Risk Score Generated from CT-Based Radiomics Signatures for Overall Survival Prediction in Non-Small Cell Lung Cancer

Le, Viet-Huan; Kha, Quang-Hien; Hung, Truong Nguyen Khanh; Le, Nguyen Quoc Khanh

doi:10.3390/cancers13143616

Cited by 39 publications

(29 citation statements)

References 48 publications

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“…With the probability calculated from the above-mentioned algorithm, researchers can turn the answer into a continuous variable to solve regression problems and vice versa. Most AI applications predicting survival [13,59], cancer risk [34][35][36][37][38][39], nodule detection [22,23], and nodule characteristics [33] are based on supervised learning.…”

Section: Supervised Learningmentioning

confidence: 99%

“…Dercle et al retrospectively analyzed the data from prospective clinical trials and found that the AI model based on the random forest algorithm and CT-based radiomic features predicted the treatment sensitivity of nivolumab with an AUC of 0.77, docetaxel with an AUC of 0.67, and gefitinib with an AUC of 0.82 [58]. CT-based radiomics models have also been reported to predict the overall survival of lung cancer [59,60].…”

Section: Medication Selectionmentioning

confidence: 99%

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Application of Artificial Intelligence in Lung Cancer

Chiu

Chao

Chen

2022

Cancers

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Lung cancer is the leading cause of malignancy-related mortality worldwide due to its heterogeneous features and diagnosis at a late stage. Artificial intelligence (AI) is good at handling a large volume of computational and repeated labor work and is suitable for assisting doctors in analyzing image-dominant diseases like lung cancer. Scientists have shown long-standing efforts to apply AI in lung cancer screening via CXR and chest CT since the 1960s. Several grand challenges were held to find the best AI model. Currently, the FDA have approved several AI programs in CXR and chest CT reading, which enables AI systems to take part in lung cancer detection. Following the success of AI application in the radiology field, AI was applied to digitalized whole slide imaging (WSI) annotation. Integrating with more information, like demographics and clinical data, the AI systems could play a role in decision-making by classifying EGFR mutations and PD-L1 expression. AI systems also help clinicians to estimate the patient’s prognosis by predicting drug response, the tumor recurrence rate after surgery, radiotherapy response, and side effects. Though there are still some obstacles, deploying AI systems in the clinical workflow is vital for the foreseeable future.

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Section: Supervised Learningmentioning

confidence: 99%

Section: Medication Selectionmentioning

confidence: 99%

Application of Artificial Intelligence in Lung Cancer

Chiu

Chao

Chen

2022

Cancers

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“…These ETs quantify ‘handcrafted (HC)’ features that quantify (i) “semantic features” that describe tumour’s visual characteristics, including tumour shape, size, necrosis and contextual information, such as tumour’s surrounding structures and; (ii) “agnostic features” that quantify statistical information of linear relationships about the pixels of the image that human observers consider important 16 (e.g., colour histograms, Haralick textures and wavelet features 21 , 22 ). Handcrafted ETs have been extensively used to predict mutation status 23 , model cancer outcomes 24 , and response to therapy 13 , 15 . However, handcrafted ETs are restricted to the human understanding of the disease and prespecified imaging representations.…”

Section: Introductionmentioning

confidence: 99%

Fused feature signatures to probe tumour radiogenomics relationships

Kumar

Fulham

Feng

et al. 2022

Sci Rep

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Radiogenomics relationships (RRs) aims to identify statistically significant correlations between medical image features and molecular characteristics from analysing tissue samples. Previous radiogenomics studies mainly relied on a single category of image feature extraction techniques (ETs); these are (i) handcrafted ETs that encompass visual imaging characteristics, curated from knowledge of human experts and, (ii) deep ETs that quantify abstract-level imaging characteristics from large data. Prior studies therefore failed to leverage the complementary information that are accessible from fusing the ETs. In this study, we propose a fused feature signature (FFSig): a selection of image features from handcrafted and deep ETs (e.g., transfer learning and fine-tuning of deep learning models). We evaluated the FFSig’s ability to better represent RRs compared to individual ET approaches with two public datasets: the first dataset was used to build the FFSig using 89 patients with non-small cell lung cancer (NSCLC) comprising of gene expression data and CT images of the thorax and the upper abdomen for each patient; the second NSCLC dataset comprising of 117 patients with CT images and RNA-Seq data and was used as the validation set. Our results show that our FFSig encoded complementary imaging characteristics of tumours and identified more RRs with a broader range of genes that are related to important biological functions such as tumourigenesis. We suggest that the FFSig has the potential to identify important RRs that may assist cancer diagnosis and treatment in the future.

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“…Radiomics is an evolving field in medical image quantitative analysis [ 30 ]. It is also known as quantitative image features.…”

Section: Introductionmentioning

confidence: 99%

Intelligent Dermatologist Tool for Classifying Multiple Skin Cancer Subtypes by Incorporating Manifold Radiomics Features Categories

Attallah

Sharkas

2021

Contrast Media & Molecular Imaging

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The rates of skin cancer (SC) are rising every year and becoming a critical health issue worldwide. SC’s early and accurate diagnosis is the key procedure to reduce these rates and improve survivability. However, the manual diagnosis is exhausting, complicated, expensive, prone to diagnostic error, and highly dependent on the dermatologist’s experience and abilities. Thus, there is a vital need to create automated dermatologist tools that are capable of accurately classifying SC subclasses. Recently, artificial intelligence (AI) techniques including machine learning (ML) and deep learning (DL) have verified the success of computer-assisted dermatologist tools in the automatic diagnosis and detection of SC diseases. Previous AI-based dermatologist tools are based on features which are either high-level features based on DL methods or low-level features based on handcrafted operations. Most of them were constructed for binary classification of SC. This study proposes an intelligent dermatologist tool to accurately diagnose multiple skin lesions automatically. This tool incorporates manifold radiomics features categories involving high-level features such as ResNet-50, DenseNet-201, and DarkNet-53 and low-level features including discrete wavelet transform (DWT) and local binary pattern (LBP). The results of the proposed intelligent tool prove that merging manifold features of different categories has a high influence on the classification accuracy. Moreover, these results are superior to those obtained by other related AI-based dermatologist tools. Therefore, the proposed intelligent tool can be used by dermatologists to help them in the accurate diagnosis of the SC subcategory. It can also overcome manual diagnosis limitations, reduce the rates of infection, and enhance survival rates.

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Risk Score Generated from CT-Based Radiomics Signatures for Overall Survival Prediction in Non-Small Cell Lung Cancer

Cited by 39 publications

References 48 publications

Application of Artificial Intelligence in Lung Cancer

Application of Artificial Intelligence in Lung Cancer

Fused feature signatures to probe tumour radiogenomics relationships

Intelligent Dermatologist Tool for Classifying Multiple Skin Cancer Subtypes by Incorporating Manifold Radiomics Features Categories

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