A novel segmentation approach for improving diagnostic accuracy of CAD systems for detecting lung cancer from chest computed tomography images

Background: The diagnosis of lung cancer (LC) may be difficult to make in the elderly. We report on the diagnostic elements available in life in an LC necropsy case series of asbestos-exposed workers and describe the frequency of non-neoplastic asbestos-related diseases as biological exposure indices. Methods: We reviewed pathologic and clinical records of an unselected series of autopsies (1997–2016) in patients with LC employed in the Monfalcone shipyards. We assessed the consistency with autopsy results of diagnoses based on, respectively, radiologic, cytologic, and histologic findings. Results: Data on 128 autopsy-confirmed LC cases were available; in life, 119 had been diagnosed as LC. Among these, 49 had histologic confirmation of diagnosis (17 with immunophenotyping); histology had been negative in 4. Cytology had been the main positive finding and the basis for diagnosis in 24 cases, but had been negative in 13. Chest computed tomography had been the basis for diagnosis in 45; in 18 cases, it had been negative. Nine patients had received a diagnosis different from LC, among whom 4 had been suspected to have malignant pleural mesothelioma by chest computed tomography. Pleural plaques were found in 124 and histologic asbestosis in 46 cases. Conclusions: Autopsies confirmed all LC diagnoses received in life, including 46 that would have been considered only possible LC based on clinical workup. The overall survival in this case series was poor. The high prevalence of pleural plaques and asbestosis suggest severity of asbestos exposures.

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Automatic Segmentation of the Gross Target Volume in Non-Small Cell Lung Cancer Using a Modified Version of ResNet

Technology in Cancer Research & Treatment ◽

10.1177/1533033820947484 ◽

2020 ◽

Vol 19 ◽

pp. 153303382094748

Author(s):

Fuli Zhang ◽

Qiusheng Wang ◽

Haipeng Li

Keyword(s):

Computed Tomography ◽

Lung Cancer ◽

Small Cell Lung Cancer ◽

Cell Lung Cancer ◽

Automatic Segmentation ◽

Target Volume ◽

Small Cell ◽

Small Cell Lung ◽

Computed Tomography Images ◽

Positive Rate

Radiotherapy plays an important role in the treatment of non-small cell lung cancer. Accurate segmentation of the gross target volume is very important for successful radiotherapy delivery. Deep learning techniques can obtain fast and accurate segmentation, which is independent of experts’ experience and saves time compared with manual delineation. In this paper, we introduce a modified version of ResNet and apply it to segment the gross target volume in computed tomography images of patients with non-small cell lung cancer. Normalization was applied to reduce the differences among images and data augmentation techniques were employed to further enrich the data of the training set. Two different residual convolutional blocks were used to efficiently extract the deep features of the computed tomography images, and the features from all levels of the ResNet were merged into a single output. This simple design achieved a fusion of deep semantic features and shallow appearance features to generate dense pixel outputs. The test loss tended to be stable after 50 training epochs, and the segmentation took 21 ms per computed tomography image. The average evaluation metrics were: Dice similarity coefficient, 0.73; Jaccard similarity coefficient, 0.68; true positive rate, 0.71; and false positive rate, 0.0012. Those results were better than those of U-Net, which was used as a benchmark. The modified ResNet directly extracted multi-scale context features from original input images. Thus, the proposed automatic segmentation method can quickly segment the gross target volume in non-small cell lung cancer cases and be applied to improve consistency in contouring.

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An artificial intelligence algorithm that identifies middle turbinate pneumatisation (concha bullosa) on sinus computed tomography scans

The Journal of Laryngology & Otology ◽

10.1017/s0022215120000444 ◽

2020 ◽

Vol 134 (4) ◽

pp. 328-331 ◽

Cited By ~ 1

Author(s):

P Parmar ◽

A-R Habib ◽

D Mendis ◽

A Daniel ◽

M Duvnjak ◽

...

Keyword(s):

Neural Network ◽

Artificial Intelligence ◽

Computed Tomography ◽

Diagnostic Accuracy ◽

Convolutional Neural Network ◽

Middle Turbinate ◽

Concha Bullosa ◽

Computed Tomography Images ◽

Computed Tomography Scans ◽

Neural Network Algorithm

AbstractObjectiveConvolutional neural networks are a subclass of deep learning or artificial intelligence that are predominantly used for image analysis and classification. This proof-of-concept study attempts to train a convolutional neural network algorithm that can reliably determine if the middle turbinate is pneumatised (concha bullosa) on coronal sinus computed tomography images.MethodConsecutive high-resolution computed tomography scans of the paranasal sinuses were retrospectively collected between January 2016 and December 2018 at a tertiary rhinology hospital in Australia. The classification layer of Inception-V3 was retrained in Python using a transfer learning method to interpret the computed tomography images. Segmentation analysis was also performed in an attempt to increase diagnostic accuracy.ResultsThe trained convolutional neural network was found to have diagnostic accuracy of 81 per cent (95 per cent confidence interval: 73.0–89.0 per cent) with an area under the curve of 0.93.ConclusionA trained convolutional neural network algorithm appears to successfully identify pneumatisation of the middle turbinate with high accuracy. Further studies can be pursued to test its ability in other clinically important anatomical variants in otolaryngology and rhinology.

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Endotracheal Metastasis Causing Airway Obstruction

Clinical Practice and Cases in Emergency Medicine ◽

10.5811/cpcem.2019.10.44964 ◽

2020 ◽

Vol 4 (1) ◽

pp. 96-98

Author(s):

Yudai Yano ◽

Takashi Fujiwara ◽

Masanobu Mizuta

Keyword(s):

Computed Tomography ◽

Lung Cancer ◽

Emergency Department ◽

Differential Diagnosis ◽

Respiratory Symptoms ◽

Primary Lung Cancer ◽

Chest Computed Tomography ◽

Important Differential Diagnosis ◽

Emergency Tracheostomy ◽

Rare Lesion

Endotracheal metastasis, a critical complication of primary lung cancer, is an extremely rare lesion. A 73-year-old woman who had previously received treatment for lung cancer presented to our emergency department with dyspnea. A chest computed tomography and nasopharyngolaryngoscopy showed an endotracheal mass below the epiglottis, obstructing the trachea almost completely. The patient had an emergency tracheostomy, and then the mass was removed via median laryngotomy. This lesion was proven to be a recurrent metastasis of lung cancer. Clinicians should recognize endotracheal metastasis as an important differential diagnosis in cancer patients presenting with respiratory symptoms.

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