Clinical impact of a deep learning system for automated detection of missed pulmonary nodules on routine body computed tomography including the chest region

Chen, Kueian; Lai, Ying‐Chieh; Vanniarajan, Balamuralidhar; Wang, Pieh-Hsu; Wang, Shao-Chung; Lin, Yu-Chun; Ng, Shu‐Hang; Tran, Pelu; Lin, Gigin

doi:10.1007/s00330-021-08412-9

Cited by 6 publications

(4 citation statements)

References 31 publications

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“…Consequently, our study showed that DL-CAD as a second reader in lung cancer screening is not cost-saving. However, it should be noted that the use of DL-CAD as a second reader can identify missed pulmonary nodules, some of which may be clinically significant [ 21 , 22 ]. This potential benefit of DL-CAD as a second reader is out of the scope of the current study.…”

Section: Discussionmentioning

confidence: 99%

Pricing and cost-saving potential for deep-learning computer-aided lung nodule detection software in CT lung cancer screening

Du,

Greuter,

Prokop

et al. 2023

Insights Imaging

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Objective An increasing number of commercial deep learning computer-aided detection (DL-CAD) systems are available but their cost-saving potential is largely unknown. This study aimed to gain insight into appropriate pricing for DL-CAD in different reading modes to be cost-saving and to determine the potentially most cost-effective reading mode for lung cancer screening. Methods In three representative settings, DL-CAD was evaluated as a concurrent, pre-screening, and second reader. Scoping review was performed to estimate radiologist reading time with and without DL-CAD. Hourly cost of radiologist time was collected for the USA (€196), UK (€127), and Poland (€45), and monetary equivalence of saved time was calculated. The minimum number of screening CTs to reach break-even was calculated for one-time investment of €51,616 for DL-CAD. Results Mean reading time was 162 (95% CI: 111–212) seconds per case without DL-CAD, which decreased by 77 (95% CI: 47–107) and 104 (95% CI: 71–136) seconds for DL-CAD as concurrent and pre-screening reader, respectively, and increased by 33–41 s for DL-CAD as second reader. This translates into €1.0–4.3 per-case cost for concurrent reading and €0.8–5.7 for pre-screening reading in the USA, UK, and Poland. To achieve break-even with a one-time investment, the minimum number of CT scans was 12,300–53,600 for concurrent reader, and 9400–65,000 for pre-screening reader in the three countries. Conclusions Given current pricing, DL-CAD must be priced substantially below €6 in a pay-per-case setting or used in a high-workload environment to reach break-even in lung cancer screening. DL-CAD as pre-screening reader shows the largest potential to be cost-saving. Critical relevance statement Deep-learning computer-aided lung nodule detection (DL-CAD) software must be priced substantially below 6 euro in a pay-per-case setting or must be used in high-workload environments with one-time investment in order to achieve break-even. DL-CAD as a pre-screening reader has the greatest cost savings potential. Key points • DL-CAD must be substantially below €6 in a pay-per-case setting to reach break-even. • DL-CAD must be used in a high-workload screening environment to achieve break-even. • DL-CAD as a pre-screening reader shows the largest potential to be cost-saving. Graphical Abstract

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Section: Discussionmentioning

confidence: 99%

Pricing and cost-saving potential for deep-learning computer-aided lung nodule detection software in CT lung cancer screening

Du,

Greuter,

Prokop

et al. 2023

Insights Imaging

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“…The basis of metabolomics is group indicator analysis, which relies on high-throughput detection and data processing. It aims to analyze the physiological state of organisms through information modelling and system integration ( 24 ). Currently, common metabolomic techniques consist of nuclear magnetic resonance, mass spectrometry, and chromatography [high performance liquid chromatography (HPLC) and gas chromatography] ( 25 ).…”

Section: Introductionmentioning

confidence: 99%

The predictive value of chest computed tomography images, tumor markers, and metabolomics in the identification of benign and malignant pulmonary nodules

Xu¹,

Ge²,

Liu³

et al. 2023

J Thorac Dis

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Background Invasive puncture biopsy is currently the main method of identifying benign and malignant pulmonary nodules (PNs). This study aimed to investigate the application effect of chest computed tomography (CT) images, tumor markers (TMs), and metabolomics in the identification of benign and malignant PNs (MPNs). Methods A total of 110 patients with PNs who were hospitalized in Dongtai Hospital of Traditional Chinese Medicine from March 2021 to March 2022 were selected as the study cohort. A retrospective analysis study of chest CT imaging, serum TMs testing, and plasma fatty acid (FA) metabolomics was performed on all participants. Results According to the pathological results, participants were divided into a MPN group (n=72) and a benign PN (BPN) group (n=38). The morphological signs of CT images, the levels and positive rate of serum TMs, and the plasma FA indicator were compared between groups. There were significant differences between the MPN group and the BPN group in the CT morphological signs, including location of PN and the number of patients with or without lobulation sign, spicule sign, and vessel convergence sign (P<0.05). Serum carcinoembryonic antigen (CEA), cytokeratin-19 fragment (CYFRA 21-1), neuron-specific enolase (NSE), and squamous cell carcinoma antigen (SCC-Ag) were not significantly different between the 2 groups. The serum contents of CEA and CYFRA 21-1 in the MPN group were remarkably higher than those in the BPN group (P<0.05). The plasma levels of palmitic acid, total omega-3 polyunsaturated FA (W3), nervonic acid, stearic acid, docosatetraenoic acid, linolenic acid, eicosapentaenoic acid, total saturated FA, and total FA were much higher in the MPN group than the BPN group (P<0.05). Conclusions In conclusion, chest CT images and TMs, combined with metabolomics, has a good application effect in the diagnosis of BPNs and MPNs, and is worthy of further promotion.

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“…A traditional method to minimize false alarms is to incorporate location, size, shape, density, texture, gradient, and upper and lower people information 21,22 . The traditional computer‐aided inspection approach has two obvious flaws 23 . The first weakness is the overall inefficiency 24 .…”

Section: Introductionmentioning

confidence: 99%

“…21,22 The traditional computer-aided inspection approach has two obvious flaws. 23 The first weakness is the overall inefficiency. 24 The second weakness is the detection hypothesis and the actual situation varies widely, affecting the total detection result.…”

Section: Introductionmentioning

confidence: 99%

A hybrid machine learning technique for early prediction of lung nodules from medical images using a learning‐based neural network classifier

Musthafa¹,

Sankar

Benil

et al. 2022

Concurrency and Computation

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Lung cancer is one of the major causes of death in the world, according to radiologists. However, a constant flow of medical images to hospitals is forcing radiologists to focus on accurate early prediction of nodules. Recently, several image-processing techniques have cooperated for the early prediction of lung nodules. However, it's hard to detect strong nodes because of lung node diversity and environmental complexity. This study presents a hybrid machine learning technique for predicting an early prognosis of lung nodules from clinical images using a learning-based neural network classifier.First, we introduce an improved snake swarm optimization with a bat model (ISSO-B) for lung nodule segmentation using statistical information. Second, we demonstrate a chaotic atom search optimization (CASO) algorithm to select the optimal best features among multiple features, which minimize the dimensionality problem. Third, we develop a hybrid learning-based deep neural network classifier (L-DNN) for nodule prediction and classification. Finally, we evaluate our proposed technique with different public datasets LIDC-IDRI and FAH-GMU. Then, performance can be compared with the latest technology in terms of accuracy, sensitivity, specificity, and area under curve (AUC).

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Clinical impact of a deep learning system for automated detection of missed pulmonary nodules on routine body computed tomography including the chest region

Cited by 6 publications

References 31 publications

Pricing and cost-saving potential for deep-learning computer-aided lung nodule detection software in CT lung cancer screening

Pricing and cost-saving potential for deep-learning computer-aided lung nodule detection software in CT lung cancer screening

The predictive value of chest computed tomography images, tumor markers, and metabolomics in the identification of benign and malignant pulmonary nodules

A hybrid machine learning technique for early prediction of lung nodules from medical images using a learning‐based neural network classifier

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