Rapid diagnosis of lung cancer and glioma based on serum Raman spectroscopy combined with deep learning

Lung cancer ranks among the most common types of cancer. Noninvasive computer-aided diagnosis can enable large-scale rapid screening of potential patients with lung cancer. Deep learning methods have already been applied for the automatic diagnosis of lung cancer in the past. Due to restrictions caused by single modality images of dataset as well as the lack of approaches that allow for a reliable extraction of fine-grained features from different imaging modalities, research regarding the automated diagnosis of lung cancer based on noninvasive clinical images requires further study. In this paper, we present a deep learning architecture that combines the fine-grained feature from PET and CT images that allow for the noninvasive diagnosis of lung cancer. The multidimensional (regarding the channel as well as spatial dimensions) attention mechanism is used to effectively reduce feature noise when extracting fine-grained features from each imaging modality. We conduct a comparative analysis of the two aspects of feature fusion and attention mechanism through quantitative evaluation metrics and the visualization of deep learning process. In our experiments, we obtained an area under the ROC curve of 0.92 (balanced accuracy = 0.72) and a more focused network attention which shows the effective extraction of the fine-grained feature from each imaging modality.

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Raman spectroscopy as a potential diagnostic tool to analyse biochemical alterations in lung cancer

The Analyst ◽

10.1039/c9an02175b ◽

2020 ◽

Vol 145 (2) ◽

pp. 385-392 ◽

Cited By ~ 1

Author(s):

Qingfeng Zheng ◽

Junyi Li ◽

Lin Yang ◽

Bo Zheng ◽

Jiangcai Wang ◽

...

Keyword(s):

Lung Cancer ◽

Raman Spectroscopy ◽

Cancer Progression ◽

Diagnostic Tool ◽

Molecular Level ◽

Rapid Diagnosis ◽

Clinical Practices ◽

Biochemical Alterations ◽

Diagnosis Tool

Raman spectroscopy can be used as a rapid diagnosis tool in lung cancer to help us understand cancer progression at molecular level and improve clinical practices.

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Rapid Diagnosis of Lung Cancer from Palpable Metastases by Needle Thrust

CHEST Journal ◽

10.1378/chest.98.6.1393 ◽

1990 ◽

Vol 98 (6) ◽

pp. 1393-1396 ◽

Cited By ~ 10

Author(s):

Jay J. Rohwedder ◽

John A. Handley ◽

David Kerr

Keyword(s):

Lung Cancer ◽

Rapid Diagnosis ◽

Diagnosis Of Lung Cancer

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Deep Learning on MRI Images for Diagnosis of Lung Cancer Spinal Bone Metastasis

Contrast Media & Molecular Imaging ◽

10.1155/2021/5294379 ◽

2021 ◽

Vol 2021 ◽

pp. 1-9

Author(s):

Xiaojie Fan ◽

Xiaoyu Zhang ◽

Zibo Zhang ◽

Yifang Jiang

Keyword(s):

Lung Cancer ◽

Deep Learning ◽

Bone Metastasis ◽

False Negative ◽

Region Growing ◽

Jaccard Coefficient ◽

Adaboost Algorithm ◽

Diagnosis Of Lung Cancer ◽

Preliminary Classification ◽

Dice Index

This paper aimed to explore the adoption of deep learning algorithms in lung cancer spinal bone metastasis diagnosis. Comprehensive analysis was carried out with the aid of AdaBoost algorithm and Chan-Vese (CV) algorithm. 87 patients with lung cancer spinal bone metastasis were taken as research subjects, and comprehensive evaluation was made in terms of preliminary classification of images, segmentation results, Dice index, and Jaccard coefficient. After the case of misjudgment on whether there was hot spot was excluded, the initial classification accuracy of the AdaBoost algorithm can reach 96.55%. True positive rate (TPR) was 2.3%, and false negative rate (FNR) was 1.15%. 45 MRI images with hot spots were utilized as test set to detect the segmentation accuracy of CV, maximum between-cluster variance method (OTSU), and region growing algorithm. The results showed that the Dice index and Jaccard coefficient of the CV algorithm were 0.8591 and 0.8002, respectively, which were considerably superior to OTSU (0.6125 and 0.5541) and region growing algorithm (0.7293 and 0.6598). In summary, the AdaBoost algorithm was adopted for image preliminary classification, and CV algorithm for image segmentation was ideal for the diagnosis of lung cancer spinal bone metastasis and it was worthy of clinical promotion.

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