deep Learning of Diffusion MRI

Moon,

Yang,

Kim

et al. 2023

Diagnostics

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Background: the objective of this study is to evaluate the predictive power of the survival model using deep learning of diffusion-weighted images (DWI) in patients with non-small-cell lung cancer (NSCLC). Methods: DWI at b-values of 0, 100, and 700 sec/mm2 (DWI0, DWI100, DWI700) were preoperatively obtained for 100 NSCLC patients who underwent curative surgery (57 men, 43 women; mean age, 62 years). The ADC0-100 (perfusion-sensitive ADC), ADC100-700 (perfusion-insensitive ADC), ADC0-100-700, and demographic features were collected as input data and 5-year survival was collected as output data. Our survival model adopted transfer learning from a pre-trained VGG-16 network, whereby the softmax layer was replaced with the binary classification layer for the prediction of 5-year survival. Three channels of input data were selected in combination out of DWIs and ADC images and their accuracies and AUCs were compared for the best performance during 10-fold cross validation. Results: 66 patients survived, and 34 patients died. The predictive performance was the best in the following combination: DWI0-ADC0-100-ADC0-100-700 (accuracy: 92%; AUC: 0.904). This was followed by DWI0-DWI700-ADC0-100-700, DWI0-DWI100-DWI700, and DWI0-DWI0-DWI0 (accuracy: 91%, 81%, 76%; AUC: 0.889, 0.763, 0.711, respectively). Survival prediction models trained with ADC performed significantly better than the one trained with DWI only (p-values < 0.05). The survival prediction was improved when demographic features were added to the model with only DWIs, but the benefit of clinical information was not prominent when added to the best performing model using both DWI and ADC. Conclusions: Deep learning may play a role in the survival prediction of lung cancer. The performance of learning can be enhanced by inputting precedented, proven functional parameters of the ADC instead of the original data of DWIs only.

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“…Prognostic prediction of NSCLC patients using deep learning models have been applied with several biomarkers such as radiologic, histopathologic, genetic, or molecular evidence [34][35][36][37] 39 . Also, Baek et al visualized the U-Net algorithm of PET/CT in NSCLC patients in the prediction of survival.…”

Section: Discussionmentioning

confidence: 99%

Predicting Lung Cancer Survival after Curative Surgery Using Deep Learning of Diffusion MRI

Moon

Yang

Kim

et al. 2022

Preprint

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The survival of lung cancer patients is expected differently according to the stage at diagnosis. However, each individual patient experiences different survival results even in the same stage group. DWI and ADC are two of widely used prognostic indicators for the prediction of survival in cancer patients. This study aims to develop a deep learning model that predicts the overall survival of non-small cell lung cancer patients using diffusion MRI. The study adapted a VGG-16 network and investigated the model’s performance using different combinations of DWI with/without ADC images. The survival model using deep learning of both DWI and ADC accurately predict the possibility of survival in five years after surgical treatment of NSCLC (up to 92%). The accuracy of results produced by the deep learning model can be enhanced by inputting precedented, proven functional parameters of ADC including the original images of DWI in survival prediction.

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A 10-Year Probability Deep Neural Network Prediction Model for Lung Cancer

Cited by 5 publications

References 24 publications

Evaluation of artificial neural network and adaptive-network-based fuzzy inference system for ovarian and lung cancer prediction

Evaluation of artificial neural network and adaptive-network-based fuzzy inference system for ovarian and lung cancer prediction

Predicting Non-Small-Cell Lung Cancer Survival after Curative Surgery via Deep Learning of Diffusion MRI

Predicting Lung Cancer Survival after Curative Surgery Using Deep Learning of Diffusion MRI

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