An Appraisal of Lung Nodules Automatic Classification Algorithms for CT Images

Wang, Xinqi; Mao, Keming; Wang, Lizhe; Yang, Peiyi; Lü, Dan; He, Ping

doi:10.3390/s19010194

Cited by 31 publications

(12 citation statements)

References 130 publications

(434 reference statements)

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“…At a first, pre-processing level, unsupervised machine learning algorithms allow the automatic stratification of BC patients realized by clustering the feature sets according to specific similarity measure [ 26 ]. In fact, this data partition is obtained through the minimization of a cost function involving distances between data and cluster prototypes, and optimal partitions are obtained through iterative optimization procedures that start from a random initialization and move from one cluster to another until no further improvement in the cost function optimization is noticed [ 27 ].…”

Section: Machine Learning and Deep Learning In The Radiomics Workflowmentioning

confidence: 99%

Overview of radiomics in breast cancer diagnosis and prognostication

et al. 2020

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Diagnosis of early invasive breast cancer relies on radiology and clinical evaluation, supplemented by biopsy confirmation. At least three issues burden this approach: a) suboptimal sensitivity and suboptimal positive predictive power of radiology screening and diagnostic approaches, respectively; b) invasiveness of biopsy with discomfort for women undergoing diagnostic tests; c) long turnaround time for recall tests. In the screening setting, radiology sensitivity is suboptimal, and when a suspicious lesion is detected and a biopsy is recommended, the positive predictive value of radiology is modest. Recent technological advances in medical imaging, especially in the field of artificial intelligence applied to image analysis, hold promise in addressing clinical challenges in cancer detection, assessment of treatment response, and monitoring disease progression. Radiomics include feature extraction from clinical images; these features are related to tumor size, shape, intensity, and texture, collectively providing comprehensive tumor characterization, the so-called radiomics signature of the tumor. Radiomics is based on the hypothesis that extracted quantitative data derives from mechanisms occurring at genetic and molecular levels. In this article we focus on the role and potential of radiomics in breast cancer diagnosis and prognostication.

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Section: Machine Learning and Deep Learning In The Radiomics Workflowmentioning

confidence: 99%

Overview of radiomics in breast cancer diagnosis and prognostication

et al. 2020

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“…Furthermore, as the malignancy estimates are created by the same panel of radiologists that record biomarkers, there is a possibility of cognitive bias in the event that the identification of descriptive biomarkers was influenced by the overall malignancy estimates [14]. Despite these potential sources of bias, LIDC-IDRI is a highly valuable resource used by many investigators to better predict malignancy suspicion levels using descriptive biomarkers, radiomic, deep image features, and combinations thereof [17]. A promising approach to potentially reduce this bias was investigated by Kang et al [16] Table 2 Best performing models that are statistically significantly rated in descending order of their average AUC values.…”

Section: Discussionmentioning

confidence: 99%

Lung Nodule Classification Using Biomarkers, Volumetric Radiomics, and 3D CNNs

et al. 2021

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We present a hybrid algorithm to estimate lung nodule malignancy that combines imaging biomarkers from Radiologist’s annotation with image classification of CT scans. Our algorithm employs a 3D Convolutional Neural Network (CNN) as well as a Random Forest in order to combine CT imagery with biomarker annotation and volumetric radiomic features. We analyze and compare the performance of the algorithm using only imagery, only biomarkers, combined imagery + biomarkers, combined imagery + volumetric radiomic features, and finally the combination of imagery + biomarkers + volumetric features in order to classify the suspicion level of nodule malignancy. The National Cancer Institute (NCI) Lung Image Database Consortium (LIDC) IDRI dataset is used to train and evaluate the classification task. We show that the incorporation of semi-supervised learning by means of K-Nearest-Neighbors (KNN) can increase the available training sample size of the LIDC-IDRI, thereby further improving the accuracy of malignancy estimation of most of the models tested although there is no significant improvement with the use of KNN semi-supervised learning if image classification with CNNs and volumetric features is combined with descriptive biomarkers. Unexpectedly, we also show that a model using image biomarkers alone is more accurate than one that combines biomarkers with volumetric radiomics, 3D CNNs, and semi-supervised learning. We discuss the possibility that this result may be influenced by cognitive bias in LIDC-IDRI because malignancy estimates were recorded by the same radiologist panel as biomarkers, as well as future work to incorporate pathology information over a subset of study participants.

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“…There is a large volume of articles devoted to multiple kinds of classifiers used such as SVM and random forest to classify pulmonary nodules or determine risk of malignancy (69) to help predict survival in lung cancer patients and concluded that the performance of these techniques, when applied to this particular database, may be on par with classical methods (71).…”

Section: Nodule Classification and Cancer Predictionmentioning

confidence: 99%

Putting artificial intelligence (AI) on the spot: machine learning evaluation of pulmonary nodules

2020

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Lung cancer remains the leading cause of cancer related death world-wide despite advances in treatment. This largely relates to the fact that many of these patients already have advanced diseases at the time of initial diagnosis. As most lung cancers present as nodules initially, an accurate classification of pulmonary nodules as early lung cancers is critical to reducing lung cancer morbidity and mortality. There have been significant recent advances in artificial intelligence (AI) for lung nodule evaluation. Deep learning (DL) and convolutional neural networks (CNNs) have shown promising results in pulmonary nodule detection and have also excelled in segmentation and classification of pulmonary nodules. This review aims to provide an overview of progress that has been made in AI recently for pulmonary nodule detection and characterization with the ultimate goal of lung cancer prediction and classification while outlining some of the pitfalls and challenges that remain to bring such advancements to routine clinical use.

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An Appraisal of Lung Nodules Automatic Classification Algorithms for CT Images

Cited by 31 publications

References 130 publications

Overview of radiomics in breast cancer diagnosis and prognostication

Overview of radiomics in breast cancer diagnosis and prognostication

Lung Nodule Classification Using Biomarkers, Volumetric Radiomics, and 3D CNNs

Putting artificial intelligence (AI) on the spot: machine learning evaluation of pulmonary nodules

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