The current issues and future perspective of artificial intelligence for developing new treatment strategy in non-small cell lung cancer: harmonization of molecular cancer biology and artificial intelligence

Tanaka, Ichidai; Furukawa, Taiki; Morise, Masahiro

doi:10.1186/s12935-021-02165-7

Cited by 23 publications

(8 citation statements)

References 102 publications

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“…Following this work, we are conducting a clinical study of multifocal HCC, yielding a more extensive data collection in the future. We will use advanced computational techniques such as artificial intelligence to optimize further the mathematical model of molecular typing ( Tanaka et al, 2021 ), and some biological intelligent interpreters ( Bu et al, 2021a ) to generate multiple biomedical knowledge. Moreover, decision tools with multimodal combinations ( Patel et al, 2021 ) could also be developed to optimize the diagnosis of multifocal HCC and thus guide the clinical treatment of liver cancer.…”

Section: Discussionmentioning

confidence: 99%

Cross Analysis of Genomic-Pathologic Features on Multiple Primary Hepatocellular Carcinoma

et al. 2022

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Hepatocellular carcinoma (HCC) is a prevalent malignancy cancer worldwide with a poor prognosis. Hepatic resection is indicated as a potentially curative option for HCC patients in the early stage. However, due to multiple nodules, it leads to clinical challenges for surgical management. Approximately 41%–75% of HCC cases are multifocal at initial diagnosis, which may arise from multicentric occurrence (MO-HCC) or intrahepatic metastasis (IM-HCC) pattern with significantly different clinical outcomes. Effectively differentiating the two mechanisms is crucial to prioritize the allocation of surgery for multifocal HCC. In this study, we collected a multifocal hepatocellular carcinoma cohort of 17 patients with a total of 34 samples. We performed whole-exome sequencing and staining of pathological HE sections for each lesion. Reconstruction of the clonal evolutionary pattern using genome mutations showed that the intrahepatic metastogenesis pattern had a poorer survival performance than independent origins, with variants in the TP53, ARID1A, and higher CNV variants occurring more significantly in the metastatic pattern. Cross-modality analysis with pathology showed that molecular classification results were consistent with pathology results in 70.6% of patients, and we found that pathology results could further complement the classification for undefined patterns of occurrence. Based on these results, we propose a model to differentiate the pattern of multifocal hepatocellular carcinoma based on the pathological results and genome mutations information, which can provide guidelines for diagnosing and treating multifocal hepatocellular carcinoma.

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Section: Discussionmentioning

confidence: 99%

Cross Analysis of Genomic-Pathologic Features on Multiple Primary Hepatocellular Carcinoma

et al. 2022

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“…Thus, the primary objective of ML is to quickly and efficiently recognize patterns within large datasets. It can produce results that are more accurate than manual human evaluations; 15 There are three distinct forms of ML: supervised learning, unsupervised learning, and reinforcement learning. 16,17 The learner parameter is changed during supervised learning to get closer to the desired outcome.…”

Section: Introductionmentioning

confidence: 99%

Artificial intelligence in lung cancer screening: Detection, classification, prediction, and prognosis

Quanyang,

Yao,

Sicong

et al. 2024

Cancer Medicine

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BackgroundThe exceptional capabilities of artificial intelligence (AI) in extracting image information and processing complex models have led to its recognition across various medical fields. With the continuous evolution of AI technologies based on deep learning, particularly the advent of convolutional neural networks (CNNs), AI presents an expanded horizon of applications in lung cancer screening, including lung segmentation, nodule detection, false‐positive reduction, nodule classification, and prognosis.MethodologyThis review initially analyzes the current status of AI technologies. It then explores the applications of AI in lung cancer screening, including lung segmentation, nodule detection, and classification, and assesses the potential of AI in enhancing the sensitivity of nodule detection and reducing false‐positive rates. Finally, it addresses the challenges and future directions of AI in lung cancer screening.ResultsAI holds substantial prospects in lung cancer screening. It demonstrates significant potential in improving nodule detection sensitivity, reducing false‐positive rates, and classifying nodules, while also showing value in predicting nodule growth and pathological/genetic typing.ConclusionsAI offers a promising supportive approach to lung cancer screening, presenting considerable potential in enhancing nodule detection sensitivity, reducing false‐positive rates, and classifying nodules. However, the universality and interpretability of AI results need further enhancement. Future research should focus on the large‐scale validation of new deep learning‐based algorithms and multi‐center studies to improve the efficacy of AI in lung cancer screening.

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“…Cellular dysfunction (cancer) in the lungs ranks subsequent among men and tenth among women worldwide. The data assumed in these statistics are an overall picture of lung cancer in the lung area system with four basic stages [ 9 , 10 , 11 ]. Lung cancer is the third greatest common malignant development after breast and colon tumors in women.…”

Section: Introductionmentioning

confidence: 99%

Multi-Process Remora Enhanced Hyperparameters of Convolutional Neural Network for Lung Cancer Prediction

et al. 2023

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In recent years, lung cancer prediction is an essential topic for reducing the death rate of humans. In the literature section, some papers are reviewed that reduce the accuracy level during the prediction stage. Hence, in this paper, we develop a Multi-Process Remora Optimized Hyperparameters of Convolutional Neural Network (MPROH-CNN) aimed at lung cancer prediction. The proposed technique can be utilized to detect the CT images of the human lung. The proposed technique proceeds with four phases, including pre-processing, feature extraction and classification. Initially, the databases are collected from the open-source system. After that, the collected CT images contain unwanted noise, which affects classification efficiency. So, the pre-processing techniques can be considered to remove unwanted noise from the input images, such as filtering and contrast enhancement. Following that, the essential features are extracted with the assistance of feature extraction techniques such as histogram, texture and wavelet. The extracted features are utilized to classification stage. The proposed classifier is a combination of the Remora Optimization Algorithm (ROA) and Convolutional Neural Network (CNN). In the CNN, the ROA is utilized for multi process optimization such as structure optimization and hyperparameter optimization. The proposed methodology is implemented in MATLAB and performances are evaluated by utilized performance matrices such as accuracy, precision, recall, specificity, sensitivity and F_Measure. To validate the projected approach, it is compared with the traditional techniques CNN, CNN-Particle Swarm Optimization (PSO) and CNN-Firefly Algorithm (FA), respectively. From the analysis, the proposed method achieved a 0.98 accuracy level in the lung cancer prediction.

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The current issues and future perspective of artificial intelligence for developing new treatment strategy in non-small cell lung cancer: harmonization of molecular cancer biology and artificial intelligence

Cited by 23 publications

References 102 publications

Cross Analysis of Genomic-Pathologic Features on Multiple Primary Hepatocellular Carcinoma

Cross Analysis of Genomic-Pathologic Features on Multiple Primary Hepatocellular Carcinoma

Artificial intelligence in lung cancer screening: Detection, classification, prediction, and prognosis

Multi-Process Remora Enhanced Hyperparameters of Convolutional Neural Network for Lung Cancer Prediction

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