Histologic subtype classification of non-small cell lung cancer using PET/CT images

Han, Yong; Ma, Yingmin; Wu, Zhiyuan; Zhang, Feng; Zheng, Deqiang; Liu, Xiangtong; Tao, Lixin; Liang, Zhigang; Liu, Yang; Li, Xia; Huang, Jian; Guo, Xiuhua

doi:10.1007/s00259-020-04771-5

Cited by 140 publications

(89 citation statements)

References 34 publications

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“…To date, it has been applied to discover potential imaging biomarkers associated with different diseases, including progression and prognosis [ [23] , [24] , [25] ]. In this study, we proposed using the NSDTCT-based GLCM algorithm to analyse the features of CT images from 96 different directions, which could capture more information than only one original level, as discussed previously [ 26 ]. Similarly, we found that some texture features are positively or negatively associated with COVID-19, which implies that the specific texture pattern could be captured and used to recognize COVID-19 images and individuals.…”

Section: Discussionmentioning

confidence: 99%

Texture feature-based machine learning classifier could assist in the diagnosis of COVID-19

Jin

et al. 2021

European Journal of Radiology

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Differentiating COVID-19 from other acute infectious pneumonias rapidly is challenging at present. This study aims to improve the diagnosis of COVID-19 using computed tomography (CT). Method: COVID-19 was confirmed mainly by virus nucleic acid testing and epidemiological history according to WHO interim guidance, while other infectious pneumonias were diagnosed by antigen testing. The texture features were extracted from CT images by two radiologists with 5 years of work experience using modified wavelet transform and matrix computation analyses. The random forest (RF) classifier was applied to identify COVID-19 patients and images. Results: We retrospectively analysed the data of 95 individuals (291 images) with COVID-19 and 96 individuals (279 images) with other acute infectious pneumonias, including 50 individuals (160 images) with influenza A/B. In total, 6 texture features showed a positive association with COVID-19, while 4 features were negatively associated. The mean AUROC, accuracy, sensitivity, and specificity values of the 5-fold test sets were 0.800, 0.722, 0.770, and 0.680 for image classification and 0.858, 0.826, 0.809, and 0.842 for individual classification, respectively. The feature 'Correlation' contributed most both at the image level and individual level, even compared with the clinical factors. In addition, the texture features could discriminate COVID-19 from influenza A/B, with an AUROC of 0.883 for images and 0.957 for individuals. Conclusions: The developed texture feature-based RF classifier could assist in the diagnosis of COVID-19, which could be a rapid screening tool in the era of pandemic.

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Section: Discussionmentioning

confidence: 99%

Texture feature-based machine learning classifier could assist in the diagnosis of COVID-19

Jin

et al. 2021

European Journal of Radiology

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“…Other studies that focused on diagnosis demonstrated the ability of PET radiomics to distinguish between malign and benign FDG-avid lesions [67,71,74] or between tuberculosis and lung cancer [75,80]. Regarding characterization, in a study including 867 patients, Han et al [129] showed that PET radiomics in combination with deep learning was able to differentiate histological subtypes of cancer, particularly, adenocarcinoma and squamous cell carcinoma. Other studies showed the ability of PET radiomics to predict EGFR mutation status [68,95,111,115] or PD-L1 expression [127].…”

Section: Lung Cancermentioning

confidence: 99%

A Systematic Review of PET Textural Analysis and Radiomics in Cancer

et al. 2021

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Background: Although many works have supported the utility of PET radiomics, several authors have raised concerns over the robustness and replicability of the results. This study aimed to perform a systematic review on the topic of PET radiomics and the used methodologies. Methods: PubMed was searched up to 15 October 2020. Original research articles based on human data specifying at least one tumor type and PET image were included, excluding those that apply only first-order statistics and those including fewer than 20 patients. Each publication, cancer type, objective and several methodological parameters (number of patients and features, validation approach, among other things) were extracted. Results: A total of 290 studies were included. Lung (28%) and head and neck (24%) were the most studied cancers. The most common objective was prognosis/treatment response (46%), followed by diagnosis/staging (21%), tumor characterization (18%) and technical evaluations (15%). The average number of patients included was 114 (median = 71; range 20–1419), and the average number of high-order features calculated per study was 31 (median = 26, range 1–286). Conclusions: PET radiomics is a promising field, but the number of patients in most publications is insufficient, and very few papers perform in-depth validations. The role of standardization initiatives will be crucial in the upcoming years.

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“…Hyun et al successfully identified the histological subtypes of lung cancer using a machine-learning algorithm with PET-based radiomic features [31]. Also, Han et al showed that machine learning/deep learning algorithms could help radiologists to differentiate the histological subtypes of non-small cell lung cancer via PET/ CT images [32]. Zhang et al confirmed that the quantified radiomics method could aid the noninvasive differentiation of autoimmune pancreatitis and PDAC in 18 F-FDG PET/CT images [26].…”

Section: Discussionmentioning

confidence: 99%

Preoperative prediction of pathological grade in pancreatic ductal adenocarcinoma based on 18F-FDG PET/CT radiomics

et al. 2021

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Purpose To develop and validate a machine learning model based on radiomic features derived from 18F-fluorodeoxyglucose (18F-FDG) positron emission tomography/computed tomography (PET/CT) images to preoperatively predict the pathological grade in patients with pancreatic ductal adenocarcinoma (PDAC). Methods A total of 149 patients (83 men, 66 women, mean age 61 years old) with pathologically proven PDAC and a preoperative 18F-FDG PET/CT scan between May 2009 and January 2016 were included in this retrospective study. The cohort of patients was divided into two separate groups for the training (99 patients) and validation (50 patients) in chronological order. Radiomics features were extracted from PET/CT images using Pyradiomics implemented in Python, and the XGBoost algorithm was used to build a prediction model. Conventional PET parameters, including standardized uptake value, metabolic tumor volume, and total lesion glycolysis, were also measured. The quality of the proposed model was appraised by means of receiver operating characteristics (ROC) and areas under the ROC curve (AUC). Results The prediction model based on a twelve-feature-combined radiomics signature could stratify PDAC patients into grade 1 and grade 2/3 groups with AUC of 0.994 in the training set and 0.921 in the validation set. Conclusion The model developed is capable of predicting pathological differentiation grade of PDAC based on preoperative 18F-FDG PET/CT radiomics features.

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Histologic subtype classification of non-small cell lung cancer using PET/CT images

Cited by 140 publications

References 34 publications

Texture feature-based machine learning classifier could assist in the diagnosis of COVID-19

Texture feature-based machine learning classifier could assist in the diagnosis of COVID-19

A Systematic Review of PET Textural Analysis and Radiomics in Cancer

Preoperative prediction of pathological grade in pancreatic ductal adenocarcinoma based on 18F-FDG PET/CT radiomics

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