Lung Cancer and Granuloma Identification Using a Deep Learning Model to Extract 3-Dimensional Radiomics Features in CT Imaging

Lin, Xiaofeng; Han, Jiao; Pang, Zengyuan; Chen, Huai; Wu, Weijie; Wang, Xiaoyi; Xiong, Lang; Huang, Yapeng; Sheng, Li; Li, Li

doi:10.1016/j.cllc.2021.02.004

Cited by 26 publications

(13 citation statements)

References 45 publications

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“…Although some scholars have tried to distinguish lung cancer from granuloma using CT features ( 32 ), we have not found any other relevant literature that specifically examines the difference between recurrence of tumor at the resection margin and granuloma in CT values. It is worth noting that enhanced CT examination has been popularized in most clinical centers in China.…”

Section: Discussionmentioning

confidence: 99%

The difference of auxiliary examination parameters between margin recurrence and granuloma on enhanced computed tomography after sublobar resection

Zheng¹,

Sun²,

Zhang³

et al. 2022

J Thorac Dis

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Background: Imaging examinations following sublobar resection of lung cancer often find thickened neoplasms around the resection margin. Identifying whether the neoplasms are postoperative recurrence or margin granulomas is vitally important. However, the identification mainly depends on the empirical judgment of the imaging department or clinicians in each clinical center at present, and there are few relevant studies, so it is hard to formulate a relatively unified standard. Therefore, we collected data from patients with thickened neoplasms around the resection margin after sublobar resection and sought to discover how to differentiate granulomas from tumor recurrence. Methods:We examined the clinical records of 15 patients with neoplasms around the margins which identified as malignant in auxiliary examination reports, and received second surgery after first sublobar resection. We collected their postoperative pathology and auxiliary examination parameters. The univariate predictors helpful in distinguishing between recurrence and granuloma were analyzed as a diagnostic test.Results: Of the 15 patients with neoplasms around the resection margin, six were diagnosed with benign granulomas, and nine were diagnosed with primary lung cancer recurrence. The results revealed that age, gender, specific surgical method, maximum standardized fluorodeoxyglucose uptake value (SUVmax), and follow-up time were not significantly different, but there were significant differences in enhanced computed tomography (CT) values in several analyses, which calculated by the hospital imaging system. The maximum CT values of the tumor recurrence and granuloma were 104.9±8.051 and 130.3±7.017 (P=0.045), the minimum CT values (15.67±5.113 vs. −17.17±4.826, P=0.0007) and in the floating range CT values (148.00±5.471 vs. 88.11±7.671, P<0.0001), respectively.Conclusions: Differentiating between tumor recurrence and granulomas after sublobar resection remains difficult. However, examining the differences in enhanced CT allows the clinician to make an informed diagnosis that aids further investigation and treatment.

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Section: Discussionmentioning

confidence: 99%

The difference of auxiliary examination parameters between margin recurrence and granuloma on enhanced computed tomography after sublobar resection

Zheng¹,

Sun²,

Zhang³

et al. 2022

J Thorac Dis

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show abstract

“…The high nucleoplasmic ratio within the tumor exhibited low-frequency Gabor features, Lin et al. ( 37 ) showed that deep learning models based on intranodal and perinodular radiomics outperformed single intranodal or perinodular models in lung cancer and granuloma discrimination.…”

Section: Discussionmentioning

confidence: 99%

“…In contrast, normal lung tissue and macrophages around the granuloma exhibit high expression of mid-frequency Gabor features, respectively. The high nucleoplasmic ratio within the tumor exhibited low-frequency Gabor features, Lin et al (37) showed that deep learning models based on intranodal and perinodular radiomics outperformed single intranodal or perinodular models in lung cancer and granuloma discrimination.…”

mentioning

confidence: 99%

Identification of pulmonary adenocarcinoma and benign lesions in isolated solid lung nodules based on a nomogram of intranodal and perinodal CT radiomic features

Peng

Chen

et al. 2022

Front. Oncol.

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To develop and validate a predictive model based on clinical radiology and radiomics to enhance the ability to distinguish between benign and malignant solitary solid pulmonary nodules. In this study, we retrospectively collected computed tomography (CT) images and clinical data of 286 patients with isolated solid pulmonary nodules diagnosed by surgical pathology, including 155 peripheral adenocarcinomas and 131 benign nodules. They were randomly divided into a training set and verification set at a 7:3 ratio, and 851 radiomic features were extracted from thin-layer enhanced venous phase CT images by outlining intranodal and perinodal regions of interest. We conducted preprocessing measures of image resampling and eigenvalue normalization. The minimum redundancy maximum relevance (mRMR) and least absolute shrinkage and selection operator (lasso) methods were used to downscale and select features. At the same time, univariate and multifactorial analyses were performed to screen clinical radiology features. Finally, we constructed a nomogram based on clinical radiology, intranodular, and perinodular radiomics features. Model performance was assessed by calculating the area under the receiver operating characteristic curve (AUC), and the clinical decision curve (DCA) was used to evaluate the clinical practicability of the models. Univariate and multivariate analyses showed that the two clinical factors of sex and age were statistically significant. Lasso screened four intranodal and four perinodal radiomic features. The nomogram based on clinical radiology, intranodular, and perinodular radiomics features showed the best predictive performance (AUC=0.95, accuracy=0.89, sensitivity=0.83, specificity=0.96), which was superior to other independent models. A nomogram based on clinical radiology, intranodular, and perinodular radiomics features is helpful to improve the ability to predict benign and malignant solitary pulmonary nodules.

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“…When high-quality scans were given as input failed to give better accuracy 70:30 84.23 [ 27 ] Multi-level Seg-Unet model with global and patch-based X-ray images for knee bone tumor detection Patch-based X-ray images for knee bone tumor detection The mean accuracy of the model was not benchmarked. Thus, we cannot rely on the model performance 70:30 84.81 [ 28 ] Lung cancer and granuloma identification using a deep learning model to extract 3-dimensional radionics features in CT imaging Tumor segmentation and radiomics feature extraction of the region of interest using gradient boosting The prediction of the model was not standard. Working efficiently when less amount of data is fed to the classifier 60:40 83.22 [ 29 ] ResBCDU-NET: A deep learning framework for lung CT image segmentation Bidirectional Convolutional Long Short-term Memory is used as an advanced integrator module This model failed in the identification of similar image densities.…”

Section: Related Workmentioning

confidence: 99%

A bi-directional deep learning architecture for lung nodule semantic segmentation

et al. 2022

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Lung nodules are abnormal growths and lesions may exist. Both lungs may have nodules. Most lung nodules are harmless (not cancerous/malignant). Pulmonary nodules are rare in lung cancer. X-rays and CT scans identify the lung nodules. Doctors may term the growth a lung spot, coin lesion, or shadow. It is necessary to obtain properly computed tomography (CT) scans of the lungs to get an accurate diagnosis and a good estimate of the severity of lung cancer. This study aims to design and evaluate a deep learning (DL) algorithm for identifying pulmonary nodules (PNs) using the LUNA-16 dataset and examine the prevalence of PNs using DB-Net. The paper states that a new, resource-efficient deep learning architecture is called for, and it has been given the name of DB-NET. When a physician orders a CT scan, they need to employ an accurate and efficient lung nodule segmentation method because they need to detect lung cancer at an early stage. However, segmentation of lung nodules is a difficult task because of the nodules' characteristics on the CT image as well as the nodules' concealed shape, visual quality, and context. The DB-NET model architecture is presented as a resource-efficient deep learning solution for handling the challenge at hand in this paper. Furthermore, it incorporates the Mish nonlinearity function and the mask class weights to improve segmentation effectiveness. In addition to the LUNA-16 dataset, which contained 1200 lung nodules collected during the LUNA-16 test, the LUNA-16 dataset was extensively used to train and assess the proposed model. The DB-NET architecture surpasses the existing U-NET model by a dice coefficient index of 88.89%, and it also achieves a similar level of accuracy to that of human experts.

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Lung Cancer and Granuloma Identification Using a Deep Learning Model to Extract 3-Dimensional Radiomics Features in CT Imaging

Cited by 26 publications

References 45 publications

The difference of auxiliary examination parameters between margin recurrence and granuloma on enhanced computed tomography after sublobar resection

The difference of auxiliary examination parameters between margin recurrence and granuloma on enhanced computed tomography after sublobar resection

Identification of pulmonary adenocarcinoma and benign lesions in isolated solid lung nodules based on a nomogram of intranodal and perinodal CT radiomic features

A bi-directional deep learning architecture for lung nodule semantic segmentation

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