DeepBTS: Prediction of Recurrence-free Survival of Non-small Cell Lung Cancer Using a Time-binned Deep Neural Network

Lee, Bora; Chun, Sang Hoon; Hong, Ji Hyung; Woo, In Sook; Kim, Seoree; Jeong, Joon Won; Kim, Jae Jun; Lee, Hyun Woo; Na, Sae Jung; Beck, Kyongmin Sarah; Gil, Bomi; An, Ho Jung; Ko, Yoon Ho

doi:10.1038/s41598-020-58722-z

Cited by 29 publications

(20 citation statements)

References 10 publications

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“…This model outperformed use of the TNM staging system alone in both the remaining 20% of the SEER data (C statistic = 0.739 vs 0.706; P < .001) and an independent validation set of 1182 patients with resected lung cancer treated at Shanghai Pulmonary Hospital in China (C statistic = 0.742 vs 0.706; P < .001). These results are in line with the accuracy of neural network models of recurrence of resected NSCLC 3 and provide additional evidence of the increasing benefit of deep learning prognostic models over standard approaches.…”

supporting

confidence: 77%

Prognosis and Treatment of Non–Small Cell Lung Cancer in the Age of Deep Learning

Howard

Pearson

2020

JAMA Netw Open

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Lung cancer is both the most common and the most deadly cancer, with more than 2 million cases diagnosed worldwide in 2018 per Global Cancer Observatory estimates and with non-small cell lung cancer (NSCLC) accounting for the great majority of cases. The 8th edition of the American Joint Committee on Cancer TNM stage groupings represents the most well-validated prognostic metric for NSCLC. However, a variety factors unaccounted for by these TNM stage groupings affect outcome, from patient-specific factors such as performance status, age, and socioeconomic status to tumor characteristics such as grade, lymphovascular invasion, programmed cell death 1 ligand 1 expression,

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supporting

confidence: 77%

Prognosis and Treatment of Non–Small Cell Lung Cancer in the Age of Deep Learning

Howard

Pearson

2020

JAMA Netw Open

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“…However, accurate postoperative prediction of NSCLC remains challenging. Lee et al [ 45 ] developed a survival analysis model based on a multilayer perceptron with semi-supervised learning neural networks to predict the 3-year postoperative recurrence risk of NSCLC patients, which outperformed the CPHM. Ensemble learning has been applied to many relevant assisted medical systems.…”

Section: Related Workmentioning

confidence: 99%

A Convolutional Neural Network-Based Intelligent Medical System with Sensors for Assistive Diagnosis and Decision-Making in Non-Small Cell Lung Cancer

Zhan

Long

Gou

et al. 2021

Sensors

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In many regions of the world, early diagnosis of non-small cell lung cancer (NSCLC) is a major challenge due to the large population and lack of medical resources, which is difficult toeffectively address via limited physician manpower alone. Therefore, we developed a convolutional neural network (CNN)-based assisted diagnosis and decision-making intelligent medical system with sensors. This system analyzes NSCLC patients’ medical records using sensors to assist staging a diagnosis and provides recommended treatment plans to physicians. To address the problem of unbalanced case samples across pathological stages, we used transfer learning and dynamic sampling techniques to reconstruct and iteratively train the model to improve the accuracy of the prediction system. In this paper, all data for training and testing the system were obtained from the medical records of 2,789,675 patients with NSCLC, which were recorded in three hospitals in China over a five-year period. When the number of case samples reached 8000, the system achieved an accuracy rate of 0.84, which is already close to that of the doctors (accuracy: 0.86). The experimental results proved that the system can quickly and accurately analyze patient data and provide decision information support for physicians.

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“…Although genomic information gives a new idea of developing prognosis prediction models, accuracy of these models has not worked out as intended, thus researchers set their sights on combining clinical data and heterogeneously expressed gene (46)(47)(48). For example, Lai et al combined seven well-known biomarkers and eight differentially expressed gene biomarkers based on microarray data with clinical information to build a deep neural network, reaching 0.8163 for AUC of ROC in predicting the 5-year survival of NSCLC patients (47), which is significantly higher than the models above in the 5-year survival prediction.…”

Section: Prognosis Based On Genomicsmentioning

confidence: 99%

“…For example, Lai et al combined seven well-known biomarkers and eight differentially expressed gene biomarkers based on microarray data with clinical information to build a deep neural network, reaching 0.8163 for AUC of ROC in predicting the 5-year survival of NSCLC patients (47), which is significantly higher than the models above in the 5-year survival prediction. Furthermore, other researchers developed binned time survival analysis (DeepBTS) models to select 14 features for prognosis prediction, which avoided manually assuming proportional hazards and approximating survival data of Cox proportional hazard model (46). In the future, DeepBTS has the potential to combine imaging information, genomic and clinicopathologic data to improve its performance.…”

Section: Prognosis Based On Genomicsmentioning

confidence: 99%

Artificial intelligence-assisted decision making for prognosis and drug efficacy prediction in lung cancer patients: a narrative review

Li¹,

Wu²,

Zhao³

et al. 2021

J Thorac Dis

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In this review, we aim to present frontier studies in patients with lung cancer as it related to artificial intelligence (AI)-assisted decision-making and summarize the latest advances, challenges and future trend in this field.Background: Despite increasing survival rate in cancer patients over the last decades, lung cancer remains one of the leading causes of death worldwide. The early diagnosis, accurate evaluation and individualized treatment are vital approaches to improve the survival rate of patients with lung cancer. Thus, decision making based on these approaches requires accuracy and efficiency beyond manpower. Recent advances in AI and precision medicine have provided a fertile environment for the development of AI-based models. These models have the potential to assist radiologists and oncologists in detecting lung cancer, predicting prognosis and developing personalized treatment plans for better outcomes of the patients.Methods: We searched literature from 2000 through July 31 th , 2021 in Medline/PubMed, the Web of Science, the Cochrane Library, ACM Digital Library, INSPEC and EMBASE. Key words such as "artificial intelligence", "AI", "deep learning", "lung cancer", "NSCLC", "SCLC" were combined to identify related literatures. These literatures were then selected by two independent authors. Articles chosen by only one author will be examined by another author to determine whether this article was relative and valuable. The selected literatures were read by all authors and discussed to draw reliable conclusions. Conclusions: AI, especially for those based on deep learning and radiomics, is capable of assisting clinical decision making from many aspects, for its quantitatively interpretation of patients' information and its potential to deal with the dynamics, individual differences and heterogeneity of lung cancer. Hopefully, remaining problems such as insufficient data and poor interpretability may be solved to put AI-based models into clinical practice.

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DeepBTS: Prediction of Recurrence-free Survival of Non-small Cell Lung Cancer Using a Time-binned Deep Neural Network

Cited by 29 publications

References 10 publications

Prognosis and Treatment of Non–Small Cell Lung Cancer in the Age of Deep Learning

Prognosis and Treatment of Non–Small Cell Lung Cancer in the Age of Deep Learning

A Convolutional Neural Network-Based Intelligent Medical System with Sensors for Assistive Diagnosis and Decision-Making in Non-Small Cell Lung Cancer

Artificial intelligence-assisted decision making for prognosis and drug efficacy prediction in lung cancer patients: a narrative review

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