CT radiomics for predicting PD-L1 expression on tumor cells in gastric cancer

Xu, Mengying; Qiao, Xiangmei; Liu, Song; ZhengLiang, Li; Ji, Chen; Li, Hui; Shi, Tingting; Lin, Li; Gu, Qingqing; Zhou, Kefeng; Zhou, Zhengyang

doi:10.21203/rs.3.rs-52520/v3

Cited by 3 publications

(4 citation statements)

References 17 publications

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“…A significant achievement in radiogenomics is developing a fully automated system combined with a radiological workflow, shown in Figure 9 [ 113 ]. This reduces the overall time involved in performing repetitive and tedious tasks while improving efficiency and productivity [ 114 , 115 ]. Another advantage is the real-time monitoring of treatment by simultaneously comparing several images from the database [ 113 , 114 ].…”

Section: What Have We Achieved So Far In Radiogenomics?mentioning

confidence: 99%

Role of Artificial Intelligence in Radiogenomics for Cancers in the Era of Precision Medicine

Saxena

Jena

Gupta

et al. 2022

Cancers

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Radiogenomics, a combination of “Radiomics” and “Genomics,” using Artificial Intelligence (AI) has recently emerged as the state-of-the-art science in precision medicine, especially in oncology care. Radiogenomics syndicates large-scale quantifiable data extracted from radiological medical images enveloped with personalized genomic phenotypes. It fabricates a prediction model through various AI methods to stratify the risk of patients, monitor therapeutic approaches, and assess clinical outcomes. It has recently shown tremendous achievements in prognosis, treatment planning, survival prediction, heterogeneity analysis, reoccurrence, and progression-free survival for human cancer study. Although AI has shown immense performance in oncology care in various clinical aspects, it has several challenges and limitations. The proposed review provides an overview of radiogenomics with the viewpoints on the role of AI in terms of its promises for computational as well as oncological aspects and offers achievements and opportunities in the era of precision medicine. The review also presents various recommendations to diminish these obstacles.

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Section: What Have We Achieved So Far In Radiogenomics?mentioning

confidence: 99%

Role of Artificial Intelligence in Radiogenomics for Cancers in the Era of Precision Medicine

Saxena

Jena

Gupta

et al. 2022

Cancers

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“…The development of a completely automated system paired with a radiological workflow, such as the one represented in Figure 8 [ 144 ], is a notable breakthrough in the field of radiogenomics. This results in a reduction in the total amount of time spent performing tasks that are repetitive and laborious, while simultaneously boosting both efficiency and productivity [ 145 , 146 ]. Another advantage is that the treatment can be monitored in real-time by comparing many photos from the database at the same time [ 144 , 146 ].…”

Section: An Overview Of Artificial Intelligence Applications In Healt...mentioning

confidence: 99%

“…This results in a reduction in the total amount of time spent performing tasks that are repetitive and laborious, while simultaneously boosting both efficiency and productivity [ 145 , 146 ]. Another advantage is that the treatment can be monitored in real-time by comparing many photos from the database at the same time [ 144 , 146 ].…”

Section: An Overview Of Artificial Intelligence Applications In Healt...mentioning

confidence: 99%

Economics of Artificial Intelligence in Healthcare: Diagnosis vs. Treatment

Khanna¹,

Maindarkar

Viswanathan

et al. 2022

Healthcare

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Motivation: The price of medical treatment continues to rise due to (i) an increasing population; (ii) an aging human growth; (iii) disease prevalence; (iv) a rise in the frequency of patients that utilize health care services; and (v) increase in the price. Objective: Artificial Intelligence (AI) is already well-known for its superiority in various healthcare applications, including the segmentation of lesions in images, speech recognition, smartphone personal assistants, navigation, ride-sharing apps, and many more. Our study is based on two hypotheses: (i) AI offers more economic solutions compared to conventional methods; (ii) AI treatment offers stronger economics compared to AI diagnosis. This novel study aims to evaluate AI technology in the context of healthcare costs, namely in the areas of diagnosis and treatment, and then compare it to the traditional or non-AI-based approaches. Methodology: PRISMA was used to select the best 200 studies for AI in healthcare with a primary focus on cost reduction, especially towards diagnosis and treatment. We defined the diagnosis and treatment architectures, investigated their characteristics, and categorized the roles that AI plays in the diagnostic and therapeutic paradigms. We experimented with various combinations of different assumptions by integrating AI and then comparing it against conventional costs. Lastly, we dwell on three powerful future concepts of AI, namely, pruning, bias, explainability, and regulatory approvals of AI systems. Conclusions: The model shows tremendous cost savings using AI tools in diagnosis and treatment. The economics of AI can be improved by incorporating pruning, reduction in AI bias, explainability, and regulatory approvals.

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“…In other words, textural features with high association with the PD-L1 expression level were those extracted from the CT portion. Recent studies have revealed that CT texture parameters could be useful for predicting PD-L1 expression in several cancers (35,36). Jiang et al also reported that textural features extracted from CT performed better than those of PET in assessing the expression status of PD-L1 in NSCLC (15).…”

Section: Representative Positron Emission Tomography/computed Tomogra...mentioning

confidence: 99%

A Machine Learning Approach Using PET/CT-based Radiomics for Prediction of PD-L1 Expression in Non-small Cell Lung Cancer

et al. 2022

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Background/Aim: We explored the prediction of programmed cell death ligand 1 (PD-L1) expression level in non-small cell lung cancer using a machine learning approach with positron emission tomography/computed tomography (PET/CT)-based radiomics. Patients and Methods: A total of 312 patients (189 adenocarcinomas, 123 squamous cell carcinomas) who underwent F-18 fluorodeoxyglucose PET/CT were retrospectively analysed. Imaging biomarkers with 46 CT and 48 PET radiomic features were extracted from segmented tumours on PET and CT images using the LIFEx package. Radiomic features were ranked, and the top five best feature subsets were selected using the Gini index based on associations with PD-L1 expression in at least 50% of tumour cells. The areas under the receiver operating characteristic curves (AUCs) of binary classifications afforded by several machine learning algorithms (random forest, neural network, Naïve Bayes, logistic regression, adaptive boosting, stochastic gradient descent, support vector machine) were compared. The model performances were tested by 10-fold cross validation. Results: We developed and validated a PET/CT-based radiomic model predicting PD-L1 expression levels in lung cancer. Long run high grey-level emphasis, homogeneity, mean Hounsfield unit, long run emphasis from CT, and maximum standardised uptake value from PET were the five best feature subsets for positive PD-L1 expression. The Naïve Bayes model (AUC=0.712), with a sensitivity of 75.3% and specificity of 58.2%, outperformed all other classifiers. It was followed by the neural network model (AUC=0.711), random forest (AUC=0.700), logistic regression (AUC=0.673) and adaptive boosting (AUC=0. 604). Conclusion: PET/CT-based radiomic features may help clinicians identify tumours with positive PD-L1 expression in a non-invasive manner using machine learning algorithms.Lung cancer remains the leading cause of cancer-related deaths worldwide, and non-small cell lung cancer (NSCLC) accounts for approximately 85% of primary lung cancers (1, 2). Despite the development of treatment strategies for NSCLC such as thoracoscopy surgery, chemotherapy, radiotherapy, and targeted therapy, the overall 5-year survival rate is still poor (3). Recently, immune checkpoint inhibitors targeting programmed cell death protein 1 (PD-1) or programmed death ligand 1 (PD-L1) have shown better survival outcomes than conventional chemotherapy in patients with advanced NSCLC (4, 5); thus, PD-L1 inhibitors have become one of the standard treatments for NSCLC. However, PD-1 and PD-L1 expression is evaluated with immunohistochemistry, making the availability of tumour tissue by biopsy mandatory in clinical practice. This procedure is invasive, time-consuming, and does not reflect the change in PD-L1 expression throughout the course of the treatment.

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CT radiomics for predicting PD-L1 expression on tumor cells in gastric cancer

Cited by 3 publications

References 17 publications

Role of Artificial Intelligence in Radiogenomics for Cancers in the Era of Precision Medicine

Role of Artificial Intelligence in Radiogenomics for Cancers in the Era of Precision Medicine

Economics of Artificial Intelligence in Healthcare: Diagnosis vs. Treatment

A Machine Learning Approach Using PET/CT-based Radiomics for Prediction of PD-L1 Expression in Non-small Cell Lung Cancer

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