Colorectal cancer with indeterminate pulmonary nodules

Jiang, Zekun; Xie, Yubao; Jing, Huang; Chen, J S

doi:10.4149/neo_2020_190613n508

Cited by 2 publications

(1 citation statement)

References 78 publications

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“…In the current clinical diagnosis of pulmonary nodules, it is generally believed that benign pulmonary nodules mainly occur in the posterior part of the upper lobe and the dorsal part of the lower lobe of the lung [14][15][16]. Most of the inflammatory nodules were found in the dorsal and inferior lobes of the lungs.…”

Section: Discussionmentioning

confidence: 99%

Computed Tomography Image under Convolutional Neural Network Deep Learning Algorithm in Pulmonary Nodule Detection and Lung Function Examination

Zhang

Huang

et al. 2021

Journal of Healthcare Engineering

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The objective of this study was to perform segmentation and extraction of CT images of pulmonary nodules based on convolutional neural networks (CNNs). The Mask-RCNN algorithm model is a typical end-to-end image segmentation model, which uses the R-FCN structure for nodule detection. The effect of applying the two algorithm models to the computed tomography (CT) diagnosis of pulmonary nodules was analyzed, and different indexes of pulmonary nodule CT images in lung function examination after algorithm optimization were compared. A total of 56 patients diagnosed with pulmonary nodules by surgery or puncture were taken as the research objects. Based on the Mask-RCNN algorithm, a model for CT image segmentation processing of pulmonary nodules was proposed. Subsequently, the 3D Faster-RCNN model was used to label the nodules in the pulmonary nodules. The experimental results showed that the trained Mask-RCNN algorithm model can effectively complete the segmentation task of lung CT images, but there was a little jitter at the boundary. The speed of R-FCN algorithm for nodular detection was 0.172 seconds/picture, and the accuracy was 88.9%. CT scans were performed on the 56 patients based on a deep learning algorithm. The results showed that 30 cases of malignant pulmonary nodules were confirmed, and the diagnostic accuracy was 93.75%. There were 22 benign lesions, the diagnostic accuracy was 91.67%, and the overall diagnostic accuracy was 92.85%. This study effectively improved the diagnostic efficiency of CT images of pulmonary nodules, and the accuracy of CT images in the diagnosis of pulmonary nodules was analyzed and evaluated. It provided theoretical support for the follow-up diagnosis of pulmonary nodules and the treatment of lung cancer. It also significantly improved the diagnostic effect and detection efficiency of pulmonary nodules.

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Section: Discussionmentioning

confidence: 99%

Computed Tomography Image under Convolutional Neural Network Deep Learning Algorithm in Pulmonary Nodule Detection and Lung Function Examination

Zhang

Huang

et al. 2021

Journal of Healthcare Engineering

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Preoperative Staging Diagnosis of Colorectal Cancer Based on Multi-Row Spiral CT

Zhao

Yujie

2021

j med imaging hlth inform

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Colorectal cancer is a common malignant tumor of digestive tract, and its incidence has increased significantly in recent years. The 5-year survival rate of patients with early local colorectal cancer is more than 90%, with local invasion of about 68%, and distant metastasis of less than 10%. Therefore, early diagnosis and early treatment is an effective means for the prevention and treatment of colorectal cancer. At present, X-ray air-barium double contrast radiography and fiberoptic endoscope are most commonly used in the examination of colorectal tumors in the world. These two methods have the advantages of simple operation, intuition and low cost. However, it is limited to judge the depth of tumor invasion, whether to break through the serosa, lymphatic metastasis, and distant metastasis. In this work, it can be confirmed that the measurement of preoperative tumor vascular volume reconstruction density is feasible in the preoperative evaluation of colorectal cancer, it will provide clinicians with more valuable information about preoperative tumor staging. Besides, multi-slice spiral CT has the advantages of less trauma, high speed, and clear image. It can not only judge the location of the tumor, the depth, and extent of tumor invasion, and show distant organ metastasis, but also has good application value in tumor staging. In this paper, the clinical data and MSCT imaging data of patients with colorectal cancer were analyzed retrospectively, and the value of MSCT in the diagnosis of colorectal cancer was discussed.

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Colorectal cancer with indeterminate pulmonary nodules

Cited by 2 publications

References 78 publications

Computed Tomography Image under Convolutional Neural Network Deep Learning Algorithm in Pulmonary Nodule Detection and Lung Function Examination

Computed Tomography Image under Convolutional Neural Network Deep Learning Algorithm in Pulmonary Nodule Detection and Lung Function Examination

Preoperative Staging Diagnosis of Colorectal Cancer Based on Multi-Row Spiral CT

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