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Development and External Validation of a Multimodal Integrated Feature Neural Network (MIFNN) for the Diagnosis of Malignancy in Small Pulmonary Nodules (≤10 mm)

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机构： [1]Department of Epidemiology and Health Statistics, Capital Medical University, No 10 Xitoutiao, You'anmen Wai, Fengtai District, Beijing, 1000069, China, Beijing, 100054, CHINA. [2]Beijing Medical Examination Center, No. 1 Building, 81 Fucheng Road Courtyard, Haidian District, Beijing, Beijing, 100069, CHINA. [3]Department of Nuclear Medicine, Xuanwu Hospital Capital Medical University, No. 45 Changchun Street, Xicheng District, Beijing, Beijing, 100053, CHINA. [4]University College Cork, College Rd, University College, Cork, Irend, Cork, T12 K8AF, IRELAND. [5]La Trobe University Department of Mathematics and Statistics, La Trobe University Melbourne Victoria 3086 Australia, Melbourne, 3086, AUSTRALIA.

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ObjectivesCurrent lung cancer screening protocols primarily evaluate pulmonary nodules, yet often neglect the malignancy risk associated with small nodules (≤10 mm). This study endeavors to optimize the management of pulmonary nodules in this population by devising and externally validating a Multimodal Integrated Feature Neural Network (MIFNN). We hypothesize that the fusion of deep learning algorithms with morphological nodule features will significantly enhance diagnostic accuracy.Materials and MethodsData were retrospectively collected from the Lung Nodule Analysis 2016 (LUNA16) dataset and four local centers in Beijing, China. The study includes patients with small pulmonary nodules (≤10 mm). We developed a neural network, termed MIFNN, that synergistically combines computed tomography (CT) images and morphological characteristics of pulmonary nodules. The network is designed to acquire clinically relevant deep learning features, thereby elevating the diagnostic accuracy of existing models. Importantly, the network's simple architecture and use of standard screening variables enable seamless integration into standard lung cancer screening protocols.ResultsIn summary, the study analyzed a total of 382 small pulmonary nodules (85 malignant) from the LUNA16 dataset and 101 small pulmonary nodules (33 malignant) obtained from four specialized centers in Beijing, China, for model training and external validation. Both internal and external validation metrics indicate that the MIFNN significantly surpasses extant state-of-the-art models, achieving an internal area under the curve (AUC) of 0.890 (95% CI: 0.848-0.932) and an external AUC of 0.843 (95% CI: 0.784-0.891).ConclusionThe MIFNN model significantly enhances the diagnostic accuracy of small pulmonary nodules, outperforming existing benchmarks by Zhang et al. with a 6.34% improvement for nodules less than 10 mm. Leveraging advanced integration techniques for imaging and clinical data, MIFNN increases the efficiency of lung cancer screenings and optimizes nodule management, potentially reducing false positives and unnecessary biopsies.© 2024 IOP Publishing Ltd.

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大类 | 4 区医学

小类 | 4 区核医学

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Q3 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING

影响因子： 1.3 最新[2023版] 1.3 最新五年平均 1.4 出版当年[2022版] 1.3 出版当年五年平均 0 出版前一年[2021版] 1.3 出版后一年[2023版]

第一作者：

第一作者机构： [1]Department of Epidemiology and Health Statistics, Capital Medical University, No 10 Xitoutiao, You'anmen Wai, Fengtai District, Beijing, 1000069, China, Beijing, 100054, CHINA. [2]Beijing Medical Examination Center, No. 1 Building, 81 Fucheng Road Courtyard, Haidian District, Beijing, Beijing, 100069, CHINA.

通讯作者：

通讯机构： [1]Department of Epidemiology and Health Statistics, Capital Medical University, No 10 Xitoutiao, You'anmen Wai, Fengtai District, Beijing, 1000069, China, Beijing, 100054, CHINA. [2]Beijing Medical Examination Center, No. 1 Building, 81 Fucheng Road Courtyard, Haidian District, Beijing, Beijing, 100069, CHINA.

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