Diagnostic Accuracy of Deep Learning and Radiomics in Lung Cancer Staging: A Systematic Review and Meta-Analysis

Zheng, Xiushan; He, Bo; Hu, Yunhai; Ren, Min; Chen, Zhiyuan; Zhang, Zhiguang; Ma, Jia; Ouyang, Lanwei; Chu, Hongmei; Gao, Huan; He, Weiqun; Liu, Tianhu; Li, Gang

doi:10.3389/fpubh.2022.938113

Cited by 23 publications

(12 citation statements)

References 43 publications

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“…Many scholars have conducted research on deep learning, recognizing its potential for automatically segmenting lesions and saving time while minimizing subjective errors. The objective of this study is to explore the application of deep learning for the segmentation of WT lesions and evaluate the feasibility of automated segmentation for imaging analysis [ 12 – 14 ].…”

Section: Introductionmentioning

confidence: 99%

Automated segmentation and volume prediction in pediatric Wilms’ tumor CT using nnu-net

Li,

Sun,

Zhang

et al. 2024

BMC Pediatr

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Background Radiologic volumetric evaluation of Wilms’ tumor (WT) is an important indicator to guide treatment decisions. However, due to the heterogeneity of the tumors, radiologists have main-guard differences in diagnosis that can lead to misdiagnosis and poor treatment. The aim of this study was to explore whether CT-based outlining of WT foci can be automated using deep learning. Methods We included CT intravenous phase images of 105 patients with WT and double-blind outlining of lesions by two radiologists. Then, we trained an automatic segmentation model using nnUnet. The Dice similarity coefficient (DSC) and 95th percentile Hausdorff distance (HD95) were used to assess the performance. Next, we optimized the automatic segmentation results based on the ratio of the three-dimensional diameter of the lesion to improve the performance of volumetric assessment. Results The DSC and HD95 was 0.83 ± 0.22 and 10.50 ± 8.98 mm. The absolute difference and percentage difference in tumor size was 72.27 ± 134.84 cm3 and 21.08% ± 30.46%. After optimization according to our method, it decreased to 40.22 ± 96.06 cm3 and 10.16% ± 9.70%. Conclusion We introduce a novel method that enhances the accuracy of predicting WT volume by integrating AI automated outlining and 3D tumor diameters. This approach surpasses the accuracy of using AI outcomes alone and has the potential to enhance the clinical evaluation of pediatric patients with WT. By intertwining AI outcomes with clinical data, this method becomes more interpretive and offers promising applications beyond Wilms tumor, extending to other pediatric diseases.

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Section: Introductionmentioning

confidence: 99%

Automated segmentation and volume prediction in pediatric Wilms’ tumor CT using nnu-net

Li,

Sun,

Zhang

et al. 2024

BMC Pediatr

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“…Some experts used ML methods to study PET/CT and CT image data of primary lung cancer undergoing lobectomy or segmentectomy to evaluate the aggressiveness of lung cancer. This study used seven ML methods such as LR, SVM, RF, KNN, light gradient boosting (LGB), 129 deep neural net (DNN), together with TabNet, 130 to establish the ensemble model (ENS) individually and jointly. All models performed well, with LR and ENS having better predictive performance than other models.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning in Diagnosis and Prognosis of Lung Cancer by PET-CT

Yuan,

An,

Zhu

et al. 2024

CMAR

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As a disease with high morbidity and high mortality, lung cancer has seriously harmed people’s health. Therefore, early diagnosis and treatment are more important. PET/CT is usually used to obtain the early diagnosis, staging, and curative effect evaluation of tumors, especially lung cancer, due to the heterogeneity of tumors and the differences in artificial image interpretation and other reasons, it also fails to entirely reflect the real situation of tumors. Artificial intelligence (AI) has been applied to all aspects of life. Machine learning (ML) is one of the important ways to realize AI. With the help of the ML method used by PET/CT imaging technology, there are many studies in the diagnosis and treatment of lung cancer. This article summarizes the application progress of ML based on PET/CT in lung cancer, in order to better serve the clinical. In this study, we searched PubMed using machine learning, lung cancer, and PET/CT as keywords to find relevant articles in the past 5 years or more. We found that PET/CT-based ML approaches have achieved significant results in the detection, delineation, classification of pathology, molecular subtyping, staging, and response assessment with survival and prognosis of lung cancer, which can provide clinicians a powerful tool to support and assist in critical daily clinical decisions. However, ML has some shortcomings such as slightly poor repeatability and reliability.

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“…Artificial intelligence has a long history in the field of pulmonary nodule detection and classification dating back to the 1960s [ 16 , 17 ]. In 2012, Lambin and colleagues coined the term radiomics to describe quantitative imaging feature extraction to achieve better diagnostic performance [ 18 ].…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Pulmonary Nodules by Radiologists vs. Radiomics in Stand-Alone and Complementary CT and MRI

Tietz,

Müller-Franzes,

Zimmermann

et al. 2024

Diagnostics

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Increased attention has been given to MRI in radiation-free screening for malignant nodules in recent years. Our objective was to compare the performance of human readers and radiomic feature analysis based on stand-alone and complementary CT and MRI imaging in classifying pulmonary nodules. This single-center study comprises patients with CT findings of pulmonary nodules who underwent additional lung MRI and whose nodules were classified as benign/malignant by resection. For radiomic features analysis, 2D segmentation was performed for each lung nodule on axial CT, T2-weighted (T2w), and diffusion (DWI) images. The 105 extracted features were reduced by iterative backward selection. The performance of radiomics and human readers was compared by calculating accuracy with Clopper–Pearson confidence intervals. Fifty patients (mean age 63 +/− 10 years) with 66 pulmonary nodules (40 malignant) were evaluated. ACC values for radiomic features analysis vs. radiologists based on CT alone (0.68; 95%CI: 0.56, 0.79 vs. 0.59; 95%CI: 0.46, 0.71), T2w alone (0.65; 95%CI: 0.52, 0.77 vs. 0.68; 95%CI: 0.54, 0.78), DWI alone (0.61; 95%CI:0.48, 0.72 vs. 0.73; 95%CI: 0.60, 0.83), combined T2w/DWI (0.73; 95%CI: 0.60, 0.83 vs. 0.70; 95%CI: 0.57, 0.80), and combined CT/T2w/DWI (0.83; 95%CI: 0.72, 0.91 vs. 0.64; 95%CI: 0.51, 0.75) were calculated. This study is the first to show that by combining quantitative image information from CT, T2w, and DWI datasets, pulmonary nodule assessment through radiomics analysis is superior to using one modality alone, even exceeding human readers’ performance.

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Diagnostic Accuracy of Deep Learning and Radiomics in Lung Cancer Staging: A Systematic Review and Meta-Analysis

Cited by 23 publications

References 43 publications

Automated segmentation and volume prediction in pediatric Wilms’ tumor CT using nnu-net

Automated segmentation and volume prediction in pediatric Wilms’ tumor CT using nnu-net

Machine Learning in Diagnosis and Prognosis of Lung Cancer by PET-CT

Evaluation of Pulmonary Nodules by Radiologists vs. Radiomics in Stand-Alone and Complementary CT and MRI

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